Vagus nerve stimulation enhances perforant path-CA3 synaptic transmission via the activation of β-adrenergic receptors and the locus coeruleus.

PubMed

Shen, Huilian; Fuchino, Yuta; Miyamoto, Daisuke; Nomura, Hiroshi; Matsuki, Norio

2012-05-01

Vagus nerve stimulation (VNS) is an approved treatment for epilepsy and depression and has cognition-enhancing effects in patients with Alzheimer's disease. The hippocampus is widely recognized to be related to epilepsy, depression, and Alzheimer's disease. One possible mechanism of VNS involves its effect on the hippocampus; i.e. it increases the release of noradrenaline in the hippocampus. However, the effect of VNS on synaptic transmission in the hippocampus is unknown. To determine whether VNS modulates neurotransmission in the hippocampus, we examined the effects of VNS on perforant path (PP)-CA3 synaptic transmission electrophysiologically in anaesthetized rats. VNS induces a persistent enhancement of PP-CA3 field excitatory post-synaptic potentials (fEPSPs). Arc, an immediate early gene, was used to identify active brain regions after VNS. The locus coeruleus (LC), which contains the perikarya of noradrenergic projections, harboured more Arc-positive cells, as measured by in-situ hybridization, after 10-min VNS. In addition, electrical lesions of LC neurons or intraventricular administration of the β-adrenergic receptor antagonist timolol prevented the enhancement of PP-CA3 responses by VNS. In conclusion, the protracted increase in PP-CA3 synaptic transmission that is induced by VNS entails activation of the LC and β-adrenergic receptors. Our novel findings suggest that information from the periphery modulates synaptic transmission in the CA3 region of the hippocampus.

Defining the neural fulcrum for chronic vagus nerve stimulation: implications for integrated cardiac control.

PubMed

Ardell, Jeffrey L; Nier, Heath; Hammer, Matthew; Southerland, E Marie; Ardell, Christopher L; Beaumont, Eric; KenKnight, Bruce H; Armour, J Andrew

2017-11-15

The evoked cardiac response to bipolar cervical vagus nerve stimulation (VNS) reflects a dynamic interaction between afferent mediated decreases in central parasympathetic drive and suppressive effects evoked by direct stimulation of parasympathetic efferent axons to the heart. The neural fulcrum is defined as the operating point, based on frequency-amplitude-pulse width, where a null heart rate response is reproducibly evoked during the on-phase of VNS. Cardiac control, based on the principal of the neural fulcrum, can be elicited from either vagus. Beta-receptor blockade does not alter the tachycardia phase to low intensity VNS, but can increase the bradycardia to higher intensity VNS. While muscarinic cholinergic blockade prevented the VNS-induced bradycardia, clinically relevant doses of ACE inhibitors, beta-blockade and the funny channel blocker ivabradine did not alter the VNS chronotropic response. While there are qualitative differences in VNS heart control between awake and anaesthetized states, the physiological expression of the neural fulcrum is maintained. Vagus nerve stimulation (VNS) is an emerging therapy for treatment of chronic heart failure and remains a standard of therapy in patients with treatment-resistant epilepsy. The objective of this work was to characterize heart rate (HR) responses (HRRs) during the active phase of chronic VNS over a wide range of stimulation parameters in order to define optimal protocols for bidirectional bioelectronic control of the heart. In normal canines, bipolar electrodes were chronically implanted on the cervical vagosympathetic trunk bilaterally with anode cephalad to cathode (n = 8, 'cardiac' configuration) or with electrode positions reversed (n = 8, 'epilepsy' configuration). In awake state, HRRs were determined for each combination of pulse frequency (2-20 Hz), intensity (0-3.5 mA) and pulse widths (130-750 μs) over 14 months. At low intensities and higher frequency VNS, HR increased during the VNS active phase owing to afferent modulation of parasympathetic central drive. When functional effects of afferent and efferent fibre activation were balanced, a null HRR was evoked (defined as 'neural fulcrum') during which HRR ≈ 0. As intensity increased further, HR was reduced during the active phase of VNS. While qualitatively similar, VNS delivered in the epilepsy configuration resulted in more pronounced HR acceleration and reduced HR deceleration during VNS. At termination, under anaesthesia, transection of the vagi rostral to the stimulation site eliminated the augmenting response to VNS and enhanced the parasympathetic efferent-mediated suppressing effect on electrical and mechanical function of the heart. In conclusion, VNS activates central then peripheral aspects of the cardiac nervous system. VNS control over cardiac function is maintained during chronic therapy. © 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

Evidence-based guideline update: Vagus nerve stimulation for the treatment of epilepsy

PubMed Central

Morris, George L.; Gloss, David; Buchhalter, Jeffrey; Mack, Kenneth J.; Nickels, Katherine; Harden, Cynthia

2013-01-01

Objective: To evaluate the evidence since the 1999 assessment regarding efficacy and safety of vagus nerve stimulation (VNS) for epilepsy, currently approved as adjunctive therapy for partial-onset seizures in patients >12 years. Methods: We reviewed the literature and identified relevant published studies. We classified these studies according to the American Academy of Neurology evidence-based methodology. Results: VNS is associated with a >50% seizure reduction in 55% (95% confidence interval [CI] 50%–59%) of 470 children with partial or generalized epilepsy (13 Class III studies). VNS is associated with a >50% seizure reduction in 55% (95% CI 46%–64%) of 113 patients with Lennox-Gastaut syndrome (LGS) (4 Class III studies). VNS is associated with an increase in ≥50% seizure frequency reduction rates of ∼7% from 1 to 5 years postimplantation (2 Class III studies). VNS is associated with a significant improvement in standard mood scales in 31 adults with epilepsy (2 Class III studies). Infection risk at the VNS implantation site in children is increased relative to that in adults (odds ratio 3.4, 95% CI 1.0–11.2). VNS is possibly effective for seizures (both partial and generalized) in children, for LGS-associated seizures, and for mood problems in adults with epilepsy. VNS may have improved efficacy over time. Recommendations: VNS may be considered for seizures in children, for LGS-associated seizures, and for improving mood in adults with epilepsy (Level C). VNS may be considered to have improved efficacy over time (Level C). Children should be carefully monitored for site infection after VNS implantation. PMID:23986299

Vagus nerve stimulation during rehabilitative training improves forelimb strength following ischemic stroke.

PubMed

Khodaparast, N; Hays, S A; Sloan, A M; Hulsey, D R; Ruiz, A; Pantoja, M; Rennaker, R L; Kilgard, M P

2013-12-01

Upper limb impairment is a common debilitating consequence of ischemic stroke. Physical rehabilitation after stroke enhances neuroplasticity and improves limb function, but does not typically restore normal movement. We have recently developed a novel method that uses vagus nerve stimulation (VNS) paired with forelimb movements to drive specific, long-lasting map plasticity in rat primary motor cortex. Here we report that VNS paired with rehabilitative training can enhance recovery of forelimb force generation following infarction of primary motor cortex in rats. Quantitative measures of forelimb function returned to pre-lesion levels when VNS was delivered during rehab training. Intensive rehab training without VNS failed to restore function back to pre-lesion levels. Animals that received VNS during rehab improved twice as much as rats that received the same rehabilitation without VNS. VNS delivered during physical rehabilitation represents a novel method that may provide long-lasting benefits towards stroke recovery. © 2013.

The pig as preclinical model for laparoscopic vagus nerve stimulation.

PubMed

Wolthuis, A M; Stakenborg, N; D'Hoore, A; Boeckxstaens, G E

2016-02-01

Cervical vagus nerve stimulation (VNS) prevents manipulation-induced intestinal inflammation and improves intestinal transit in a mouse model of postoperative ileus (POI). Cervical VNS, however, is accompanied by cardiovascular and respiratory side effects. In view of potential clinical application, we therefore evaluated the safety and feasibility of abdominal VNS via laparoscopic approach in a porcine model. Six pigs were used in a non-survival study for both cervical and abdominal VNS. Two cardiac pacing electrodes were positioned around the right cervical and posterior abdominal vagus nerve and connected to an external stimulator. VNS was performed using four different settings (5 and 20 Hz, 0.5 and 1 ms pulse width) during 2 min with ECG recording. Laparoscopic VNS was timed and videotaped, and technical difficulties were noted. A validated National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire was used to evaluate the task and workload. The procedure was completed in all pigs with 4-port laparoscopic technique. Cervical and abdominal VNS were performed after correct identification and isolation of the nerve, and positioning of the electrodes around the nerve. Median laparoscopic operating time was 16 min (range 8-33 min), and median NASA-TLX was 31 (range 11-74). No major complications were encountered. Reduction of heart rate was between 5.5 and 14% for cervical VNS and undetectable for abdominal VNS. In a porcine model, laparoscopic VNS is feasible and safe with cardiac pacing electrodes and may lead to a similar novel approach in humans in the near future.

On Quantitative Biomarkers of VNS Therapy Using EEG and ECG Signals.

PubMed

Ravan, Maryam; Sabesan, Shivkumar; D'Cruz, O'Neill

2017-02-01

The goal of this work is to objectively evaluate the effectiveness of neuromodulation therapies, specifically, Vagus nerve stimulation (VNS) in reducing the severity of seizures in patients with medically refractory epilepsy. Using novel quantitative features obtained from combination of electroencephalographic (EEG) and electrocardiographic (ECG) signals around seizure events in 16 patients who underwent implantation of closed-loop VNS therapy system, namely AspireSR, we evaluated if automated delivery of VNS at the time of seizure onset reduces the severity of seizures by reducing EEG spatial synchronization as well as the duration and magnitude of heart rate increase. Unsupervised classification was subsequently applied to test the discriminative ability and validity of these features to measure responsiveness to VNS therapy. Results of application of this methodology to compare 105 pre-VNS treatment and 107 post-VNS treatment seizures revealed that seizures that were acutely stimulated using VNS had a reduced ictal spread as well as reduced impact on cardiovascular function compared to the ones that occurred prior to any treatment. Furthermore, application of an unsupervised fuzzy-c-mean classifier to evaluate the ability of the combined EEG-ECG based features to classify pre and post-treatment seizures achieved a classification accuracy of 85.85%. These results indicate the importance of timely delivery of VNS to reduce seizure severity and thus help achieve better seizure control for patients with epilepsy. The proposed set of quantitative features could be used as potential biomarkers for predicting long-term response to VNS therapy.

Evidence-Based Guideline Update: Vagus Nerve Stimulation for the Treatment of Epilepsy

PubMed Central

Morris, George L.; Gloss, David; Buchhalter, Jeffrey; Mack, Kenneth J.; Nickels, Katherine; Harden, Cynthia

2013-01-01

OBJECTIVE: To evaluate the evidence since the 1999 assessment regarding efficacy and safety of vagus nerve stimulation (VNS) for epilepsy, currently approved as adjunctive therapy for partial-onset seizures in patients >12 years. METHODS: We reviewed the literature and identified relevant published studies. We classified these studies according to the American Academy of Neurology evidence-based methodology. RESULTS: VNS is associated with a >50% seizure reduction in 55% (95% confidence interval [CI] 50%–59%) of 470 children with partial or generalized epilepsy (13 Class III studies). VNS is associated with a >50% seizure reduction in 55% (95% CI 46%–64%) of 113 patients with Lennox-Gastaut syndrome (LGS) (4 Class III studies). VNS is associated with an increase in ≥50% seizure frequency reduction rates of ~7% from 1 to 5 years postim-plantation (2 Class III studies). VNS is associated with a significant improvement in standard mood scales in 31 adults with epilepsy (2 Class III studies). Infection risk at the VNS implantation site in children is increased relative to that in adults (odds ratio 3.4, 95% CI 1.0–11.2). VNS is possibly effective for seizures (both partial and generalized) in children, for LGS-associated seizures, and for mood problems in adults with epilepsy. VNS may have improved efficacy over time. RECOMMENDATIONS: VNS may be considered for seizures in children, for LGS-associated seizures, and for improving mood in adults with epilepsy (Level C). VNS may be considered to have improved efficacy over time (Level C). Children should be carefully monitored for site infection after VNS implantation. Neurology® 2013;81:1–7 PMID:24348133

Vagus nerve stimulation mitigates intrinsic cardiac neuronal and adverse myocyte remodeling postmyocardial infarction

PubMed Central

Beaumont, Eric; Southerland, Elizabeth M.; Hardwick, Jean C.; Wright, Gary L.; Ryan, Shannon; Li, Ying; KenKnight, Bruce H.; Armour, J. Andrew

2015-01-01

This paper aims to determine whether chronic vagus nerve stimulation (VNS) mitigates myocardial infarction (MI)-induced remodeling of the intrinsic cardiac nervous system (ICNS), along with the cardiac tissue it regulates. Guinea pigs underwent VNS implantation on the right cervical vagus. Two weeks later, MI was produced by ligating the ventral descending coronary artery. VNS stimulation started 7 days post-MI (20 Hz, 0.9 ± 0.2 mA, 14 s on, 48 s off; VNS-MI, n = 7) and was compared with time-matched MI animals with sham VNS (MI n = 7) vs. untreated controls (n = 8). Echocardiograms were performed before and at 90 days post-MI. At termination, IC neuronal intracellular voltage recordings were obtained from whole-mount neuronal plexuses. MI increased left ventricular end systolic volume (LVESV) 30% (P = 0.027) and reduced LV ejection fraction (LVEF) 6.5% (P < 0.001) at 90 days post-MI compared with baseline. In the VNS-MI group, LVESV and LVEF did not differ from baseline. IC neurons showed depolarization of resting membrane potentials and increased input resistance in MI compared with VNS-MI and sham controls (P < 0.05). Neuronal excitability and sensitivity to norepinephrine increased in MI and VNS-MI groups compared with controls (P < 0.05). Synaptic efficacy, as determined by evoked responses to stimulating input axons, was reduced in VNS-MI compared with MI or controls (P < 0.05). VNS induced changes in myocytes, consistent with enhanced glycogenolysis, and blunted the MI-induced increase in the proapoptotic Bcl-2-associated X protein (P < 0.05). VNS mitigates MI-induced remodeling of the ICNS, correspondingly preserving ventricular function via both neural and cardiomyocyte-dependent actions. PMID:26276818

Vagus nerve stimulation mitigates intrinsic cardiac neuronal and adverse myocyte remodeling postmyocardial infarction.

PubMed

Beaumont, Eric; Southerland, Elizabeth M; Hardwick, Jean C; Wright, Gary L; Ryan, Shannon; Li, Ying; KenKnight, Bruce H; Armour, J Andrew; Ardell, Jeffrey L

2015-10-01

This paper aims to determine whether chronic vagus nerve stimulation (VNS) mitigates myocardial infarction (MI)-induced remodeling of the intrinsic cardiac nervous system (ICNS), along with the cardiac tissue it regulates. Guinea pigs underwent VNS implantation on the right cervical vagus. Two weeks later, MI was produced by ligating the ventral descending coronary artery. VNS stimulation started 7 days post-MI (20 Hz, 0.9 ± 0.2 mA, 14 s on, 48 s off; VNS-MI, n = 7) and was compared with time-matched MI animals with sham VNS (MI n = 7) vs. untreated controls (n = 8). Echocardiograms were performed before and at 90 days post-MI. At termination, IC neuronal intracellular voltage recordings were obtained from whole-mount neuronal plexuses. MI increased left ventricular end systolic volume (LVESV) 30% (P = 0.027) and reduced LV ejection fraction (LVEF) 6.5% (P < 0.001) at 90 days post-MI compared with baseline. In the VNS-MI group, LVESV and LVEF did not differ from baseline. IC neurons showed depolarization of resting membrane potentials and increased input resistance in MI compared with VNS-MI and sham controls (P < 0.05). Neuronal excitability and sensitivity to norepinephrine increased in MI and VNS-MI groups compared with controls (P < 0.05). Synaptic efficacy, as determined by evoked responses to stimulating input axons, was reduced in VNS-MI compared with MI or controls (P < 0.05). VNS induced changes in myocytes, consistent with enhanced glycogenolysis, and blunted the MI-induced increase in the proapoptotic Bcl-2-associated X protein (P < 0.05). VNS mitigates MI-induced remodeling of the ICNS, correspondingly preserving ventricular function via both neural and cardiomyocyte-dependent actions. Copyright © 2015 the American Physiological Society.

A gene co-expression network model identifies yield-related vicinity networks in Jatropha curcas shoot system.

PubMed

Govender, Nisha; Senan, Siju; Mohamed-Hussein, Zeti-Azura; Wickneswari, Ratnam

2018-06-15

The plant shoot system consists of reproductive organs such as inflorescences, buds and fruits, and the vegetative leaves and stems. In this study, the reproductive part of the Jatropha curcas shoot system, which includes the aerial shoots, shoots bearing the inflorescence and inflorescence were investigated in regard to gene-to-gene interactions underpinning yield-related biological processes. An RNA-seq based sequencing of shoot tissues performed on an Illumina HiSeq. 2500 platform generated 18 transcriptomes. Using the reference genome-based mapping approach, a total of 64 361 genes was identified in all samples and the data was annotated against the non-redundant database by the BLAST2GO Pro. Suite. After removing the outlier genes and samples, a total of 12 734 genes across 17 samples were subjected to gene co-expression network construction using petal, an R library. A gene co-expression network model built with scale-free and small-world properties extracted four vicinity networks (VNs) with putative involvement in yield-related biological processes as follow; heat stress tolerance, floral and shoot meristem differentiation, biosynthesis of chlorophyll molecules and laticifers, cell wall metabolism and epigenetic regulations. Our VNs revealed putative key players that could be adapted in breeding strategies for J. curcas shoot system improvements.

The vagal nerve stimulation outcome, and laryngeal effect: Otolaryngologists roles and perspective.

PubMed

Al Omari, Ahmad I; Alzoubi, Firas Q; Alsalem, Mohammad M; Aburahma, Samah K; Mardini, Diala T; Castellanos, Paul F

Epilepsy is one of the most common neurologic disorders. Vagus nerve stimulation (VNS), first investigated in 1938 and subsequently studied as a potential therapy for epilepsy. The FDA approved the use of VNS in 1997 as an adjunctive non-pharmacologic symptomatic treatment option for refractory epilepsy for adults and adolescents over 12years. VNS can cause laryngeal and voice side effects that can be managed by otolaryngologists safely and effectively. This study is to review the outcomes of vagal nerve stimulator (VNS) implantation in terms of the surgical procedures, complications, seizure frequency, and the clinical effect on larynx and vocal folds motion. Series of thirty consecutive patients who had VNS implantation between 2007 and 2014 were recruited. Seizure-frequency outcome, surgical complications and device adverse effects of VNS were retrospectively reviewed. Additional evaluation included use of the Voice Handicap Index and Maximum Phonation Time (MPT) were conducted before and after the implantation. Videolaryngoscopy was used to evaluate the vocal fold mobility before and after the VNS implantation. Seizure frequency reduction over a minimum of 2years of follow up demonstrated: 100% in seizure frequency reduction in 1 patient, drastic reduction in seizure frequency (70-90%) in 9 patients, a good reduction in terms of seizure frequency (50%) in 8 patients, a 30% reduction in 5 patients, no response in 6 patients, and 1 patient had increased frequency. The most commonly reported adverse effects after VNS activation were coughing and voice changes with pitch breaks, as well as mild intermittent shortness of breath in 33% of patients. For those patients secondary supraglottic muscle tension and hyper function with reduced left vocal fold mobility were noticed on videolaryngoscopy, though none had aspiration problems. Surgical complications included a wound dehiscence in one patient (3%) which was surgically managed, minor intra-operative bleeding 3%; a superficial wound infection in one patient (3%) which was treated conservatively, none of the complications necessitated VNS removal. VNS appears to be an effective non-pharmacologic adjuvant therapy in patients with medically refractory seizures. With the favorable adverse-effect profile previously described, VNS is generally well tolerated and of a great benefit to such patients. Laryngeal side effects, of which hoarseness being of the greatest repetition, are the most common after the VNS implantation. VNS can affect the voice and reduced vocal cord motion on the implantation side with secondary supraglottic muscle tension. Otolaryngologists are not only capable of performing VNS implantation, but can also manage surgical complications, assess laryngeal side effects and treat them as needed. Copyright © 2017 Elsevier Inc. All rights reserved.

Optimization of epilepsy treatment with vagus nerve stimulation

NASA Astrophysics Data System (ADS)

Uthman, Basim; Bewernitz, Michael; Liu, Chang-Chia; Ghacibeh, Georges

2007-11-01

Epilepsy is one of the most common chronic neurological disorders that affects close to 50 million people worldwide. Antiepilepsy drugs (AEDs), the main stay of epilepsy treatment, control seizures in two thirds of patients only. Other therapies include the ketogenic diet, ablative surgery, hormonal treatments and neurostimulation. While other approaches to stimulation of the brain are currently in the experimental phase vagus nerve stimulation (VNS) has been approved by the FDA since July 1997 for the adjunctive treatment of intractable partial onset epilepsy with and without secondary generalization in patients twelve years of age or older. The safety and efficacy of VNS have been proven and duplicated in two subsequent double-blinded controlled studies after two pilot studies demonstrated the feasibility of VNS in man. Long term observational studies confirmed the safety of VNS and that its effectiveness is sustained over time. While AEDs influence seizure thresholds via blockade or modulation of ionic channels, inhibit excitatory neurotransmitters or enhance inhibitory neurotransmitters the exact mechanism of action of VNS is not known. Neuroimaging studies revealed that VNS increases blood flow in certain regions of the brain such as the thalamus. Chemical lesions in the rat brains showed that norepinephrine is an important link in the anticonvulsant effect of VNS. Analysis of cerebrospinal fluid obtained from patients before and after treatment with VNS showed modest decreases in excitatory neurotransmitters. Although Hammond et al. reported no effect of VNS on scalp EEG by visual analysis and Salinsky et al. found no effect of VNS on scalp EEG by spectral analysis, Kuba et al. suggested that VNS reduces interictal epileptiform activity. Further, nonlinear dynamical analysis of the electroencephalogram in the rat and man have reportedly shown predictable changes (decrease in the short term Lyapunov exponent STLmax and T-index) more than an hour prior to the clinical or electroencephalographic seizure onset. It is possible that intermittent VNS maintains chaoticity of brain activity in patients with epilepsy that respond to this therapy. The most optimal stimulation parameters of VNS are not known and further study of nonlinear dynamics of brain activity may shed some light on more effective interception or prevention of seizures. Online real time analysis may allow on-demand stimulation rather than hit-or-miss approach

Validation and properties of the verbal numeric scale in children with acute pain.

PubMed

Bailey, Benoit; Daoust, Raoul; Doyon-Trottier, Evelyne; Dauphin-Pierre, Sabine; Gravel, Jocelyn

2010-05-01

Although the verbal numeric scale (VNS) is used frequently at patients' bedsides, it has never been formally validated in children with acute pain. In order to validate this scale, a prospective cohort study was performed in children between 8 and 17years presenting to a pediatric emergency department (ED) with acute pain. Pain was graded using the VNS, the visual analogue scale (VAS), and the verbal rating scale (VRS). A second assessment was done before discharge. We determined a priori that in order to be valid, the VNS would need to: correlate with the VAS (concurrent validity); decrease after intervention to reduce pain (construct validity); and be associated with the VRS categories (content validity). The VNS interchangeability with the VAS, its minimal clinically significant difference, and test-retest reliability were also determined. A total of 202 patients (mean age: 12.2+/-2.6years) were enrolled. The VNS correlated with the VAS: r(ic)=0.93, p<0.001. There were differences in the VNS before versus after interventions (p<0.001), and between VRS categories (mild versus moderate, p<0.001; moderate versus severe, p<0.001). The 95% limits of agreement (interchangeability) between VNS/VAS were outside the a priori set limit of +/-2.0: -1.8, 2.5. The VNS minimal clinically significant difference was 1. The VNS had good test-retest reliability with 95% limits of agreement of -0.9 and 1.2. In conclusion, the VNS provides a valid and reliable scale to evaluate acute pain in children aged 8-17years but is not interchangeable with the VAS. Copyright 2009 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Vagus nerve stimulation mitigates intrinsic cardiac neuronal remodeling and cardiac hypertrophy induced by chronic pressure overload in guinea pig

PubMed Central

Beaumont, Eric; Wright, Gary L.; Southerland, Elizabeth M.; Li, Ying; Chui, Ray; KenKnight, Bruce H.; Armour, J. Andrew

2016-01-01

Our objective was to determine whether chronic vagus nerve stimulation (VNS) mitigates pressure overload (PO)-induced remodeling of the cardioneural interface. Guinea pigs (n = 48) were randomized to right or left cervical vagus (RCV or LCV) implant. After 2 wk, chronic left ventricular PO was induced by partial (15–20%) aortic constriction. Of the 31 animals surviving PO induction, 10 were randomized to RCV VNS, 9 to LCV VNS, and 12 to sham VNS. VNS was delivered at 20 Hz and 1.14 ± 0.03 mA at a 22% duty cycle. VNS commenced 10 days after PO induction and was maintained for 40 days. Time-matched controls (n = 9) were evaluated concurrently. Echocardiograms were obtained before and 50 days after PO. At termination, intracellular current-clamp recordings of intrinsic cardiac (IC) neurons were studied in vitro to determine effects of therapy on soma characteristics. Ventricular cardiomyocyte sizes were assessed with histology along with immunoblot analysis of selected proteins in myocardial tissue extracts. In sham-treated animals, PO increased cardiac output (34%, P < 0.004), as well as systolic (114%, P < 0.04) and diastolic (49%, P < 0.002) left ventricular volumes, a hemodynamic response prevented by VNS. PO-induced enhancements of IC synaptic efficacy and muscarinic sensitivity of IC neurons were mitigated by chronic VNS. Increased myocyte size, which doubled in PO (P < 0.05), was mitigated by RCV. PO hypertrophic myocardium displayed decreased glycogen synthase (GS) protein levels and accumulation of the phosphorylated (inactive) form of GS. These PO-induced changes in GS were moderated by left VNS. Chronic VNS targets IC neurons accompanying PO to obtund associated adverse cardiomyocyte remodeling. PMID:26993230

Vagus nerve stimulation mitigates intrinsic cardiac neuronal remodeling and cardiac hypertrophy induced by chronic pressure overload in guinea pig.

PubMed

Beaumont, Eric; Wright, Gary L; Southerland, Elizabeth M; Li, Ying; Chui, Ray; KenKnight, Bruce H; Armour, J Andrew; Ardell, Jeffrey L

2016-05-15

Our objective was to determine whether chronic vagus nerve stimulation (VNS) mitigates pressure overload (PO)-induced remodeling of the cardioneural interface. Guinea pigs (n = 48) were randomized to right or left cervical vagus (RCV or LCV) implant. After 2 wk, chronic left ventricular PO was induced by partial (15-20%) aortic constriction. Of the 31 animals surviving PO induction, 10 were randomized to RCV VNS, 9 to LCV VNS, and 12 to sham VNS. VNS was delivered at 20 Hz and 1.14 ± 0.03 mA at a 22% duty cycle. VNS commenced 10 days after PO induction and was maintained for 40 days. Time-matched controls (n = 9) were evaluated concurrently. Echocardiograms were obtained before and 50 days after PO. At termination, intracellular current-clamp recordings of intrinsic cardiac (IC) neurons were studied in vitro to determine effects of therapy on soma characteristics. Ventricular cardiomyocyte sizes were assessed with histology along with immunoblot analysis of selected proteins in myocardial tissue extracts. In sham-treated animals, PO increased cardiac output (34%, P < 0.004), as well as systolic (114%, P < 0.04) and diastolic (49%, P < 0.002) left ventricular volumes, a hemodynamic response prevented by VNS. PO-induced enhancements of IC synaptic efficacy and muscarinic sensitivity of IC neurons were mitigated by chronic VNS. Increased myocyte size, which doubled in PO (P < 0.05), was mitigated by RCV. PO hypertrophic myocardium displayed decreased glycogen synthase (GS) protein levels and accumulation of the phosphorylated (inactive) form of GS. These PO-induced changes in GS were moderated by left VNS. Chronic VNS targets IC neurons accompanying PO to obtund associated adverse cardiomyocyte remodeling. Copyright © 2016 the American Physiological Society.

Clinical outcomes of VNS therapy with AspireSR® (including cardiac-based seizure detection) at a large complex epilepsy and surgery centre.

PubMed

Hamilton, Preci; Soryal, Imad; Dhahri, Prince; Wimalachandra, Welege; Leat, Anna; Hughes, Denise; Toghill, Nicole; Hodson, James; Sawlani, Vijay; Hayton, Tom; Samarasekera, Shanika; Bagary, Manny; McCorry, Dougall; Chelvarajah, Ramesh

2018-05-01

To compare the efficacy of AspireSR ® to preceding VNS battery models for battery replacements, and to determine the efficacy of the AspireSR ® for new implants. Data were collected retrospectively from patients with epilepsy who had VNS AspireSR ® implanted over a three-year period between June 2014 and June 2017 by a single surgeon. Cases were divided into two cohorts, those in whom the VNS was a new insertion, and those in whom the VNS battery was changed from a previous model to AspireSR ® . Within each group, the seizure burden was compared between the periods before and after insertion of AspireSR ® . Fifty-one patients with a newly inserted AspireSR ® VNS model had a significant reduction in seizure frequency (p < 0.001), with 59% (n = 30) reporting ≥50% reduction. Of the 62 patients who had an existing VNS, 53% (n = 33) reported ≥50% reduction in seizure burden when the original VNS was inserted. After the battery was changed to the AspireSR ® , 71% (n = 44) reported a further reduction of ≥50% in their seizure burden. The size of this reduction was at least as large as that resulting from the insertion of their existing VNS in 98% (61/62) of patients. The results suggest that approximately 70% of patients with existing VNS insertions could have significant additional benefit from cardiac based seizure detection and closed loop stimulation from the AspireSR ® device. For new insertions, the AspireSR ® device has efficacy in 59% of patients. The 'rule of thirds' used in counseling patients may need to be modified accordingly. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Chronic migraine headache prevention with noninvasive vagus nerve stimulation: The EVENT study.

PubMed

Silberstein, Stephen D; Calhoun, Anne H; Lipton, Richard B; Grosberg, Brian M; Cady, Roger K; Dorlas, Stefanie; Simmons, Kristy A; Mullin, Chris; Liebler, Eric J; Goadsby, Peter J; Saper, Joel R

2016-08-02

To evaluate the feasibility, safety, and tolerability of noninvasive vagus nerve stimulation (nVNS) for the prevention of chronic migraine (CM) attacks. In this first prospective, multicenter, double-blind, sham-controlled pilot study of nVNS in CM prophylaxis, adults with CM (≥15 headache d/mo) entered the baseline phase (1 month) and were subsequently randomized to nVNS or sham treatment (2 months) before receiving open-label nVNS treatment (6 months). The primary endpoints were safety and tolerability. Efficacy endpoints in the intent-to-treat population included change in the number of headache days per 28 days and acute medication use. Fifty-nine participants (mean age, 39.2 years; mean headache frequency, 21.5 d/mo) were enrolled. During the randomized phase, tolerability was similar for nVNS (n = 30) and sham treatment (n = 29). Most adverse events were mild/moderate and transient. Mean changes in the number of headache days were -1.4 (nVNS) and -0.2 (sham) (Δ = 1.2; p = 0.56). Twenty-seven participants completed the open-label phase. For the 15 completers initially assigned to nVNS, the mean change from baseline in headache days after 8 months of treatment was -7.9 (95% confidence interval -11.9 to -3.8; p < 0.01). Therapy with nVNS was well-tolerated with no safety issues. Persistent prophylactic use may reduce the number of headache days in CM; larger sham-controlled studies are needed. NCT01667250. This study provides Class II evidence that for patients with CM, nVNS is safe, is well-tolerated, and did not significantly change the number of headache days. This pilot study lacked the precision to exclude important safety issues or benefits of nVNS. © 2016 American Academy of Neurology.

Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports

PubMed Central

Schilde, M.; Doerner, K.F.; Hartl, R.F.

2011-01-01

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15%. Moreover, improvements of up to 41% can be achieved for some test instances. PMID:23543641

Reorganization of Motor Cortex by Vagus Nerve Stimulation Requires Cholinergic Innervation.

PubMed

Hulsey, Daniel R; Hays, Seth A; Khodaparast, Navid; Ruiz, Andrea; Das, Priyanka; Rennaker, Robert L; Kilgard, Michael P

2016-01-01

Vagus nerve stimulation (VNS) paired with forelimb training drives robust, specific reorganization of movement representations in the motor cortex. The mechanisms that underlie VNS-dependent enhancement of map plasticity are largely unknown. The cholinergic nucleus basalis (NB) is a critical substrate in cortical plasticity, and several studies suggest that VNS activates cholinergic circuitry. We examined whether the NB is required for VNS-dependent enhancement of map plasticity in the motor cortex. Rats were trained to perform a lever pressing task and then received injections of the immunotoxin 192-IgG-saporin to selectively lesion cholinergic neurons of the NB. After lesion, rats underwent five days of motor training during which VNS was paired with successful trials. At the conclusion of behavioral training, intracortical microstimulation was used to document movement representations in motor cortex. VNS paired with forelimb training resulted in a substantial increase in the representation of proximal forelimb in rats with an intact NB compared to untrained controls. NB lesions prevent this VNS-dependent increase in proximal forelimb area and result in representations similar to untrained controls. Motor performance was similar between groups, suggesting that differences in forelimb function cannot account for the difference in proximal forelimb representation. Together, these findings indicate that the NB is required for VNS-dependent enhancement of plasticity in the motor cortex and may provide insight into the mechanisms that underlie the benefits of VNS therapy. Copyright © 2016 Elsevier Inc. All rights reserved.

Does enteral nutrition affect clinical outcome? A systematic review of the randomized trials.

PubMed

Koretz, Ronald L; Avenell, Alison; Lipman, Timothy O; Braunschweig, Carol L; Milne, Anne C

2007-02-01

Both parenteral nutrition (PN) and enteral nutrition (EN) are widely advocated as adjunctive care in patients with various diseases. A systematic review of 82 randomized controlled trials (RCTs) of PN published in 2001 found little, if any, effect on mortality, morbidity, or duration of hospital stay; in some situations, PN increased infectious complication rates. The objective was to assess the effect of EN or volitional nutrition support (VNS) in individual disease states from available RCTs. We conducted a systematic review. RCTs comparing EN or VNS with untreated controls, or comparing EN with PN, were identified and separated according to the underlying disease state. Meta-analysis was performed when at least three RCTs provided data. The evidence from the RCTs was summarized into one of five grades. A or B, respectively, indicated the presence of strong or weak (low-quality RCTs) evidence supporting the use of the intervention. C indicated a lack of adequate evidence to make any decision about efficacy. D indicated that limited data could not support the intervention. E indicated either that strong data found no effect, or that either strong or weak data suggested that the intervention caused harm. RCTs could include either hospitalized or nonhospitalized patients. The EN or VNS had to be provided as part of a treatment plan for an underlying disease process. The RCT had to compare recipients of either EN or VNS with controls not receiving any type of artificial nutrition or had to compare recipients of EN with recipients of PN. These were mortality, morbidity (disease specific), duration of hospitalization, cost, or interventional complications. SUMMARY OF GRADING: A: No indication was identified. B: EN or VNS in the perioperative patient or in patients with chronic liver disease; EN in critically ill patients or low birth weight infants (trophic feeding); VNS in malnourished geriatric patients. (The low-quality trials found a significant difference in survival favoring the VNS recipients in the malnourished geriatric patient trials; two high-quality trials found nonsignificant differences that favored VNS as well.) C: EN or VNS in liver transplantation, cystic fibrosis, renal failure, pediatric conditions other than low birth weight infants, well-nourished geriatric patients, nonstroke neurologic conditions, AIDS; EN in acute pancreatitis, chronic obstructive pulmonary disease, nonmalnourished geriatric patients; VNS in inflammatory bowel disease, arthritis, cardiac disease, pregnancy, allergic patients, preoperative bowel preparation. D: EN or VNS in patients receiving nonsurgical cancer treatment or in patients with hip fractures; EN in patients with inflammatory bowel disease; VNS in patients with chronic obstructive pulmonary disease. E: EN in the first week in dysphagic, or VNS at any time in nondysphagic, stroke patients who are not malnourished; dysphagia persisting for weeks will presumably ultimately require EN. There is strong evidence for not using EN in the first week in dysphagic, and not using VNS at all in nondysphagic, stroke patients who are not malnourished. There is reasonable evidence for using VNS in malnourished geriatric patients. The recommendations to consider EN/VNS in perioperative/liver/critically ill/low birth weight patients are limited by the low quality of the RCTs. No evidence could be identified to justify the use of EN/VNS in other disease states.

Vagus nerve stimulation blocks vascular permeability following burn injury in both local and distal sites

PubMed Central

Ortiz-Pomales, Yan T; Krzyzaniak, Michael; Coimbra, Raul; Baird, Andrew; Eliceiri, Brian P.

2012-01-01

Recent studies have shown that vagus nerve stimulation (VNS) can block the burn injury-induced systemic inflammatory response (SIRS). In this study we examined the potential for VNS to modulate vascular permeability (VP) in local sites (i.e. skin) and in secondary sites (i.e. lung) following burn injury. In a 30% total body surface area burn injury model, VP was measured using intravascular fluorescent dextran for quantification of the VP response in skin and lung. A peak in VP of the skin was observed 24 hours post-burn injury, that was blocked by VNS. Moreover, in the lung, VNS led to a reduction in burn-induced VP compared to sham-treated animals subjected to burn injury alone. The protective effects of VNS in this model were independent of the spleen, suggesting that the spleen was not a direct mediator of VNS. These studies identify a role for VNS in the regulation of VP in burns, with the translational potential of attenuating lung complications following burn injury. PMID:22694873

Vagus nerve stimulation ameliorated deficits in one-way active avoidance learning and stimulated hippocampal neurogenesis in bulbectomized rats.

PubMed

Gebhardt, Nils; Bär, Karl-Jürgen; Boettger, Michael K; Grecksch, Gisela; Keilhoff, Gerburg; Reichart, Rupert; Becker, Axel

2013-01-01

Vagus nerve stimulation (VNS) has been introduced as a therapeutic option for treatment-resistant depression. The neural and chemical mechanisms responsible for the effects of VNS are largely unclear. Bilateral removal of the olfactory bulbs (OBX) is a validated animal model in depression research. We studied the effects of vagus nerve stimulation (VNS) on disturbed one-way active avoidance learning and neurogenesis in the hippocampal dentate gyrus of rats. After a stimulation period of 3 weeks, OBX rats acquired the learning task as controls. In addition, the OBX-related decrease of neuronal differentiated BrdU positive cells in the dentate gyrus was prevented by VNS. This suggests that chronic VNS and changes in hippocampal neurogenesis induced by VNS may also account for the amelioration of behavioral deficits in OBX rats. To the best of our knowledge, this is the first report on the restorative effects of VNS on behavioral function in an animal model of depression that can be compared with the effects of antidepressants. Copyright © 2013 Elsevier Inc. All rights reserved.

Repeatedly pairing vagus nerve stimulation with a movement reorganizes primary motor cortex.

PubMed

Porter, Benjamin A; Khodaparast, Navid; Fayyaz, Tabbassum; Cheung, Ryan J; Ahmed, Syed S; Vrana, William A; Rennaker, Robert L; Kilgard, Michael P

2012-10-01

Although sensory and motor systems support different functions, both systems exhibit experience-dependent cortical plasticity under similar conditions. If mechanisms regulating cortical plasticity are common to sensory and motor cortices, then methods generating plasticity in sensory cortex should be effective in motor cortex. Repeatedly pairing a tone with a brief period of vagus nerve stimulation (VNS) increases the proportion of primary auditory cortex responding to the paired tone (Engineer ND, Riley JR, Seale JD, Vrana WA, Shetake J, Sudanagunta SP, Borland MS, Kilgard MP. 2011. Reversing pathological neural activity using targeted plasticity. Nature. 470:101-104). In this study, we predicted that repeatedly pairing VNS with a specific movement would result in an increased representation of that movement in primary motor cortex. To test this hypothesis, we paired VNS with movements of the distal or proximal forelimb in 2 groups of rats. After 5 days of VNS movement pairing, intracranial microstimulation was used to quantify the organization of primary motor cortex. Larger cortical areas were associated with movements paired with VNS. Rats receiving identical motor training without VNS pairing did not exhibit motor cortex map plasticity. These results suggest that pairing VNS with specific events may act as a general method for increasing cortical representations of those events. VNS movement pairing could provide a new approach for treating disorders associated with abnormal movement representations.

Muscarinic contribution to the acute cortical effects of vagus nerve stimulation

NASA Astrophysics Data System (ADS)

Nichols, Justin A.

2011-12-01

Electrical stimulation of the vagus nerve (VNS) has been used to treat more than 60,000 patients with drug-resistant epilepsy and is under investigation as a treatment for several other neurological disorders and conditions. Among these, VNS increases memory performance and enhances recovery of motor and cognitive function in animal models of traumatic brain injury. Recent research indicates that pairing brief VNS with tones multiple-times a day for several weeks induces long-term, input specific cortical plasticity, which can be used to re-normalize the pathological cortical reorganization and eliminate a behavioral correlate of chronic tinnitus in noise exposed rats. Despite the therapeutic potential, the mechanisms of action of VNS remain speculative. In chapter 2 of this dissertation, the acute effects of VNS on cortical synchrony, excitability, and temporal processing are examined. In anesthetized rats implanted with multi-electrode arrays, VNS increased and decorrelated spontaneous multi-unit activity, and suppressed entrainment to repetitive noise burst stimulation at 6 to 8 Hz, but not after systemic administration of the muscarinic antagonist scopolamine. Chapter 3 focuses on VNS-tone pairing induced cortical plasticity. Pairing VNS with a tone one hundred times in anesthetized rats resulted in frequency specific plasticity in 31% of the auditory cortex sites. Half of these sites exhibited a frequency specific increase in firing rate and half exhibited a frequency specific decrease. Muscarinic receptor blockade with scopolamine almost entirely prevented the frequency specific increases, but not decreases. Collectively, these experiments demonstrate the capacity for VNS to not only acutely influence cortical synchrony, and excitability, but to also influence temporal and spectral tuning via muscarinic receptor activation. These results strengthen the hypothesis that acetylcholine and muscarinic receptors are involved in the mechanisms of action of VNS and are discussed with respect to their possible implications for sensory processing, neural plasticity, and epilepsy.

Vagal Nerve Stimulation Evoked Heart Rate Changes and Protection from Cardiac Remodeling.

PubMed

Agarwal, Rahul; Mokelke, Eric; Ruble, Stephen B; Stolen, Craig M

2016-02-01

This study investigated whether vagal nerve stimulation (VNS) leads to improvements in ischemic heart failure via heart rate modulation. At 7 ± 1 days post left anterior descending artery (LAD) ligation, 63 rats with myocardial infarctions (MI) were implanted with ECG transmitters and VNS devices (MI + VNS, N = 44) or just ECG transmitters (MI, N = 17). VNS stimulation was active from 14 ± 1 days to 8 ± 1 weeks post MI. The average left ventricular (LV) end diastolic volumes at 8 ± 1 weeks were MI = 672.40 μl and MI + VNS = 519.35 μl, p = 0.03. The average heart weights, normalized to body weight (± std) at 14 ± 1 weeks were MI = 3.2 ± 0.6 g*kg(-1) and MI + VNS = 2.9 ± 0.3 g*kg(-1), p = 0.03. The degree of cardiac remodeling was correlated with the magnitude of acute VNS-evoked heart rate (HR) changes. Further research is required to determine if the acute heart rate response to VNS activation is useful as a heart failure biomarker or as a tool for VNS therapy characterization.

Rates and Predictors of Seizure Freedom With Vagus Nerve Stimulation for Intractable Epilepsy

PubMed Central

Rolston, John D.; Wright, Clinton W.; Hassnain, Kevin H.; Chang, Edward F.

2015-01-01

BACKGROUND: Neuromodulation-based treatments have become increasingly important in epilepsy treatment. Most patients with epilepsy treated with neuromodulation do not achieve complete seizure freedom, and, therefore, previous studies of vagus nerve stimulation (VNS) therapy have focused instead on reduction of seizure frequency as a measure of treatment response. OBJECTIVE: To elucidate rates and predictors of seizure freedom with VNS. METHODS: We examined 5554 patients from the VNS therapy Patient Outcome Registry, and also performed a systematic review of the literature including 2869 patients across 78 studies. RESULTS: Registry data revealed a progressive increase over time in seizure freedom after VNS therapy. Overall, 49% of patients responded to VNS therapy 0 to 4 months after implantation (≥50% reduction seizure frequency), with 5.1% of patients becoming seizure-free, while 63% of patients were responders at 24 to 48 months, with 8.2% achieving seizure freedom. On multivariate analysis, seizure freedom was predicted by age of epilepsy onset >12 years (odds ratio [OR], 1.89; 95% confidence interval [CI], 1.38-2.58), and predominantly generalized seizure type (OR, 1.36; 95% CI, 1.01-1.82), while overall response to VNS was predicted by nonlesional epilepsy (OR, 1.38; 95% CI, 1.06-1.81). Systematic literature review results were consistent with the registry analysis: At 0 to 4 months, 40.0% of patients had responded to VNS, with 2.6% becoming seizure-free, while at last follow-up, 60.1% of individuals were responders, with 8.0% achieving seizure freedom. CONCLUSION: Response and seizure freedom rates increase over time with VNS therapy, although complete seizure freedom is achieved in a small percentage of patients. ABBREVIATIONS: AED, antiepileptic drug VNS, vagus nerve stimulation PMID:26645965

Transcutaneous Vagus Nerve Stimulation: A Promising Method for Treatment of Autism Spectrum Disorders

PubMed Central

Jin, Yu; Kong, Jian

2017-01-01

Transcutaneous Vagus Nerve Stimulation (tVNS) on the auricular branch of the vagus nerve has been receiving attention due to its therapeutic potential for neuropsychiatric disorders. Although the mechanism of tVNS is not yet completely understood, studies have demonstrated the potential role of vagal afferent nerve stimulation in the regulation of mood and visceral state associated with social communication. In addition, a growing body of evidence shows that tVNS can activate the brain regions associated with Autism Spectrum Disorder (ASD), trigger neuroimmune modulation and produce treatment effects for comorbid disorders of ASD such as epilepsy and depression. We thus hypothesize that tVNS may be a promising treatment for ASD, not only for comorbid epilepsy and depression, but also for the core symptoms of ASD. The goal of this manuscript is to summarize the findings and rationales for applying tVNS to treat ASD and propose potential parameters for tVNS treatment of ASD. PMID:28163670

The role of laryngeal electromyography in vagus nerve stimulation-related vocal fold dysmotility.

PubMed

Saibene, Alberto M; Zambrelli, Elena; Pipolo, Carlotta; Maccari, Alberto; Felisati, Giovanni; Felisati, Elena; Furia, Francesca; Vignoli, Aglaia; Canevini, Maria Paola; Alfonsi, Enrico

2017-03-01

Vagus nerve stimulation (VNS) is a useful tool for drug-resistant epilepsy, but it induces known laryngeal side effects, with a significant role on patients' quality of life. VNS patients may show persistent left vocal fold (LVF) palsy at rest and/or recurrent LVF adduction during stimulation. This study aims at electromyographically evaluating laryngeal muscles abnormalities in VNS patients. We compared endoscopic laryngeal evaluation data in six VNS patients with laryngeal muscle electromyography (LMEMG) carried out on the thyroarytenoid, cricothyroid, posterior cricoarytenoid, and cricopharyngeal muscles. Endoscopy showed LVF palsy at rest in 3/6 patients in whom LMEMG documented a tonic spastic activity with reduced phasic modulation. In four out of six patients with recurrent LVF adduction during VNS activation, LMEMG showed a compound muscle action potential persisting for the whole stimulation. This is the first LMEMG report of VNS-induced motor unit activation via recurrent laryngeal nerve and upper laryngeal nerve stimulation. LMEMG data were could, therefore, be considered consistent with the endoscopic laryngeal examination in all patient.

Rapid Remission of Conditioned Fear Expression with Extinction Training Paired with Vagus Nerve Stimulation

PubMed Central

Peña, David F.; Engineer, Navzer D.; McIntyre, Christa K.

2012-01-01

Background Fearful experiences can produce long-lasting and debilitating memories. Extinction of conditioned fear requires consolidation of new memories that compete with fearful associations. In human subjects, as well as rats, posttraining stimulation of the vagus nerve enhances memory consolidation. Subjects with posttraumatic stress disorder (PTSD) show impaired extinction of conditioned fear. The objective of this study was to determine whether vagus nerve stimulation (VNS) can enhance the consolidation of extinction of conditioned fear. Methods Male Sprague-Dawley rats were trained on an auditory fear conditioning task followed by 1–10 days of extinction training. Treatment with vagus nerve or sham stimulation was administered concurrently with exposure to the fear conditioned stimulus. Another group was given VNS and extinction training but the VNS was not paired with exposure to conditioned cues. Retention of fear conditioning was tested 24 hours after each treatment. Results VNS paired with exposure to conditioned cues enhanced the extinction of conditioned fear. After a single extinction trial, rats given VNS stimulation demonstrated a significantly lower level of freezing, compared to that of sham controls. When extinction trials were extended to 10 days, paired VNS accelerated extinction of the conditioned response. Conclusions Extinction paired with VNS is more rapid than extinction paired with sham stimulation. As it is currently approved by the Federal Food and Drug Administration for depression and seizure prevention, VNS is a readily-available and promising adjunct to exposure therapy for the treatment of severe anxiety disorders. PMID:23245749

Use of social media to assess the effectiveness of vagal nerve stimulation in Dravet syndrome: A caregiver's perspective.

PubMed

Ali, Rushna; Elsayed, Mona; Kaur, Manpreet; Air, Ellen; Mahmood, Naznin; Constantinou, Jules; Schwalb, Jason

2017-04-15

Dravet syndrome (DS) is a rare genetic epilepsy syndrome which is particularly pharmacoresistant. Vagus nerve stimulation (VNS) is commonly used in the treatment of DS as an adjunct to medical therapy. A meaningful assessment of post-surgical outcomes with VNS is difficult given the rarity of the condition. In a novel approach, we used social media to contact patients with DS to gather data on post-surgical seizure reduction and overall satisfaction with VNS. A survey consisting of 10 questions was posted to a social media webpage for a DS support group moderated by the Dravet Syndrome Foundation. The results were analyzed and percentages reported using the integrated SurveyMonkey analytical software. 49 responses were received. We found that 28.5% of patients had a >50% reduction in seizure frequency after VNS placement, 55.8% felt that VNS therapy had helped to reduce seizure frequency, and 83.7% felt that seizure severity had improved. Of the respondents, 75% felt that they would undergo VNS implantation again for similar outcomes. We employed the novel technique of using social media to gather the largest set of self-reported outcomes of VNS therapy for Dravet syndrome. As corroborated by prior studies of VNS effectiveness in Dravet syndrome, there is significant albeit limited improvement in seizure control. Our study shows that despite this limitation, it is still considered a useful treatment adjunct from a caregiver's perspective. Copyright © 2017 Elsevier B.V. All rights reserved.

Effect of transcutaneous auricular vagus nerve stimulation on impaired glucose tolerance: a pilot randomized study.

PubMed

Huang, Feng; Dong, Jianxun; Kong, Jian; Wang, Hongcai; Meng, Hong; Spaeth, Rosa B; Camhi, Stephanie; Liao, Xing; Li, Xia; Zhai, Xu; Li, Shaoyuan; Zhu, Bing; Rong, Peijing

2014-06-26

Impaired glucose tolerance (IGT) is a pre-diabetic state of hyperglycemia that is associated with insulin resistance, increased risk of type II diabetes, and cardiovascular pathology. Recently, investigators hypothesized that decreased vagus nerve activity may be the underlying mechanism of metabolic syndrome including obesity, elevated glucose levels, and high blood pressure. In this pilot randomized clinical trial, we compared the efficacy of transcutaneous auricular vagus nerve stimulation (taVNS) and sham taVNS on patients with IGT. 72 participants with IGT were single-blinded and were randomly allocated by computer-generated envelope to either taVNS or sham taVNS treatment groups. In addition, 30 IGT adults were recruited as a control population and not assigned treatment so as to monitor the natural fluctuation of glucose tolerance in IGT patients. All treatments were self-administered by the patients at home after training at the hospital. Patients were instructed to fill in a patient diary booklet each day to describe any side effects after each treatment. The treatment period was 12 weeks in duration. Baseline comparison between treatment and control group showed no difference in weight, BMI, or measures of systolic blood pressure, diastolic blood pressure, fasting plasma glucose (FPG), 2-hour plasma glucose (2hPG), or glycosylated hemoglobin (HbAlc). 100 participants completed the study and were included in data analysis. Two female patients (one in the taVNS group, one in the sham taVNS group) dropped out of the study due to stimulation-evoked dizziness. The symptoms were relieved after stopping treatment. Compared with sham taVNS, taVNS significantly reduced the two-hour glucose tolerance (F(2) = 5.79, p = 0.004). In addition, we found that taVNS significantly decreased (F(1) = 4.21, p = 0.044) systolic blood pressure over time compared with sham taVNS. Compared with the no-treatment control group, patients receiving taVNS significantly differed in measures of FPG (F(2) = 10.62, p < 0.001), 2hPG F(2) = 25.18, p < 0.001) and HbAlc (F(1) = 12.79, p = 0.001) over the course of the 12 week treatment period. Our study suggests that taVNS is a promising, simple, and cost-effective treatment for IGT/ pre-diabetes with only slight risk of mild side-effects.

Temporal plasticity in auditory cortex improves neural discrimination of speech sounds

PubMed Central

Engineer, Crystal T.; Shetake, Jai A.; Engineer, Navzer D.; Vrana, Will A.; Wolf, Jordan T.; Kilgard, Michael P.

2017-01-01

Background Many individuals with language learning impairments exhibit temporal processing deficits and degraded neural responses to speech sounds. Auditory training can improve both the neural and behavioral deficits, though significant deficits remain. Recent evidence suggests that vagus nerve stimulation (VNS) paired with rehabilitative therapies enhances both cortical plasticity and recovery of normal function. Objective/Hypothesis We predicted that pairing VNS with rapid tone trains would enhance the primary auditory cortex (A1) response to unpaired novel speech sounds. Methods VNS was paired with tone trains 300 times per day for 20 days in adult rats. Responses to isolated speech sounds, compressed speech sounds, word sequences, and compressed word sequences were recorded in A1 following the completion of VNS-tone train pairing. Results Pairing VNS with rapid tone trains resulted in stronger, faster, and more discriminable A1 responses to speech sounds presented at conversational rates. Conclusion This study extends previous findings by documenting that VNS paired with rapid tone trains altered the neural response to novel unpaired speech sounds. Future studies are necessary to determine whether pairing VNS with appropriate auditory stimuli could potentially be used to improve both neural responses to speech sounds and speech perception in individuals with receptive language disorders. PMID:28131520

An analysis of quality of life (QOL) in patients with epilepsy and comorbid psychogenic nonepileptic seizures (PNES) after vagus nerve stimulation (VNS).

PubMed

Vivas, Andrew C; Reitano, Christian J; Waseem, Hena; Benbadis, Selim R; Vale, Fernando L

2017-08-01

Patients with epilepsy (PWE) may suffer from comorbid psychogenic nonepileptic seizures (PNES). The efficacy of vagus nerve stimulation (VNS) in the treatment of epilepsy and depression is established, however the impact on PNES is unknown. Since many patients with PNES have comorbid depression, we explored the impact on quality of life (QOL) that VNS has on PWE and PNES. The video electroencephalogram (vEEG) of all patients who underwent VNS at our institution was reviewed. Patients diagnosed with both psychogenic seizures and epileptic seizures on their vEEG were included in this study. These patients were contacted, and given a QOLIE-31 survey to assess their quality of life after VNS. Patients also completed a separate survey created by our group to categorize the quartile of their improvement. Pre-operative psychiatric disease was retrospectively reviewed. From a period of 2001 to 2016, 518 patients underwent placement of VNS for drug resistant epilepsy (DRE) at our institution. In total, 16 patients were diagnosed with both epilepsy and PNES. 11/16 patients responded to our questionnaire and survey. 9 out of 11 patients felt that their epileptic seizures had improved after VNS, while 7 of the 11 patients felt that their psychogenic episodes had improved. 2(28.6%), 1 (14.3%), and 4 (57.1%) of participants said their PNES improved by 25-50%, 50-75%, and 75-100%, respectively. 3(27.3%), 3 (27.3%), 1 (9.1%), and 4 (36.4%) of the participants said their epileptic seizures improved by 0-25%, 25-50%, 50-75%, and 75-100%, respectively. The average overall score for quality of life for the study participants was found to be 51 (±8) out of 100. Patients with epilepsy and comorbid PNES may benefit from VNS. It is unclear whether the benefit is conferred strictly from decreased epileptic seizure burden. The possible effect on PNES may be related to the known effect of VNS on depression. Further studies are necessary to elucidate the role of VNS in the treatment of PNES and possibly other psychiatric disease. Copyright © 2017 Elsevier Inc. All rights reserved.

Vagal nerve stimulator: Evolving trends

PubMed Central

Ogbonnaya, Sunny; Kaliaperumal, Chandrasekaran

2013-01-01

Over three decades ago, it was found that intermittent electrical stimulation from the vagus nerve produces inhibition of neural processes, which can alter brain activity and terminate seizures. This paved way for the concept of vagal nerve stimulator (VNS). We describe the evolution of the VNS and its use in different fields of medicine. We also review the literature focusing on the mechanism of action of VNS producing desired effects in different conditions. PUBMED and EMBASE search was performed for ‘VNS’ and its use in refractory seizure management, depression, obesity, memory, and neurogenesis. VNS has been in vogue over for the past three decades and has proven to reduce the intensity and frequency of seizure by 50% in the management of refractory seizures. Apart from this, VNS has been shown to promote neurogenesis in the dentate gyrus of rat hippocampus after 48 hours of stimulation of the vagus nerve. Improvement has also been observed in non-psychotic major depression from a randomized trial conducted 7 years ago. The same concept has been utilized to alter behavior and cognition in rodents, and good improvement has been observed. Recent studies have proven that VNS is effective in obesity management in patients with depression. Several hypotheses have been postulated for the mechanism of action of VNS contributing to its success. VNS has gained significant popularity with promising results in epilepsy surgery and treatment-resistant depression. The spectrum of its use has also extended to other fields of medicine including obesity, memory, and neurogenesis, and there is still a viable scope for its utility in the future. PMID:23633829

Low-Level Vagus Nerve Stimulation Attenuates Myocardial Ischemic Reperfusion Injury by Antioxidative Stress and Antiapoptosis Reactions in Canines.

PubMed

Chen, Mingxian; Zhou, Xiaoya; Yu, Lilei; Liu, Qiming; Sheng, Xia; Wang, Zhuo; Wang, Songyun; Jiang, Hong; Zhou, Shenghua

2016-02-01

Low-level vagus nerve stimulation (LL-VNS) has been demonstrated to protect myocardium against acute ischemia/reperfusion (I/R) injury. However, the underlying mechanism of this protective effect remains unknown. This study aimed to test the hypothesis that LL-VNS exerts cardioprotective effect on acute I/R injury in canines via antioxidative stress and antiapoptosis reactions. Thirty anesthetized mongrel dogs were randomly divided into three groups: I/R group (N = 12, the left anterior descending coronary artery was occluded for 1 hour following by 1 hour reperfusion), LL-VNS group (N = 9, I/R plus LL-VNS), and sham group (N = 9, sham surgery without LL-VNS). The voltage threshold was set at 80% of the voltage required to slow the sinus rate. Infarct size was assessed with Evans Blue and triphenyltetrazolium chloride. Activity assays, TUNEL staining, and western blotting were performed to determine markers of oxidative stress and apoptosis. LL-VNS significantly decreased the incidence of ventricular arrhythmias, increased vagal tone, as confirmed by heart rate viability, and reduced infarct size compared with the I/R group. This improvement was associated with a reduction in myocardial neutrophil infiltration, the inhibition of oxidative stress, and the suppression in cardiomyocyte apoptosis. In contrast, the lack of LL-VNS in the I/R group induced the opposite effect compared with the sham group. LL-VNS exerts protective effects on myocardial I/R injury. Its potential mechanisms involve the suppression of oxidative stress and cellular apoptosis. © 2015 Wiley Periodicals, Inc.

Vagus Nerve Stimulation Reduces Cocaine Seeking and Alters Plasticity in the Extinction Network

ERIC Educational Resources Information Center

Childs, Jessica E.; DeLeon, Jaime; Nickel, Emily; Kroener, Sven

2017-01-01

Drugs of abuse cause changes in the prefrontal cortex (PFC) and associated regions that impair inhibitory control over drug-seeking. Breaking the contingencies between drug-associated cues and the delivery of the reward during extinction learning reduces rates of relapse. Here we used vagus nerve stimulation (VNS) to induce targeted synaptic…

Safety, Feasibility, and Efficacy of Vagus Nerve Stimulation Paired With Upper-Limb Rehabilitation After Ischemic Stroke.

PubMed

Dawson, Jesse; Pierce, David; Dixit, Anand; Kimberley, Teresa J; Robertson, Michele; Tarver, Brent; Hilmi, Omar; McLean, John; Forbes, Kirsten; Kilgard, Michael P; Rennaker, Robert L; Cramer, Steven C; Walters, Matthew; Engineer, Navzer

2016-01-01

Recent animal studies demonstrate that vagus nerve stimulation (VNS) paired with movement induces movement-specific plasticity in motor cortex and improves forelimb function after stroke. We conducted a randomized controlled clinical pilot study of VNS paired with rehabilitation on upper-limb function after ischemic stroke. Twenty-one participants with ischemic stroke >6 months before and moderate to severe upper-limb impairment were randomized to VNS plus rehabilitation or rehabilitation alone. Rehabilitation consisted of three 2-hour sessions per week for 6 weeks, each involving >400 movement trials. In the VNS group, movements were paired with 0.5-second VNS. The primary objective was to assess safety and feasibility. Secondary end points included change in upper-limb measures (including the Fugl-Meyer Assessment-Upper Extremity). Nine participants were randomized to VNS plus rehabilitation and 11 to rehabilitation alone. There were no serious adverse device effects. One patient had transient vocal cord palsy and dysphagia after implantation. Five had minor adverse device effects including nausea and taste disturbance on the evening of therapy. In the intention-to-treat analysis, the change in Fugl-Meyer Assessment-Upper Extremity scores was not significantly different (between-group difference, 5.7 points; 95% confidence interval, -0.4 to 11.8). In the per-protocol analysis, there was a significant difference in change in Fugl-Meyer Assessment-Upper Extremity score (between-group difference, 6.5 points; 95% confidence interval, 0.4 to 12.6). This study suggests that VNS paired with rehabilitation is feasible and has not raised safety concerns. Additional studies of VNS in adults with chronic stroke will now be performed. URL: https://www.clinicaltrials.gov. Unique identifier: NCT01669161. © 2015 The Authors.

Safety, Feasibility, and Efficacy of Vagus Nerve Stimulation Paired With Upper-Limb Rehabilitation After Ischemic Stroke

PubMed Central

Pierce, David; Dixit, Anand; Kimberley, Teresa J.; Robertson, Michele; Tarver, Brent; Hilmi, Omar; McLean, John; Forbes, Kirsten; Kilgard, Michael P.; Rennaker, Robert L.; Cramer, Steven C.; Walters, Matthew; Engineer, Navzer

2016-01-01

Background and Purpose— Recent animal studies demonstrate that vagus nerve stimulation (VNS) paired with movement induces movement-specific plasticity in motor cortex and improves forelimb function after stroke. We conducted a randomized controlled clinical pilot study of VNS paired with rehabilitation on upper-limb function after ischemic stroke. Methods— Twenty-one participants with ischemic stroke >6 months before and moderate to severe upper-limb impairment were randomized to VNS plus rehabilitation or rehabilitation alone. Rehabilitation consisted of three 2-hour sessions per week for 6 weeks, each involving >400 movement trials. In the VNS group, movements were paired with 0.5-second VNS. The primary objective was to assess safety and feasibility. Secondary end points included change in upper-limb measures (including the Fugl–Meyer Assessment-Upper Extremity). Results— Nine participants were randomized to VNS plus rehabilitation and 11 to rehabilitation alone. There were no serious adverse device effects. One patient had transient vocal cord palsy and dysphagia after implantation. Five had minor adverse device effects including nausea and taste disturbance on the evening of therapy. In the intention-to-treat analysis, the change in Fugl–Meyer Assessment-Upper Extremity scores was not significantly different (between-group difference, 5.7 points; 95% confidence interval, −0.4 to 11.8). In the per-protocol analysis, there was a significant difference in change in Fugl–Meyer Assessment-Upper Extremity score (between-group difference, 6.5 points; 95% confidence interval, 0.4 to 12.6). Conclusions— This study suggests that VNS paired with rehabilitation is feasible and has not raised safety concerns. Additional studies of VNS in adults with chronic stroke will now be performed. Clinical Trial Registration— URL: https://www.clinicaltrials.gov. Unique identifier: NCT01669161. PMID:26645257

Vagus Nerve Stimulation in children: A focus on intellectual disability.

PubMed

Sourbron, Jo; Klinkenberg, Sylvia; Kessels, Alfons; Schelhaas, Helenius Jurgen; Lagae, Lieven; Majoie, Marian

2017-05-01

Vagus Nerve Stimulation (VNS) can be an efficacious add-on treatment in patients with drug-resistant epilepsy, who are not eligible for surgery. Evidence of VNS efficacy in children with intellectual disability (ID) is scarce. The purpose of this study was to review all available VNS data in the pediatric population (≤18 years old) and focus on the subpopulation with ID since appropriate treatment of these children is often challenging and complex. Cochrane, EMBASE, PubMed and MEDLINE were used to collect all research associated to VNS and ID (or synonyms) leading to a total of 37 studies. Seven studies showed the results of patients with ID and those without separately; thereby only these studies were included in the VNS meta-analysis. Our meta-analysis showed that VNS was less effective in pediatric epilepsy patients with ID compared to those without ID (Mantel-Haenszel meta-analysis; p = 0.028, OR 0.18 (CI 95% 0.039-0.84)). However, there were no prospective controlled studies. Numerous studies reported quality of life (QoL) improvements in this subpopulation. The most common adverse events were transient and well tolerated. Side effects on cognition and behavior were not reported. These results might be a reason to consider VNS early on in the treatment of this subgroup. The significantly greater amount of retrospective studies, differences in follow-up (FU), lack of control data, heterogeneous series and limited number of patients could have biased the outcome measurements. Hence, current data do not exclude VNS for children with drug-resistant epilepsy and ID but should be interpreted with caution. Copyright © 2017 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Parenting stress in parents of children with refractory epilepsy before and after vagus nerve stimulation implantation.

PubMed

Li, Sung-Tse; Chiu, Nan-Chang; Kuo, Yung-Ting; Shen, Ein-Yiao; Tsai, Pei-Chieh; Ho, Che-Sheng; Wu, Wen-Hsiang; Chen, Juei-Chao

2017-12-01

The purpose of this study was to evaluate parenting stress in parents of children with refractory epilepsy before and after their children received vagus nerve stimulation (VNS) implantation. Parents of children with refractory epilepsy completed the Parenting Stress Index (PSI) under a psychologist's assessment before and at least 12 months after their children received VNS implantation. The PSI questionnaire measures parenting stress in two domains; a parent domain with seven subscales, and a child domain with six. Age, gender, epilepsy comorbidity, VNS implantation date, seizure frequency, and anticonvulsant history before and after VNS implantation were obtained from reviews of medical charts. In total, 30 parents completed the first and follow-up PSI questionnaires. Seventeen of their children (56.7%) were boys. The children aged from 1 to 12 years (7.43 ± 3.59 years, mean ± SD). After VNS implantation, the mean total parenting stress scores decreased from 282.1 ± 38.0 to 272.4 ± 42.9. A significant decrease was found on the spouse subscale of the parent domain. For the parents of boys, the mean total parenting stress scores decreased significantly. The mean total parenting stress scores also decreased significantly for parents of epileptic children without autism and who did not taper off the number of different anticonvulsants used after VNS. VNS is an advisable choice to treat refractory epilepsy. Our study showed that 12 months or more after VNS implantation, seizure frequency and parenting stress typically decreased. However, in some special cases the parenting stress may increase, and external help may be required to support these patients and their parents. Copyright © 2017. Published by Elsevier B.V.

Scaling view by the Virtual Nature Systems

NASA Astrophysics Data System (ADS)

Klenov, Valeriy

2010-05-01

The Virtual Nature System is irreplaceable for research and evaluation for governing processes on the Earth. Processes on the Earth depends on external exogenous and endogenous influences, and on own dynamics of the Actual Nature Systems (ANS). To select part of the actors is impossible without take in account factor of the Time, factor for information safety during the Time. The stochastic nature of external influences and stochastic pattern for dynamics of Nature systems complicates evaluation of 2D threat of disasters. These are multi-layer, multi-scale, and multi-driven structures of surface processes. Their spatial-temporal overlapping of them generates relatively stable structure of river basins and of river net. Dynamics of processes in river basins results in remove of the former sediments and levels, and in displace of erosion/sedimentation pattern, in destroy and dissipation for a memory the ANS. This complex process results in the Information Loss Law (ILL) in the ANS, which gradually cut off own Past. This view on the GeoDynamics appeared after long time field measurements thousands of terrace levels, hundreds of terrace ranks, and terrace complexes in river basins (Klenov, 1986, 2004). Action of the ILL leads to blanks in natural records, which are non-linearly increasing to the Past, and in appearance of false trends in the records. This temporal barrier prevents evaluation of the history. The way to view spatial-temporal dynamics of the ANS is creation for the portrait Virtual Nature Systems, as acting doubles of the actual nature systems (ANS). Exogenous and endogenous influences are governing drivers of the ANS and of corresponding VNS. The VNS is necessary for research of spatial-temporal GeoDynamics. Unfortunately, the ILL is working not only for the Past, but also restrict ‘view' the Future. It is because of future drivers are yet unknown with necessary exactness, and due high sensitivity of nature systems to external pressure. However, a time for validation of the VNS is short to receive non distorted records, but it provides satisfactory validation of the VNS, and provides satisfactory evaluation for stochastic patterns of disasters (floods and debris flows). The VNS gives a chance to divide exogenous (climatic) from tectonic influences. This property is invaluable for monitoring and scenarios of land use, engineering, and other human activity, under simultaneous climatic and tectonic impacts, for evaluation of threat's areas and tracks. The continually measured stochastic spatial-temporal interception of external impacts (storms, precipitations, of tectonic distorts, earthquakes, and others), does not make problems for the VNS (acting by observed records), and by imply the Moving Digital Earth (MDE) technology (by immediate reforming of external drivers to natural processes). It is a goal for the VNS and MDE, which becomes possible by remote sensing, by powerful computers, and by fast communications. The VNS/MDE presents corresponding mapping for processes in any area. Instead of problems of scaling the current task is to provide necessary spatial resolution of the basic multi-layer Matrix of variables and parameters. Problems are in procedures for filling up of large multi-layer M, quick computing and mapping of large areas. The scaling depends on a task. The acceptable spatial resolution of the Matrix must perceive in view to hazardous processes with acceptable in resolution. During the VNS practice were evaluated any imagined combines of exogenous-endogenous impacts (from linear to circle distort, blocks, volcanoes, earthquakes, and others, in a variety of scales from local to sub-continental. The single principle for choose a scale is that spatial resolution (cell size) should not ignore important details of the Earth. For the Rhine Basin was computed influence of small smooth tectonic distorts in a large area. It was resulted in essential change for pattern of erosion/sedimentation on a land, and in Coastal Zone. For small basis were computed scenarios for complex tectonic distorts, earthquakes, resulted in decreasing of soil/rock resistance and in sharp increasing of catastrophic debris flows and flash floods. Any scenarios are possible for the verified/validated VNS. The VNS is valuable for any area, and the MDE has a skill for mapping the soon Future, and for mapping of threats' areas and tracks.

Vagus nerve stimulation as a potential adjuvant to behavioral therapy for autism and other neurodevelopmental disorders.

PubMed

Engineer, Crystal T; Hays, Seth A; Kilgard, Michael P

2017-01-01

Many children with autism and other neurodevelopmental disorders undergo expensive, time-consuming behavioral interventions that often yield only modest improvements. The development of adjunctive interventions that can increase the benefit of rehabilitation therapies is essential in order to improve the lives of individuals with neurodevelopmental disorders. Vagus nerve stimulation (VNS) is an FDA approved therapy that is safe and effective in reducing seizure frequency and duration in individuals with epilepsy. Individuals with neurodevelopmental disorders often exhibit decreased vagal tone, and studies indicate that VNS can be used to overcome an insufficient vagal response. Multiple studies have also documented significant improvements in quality of life after VNS therapy in individuals with neurodevelopmental disorders. Moreover, recent findings indicate that VNS significantly enhances the benefits of rehabilitative training in animal models and patients, leading to greater recovery in a variety of neurological diseases. Here, we review these findings and provide a discussion of how VNS paired with rehabilitation may yield benefits in the context of neurodevelopmental disorders. VNS paired with behavioral therapy may represent a potential new approach to enhance rehabilitation that could significantly improve the outcomes of individuals with neurodevelopmental disorders.

Pairing tone trains with vagus nerve stimulation induces temporal plasticity in auditory cortex.

PubMed

Shetake, Jai A; Engineer, Navzer D; Vrana, Will A; Wolf, Jordan T; Kilgard, Michael P

2012-01-01

The selectivity of neurons in sensory cortex can be modified by pairing neuromodulator release with sensory stimulation. Repeated pairing of electrical stimulation of the cholinergic nucleus basalis, for example, induces input specific plasticity in primary auditory cortex (A1). Pairing nucleus basalis stimulation (NBS) with a tone increases the number of A1 neurons that respond to the paired tone frequency. Pairing NBS with fast or slow tone trains can respectively increase or decrease the ability of A1 neurons to respond to rapidly presented tones. Pairing vagus nerve stimulation (VNS) with a single tone alters spectral tuning in the same way as NBS-tone pairing without the need for brain surgery. In this study, we tested whether pairing VNS with tone trains can change the temporal response properties of A1 neurons. In naïve rats, A1 neurons respond strongly to tones repeated at rates up to 10 pulses per second (pps). Repeatedly pairing VNS with 15 pps tone trains increased the temporal following capacity of A1 neurons and repeatedly pairing VNS with 5 pps tone trains decreased the temporal following capacity of A1 neurons. Pairing VNS with tone trains did not alter the frequency selectivity or tonotopic organization of auditory cortex neurons. Since VNS is well tolerated by patients, VNS-tone train pairing represents a viable method to direct temporal plasticity in a variety of human conditions associated with temporal processing deficits. Copyright © 2011 Elsevier Inc. All rights reserved.

Vagus nerve stimulation therapy in partial epilepsy: a review.

PubMed

Panebianco, Mariangela; Zavanone, Chiara; Dupont, Sophie; Restivo, Domenico A; Pavone, Antonino

2016-09-01

Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked epileptic seizures. The majority of people given a diagnosis of epilepsy have a good prognosis, but 20-30 % will develop drug-resistant epilepsy. Vagus nerve stimulation (VNS) is a neuromodulatory treatment that is used as an adjunctive therapy for treating people with medically refractory epilepsy. It consists of chronic intermittent electrical stimulation of the vagus nerve, delivered by a programmable pulse generator (Neuro-Cybernetic Prosthesis). In 1997, the Food and Drug Administration approved VNS as adjunctive treatment for medically refractory partial-onset seizures in adults and adolescents. This article reviews the literature from 1988 to nowadays. We discuss thoroughly the anatomy and physiology of vagus nerve and the potential mechanisms of actions and clinical applications involved in VNS therapy, as well as the management, safety, tolerability and effectiveness of VNS therapy. VNS for partial seizures appears to be an effective and well tolerated treatment in adult and pediatric patients. People noted improvements in feelings of well-being, alertness, memory and thinking skills, as well as mood. The adverse effect profile is substantially different from the adverse effect profile associated with antiepileptic drugs, making VNS a potential alternative for patients with difficulty tolerating antiepileptic drug adverse effects. Despite the passing years and the advent of promising neuromodulation technologies, VNS remains an efficacy treatment for people with medically refractory epilepsy. Past and ongoing investigations in other indications have provided signals of the therapeutic potential in a wide variety of conditions.

Tuberous Sclerosis with Epilepsy

DTIC Science & Technology

2009-02-01

seizures. She uses a vagal nerve stimulator (VNS) set at maximum level, and she is maintained on a modified ketogenic diet to help control seizure...refractory to medical treatment include ketogenic diet or VNS. The anticonvulsant effects of the ketogenic diet are a result of the persistent state of...patient’s situation improved seizure control was achieved with a combination of a modified ketogenic diet , VNS, and depot medroxyprogesterone acetate

Quality-of-life metrics with vagus nerve stimulation for epilepsy from provider survey data.

PubMed

Englot, Dario J; Hassnain, Kevin H; Rolston, John D; Harward, Stephen C; Sinha, Saurabh R; Haglund, Michael M

2017-01-01

Drug-resistant epilepsy is a devastating disorder associated with diminished quality of life (QOL). Surgical resection leads to seizure freedom and improved QOL in many epilepsy patients, but not all individuals are candidates for resection. In these cases, neuromodulation-based therapies such as vagus nerve stimulation (VNS) are often used, but most VNS studies focus exclusively on reduction of seizure frequency. QOL changes and predictors with VNS remain poorly understood. Using the VNS Therapy Patient Outcome Registry, we examined 7 metrics related to QOL after VNS for epilepsy in over 5000 patients (including over 3000 with ≥12months follow-up), as subjectively assessed by treating physicians. Trends and predictors of QOL changes were examined and related to post-operative seizure outcome and likelihood of VNS generator replacement. After VNS therapy, physicians reported patient improvement in alertness (58-63%, range over follow-up period), post-ictal state (55-62%), cluster seizures (48-56%), mood change (43-49%), verbal communication (38-45%), school/professional achievements (29-39%), and memory (29-38%). Predictors of net QOL improvement included shorter time to implant (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.1-1.6), generalized seizure type (OR, 1.2; 95% CI, 1.0-1.4), female gender (OR, 1.2; 95% CI, 1.0-1.4), and Caucasian ethnicity (OR, 1.3; 95% CI, 1.0-1.5). No significant trends were observed over time. Patients with net QOL improvement were more likely to have favorable seizure outcomes (chi square [χ 2 ]=148.1, p<0.001) and more likely to undergo VNS generator replacement (χ 2 =68.9, p<0.001) than those with worsened/unchanged QOL. VNS for drug-resistant epilepsy is associated with improvement on various QOL metrics subjectively rated by physicians. QOL improvement is associated with favorable seizure outcome and a higher likelihood of generator replacement, suggesting satisfaction with therapy. It is important to consider QOL metrics in neuromodulation for epilepsy, given the deleterious effects of seizures on patient QOL. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation.

PubMed

Bula, Gustavo Alfredo; Prodhon, Caroline; Gonzalez, Fabio Augusto; Afsar, H Murat; Velasco, Nubia

2017-02-15

This work focuses on the Heterogeneous Fleet Vehicle Routing problem (HFVRP) in the context of hazardous materials (HazMat) transportation. The objective is to determine a set of routes that minimizes the total expected routing risk. This is a nonlinear function, and it depends on the vehicle load and the population exposed when an incident occurs. Thus, a piecewise linear approximation is used to estimate it. For solving the problem, a variant of the Variable Neighborhood Search (VNS) algorithm is employed. To improve its performance, a post-optimization procedure is implemented via a Set Partitioning (SP) problem. The SP is solved on a pool of routes obtained from executions of the local search procedure embedded on the VNS. The algorithm is tested on two sets of HFVRP instances based on literature with up to 100 nodes, these instances are modified to include vehicle and arc risk parameters. The results are competitive in terms of computational efficiency and quality attested by a comparison with Mixed Integer Linear Programming (MILP) previously proposed. Copyright © 2016 Elsevier B.V. All rights reserved.

Vagus nerve stimulation for 6- to 12-year-old children with refractory epilepsy: Impact on seizure frequency and parenting stress index.

PubMed

Fan, Hueng-Chuen; Hsu, Ting-Rong; Chang, Kai-Ping; Chen, Shyi-Jou; Tsai, Jeng-Dau

2018-06-01

Refractory epilepsy (RE) is frequently associated with neuropsychological impairment in children and may disrupt their social development. Vagus nerve stimulation (VNS) had been reported to have beneficial effects on behavioral outcomes. The aim of this study was to compare Parenting Stress Index (PSI) scores before and after VNS device implantation in children with RE, especially those who experienced seizure frequency reduction. We conducted a one-group pretest-posttest study in school age children with RE. Seizure frequency and PSI were recorded at 12months after VNS device implantation. Treatment with VNS was significantly associated with reduced seizure frequency and parental stress as measured by PSI. Factors contributing to seizure frequency included idiopathic/cryptogenic etiology and neurobehavioral comorbidities. In children with reduced seizure frequency, statistically significant improvements in the child domain of the PSI on the subscales of mood and reinforces parent were found. In the parent domain, the scores for social isolation were reduced. Treatment with VNS was significantly associated with reduced seizure frequency and improved PSI scores, especially within the child domain on the mood and reinforces parent subscales. These findings suggest that VNS reduced not only seizure frequency but also the psychological burden on children with RE. Copyright © 2017 Elsevier Inc. All rights reserved.

Mixed evidence for the potential of non-invasive transcutaneous vagal nerve stimulation to improve the extinction and retention of fear.

PubMed

Burger, A M; Verkuil, B; Fenlon, H; Thijs, L; Cools, L; Miller, H C; Vervliet, B; Van Diest, I

2017-10-01

Extinction memories are fragile and their formation has been proposed to partially rely on vagus nerve activity. We tested whether stimulating the auricular branch of the vagus (transcutaneous VNS; tVNS) accelerates extinction and reduces spontaneous recovery of fear. Forty-two healthy students participated in a 3-day fear conditioning study, where we tested fear acquisition (day 1), fear extinction (day 2) and the retention of the extinction memory (day 3). During extinction, participants were randomly allocated to receive tVNS or sham stimulation concurrently with each CS presentation. During the acquisition and retention phases, all participants received sham stimulation. Indexes of fear included US-expectancy, startle blink EMG and skin conductance responses. Results showed successful acquisition and extinction of fear in all measures. tVNS facilitated the extinction of declarative fear (US expectancy ratings), but did not promote a stronger retention of the declarative extinction memory. No clear effects of tVNS on extinction and retention of extinction were found for the psychophysiological indexes. The present findings provide tentative indications that tVNS could be a promising tool to improve fear extinction and call for larger scale studies to replicate these effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

Chronic cuffing of cervical vagus nerve inhibits efferent fiber integrity in rat model

NASA Astrophysics Data System (ADS)

Somann, Jesse P.; Albors, Gabriel O.; Neihouser, Kaitlyn V.; Lu, Kun-Han; Liu, Zhongming; Ward, Matthew P.; Durkes, Abigail; Robinson, J. Paul; Powley, Terry L.; Irazoqui, Pedro P.

2018-06-01

Objective. Numerous studies of vagal nerve stimulation (VNS) have been published showing it to be a potential treatment for chronic inflammation and other related diseases and disorders. Studies in recent years have shown that electrical stimulation of the vagal efferent fibers can artificially modulate cytokine levels and reduce systematic inflammation. Most VNS research in the treatment of inflammation have been acute studies on rodent subjects. Our study tested VNS on freely moving animals by stimulating and recording from the cervical vagus with nerve cuff electrodes over an extended period of time. Approach. We used methods of electrical stimulation, retrograde tracing (using Fluorogold) and post necropsy histological analysis of nerve tissue, flow cytometry to measure plasma cytokine levels, and MRI scanning of gastric emptying. This novel combination of methods allowed examination of physiological aspects of VNS previously unexplored. Main results. Through our study of 53 rat subjects, we found that chronically cuffing the left cervical vagus nerve suppressed efferent Fluorogold transport in 43 of 44 animals (36 showed complete suppression). Measured cytokine levels and gastric emptying rates concurrently showed nominal differences between chronically cuffed rats and those tested with similar acute methods. Meanwhile, results of electrophysiological and histological tests of the cuffed nerves revealed them to be otherwise healthy, consistent with previous literature. Significance. We hypothesize that due to these unforeseen and unexplored physiological consequences of the chronically cuffed vagus nerve in a rat, that inflammatory modulation and other vagal effects by VNS may become unreliable in chronic studies. Given our findings, we submit that it would benefit the VNS community to re-examine methods used in previous literature to verify the efficacy of the rat model for chronic VNS studies.

Thoracic spinal cord and cervical vagosympathetic neuromodulation obtund nodose sensory transduction of myocardial ischemia.

PubMed

Salavatian, Siamak; Beaumont, Eric; Gibbons, David; Hammer, Matthew; Hoover, Donald B; Armour, J Andrew; Ardell, Jeffrey L

2017-12-01

Autonomic regulation therapy involving either vagus nerve stimulation (VNS) or spinal cord stimulation (SCS) represents emerging bioelectronic therapies for heart disease. The objective of this study was to determine if VNS and/or SCS modulate primary cardiac afferent sensory transduction of the ischemic myocardium. Using extracellular recordings in 19 anesthetized canines, of 88 neurons evaluated, 36 ventricular-related nodose ganglia sensory neurons were identified by their functional activity responses to epicardial touch, chemical activation of their sensory neurites (epicardial veratridine) and great vessel (descending aorta or inferior vena cava) occlusion. Neural responses to 1min left anterior descending (LAD) coronary artery occlusion (CAO) were then evaluated. These interventions were then studied following either: i) SCS [T1-T3 spinal level; 50Hz, 90% motor threshold] or ii) cervical VNS [15-20Hz; 1.2× threshold]. LAD occlusion activated 66% of identified nodose ventricular sensory neurons (0.33±0.08-0.79±0.20Hz; baseline to CAO; p<0.002). Basal activity of cardiac-related nodose neurons was differentially reduced by VNS (0.31±0.11 to 0.05±0.02Hz; p<0.05) as compared to SCS (0.36±0.12 to 0.28±0.14, p=0.59), with their activity response to transient LAD CAO being suppressed by either SCS (0.85±0.39-0.11±0.04Hz; p<0.03) or VNS (0.75±0.27-0.12±0.05Hz; p<0.04). VNS did not alter evoked neural responses of cardiac-related nodose neurons to great vessel occlusion. Both VNS and SCS obtund ventricular ischemia induced enhancement of nodose afferent neuronal inputs to the medulla. Copyright © 2017 Elsevier B.V. All rights reserved.

Vagus nerve stimulation improves locomotion and neuronal populations in a model of Parkinson's disease.

PubMed

Farrand, Ariana Q; Helke, Kristi L; Gregory, Rebecca A; Gooz, Monika; Hinson, Vanessa K; Boger, Heather A

Parkinson's disease (PD) is a progressive, neurodegenerative disorder with no disease-modifying therapies, and symptomatic treatments are often limited by debilitating side effects. In PD, locus coeruleus noradrenergic (LC-NE) neurons degenerate prior to substantia nigra dopaminergic (SN-DA) neurons. Vagus nerve stimulation (VNS) activates LC neurons, and decreases pro-inflammatory markers, allowing improvement of LC targets, making it a potential PD therapeutic. To assess therapeutic potential of VNS in a PD model. To mimic the progression of PD degeneration, rats received a systemic injection of noradrenergic neurotoxin DSP-4, followed one week later by bilateral intrastriatal injection of dopaminergic neurotoxin 6-hydroxydopamine. At this time, a subset of rats also had vagus cuffs implanted. After eleven days, rats received a precise VNS regimen twice a day for ten days, and locomotion was measured during each afternoon session. Immediately following final stimulation, rats were euthanized, and left dorsal striatum, bilateral SN and LC were sectioned for immunohistochemical detection of monoaminergic neurons (tyrosine hydroxylase, TH), α-synuclein, astrocytes (GFAP) and microglia (Iba-1). VNS significantly increased locomotion of lesioned rats. VNS also resulted in increased expression of TH in striatum, SN, and LC; decreased SN α-synuclein expression; and decreased expression of glial markers in the SN and LC of lesioned rats. Additionally, saline-treated rats after VNS, had higher LC TH and lower SN Iba-1. Our findings of increased locomotion, beneficial effects on LC-NE and SN-DA neurons, decreased α-synuclein density in SN TH-positive neurons, and neuroinflammation suggest VNS has potential as a novel PD therapeutic. Copyright © 2017 Elsevier Inc. All rights reserved.

Effects of vagus nerve stimulation on extinction of conditioned fear and post-traumatic stress disorder symptoms in rats.

PubMed

Noble, L J; Gonzalez, I J; Meruva, V B; Callahan, K A; Belfort, B D; Ramanathan, K R; Meyers, E; Kilgard, M P; Rennaker, R L; McIntyre, C K

2017-08-22

Exposure-based therapies help patients with post-traumatic stress disorder (PTSD) to extinguish conditioned fear of trauma reminders. However, controlled laboratory studies indicate that PTSD patients do not extinguish conditioned fear as well as healthy controls, and exposure therapy has high failure and dropout rates. The present study examined whether vagus nerve stimulation (VNS) augments extinction of conditioned fear and attenuates PTSD-like symptoms in an animal model of PTSD. To model PTSD, rats were subjected to a single prolonged stress (SPS) protocol, which consisted of restraint, forced swim, loss of consciousness, and 1 week of social isolation. Like PTSD patients, rats subjected to SPS show impaired extinction of conditioned fear. The SPS procedure was followed, 1 week later, by auditory fear conditioning (AFC) and extinction. VNS or sham stimulation was administered during half of the extinction days, and was paired with presentations of the conditioned stimulus. One week after completion of extinction training, rats were given a battery of behavioral tests to assess anxiety, arousal and avoidance. Results indicated that rats given SPS 1 week prior to AFC (PTSD model) failed to extinguish the freezing response after eleven consecutive days of extinction. Administration of VNS reversed the extinction impairment and attenuated reinstatement of the conditioned fear response. Delivery of VNS during extinction also eliminated the PTSD-like symptoms, such as anxiety, hyperarousal and social avoidance for more than 1 week after VNS treatment. These results provide evidence that extinction paired with VNS treatment can lead to remission of fear and improvements in PTSD-like symptoms. Taken together, these findings suggest that VNS may be an effective adjunct to exposure therapy for the treatment of PTSD.

Transcutaneous Auricular Vagus Nerve Stimulation with Concurrent Upper Limb Repetitive Task Practice for Poststroke Motor Recovery: A Pilot Study.

PubMed

Redgrave, Jessica N; Moore, Lucy; Oyekunle, Tosin; Ebrahim, Maryam; Falidas, Konstantinos; Snowdon, Nicola; Ali, Ali; Majid, Arshad

2018-03-23

Invasive vagus nerve stimulation (VNS) has the potential to enhance the effects of physiotherapy for upper limb motor recovery after stroke. Noninvasive, transcutaneous auricular branch VNS (taVNS) may have similar benefits, but this has not been evaluated in stroke recovery. We sought to determine the feasibility of taVNS delivered alongside upper limb repetitive task-specific practice after stroke and its effects on a range of outcome measures evaluating limb function. Thirteen participants at more than 3 months postischemic stroke with residual upper limb dysfunction were recruited from the community of Sheffield, United Kingdom (October-December 2016). Participants underwent 18 × 1-hour sessions over 6 weeks in which they made 30-50 repetitions of 8-10 arm movements concurrently with taVNS (NEMOS; Cerbomed, Erlangen, Germany, 25 Hz, .1-millisecond pulse width) at maximum tolerated intensity (mA). An electrocardiogram and rehabilitation outcome scores were obtained at each visit. Qualitative interviews determined the acceptability of taVNS to participants. Median time after stroke was 1.16 years, and baseline median/interquartile range upper limb Fugl-Meyer (UFM) score was 63 (54.5-99.5). Participants attended 92% of the planned treatment sessions. Three participants reported side effects, mainly fatigue, but all performed mean of more than 300 arm repetitions per session with no serious adverse events. There was a significant change in the UFM score with a mean increase per participant of 17.1 points (standard deviation 7.8). taVNS is feasible and well-tolerated alongside upper limb repetitive movements in poststroke rehabilitation. The motor improvements observed justify a phase 2 trial in patients with residual arm weakness. Copyright © 2018 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Ablation of the sphenopalatine ganglion does not attenuate the infarct reducing effect of vagus nerve stimulation

PubMed Central

Ay, Ilknur; Ay, Hakan

2013-01-01

Electrical stimulation of the cervical vagus nerve reduces infarct size by approximately 50% after cerebral ischemia in rats. The mechanism of ischemic protection by vagus nerve stimulation (VNS) is not known. In this study, we investigated whether the infarct reducing effect of VNS was mediated by activation of the parasympathetic vasodilator fibers that originate from the sphenopalatine ganglion (SPG) and innervate the anterior cerebral circulation. We examined the effects of electrical stimulation of the cervical vagus nerve in two groups of rats: one with and one without SPG ablation. Electrical stimulation was initiated 30 min after induction of ischemia, and lasted for 1h. Measurement of infarct size 24h later revealed that the volume of ischemic damage was smaller in those animals that received VNS treatment (41.32 ± 2.07% vs. 24.19 ± 2.62% of the contralateral hemispheric volume, n=6 in both; p<0.05). SPG ablation did not abolish this effect; the reduction in infarct volume following VNS was 58% in SPG-damaged animals, 41% in SPG-intact animals (p>0.05). In both SPG-intact and SPG-damaged animals VNS treatment resulted in better motor outcome (p<0.05 vs. corresponding controls for both). Our findings show that VNS can protect the brain against acute ischemic injury, and that this effect is not mediated by SPG projections. PMID:23273773

The neuropsychological outcome of pediatric patients with refractory epilepsy treated with VNS--A 24-month follow-up in Taiwan.

PubMed

Tsai, Jeng-Dau; Chang, Ying-Chao; Lin, Lung-Chang; Hung, Kun-Long

2016-03-01

Multiple studies have reported the benefits of vagus nerve stimulation (VNS) on neuropsychological outcomes. The aim of this study was to investigate how VNS affects cognition and psychosocial adjustment in children with refractory epilepsy (RE), and to determine the efficacy of VNS in a Taiwanese population. We conducted a one-group pretest-posttest study on pediatric patients with RE. The study comprised 19 males and 18 females, all aged <18 years. We recorded seizure frequency at 3, 12, and 24 months after VNS device implantation. Intelligence quotients (IQ) were assessed using the Wechsler Intelligence Scale for Children - IV. The Parental Stress Index (PSI) scores were evaluated by a pediatric psychologist. Vagus nerve stimulation device implantation significantly reduced seizure frequency at 3, 12 and 24 months, especially in young children (<12 years). No significant improvement in IQ test performance was observed, though there were significant improvements in the PSI, especially in young children. Vagus nerve stimulation device implantation does not significantly improve cognition function, but it does significantly reduce seizure frequency and stress in parent-child relationships, especially in young children (<12 years). These findings suggest that VNS should be considered as an alternative therapy for patients proven to have seizures that are medically refractory, especially those younger than 12 years of age. Copyright © 2015 Elsevier Inc. All rights reserved.

Vagus nerve stimulation mediates protection from kidney ischemia-reperfusion injury through α7nAChR+ splenocytes.

PubMed

Inoue, Tsuyoshi; Abe, Chikara; Sung, Sun-Sang J; Moscalu, Stefan; Jankowski, Jakub; Huang, Liping; Ye, Hong; Rosin, Diane L; Guyenet, Patrice G; Okusa, Mark D

2016-05-02

The nervous and immune systems interact in complex ways to maintain homeostasis and respond to stress or injury, and rapid nerve conduction can provide instantaneous input for modulating inflammation. The inflammatory reflex referred to as the cholinergic antiinflammatory pathway regulates innate and adaptive immunity, and modulation of this reflex by vagus nerve stimulation (VNS) is effective in various inflammatory disease models, such as rheumatoid arthritis and inflammatory bowel disease. Effectiveness of VNS in these models necessitates the integration of neural signals and α7 nicotinic acetylcholine receptors (α7nAChRs) on splenic macrophages. Here, we sought to determine whether electrical stimulation of the vagus nerve attenuates kidney ischemia-reperfusion injury (IRI), which promotes the release of proinflammatory molecules. Stimulation of vagal afferents or efferents in mice 24 hours before IRI markedly attenuated acute kidney injury (AKI) and decreased plasma TNF. Furthermore, this protection was abolished in animals in which splenectomy was performed 7 days before VNS and IRI. In mice lacking α7nAChR, prior VNS did not prevent IRI. Conversely, adoptive transfer of VNS-conditioned α7nAChR splenocytes conferred protection to recipient mice subjected to IRI. Together, these results demonstrate that VNS-mediated attenuation of AKI and systemic inflammation depends on α7nAChR-positive splenocytes.

Adverse Effects and Surgical Complications in Pediatric Patients Undergoing Vagal Nerve Stimulation for Drug-Resistant Epilepsy.

PubMed

Trezza, A; Landi, A; Grioni, D; Pirillo, D; Fiori, L; Giussani, C; Sganzerla, E P

2017-01-01

Vagal nerve stimulation (VNS) is an effective treatment for drug-resistant epilepsy that is not suitable for resective surgery, both in adults and in children. Few reports describe the adverse effects and complications of VNS. The aim of our study was to present a series of 33 pediatric patients who underwent VNS for drug-resistant epilepsy and to discuss the adverse effects and complications through a review of the literature.The adverse effects of VNS are usually transient and are dependent on stimulation of the vagus and its efferent fibers; surgical complications of the procedure may be challenging and patients sometimes require further surgery; generally these complications affect VNS efficacy; in addition, hardware complications also have to be taken into account.In our experience and according to the literature, adverse effects and surgical and hardware complications are uncommon and can usually be managed definitely. Careful selection of patients, particularly from a respiratory and cardiac point of view, has to be done before surgery to limit the incidence of some adverse effects.

Impact of Anesthetics on Immune Functions in a Rat Model of Vagus Nerve Stimulation

PubMed Central

Picq, Chloé A.; Clarençon, Didier; Sinniger, Valérie E.; Bonaz, Bruno L.; Mayol, Jean-François S.

2013-01-01

Vagus nerve stimulation (VNS) has been successfully performed in animals for the treatment of different experimental models of inflammation. The anti-inflammatory effect of VNS involves the release of acetylcholine by vagus nerve efferent fibers inhibiting pro-inflammatory cytokines (e.g. TNF-α) produced by macrophages. Moreover, it has recently been demonstrated that splenic lymphocytic populations may also be involved. As anesthetics can modulate the inflammatory response, the current study evaluated the effect of two different anesthetics, isoflurane and pentobarbital, on splenic cellular and molecular parameters in a VNS rat model. Spleens were collected for the characterization of lymphocytes sub-populations by flow cytometry and quantification of cytokines secretion after in vitro activation. Different results were observed depending on the anesthetic used. The use of isoflurane displayed a non-specific effect of VNS characterized by a decrease of most splenic lymphocytes sub-populations studied, and also led to a significantly lower TNF-α secretion by splenocytes. However, the use of pentobarbital brought to light immune modifications in non-stimulated animals that were not observed with isoflurane, and also revealed a specific effect of VNS, notably at the level of T lymphocytes’ activation. These differences between the two anesthetics could be related to the anti-inflammatory properties of isoflurane. In conclusion, pentobarbital is more adapted than isoflurane in the study of the anti-inflammatory effect of VNS on an anesthetized rat model in that it allows more accurate monitoring of subtle immunomodulatory processes. PMID:23840592

Factors associated with the use of home-visit nursing services covered by the long-term care insurance in rural Japan: a cross-sectional study.

PubMed

Kashiwagi, Masayo; Tamiya, Nanako; Sato, Mikiya; Yano, Eiji

2013-01-02

In Japan, there is a large increase in the number of elderly persons who potentially need home-visit nursing services (VNS). However, the number of persons using the VNS has increased only little in comparison to the number of individuals who use home social services, which are also covered by the Long-Term Care Insurance (LTCI) system. This cross-sectional study investigated the predictors of the VNS used under the LTCI system in Japan. We used 1,580 claim data from all the users of community-based services and 1,574 interview survey data collected in 2001 from the six municipal bodies in Japan. After we merged the two datasets, 1,276 users of community-based services under the LTCI were analyzed. Multiple logistic regression models stratified by care needs levels were used for analysis. Only 8.3% of the study subjects were VNS users. Even among study participants within the higher care-needs level, only 22.0% were VNS users. In the lower care level group, people with a higher care level (OR: 3.50, 95% CI: 1.50-8.93), those whose condition needed long term care due to respiratory or heart disease (OR: 4.31, 95% CI: 1.88-89.20), those whose period of needing care was two years or more (OR: 2.01, 95% CI: 1.14-3.48), those whose service plan was created by a medical care management agency (OR: 2.39, 95% CI: 1.31-4.33), those living with family (OR: 1.86, 95% CI: 1.00-3.42), and those who use home-help services (OR: 2.12, 95% CI: 1.17-3.83) were more likely to use the VNS. In the higher care level group, individuals with higher care level (OR: 3.63, 95% CI: 1.56-8.66), those with higher income (OR: 3.79, 95% CI: 1.01-14.25), and those who had regular hospital visits before entering the LTCI (OR: 2.36, 95% CI: 1.11-5.38) were more likely to use the VNS. Our results suggested that VNS use is limited due to management by non-medical care management agencies, due to no caregivers being around or a low income household. The findings of this study provide valuable insight for LTCI policy makers: the present provision of VNS should be reconsidered.

Left Atrial Size and Function in a Canine Model of Chronic Atrial Fibrillation and Heart Failure

PubMed Central

Goldberg, Adam; Kusunose, Kenya; Qamruddin, Salima; Rodriguez, L. Leonardo; Mazgalev, Todor N.; Griffin, Brian P.; Van Wagoner, David R.; Zhang, Youhua; Popović, Zoran B.

2016-01-01

Background Our aim was to assess how atrial fibrillation (AF) induction, chronicity, and RR interval irregularity affect left atrial (LA) function and size in the setting of underlying heart failure (HF), and to determine whether AF effects can be mitigated by vagal nerve stimulation (VNS). Methods HF was induced by 4-weeks of rapid ventricular pacing in 24 dogs. Subsequently, AF was induced and maintained by atrial pacing at 600 bpm. Dogs were randomized into control (n = 9) and VNS (n = 15) groups. In the VNS group, atrioventricular node fat pad stimulation (310 μs, 20 Hz, 3–7 mA) was delivered continuously for 6 months. LA volume and LA strain data were calculated from bi-weekly echocardiograms. Results RR intervals decreased with HF in both groups (p = 0.001), and decreased further during AF in control group (p = 0.014), with a non-significant increase in the VNS group during AF. LA size increased with HF (p<0.0001), with no additional increase during AF. LA strain decreased with HF (p = 0.025) and further decreased after induction of AF (p = 0.0001). LA strain decreased less (p = 0.001) in the VNS than in the control group. Beat-by-beat analysis showed a curvilinear increase of LA strain with longer preceding RR interval, (r = 0.45, p <0.0001) with LA strain 1.1% higher (p = 0.02) in the VNS-treated animals, independent of preceding RR interval duration. The curvilinear relationship between ratio of preceding and pre-preceding RR intervals, and subsequent LA strain was weaker, (r = 0.28, p = 0.001). However, VNS-treated animals again had higher LA strain (by 2.2%, p = 0.002) independently of the ratio of preceding and pre-preceding RR intervals. Conclusions In the underlying presence of pacing-induced HF, AF decreased LA strain, with little impact on LA size. LA strain depends on the preceding RR interval duration. PMID:26771573

Acute effect of Vagus nerve stimulation parameters on cardiac chronotropic, inotropic, and dromotropic responses

NASA Astrophysics Data System (ADS)

Ojeda, David; Le Rolle, Virginie; Romero-Ugalde, Hector M.; Gallet, Clément; Bonnet, Jean-Luc; Henry, Christine; Bel, Alain; Mabo, Philippe; Carrault, Guy; Hernández, Alfredo I.

2017-11-01

Vagus nerve stimulation (VNS) is an established therapy for drug-resistant epilepsy and depression, and is considered as a potential therapy for other pathologies, including Heart Failure (HF) or inflammatory diseases. In the case of HF, several experimental studies on animals have shown an improvement in the cardiac function and a reverse remodeling of the cardiac cavity when VNS is applied. However, recent clinical trials have not been able to reproduce the same response in humans. One of the hypothesis to explain this lack of response is related to the way in which stimulation parameters are defined. The combined effect of VNS parameters is still poorly-known, especially in the case of VNS synchronously delivered with cardiac activity. In this paper, we propose a methodology to analyze the acute cardiovascular effects of VNS parameters individually, as well as their interactive effects. A Latin hypercube sampling method was applied to design a uniform experimental plan. Data gathered from this experimental plan was used to produce a Gaussian process regression (GPR) model in order to estimate unobserved VNS sequences. Finally, a Morris screening sensitivity analysis method was applied to each obtained GPR model. Results highlight dominant effects of pulse current, pulse width and number of pulses over frequency and delay and, more importantly, the degree of interactions between these parameters on the most important acute cardiovascular responses. In particular, high interacting effects between current and pulse width were found. Similar sensitivity profiles were observed for chronotropic, dromotropic and inotropic effects. These findings are of primary importance for the future development of closed-loop, personalized neuromodulator technologies.

The visible, near-infrared and short wave infrared channels of the EarthCARE multi-spectral imager

NASA Astrophysics Data System (ADS)

Doornink, J.; de Goeij, B.; Marinescu, O.; Meijer, E.; Vink, R.; van Werkhoven, W.; van't Hof, A.

2017-11-01

The EarthCARE satellite mission objective is the observation of clouds and aerosols from low Earth orbit. The payload will include active remote sensing instruments being the W-band Cloud Profiling Radar (CPR) and the ATLID LIDAR. These are supported by the passive instruments Broadband Radiometer (BBR) and the Multispectral Imager (MSI) providing the radiometric and spatial context of the ground scene being probed. The MSI will form Earth images over a swath width of 150 km; it will image the Earth atmosphere in 7 spectral bands. The MSI instrument consists of two parts: the Visible, Near infrared and Short wave infrared (VNS) unit, and the Thermal InfraRed (TIR) unit. Subject of this paper is the VNS unit. In the VNS optical unit, the ground scene is imaged in four spectral bands onto four linear detectors via separate optical channels. Driving requirements for the VNS instrument performance are the spectral sensitivity including out-of-band rejection, the MTF, co-registration and the inter-channel radiometric accuracy. The radiometric accuracy performance of the VNS is supported by in-orbit calibration, in which direct solar radiation is fed into the instrument via a set of quasi volume diffusers. The compact optical concept with challenging stability requirements together with the strict thermal constraints have led to a sophisticated opto-mechanical design. This paper, being the second of a sequence of two on the Multispectral Imager describes the VNS instrument concept chosen to fulfil the performance requirements within the resource and accommodation constraints.

The effect of vagus nerve stimulation on response inhibition.

PubMed

Schevernels, Hanne; van Bochove, Marlies E; De Taeye, Leen; Bombeke, Klaas; Vonck, Kristl; Van Roost, Dirk; De Herdt, Veerle; Santens, Patrick; Raedt, Robrecht; Boehler, C Nico

2016-11-01

In the current study, we explored whether vagus nerve stimulation (VNS) in patients with epilepsy, which is believed to increase norepinephrine (NE) levels via activation of the locus coeruleus, would positively affect response inhibition. Moreover, we tried to identify the dynamics of the underlying neural processes by investigating event-related potentials (ERPs) and pupil size. Patients performed a stop-signal task once when stimulation was switched on and once when it was switched off. We found a correlational pattern suggesting that patients who clinically benefit more from VNS treatment also show a larger behavioral advantage, in terms of faster response inhibition, when the vagus nerve is being stimulated. Event-related potential (ERP) results suggested more pronounced reactive inhibition when stimulation was switched on, independent of the individual amount of seizure reduction. Transient go-locked pupil size was increased from go trials to successful stop trials to unsuccessful stop trials but without displaying a clear VNS effect, which however, might relate to limited sensitivity. We conclude that VNS likely has a positive effect on response inhibition, at least in patients with epilepsy that benefit clinically from the treatment, presumably relating to enhancements of response-inhibition mechanisms and, therefore, identify enhanced response inhibition as a possible cognitive benefit of VNS. Copyright © 2016 Elsevier Inc. All rights reserved.

Transcutaneous vagus nerve stimulation (tVNS) enhances divergent thinking.

PubMed

Colzato, Lorenza S; Ritter, Simone M; Steenbergen, Laura

2018-03-01

Creativity is one of the most important cognitive skills in our complex and fast-changing world. Previous correlative evidence showed that gamma-aminobutyric acid (GABA) is involved in divergent but not convergent thinking. In the current study, a placebo/sham-controlled, randomized between-group design was used to test a causal relation between vagus nerve and creativity. We employed transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique to stimulate afferent fibers of the vagus nerve and speculated to increase GABA levels, in 80 healthy young volunteers. Creative performance was assessed in terms of divergent thinking (Alternate Uses Task) and convergent thinking tasks (Remote Associates Test, Creative Problem Solving Task, Idea Selection Task). Results demonstrate active tVNS, compared to sham stimulation, enhanced divergent thinking. Bayesian analysis reported the data to be inconclusive regarding a possible effect of tVNS on convergent thinking. Therefore, our findings corroborate the idea that the vagus nerve is causally involved in creative performance. Even thought we did not directly measure GABA levels, our results suggest that GABA (likely to be increased in active tVNS condition) supports the ability to select among competing options in high selection demand (divergent thinking) but not in low selection demand (convergent thinking). Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Vagus Nerve Stimulation in Intractable Childhood Epilepsy: a Korean Multicenter Experience

PubMed Central

You, Su Jeong; Kang, Hoon-Chul; Kim, Heung Dong; Kim, Deok-Soo; Hwang, Yong Soon; Kim, Dong Suk; Lee, Jung-Kyo; Park, Sang Keun

2007-01-01

We evaluated the long-term outcome of vagus nerve stimulation (VNS) in 28 children with refractory epilepsy. Of these 28 children, 15 (53.6%) showed a >50% reduction in seizure frequency and 9 (32.1%) had a >75% reduction. When we compared seizure reduction rates according to seizure types (generalized vs. partial) and etiologies (symptomatic vs. cryptogenic), we found no significant differences. In addition, there was no correlation between the length of the stimulation period and treatment effect. The seizure reduction rate, however, tended to be inversely related to the seizure duration before VNS implantation and age at the time of VNS therapy. VNS also improved quality of life in this group of patients, including improved memory in 9 (32.1%), improved mood in 12 (42.9%), improved behavior in 11 (39.3%), improved altertness in 12 (42.9%), improved achievement in 6 (21.4%), and improved verbal skills in 8 (28.6%). Adverse events included hoarseness in 7 patients, dyspnea at sleep in 2 patients, and wound infection in 1 patient, but all were transient and successfully managed by careful follow-up and adjustment of parameters. These results indicate that VNS is a safe and effective alternative therapy for pediatric refractory epilepsy, without significant adverse events. PMID:17596651

An alternative therapy for drug-resistant epilepsy: transcutaneous auricular vagus nerve stimulation.

PubMed

Rong, Peijing; Liu, Aihua; Zhang, Jianguo; Wang, Yuping; Yang, Anchao; Li, Liang; Ben, Hui; Li, Liping; Liu, Rupeng; He, Wei; Liu, Huanguang; Huang, Feng; Li, Xia; Wu, Peng; Zhu, Bing

2014-01-01

Previous studies demonstrated that vagus nerve stimulation (VNS) is an effective therapy for drug-resistant epilepsy. Acupuncture is also used to treat epilepsy. This study was designed to examine the safety and effectiveness of transcutaneous auricular vagus nerve stimulation (ta-VNS) for patients with drug-resistant epilepsy. A total of 50 volunteer patients with drug-resistant epilepsy were selected for a random clinical trial to observe the therapeutic effect of ta-VNS. The seizure frequency, quality of life, and severity were assessed in weeks 8, 16, and 24 of the treatment according to the percentage of seizure frequency reduction. In the pilot study, 47 of the 50 epilepsy patients completed the 24-week treatment; three dropped off. After 8-week treatment, six of the 47 patients (12%) were seizure free and 12 (24%) had a reduction in seizure frequency. In week 16 of the continuous treatment, six of the 47 patients (12%) were seizure free; 17 (34%) had a reduction in seizure frequency. After 24 weeks' treatment, eight patients (16%) were seizure free; 19 (38%) had reduced seizure frequency. Similar to the therapeutic effect of VNS, ta-VNS can suppress epileptic seizures and is a safe, effective, economical, and widely applicable treatment option for drug-resistant epilepsy. (ChiCTR-TRC-10001023).

Transcutaneous vagus nerve stimulation (tVNS) enhances conflict-triggered adjustment of cognitive control.

PubMed

Fischer, Rico; Ventura-Bort, Carlos; Hamm, Alfons; Weymar, Mathias

2018-04-24

Response conflicts play a prominent role in the flexible adaptation of behavior as they represent context-signals that indicate the necessity for the recruitment of cognitive control. Previous studies have highlighted the functional roles of the affectively aversive and arousing quality of the conflict signal in triggering the adaptation process. To further test this potential link with arousal, participants performed a response conflict task in two separate sessions with either transcutaneous vagus nerve stimulation (tVNS), which is assumed to activate the locus coeruleus-noradrenaline (LC-NE) system, or with neutral sham stimulation. In both sessions the N2 and P3 event-related potentials (ERP) were assessed. In line with previous findings, conflict interference, the N2 and P3 amplitude were reduced after conflict. Most importantly, this adaptation to conflict was enhanced under tVNS compared to sham stimulation for conflict interference and the N2 amplitude. No effect of tVNS on the P3 component was found. These findings suggest that tVNS increases behavioral and electrophysiological markers of adaptation to conflict. Results are discussed in the context of the potentially underlying LC-NE and other neuromodulatory (e.g., GABA) systems. The present findings add important pieces to the understanding of the neurophysiological mechanisms of conflict-triggered adjustment of cognitive control.

3 Tesla MRI of patients with a vagus nerve stimulator: initial experience using a T/R head coil under controlled conditions.

PubMed

Gorny, Krzysztof R; Bernstein, Matt A; Watson, Robert E

2010-02-01

To assess safety of clinical MRI of the head in patients with implanted model 100, 102, and 103 vagus nerve stimulation (VNS) Therapy Systems (Cyberonics, Inc., Houston, TX) in 3.0 Tesla MRI (GE Healthcare, Milwaukee, WI). The distributions of the radiofrequency B(1) (+)-field produced by the clinically used transmit/receive (T/R) head coil (Advanced Imaging Research Incorporated, Cleveland, OH) and body coil were measured in a head and shoulders phantom. These measurements were supplemented by temperature measurements on the lead tips and the implantable pulse generator (IPG) of the VNS devices in a head and torso phantom with the same two coils. Clinical 3T MRI head scans were then acquired under highly controlled conditions in a series of 17 patients implanted with VNS. Phantom studies showed only weak B(1) (+) fields at the location of the VNS IPG and leads for MRI scans using the T/R head coil. The MRI-related heating on a VNS scanned in vitro at 3T was also found to be minimal (0.4-0.8 degrees C at the leads, negligible at the IPG). The patient MRI examinations were completed successfully without any adverse incidents. No patient reported any heating, discomfort, or any other unusual sensation. Safe clinical MRI head scanning of patients with implanted VNS is shown to be feasible on a GE Signa Excite 3T MRI system using one specific T/R head coil. These results apply to this particular MRI system configuration. Extrapolation or generalization of these results to more general or less controlled imaging situations without supporting data of safety is highly discouraged.

Vagus nerve stimulation therapy in a developing country: a long term follow up study and cost utility analysis.

PubMed

Aburahma, Samah K; Alzoubi, Firas Q; Hammouri, Hanan M; Masri, Amira

2015-02-01

To evaluate clinical outcomes, quality-adjusted life years (QALY), cost effectiveness and cost utility associated with VNS therapy in children with refractory epilepsy in a developing country. Retrospective review of all children who underwent VNS implantation at King Abdullah University Hospital and Jordan University Hospital in Jordan. Twenty eight patients (16 males) had implantation of the VNS therapy system between the years 2007 and 2011. Mean age at implantation was 9.4 years. Mean duration of epilepsy prior to implantation was 6.5 years. The most common seizure type was generalized tonic clonic seizures. Fifteen patients showed a 50% or more reduction in seizure frequency. There was a significant reduction in total number of seizures (p=0.002) and emergency room (ER) visits (p=0.042) after VNS therapy. Atonic seizures were more likely to respond than generalized tonic clonic seizures, p=0.034. Direct hospital costs prior to VNS implantation were analyzed in relation to ER visits and intensive care unit (ICU) admissions. Cost savings per patient did reduce the financial burden of the device by about 30%. There was a QALY gain per lifetime of 3.78 years for children and 1 year for adolescents. Response to VNS implantation in Jordan was favorable and similar to what has been previously reported. QALY gain and cost per QALY analysis were encouraging. Cost savings were related to reduction in seizure severity. In circumstances of limited resources as in developing countries, targeting patients with frequent utilization of health services would improve cost effectiveness. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Vagus nerve stimulation inhibits seizure activity and protects blood-brain barrier integrity in kindled rats with cortical dysplasia.

PubMed

Kaya, Mehmet; Orhan, Nurcan; Karabacak, Emrah; Bahceci, Metin Berkant; Arican, Nadir; Ahishali, Bulent; Kemikler, Gonul; Uslu, Atilla; Cevik, Aydin; Yilmaz, Canan Ugur; Kucuk, Mutlu; Gürses, Candan

2013-03-12

This study investigates the effects of vagus nerve stimulation (VNS) on seizure severity and blood-brain barrier (BBB) integrity in kindled rats with cortical dysplasia (CD). Pregnant rats were exposed to 145 cGy of gamma-irradiation on day 17 of pregnancy. In offsprings, kindling was induced by giving subconvulsive doses of pentylenetetrazole. Left VNS was performed for 48 h at output currents of 0.5 or 1 mA. Horseradish peroxidase (HRP) was used to study the BBB permeability. Immunohistochemistry for occludin and P-glycoprotein (P-gp) was also performed. Kindled rats with CD exhibited seizures with mean Racine's scores of 3.57 ± 1.2 during video EEG recording. Kindled animals with CD receiving VNS at 0.5 and 1.0 mA did not exhibit either clinical or electrophysiological signs of seizure. Immunostaining for occludin, a tight junction protein, in hippocampus remained relatively intact in all groups. VNS-treated and -untreated kindled animals with CD revealed intense immunostaining for P-gp in hippocampal formation (P<0.01). Electron microscopic observations revealed frequent transport vesicles containing electron-dense HRP reaction products in the cytoplasm of brain capillary endothelial cells in both cerebral cortex and hippocampus of kindled animals with CD. Those which were exposed to 1 mA VNS were observed to have brain capillary endothelial cells largely devoid of HRP reaction products in both cerebral cortex and hippocampus. The results of this study suggest that VNS therapy at 1 mA inhibits seizure activity and protects BBB integrity by limiting the enhancement of transcellular pathway in kindled animals with CD. Copyright © 2013 Elsevier Inc. All rights reserved.

A prospective, multicenter study of cardiac-based seizure detection to activate vagus nerve stimulation.

PubMed

Boon, Paul; Vonck, Kristl; van Rijckevorsel, Kenou; El Tahry, Riem; Elger, Christian E; Mullatti, Nandini; Schulze-Bonhage, Andreas; Wagner, Louis; Diehl, Beate; Hamer, Hajo; Reuber, Markus; Kostov, Hrisimir; Legros, Benjamin; Noachtar, Soheyl; Weber, Yvonne G; Coenen, Volker A; Rooijakkers, Herbert; Schijns, Olaf E M G; Selway, Richard; Van Roost, Dirk; Eggleston, Katherine S; Van Grunderbeek, Wim; Jayewardene, Amara K; McGuire, Ryan M

2015-11-01

This study investigates the performance of a cardiac-based seizure detection algorithm (CBSDA) that automatically triggers VNS (NCT01325623). Thirty-one patients with drug resistant epilepsy were evaluated in an epilepsy monitoring unit (EMU) to assess algorithm performance and near-term clinical benefit. Long-term efficacy and safety were evaluated with combined open and closed-loop VNS. Sixty-six seizures (n=16 patients) were available from the EMU for analysis. In 37 seizures (n=14 patients) a ≥ 20% heart rate increase was found and 11 (n=5 patients) were associated with ictal tachycardia (iTC, 55% or 35 bpm heart rate increase, minimum of 100 bpm). Multiple CBSDA settings achieved a sensitivity of ≥ 80%. False positives ranged from 0.5 to 7.2/h. 27/66 seizures were stimulated within ± 2 min of seizure onset. In 10/17 of these seizures, where triggered VNS overlapped with ongoing seizure activity, seizure activity stopped during stimulation. Physician-scored seizure severity (NHS3-scale) showed significant improvement for complex partial seizures (CPS) at EMU discharge and through 12 months (p<0.05). Patient-scored seizure severity (total SSQ score) showed significant improvement at 3 and 6 months. Quality of life (total QOLIE-31-P score) showed significant improvement at 12 months. The responder rate (≥ 50% reduction in seizure frequency) at 12 months was 29.6% (n=8/27). Safety profiles were comparable to prior VNS trials. The investigated CBSDA has a high sensitivity and an acceptable specificity for triggering VNS. Despite the moderate effects on seizure frequency, combined open- and closed-loop VNS may provide valuable improvements in seizure severity and QOL in refractory epilepsy patients. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Vagal nerve stimulation modulates the dendritic cell profile in posthemorrhagic shock mesenteric lymph.

PubMed

Morishita, Koji; Costantini, Todd W; Eliceiri, Brian; Bansal, Vishal; Coimbra, Raul

2014-03-01

Previous studies have established that posthemorrhagic shock mesenteric lymph (PHSML) contains proinflammatory mediators, while the cellular basis of PHSML is less well characterized in acute models of injury. CD103 dendritic cells (DCs) have been identified in the mesenteric lymph (ML) in models of chronic intestinal inflammation, suggesting an important role in the gut response to injury. We have previously demonstrated the ability of vagal nerve stimulation (VNS) to prevent gut barrier failure after trauma/hemorrhagic shock (T/HS); however, the ability of VNS to alter ML DCs is unknown. We hypothesized that the CD103 MHC-II DC population would change in PHSML and that VNS would prevent injury-induced changes in this population in PHSML. Male Sprague-Dawley rats were randomly assigned to trauma/sham shock or T/HS. T/HS was induced by midline laparotomy and 60 minutes of HS (blood pressure, 35 mm Hg), followed by fluid resuscitation. A separate cohort of animals underwent cervical VNS after the HS phase. Gut tissue was harvested at 2 hours after injury for histologic analysis. ML was collected during the pre-HS, HS, and post-HS phase. For flow cytometric analysis, ML cells were subjected to staining with CD103 and MHC-II antibodies, and this cell population was compared in the pre-HS and post-HS phase from the same animal. The CD4Foxp3 cell (T reg) population in the ML node (MLN) was also tested to determine effects of CD103 DC modulation in the ML. VNS reduced histologic gut injury and ML flow seen after injury. The CD103 MHC-II DC population in the PHSML was significantly decreased compared with pre-HS and was associated with decreased T reg expression in the MLN. VNS prevented the injury-induced decrease in the CD103 MHC-II+ DC population in the ML and restored the T reg population in the MLN. These findings suggest that VNS mediates the inflammatory responses in ML DCs and MLN T reg cells by affecting the set point of T/HS responsiveness.

Failure of a vagus nerve stimulator following a nearby lightning strike.

PubMed

Terry, Garth E; Conry, Joan A; Taranto, Eleanor; Yaun, Amanda

2011-01-01

We recently reported our experience with implanted vagus nerve stimulators (VNS) in 62 children over a 7-year period. Here, we present a case of a VNS that successfully reduced the number and severity of seizures in a patient with an unusual seizure pattern, and failed to function shortly after a lightning storm. To our knowledge, the failure of VNS or any implantable electrical devices by lightning has not been reported in the literature. This mechanism of electrical interference, while unusual, may require more attention as these devices are expected to be used more frequently. Copyright © 2011 S. Karger AG, Basel.

Vagus nerve stimulation: state of the art of stimulation and recording strategies to address autonomic function neuromodulation

NASA Astrophysics Data System (ADS)

Guiraud, David; Andreu, David; Bonnet, Stéphane; Carrault, Guy; Couderc, Pascal; Hagège, Albert; Henry, Christine; Hernandez, Alfredo; Karam, Nicole; Le Rolle, Virginie; Mabo, Philippe; Maciejasz, Paweł; Malbert, Charles-Henri; Marijon, Eloi; Maubert, Sandrine; Picq, Chloé; Rossel, Olivier; Bonnet, Jean-Luc

2016-08-01

Objective. Neural signals along the vagus nerve (VN) drive many somatic and autonomic functions. The clinical interest of VN stimulation (VNS) is thus potentially huge and has already been demonstrated in epilepsy. However, side effects are often elicited, in addition to the targeted neuromodulation. Approach. This review examines the state of the art of VNS applied to two emerging modulations of autonomic function: heart failure and obesity, especially morbid obesity. Main results. We report that VNS may benefit from improved stimulation delivery using very advanced technologies. However, most of the results from fundamental animal studies still need to be demonstrated in humans.

Noachian Climate of Mars: Insights from Noachian Stratigraphy and Valley Networks System Formation Times

NASA Astrophysics Data System (ADS)

Head, J. W., III

2017-12-01

Noachian climate models have been proposed in order to account for 1) observed fluvial and lacustrine activity, 2) weathering processes producing phyllosilicates, and 3) an unusual impact record including three major impact basins and unusual degradation processes. We adopt a stratigraphic approach in order place these observations in a temporal context. Formation of the major impact basins Hellas, Isidis and Argyre in earlier Noachian profoundly influenced the uplands geology and appears to have occurred concurrently with major phyllosilicate and related surface occurrences/deposits; the immediate aftermath of these basins appears to have created a temporary hot and wet surface environment with significant effect on surface morphology and alteration processes. Formation of Late Noachian-Early Hesperian valley network systems (VNS) signaled the presence of warm/wet conditions generating several hypotheses for climates permissive of these conditions. We examined estimates for the time required to carve channels/deltas and total duration implied by plausible intermittencies. Synthesis of required timescales show that the total time to carve the VN does not exceed 106 years, < 0.25% of the total Noachian. What climate models can account for the VNS? 1) Warm and wet/semiarid/arid climate: Sustained background MAT >273 K, hydrological system vertically integrated, and rainfall occurs to recharge the aquifer. 2) Cold and Icy climate warmed by greenhouse gases or episodic stochastic events: Climate is sustained cold/icy, but greenhouse gases of unspecified nature/amount/duration elevate MAT by several tens of Kelvins, bringing the annual temperature range into the realm where peak seasonal temperatures (PST) exceed 273 K. In this climate environment, analogous to the Antarctic Dry Valleys, seasonal summer temperatures above 273 K are sufficient to melt snow/ice and form fluvial and lacustrine features, but MAT is well below 273 K (253 K); punctuated warming alternatives include impacts or volcanic eruptions. We conclude that a cold and icy background climate with modest greenhouse warming or punctuated warming and melting events for the VNs origin is consistent with: 1) the estimated durations of continuous VN formation (<105 years) and 2) VN system estimated recurrence rates (106-107 years).

A putative functional vomeronasal system in anuran tadpoles

PubMed Central

Jungblut, Lucas David; Pozzi, Andrea Gabriela; Paz, Dante Agustín

2012-01-01

We investigated the occurrence and anatomy of the vomeronasal system (VNS) in tadpoles of 13 different anuran species. All of the species possessed a morphologically fully developed VNS with a highly conserved anatomical organisation. We found that a bean-shaped vomeronasal organ (VNO) developed early in the tadpoles, during the final embryonic stages, and was located in the anteromedial nasal region. Histology revealed the presence of bipolar chemosensory neurones in the VNO that were immunoreactive for the Gαo protein. Tract-tracing experiments demonstrated that chemosensory neurones from the VNO reach specific areas in the brain, where a discernible accessory olfactory bulb (AOB) could be observed. The AOB was located in the ventrolateral side of the anterior telencephalon, somewhat caudal to the main olfactory bulb. Synaptophysin-like immunodetection revealed that synaptic contacts between VNO and AOB are established during early larval stages. Moreover, using lectin staining, we identified glomerular structures in the AOB in most of the species that we examined. According to our findings, a significant maturation in the VNS is achieved in anuran larvae. Recent published evidence strongly suggests that the VNS appeared early in vertebrate evolution and was already present in the aquatic last common ancestor of lungfish and tetrapods. In this context, tadpoles may be a good model in which to investigate the anatomical, biochemical and functional aspects of the VNS in an aquatic environment. PMID:22774780

Amelioration of intractable epilepsy by adjunct vagus nerve stimulation therapy in a girl with a CDKL5 mutation.

PubMed

Baba, Shimpei; Sugawara, Yuji; Moriyama, Kengo; Inaji, Motoki; Maehara, Taketoshi; Yamamoto, Toshiyuki; Morio, Tomohiro

2017-04-01

We report the case of on an 8-year-old girl with a cyclin-dependent kinase-like 5 mutation and who underwent vagus nerve stimulation (VNS) therapy for 2years. She had developed epilepsy at the age of 6months and had severe developmental delays. Initially, she had tonic and tonic-clonic seizures; however, around the age of 5years, she also developed epileptic spasms. These seizures were never completely controlled by conventional medical treatments. At the age of 7, after VNS initiation, her seizure frequency markedly reduced, and abnormal electrical activities on her electroencephalography tests strikingly decreased. Moreover, using questionnaires, we confirmed an improvement in her quality of life in the fields of alertness and activity. Although the efficacy of VNS therapy for patients with intractable epilepsy associated with a genetic anomaly has not been fully established, adjunctive VNS therapy may widen the scope of treatment choices available to these patients. Copyright © 2016 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Genetic dissection of pheromone processing reveals main olfactory system-mediated social behaviors in mice.

PubMed

Matsuo, Tomohiko; Hattori, Tatsuya; Asaba, Akari; Inoue, Naokazu; Kanomata, Nobuhiro; Kikusui, Takefumi; Kobayakawa, Reiko; Kobayakawa, Ko

2015-01-20

Most mammals have two major olfactory subsystems: the main olfactory system (MOS) and vomeronasal system (VNS). It is now widely accepted that the range of pheromones that control social behaviors are processed by both the VNS and the MOS. However, the functional contributions of each subsystem in social behavior remain unclear. To genetically dissociate the MOS and VNS functions, we established two conditional knockout mouse lines that led to either loss-of-function in the entire MOS or in the dorsal MOS. Mice with whole-MOS loss-of-function displayed severe defects in active sniffing and poor survival through the neonatal period. In contrast, when loss-of-function was confined to the dorsal MOB, sniffing behavior, pheromone recognition, and VNS activity were maintained. However, defects in a wide spectrum of social behaviors were observed: attraction to female urine and the accompanying ultrasonic vocalizations, chemoinvestigatory preference, aggression, maternal behaviors, and risk-assessment behaviors in response to an alarm pheromone. Functional dissociation of pheromone detection and pheromonal induction of behaviors showed the anterior olfactory nucleus (AON)-regulated social behaviors downstream from the MOS. Lesion analysis and neural activation mapping showed pheromonal activation in multiple amygdaloid and hypothalamic nuclei, important regions for the expression of social behavior, was dependent on MOS and AON functions. Identification of the MOS-AON-mediated pheromone pathway may provide insights into pheromone signaling in animals that do not possess a functional VNS, including humans.

Application of Rubber Band with Hooks on Both Ends for Vagus Nerve Stimulator Implantation.

PubMed

Hosoyama, Hiroshi; Hanaya, Ryosuke; Otsubo, Toshiaki; Sato, Masanori; Kashida, Yumi; Sugata, Sei; Katagiri, Masaya; Iida, Koji; Arita, Kazunori

2018-03-01

Vagus nerve stimulation (VNS) is a valuable therapeutic option for many types of drug-resistant epilepsy. Muscle hooks and carotid endarterectomy rings have been used for cervical delamination preceding the implantation of stimulation electrodes. The attachment on both sides of a rubber band of Kamiyama-style hanging needles, as are used for scalp and dural retraction during craniotomy, yields a useful tool for VNS implantation. Here we report our experience with this method. We present our method using a rubber band plus hooks and a review of 21 consecutive patients who underwent VNS implantation using our rubber band-plus-hooks method. None of the 21 patients experienced intraoperative or perioperative complications. Hooks placed in connective tissue around the common carotid artery and jugular vein raised the vagus nerve by elevating the carotid sheath. A single surgeon was able to perform all cervical manipulations under a surgical microscope. The average operation time in this series of 21 patients was 137 minutes. The use of hooks attached to both sides of a rubber band rendered VNS implantation safer by lifting the vagus nerve and standardizing the procedure. Copyright © 2017 Elsevier Inc. All rights reserved.

Crosstalk-aware virtual network embedding over inter-datacenter optical networks with few-mode fibers

NASA Astrophysics Data System (ADS)

Huang, Haibin; Guo, Bingli; Li, Xin; Yin, Shan; Zhou, Yu; Huang, Shanguo

2017-12-01

Virtualization of datacenter (DC) infrastructures enables infrastructure providers (InPs) to provide novel services like virtual networks (VNs). Furthermore, optical networks have been employed to connect the metro-scale geographically distributed DCs. The synergistic virtualization of the DC infrastructures and optical networks enables the efficient VN service over inter-DC optical networks (inter-DCONs). While the capacity of the used standard single-mode fiber (SSMF) is limited by their nonlinear characteristics. Thus, mode-division multiplexing (MDM) technology based on few-mode fibers (FMFs) could be employed to increase the capacity of optical networks. Whereas, modal crosstalk (XT) introduced by optical fibers and components deployed in the MDM optical networks impacts the performance of VN embedding (VNE) over inter-DCONs with FMFs. In this paper, we propose a XT-aware VNE mechanism over inter-DCONs with FMFs. The impact of XT is considered throughout the VNE procedures. The simulation results show that the proposed XT-aware VNE can achieves better performances of blocking probability and spectrum utilization compared to conventional VNE mechanisms.

Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder

PubMed Central

Giacobbe, Peter; Kennedy, Sidney H.; Blumberger, Daniel M.; Daskalakis, Zafiris J.; Downar, Jonathan; Modirrousta, Mandana; Patry, Simon; Vila-Rodriguez, Fidel; Lam, Raymond W.; MacQueen, Glenda M.; Parikh, Sagar V.; Ravindran, Arun V.

2016-01-01

Background: The Canadian Network for Mood and Anxiety Treatments (CANMAT) conducted a revision of the 2009 guidelines by updating the evidence and recommendations. The scope of the 2016 guidelines remains the management of major depressive disorder (MDD) in adults, with a target audience of psychiatrists and other mental health professionals. Methods: Using the question-answer format, we conducted a systematic literature search focusing on systematic reviews and meta-analyses. Evidence was graded using CANMAT-defined criteria for level of evidence. Recommendations for lines of treatment were based on the quality of evidence and clinical expert consensus. “Neurostimulation Treatments” is the fourth of six sections of the 2016 guidelines. Results: Evidence-informed responses were developed for 31 questions for 6 neurostimulation modalities: 1) transcranial direct current stimulation (tDCS), 2) repetitive transcranial magnetic stimulation (rTMS), 3) electroconvulsive therapy (ECT), 4) magnetic seizure therapy (MST), 5) vagus nerve stimulation (VNS), and 6) deep brain stimulation (DBS). Most of the neurostimulation treatments have been investigated in patients with varying degrees of treatment resistance. Conclusions: There is increasing evidence for efficacy, tolerability, and safety of neurostimulation treatments. rTMS is now a first-line recommendation for patients with MDD who have failed at least 1 antidepressant. ECT remains a second-line treatment for patients with treatment-resistant depression, although in some situations, it may be considered first line. Third-line recommendations include tDCS and VNS. MST and DBS are still considered investigational treatments. PMID:27486154

Online patient information on Vagus Nerve Stimulation: How reliable is it for facilitating shared decision making?

PubMed

Ved, Ronak; Cobbold, Naomi; Igbagiri, Kueni; Willis, Mark; Leach, Paul; Zaben, Malik

2017-08-01

This study evaluates the quality of information available on the internet for carers of children with epilepsy considering treatment with Vagus Nerve Stimulation (VNS). Selected key phrases were entered into two popular search engines (Google™, Yahoo™). These phrases were: "Vagus nerve stimulator", alone and in combination with "childhood epilepsy", "paediatric epilepsy" and "epilepsy in childhood"; "VNS", and "VNS epilepsy". The first 50 hits per search were then screened. Of 600 identified sites, duplicated (262), irrelevant (230) and inaccessible (15) results were excluded. 93 websites were identified for evaluation using the DISCERN instrument, an online validation tool for patient information websites. The mean DISCERN score of all analysed websites was 39/80 (49%; SD 13.5). This equates to Fair to borderline Poor global quality, (Excellent=80-63; Good=62-51; Fair=50-39; Poor=38-27; Very poor=26-15). None of the analysed sites obtained an Excellent quality rating. 13% (12) obtained a Good score, 40% (37) obtained an Average score, 35% (33) obtained a Poor score, and 12% (11) obtained a Very poor score. The cohort of websites scored particularly poorly on assessment of whether reliable, holistic information was presented, for instance provision of reliable sources, (28%, SD 18) and discussion of alternative treatments, (30%, SD 14). To facilitate patient-centred shared decision-making, high quality information needs to be available for patients and families considering VNS. This study identifies that such information is difficult to locate on the internet. There is a need to develop focussed and reliable online patient resources for VNS. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Vagus nerve stimulation in 15 children with therapy resistant epilepsy; its impact on cognition, quality of life, behaviour and mood.

PubMed

Hallböök, Tove; Lundgren, Johan; Stjernqvist, Karin; Blennow, Gösta; Strömblad, Lars-Göran; Rosén, Ingmar

2005-10-01

Vagus nerve stimulation (VNS) is a neurophysiologic treatment for patients with refractory epilepsy. There is growing evidence of additional quality of life (QOL) benefits of VNS. We report the effects of VNS on seizure frequency and severity and how these changes are related to cognitive abilities, QOL, behaviour and mood in 15 children with medically refractory and for surgery not eligible epilepsy. Initially, and after 3 and 9 months of VNS-treatment, 15 children were investigated with Bayley Scales of Infant Development (BSID), Wechsler Preschool and Primary Scale of Intelligence (WPPSI-R), Wechlser Intelligence Scales for Children (WISC-III) depending on the child's level of functioning, a Visual Analogue Scale for validating QOL, Child Behaviour Checklist (CBCL) for quantifying behaviour problems, Dodrill Mood Analogue Scale and Birleson Depression Self-Rating Scale, and the National Hospital Seizure Severity Scale (NHS3). A diary of seizure frequency was collected. Six of 15 children showed a 50% or more reduction in seizure frequency; one of these became seizure-free. Two children had a 25-50% seizure reduction. Two children showed increased seizure frequency. In 13 of 15 children there was an improvement in NHS3. The parents reported shorter duration of seizure and recovery phase. There were no changes in cognitive functioning. Twelve children showed an improvement in QOL. Eleven of these also improved in seizure severity and mood and five also in depressive parameters. This study has shown a good anti-seizure effect of VNS, an improvement in seizure severity and in QOL and a tendency to improvement over time regarding behaviour, mood and depressive parameters. The improvement in seizure severity, QOL, behaviour, mood and depressive parameters was not related to the anti-seizure effect.

Vagus nerve stimulator implantation for epilepsy in a paediatric hospital: outcomes and effect on quality of life.

PubMed

Ulate-Campos, A; Cean-Cabrera, L; Petanas-Argemi, J; García-Fructuoso, G; Aparicio, J; López-Sala, A; Palacio-Navarro, A; Mas, M J; Muchart, J; Rebollo, M; Sanmartí, F X

2015-10-01

Epilepsy, which is present in 0.5% to 1% of the paediatric population, is one of the most frequent childhood neurological disorders. Approximately 20% to 30% of these cases will be drug-resistant. The objective of this study is to describe the impact of vagal nerve stimulation (VNS) on seizures and quality of life in a sample of 30 patients. Descriptive, retrospective study of all patients with a VNS device implanted between 2008 and 2013 in a single paediatric hospital, based on patients' medical records. Quality of life was assessed using the Spanish scale for quality of life in children with epilepsy, completed by means of a telephone interview. We describe a population of 19 boys (64%) and 11 girls (36%) with a mean age at seizure onset of 21 months (1-144 months). The mean age of VNS implantation was 11.89 years. Follow-up periods ranged from 6 to 36 months. Mean reduction in seizures at 6 months was 38%, with a reduction of 43% at 12 months, 42% at 24 months, and 54% at 36 months. At least half of all patients were classified as responders. According to the quality of life scale, 54% of the families rated the effect of VNS as either very good or good while 39% rated it as fair. VNS is a safe palliative treatment that is generally well tolerated. It is partially effective for controlling drug-resistant epilepsy and exerts a positive effect on quality of life. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

TRIP effect in austenitic-martensitic VNS9-Sh steel at various strain rates

NASA Astrophysics Data System (ADS)

Terent'ev, V. F.; Slizov, A. K.; Prosvirnin, D. V.

2016-10-01

The mechanical properties of austenitic-martensitic VNS9-Sh (23Kh15N5AM3-Sh) steel are studied at a static strain rate from 4.1 × 10-5 to 17 × 10-3 s-1 (0.05-20 mm/min). It is found that, as the strain rate increases, the ultimate tensile strength decreases and the physical yield strength remains unchanged (≈1400 MPa). As the strain rate increases, the yield plateau remains almost unchanged and the relative elongation decreases continuously. Because of high microplastic deformation, the conventional yield strength is lower than the physical yield strength over the entire strain rate range under study. The influence of the TRIP effect on the changes in the mechanical properties of VNS9-Sh steel at various strain rates is discussed.

Pharyngeal dysesthesia in refractory complex partial epilepsy: new seizure or adverse effect of vagal nerve stimulation?

PubMed

Akman, Cigdem; Riviello, James J; Madsen, Joseph R; Bergin, Ann M

2003-06-01

Sensory symptoms are commonly seen in association with focal epilepsy, but viscerosensory auras, such as pharyngeal dysesthesias, are rarely the main clinical manifestation. With the introduction of vagal nerve stimulation (VNS) for medically refractory epilepsy, viscerosensory symptoms commonly occur as an adverse effect of VNS. Voice alterations (hoarseness or tremulousness), local neck or throat pain, and cough are the most common adverse effects seen during active stimulation (on-time). Numbness of the throat, neck, or chin, as well as a tingling sensation of the neck and throat is directly related to stimulation intensity. We present a case in which recurrent pharyngeal sensations caused a diagnostic dilemma and in which monitoring the VNS artifact during video/EEG and correlating this with clinical symptoms helped determine the etiology of the recurrent sensory symptoms.

High-resolution measurement of electrically-evoked vagus nerve activity in the anesthetized dog

NASA Astrophysics Data System (ADS)

Yoo, Paul B.; Lubock, Nathan B.; Hincapie, Juan G.; Ruble, Stephen B.; Hamann, Jason J.; Grill, Warren M.

2013-04-01

Objective. Not fully understanding the type of axons activated during vagus nerve stimulation (VNS) is one of several factors that limit the clinical efficacy of VNS therapies. The main goal of this study was to characterize the electrical recruitment of both myelinated and unmyelinated fibers within the cervical vagus nerve. Approach. In anesthetized dogs, recording nerve cuff electrodes were implanted on the vagus nerve following surgical excision of the epineurium. Both the vagal electroneurogram (ENG) and laryngeal muscle activity were recorded in response to stimulation of the right vagus nerve. Main results. Desheathing the nerve significantly increased the signal-to-noise ratio of the ENG by 1.2 to 9.9 dB, depending on the nerve fiber type. Repeated VNS following nerve transection or neuromuscular block (1) enabled the characterization of A-fibers, two sub-types of B-fibers, and unmyelinated C-fibers, (2) confirmed the absence of stimulation-evoked reflex compound nerve action potentials in both the ipsilateral and contralateral vagus nerves, and (3) provided evidence of stimulus spillover into muscle tissue surrounding the stimulating electrode. Significance. Given the anatomical similarities between the canine and human vagus nerves, the results of this study provide a template for better understanding the nerve fiber recruitment patterns associated with VNS therapies.

Central-peripheral neural network interactions evoked by vagus nerve stimulation: functional consequences on control of cardiac function.

PubMed

Ardell, Jeffrey L; Rajendran, Pradeep S; Nier, Heath A; KenKnight, Bruce H; Armour, J Andrew

2015-11-15

Using vagus nerve stimulation (VNS), we sought to determine the contribution of vagal afferents to efferent control of cardiac function. In anesthetized dogs, the right and left cervical vagosympathetic trunks were stimulated in the intact state, following ipsilateral or contralateral vagus nerve transection (VNTx), and then following bilateral VNTx. Stimulations were performed at currents from 0.25 to 4.0 mA, frequencies from 2 to 30 Hz, and a 500-μs pulse width. Right or left VNS evoked significantly greater current- and frequency-dependent suppression of chronotropic, inotropic, and lusitropic function subsequent to sequential VNTx. Bradycardia threshold was defined as the current first required for a 5% decrease in heart rate. The threshold for the right vs. left vagus-induced bradycardia in the intact state (2.91 ± 0.18 and 3.47 ± 0.20 mA, respectively) decreased significantly with right VNTx (1.69 ± 0.17 mA for right and 3.04 ± 0.27 mA for left) and decreased further following bilateral VNTx (1.29 ± 0.16 mA for right and 1.74 ± 0.19 mA for left). Similar effects were observed following left VNTx. The thresholds for afferent-mediated effects on cardiac parameters were 0.62 ± 0.04 and 0.65 ± 0.06 mA with right and left VNS, respectively, and were reflected primarily as augmentation. Afferent-mediated tachycardias were maintained following β-blockade but were eliminated by VNTx. The increased effectiveness and decrease in bradycardia threshold with sequential VNTx suggest that 1) vagal afferents inhibit centrally mediated parasympathetic efferent outflow and 2) the ipsilateral and contralateral vagi exert a substantial buffering capacity. The intact threshold reflects the interaction between multiple levels of the cardiac neural hierarchy. Copyright © 2015 the American Physiological Society.

Central-peripheral neural network interactions evoked by vagus nerve stimulation: functional consequences on control of cardiac function

PubMed Central

Rajendran, Pradeep S.; Nier, Heath A.; KenKnight, Bruce H.; Armour, J. Andrew

2015-01-01

Using vagus nerve stimulation (VNS), we sought to determine the contribution of vagal afferents to efferent control of cardiac function. In anesthetized dogs, the right and left cervical vagosympathetic trunks were stimulated in the intact state, following ipsilateral or contralateral vagus nerve transection (VNTx), and then following bilateral VNTx. Stimulations were performed at currents from 0.25 to 4.0 mA, frequencies from 2 to 30 Hz, and a 500-μs pulse width. Right or left VNS evoked significantly greater current- and frequency-dependent suppression of chronotropic, inotropic, and lusitropic function subsequent to sequential VNTx. Bradycardia threshold was defined as the current first required for a 5% decrease in heart rate. The threshold for the right vs. left vagus-induced bradycardia in the intact state (2.91 ± 0.18 and 3.47 ± 0.20 mA, respectively) decreased significantly with right VNTx (1.69 ± 0.17 mA for right and 3.04 ± 0.27 mA for left) and decreased further following bilateral VNTx (1.29 ± 0.16 mA for right and 1.74 ± 0.19 mA for left). Similar effects were observed following left VNTx. The thresholds for afferent-mediated effects on cardiac parameters were 0.62 ± 0.04 and 0.65 ± 0.06 mA with right and left VNS, respectively, and were reflected primarily as augmentation. Afferent-mediated tachycardias were maintained following β-blockade but were eliminated by VNTx. The increased effectiveness and decrease in bradycardia threshold with sequential VNTx suggest that 1) vagal afferents inhibit centrally mediated parasympathetic efferent outflow and 2) the ipsilateral and contralateral vagi exert a substantial buffering capacity. The intact threshold reflects the interaction between multiple levels of the cardiac neural hierarchy. PMID:26371171

[Structural-functional reserves of the vegetative nervous system in pilots flying high maneuver aircrafts].

PubMed

Sukhoterin, A F; Pashchenko, P S

2014-01-01

Purpose of the work was to analyze morbidity among pilots of different categories of aircraft, and to investigate reactivity of the vegetative nervous system (VNS) in pilots flying high maneuver aircrafts varying in age and flying time. Morbidity was deduced from the data of aviation medical exams. The VNS investigation involved 56 pilots of fighter and assault aircrafts both in the inter-flight periods and during duty shifts. Cytochemistry was used to measure glycogen in peripheral blood neutrophils in 77 pilots. It was shown that the pre-stress condition in pilots with the flying time more than 1000 hours may transform to chronic stress, provided that the flight duties remain heavy. According to the cytochemical data, concentration of neutrophilic glycogen indicating the energy potential of peripheral blood leukocytes is controlled by hormones secreted by the VNS sympathetic and parasympathetic components.

Auricular acupuncture and biomedical research--A promising Sino-Austrian research cooperation.

PubMed

Rong, Pei-Jing; Zhao, Jing-Jun; Li, Yu-Qing; Litscher, Daniela; Li, Shao-yuan; Gaischek, Ingrid; Zhai, Xu; Wang, Lu; Luo, Man; Litscher, Gerhard

2015-12-01

Treatment by auricular acupuncture has a long history. Ear-acupoint research has been advancing step by step in China and also in Europe. Auricles are rich in nerves, therefore a close relationship with different functions of the human body has been proved by the research teams of the two main authors of this article from China and Austria. In recent years, great progress has been made in the research of regulating human body functions through electroacupuncture at the auricular branch of the vagus nerve, which is part of auricular acupuncture therapy. It is well known that the auricular branch of the vagus nerve is the only peripheral pathway to the cerebral cortex. Studies of the Chinese team on hypertension, diabetes, epilepsy and depression have shown that the mechanism of auricular vagus nerve stimulation (VNS) may be comparable with cervical VNS in terms of pathways. Auricular VNS has a broad clinical application prospect.

Tuberous Sclerosis Complex National Database

DTIC Science & Technology

2005-10-01

monotherapy LIAED dosage reduction ElDiscontinuation of AED LURemoval of VNS device O1Discontinuation of Ketogenic Diet U Seizure remission Surgical...34* Treatments "* VNS "* Ketogenic Diet "* AEDs W81XWH-04-1-0896 Annual Report 10/05 Tuberous Sclerosis Complex National Database App. H - Page 1 of 3 PI: Steven P...Page 20 of 29 Date last modified 7/14/05 Subject name: First, Middle, Last DOB: LiKetogenic diet LiEpilepsy surgery (if checked, complete the separate

Korean Affairs Report.

DTIC Science & Technology

1986-10-20

Assails Chon’s Linking Explosion With Chongnyon (KCNA, 26 Sep 86) 31 "" 3i "" VNS on Chon Kyong-hwan Bribery Case (Yun Chong- won ; Voice of...Myong-nam; Pyongyang Domestic Service, 25 Sep 86) .. 42 VNS on People’s Rejection of Asiad, Olympics (Ko Il-chol, Min Hye -kyong; Voice of National...86) 109 INTERNATIONAL COMMENTARY United States Accused of Plotting Chemical Warfare (Pak Won -hae; MINJU CH0S0N, 3 Aug 86) 114 /7310 - c

Somatic therapies for treatment-resistant depression: ECT, TMS, VNS, DBS.

PubMed

Cusin, Cristina; Dougherty, Darin D

2012-08-17

The field of non-pharmacological therapies for treatment resistant depression (TRD) is rapidly evolving and new somatic therapies are valuable options for patients who have failed numerous other treatments. A major challenge for clinicians (and patients alike) is how to integrate the results from published clinical trials in the clinical decision-making process.We reviewed the literature for articles reporting results for clinical trials in particular efficacy data, contraindications and side effects of somatic therapies including electroconvulsive therapy (ECT), transcranial magnetic stimulation (TMS), vagal nerve stimulation (VNS) and deep brain stimulation (DBS). Each of these devices has an indication for patients with different level of treatment resistance, based on acuteness of illness, likelihood of response, costs and associated risks. ECT is widely available and its effects are relatively rapid in severe TRD, but its cognitive adverse effects may be cumbersome. TMS is safe and well tolerated, and it has been approved by FDA for adults who have failed to respond to one antidepressant, but its use in TRD is still controversial as it is not supported by rigorous double-blind randomized clinical trials. The options requiring surgical approach are VNS and DBS. VNS has been FDA-approved for TRD, however it is not indicated for management of acute illness. DBS for TRD is still an experimental area of investigation and double-blind clinical trials are underway.

Modern management of epilepsy: Vagus nerve stimulation.

PubMed

Ben-Menachem, E

1996-12-01

Vagus nerve stimulation (VNS) was first tried as a treatment for seizure patients in 1988. The idea to stimulate the vagus nerve and disrupt or prevent seizures was proposed by Jacob Zabarra. He observed a consistent finding among several animal studies which indicated that stimulation of the vagus nerve could alter the brain wave patterns of the animals under study. His hypothesis formed the basis for the development of the vagus nerve stimulator, an implantable device similar to a pacemaker, which is implanted in the left chest and attached to the left vagus nerve via a stimulating lead. Once implanted, the stimulator is programmed by a physician to deliver regular stimulation 24 hours a day regardless of seizure activity. Patients can also activate extra 'on-demand' stimulation with a handheld magnet. Clinical studies have demonstrated VNS therapy to be a safe and effective mode of treatment when added to the existing regimen of severe, refractory patients with epilepsy. Efficacy ranges from seizure free to no response with the majority of patients (> 50%) reporting at least a 50% improvement in number of seizures after 1.5 years of treatment. The side-effect profile is unique and mostly includes stimulation-related sensations in the neck and throat. The mechanism of action for VNS is not clearly understood although two theories have emerged. First, the direct connection theory hypothesizes that the anticonvulsant action of VNS is caused by a threshold raising effect of the connections to the nucleus of the solitary tract and on to other structures. The second is the concept that chronic stimulation of the vagus nerve increases the amount of inhibitory neurotransmitters and decreases the amount of excitatory neurotransmitters. Additional research into the optimal use of VNS is ongoing. Animal and clinical research have produced some interesting new data suggesting there are numerous ways to improve the clinical performance of vagus nerve stimulation as a treatment for refractory patients.

Stimulation of the nervous system for the management of seizures: current and future developments.

PubMed

Murphy, Jerome V; Patil, Arunangelo

2003-01-01

Vagal nerve stimulation (VNS) for the treatment of refractory epilepsy appears to have started from the theory that since VNS can alter the EEG, it may influence epilepsy. It proved effective in several models of epilepsy and was then tried in short-term, open-label and double-blind trials, leading to approval in Canada, Europe and the US. Follow-up observations in these patients demonstrated continued improvement in seizure control for up to 2 years. Close to 50% of treated patients have achieved at least a 50% reduction in seizure frequency. This therapy was also useful as rescue therapy for ongoing seizures in some patients; many patients are more alert. The initial trials were completed in patients >/=12 years of age with refractory partial seizures. Subsequently, similar benefits were shown in patients with tuberous sclerosis complex, Lennox-Gastaut syndrome, hypothalamic hamartomas and primary generalised seizures. Implanting the generator and leads is technically easy, and complications are few. The method of action is largely unknown, although VNS appears to alter metabolic activity in specific brain nuclei. Considering that improvement in mood is frequently found in patients using VNS, it has undergone trials in patients with depression. Other illnesses deserving exploration with this unusual therapy are Alzheimer's disease and autism. Some aspects of VNS have proven disappointing. Although patients have fewer seizures, the number of antiepileptic drugs they take is not significantly reduced. In addition, there is no way to accurately predict the end of life of the generator. Optimal stimulation parameters, if they exist, are unknown. Deep brain stimulation is a new method for controlling medically refractory seizures. It is based on the observation that thalamic stimulation can influence the EEG over a wide area. Several thalamic nuclei have been the object of stimulation in different groups of patients. Intraoperative brain imaging is essential for electrode placement. The procedure is done under local anaesthesia. Experience with this therapy is currently limited, but growing.

Transcutaneous vagus nerve stimulation (tVNS) enhances recognition of emotions in faces but not bodies.

PubMed

Sellaro, Roberta; de Gelder, Beatrice; Finisguerra, Alessandra; Colzato, Lorenza S

2018-02-01

The polyvagal theory suggests that the vagus nerve is the key phylogenetic substrate enabling optimal social interactions, a crucial aspect of which is emotion recognition. A previous study showed that the vagus nerve plays a causal role in mediating people's ability to recognize emotions based on images of the eye region. The aim of this study is to verify whether the previously reported causal link between vagal activity and emotion recognition can be generalized to situations in which emotions must be inferred from images of whole faces and bodies. To this end, we employed transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique that causes the vagus nerve to fire by the application of a mild electrical stimulation to the auricular branch of the vagus nerve, located in the anterior protuberance of the outer ear. In two separate sessions, participants received active or sham tVNS before and while performing two emotion recognition tasks, aimed at indexing their ability to recognize emotions from facial and bodily expressions. Active tVNS, compared to sham stimulation, enhanced emotion recognition for whole faces but not for bodies. Our results confirm and further extend recent observations supporting a causal relationship between vagus nerve activity and the ability to infer others' emotional state, but restrict this association to situations in which the emotional state is conveyed by the whole face and/or by salient facial cues, such as eyes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of selective vagal nerve stimulation on blood pressure, heart rate and respiratory rate in rats under metoprolol medication.

PubMed

Gierthmuehlen, Mortimer; Plachta, Dennis T T

2016-02-01

Selective vagal nerve stimulation (sVNS) has been shown to reduce blood pressure without major side effects in rats. This technology might be the key to non-medical antihypertensive treatment in patients with therapy-resistant hypertension. β-blockers are the first-line therapy of hypertension and have in general a bradycardic effect. As VNS itself can also promote bradycardia, it was the aim of this study to investigate the influence of the β1-selective blocker Metoprolol on the effect of sVNS especially with respect to the heart rate. In 10 male Wistar rats, a polyimide multichannel-cuff electrode was placed around the vagal nerve bundle to selectively stimulate the aortic depressor nerve fibers. The stimulation parameters were adapted to the thresholds of individual animals and were in the following ranges: frequency 30-50 Hz, amplitude 0.3-1.8 mA and pulse width 0.3-1.3 ms. Blood pressure responses were detected with a microtip transducer in the carotid artery, and electrocardiography was recorded with s.c. chest electrodes. After IV administration of Metoprolol (2 mg kg(-1) body weight), the animals' mean arterial blood pressure (MAP) and heart rate (HR) decreased significantly. Although the selective electrical stimulation of the baroreceptive fibers reduced MAP and HR, both effects were significantly alleviated by Metoprolol. As a side effect, the rate of stimulation-induced apnea significantly increased after Metoprolol administration. sVNS can lower the MAP under Metoprolol without causing severe bradycardia.

Evoked Pain Analgesia in Chronic Pelvic Pain Patients using Respiratory-gated Auricular Vagal Afferent Nerve Stimulation

PubMed Central

Napadow, Vitaly; Edwards, Robert R; Cahalan, Christine M; Mensing, George; Greenbaum, Seth; Valovska, Assia; Li, Ang; Kim, Jieun; Maeda, Yumi; Park, Kyungmo; Wasan, Ajay D.

2012-01-01

Objective Previous Vagus Nerve Stimulation (VNS) studies have demonstrated anti-nociceptive effects, and recent non-invasive approaches; termed transcutaneous-VNS, or t-VNS, have utilized stimulation of the auricular branch of the vagus nerve in the ear. The dorsal medullary vagal system operates in tune with respiration, and we propose that supplying vagal afferent stimulation gated to the exhalation phase of respiration can optimize t-VNS. Design counterbalanced, crossover study. Patients patients with chronic pelvic pain (CPP) due to endometriosis in a specialty pain clinic. Interventions/Outcomes We evaluated evoked pain analgesia for Respiratory-gated Auricular Vagal Afferent Nerve Stimulation (RAVANS) compared with Non-Vagal Auricular Stimulation (NVAS). RAVANS and NVAS were evaluated in separate sessions spaced at least one week apart. Outcome measures included deep tissue pain intensity, temporal summation of pain, and anxiety ratings, which were assessed at baseline, during active stimulation, immediately following stimulation, and 15 minutes after stimulus cessation. Results RAVANS demonstrated a trend for reduced evoked pain intensity and temporal summation of mechanical pain, and significantly reduced anxiety in N=15 CPP patients, compared to NVAS, with moderate to large effect sizes (eta2>0.2). Conclusion Chronic pain disorders such as CPP are in great need of effective, non-pharmacological options for treatment. RAVANS produced promising anti-nociceptive effects for QST outcomes reflective of the noted hyperalgesia and central sensitization in this patient population. Future studies should evaluate longer-term application of RAVANS to examine its effects on both QST outcomes and clinical pain. PMID:22568773

Feasibility, safety and efficacy of transcutaneous vagus nerve stimulation in chronic tinnitus: an open pilot study.

PubMed

Kreuzer, Peter M; Landgrebe, Michael; Resch, Markus; Husser, Oliver; Schecklmann, Martin; Geisreiter, Florian; Poeppl, Timm B; Prasser, Sarah J; Hajak, Goeran; Rupprecht, Rainer; Langguth, Berthold

2014-01-01

Vagus nerve stimulation represents an established treatment strategy for epilepsy and affective disorders. Recently, positive effects were also shown in animals and humans with tinnitus. Here we report the results of an open pilot study exploring feasibility, safety and efficacy of tVNS in the treatment of chronic tinnitus. Fifty patients with chronic tinnitus underwent tVNS in an open single-armed pilot study which was conducted in two phases applying two different stimulating devices (Cerbomed CM02 and NEMOS). Clinical assessment was based on Tinnitus Questionnaire (TQ), Tinnitus Handicap Inventory (THI), Beck Depression Inventory (BDI), WHO Quality of Life, and various numeric rating scales. Primary outcome was defined as change in TQ (baseline vs. final visit in week 24). The study has been registered with clinicaltrials.gov (NCT01176734). Primary analysis indicated mean TQ reductions of 3.7 points (phase 1) and 2.8 points (phase 2) significant for the first study phase. Secondary analyses indicated a significant BDI reduction for phase 1 (uncorrected for multiple testing), but no further systematic or significant effects. Adverse events included twitching and pressure at electrode placement site. The occurrence of one hospitalization because of palpations and the development of a left bundle branch block were considered as unrelated to the intervention. Cognitive testing revealed no significant changes. Our data demonstrate the feasibility of tVNS over a period of 6 months. There was no clinically relevant improvement of tinnitus complaints. Our data suggest tVNS to be considered safe in patients without a history of cardiac disease. Copyright © 2014 Elsevier Inc. All rights reserved.

Vagal Nerve Stimulator Malfunction with Change in Neck Position: Case Report and Literature Review.

PubMed

D'Agostino, Erin; Makler, Vyacheslav; Bauer, David F

2018-06-01

Vagal nerve stimulation is a safe and well-tolerated treatment for drug-resistant epilepsy. Complications and failure of the device can result from lead fracture, device malfunction, disconnection, or battery displacement and can result in a variety of symptoms. We present an interesting case of stimulator malfunction with increased impedance change seen only with a change in head position. The patient is a 25-year-old male with a vagal nerve stimulator (VNs) placed for medically refractory epilepsy who presented with neck pain and an electrical pulling sensation in his neck whenever he turned his head to the right. Initial interrogation of the VNs showed normal impedance. Subsequent interrogation with the patient's head turned found increased impedance only when the head was turned to the right. The patient had successful removal and replacement of the device with resolution of his preoperative complaints. Partial lead fracture was seen at explant. VNs malfunction can present in atypical ways. Positional maneuvers may help with its timely diagnosis. Copyright © 2018 Elsevier Inc. All rights reserved.

Non-pharmacological biological treatment approaches to difficult-to-treat depression.

PubMed

Fitzgerald, Paul B

2013-09-16

There has been substantial recent interest in novel brain stimulation treatments for difficult-to-treat depression. Electroconvulsive therapy (ECT) is a well established, effective treatment for severe depression. ECT's problematic side-effect profile and questions regarding optimal administration methods continue to be investigated. Magnetic seizure therapy, although very early in development, shows promise, with potentially similar efficacy to ECT but fewer side effects. Vagus nerve stimulation (VNS) and repetitive transcranial magnetic stimulation (rTMS) are clinically available in some countries. Limited research suggests VNS has potentially long-lasting antidepressant effects in a small group of patients. Considerable research supports the efficacy of rTMS. Both techniques require further study of optimal treatment parameters. Transcranial direct current stimulation may provide a low-cost antidepressant option if its efficacy is substantiated in larger samples. Deep brain stimulation is likely to remain reserved for patients with the most severe and difficult-to-treat depression, requiring further exploration of administration methods and its role in depression therapy. New and innovative forms of brain stimulation, including low-intensity ultrasound, low-field magnetic stimulation and epidural stimulation of the cortical surface, are in early stages of exploration and are yet to move into the clinical domain. Ongoing work is required to define which brain stimulation treatments are likely to be most useful, and in which patient groups. Clinical service development of brain stimulation treatments will likely be inconsistent and variable.

Comparing neurostimulation technologies in refractory focal-onset epilepsy.

PubMed

Gooneratne, Inuka Kishara; Green, Alexander L; Dugan, Patricia; Sen, Arjune; Franzini, Angelo; Aziz, Tipu; Cheeran, Binith

2016-11-01

For patients with pharmacoresistant focal epilepsy in whom surgical resection of the epileptogenic focus fails or was not feasible in the first place, there were few therapeutic options. Increasingly, neurostimulation provides an alternative treatment strategy for these patients. Vagal nerve stimulation (VNS) is well established. Deep brain stimulation (DBS) and cortical responsive stimulation (CRS) are newer neurostimulation therapies with recently published long-term efficacy and safety data. In this literature review, we introduce these therapies to a non-specialist audience. Furthermore, we compare and contrast long-term (5-year) outcomes of newer neurostimulation techniques with the more established VNS. A search to identify all studies reporting long-term efficacy (>5 years) of VNS, CRS and DBS in patients with refractory focal/partial epilepsy was conducted using PubMed and Cochrane databases. The outcomes compared were responder rate, percentage seizure frequency reduction, seizure freedom, adverse events, neuropsychological outcome and quality of life. We identified 1 study for DBS, 1 study for CRS and 4 studies for VNS. All neurostimulation technologies showed long-term efficacy, with progressively better seizure control over time. Sustained improvement in quality of life measures was demonstrated in all modalities. Intracranial neurostimulation had a greater side effect profile compared with extracranial stimulation, though all forms of stimulation are safe. Methodological differences between the studies mean that direct comparisons are not straightforward. We have synthesised the findings of this review into a pragmatic decision tree, to guide the further management of the individual patient with pharmacoresistant focal-onset epilepsy. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

On-off closed-loop control of vagus nerve stimulation for the adaptation of heart rate.

PubMed

Ugalde, Hector Romero; Le Rolle, Virginie; Bel, Alain; Bonnet, Jean-Luc; Andreu, David; Mabo, Philippe; Carrault, Guy; Hernández, Alfredo I

2014-01-01

Vagus nerve stimulation (VNS) is a potential therapeutic approach in a number of clinical applications. Although VNS is commonly delivered in an open-loop approach, it is now recognized that closed-loop approaches may be necessary to optimize the therapy and minimize side effects of neuro-stimulation devices. In this paper, we describe a prototype system for real-time control of the instantaneous heart rate, working synchronously with the heart period. As a first step, an on-off control method has been integrated. The system is evaluated on one sheep with induced heart failure, showing the interest of the proposed approach.

Virtual Network Embedding via Monte Carlo Tree Search.

PubMed

Haeri, Soroush; Trajkovic, Ljiljana

2018-02-01

Network virtualization helps overcome shortcomings of the current Internet architecture. The virtualized network architecture enables coexistence of multiple virtual networks (VNs) on an existing physical infrastructure. VN embedding (VNE) problem, which deals with the embedding of VN components onto a physical network, is known to be -hard. In this paper, we propose two VNE algorithms: MaVEn-M and MaVEn-S. MaVEn-M employs the multicommodity flow algorithm for virtual link mapping while MaVEn-S uses the shortest-path algorithm. They formalize the virtual node mapping problem by using the Markov decision process (MDP) framework and devise action policies (node mappings) for the proposed MDP using the Monte Carlo tree search algorithm. Service providers may adjust the execution time of the MaVEn algorithms based on the traffic load of VN requests. The objective of the algorithms is to maximize the profit of infrastructure providers. We develop a discrete event VNE simulator to implement and evaluate performance of MaVEn-M, MaVEn-S, and several recently proposed VNE algorithms. We introduce profitability as a new performance metric that captures both acceptance and revenue to cost ratios. Simulation results show that the proposed algorithms find more profitable solutions than the existing algorithms. Given additional computation time, they further improve embedding solutions.

Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints

NASA Astrophysics Data System (ADS)

Tang, Qiuhua; Li, Zixiang; Zhang, Liping; Floudas, C. A.; Cao, Xiaojun

2015-09-01

Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Development of Tools and Techniques for Processing STORRM Flight Data

NASA Technical Reports Server (NTRS)

Robinson, Shane; D'Souza, Christopher

2011-01-01

While at JSC for the summer of 2011, I was assigned to work on the sensor test for Orion relative-navigation risk mitigation (STORRM) development test objective (DTO). The STORRM DTO was flown on-board Endeavor during STS-134. The objective of the STORRM DTO is to test the visual navigation system (VNS), which will be used as the primary relative navigation sensor for the Orion spacecraft. The VNS is a flash lidar system intended to provide both line of sight and range information during rendezvous and proximity operations. The STORRM DTO also serves as a testbed for the high-resolution docking camera. This docking camera will be used to provide piloting cues for the crew during proximity operations. These instruments were mounted next to the trajectory control sensor (TCS) in Endeavour s payload bay. My principle objective for the summer was to generate a best estimated trajectory (BET) for Endeavor using the flight data collected by the VNS during rendezvous and the unprecedented re-rendezvous with the ISS. I processed the raw images from the VNS to produce range and bearing measurements. I then aggregated these measurements and extracted the measurements corresponding to individual reflectors. I combined the information contained in these measurements with data from the Endeavour's inertial sensors using Kalman smoothing techniques to ultimately produce a BET. This work culminated with a final presentation of the result to division management. Development of this tool required that traditional linear smoothing techniques be modified in a novel fashion to permit for the inclusion of non-linear measurements. This internship has greatly helped me further my career by providing exposure to real engineering projects. I also have benefited immensely from the mentorship of the engineers working on these projects. Many of the lessons I learned and experiences I had are of particular value because then can only be found in a place like JSC.

Variation in nuclear size and PD-L2 positivity correlate with aggressive chromophobe renal cell carcinoma.

PubMed

Mostafa, Mohamed E; Abdelkader, Amrou; Kuroda, Naoto; Pérez-Montiel, Delia; Banerjee, Anjishnu; Hes, Ondrej; Iczkowski, Kenneth A

2018-06-01

Chromophobe renal cell carcinoma (CRCC) is not amenable to International Society for Urologic Pathology-endorsed nucleolar grading. Novel grading approaches were proposed, but the rarity of adverse pathology hampers their discriminatory value. We investigate simple linear micrometer measurements and a proposed immunostain in CRCCs. 32 patients' CRCCs were studied: 12 adverse cases (stage pT3, recurrence, or metastasis), 15 controls (stage ≤pT2, no recurrence or metastasis after >3 years), and 8 metastases (3 were paired with primary adverse cases). The ratio of greatest dimensions of largest and smallest nuclei, in each of 5 "worst" high-power fields, excluding those with degenerative features, was designated variation in nuclear size (VNS). Percent multinucleate cells (PMC) were also counted. Mouse anti PD-L2 monoclonal antibody immunostaining was performed. Mean VNS measured in adverse primary and control primary tumors were 3.7 ± 0.5 and 2.4 ± 0.4 respectively (P < .001), and 3.4 ± 0.4 for metastases (P < .001). Optimal VNS cut-off was 2.5, with sensitivity and specificity 0.85 and 0.81, respectively. PMCs were 6.0 ± 3.0 for adverse group, 5.7 ± 2.7 for controls, and 4.1 ± 1.6 for metastases (P = NS). PD-L2 could not discriminate adverse versus good primary tumors (χ 2 1.6, P = .2), but was higher in metastases (χ 2 6.9, P < .01), or metastases plus adverse primary tumors (χ 2 4.8, P = .03), compared to good-pathology primary tumors. In conclusion, VNS is an easily obtained measurement that can predict adverse behavior of chromophobe RCC, and may impart value for needle biopsy reporting and the choice of active surveillance. PD-L2 was elevated in metastases but was less useful for primary tumors. Copyright © 2018 Elsevier Inc. All rights reserved.

Brain stimulation in posttraumatic stress disorder

PubMed Central

Novakovic, Vladan; Sher, Leo; Lapidus, Kyle A.B.; Mindes, Janet; A.Golier, Julia; Yehuda, Rachel

2011-01-01

Posttraumatic stress disorder (PTSD) is a complex, heterogeneous disorder that develops following trauma and often includes perceptual, cognitive, affective, physiological, and psychological features. PTSD is characterized by hyperarousal, intrusive thoughts, exaggerated startle response, flashbacks, nightmares, sleep disturbances, emotional numbness, and persistent avoidance of trauma-associated stimuli. The efficacy of available treatments for PTSD may result in part from relief of associated depressive and anxiety-related symptoms in addition to treatment of core symptoms that derive from reexperiencing, numbing, and hyperarousal. Diverse, heterogeneous mechanisms of action and the ability to act broadly or very locally may enable brain stimulation devices to address PTSD core symptoms in more targeted ways. To achieve this goal, specific theoretical bases derived from novel, well-designed research protocols will be necessary. Brain stimulation devices include both long-used and new electrical and magnetic devices. Electroconvulsive therapy (ECT) and Cranial electrotherapy stimulation (CES) have both been in use for decades; transcranial magnetic stimulation (TMS), magnetic seizure therapy (MST), deep brain stimulation (DBS), transcranial Direct Current Stimulation (tDCS), and vagus nerve stimulation (VNS) have been developed recently, over approximately the past twenty years. The efficacy of brain stimulation has been demonstrated as a treatment for psychiatric and neurological disorders such as anxiety (CES), depression (ECT, CES, rTMS, VNS, DBS), obsessive-compulsive disorder (OCD) (DBS), essential tremor, dystonia (DBS), epilepsy (DBS, VNS), Parkinson Disease (DBS), pain (CES), and insomnia (CES). To date, limited data on brain stimulation for PTSD offer only modest guidance. ECT has shown some efficacy in reducing comorbid depression in PTSD patients but has not been demonstrated to improve most core PTSD symptoms. CES and VNS have shown some efficacy in reducing anxiety, findings that may suggest possible utility in relieving PTSD-associated anxiety. Treatment of animal models of PTSD with DBS suggests potential human benefit. Additional research and novel treatment options for PTSD are urgently needed. The potential usefulness of brain stimulation in treating PTSD deserves further exploration. PMID:22893803

Microsurgical endoscopy-assisted anterior corpus callosotomy for drug-resistant epilepsy in an adult unresponsive to vagus nerve stimulation.

PubMed

Nasi, Davide; Iacoangeli, Maurizio; Di Somma, Lucia; Dobran, Mauro; Di Rienzo, Alessandro; Gladi, Maurizio; Benigni, Roberta; Passamonti, Claudia; Zamponi, Nelia; Scerrati, Massimo

2016-01-01

Because most of the corpus callosotomy (CC) series available in literature were published before the advent of vagus nerve stimulation (VNS), the efficacy of CC in patients with inadequate response to VNS remains unclear, especially in adult patients. We present the case of a 21-year-old female with medically refractory drop attacks that began at the age of 8 years, which resulted in the patient being progressively unresponsive to vagus nerve stimulation implanted at the age of 14 years. Corpus callosotomy was recommended to reduce the number of drop attacks. However, the patient had only mild cognitive impairments and no neurological deficits. For this reason, we were forced to plan a surgical approach able to maximize the disconnection for good seizure control while, at the same time, minimizing sequelae from disconnection syndromes and neurosurgical complications because in such cases of long-lasting epilepsy the gyri cinguli and the arteries can be tenaciously adherent and dislocated with all the normal anatomy altered. In this scenario, we opted for a microsurgical endoscopy-assisted anterior two-thirds corpus callosotomy. The endoscopic minimally invasive approach proved to be quite adequate in this technically demanding case and confirmed that CC may offer advantages, with good results, even in adult patients with drop attacks who have had inadequate response to VNS.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

PubMed

Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio

2014-05-10

Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

PubMed Central

2014-01-01

Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957

Help With Depression

MedlinePlus

... techniques that focus on neuromodulation, which incorporates electrical, magnetic or other forms of energy to stimulate brain ... electroconvulsive therapy (ECT), vagus-nerve stimulation (VNS), transcranial magnetic stimulation (TMS) and the experimental deep-brain stimulation ( ...

Vagus Nerve Stimulation for Treating Epilepsy

MedlinePlus

... and their FAMILIES VAGUS NERVE STIMULATION FOR TREATING EPILEPSY This information sheet is provided to help you ... how vagus nerve stimulation (VNS) may help treat epilepsy. The American Academy of Neurology (AAN) is the ...

Evoked pain analgesia in chronic pelvic pain patients using respiratory-gated auricular vagal afferent nerve stimulation.

PubMed

Napadow, Vitaly; Edwards, Robert R; Cahalan, Christine M; Mensing, George; Greenbaum, Seth; Valovska, Assia; Li, Ang; Kim, Jieun; Maeda, Yumi; Park, Kyungmo; Wasan, Ajay D

2012-06-01

Previous vagus nerve stimulation (VNS) studies have demonstrated antinociceptive effects, and recent noninvasive approaches, termed transcutaneous-vagus nerve stimulation (t-VNS), have utilized stimulation of the auricular branch of the vagus nerve in the ear. The dorsal medullary vagal system operates in tune with respiration, and we propose that supplying vagal afferent stimulation gated to the exhalation phase of respiration can optimize t-VNS. Counterbalanced, crossover study. Patients with chronic pelvic pain (CPP) due to endometriosis in a specialty pain clinic. INTERVENTIONS/OUTCOMES: We evaluated evoked pain analgesia for respiratory-gated auricular vagal afferent nerve stimulation (RAVANS) compared with nonvagal auricular stimulation (NVAS). RAVANS and NVAS were evaluated in separate sessions spaced at least 1 week apart. Outcome measures included deep-tissue pain intensity, temporal summation of pain, and anxiety ratings, which were assessed at baseline, during active stimulation, immediately following stimulation, and 15 minutes after stimulus cessation. RAVANS demonstrated a trend for reduced evoked pain intensity and temporal summation of mechanical pain, and significantly reduced anxiety in N = 15 CPP patients, compared with NVAS, with moderate to large effect sizes (η(2) > 0.2). Chronic pain disorders such as CPP are in great need of effective, nonpharmacological options for treatment. RAVANS produced promising antinociceptive effects for quantitative sensory testing (QST) outcomes reflective of the noted hyperalgesia and central sensitization in this patient population. Future studies should evaluate longer-term application of RAVANS to examine its effects on both QST outcomes and clinical pain. Wiley Periodicals, Inc.

A hybrid approach for integrated healthcare cooperative purchasing and supply chain configuration.

PubMed

Rego, Nazaré; Claro, João; Pinho de Sousa, Jorge

2014-12-01

This paper presents an innovative and flexible approach for recommending the number, size and composition of purchasing groups, for a set of hospitals willing to cooperate, while minimising their shared supply chain costs. This approach makes the financial impact of the various cooperation alternatives transparent to the group and the individual participants, opening way to a negotiation process concerning the allocation of the cooperation costs and gains. The approach was developed around a hybrid Variable Neighbourhood Search (VNS)/Tabu Search metaheuristic, resulting in a flexible tool that can be applied to purchasing groups with different characteristics, namely different operative and market circumstances, and to supply chains with different topologies and atypical cost characteristics. Preliminary computational results show the potential of the approach in solving a broad range of problems.

A New Soft Computing Method for K-Harmonic Means Clustering.

PubMed

Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

2016-01-01

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

[Treatment of status migrainosus by general anesthesia: a case report].

PubMed

Udelsmann, Artur; Saccomani, Priscila; Dreyer, Elisabeth; Costa, Alberto Luiz Cunha da

2015-01-01

The status migrainosus is a complication of migraine characterizedby severe headache for more than 72 h that did not respond to treatment, with risk of strokeand suicide. Researches on treatment are directed to drugs that stimulate GABA receptors; propofol and isoflurane act on sub-GABAa receptors and theoretically could be interesting. The first has been the subject of research in severe migraine. Opioids are employed in pain, and its use in chronic headache is debatable, but these agents are employed in acute cases. The goalis to present a case of refractory status migrainosus in that we decided to break the pain cycle by general anesthesia. Female patient, aged 50 years, with status migrainosus, in the last five days withvisits to the emergency department, medicated parenterally with various agents without result. Without comorbidities, dehydrated, described her pain as "well over 10" in Visual NumericScale (VNS). After consulting the literature, and given the apparent severity of the condition, we opted for a general anesthesia: induction with fentanyl, propofol, and vecuroniumand maintenance with isoflurane and propofol for two hours. Following the treatment, in the postanesthetic recuperation (PAR), the patient related her pain as VNS 3, and was released after five hours with VNS 2. Subsequently, her preventive treatment was resumed. Status migrainosus is a rare disabling complication and anesthetics have been the subject of research in its treatment; the option for general anesthesia with agents that stimulate GABA receptors, propofol and isoflurane, in association with fentanyl, proved effective and should encourage new research. Copyright © 2013 Sociedade Brasileira de Anestesiologia. Publicado por Elsevier Editora Ltda. All rights reserved.

Development of detailed design concepts for the EarthCARE multi-spectral imager

NASA Astrophysics Data System (ADS)

Lobb, Dan; Escadero, Isabel; Chang, Mark; Gode, Sophie

2017-11-01

The EarthCARE mission is dedicated to the study of clouds by observations from a satellite in low Earth orbit. The payload will include major radar and LIDAR instruments, supported by a multi-spectral imager (MSI) and a broadband radiometer. The paper describes development of detailed design concepts for the MSI, and analysis of critical performance parameters. The MSI will form Earth images at 500m ground sample distance (GSD) over a swath width of 150km, from a nominal platform altitude of around 400km. The task of the MSI is to provide spatial context for the single-point measurements made by the radar and LIDAR systems; it will image Earth in 7 spectral bands: one visible, one near-IR, two short-wave IR and three thermal IR. The MSI instrument will be formed in two parts: a visible-NIR-SWIR (VNS) system, radiometrically calibrated using a sunilluminated diffuser, and a thermal IR (TIR) system calibrated using cold space and an internal black-body. The VNS system will perform push-broom imaging, using linear array detectors (silicon and InGaAs) and 4 separate lenses. The TIR system will use a microbolometer array detector in a time delay and integration (TDI) mode. Critical issues discussed for the VNS system include detector selection and detailed optical design trade-offs. The latter are related to the desirability of dichroics to achieve a common aperture, which influences the calibration hardware and lens design. The TIR system's most significant problems relate to control of random noise and bias errors, requiring optimisation of detector operation and calibration procedures.

Effects of Ultrasound-guided intra-articular ketorolac injection with capsular distension.

PubMed

Ahn, Jae Ki; Kim, Jongwoo; Lee, Sang Jae; Park, Yongbum; Bae, Byung; Lee, Woo

2015-01-01

Frozen shoulder is a painful condition with gradual onset and loss of range of motion in the glenohumeral joint. To investigate the efficacy of ultrasound(US)-guided intra-articular (IA) ketorolac injection with capsular distension compared with steroid injection alone in patients with frozen shoulder by assessing pain relief, functional improvements, and range of motion at 1,3 and 6 months after the last injections. Between January 2009 and December 2012, 121 patient were treated with US-guided IA steroid injection or IA ketorolac injection with capsular distension for frozen shoulder. Patients (n= 57) of US-guided IA steroid injection group were administered with a mixture of 0.5% lidocaine (4 ml) plus triamcinolone (40 mg/ml; 1 ml) and patients (n= 64) of US-guided IA ketorolac injection with capsular distension group were administered by using 0.5% lidocaine (19 mL) plus ketorolac (30 mg/ml; 1 mL) for capsular distension. Outcome measurement was assessed by Shoulder Pain and Disability Index (SPADI), Verbal Numeric pain Scale (VNS) and passive range of motion (ROM) before injections and at 1, 3 and 6 months after the last injections. We regarded the outcomes as a success if patients obtained significant pain relief (as measured by > 50% improvement in the VNS score and 20 point improvement in the SPASI) at 1, 3 and 6 months after the last injections. SPADI, VNS and passive ROM were improved 1, 3 and 6 months after the last injections in both groups. The statistical differences were not observed in SPADI, VNS between groups (p< 0.05). Successful treatment rate were not significantly different between the groups as well as in 1, 3 and 6 month outcomes. However, greater improvement was found in a matter of range of motion in patients receiving IA ketorolac injection with capsular distension than participants receiving US-guided IA steroid injection alone. Significant differences in improvement at 3 and 6 months were observed for shoulder passive abduction and external rotation (p< 0.05). IA ketorolac injection with capsular distension was shown to be a treatment method as effective as the steroid injection alone in pain relief and functional improvement in patient with frozen shoulder and more improvement in passive abduction and external rotation than steroid injection alone at 3 and 6 months.

Time perception in patients with major depressive disorder during vagus nerve stimulation.

PubMed

Biermann, T; Kreil, S; Groemer, T W; Maihöfner, C; Richter-Schmiedinger, T; Kornhuber, J; Sperling, W

2011-07-01

Affective disorders may affect patients' time perception. Several studies have described time as a function of the frontal lobe. The activating eff ects of vagus nerve stimulation on the frontal lobe might also modulate time perception in patients with major depressive disorder (MDD). Time perception was investigated in 30 patients with MDD and in 7 patients with therapy-resistant MDD. In these 7 patients, a VNS system was implanted and time perception was assessed before and during stimulation. A time estimation task in which patients were asked "How many seconds have passed?" tested time perception at 4 defined time points (34 s, 77 s, 192 s and 230 s). The differences between the estimated and actual durations were calculated and used for subsequent analysis. Patients with MDD and healthy controls estimated the set time points relatively accurately. A general linear model revealed a significant main eff ect of group but not of age or sex. The passing of time was perceived as significantly slower in patients undergoing VNS compared to patients with MDD at all time points (T34: t = − 4.2; df = 35; p < 0.001; T77: t = − 4.8; df = 35; p < 0.001; T192: t = − 2.0; df = 35; p = 0.059; T230 t = −2.2; df = 35; p = 0.039) as well as compared to healthy controls (at only T77: t = 4.1; df = 35; p < 0.001). There were no differences in time perception with regard to age, sex or polarity of depression (uni- or bipolar). VNS is capable of changing the perception of time. This discovery furthers the basic research on circadian rhythms in patients with psychiatric disorders.

Treatment of status migrainosus by general anesthesia: a case report.

PubMed

Udelsmann, Artur; Saccomani, Priscila; Dreyer, Elisabeth; da Costa, Alberto Luiz Cunha

2015-01-01

The status migrainosus is a complication of migraine characterized by severe headache for more than 72h that did not respond to treatment, with risk of stroke and suicide. Researches on treatment are directed to drugs that stimulate GABA receptors; propofol and isoflurane act on sub-GABAa receptors and theoretically could be interesting. The first has been the subject of research in severe migraine. Opioids are employed in pain, and its use in chronic headache is debatable, but these agents are employed in acute cases. The goal is to present a case of refractory status migrainosus in that we decided to break the pain cycle by general anesthesia. Female patient, aged 50 years, with status migrainosus, in the last five days with visits to the emergency department, medicated parenterally with various agents without result. Without comorbidities, dehydrated, described her pain as "well over 10" in Visual Numeric Scale (VNS). After consulting the literature, and given the apparent severity of the condition, we opted for a general anesthesia: induction with fentanyl, propofol, and vecuronium and maintenance with isoflurane and propofol for two hours. Following the treatment, in the postanesthetic recuperation (PAR), the patient related her pain as VNS 3, and was released after five hours with VNS 2. Subsequently, her preventive treatment was resumed. Status migrainosus is a rare disabling complication and anesthetics have been the subject of research in its treatment; the option for general anesthesia with agents that stimulate GABA receptors, propofol and isoflurane, in association with fentanyl, proved effective and should encourage new research. Copyright © 2013 Sociedade Brasileira de Anestesiologia. Published by Elsevier Editora Ltda. All rights reserved.

Long term effects on epileptiform activity with vagus nerve stimulation in children.

PubMed

Hallböök, Tove; Lundgren, Johan; Blennow, Gösta; Strömblad, Lars-Göran; Rosén, Ingmar

2005-12-01

We report long-term effects of vagus nerve stimulation (VNS) on epileptiform activity in 15 children, and how these changes are related to activity stage and to clinical effects on seizure reduction, seizure severity (NHS3) and quality of life (QOL). Initially, and after 3 and 9 months of VNS-treatment, 15 children were investigated with 24 h ambulatory EEG monitoring for spike detection. The number of interictal epileptiform discharges (IEDs) and the inter spike intervals (ISIs) were analysed during 2 h in the awake state, and 1h of rapid eye movement (REM)-, spindle- and delta-sleep, respectively. Total number and duration of electrographic seizure episodes were also analysed. At 9 months the total number of IEDs was significantly reduced (p=0.04). There was a tendency of reduction in all activity stages, and significantly so in delta-sleep (p=0.008). Total electrographic seizure number was significantly reduced in the 24 h EEG at 3 and 9 months (p=0.03, 0.05). There was a significant concordance in direction of changes in epileptiform activity and electrographic seizures at 9 months (p=0.04). Concordance in direction of changes was seen in 9 of 15 children between clinical seizures and IED (p>0.3), in 10 of 15 children between QOL and IED (p=0.3) and in 8 of 15 children between NHS3 and IED (p>0.3). There was no direct correlation between the extent of improvement in these clinical data and the degree of spike reduction. This study shows that VNS reduces IEDs especially in REM and delta sleep, as well as the number of electrographic seizures. It also shows a concordance between reduction in IEDs and electrographic seizures.

Autonomic regulation therapy to enhance myocardial function in heart failure patients: the ANTHEM‐HFpEF study

PubMed Central

DiCarlo, Lorenzo A.; Libbus, Imad; Kumar, H. Uday; Mittal, Sanjay; Premchand, Rajendra K.; Amurthur, Badri; KenKnight, Bruce H.; Ardell, Jeffrey L.

2017-01-01

Abstract Background Approximately half of the patients presenting with new‐onset heart failure (HF) have HF with preserved left ventricular ejection fraction (HFpEF) and HF with mid‐range left ventricular ejection fraction (HFmrEF). These patients have neurohormonal activation like that of HF with reduced ejection fraction; however, beta‐blockers and angiotensin‐converting enzyme inhibitors have not been shown to improve their outcomes, and current treatment for these patients is symptom based and empiric. Sympathoinhibition using parasympathetic stimulation has been shown to improve central and peripheral aspects of the cardiac nervous system, reflex control, induce myocyte cardioprotection, and can lead to regression of left ventricular hypertrophy. Beneficial effects of autonomic regulation therapy (ART) using vagus nerve stimulation (VNS) have also been observed in several animal models of HFpEF, suggesting a potential role for ART in patients with this disease. Methods The Autonomic Neural Regulation Therapy to Enhance Myocardial Function in Patients with Heart Failure and Preserved Ejection Fraction (ANTHEM‐HFpEF) study is designed to evaluate the feasibility, tolerability, and safety of ART using right cervical VNS in patients with chronic, stable HFpEF and HFmrEF. Patients with symptomatic HF and HFpEF or HFmrEF fulfilling the enrolment criteria will receive chronic ART with a subcutaneous VNS system attached to the right cervical vagus nerve. Safety parameters will be continuously monitored, and cardiac function and HF symptoms will be assessed every 3 months during a post‐titration follow‐up period of at least 12 months. Conclusions The ANTHEM‐HFpEF study is likely to provide valuable information intended to expand our understanding of the potential role of ART in patients with chronic symptomatic HFpEF and HFmrEF. PMID:29283224

Treatment-resistant depression and suicidality.

PubMed

Bergfeld, Isidoor O; Mantione, Mariska; Figee, Martijn; Schuurman, P Richard; Lok, Anja; Denys, Damiaan

2018-08-01

Thirty percent of patients with treatment-resistant depression (TRD) attempt suicide at least once during their lifetime. However, it is unclear what the attempted and completed suicide incidences are in TRD patients after initiating a treatment, and whether specific treatments increase or decrease these incidences. We searched PubMed systematically for studies of depressed patients who failed at least two antidepressant therapies and were followed for at least three months after initiating a treatment. We estimated attempted and completed suicide incidences using a Poisson meta-analysis. Given the lack of controlled comparisons, we used a meta-regression to estimate whether these incidences differed between treatments. We included 30 studies investigating suicidality in 32 TRD samples, undergoing deep brain stimulation (DBS, n = 9), vagal nerve stimulation (VNS, n = 9), electroconvulsive therapy (ECT, n = 5), treatment-as-usual (n = 3), capsulotomy (n = 2), cognitive behavioral therapy (n = 2), ketamine (n = 1), and epidural cortical stimulation (n = 1). The overall incidence of completed suicides was 0.47 per 100 patient years (95% CI: 0.22-1.00), and of attempted suicides 4.66 per 100 patient years (95% CI: 3.53-6.23). No differences were found in incidences following DBS, VNS or ECT. Suicidality is poorly recorded in many studies limiting the number of studies available. The completed and attempted suicide incidences are high (0.47 and 4.66 per 100 patient years respectively), but these incidences did not differ between three end of the line treatments (DBS, VNS or ECT). Given the high suicide risk in TRD patients, clinical trials should consider suicidality as an explicit outcome measure. Copyright © 2018 Elsevier B.V. All rights reserved.

The Sensor Test for Orion RelNav Risk Mitigation Development Test Objective

NASA Technical Reports Server (NTRS)

Christian, John A.; Hinkel, Heather; Maguire, Sean

2011-01-01

The Sensor Test for Orion Relative-Navigation Risk Mitigation (STORRM) Development Test Objective (DTO) ew aboard the Space Shuttle Endeavour on STS-134, and was designed to characterize the performance of the ash LIDAR being developed for the Orion. This ash LIDAR, called the Vision Navigation Sensor (VNS), will be the primary navigation instrument used by the Orion vehicle during rendezvous, proximity operations, and docking. This paper provides an overview of the STORRM test objectives and the concept of operations. It continues with a description of the STORRM's major hardware compo nents, which include the VNS and the docking camera. Next, an overview of crew and analyst training activities will describe how the STORRM team prepared for flight. Then an overview of how insight data collection and analysis actually went is presented. Key ndings and results from this project are summarized, including a description of "truth" data. Finally, the paper concludes with lessons learned from the STORRM DTO.

Brain Stimulation in Alzheimer's Disease.

PubMed

Chang, Chun-Hung; Lane, Hsien-Yuan; Lin, Chieh-Hsin

2018-01-01

Brain stimulation techniques can modulate cognitive functions in many neuropsychiatric diseases. Pilot studies have shown promising effects of brain stimulations on Alzheimer's disease (AD). Brain stimulations can be categorized into non-invasive brain stimulation (NIBS) and invasive brain stimulation (IBS). IBS includes deep brain stimulation (DBS), and invasive vagus nerve stimulation (VNS), whereas NIBS includes transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), electroconvulsive treatment (ECT), magnetic seizure therapy (MST), cranial electrostimulation (CES), and non-invasive VNS. We reviewed the cutting-edge research on these brain stimulation techniques and discussed their therapeutic effects on AD. Both IBS and NIBS may have potential to be developed as novel treatments for AD; however, mixed findings may result from different study designs, patients selection, population, or samples sizes. Therefore, the efficacy of NIBS and IBS in AD remains uncertain, and needs to be further investigated. Moreover, more standardized study designs with larger sample sizes and longitudinal follow-up are warranted for establishing a structural guide for future studies and clinical application.

Synthesis and purification of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB)

DOEpatents

Mitchell, Alexander R [Livermore, CA; Coburn, Michael D [Santa Fe, NM; Lee, Gregory S [San Ramon, CA; Schmidt, Robert D [Livermore, CA; Pagoria, Philip F [Livermore, CA; Hsu, Peter C [Pleasanton, CA

2006-06-06

A method to convert surplus nitroarene explosives (picric acid, ammonium picrate,) into TATB is described. The process comprises three major steps: conversion of picric acid/ammonium picrate into picramide; conversion of picramide to TATB through vicarious nucleophilic substitution (VNS) of hydrogen chemistry; and purification of TATB.

The Sensor Test for Orion RelNav Risk Mitigation (STORRM) Development Test Objective

NASA Technical Reports Server (NTRS)

Christian, John A.; Hinkel, Heather; D'Souza, Christopher N.; Maguire, Sean; Patangan, Mogi

2011-01-01

The Sensor Test for Orion Relative-Navigation Risk Mitigation (STORRM) Development Test Objective (DTO) flew aboard the Space Shuttle Endeavour on STS-134 in May- June 2011, and was designed to characterize the performance of the flash LIDAR and docking camera being developed for the Orion Multi-Purpose Crew Vehicle. The flash LIDAR, called the Vision Navigation Sensor (VNS), will be the primary navigation instrument used by the Orion vehicle during rendezvous, proximity operations, and docking. The DC will be used by the Orion crew for piloting cues during docking. This paper provides an overview of the STORRM test objectives and the concept of operations. It continues with a description of STORRM's major hardware components, which include the VNS, docking camera, and supporting avionics. Next, an overview of crew and analyst training activities will describe how the STORRM team prepared for flight. Then an overview of in-flight data collection and analysis is presented. Key findings and results from this project are summarized. Finally, the paper concludes with lessons learned from the STORRM DTO.

Human papilloma virus lesions of the oral cavity: healing and relapse after treatment with 810-980 nm diode laser.

PubMed

Angiero, Francesca; Buccianti, Alberto; Parma, Luisa; Crippa, Rolando

2015-02-01

This study evaluated the therapeutic efficacy of laser therapy in treating oral human papilloma virus (HPV) lesions. In particular, mode of action, healing, postoperative patient compliance, visual numeric scale (VNS) pain index, and recurrence were analyzed. During 2001-2012, in 170 patients (80 women and 90 men), 174 intraoral and lip HPV lesions were detected and excised by diode laser of different wavelengths (810-980 nm), with an average power of 2.1 W, in continuous wave mode, using 300 to 320 μm optical fibers. In most cases (95.4%), complete healing occurred in the first 30 days. There were no adverse effects and all patients were carefully followed up until complete healing occurred, documenting any complications. There was only one recurrence, which was later treated successfully; the mean VNS pain score was below one. In treating HPV lesions, the diode laser is not only a valuable tool for their eradication but especially it reduces relapses, thanks to the characteristics of the laser light.

What is the best term in Spanish to express the concept of cancer-related fatigue?

PubMed

Centeno, Carlos; Portela Tejedor, María Angustias; Carvajal, Ana; San Miguel, Maria Teresa; Urdiroz, Julia; Ramos, Luis; De Santiago, Ana

2009-05-01

Fatigue is one of the most frequent symptoms in patients with cancer. No adequate term in Spanish has been defined to describe the English concept of fatigue. To identify the most suitable Spanish words that define the concept of fatigue and to check psychometric characteristics. Consensus with professional experts on Spanish words that best suit the English concept of fatigue. A prospective study on oncologic patients was also undertaken, which included an evaluation of the intensity of fatigue through visual numeric scales (VNS) where the words had been previously selected. The fatigue subscale of the Functional Assessment of Cancer Therapy-Fatigue (FACT-F) questionnaire was taken as a reference. The experts highlighted the words cansancio, agotamiento, and debilidad (tiredness, exhaustion, and weakness) as the terms that best defined the concept of fatigue. In the psychometric assessment study, 100 patients were included, of which 61 (61%) presented diagnostic values for cancer-related fatigue in the FACT-F fatigue subscale (score 34/52 or lower). The VNS for the chosen terms obtained a high correlation with the FACT-F fatigue subscale results: cansancio (tiredness) r = -0.71, agotamiento (exhaustion) r = -0.74, debilidad (weakness) r = -0.74, with no statistical differences between them. For the detection of fatigue by means of the VNS, tiredness (cutoff point > or =4/10) gave sensitivity (S) 0.90 and specificity (E) 0.72; exhaustion (cutoff point > or =3/10) S 0.95 and E 0.90 and weakness (cutoff point > or =4/10) S 0.92 and E 0.72. The ROC curve was 0.88 for tiredness, 0.94 for exhaustion, and 0.92 for weakness, with no significant difference between the areas mentioned. The terms cansancio, agotamiento, and debilidad (tiredness, exhaustion, and weakness) are suitable for defining the English concept of fatigue in Spanish, and should be the preferred option for inclusion in evaluation tools.

[CHARACTERISTICS OF COMBINED ANESTHESIA WITH EPIDURAL COMPONENTE DEPENDING ON VEGETATIVE NERVOUS SYSTEM TYPE].

PubMed

Hasanov, F J; Aslanov, A A; Muradov, N F; Namazova, K N

2016-01-01

The research objective was to study the characteristics of combined anesthesia with epidural componente (CAEC) depending on vegetative nervous system type (VNS) in patients who underwent large scale traumatic surgical operations on abdominal cavity organs. The scientific research was conducted in Anaesthesiology--Reanimation Department of the Scientific Surgical Centre named after acad. MA. Topchubashev, the Ministry of Health of the Azerbaijan Republic. The research objects were 69 patients who underwent operations in conditions of CAEC due to different serious surgical pathologies of abdominal cavity organs. VNS type was identified based on electroencephalogram, Cerdo Vegetative Index (CVI), Hildebrandt coefficient (HC) and single neurophysiological tests. The patients were divided into three groups depending on VNS type: I--normotonics--17 patients (24.7%), II--sympathatonics--25 patients (36.2%), and III--vagotonics--27 patients (39.1%). Blood adrenocorticotropic hormone (ACTH) and cortisol concentration were studied in 3 stages: I -preoperative, II--operation traumatic stage, III--the 1st postoperative days. The other indicators (heart rate, systolic blood pressure--SBP, dyastolic blood pressure--DBR average blood pressure--BP ave., pulse oximetry SpO₂, ECG, gases in blood and acid-base balance, electrolytes, blood glucose level, myocardium oxygen demand--MOD) were registered after 20 minutes and the 2nd day after operation besides the above stages. The research results indicated that it is possible to define the vegetative nervous system type superiority based on complex of single tests data, EEG, ECG, Cerdo Vegetative Index, Hildebrandt coefficient. CAEC can be considered optimun alternative of general anesthesia ensuring neurohumoral and hemodynamic stability in large scale, traumatic operations on abdominal cavity organs. Clinical course of CAEC is characterized by firmer hemodynamic and humoral stability in patients with functional balance of sympathetic and parasympathetic divisions of vegetative nervous system, that is in normotonics in comparison with sympathico-, and parasympathotonics.

Extracellular pH monitoring for use in closed-loop vagus nerve stimulation

NASA Astrophysics Data System (ADS)

Cork, Simon C.; Eftekhar, Amir; Mirza, Khalid B.; Zuliani, Claudio; Nikolic, Konstantin; Gardiner, James V.; Bloom, Stephen R.; Toumazou, Christofer

2018-02-01

Objective. Vagal nerve stimulation (VNS) has shown potential benefits for obesity treatment; however, current devices lack physiological feedback, which limit their efficacy. Changes in extracellular pH (pHe) have shown to be correlated with neural activity, but have traditionally been measured with glass microelectrodes, which limit their in vivo applicability. Approach. Iridium oxide has previously been shown to be sensitive to fluctuations in pH and is biocompatible. Iridium oxide microelectrodes were inserted into the subdiaphragmatic vagus nerve of anaesthetised rats. Introduction of the gut hormone cholecystokinin (CCK) or distension of the stomach was used to elicit vagal nerve activity. Main results. Iridium oxide microelectrodes have sufficient pH sensitivity to readily detect changes in pHe associated with both CCK and gastric distension. Furthermore, a custom-made Matlab script was able to use these changes in pHe to automatically trigger an implanted VNS device. Significance. This is the first study to show pHe changes in peripheral nerves in vivo. In addition, the demonstration that iridium oxide microelectrodes are sufficiently pH sensitive as to measure changes in pHe associated with physiological stimuli means they have the potential to be integrated into closed-loop neurostimulating devices.

46 CFR 28.575 - Severe wind and roll.

Code of Federal Regulations, 2014 CFR

2014-10-01

... the number of elements in the series; Vn=S[0.124LN(0.3048hn)+0.772], in feet per second S[0.127LN(hn)+0.772], in meters per second and is the wind speed for profile element “n” on a vessel; S=64 (19.5... is calculated by the following formula: A1=109kXY[Square root of (rs)], in degrees, where: s,X,Y...

46 CFR 28.575 - Severe wind and roll.

Code of Federal Regulations, 2012 CFR

2012-10-01

... the number of elements in the series; Vn=S[0.124LN(0.3048hn)+0.772], in feet per second S[0.127LN(hn)+0.772], in meters per second and is the wind speed for profile element “n” on a vessel; S=64 (19.5... is calculated by the following formula: A1=109kXY[Square root of (rs)], in degrees, where: s,X,Y...

46 CFR 28.575 - Severe wind and roll.

Code of Federal Regulations, 2011 CFR

2011-10-01

... the number of elements in the series; Vn=S[0.124LN(0.3048hn)+0.772], in feet per second S[0.127LN(hn)+0.772], in meters per second and is the wind speed for profile element “n” on a vessel; S=64 (19.5... is calculated by the following formula: A1=109kXY[Square root of (rs)], in degrees, where: s,X,Y...

46 CFR 28.575 - Severe wind and roll.

Code of Federal Regulations, 2013 CFR

2013-10-01

... the number of elements in the series; Vn=S[0.124LN(0.3048hn)+0.772], in feet per second S[0.127LN(hn)+0.772], in meters per second and is the wind speed for profile element “n” on a vessel; S=64 (19.5... is calculated by the following formula: A1=109kXY[Square root of (rs)], in degrees, where: s,X,Y...

46 CFR 28.575 - Severe wind and roll.

Code of Federal Regulations, 2010 CFR

2010-10-01

... the number of elements in the series; Vn=S[0.124LN(0.3048hn)+0.772], in feet per second S[0.127LN(hn)+0.772], in meters per second and is the wind speed for profile element “n” on a vessel; S=64 (19.5... is calculated by the following formula: A1=109kXY[Square root of (rs)], in degrees, where: s,X,Y...

Boron Hydrides

DTIC Science & Technology

1946-07-01

4cCdocmpcsiti- a c~f wntrca * s~r,,np1. Z P. R. XII, 6; XVII, 4.. 1. i~eu~u~ioa ýf hy,’-.r-ijn pr-babl’ ný.t -h s:bizu£ctr P. R. xII, ý;s XVII!, -1. 2. Vrt ...hour at 200OCr, brown dccozzposition preducts ~were depositod uniformly over tho hoatcd G1aiss surfae* The tube vrts opened and the hydro~cni vns renoved

Darwin revisited: The vagus nerve is a causal element in controlling recognition of other's emotions.

PubMed

Colzato, Lorenza S; Sellaro, Roberta; Beste, Christian

2017-07-01

Charles Darwin proposed that via the vagus nerve, the tenth cranial nerve, emotional facial expressions are evolved, adaptive and serve a crucial communicative function. In line with this idea, the later-developed polyvagal theory assumes that the vagus nerve is the key phylogenetic substrate that regulates emotional and social behavior. The polyvagal theory assumes that optimal social interaction, which includes the recognition of emotion in faces, is modulated by the vagus nerve. So far, in humans, it has not yet been demonstrated that the vagus plays a causal role in emotion recognition. To investigate this we employed transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique that modulates brain activity via bottom-up mechanisms. A sham/placebo-controlled, randomized cross-over within-subjects design was used to infer a causal relation between the stimulated vagus nerve and the related ability to recognize emotions as indexed by the Reading the Mind in the Eyes Test in 38 healthy young volunteers. Active tVNS, compared to sham stimulation, enhanced emotion recognition for easy items, suggesting that it promoted the ability to decode salient social cues. Our results confirm that the vagus nerve is causally involved in emotion recognition, supporting Darwin's argumentation. Copyright © 2017 Elsevier Ltd. All rights reserved.

POSE Algorithms for Automated Docking

NASA Technical Reports Server (NTRS)

Heaton, Andrew F.; Howard, Richard T.

2011-01-01

POSE (relative position and attitude) can be computed in many different ways. Given a sensor that measures bearing to a finite number of spots corresponding to known features (such as a target) of a spacecraft, a number of different algorithms can be used to compute the POSE. NASA has sponsored the development of a flash LIDAR proximity sensor called the Vision Navigation Sensor (VNS) for use by the Orion capsule in future docking missions. This sensor generates data that can be used by a variety of algorithms to compute POSE solutions inside of 15 meters, including at the critical docking range of approximately 1-2 meters. Previously NASA participated in a DARPA program called Orbital Express that achieved the first automated docking for the American space program. During this mission a large set of high quality mated sensor data was obtained at what is essentially the docking distance. This data set is perhaps the most accurate truth data in existence for docking proximity sensors in orbit. In this paper, the flight data from Orbital Express is used to test POSE algorithms at 1.22 meters range. Two different POSE algorithms are tested for two different Fields-of-View (FOVs) and two different pixel noise levels. The results of the analysis are used to predict future performance of the POSE algorithms with VNS data.

A multipopulation PSO based memetic algorithm for permutation flow shop scheduling.

PubMed

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.

Software applications to three-dimensional visualization of forest landscapes -- A case study demontrating the use of visual nature studio (VNS) in visualizing fire spread in forest landscapes

Treesearch

Brian J. Williams; Bo Song; Chou Chiao-Ying; Thomas M. Williams; John Hom

2010-01-01

Three-dimensional (3D) visualization is a useful tool that depicts virtual forest landscapes on computer. Previous studies in visualization have required high end computer hardware and specialized technical skills. A virtual forest landscape can be used to show different effects of disturbances and management scenarios on a computer, which allows observation of forest...

Scaling view by the Virtual Nature Systems

NASA Astrophysics Data System (ADS)

Klenov, Valeriy

2010-05-01

The Actual Nature Systems (ANS) continually are under spatial-temporal governing external influences from other systems (Meteorology and Geophysics). This influences provide own spatial temporal patterns on the Earth Nature Systems, which reforms these influences by own manner and scales. These at last three systems belong to the Open Non Equilibrium Nature Systems (ONES). The Geophysics and Meteorology Systems are both governing for the ANS on the Earth. They provide as continual energetic pressure and impacts, and direct Extremes from the both systems to the ANS on Earth surface (earthquakes, storms, and others). The Geodynamics of the ANS is under mixing of influence for both systems, on their scales and on dynamics of their spatial-temporal structures, and by own ANS properties, as the ONES. To select influences of external systems on the Earth systems always is among major tasks of the Geomorphology. Mixing of the Systems scales and dynamics provide specific properties for the memory of Earth system. The memory of the ANS has practical value for their multi-purpose management. The knowledge of these properties is the key for research spatial-temporal GeoDynamics and Trends of Earth Nature Systems. Selection of the influences in time and space requires for special tool, requires elaboration and action of the Virtual Nature Systems (VNS), which are enliven computer doubles for analysis Geodynamics of the ANS. The Experience on the VNS enables to assess influence of each and both external factors on the ANS. It is source of knowledge for regional tectonic and climate oscillations, trends, and threats. Research by the VNS for spatial-temporal dynamics and structures of stochastic regimes of governing systems and processes results in stochastic GeoDynamics of environmental processes, in forming of false trends and blanks in natural records. This ‘wild dance' of 2D stochastic patterns and their interaction each other and generates acting structures of river nets, and of river basins, in multi-layer, multi-scale, and multi-driven structures of surface processes. It results in the Information Loss Law for observed memory of the VNS (and of external drivers) which gradually cut off own Past and distort own history. This view on the GeoDynamics appeared after long time field measurements thousand of terrace levels, hundreds of terrace ranks, and many terrace complexes in river basins of all scales - for the purpose to recognize their deforming by climatic and tectonic spatial-temporal influences. The method for following up of terrace levels along valleys was used in the Geomorphology and Geology for a long time, by linking fragments of level to ‘cycles'. It gradually linked them by heights above riverbed. The understanding of this logical mistake was happened (as insight) during observing from upstream a valley. All fragmental levels downstream were good visible, without chances for their correlation ‘by height' or ‘by number'. Instead of link of fragments, this explains process of river valleys' stochastic GeoDynamics by properties of the ONES (I. Prigogine et al., 1984) to generate oscillations. Is only first view, but later it turned to simple mechanic of Information Loss Law action in the GeoInformatics for Nature Systems (Klenov, 1980, et al.). The Information Loss distorts and destroys natural records (sources for data on the Past exogenous and endogenous rivers). This simple equation was received by multiple measures of terrace rank, and other natural records. It explains origin of false trend in natural records, destroys most own history by stochastic dynamics of the ONES. It prevents to restore of nature records as a memory of the Past. Non-disturbed is only small time between the Past and the Future, which looks like a peak between two non-linear losses. The history of Past (of the ANS, and of external drivers) are destroyed by the ANS. The Future becomes none determined due unknown 2D data of future external influences. However, the effect is the reliable Outstripping Monitoring for impending disasters and of other processes with satisfactory exactness. It was proved by direct validations (by use observed records). The conclusions are as follows: The ILL is mechanics for dissipation the Past and indeterminism the Future of the Nature. Moving back along the VNS' Phase Trajectory changes a view on natural records, and is chance to restore history of the ANS and its external drivers.

The effectiveness of the low-power laser and kinesiotaping in the treatment of carpal tunnel syndrome, a pilot study.

PubMed

Güner, A; Altan, L; Kasapoğlu Aksoy, M

2018-05-01

In mild and moderate cases of carpal tunnel syndrome (CTS), the conservative approach is suggested. The purpose of this study is to assess and compare the effect of low-power laser versus the combination of low-power laser and kinesiotaping on pain, muscle strength, functionality, and electrophysiologic parameters in the patients with CTS. The study was planned as single-blind, prospective, randomized control. 64 hands diagnosed with CTS were included in the study. The patients were randomly divided into three groups by closed envelope method. Low-power laser therapy was applied to Group 1 (21 hands), kinesiotaping and low-power laser therapy in group 2 (22 hands), sham laser therapy in Group 3 (21 hands). All patients were assessed by visual numeric pain scale (VNS), hand grip strength (HGS), finger pinch strength (FPS), the Boston Carpal Tunnel Syndrome Questionnaire (BCTSQ), before treatment, after treatment (3rd week), and after (12th week) 3 months the treatment with the same physician. Motor and sensory nerve conduction studies were performed with electroneuromyography (ENMG) before the treatment (0th week) and at the end of the 12th week. Comparison of the group 1 with the group 3 showed significantly better improvement in the former in VNS, BCTSQ at 3rd week and 12th week compared to 0th week, and in FPS and HGS at 3rd week. Comparison of the group 2 with the group 3 showed significantly better improvement in the former VNS, BCTSQ, FPS and HGS at 3rd and 12th week compared to 0th week. When Group 1 and Group 2 were compared there was no statistically significant difference in any parameters in the 3rd week, but there was a statistically significant difference in favor of group 2 in FPS and HGS parameters at the 12th week. We have found that the kinesiotaping method applied with low-power laser treatment does not provide any additional benefit to the low-power laser treatment in the short term, however, in the long term, the increase in the HGS and FPS has occurred. In conclusion, low-power laser and kinesiotaping method in the treatment of CTS may be an effective and reliable treatment option in clinical parameters.

Gas Warfare in World War I. The 3rd. Division at Chateau Thierry, July 1918

DTIC Science & Technology

1959-07-01

I hvt ’.o 1, 17 Jun (Vb, i p. 35 - 36’. 1Jnf.-D>in -i. 10. 1andý’er:Divu i -@Una 5O~fg opv ~lGe vns me., 44 %~ Aw sw1 .~.g P.. 9.s KAi,, .I P LAM... indoors (where delay in masking or escaping to the open was almost certain to rqsult in casualties]. Shell fire and a general sense of insecurity

Transcutaneous electrical nerve stimulation (TENS) for pain control after vaginal delivery and cesarean section.

PubMed

Kayman-Kose, Seda; Arioz, Dagistan Tolga; Toktas, Hasan; Koken, Gulengul; Kanat-Pektas, Mine; Kose, Mesut; Yilmazer, Mehmet

2014-10-01

The present study aims to determine the efficiency and reliability of transcutaneous electrical nerve stimulation (TENS) in the management of pain related with uterine contractions after vaginal delivery and the pain related with both abdominal incision uterine contractions after cesarean section. A hundred healthy women who underwent cesarean section under general anesthesia were randomly assigned to the placebo group (Group 1) or the TENS group (Group 2), while 100 women who delivered by vaginal route without episiotomy were randomized into the placebo group (Group 3) or the TENS group (Group 4). The patients in Group 2 had statistically lower visual analog scale (VAS) and verbal numerical scale (VNS) scores than the patients in Group 1 (p < 0.001 for both). The patients in Group 4 had statistically lower VAS and VNS scores than the patients in Group 3 (p = 0.022 and p = 0.005, respectively). The analgesic requirement at the eighth hour of cesarean section was significantly lower in the patients who were treated with TENS (p = 0.006). The need for analgesics at the eighth hour of vaginal delivery was statistically similar in the patients who were treated with TENS and the patients who received placebo (p = 0.830). TENS is an effective, reliable, practical and easily available modality of treatment for postpartum pain.

Nonuniform quantum turbulence in superfluids

NASA Astrophysics Data System (ADS)

Nemirovskii, Sergey K.

2018-04-01

The problem of quantum turbulence in a channel with an inhomogeneous counterflow of superfluid turbulent helium is studied. The counterflow velocity Vns x(y ) along the channel is supposed to have a parabolic profile in the transverse direction y . Such statement corresponds to the recent numerical simulation by Khomenko et al. [Phys. Rev. B 91, 180504 (2015), 10.1103/PhysRevB.91.180504]. The authors reported about a sophisticated behavior of the vortex-line density (VLD) L (r ,t ) , different from L ∝Vns x(y) 2 , which follows from the straightforward application of the conventional Vinen theory. It is clear that Vinen theory should be refined by taking into account transverse effects, and the way it ought to be done is the subject of active discussion in the literature. In this work, we discuss several possible mechanisms of the transverse flux of VLD L (r ,t ) which should be incorporated in the standard Vinen equation to describe adequately the inhomogeneous quantum turbulence. It is shown that the most effective among these mechanisms is the one that is related to the phase-slippage phenomenon. The use of this flux in the modernized Vinen equation corrects the situation with an unusual distribution of the vortex-line density, and satisfactorily describes the behavior L (r ,t ) both in stationary and nonstationary situations. The general problem of the phenomenological Vinen theory in the case of nonuniform and nonstationary quantum turbulence is thoroughly discussed.

Effect of noninvasive vagus nerve stimulation on acute migraine: an open-label pilot study.

PubMed

Goadsby, P J; Grosberg, B M; Mauskop, A; Cady, R; Simmons, K A

2014-10-01

We sought to assess a novel, noninvasive, portable vagal nerve stimulator (nVNS) for acute treatment of migraine. Participants with migraine with or without aura were eligible for an open-label, single-arm, multiple-attack study. Up to four migraine attacks were treated with two 90-second doses, at 15-minute intervals delivered to the right cervical branch of the vagus nerve within a six-week time period. Subjects were asked to self-treat at moderate or severe pain, or after 20 minutes of mild pain. Of 30 enrolled patients (25 females, five males, median age 39), two treated no attacks, and one treated aura only, leaving a Full Analysis Set of 27 treating 80 attacks with pain. An adverse event was reported in 13 patients, notably: neck twitching (n = 1), raspy voice (n = 1) and redness at the device site (n = 1). No unanticipated, serious or severe adverse events were reported. The pain-free rate at two hours was four of 19 (21%) for the first treated attack with a moderate or severe headache at baseline. For all moderate or severe attacks at baseline, the pain-free rate was 12/54 (22%). nVNS may be an effective and well-tolerated acute treatment for migraine in certain patients. © International Headache Society 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Construction, Test and Delivery of Standardized Coaxial Magnetron.

DTIC Science & Technology

1981-09-23

Cavity Pressurization and Pressure Switch . .. 13 4.0 PERFORNANCE OF BAND IV, VMS-1104 COAXIAL AAGNETRON. 14 4.1 Initial Test of Band IV, VNS-1I04, S/N...of the magnetron. 3.7 Ship Air Pressure Switch The insertion of the magnetron deeper into the cabinet to obtain proper alignment of the tube and...waveguide run required reloca- tion of the ship air pressure switch . The switch was moved approxi- mately two inches deeper into the cabinet and rebolted

A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling

PubMed Central

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP. PMID:24453841

Osteopathic manipulative treatment is effective on pain control associated to spinal cord injury.

PubMed

Arienti, C; Daccò, S; Piccolo, I; Redaelli, T

2011-04-01

This study was designed as an experimental study (trial). To verify the effects of the association between conventional pharmacological treatment and osteopathic manipulative treatment (OMT) for chronic pain management in spinal cord injury (SCI). This study was carried out at Spinal Unit, Ospedale Niguarda Ca' Granda, Milan, Italy. Istituto Superiore di Osteopatia, Milan, Italy. We enrolled 47 patients with SCI, 26 with pain of both nociceptive and neuropathic origin, and 21 with pure neuropathic pain. In all, 33 patients had a complete spinal cord lesion (ASIA level A) and 14 had incomplete lesion (ASIA level B, C and D). The patients were subdivided in a pharmacological group (Ph), a pharmacological osteopathic (PhO) group and a osteopathic (Os) group. The verbal numeric scale (VNS) was used at various time intervals to evaluate treatment outcomes. Ph patients reached a 24% improvement in their pain perception, assessed by the VNS scale after 3 weeks of treatment, whereas Os patients reached a 16% improvement in their pain perception for the same weeks. Both treatments per se failed to induce further improvements at later time points. In contrast, the combination of the two approaches yielded a significantly better pain relief both in patients with nociceptive or pure neuropathic pain in the PhO group. Our results suggest the OMT is a feasible approach in patients in whom available drugs cannot be used. Moreover, a benefit can be expected by the association of OMT in patients treated according to existing pharmacological protocols.

Arthrobotrys oligospora-mediated biological control of diseases of tomato (Lycopersicon esculentum Mill.) caused by Meloidogyne incognita and Rhizoctonia solani.

PubMed

Singh, U B; Sahu, A; Sahu, N; Singh, R K; Renu, S; Singh, D P; Manna, M C; Sarma, B K; Singh, H B; Singh, K P

2013-01-01

To study the biocontrol potential of nematode-trapping fungus Arthrobotrys oligospora in protecting tomato (Lycopersicon esculentum Mill.) against Meloidogyne incognita and Rhizoctonia solani under greenhouse and field conditions. Five isolates of the nematode-trapping fungus Arthrobotrys oligospora isolated from different parts of India were tested against Meloidogyne incognita and Rhizoctonia solani in tomato (Lycopersicon esculentum Mill.) plants grown under greenhouse and field conditions. Arthrobotrys oligospora-treated plants showed enhanced growth in terms of shoot and root length and biomass, chlorophyll and total phenolic content and high phenylalanine ammonia lyase activity in comparison with M. incognita- and R. solani-inoculated plants. Biochemical profiling when correlated with disease severity and intensity in A. oligospora-treated and untreated plants indicate that A. oligospora VNS-1 offered significant disease reduction in terms of number of root galls, seedling mortality, lesion length, disease index, better plant growth and fruit yield as compared to M. incognita- and R. solani-challenged plants. The result established that A. oligospora VNS-1 has the potential to provide bioprotection agents against M. incognita and R. solani. Arthrobotrys oligospora can be a better environment friendly option and can be incorporated in the integrated disease management module of crop protection. Application of A. oligospora not only helps in the control of nematodes but also increases plant growth and enhances nutritional value of tomato fruits. Thus, it proves to be an excellent biocontrol as well as plant growth promoting agent. © 2012 The Society for Applied Microbiology.

Heterogeneous distribution of G protein alpha subunits in the main olfactory and vomeronasal systems of Rhinella (Bufo) arenarum tadpoles.

PubMed

Jungblut, Lucas D; Paz, Dante A; López-Costa, Juan J; Pozzi, Andrea G

2009-10-01

We evaluated the presence of G protein subtypes Galpha(o), Galpha(i2), and Galpha(olf) in the main olfactory system (MOS) and accessory or vomeronasal system (VNS) of Rhinella (Bufo) arenarum tadpoles, and here describe the fine structure of the sensory cells in the olfactory epithelium (OE) and vomeronasal organ (VNO). The OE shows olfactory receptor neurons (ORNs) with cilia in the apical surface, and the vomeronasal receptor neurons (VRNs) of the VNO are covered with microvilli. Immunohistochemistry detected the presence of at least two segregated populations of ORNs throughout the OE, coupled to Galpha(olf) and Galpha(o). An antiserum against Galpha(i2) was ineffective in staining the ORNs. In the VNO, Galpha(o) neurons stained strongly but lacked immunoreactivity to any other Galpha subunit in all larval stages analyzed. Western blot analyses and preabsorption experiments confirmed the specificity of the commercial antisera used. The functional significance of the heterogeneous G-protein distribution in R. arenarum tadpoles is not clear, but the study of G- protein distributions in various amphibian species is important, since this vertebrate group played a key role in the evolution of tetrapods. A more complete knowledge of the amphibian MOS and VNS would help to understand the functional organization and evolution of vertebrate chemosensory systems. This work demonstrates, for the first time, the existence of a segregated distribution of G-proteins in the OE of R. arenarum tadpoles.

Late Noachian Climate Of Mars: Constraints From Valley Network System Formation Times And The Intermittencies (Episodic/Periodic And Punctuated).

NASA Astrophysics Data System (ADS)

Head, James

2017-04-01

Formation of Late Noachian-Early Hesperian (LN-EH) valley network systems (VNS) signaled the presence of warm/wet conditions generating several hypotheses for climates permissive of these conditions. To constrain options for the ambient Noachian climate, we examine estimates for time required to carve channels/deltas and total duration implied by plausible intermittencies. Formation Times for VN, OBL, Deltas, Fans: A synthesis of required timescales show that even with the longest estimated continuous duration of VN formation/intermittencies, total time to carve the VN does not exceed 106 years, <˜0.25% of the total Noachian. Intermittency/episodicity assumptions are climate-model dependent (e.g., most workers use Earth-like fluvial activity and intermittency). Noachian-Early Hesperian Climate Models: 1) Warm and wet/semiarid/arid climate: Sustained background MAT >273 K, hydrological system vertically integrated, and rainfall occurs to recharge the aquifer. Two subtypes: a) "Rainfall/Fluvial Erosion-Dominated Warm and Wet Model": "Rainfall and surface runoff" persist throughout Noachian to explain crater degradation, and a LN-EH short rapidly ending terminal epoch. b) "Recharge Evaporation/Evaporite Dominated Warm and Wet Model": Sustained period of equatorial/mid-latitude precipitation and a vertically integrated hydrological system driven by evaporative upwelling and fluctuating shallow water table playa environments account for sulfate evaporate environments at Meridiani Planum. Sustained temperatures >273 K are required for extended periods (107-108 years). 2) Cold and icy climate: Sustained background temperatures extremely low (MAT ˜225 K), cryosphere is globally continuous, hydrological system is horizontally stratified, separating groundwater system from surface; no combination of spin-axis/orbital perturbations can raise MAT to 273 K. Adiabatic cooling effects transfer water to high altitudes, leading to "Late Noachian Icy Highlands Model". VNS cannot form in this nominal climate environment without special circumstances (e.g., impacts or volcanic eruptions elevate of temperatures by >˜50 K to induce melting and fluvial/lacustrine activity). 3) Cold and Icy climate warmed by greenhouse gases: The climate is sustained cold/icy model, but greenhouse gases of unspecified nature/amount/duration elevate MAT by several tens of Kelvins (say 25 K, to MAT 250 K), bringing annual temperature range into the realm where peak seasonal temperatures (PST) exceed 273 K. In this climate environment, analogous to the Antarctic Dry Valleys, seasonal summer temperatures above 273 K are sufficient to melt snow/ice and form fluvial and lacustrine features, but MAT is well below 273 K (253 K). Fluvial systems driven by episodic/periodic intermittency typically involve short intermittency time-scales (10-106 years) but require a warm climate (MAT >273 K) to be sustained for >0.4 x 109 years. Fluvial systems driven by punctuated intermittency typically involve short duration time-scales (10-105 years) but only require a warm climate (MAT >273 K) for the very short duration of the climatic impact of the punctuated event (102-105 years). We conclude that a cold and icy background climate with punctuated intermittency of warming and melting events is consistent with: 1) the estimated durations of continuous VN formation (<105 years) and 2) VN system estimated recurrence rates (106-107 years).

National Program for Inspection of Non-Federal Dams. Williams Pond Dam (CT 00551), Thames River Basin, Lebanon, Connecticut. Phase I Inspection Program.

DTIC Science & Technology

1978-10-01

GRA&I UnTucea B WILLIAMS POND DAM ~~1Z~ CT 00551 _ Distribution/ Availabilit Y Codes Avail and/or Dis~tj pecialS RIVER BASIN ~lIILEBANON, COXNNECTICUT...Inspection Report. Alternatives to these recommendations r 1 would include reducing the Williams Pond water levels during expected periods of intense storm...Materials Branch Engi’neering Division FRED J. VNS. Jr., Member Chief, De ’ggn Branch Engineering Division SAUL COOPER, -r Chief, Water Control Branch

Noninvasive techniques for probing neurocircuitry and treating illness: vagus nerve stimulation (VNS), transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS)

PubMed Central

George, Mark S; Aston-Jones, Gary

2010-01-01

Although the preceding chapters discuss much of the new knowledge of neurocircuitry of neuropsychiatric diseases, and an invasive approach to treatment, this chapter describes and reviews the noninvasive methods of testing circuit-based theories and treating neuropsychiatric diseases that do not involve implanting electrodes into the brain or on its surface. These techniques are transcranial magnetic stimulation, vagus nerve stimulation, and transcranial direct current stimulation. Two of these approaches have FDA approval as therapies. PMID:19693003

A systematic review of the effects of neuromodulation on eating and body weight: evidence from human and animal studies.

PubMed

McClelland, Jessica; Bozhilova, Natali; Campbell, Iain; Schmidt, Ulrike

2013-11-01

Eating disorders (ED) are chronic and sometimes deadly illnesses. Existing treatments have limited proven efficacy, especially in the case of adults with anorexia nervosa (AN). Emerging neural models of ED provide a rationale for more targeted, brain-directed interventions. This systematic review has examined the effects of neuromodulation techniques on eating behaviours and body weight and assessed their potential for therapeutic use in ED. All articles in PubMed, PsychInfo and Web of Knowledge were considered and screened against a priori inclusion/exclusion criteria. The effects of repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation, vagus nerve stimulation (VNS) and deep brain stimulation (DBS) were examined across studies in ED samples, other psychiatric and neurological disorders, and animal models. Sixty studies were identified. There is evidence for ED symptom reduction following rTMS and DBS in both AN and bulimia nervosa. Findings from studies of other psychiatric and neurological disorders and from animal studies demonstrate that increases in food intake and body weight can be achieved following DBS and that VNS has potential value as a means of controlling eating and inducing weight loss. Neuromodulation tools have potential for reducing ED symptomatology and related behaviours, and for altering food intake and body weight. In response to such findings, and emerging neural models of ED, treatment approaches are highly unlikely to remain 'brainless'. More research is required to evaluate the potential of neuromodulation procedures for improving long-term outcomes in ED. Copyright © 2013 John Wiley & Sons, Ltd and Eating Disorders Association.

Cervical Sympathetic Chain Schwannoma Masquerading as a Vagus Nerve Schwannoma Complicated by Postoperative Horner's Syndrome and Facial Pain: A Case Report.

PubMed

Baker, Austin T; Homewood, Tyler J; Baker, Terry R

2018-06-09

Cervical Sympathetic Chain Schwannomas (CSCS) of the carotid sheath are rare neoplasms that can be misdiagnosed on imaging. The following case documents a rare incident of a misdiagnosed CSCS with unusual outcomes of permanent Horner's syndrome and facial pain. A 36-year-old female presented with a slow-growing neck mass. CT and MRI led to a preoperative diagnosis of vagus nerve schwannoma (VNS). However, surgical treatment revealed the mass to be involved with the cervical sympathetic chain rather than the vagus nerve. The diagnosis was corrected to CSCS and the nerve was resected with the mass. The patient presented postoperatively with Horner's syndrome and severe facial pain. These symptoms persisted despite two years of medical management. Studies indicate that imaging trends used for distinction between VNS and CSCS show inconsistencies in making preoperative diagnoses. Recent literature reveals helpful criteria for improving diagnostic standards that assist with preoperative patient counseling. In addition, postoperative outcomes, such as temporary, asymptomatic Horner's syndrome are common in CSCS. The following case report exemplifies the difficulties in diagnosis and addresses the unique complications of facial pain and permanent Horner's syndrome. This case report examines postoperative outcomes and improves clinician awareness of the potential for misdiagnosis of a rare neoplasm and the recently improved diagnostic measures, providing for higher quality preoperative counseling. Future research is recommended to confirm and improve diagnostic guidelines and accuracy. Additional studies may focus on evaluating the effects of incorrect preoperative diagnosis on postoperative complication rates. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Postembryonic lineages of the Drosophila ventral nervous system: Neuroglian expression reveals the adult hemilineage associated fiber tracts in the adult thoracic neuromeres.

PubMed

Shepherd, David; Harris, Robin; Williams, Darren W; Truman, James W

2016-09-01

During larval life most of the thoracic neuroblasts (NBs) in Drosophila undergo a second phase of neurogenesis to generate adult-specific neurons that remain in an immature, developmentally stalled state until pupation. Using a combination of MARCM and immunostaining with a neurotactin antibody, Truman et al. (2004; Development 131:5167-5184) identified 24 adult-specific NB lineages within each thoracic hemineuromere of the larval ventral nervous system (VNS), but because of the neurotactin labeling of lineage tracts disappearing early in metamorphosis, they were unable extend the identification of these lineages into the adult. Here we show that immunostaining with an antibody against the cell adhesion molecule neuroglian reveals the same larval secondary lineage projections through metamorphosis and bfy identifying each neuroglian-positive tract at selected stages we have traced the larval hemilineage tracts for all three thoracic neuromeres through metamorphosis into the adult. To validate tract identifications we used the genetic toolkit developed by Harris et al. (2015; Elife 4) to preserve hemilineage-specific GAL4 expression patterns from larval into the adult stage. The immortalized expression proved a powerful confirmation of the analysis of the neuroglian scaffold. This work has enabled us to directly link the secondary, larval NB lineages to their adult counterparts. The data provide an anatomical framework that 1) makes it possible to assign most neurons to their parent lineage and 2) allows more precise definitions of the neuronal organization of the adult VNS based in developmental units/rules. J. Comp. Neurol. 524:2677-2695, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Postembryonic lineages of the Drosophila ventral nervous system: Neuroglian expression reveals the adult hemilineage associated fiber tracts in the adult thoracic neuromeres

PubMed Central

Harris, Robin; Williams, Darren W.; Truman, James W.

2016-01-01

During larval life most of the thoracic neuroblasts (NBs) in Drosophila undergo a second phase of neurogenesis to generate adult‐specific neurons that remain in an immature, developmentally stalled state until pupation. Using a combination of MARCM and immunostaining with a neurotactin antibody, Truman et al. (2004; Development 131:5167–5184) identified 24 adult‐specific NB lineages within each thoracic hemineuromere of the larval ventral nervous system (VNS), but because of the neurotactin labeling of lineage tracts disappearing early in metamorphosis, they were unable extend the identification of these lineages into the adult. Here we show that immunostaining with an antibody against the cell adhesion molecule neuroglian reveals the same larval secondary lineage projections through metamorphosis and bfy identifying each neuroglian‐positive tract at selected stages we have traced the larval hemilineage tracts for all three thoracic neuromeres through metamorphosis into the adult. To validate tract identifications we used the genetic toolkit developed by Harris et al. (2015; Elife 4) to preserve hemilineage‐specific GAL4 expression patterns from larval into the adult stage. The immortalized expression proved a powerful confirmation of the analysis of the neuroglian scaffold. This work has enabled us to directly link the secondary, larval NB lineages to their adult counterparts. The data provide an anatomical framework that 1) makes it possible to assign most neurons to their parent lineage and 2) allows more precise definitions of the neuronal organization of the adult VNS based in developmental units/rules. J. Comp. Neurol. 524:2677–2695, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc. PMID:26878258

Vagus nerve stimulation magnet activation for seizures: a critical review.

PubMed

Fisher, R S; Eggleston, K S; Wright, C W

2015-01-01

Some patients receiving VNS Therapy report benefit from manually activating the generator with a handheld magnet at the time of a seizure. A review of 20 studies comprising 859 subjects identified patients who reported on-demand magnet mode stimulation to be beneficial. Benefit was reported in a weighted average of 45% of patients (range 0-89%) using the magnet, with seizure cessation claimed in a weighted average of 28% (range 15-67%). In addition to seizure termination, patients sometimes reported decreased intensity or duration of seizures or the post-ictal period. One study reported an isolated instance of worsening with magnet stimulation (Arch Pediatr Adolesc Med, 157, 2003 and 560). All of the reviewed studies assessed adjunctive magnet use. No studies were designed to provide Level I evidence of efficacy of magnet-induced stimulation. Retrospective analysis of one pivotal randomized trial of VNS therapy showed significantly more seizures terminated or improved in the active stimulation group vs the control group. Prospective, controlled studies would be required to isolate the effect and benefit of magnet mode stimulation and to document that the magnet-induced stimulation is the proximate cause of seizure reduction. Manual application of the magnet to initiate stimulation is not always practical because many patients are immobilized or unaware of their seizures, asleep or not in reach of the magnet. Algorithms based on changes in heart rate at or near the onset of the seizure provide a methodology for automated responsive stimulation. Because literature indicates additional benefits from on-demand magnet mode stimulation, a potential role exists for automatic activation of stimulation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Automatic Vagus Nerve Stimulation Triggered by Ictal Tachycardia: Clinical Outcomes and Device Performance--The U.S. E-37 Trial.

PubMed

Fisher, Robert S; Afra, Pegah; Macken, Micheal; Minecan, Daniela N; Bagić, Anto; Benbadis, Selim R; Helmers, Sandra L; Sinha, Saurabh R; Slater, Jeremy; Treiman, David; Begnaud, Jason; Raman, Pradheep; Najimipour, Bita

2016-02-01

The Automatic Stimulation Mode (AutoStim) feature of the Model 106 Vagus Nerve Stimulation (VNS) Therapy System stimulates the left vagus nerve on detecting tachycardia. This study evaluates performance, safety of the AutoStim feature during a 3-5-day Epilepsy Monitoring Unit (EMU) stay and long- term clinical outcomes of the device stimulating in all modes. The E-37 protocol (NCT01846741) was a prospective, unblinded, U.S. multisite study of the AspireSR(®) in subjects with drug-resistant partial onset seizures and history of ictal tachycardia. VNS Normal and Magnet Modes stimulation were present at all times except during the EMU stay. Outpatient visits at 3, 6, and 12 months tracked seizure frequency, severity, quality of life, and adverse events. Twenty implanted subjects (ages 21-69) experienced 89 seizures in the EMU. 28/38 (73.7%) of complex partial and secondarily generalized seizures exhibited ≥20% increase in heart rate change. 31/89 (34.8%) of seizures were treated by Automatic Stimulation on detection; 19/31 (61.3%) seizures ended during the stimulation with a median time from stimulation onset to seizure end of 35 sec. Mean duty cycle at six-months increased from 11% to 16%. At 12 months, quality of life and seizure severity scores improved, and responder rate was 50%. Common adverse events were dysphonia (n = 7), convulsion (n = 6), and oropharyngeal pain (n = 3). The Model 106 performed as intended in the study population, was well tolerated and associated with clinical improvement from baseline. The study design did not allow determination of which factors were responsible for improvements. © 2015 The Authors. Neuromodulation: Technology at the Neural Interface published by Wiley Periodicals, Inc. on behalf of International Neuromodulation Society.

State Space Model with hidden variables for reconstruction of gene regulatory networks.

PubMed

Wu, Xi; Li, Peng; Wang, Nan; Gong, Ping; Perkins, Edward J; Deng, Youping; Zhang, Chaoyang

2011-01-01

State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN. True GRNs and synthetic gene expression datasets were generated using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks. Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN. This study provides useful information in handling the hidden variables and improving the inference precision.

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

PubMed Central

Gopal, Shruti; Miller, Robyn L.; Michael, Andrew; Adali, Tulay; Cetin, Mustafa; Rachakonda, Srinivas; Bustillo, Juan R.; Cahill, Nathan; Baum, Stefi A.; Calhoun, Vince D.

2016-01-01

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects. PMID:26106217

Influence of the tempering temperature on the mechanical properties and the phase composition of thin sheet TRIP steel

NASA Astrophysics Data System (ADS)

Terent'ev, V. F.; Eliseev, E. A.; Matyunin, V. M.; Slizov, A. K.; Marchenkov, A. Yu.; Sirotinkin, V. P.; Baikin, A. S.; Seval'nev, G. S.

2017-10-01

The strength and the plasticity properties of sheet high-strength austenitic-martensitic VNS9-Sh TRIP steel (23Kh15N5AM3-Sh) are studied as functions of the tempering temperature in the range 125-600°C. A nonmonotonic decease in the strength and the plasticity properties of the steel has been detected when the tempering temperature increases, and they increase in the range 300-450°C. The influence of aging processes, the precipitation of carbide, and the phase transformations in tempering on the mechanical properties of austenitic-martensitic corrosion-resistant steel is discussed.

Periodicity and stability for variable-time impulsive neural networks.

PubMed

Li, Hongfei; Li, Chuandong; Huang, Tingwen

2017-10-01

The paper considers a general neural networks model with variable-time impulses. It is shown that each solution of the system intersects with every discontinuous surface exactly once via several new well-proposed assumptions. Moreover, based on the comparison principle, this paper shows that neural networks with variable-time impulse can be reduced to the corresponding neural network with fixed-time impulses under well-selected conditions. Meanwhile, the fixed-time impulsive systems can be regarded as the comparison system of the variable-time impulsive neural networks. Furthermore, a series of sufficient criteria are derived to ensure the existence and global exponential stability of periodic solution of variable-time impulsive neural networks, and to illustrate the same stability properties between variable-time impulsive neural networks and the fixed-time ones. The new criteria are established by applying Schaefer's fixed point theorem combined with the use of inequality technique. Finally, a numerical example is presented to show the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Short- and long-term results of an inpatient programme to manage Complex Regional Pain Syndrome in children and adolescents

PubMed Central

Cucchiaro, Giovanni; Craig, Kevin; Marks, Kerri; Cooley, Kristin; Cox, Thalitha Kay Black; Schwartz, Jennifer

2017-01-01

The aim of this retrospective study was to determine whether an inpatient approach and the use of regional anaesthesia techniques can accelerate the recovery to normal functions in children with Complex Regional Pain Syndrome (CRPS). This study looked at the data of patients admitted to the rehabilitation unit with a diagnosis of CRPS from January 2010 to April 2015. Variables such as hospital stay, medications administered, regional anaesthesia procedures, changes in functional status prior to treatment and at the time of discharge, psychological evaluation and diagnosis were evaluated. A total of 31 patients (21 females and 10 males) were admitted with a diagnosis of CRPS 1 and 2. In all, 97% of the patients received a peripheral or central nerve catheter for an average of 4 days with pain scores of Verbal Numeric Scale (VNS) score = 1.0 ± 0.7 and an average length of hospital stay of 8.2 ± 2.6 days. The modified Functional Independence Measure for Children (WeeFIM) scores and Canadian Association of Occupational Therapists tests significantly improved at the time of hospital discharge, as well as their pain scores, which decreased from 8.2 ± 2 to 1.6 ± 3. In conclusion, these data suggest that the use of regional anaesthesia techniques and an intensive inpatient rehabilitation programme could accelerate the recovery of children with CRPS. PMID:28491301

Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.

PubMed

Tsai, Kuo-Ting; Hu, Chin-Kun; Li, Kuan-Wei; Hwang, Wen-Liang; Chou, Ya-Hui

2018-05-23

Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. We found that inhibitory interneurons enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, we have described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. In this work, we evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems.

Continuous-variable quantum network coding for coherent states

NASA Astrophysics Data System (ADS)

Shang, Tao; Li, Ke; Liu, Jian-wei

2017-04-01

As far as the spectral characteristic of quantum information is concerned, the existing quantum network coding schemes can be looked on as the discrete-variable quantum network coding schemes. Considering the practical advantage of continuous variables, in this paper, we explore two feasible continuous-variable quantum network coding (CVQNC) schemes. Basic operations and CVQNC schemes are both provided. The first scheme is based on Gaussian cloning and ADD/SUB operators and can transmit two coherent states across with a fidelity of 1/2, while the second scheme utilizes continuous-variable quantum teleportation and can transmit two coherent states perfectly. By encoding classical information on quantum states, quantum network coding schemes can be utilized to transmit classical information. Scheme analysis shows that compared with the discrete-variable paradigms, the proposed CVQNC schemes provide better network throughput from the viewpoint of classical information transmission. By modulating the amplitude and phase quadratures of coherent states with classical characters, the first scheme and the second scheme can transmit 4{log _2}N and 2{log _2}N bits of information by a single network use, respectively.

Discrete-time BAM neural networks with variable delays

NASA Astrophysics Data System (ADS)

Liu, Xin-Ge; Tang, Mei-Lan; Martin, Ralph; Liu, Xin-Bi

2007-07-01

This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development.

Tracking and Data Relay Satellite System (TDRSS) Support of User Spacecraft without TDRSS Transponders

NASA Technical Reports Server (NTRS)

Jackson, James A.; Marr, Greg C.; Maher, Michael J.

1995-01-01

NASA GSFC VNS TSG personnel have proposed the use of TDRSS to obtain telemetry and/or S-band one-way return Doppler tracking data for spacecraft which do not have TDRSS-compatible transponders and therefore were never considered candidates for TDRSS support. For spacecraft with less stable local oscillators (LO), one-way return Doppler tracking data is typically of poor quality. It has been demonstrated using UARS, WIND, and NOAA-J tracking data that the simultaneous use of two TDRSS spacecraft can yield differenced one-way return Doppler data of high quality which is usable for orbit determination by differencing away the effects of oscillator instability.

Hemicrania Continua: Beneficial Effect of Non-Invasive Vagus Nerve Stimulation in a Patient With a Contraindication for Indomethacin.

PubMed

Eren, Ozan; Straube, Andreas; Schöberl, Florian; Schankin, Christoph

2017-02-01

Hemicrania continua (HC) is a primary chronic headache disorder, characterized by a continuous and strictly unilateral headache, with possible cranial autonomic symptoms during episodes of pain exacerbation. The unilateral headache generally responds well to indomethacin; however, continuous indomethacin intake is often not tolerated due to severe adverse effects, like hypertension, gastrointestinal discomfort (especially if combined with aspirin), slightly increased risk of vascular events, and bronchial spasms. Therefore, alternative treatment options are desperately needed. Non-invasive vagus nerve stimulation (nVNS) has been shown to be effective in patients with cluster headache, another trigeminal autonomic cephalalgia (TAC), with cranial parasympathetic autonomic activation during the attacks. © 2016 American Headache Society.

Latent variable models are network models.

PubMed

Molenaar, Peter C M

2010-06-01

Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

Modeling Psychological Attributes in Psychology – An Epistemological Discussion: Network Analysis vs. Latent Variables

PubMed Central

Guyon, Hervé; Falissard, Bruno; Kop, Jean-Luc

2017-01-01

Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes. PMID:28572780

Chaotic simulated annealing by a neural network with a variable delay: design and application.

PubMed

Chen, Shyan-Shiou

2011-10-01

In this paper, we have three goals: the first is to delineate the advantages of a variably delayed system, the second is to find a more intuitive Lyapunov function for a delayed neural network, and the third is to design a delayed neural network for a quadratic cost function. For delayed neural networks, most researchers construct a Lyapunov function based on the linear matrix inequality (LMI) approach. However, that approach is not intuitive. We provide a alternative candidate Lyapunov function for a delayed neural network. On the other hand, if we are first given a quadratic cost function, we can construct a delayed neural network by suitably dividing the second-order term into two parts: a self-feedback connection weight and a delayed connection weight. To demonstrate the advantage of a variably delayed neural network, we propose a transiently chaotic neural network with variable delay and show numerically that the model should possess a better searching ability than Chen-Aihara's model, Wang's model, and Zhao's model. We discuss both the chaotic and the convergent phases. During the chaotic phase, we simply present bifurcation diagrams for a single neuron with a constant delay and with a variable delay. We show that the variably delayed model possesses the stochastic property and chaotic wandering. During the convergent phase, we not only provide a novel Lyapunov function for neural networks with a delay (the Lyapunov function is independent of the LMI approach) but also establish a correlation between the Lyapunov function for a delayed neural network and an objective function for the traveling salesman problem. © 2011 IEEE

Safety and efficacy of vagus nerve stimulation paired with tones for the treatment of tinnitus: a case series.

PubMed

De Ridder, Dirk; Vanneste, Sven; Engineer, Navzer D; Kilgard, Michael P

2014-02-01

Classical neuromodulation applies current to the nervous system in an attempt to alter ongoing activity. However, classical neuromodulation interferes with activity but does not drive it in a controlled way. Recently, an animal study demonstrated it is possible to drive plasticity in a controlled way by using stimulation of the vagus nerve paired with tones. This reversed the tinnitus percept and pathological neural plasticity in noise-exposed rats with behavioral characteristics of tinnitus. The aim of the current study was to translate this innovative neuromodulation method to humans suffering from tinnitus. Ten patients with severe chronic tinnitus were implanted with electrodes on their left vagus nerve. Two and a half hours each day for 20 days, the patients heard tones, excluding the tinnitus-matched frequency, paired with brief electrical stimulation of the vagus nerve. The therapy was well tolerated, and no patient withdrew from the study due to complications or side-effects. Four of the ten patients exhibited clinically meaningful improvements in their tinnitus, both for the affective component, as quantified by the Tinnitus Handicap Inventory, and for the sound percept, as quantified by the minimum masking level. These improvements were stable for more than two months after the end of therapy. Of the ten patients, five were on medications that included muscarinic antagonists, norepinephrine agonists, and γ-amino butyric acid agonists, thereby possibly interfering with acetylcholine and norepinephrine release induced by vagus nerve stimulation (VNS) and essential for inducing plasticity. These patients had no improvement in contrast to medication-free patients. VNS paired with tones excluding the tinnitus-matched frequency is safe and feasible. It seems to exert a beneficial effect in nonmedication-taking patients, both with regard to the perceived sound and the distress. Further studies are therefore mandated. © 2013 International Neuromodulation Society.

Inguinal hernia repair in day surgery: the role of MAC (Monitored Anesthesia Care) with remifentanil

PubMed Central

USAI, S.; AMATUCCI, C.; PEROTTI, B.; RUGGERI, L.; ILLUMINATI, G.; TELLAN, G.

2017-01-01

Background The extension of indications for procedures in a Day Surgery (DS) setting has led to changes in the anesthetic and surgical treatment of Inguinal Hernias (IH). According to the recommendations of the European Hernia Society, the treatment of IH in DS units should be performed under Monitored Anesthesia Care (MAC). Patients and methods 960 patients underwent IH repairs over a period of 24 months. The patients were randomly divided into two groups: R (remifentanil) and F (fentanyl); the group F was considered as a control group. The exclusion criteria in both group were: morbid obesity (BMI>40 or BMI>35 in association with high blood pressure or diabetes); coagulopathy; OSAS (obstructive sleep apnea syndrome) with AHI >10; cardiovascular, respiratory, renal, hepatic or metabolic disease; history of substances abuse; GERD-related esophagitis (gastro-esophageal reflux disease); chronic analgesic use; allergy to local anesthetic and ASA>III. Patients reported their level of pain on a verbal numeric scale (VNS), with scores ranging from 0 to 10. For each patient systolic and diastolic blood pressure (SBP and DBP), mean arterial pressure (MAP), heart rate (HR) and peripheral oxygen saturation (SpO2) were recorded. The results are presented as the mean value ± standard deviations; statistical analysis was performed using Student’s t-test. Results Amongst the 960 procedures, complications or side effects related to the anesthetic techniques didn’t occur; no procedure-related complications requiring mechanical ventilation support were reported. Our research focused on evaluating remifentanil effectiveness in pain control and its impact on hemodynamic stability and respiratory function. There was a significant difference between the two groups with regard to the VNS. Conclusions Remifentanil, is an excellent drug for pain control during intra-operative procedures, that allows an optimal hemodynamic stability for IH repairs in a DS setting, due to its pharmacokinetic and pharmacodynamic properties and few adverse effects. PMID:29442057

A translational study of resistance emergence using sequential direct-acting antiviral agents for hepatitis C using ultra-deep sequencing.

PubMed

Abe, Hiromi; Hayes, C Nelson; Hiraga, Nobuhiko; Imamura, Michio; Tsuge, Masataka; Miki, Daiki; Takahashi, Shoichi; Ochi, Hidenori; Chayama, Kazuaki

2013-09-01

Direct-acting antiviral agents (DAAs) against hepatitis C virus (HCV) have recently been developed and are ultimately hoped to replace interferon-based therapy. However, DAA monotherapy results in rapid emergence of resistant strains and DAAs must be used in combinations that present a high genetic barrier to resistance, although viral kinetics of multidrug-resistant strains remain poorly characterized. The aim of this study is to track the emergence and fitness of resistance using combinations of telaprevir and NS5A or NS5B inhibitors with genotype 1b clones. HCV-infected chimeric mice were treated with DAAs, and resistance was monitored using direct and ultra-deep sequencing. Combination therapy with telaprevir and BMS-788329 (NS5A inhibitor) reduced serum HCV RNA to undetectable levels. The presence of an NS3-V36A telaprevir resistance mutation resulted in poor response to telaprevir monotherapy but showed significant HCV reduction when telaprevir was combined with BMS-788329. However, a BMS-788329-resistant strain emerged at low frequency. Infection with a BMS-788329-resistant NS5A-L31V mutation rapidly resulted in gain of an additional NS5A-Y93A mutation that conferred telaprevir resistance during combination therapy. Infection with dual NS5AL31V/NS5AY93H mutations resulted in poor response to combination therapy and development of telaprevir resistance. Although HCV RNA became undetectable soon after the beginning of combination therapy with BMS-788329 and BMS-821095 (NS5B inhibitor), rebound with emergence of resistance against all three drugs occurred. Triple resistance also occurred following infection with the NS3V36A/NS5AL31V/NS5AY93H triple mutation. Resistant strains easily develop from cloned virus strains. Sequential use of DAAs should be avoided to prevent emergence of multidrug-resistant strains.

Wave-variable framework for networked robotic systems with time delays and packet losses

NASA Astrophysics Data System (ADS)

Puah, Seng-Ming; Liu, Yen-Chen

2017-05-01

This paper investigates the problem of networked control system for nonlinear robotic manipulators under time delays and packet loss by using passivity technique. With the utilisation of wave variables and a passive remote controller, the networked robotic system is demonstrated to be stable with guaranteed position regulation. For the input/output signals of robotic systems, a discretisation block is exploited to convert continuous-time signals to discrete-time signals, and vice versa. Subsequently, we propose a packet management, called wave-variable modulation, to cope with the proposed networked robotic system under time delays and packet losses. Numerical examples and experimental results are presented to demonstrate the performance of the proposed wave-variable-based networked robotic systems.

Self-reported personality variability across the social network is associated with interpersonal dysfunction.

PubMed

Clifton, Allan; Kuper, Laura E

2011-04-01

We describe 2 studies (n=52 and n=82) examining variability in perceptions of personality using a social network methodology. Undergraduate participants completed self-report measures of personality and interpersonal dysfunction and then subsequently reported on their personalities with each of 30 members of their social networks. Results across the 2 studies found substantial variability in participants' perceived personalities within their social networks. Measures of interpersonal dysfunction were associated with the amount of variability in dyadic ratings of personality, specifically Agreeableness and Openness to Experience. Results suggest that personality variability across interpersonal contexts may be an important individual difference related to social behavior and dysfunction. © 2011 The Authors. Journal of Personality © 2011, Wiley Periodicals, Inc.

Analysis of blocking probability for OFDM-based variable bandwidth optical network

NASA Astrophysics Data System (ADS)

Gong, Lei; Zhang, Jie; Zhao, Yongli; Lin, Xuefeng; Wu, Yuyao; Gu, Wanyi

2011-12-01

Orthogonal Frequency Division Multiplexing (OFDM) has recently been proposed as a modulation technique. For optical networks, because of its good spectral efficiency, flexibility, and tolerance to impairments, optical OFDM is much more flexible compared to traditional WDM systems, enabling elastic bandwidth transmissions, and optical networking is the future trend of development. In OFDM-based optical network the research of blocking rate has very important significance for network assessment. Current research for WDM network is basically based on a fixed bandwidth, in order to accommodate the future business and the fast-changing development of optical network, our study is based on variable bandwidth OFDM-based optical networks. We apply the mathematical analysis and theoretical derivation, based on the existing theory and algorithms, research blocking probability of the variable bandwidth of optical network, and then we will build a model for blocking probability.

Mean Field Analysis of Stochastic Neural Network Models with Synaptic Depression

NASA Astrophysics Data System (ADS)

Yasuhiko Igarashi,; Masafumi Oizumi,; Masato Okada,

2010-08-01

We investigated the effects of synaptic depression on the macroscopic behavior of stochastic neural networks. Dynamical mean field equations were derived for such networks by taking the average of two stochastic variables: a firing-state variable and a synaptic variable. In these equations, the average product of thesevariables is decoupled as the product of their averages because the two stochastic variables are independent. We proved the independence of these two stochastic variables assuming that the synaptic weight Jij is of the order of 1/N with respect to the number of neurons N. Using these equations, we derived macroscopic steady-state equations for a network with uniform connections and for a ring attractor network with Mexican hat type connectivity and investigated the stability of the steady-state solutions. An oscillatory uniform state was observed in the network with uniform connections owing to a Hopf instability. For the ring network, high-frequency perturbations were shown not to affect system stability. Two mechanisms destabilize the inhomogeneous steady state, leading to two oscillatory states. A Turing instability leads to a rotating bump state, while a Hopf instability leads to an oscillatory bump state, which was previously unreported. Various oscillatory states take place in a network with synaptic depression depending on the strength of the interneuron connections.

The EarthCARE multi spectral imager thermal infrared optical unit

NASA Astrophysics Data System (ADS)

Chang, M. P. J. L.; Woods, D.; Baister, Guy; Lobb, Dan; Wood, Trevor

2017-11-01

The EarthCARE satellite mission objective is the observation of clouds and aerosols from low Earth orbit. The key spatial context providing instrument within the payload suite of 4 instruments is the Multi-Spectral Imager (MSI), previously described in [1]. The MSI is intended to provide information on the horizontal variability of the atmospheric conditions and to identify e.g. cloud type, textures, and temperature. It will form Earth images at 500m ground sample distance (GSD) over a swath width of 150km; it will image Earth in 7 spectral bands: one visible, one near-IR, two short-wave IR and three thermal IR. The instrument will be comprised of two key parts: • a visible-NIR-SWIR (VNS) optical unit radiometrically calibrated using a sun illuminated quasivolume diffuser and shutter system • a thermal IR (TIR) optical unit radiometrically calibrated using cold space and an internal black-body. This paper, being the first of a sequence of two, will provide an overview of the MSI and enter into more detail the critical performance parameters and detailed design the MSI TIR optical design. The TIR concept is to provide pushbroom imaging of its 3 bands through spectral separation from a common aperture. The result is an efficient, well controlled optical design without the need for multiple focal plane arrays. The designed focal plane houses an area array detector and will meet a challenging set of requirements, including radiometric resolution, accuracy, distortion and MTF.

Variable Neural Adaptive Robust Control: A Switched System Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

2015-05-01

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewisemore » quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.« less

Variable Discretisation for Anomaly Detection using Bayesian Networks

DTIC Science & Technology

2017-01-01

UNCLASSIFIED DST- Group –TR–3328 1 Introduction Bayesian network implementations usually require each variable to take on a finite number of mutually...UNCLASSIFIED Variable Discretisation for Anomaly Detection using Bayesian Networks Jonathan Legg National Security and ISR Division Defence Science...and Technology Group DST- Group –TR–3328 ABSTRACT Anomaly detection is the process by which low probability events are automatically found against a

Animal-to-animal variability in the phasing of the crustacean cardiac motor pattern: an experimental and computational analysis

PubMed Central

Williams, Alex H.; Kwiatkowski, Molly A.; Mortimer, Adam L.; Marder, Eve; Zeeman, Mary Lou

2013-01-01

The cardiac ganglion (CG) of Homarus americanus is a central pattern generator that consists of two oscillatory groups of neurons: “small cells” (SCs) and “large cells” (LCs). We have shown that SCs and LCs begin their bursts nearly simultaneously but end their bursts at variable phases. This variability contrasts with many other central pattern generator systems in which phase is well maintained. To determine both the consequences of this variability and how CG phasing is controlled, we modeled the CG as a pair of Morris-Lecar oscillators coupled by electrical and excitatory synapses and constructed a database of 15,000 simulated networks using random parameter sets. These simulations, like our experimental results, displayed variable phase relationships, with the bursts beginning together but ending at variable phases. The model suggests that the variable phasing of the pattern has important implications for the functional role of the excitatory synapses. In networks in which the two oscillators had similar duty cycles, the excitatory coupling functioned to increase cycle frequency. In networks with disparate duty cycles, it functioned to decrease network frequency. Overall, we suggest that the phasing of the CG may vary without compromising appropriate motor output and that this variability may critically determine how the network behaves in response to manipulations. PMID:23446690

An ANOVA approach for statistical comparisons of brain networks.

PubMed

Fraiman, Daniel; Fraiman, Ricardo

2018-03-16

The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

Effects of spatial constraints on channel network topology: Implications for geomorphological inference

NASA Astrophysics Data System (ADS)

Cabral, Mariza Castanheira De Moura Da Costa

In the fifty-two years since Robert Horton's 1945 pioneering quantitative description of channel network planform (or plan view morphology), no conclusive findings have been presented that permit inference of geomorphological processes from any measures of network planform. All measures of network planform studied exhibit limited geographic variability across different environments. Horton (1945), Langbein et al. (1947), Schumm (1956), Hack (1957), Melton (1958), and Gray (1961) established various "laws" of network planform, that is, statistical relationships between different variables which have limited variability. A wide variety of models which have been proposed to simulate the growth of channel networks in time over a landsurface are generally also in agreement with the above planform laws. An explanation is proposed for the generality of the channel network planform laws. Channel networks must be space filling, that is, they must extend over the landscape to drain every hillslope, leaving no large undrained areas, and with no crossing of channels, often achieving a roughly uniform drainage density in a given environment. It is shown that the space-filling constraint can reduce the sensitivity of planform variables to different network growth models, and it is proposed that this constraint may determine the planform laws. The "Q model" of network growth of Van Pelt and Verwer (1985) is used to generate samples of networks. Sensitivity to the model parameter Q is markedly reduced when the networks generated are required to be space filling. For a wide variety of Q values, the space-filling networks are in approximate agreement with the various channel network planform laws. Additional constraints, including of energy efficiency, were not studied but may further reduce the variability of planform laws. Inference of model parameter Q from network topology is successful only in networks not subject to spatial constraints. In space-filling networks, for a wide range of Q values, the maximal-likelihood Q parameter value is generally in the vicinity of 1/2, which yields topological randomness. It is proposed that space filling originates the appearance of randomness in channel network topology, and may cause difficulties to geomorphological inference from network planform.

Price of anarchy on heterogeneous traffic-flow networks.

PubMed

Rose, A; O'Dea, R; Hopcraft, K I

2016-09-01

The efficiency of routing traffic through a network, comprising nodes connected by links whose cost of traversal is either fixed or varies in proportion to volume of usage, can be measured by the "price of anarchy." This is the ratio of the cost incurred by agents who act to minimize their individual expenditure to the optimal cost borne by the entire system. As the total traffic load and the network variability-parameterized by the proportion of variable-cost links in the network-changes, the behaviors that the system presents can be understood with the introduction of a network of simpler structure. This is constructed from classes of nonoverlapping paths connecting source to destination nodes that are characterized by the number of variable-cost edges they contain. It is shown that localized peaks in the price of anarchy occur at critical traffic volumes at which it becomes beneficial to exploit ostensibly more expensive paths as the network becomes more congested. Simulation results verifying these findings are presented for the variation of the price of anarchy with the network's size, aspect ratio, variability, and traffic load.

Relationship of Social Network to Protective Factors in Suicide and Alcohol Use Disorder Intervention for Rural Yup’ik Alaska Native Youth

PubMed Central

Philip, Jacques; Ford, Tara; Henry, David; Rasmus, Stacy; Allen, James

2015-01-01

Suicide and alcohol use disorders are significant Alaska Native health disparities, yet there is limited understanding of protection and no studies of social network factors in protection in this or other populations. The Qungasvik intervention enhances protective factors from suicide and alcohol use disorders through activities grounded in Yup’ik cultural practices and values. Identification of social network factors associated with protection within the cultural context of these tight, close knit, and high density rural Yup’ik Alaska Native communities in southwest Alaska can help identify effective prevention strategies for suicide and alcohol use disorder risk. Using data from ego-centered social network and protective factors from suicide and alcohol use disorders surveys with 50 Yup’ik adolescents, we provide descriptive data on structural and network composition variables, identify key network variables that explain major proportions of the variance in a four principal component structure of these network variables, and demonstrate the utility of these key network variables as predictors of family and community protective factors from suicide and alcohol use disorder risk. Connections to adults and connections to elders, but not peer connections, emerged as predictors of family and community level protection, suggesting these network factors as important intervention targets for intervention. PMID:27110094

Predictive Coding of Dynamical Variables in Balanced Spiking Networks

PubMed Central

Boerlin, Martin; Machens, Christian K.; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated. PMID:24244113

Variability in functional brain networks predicts expertise during action observation.

PubMed

Amoruso, Lucía; Ibáñez, Agustín; Fonseca, Bruno; Gadea, Sebastián; Sedeño, Lucas; Sigman, Mariano; García, Adolfo M; Fraiman, Ricardo; Fraiman, Daniel

2017-02-01

Observing an action performed by another individual activates, in the observer, similar circuits as those involved in the actual execution of that action. This activation is modulated by prior experience; indeed, sustained training in a particular motor domain leads to structural and functional changes in critical brain areas. Here, we capitalized on a novel graph-theory approach to electroencephalographic data (Fraiman et al., 2016) to test whether variability in functional brain networks implicated in Tango observation can discriminate between groups differing in their level of expertise. We found that experts and beginners significantly differed in the functional organization of task-relevant networks. Specifically, networks in expert Tango dancers exhibited less variability and a more robust functional architecture. Notably, these expertise-dependent effects were captured within networks derived from electrophysiological brain activity recorded in a very short time window (2s). In brief, variability in the organization of task-related networks seems to be a highly sensitive indicator of long-lasting training effects. This finding opens new methodological and theoretical windows to explore the impact of domain-specific expertise on brain plasticity, while highlighting variability as a fruitful measure in neuroimaging research. Copyright © 2016 Elsevier Inc. All rights reserved.

Decoding the spatial signatures of multi-scale climate variability - a climate network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Jajcay, N.; Wiedermann, M.; Ekhtiari, N.; Palus, M.

2017-12-01

During the last years, the application of complex networks as a versatile tool for analyzing complex spatio-temporal data has gained increasing interest. Establishing this approach as a new paradigm in climatology has already provided valuable insights into key spatio-temporal climate variability patterns across scales, including novel perspectives on the dynamics of the El Nino Southern Oscillation or the emergence of extreme precipitation patterns in monsoonal regions. In this work, we report first attempts to employ network analysis for disentangling multi-scale climate variability. Specifically, we introduce the concept of scale-specific climate networks, which comprises a sequence of networks representing the statistical association structure between variations at distinct time scales. For this purpose, we consider global surface air temperature reanalysis data and subject the corresponding time series at each grid point to a complex-valued continuous wavelet transform. From this time-scale decomposition, we obtain three types of signals per grid point and scale - amplitude, phase and reconstructed signal, the statistical similarity of which is then represented by three complex networks associated with each scale. We provide a detailed analysis of the resulting connectivity patterns reflecting the spatial organization of climate variability at each chosen time-scale. Global network characteristics like transitivity or network entropy are shown to provide a new view on the (global average) relevance of different time scales in climate dynamics. Beyond expected trends originating from the increasing smoothness of fluctuations at longer scales, network-based statistics reveal different degrees of fragmentation of spatial co-variability patterns at different scales and zonal shifts among the key players of climate variability from tropically to extra-tropically dominated patterns when moving from inter-annual to decadal scales and beyond. The obtained results demonstrate the potential usefulness of systematically exploiting scale-specific climate networks, whose general patterns are in line with existing climatological knowledge, but provide vast opportunities for further quantifications at local, regional and global scales that are yet to be explored.

Characterizing Individual Differences in Functional Connectivity Using Dual-Regression and Seed-Based Approaches

PubMed Central

Smith, David V.; Utevsky, Amanda V.; Bland, Amy R.; Clement, Nathan; Clithero, John A.; Harsch, Anne E. W.; Carter, R. McKell; Huettel, Scott A.

2014-01-01

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent components analysis (ICA). We estimated voxelwise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity. PMID:24662574

Neural Network Machine Learning and Dimension Reduction for Data Visualization

NASA Technical Reports Server (NTRS)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

Individual differences and time-varying features of modular brain architecture.

PubMed

Liao, Xuhong; Cao, Miao; Xia, Mingrui; He, Yong

2017-05-15

Recent studies have suggested that human brain functional networks are topologically organized into functionally specialized but inter-connected modules to facilitate efficient information processing and highly flexible cognitive function. However, these studies have mainly focused on group-level network modularity analyses using "static" functional connectivity approaches. How these extraordinary modular brain structures vary across individuals and spontaneously reconfigure over time remain largely unknown. Here, we employed multiband resting-state functional MRI data (N=105) from the Human Connectome Project and a graph-based modularity analysis to systematically investigate individual variability and dynamic properties in modular brain networks. We showed that the modular structures of brain networks dramatically vary across individuals, with higher modular variability primarily in the association cortex (e.g., fronto-parietal and attention systems) and lower variability in the primary systems. Moreover, brain regions spontaneously changed their module affiliations on a temporal scale of seconds, which cannot be simply attributable to head motion and sampling error. Interestingly, the spatial pattern of intra-subject dynamic modular variability largely overlapped with that of inter-subject modular variability, both of which were highly reproducible across repeated scanning sessions. Finally, the regions with remarkable individual/temporal modular variability were closely associated with network connectors and the number of cognitive components, suggesting a potential contribution to information integration and flexible cognitive function. Collectively, our findings highlight individual modular variability and the notable dynamic characteristics in large-scale brain networks, which enhance our understanding of the neural substrates underlying individual differences in a variety of cognition and behaviors. Copyright © 2017 Elsevier Inc. All rights reserved.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

PubMed Central

Bi, Zedong; Zhou, Changsong

2016-01-01

Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy). PMID:27555816

An approach to online network monitoring using clustered patterns

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Jinoh; Sim, Alex; Suh, Sang C.

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regardmore » to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.« less

Social Network Analysis for Assessing College-Aged Adults' Health: A Systematic Review.

PubMed

Patterson, Megan S; Go Odson, Patricia

2018-04-13

Social network analysis (SNA) is a useful, emerging method for studying health. College students are especially prone to social influence when it comes to health. This review aimed to identify network variables related to college student health and determine how SNA was used in the literature. A systematic review of relevant literature was conducted in October 2015. Studies employing egocentric or whole network analysis to study college student health were included. We used Garrard's Matrix Method to extract data from reviewed articles (n = 15). Drinking, smoking, aggression, homesickness, and stress were predicted by network variables in the reviewed literature. Methodological inconsistencies concerning boundary specification, data collection, nomination limits, and statistical analyses were revealed across studies. Results show the consistent relationship between network variables and college health outcomes, justifying further use of SNA to research college health. Suggestions and considerations for future use of SNA are provided.

An approach to online network monitoring using clustered patterns

DOE PAGES

Kim, Jinoh; Sim, Alex; Suh, Sang C.; ...

2017-03-13

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regardmore » to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.« less

Upstream and downstream correlates of older people's engagement in social networks: what are their effects on health over time?

PubMed

Stephens, Christine; Noone, Jack; Alpass, Fiona

2014-01-01

This study tested the effects of social network engagement and social support on the health of older people moving into retirement, using a model which includes social context variables. A prospective survey of a New Zealand population sample aged 54-70 at baseline (N = 2,282) was used to assess the effects on mental and physical health across time. A structural equation model assessed pathways from the social context variables through network engagement to social support and then to mental and physical health 2 years later. The proposed model of effects on mental health was supported when gender, economic living standards, and ethnicity were included along with the direct effects of these variables on social support. These findings confirm the importance of taking social context variables into account when considering social support networks. Social engagement appears to be an important aspect of social network functioning which could be investigated further.

Epidemics in networks: a master equation approach

NASA Astrophysics Data System (ADS)

Cotacallapa, M.; Hase, M. O.

2016-02-01

A problem closely related to epidemiology, where a subgraph of ‘infected’ links is defined inside a larger network, is investigated. This subgraph is generated from the underlying network by a random variable, which decides whether a link is able to propagate a disease/information. The relaxation timescale of this random variable is examined in both annealed and quenched limits, and the effectiveness of propagation of disease/information is analyzed. The dynamics of the model is governed by a master equation and two types of underlying network are considered: one is scale-free and the other has exponential degree distribution. We have shown that the relaxation timescale of the contagion variable has a major influence on the topology of the subgraph of infected links, which determines the efficiency of spreading of disease/information over the network.

Directional semivariogram analysis to identify and rank controls on the spatial variability of fracture networks

NASA Astrophysics Data System (ADS)

Hanke, John R.; Fischer, Mark P.; Pollyea, Ryan M.

2018-03-01

In this study, the directional semivariogram is deployed to investigate the spatial variability of map-scale fracture network attributes in the Paradox Basin, Utah. The relative variability ratio (R) is introduced as the ratio of integrated anisotropic semivariogram models, and R is shown to be an effective metric for quantifying the magnitude of spatial variability for any two azimuthal directions. R is applied to a GIS-based data set comprising roughly 1200 fractures, in an area which is bounded by a map-scale anticline and a km-scale normal fault. This analysis reveals that proximity to the fault strongly influences the magnitude of spatial variability for both fracture intensity and intersection density within 1-2 km. Additionally, there is significant anisotropy in the spatial variability, which is correlated with trends of the anticline and fault. The direction of minimum spatial correlation is normal to the fault at proximal distances, and gradually rotates and becomes subparallel to the fold axis over the same 1-2 km distance away from the fault. We interpret these changes to reflect varying scales of influence of the fault and the fold on fracture network development: the fault locally influences the magnitude and variability of fracture network attributes, whereas the fold sets the background level and structure of directional variability.

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.

PubMed

Smith, David V; Utevsky, Amanda V; Bland, Amy R; Clement, Nathan; Clithero, John A; Harsch, Anne E W; McKell Carter, R; Huettel, Scott A

2014-07-15

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust-yet frequently ignored-neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity. Copyright © 2014 Elsevier Inc. All rights reserved.

Evaluation of feasibility of 1.5 Tesla prostate MRI using body coil RF transmit in a patient with an implanted vagus nerve stimulator.

PubMed

Favazza, Christopher P; Edmonson, Heidi A; Ma, Chi; Shu, Yunhong; Felmlee, Joel P; Watson, Robert E; Gorny, Krzysztof R

2017-11-01

To assess risks of RF-heating of a vagus nerve stimulator (VNS) during 1.5 T prostate MRI using body coil transmit and to compare these risks with those associated with MRI head exams using a transmit/receive head coil. Spatial distributions of radio-frequency (RF) B1 fields generated by transmit/receive (T/R) body and head coils were empirically assessed along the long axis of a 1.5 T MRI scanner bore. Measurements were obtained along the center axis of the scanner and laterally offset by 15 cm (body coil) and 7 cm (head coil). RF-field measurements were supplemented with direct measurements of RF-heating of 15 cm long copper wires affixed to and submerged in the "neck" region of the gelled saline-filled (sodium chloride and polyacrylic acid) "head-and-torso" phantom. Temperature elevations at the lead tips were measured using fiber-optic thermometers with the phantom positioned at systematically increased distances from the scanner isocenter. B1 field measurements demonstrated greater than 10 dB reduction in RF power at distances beyond 28 cm and 24 cm from isocenter for body and head coil, respectively. Moreover, RF power from body coil transmit at distances greater than 32 cm from isocenter was found to be lower than from the RF power from head coil transmit measured at locations adjacent to the coil array at its opening. Correspondingly, maximum temperature elevations at the tips of the copper wires decreased with increasing distance from isocenter - from 7.4°C at 0 cm to no appreciable heating at locations beyond 40 cm. For the particular scanner model evaluated in this study, positioning an implanted VNS farther than 32 cm from isocenter (configuration achievable for prostate exams) can reduce risks of RF-heating resulting from the body coil transmit to those associated with using a T/R head coil. © 2017 American Association of Physicists in Medicine.

SIMRAND I- SIMULATION OF RESEARCH AND DEVELOPMENT PROJECTS

NASA Technical Reports Server (NTRS)

Miles, R. F.

1994-01-01

The Simulation of Research and Development Projects program (SIMRAND) aids in the optimal allocation of R&D resources needed to achieve project goals. SIMRAND models the system subsets or project tasks as various network paths to a final goal. Each path is described in terms of task variables such as cost per hour, cost per unit, availability of resources, etc. Uncertainty is incorporated by treating task variables as probabilistic random variables. SIMRAND calculates the measure of preference for each alternative network. The networks yielding the highest utility function (or certainty equivalence) are then ranked as the optimal network paths. SIMRAND has been used in several economic potential studies at NASA's Jet Propulsion Laboratory involving solar dish power systems and photovoltaic array construction. However, any project having tasks which can be reduced to equations and related by measures of preference can be modeled. SIMRAND analysis consists of three phases: reduction, simulation, and evaluation. In the reduction phase, analytical techniques from probability theory and simulation techniques are used to reduce the complexity of the alternative networks. In the simulation phase, a Monte Carlo simulation is used to derive statistics on the variables of interest for each alternative network path. In the evaluation phase, the simulation statistics are compared and the networks are ranked in preference by a selected decision rule. The user must supply project subsystems in terms of equations based on variables (for example, parallel and series assembly line tasks in terms of number of items, cost factors, time limits, etc). The associated cumulative distribution functions and utility functions for each variable must also be provided (allowable upper and lower limits, group decision factors, etc). SIMRAND is written in Microsoft FORTRAN 77 for batch execution and has been implemented on an IBM PC series computer operating under DOS.

Optimization of rainfall networks using information entropy and temporal variability analysis

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin

2018-04-01

Rainfall networks are the most direct sources of precipitation data and their optimization and evaluation are essential and important. Information entropy can not only represent the uncertainty of rainfall distribution but can also reflect the correlation and information transmission between rainfall stations. Using entropy this study performs optimization of rainfall networks that are of similar size located in two big cities in China, Shanghai (in Yangtze River basin) and Xi'an (in Yellow River basin), with respect to temporal variability analysis. Through an easy-to-implement greedy ranking algorithm based on the criterion called, Maximum Information Minimum Redundancy (MIMR), stations of the networks in the two areas (each area is further divided into two subareas) are ranked during sliding inter-annual series and under different meteorological conditions. It is found that observation series with different starting days affect the ranking, alluding to the temporal variability during network evaluation. We propose a dynamic network evaluation framework for considering temporal variability, which ranks stations under different starting days with a fixed time window (1-year, 2-year, and 5-year). Therefore, we can identify rainfall stations which are temporarily of importance or redundancy and provide some useful suggestions for decision makers. The proposed framework can serve as a supplement for the primary MIMR optimization approach. In addition, during different periods (wet season or dry season) the optimal network from MIMR exhibits differences in entropy values and the optimal network from wet season tended to produce higher entropy values. Differences in spatial distribution of the optimal networks suggest that optimizing the rainfall network for changing meteorological conditions may be more recommended.

Analysis and Design of Complex Network Environments

DTIC Science & Technology

2014-02-01

entanglements among un- measured variables. This “potential entanglement ” type of network complexity is previously unaddressed in the literature, yet it...Appreciating the power of structural representations that allow for potential entanglement among unmeasured variables to simplify network inference problems...rely on the idea of subsystems and allows for potential entanglement among unmeasured states. As a result, inferring a system’s signal structure

Investigation of Social Studies Teachers' Intended Uses of Social Networks in Terms of Various Variables

ERIC Educational Resources Information Center

Akgün, Ismail Hakan

2016-01-01

The aim of this research is to determine Social Studies teacher candidates' intended uses of social networks in terms of various variables. The research was carried out by using screening model of quantitative research methods. In the study, "The Social Network Intended Use Scale" was used as a data collection tool. As a result of the…

Adaptive Synchronization of Fractional Order Complex-Variable Dynamical Networks via Pinning Control

NASA Astrophysics Data System (ADS)

Ding, Da-Wei; Yan, Jie; Wang, Nian; Liang, Dong

2017-09-01

In this paper, the synchronization of fractional order complex-variable dynamical networks is studied using an adaptive pinning control strategy based on close center degree. Some effective criteria for global synchronization of fractional order complex-variable dynamical networks are derived based on the Lyapunov stability theory. From the theoretical analysis, one concludes that under appropriate conditions, the complex-variable dynamical networks can realize the global synchronization by using the proper adaptive pinning control method. Meanwhile, we succeed in solving the problem about how much coupling strength should be applied to ensure the synchronization of the fractional order complex networks. Therefore, compared with the existing results, the synchronization method in this paper is more general and convenient. This result extends the synchronization condition of the real-variable dynamical networks to the complex-valued field, which makes our research more practical. Finally, two simulation examples show that the derived theoretical results are valid and the proposed adaptive pinning method is effective. Supported by National Natural Science Foundation of China under Grant No. 61201227, National Natural Science Foundation of China Guangdong Joint Fund under Grant No. U1201255, the Natural Science Foundation of Anhui Province under Grant No. 1208085MF93, 211 Innovation Team of Anhui University under Grant Nos. KJTD007A and KJTD001B, and also supported by Chinese Scholarship Council

Variable weight spectral amplitude coding for multiservice OCDMA networks

NASA Astrophysics Data System (ADS)

Seyedzadeh, Saleh; Rahimian, Farzad Pour; Glesk, Ivan; Kakaee, Majid H.

2017-09-01

The emergence of heterogeneous data traffic such as voice over IP, video streaming and online gaming have demanded networks with capability of supporting quality of service (QoS) at the physical layer with traffic prioritisation. This paper proposes a new variable-weight code based on spectral amplitude coding for optical code-division multiple-access (OCDMA) networks to support QoS differentiation. The proposed variable-weight multi-service (VW-MS) code relies on basic matrix construction. A mathematical model is developed for performance evaluation of VW-MS OCDMA networks. It is shown that the proposed code provides an optimal code length with minimum cross-correlation value when compared to other codes. Numerical results for a VW-MS OCDMA network designed for triple-play services operating at 0.622 Gb/s, 1.25 Gb/s and 2.5 Gb/s are considered.

A virtual water network of the Roman world

NASA Astrophysics Data System (ADS)

Dermody, B. J.; van Beek, R. P. H.; Meeks, E.; Klein Goldewijk, K.; Scheidel, W.; van der Velde, Y.; Bierkens, M. F. P.; Wassen, M. J.; Dekker, S. C.

2014-12-01

The Romans were perhaps the most impressive exponents of water resource management in preindustrial times with irrigation and virtual water trade facilitating unprecedented urbanization and socioeconomic stability for hundreds of years in a region of highly variable climate. To understand Roman water resource management in response to urbanization and climate variability, a Virtual Water Network of the Roman World was developed. Using this network we find that irrigation and virtual water trade increased Roman resilience to interannual climate variability. However, urbanization arising from virtual water trade likely pushed the Empire closer to the boundary of its water resources, led to an increase in import costs, and eroded its resilience to climate variability in the long term. In addition to improving our understanding of Roman water resource management, our cost-distance-based analysis illuminates how increases in import costs arising from climatic and population pressures are likely to be distributed in the future global virtual water network.

A virtual water network of the Roman world

NASA Astrophysics Data System (ADS)

Dermody, B. J.; van Beek, R. P. H.; Meeks, E.; Klein Goldewijk, K.; Scheidel, W.; van der Velde, Y.; Bierkens, M. F. P.; Wassen, M. J.; Dekker, S. C.

2014-06-01

The Romans were perhaps the most impressive exponents of water resource management in preindustrial times with irrigation and virtual water trade facilitating unprecedented urbanisation and socioeconomic stability for hundreds of years in a region of highly variable climate. To understand Roman water resource management in response to urbanisation and climate variability, a Virtual Water Network of the Roman World was developed. Using this network we find that irrigation and virtual water trade increased Roman resilience to climate variability in the short term. However, urbanisation arising from virtual water trade likely pushed the Empire closer to the boundary of its water resources, led to an increase in import costs, and reduced its resilience to climate variability in the long-term. In addition to improving our understanding of Roman water resource management, our cost-distance based analysis illuminates how increases in import costs arising from climatic and population pressures are likely to be distributed in the future global virtual water network.

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion.

PubMed

Kong, Ru; Li, Jingwei; Orban, Csaba; Sabuncu, Mert R; Liu, Hesheng; Schaefer, Alexander; Sun, Nanbo; Zuo, Xi-Nian; Holmes, Avram J; Eickhoff, Simon B; Yeo, B T Thomas

2018-06-06

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

Evaluation of Deep Learning Models for Predicting CO2 Flux

NASA Astrophysics Data System (ADS)

Halem, M.; Nguyen, P.; Frankel, D.

2017-12-01

Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

Emergence of context-dependent variability across a basal ganglia network.

PubMed

Woolley, Sarah C; Rajan, Raghav; Joshua, Mati; Doupe, Allison J

2014-04-02

Context dependence is a key feature of cortical-basal ganglia circuit activity, and in songbirds the cortical outflow of a basal ganglia circuit specialized for song, LMAN, shows striking increases in trial-by-trial variability and bursting when birds sing alone rather than to females. To reveal where this variability and its social regulation emerge, we recorded stepwise from corticostriatal (HVC) neurons and their target spiny and pallidal neurons in Area X. We find that corticostriatal and spiny neurons both show precise singing-related firing across both social settings. Pallidal neurons, in contrast, exhibit markedly increased trial-by-trial variation when birds sing alone, created by highly variable pauses in firing. This variability persists even when recurrent inputs from LMAN are ablated. These data indicate that variability and its context sensitivity emerge within the basal ganglia network, suggest a network mechanism for this emergence, and highlight variability generation and regulation as basal ganglia functions. Copyright © 2014 Elsevier Inc. All rights reserved.

Emergence of context-dependent variability across a basal ganglia network

PubMed Central

Woolley, Sarah C.; Rajan, Raghav; Joshua, Mati; Doupe, Allison J.

2014-01-01

Summary Context-dependence is a key feature of cortical-basal ganglia circuit activity, and in songbirds, the cortical outflow of a basal ganglia circuit specialized for song, LMAN, shows striking increases in trial-by-trial variability and bursting when birds sing alone rather than to females. To reveal where this variability and its social regulation emerge, we recorded stepwise from cortico-striatal (HVC) neurons and their target spiny and pallidal neurons in Area X. We find that cortico-striatal and spiny neurons both show precise singing-related firing across both social settings. Pallidal neurons, in contrast, exhibit markedly increased trial-by-trial variation when birds sing alone, created by highly variable pauses in firing. This variability persists even when recurrent inputs from LMAN are ablated. These data indicate that variability and its context-sensitivity emerge within the basal ganglia network, suggest a network mechanism for this emergence, and highlight variability generation and regulation as basal ganglia functions. PMID:24698276

Poisson-Like Spiking in Circuits with Probabilistic Synapses

PubMed Central

Moreno-Bote, Rubén

2014-01-01

Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex. PMID:25032705

A review of covariate selection for non-experimental comparative effectiveness research.

PubMed

Sauer, Brian C; Brookhart, M Alan; Roy, Jason; VanderWeele, Tyler

2013-11-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for a common cause pathway between treatment and outcome can remove confounding, whereas adjustment for other structural types may increase bias. For this reason, variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely known. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher's knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. Copyright © 2013 John Wiley & Sons, Ltd.

A Review of Covariate Selection for Nonexperimental Comparative Effectiveness Research

PubMed Central

Sauer, Brian C.; Brookhart, Alan; Roy, Jason; Vanderweele, Tyler

2014-01-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research (CER), and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for on a common cause pathway between treatment and outcome can remove confounding, while adjustment for other structural types may increase bias. For this reason variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely know. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses the high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher’s knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically-derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. PMID:24006330

Inverse Ising problem in continuous time: A latent variable approach

NASA Astrophysics Data System (ADS)

Donner, Christian; Opper, Manfred

2017-12-01

We consider the inverse Ising problem: the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood into a form which allows for simple iterative inference algorithms with analytical updates. The variables are (1) Poisson variables to linearize an exponential term which is typical for point process likelihoods and (2) Pólya-Gamma variables, which make the likelihood quadratic in the coupling parameters. Using the augmented likelihood, we derive an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimate of network parameters. Using a third set of latent variables we extend the EM algorithm to sparse couplings via L1 regularization. Finally, we develop an efficient approximate Bayesian inference algorithm using a variational approach. We demonstrate the performance of our algorithms on data simulated from an Ising model. For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

Variable disparity estimation based intermediate view reconstruction in dynamic flow allocation over EPON-based access networks

NASA Astrophysics Data System (ADS)

Bae, Kyung-Hoon; Lee, Jungjoon; Kim, Eun-Soo

2008-06-01

In this paper, a variable disparity estimation (VDE)-based intermediate view reconstruction (IVR) in dynamic flow allocation (DFA) over an Ethernet passive optical network (EPON)-based access network is proposed. In the proposed system, the stereoscopic images are estimated by a variable block-matching algorithm (VBMA), and they are transmitted to the receiver through DFA over EPON. This scheme improves a priority-based access network by converting it to a flow-based access network with a new access mechanism and scheduling algorithm, and then 16-view images are synthesized by the IVR using VDE. Some experimental results indicate that the proposed system improves the peak-signal-to-noise ratio (PSNR) to as high as 4.86 dB and reduces the processing time to 3.52 s. Additionally, the network service provider can provide upper limits of transmission delays by the flow. The modeling and simulation results, including mathematical analyses, from this scheme are also provided.

The role of social and cognitive processes in the relationship between fear network and psychological distress among parents of children undergoing hematopoietic stem cell transplantation.

PubMed

Virtue, Shannon Myers; Manne, Sharon; Mee, Laura; Bartell, Abraham; Sands, Stephen; Ohman-Strickland, Pamela; Gajda, Tina Marie

2014-09-01

The current study examined whether cognitive and social processing variables mediated the relationship between fear network and depression among parents of children undergoing hematopoietic stem cell transplant (HSCT). Parents whose children were initiating HSCT (N = 179) completed survey measures including fear network, Beck Depression Inventory, cognitive processing variables (positive reappraisal and self-blame) and social processing variables (emotional support and holding back from sharing concerns). Fear network was positively correlated with depression (p < .001). Self-blame and holding back emerged as individual partial mediators in the relationship between fear network and depression. Together they accounted for 34.3% of the variance in the relationship between fear network and depression. Positive reappraisal and emotional support did not have significant mediating effects. Social and cognitive processes, specifically self-blame and holding back from sharing concerns, play a negative role in parents' psychological adaptation to fears surrounding a child's HSCT.

The Role of Social and Cognitive Processes in the Relationship between Fear Network and Psychological Distress among Parents of Children Undergoing Hematopoietic Stem Cell Transplantation

PubMed Central

Virtue, Shannon Myers; Manne, Sharon; Mee, Laura; Bartell, Abraham; Sands, Stephen; Ohman-Strickland, Pamela; Gajda, Tina Marie

2014-01-01

The current study examined whether cognitive and social processing variables mediated the relationship between fear network and depression among parents of children undergoing hematopoietic stem cell transplant (HSCT). Parents whose children were initiating HSCT (N = 179) completed survey measures including fear network, Beck Depression Inventory (BDI), cognitive processing variables (positive reappraisal and self-blame) and social processing variables (emotional support and holding back from sharing concerns). Fear network was positively correlated with depression (p < .001). Self-blame and holding back emerged as individual partial mediators in the relationship between fear network and depression. Together they accounted for 34.3% of the variance in the relationship between fear network and depression. Positive reappraisal and emotional support did not have significant mediating effects. Social and cognitive processes, specifically self-blame and holding back from sharing concerns, play a negative role in parents’ psychological adaptation to fears surrounding a child’s HSCT. PMID:25081956

Social network type and morale in old age.

PubMed

Litwin, H

2001-08-01

The aim of this research was to derive network types among an elderly population and to examine the relationship of network type to morale. Secondary analysis of data compiled by the Israeli Central Bureau of Statistics (n = 2,079) was employed, and network types were derived through K-means cluster analysis. Respondents' morale scores were regressed on network types, controlling for background and health variables. Five network types were derived. Respondents in diverse or friends networks reported the highest morale; those in exclusively family or restricted networks had the lowest. Multivariate regression analysis underscored that certain network types were second among the study variables in predicting respondents' morale, preceded only by disability level (Adjusted R(2) =.41). Classification of network types allows consideration of the interpersonal environments of older people in relation to outcomes of interest. The relative effects on morale of elective versus obligated social ties, evident in the current analysis, is a case in point.

Detecting Anomalies in Process Control Networks

NASA Astrophysics Data System (ADS)

Rrushi, Julian; Kang, Kyoung-Don

This paper presents the estimation-inspection algorithm, a statistical algorithm for anomaly detection in process control networks. The algorithm determines if the payload of a network packet that is about to be processed by a control system is normal or abnormal based on the effect that the packet will have on a variable stored in control system memory. The estimation part of the algorithm uses logistic regression integrated with maximum likelihood estimation in an inductive machine learning process to estimate a series of statistical parameters; these parameters are used in conjunction with logistic regression formulas to form a probability mass function for each variable stored in control system memory. The inspection part of the algorithm uses the probability mass functions to estimate the normalcy probability of a specific value that a network packet writes to a variable. Experimental results demonstrate that the algorithm is very effective at detecting anomalies in process control networks.

Lognormal kriging for the assessment of reliability in groundwater quality control observation networks

USGS Publications Warehouse

Candela, L.; Olea, R.A.; Custodio, E.

1988-01-01

Groundwater quality observation networks are examples of discontinuous sampling on variables presenting spatial continuity and highly skewed frequency distributions. Anywhere in the aquifer, lognormal kriging provides estimates of the variable being sampled and a standard error of the estimate. The average and the maximum standard error within the network can be used to dynamically improve the network sampling efficiency or find a design able to assure a given reliability level. The approach does not require the formulation of any physical model for the aquifer or any actual sampling of hypothetical configurations. A case study is presented using the network monitoring salty water intrusion into the Llobregat delta confined aquifer, Barcelona, Spain. The variable chloride concentration used to trace the intrusion exhibits sudden changes within short distances which make the standard error fairly invariable to changes in sampling pattern and to substantial fluctuations in the number of wells. ?? 1988.

Defining and quantifying state of good repair (SGR) for the pedestrian network.

DOT National Transportation Integrated Search

2015-02-01

State of Good Repair is difficult to quantify in a pedestrian context. Dozens and dozens of : variables can affect the utility of the pedestrian network, and these variables change depending : upon the environmental context (urban, suburban, rural). ...

Deploying temporary networks for upscaling of sparse network stations

NASA Astrophysics Data System (ADS)

Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane

2016-10-01

Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.

Complexity in relational processing predicts changes in functional brain network dynamics.

PubMed

Cocchi, Luca; Halford, Graeme S; Zalesky, Andrew; Harding, Ian H; Ramm, Brentyn J; Cutmore, Tim; Shum, David H K; Mattingley, Jason B

2014-09-01

The ability to link variables is critical to many high-order cognitive functions, including reasoning. It has been proposed that limits in relating variables depend critically on relational complexity, defined formally as the number of variables to be related in solving a problem. In humans, the prefrontal cortex is known to be important for reasoning, but recent studies have suggested that such processes are likely to involve widespread functional brain networks. To test this hypothesis, we used functional magnetic resonance imaging and a classic measure of deductive reasoning to examine changes in brain networks as a function of relational complexity. As expected, behavioral performance declined as the number of variables to be related increased. Likewise, increments in relational complexity were associated with proportional enhancements in brain activity and task-based connectivity within and between 2 cognitive control networks: A cingulo-opercular network for maintaining task set, and a fronto-parietal network for implementing trial-by-trial control. Changes in effective connectivity as a function of increased relational complexity suggested a key role for the left dorsolateral prefrontal cortex in integrating and implementing task set in a trial-by-trial manner. Our findings show that limits in relational processing are manifested in the brain as complexity-dependent modulations of large-scale networks. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Evaluating the effect of internal aperture variability on transport in kilometer scale discrete fracture networks

DOE PAGES

Makedonska, Nataliia; Hyman, Jeffrey D.; Karra, Satish; ...

2016-08-01

The apertures of natural fractures in fractured rock are highly heterogeneous. However, in-fracture aperture variability is often neglected in flow and transport modeling and individual fractures are assumed to have uniform aperture distribution. The relative importance of in-fracture variability in flow and transport modeling within kilometer-scale fracture networks has been under debate for a long time, since the flow in each single fracture is controlled not only by in-fracture variability but also by boundary conditions. Computational limitations have previously prohibited researchers from investigating the relative importance of in-fracture variability in flow and transport modeling within large-scale fracture networks. We addressmore » this question by incorporating internal heterogeneity of individual fractures into flow simulations within kilometer scale three-dimensional fracture networks, where fracture intensity, P 32 (ratio between total fracture area and domain volume) is between 0.027 and 0.031 [1/m]. The recently developed discrete fracture network (DFN) simulation capability, dfnWorks, is used to generate kilometer scale DFNs that include in-fracture aperture variability represented by a stationary log-normal stochastic field with various correlation lengths and variances. The Lagrangian transport parameters, non-reacting travel time, , and cumulative retention, , are calculated along particles streamlines. As a result, it is observed that due to local flow channeling early particle travel times are more sensitive to in-fracture aperture variability than the tails of travel time distributions, where no significant effect of the in-fracture aperture variations and spatial correlation length is observed.« less

Evaluating the effect of internal aperture variability on transport in kilometer scale discrete fracture networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makedonska, Nataliia; Hyman, Jeffrey D.; Karra, Satish

The apertures of natural fractures in fractured rock are highly heterogeneous. However, in-fracture aperture variability is often neglected in flow and transport modeling and individual fractures are assumed to have uniform aperture distribution. The relative importance of in-fracture variability in flow and transport modeling within kilometer-scale fracture networks has been under debate for a long time, since the flow in each single fracture is controlled not only by in-fracture variability but also by boundary conditions. Computational limitations have previously prohibited researchers from investigating the relative importance of in-fracture variability in flow and transport modeling within large-scale fracture networks. We addressmore » this question by incorporating internal heterogeneity of individual fractures into flow simulations within kilometer scale three-dimensional fracture networks, where fracture intensity, P 32 (ratio between total fracture area and domain volume) is between 0.027 and 0.031 [1/m]. The recently developed discrete fracture network (DFN) simulation capability, dfnWorks, is used to generate kilometer scale DFNs that include in-fracture aperture variability represented by a stationary log-normal stochastic field with various correlation lengths and variances. The Lagrangian transport parameters, non-reacting travel time, , and cumulative retention, , are calculated along particles streamlines. As a result, it is observed that due to local flow channeling early particle travel times are more sensitive to in-fracture aperture variability than the tails of travel time distributions, where no significant effect of the in-fracture aperture variations and spatial correlation length is observed.« less

A Wild Weasel Penetration Model.

DTIC Science & Technology

1982-03-01

event 13, and node WM. Global variable XX(48) counts the WWs as they reach the home point. The network logic for WWI and WW2 is identical. Each WW...the same no matter if the aircraft is WWI or WW2 . Radar-Attack Profile In the radar-attack po. tion of the network threat radars engage both attack...Systems Dispersion on LOC XX(52) *State Variable--see text. * 94 variable. (The entry positions of WW1 and WW2 are changed with state variables SS(25) and

The Development of a High-Throughput/Combinatorial Workflow for the Study of Porous Polymer Networks

DTIC Science & Technology

2012-04-05

poragen composition , poragen level, and cure temperature. A total of 216 unique compositions were prepared. Changes in opacity of the blends as they cured...allowed for the identification of compositional variables and process variables that enabled the production of porous networks. Keywords: high...in polymer network cross-link density,poragen composition , poragen level, and cure temperature. A total of 216 unique compositions were prepared

Sensitivity of marine protected area network connectivity to atmospheric variability

NASA Astrophysics Data System (ADS)

Fox, Alan D.; Henry, Lea-Anne; Corne, David W.; Roberts, J. Murray

2016-11-01

International efforts are underway to establish well-connected systems of marine protected areas (MPAs) covering at least 10% of the ocean by 2020. But the nature and dynamics of ocean ecosystem connectivity are poorly understood, with unresolved effects of climate variability. We used 40-year runs of a particle tracking model to examine the sensitivity of an MPA network for habitat-forming cold-water corals in the northeast Atlantic to changes in larval dispersal driven by atmospheric cycles and larval behaviour. Trajectories of Lophelia pertusa larvae were strongly correlated to the North Atlantic Oscillation (NAO), the dominant pattern of interannual atmospheric circulation variability over the northeast Atlantic. Variability in trajectories significantly altered network connectivity and source-sink dynamics, with positive phase NAO conditions producing a well-connected but asymmetrical network connected from west to east. Negative phase NAO produced reduced connectivity, but notably some larvae tracked westward-flowing currents towards coral populations on the mid-Atlantic ridge. Graph theoretical metrics demonstrate critical roles played by seamounts and offshore banks in larval supply and maintaining connectivity across the network. Larval longevity and behaviour mediated dispersal and connectivity, with shorter lived and passive larvae associated with reduced connectivity. We conclude that the existing MPA network is vulnerable to atmospheric-driven changes in ocean circulation.

The association between social network relationships and depressive symptoms among older Americans: what matters most?

PubMed

Litwin, Howard

2011-08-01

Although social network relationships are linked to mental health in late life, it is still unclear whether it is the structure of social networks or their perceived quality that matters. The current study regressed a dichotomous 8-item version of the Center for Epidemiological Studies Depression Scale (CESD-8) score on measures of social network relationships among Americans, aged 65-85 years, from the first wave of the National Social Life, Health and Aging Project. The network indicators included a structural variable - social network type - and a series of relationship quality indicators: perceived positive and negative ties with family, friends and spouse/ partner. Multivariate logistic regression analyses controlled for age, gender, education, income, race/ethnicity, religious affiliation, functional health and physical health. The perceived social network quality variables were unrelated to the presence of a high level of depressive symptoms, but social network type maintained an association with this mental health outcome even after controlling for confounders. Respondents embedded in resourceful social network types in terms of social capital--"diverse," "friend" and "congregant" networks--reported less presence of depressive symptoms, to varying degrees. The results show that the structure of the network seems to matter more than the perceived quality of the ties as an indicator of depressive symptoms. Moreover, the composite network type variable stands out in capturing the differences in mental state. The construct of network type should be incorporated in mental health screening among older people who reside in the community. One's social network type can be an important initial indicator that one is at risk.

A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions.

PubMed

Samal, Satya Swarup; Grigoriev, Dima; Fröhlich, Holger; Weber, Andreas; Radulescu, Ovidiu

2015-12-01

Model reduction of biochemical networks relies on the knowledge of slow and fast variables. We provide a geometric method, based on the Newton polytope, to identify slow variables of a biochemical network with polynomial rate functions. The gist of the method is the notion of tropical equilibration that provides approximate descriptions of slow invariant manifolds. Compared to extant numerical algorithms such as the intrinsic low-dimensional manifold method, our approach is symbolic and utilizes orders of magnitude instead of precise values of the model parameters. Application of this method to a large collection of biochemical network models supports the idea that the number of dynamical variables in minimal models of cell physiology can be small, in spite of the large number of molecular regulatory actors.

How heart rate variability affects emotion regulation brain networks.

PubMed

Mather, Mara; Thayer, Julian

2018-02-01

Individuals with high heart rate variability tend to have better emotional well-being than those with low heart rate variability, but the mechanisms of this association are not yet clear. In this paper, we propose the novel hypothesis that by inducing oscillatory activity in the brain, high amplitude oscillations in heart rate enhance functional connectivity in brain networks associated with emotion regulation. Recent studies using daily biofeedback sessions to increase the amplitude of heart rate oscillations suggest that high amplitude physiological oscillations have a causal impact on emotional well-being. Because blood flow timing helps determine brain network structure and function, slow oscillations in heart rate have the potential to strengthen brain network dynamics, especially in medial prefrontal regulatory regions that are particularly sensitive to physiological oscillations.

Optimizing Placement of Weather Stations: Exploring Objective Functions of Meaningful Combinations of Multiple Weather Variables

NASA Astrophysics Data System (ADS)

Snyder, A.; Dietterich, T.; Selker, J. S.

2017-12-01

Many regions of the world lack ground-based weather data due to inadequate or unreliable weather station networks. For example, most countries in Sub-Saharan Africa have unreliable, sparse networks of weather stations. The absence of these data can have consequences on weather forecasting, prediction of severe weather events, agricultural planning, and climate change monitoring. The Trans-African Hydro-Meteorological Observatory (TAHMO.org) project seeks to address these problems by deploying and operating a large network of weather stations throughout Sub-Saharan Africa. To design the TAHMO network, we must determine where to place weather stations within each country. We should consider how we can create accurate spatio-temporal maps of weather data and how to balance the desired accuracy of each weather variable of interest (precipitation, temperature, relative humidity, etc.). We can express this problem as a joint optimization of multiple weather variables, given a fixed number of weather stations. We use reanalysis data as the best representation of the "true" weather patterns that occur in the region of interest. For each possible combination of sites, we interpolate the reanalysis data between selected locations and calculate the mean average error between the reanalysis ("true") data and the interpolated data. In order to formulate our multi-variate optimization problem, we explore different methods of weighting each weather variable in our objective function. These methods include systematic variation of weights to determine which weather variables have the strongest influence on the network design, as well as combinations targeted for specific purposes. For example, we can use computed evapotranspiration as a metric that combines many weather variables in a way that is meaningful for agricultural and hydrological applications. We compare the errors of the weather station networks produced by each optimization problem formulation. We also compare these errors to those of manually designed weather station networks in West Africa, planned by the respective host-country's meteorological agency.

The Relationships between Selected Organizational Variables and ATM Technology Adoption in Campus Networking.

ERIC Educational Resources Information Center

Yao, Engui

1998-01-01

Determines the relationships between ATM (Asynchronous Transfer Mode) adoption and four organizational variables: university size, type, finances, and information-processing maturity. Identifies the current status of ATM adoption in campus networking in the United States. Contains 33 references. (DDR)

Exploiting Data Missingness in Bayesian Network Modeling

NASA Astrophysics Data System (ADS)

Rodrigues de Morais, Sérgio; Aussem, Alex

This paper proposes a framework built on the use of Bayesian networks (BN) for representing statistical dependencies between the existing random variables and additional dummy boolean variables, which represent the presence/absence of the respective random variable value. We show how augmenting the BN with these additional variables helps pinpoint the mechanism through which missing data contributes to the classification task. The missing data mechanism is thus explicitly taken into account to predict the class variable using the data at hand. Extensive experiments on synthetic and real-world incomplete data sets reveals that the missingness information improves classification accuracy.

Using Equation-Free Computation to Accelerate Network-Free Stochastic Simulation of Chemical Kinetics.

PubMed

Lin, Yen Ting; Chylek, Lily A; Lemons, Nathan W; Hlavacek, William S

2018-06-21

The chemical kinetics of many complex systems can be concisely represented by reaction rules, which can be used to generate reaction events via a kinetic Monte Carlo method that has been termed network-free simulation. Here, we demonstrate accelerated network-free simulation through a novel approach to equation-free computation. In this process, variables are introduced that approximately capture system state. Derivatives of these variables are estimated using short bursts of exact stochastic simulation and finite differencing. The variables are then projected forward in time via a numerical integration scheme, after which a new exact stochastic simulation is initialized and the whole process repeats. The projection step increases efficiency by bypassing the firing of numerous individual reaction events. As we show, the projected variables may be defined as populations of building blocks of chemical species. The maximal number of connected molecules included in these building blocks determines the degree of approximation. Equation-free acceleration of network-free simulation is found to be both accurate and efficient.

Entropy-Based Analysis and Bioinformatics-Inspired Integration of Global Economic Information Transfer

PubMed Central

An, Sungbae; Kwon, Young-Kyun; Yoon, Sungroh

2013-01-01

The assessment of information transfer in the global economic network helps to understand the current environment and the outlook of an economy. Most approaches on global networks extract information transfer based mainly on a single variable. This paper establishes an entirely new bioinformatics-inspired approach to integrating information transfer derived from multiple variables and develops an international economic network accordingly. In the proposed methodology, we first construct the transfer entropies (TEs) between various intra- and inter-country pairs of economic time series variables, test their significances, and then use a weighted sum approach to aggregate information captured in each TE. Through a simulation study, the new method is shown to deliver better information integration compared to existing integration methods in that it can be applied even when intra-country variables are correlated. Empirical investigation with the real world data reveals that Western countries are more influential in the global economic network and that Japan has become less influential following the Asian currency crisis. PMID:23300959

Entropy-based analysis and bioinformatics-inspired integration of global economic information transfer.

PubMed

Kim, Jinkyu; Kim, Gunn; An, Sungbae; Kwon, Young-Kyun; Yoon, Sungroh

2013-01-01

The assessment of information transfer in the global economic network helps to understand the current environment and the outlook of an economy. Most approaches on global networks extract information transfer based mainly on a single variable. This paper establishes an entirely new bioinformatics-inspired approach to integrating information transfer derived from multiple variables and develops an international economic network accordingly. In the proposed methodology, we first construct the transfer entropies (TEs) between various intra- and inter-country pairs of economic time series variables, test their significances, and then use a weighted sum approach to aggregate information captured in each TE. Through a simulation study, the new method is shown to deliver better information integration compared to existing integration methods in that it can be applied even when intra-country variables are correlated. Empirical investigation with the real world data reveals that Western countries are more influential in the global economic network and that Japan has become less influential following the Asian currency crisis.

Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.

PubMed

Caillet, Pascal; Klemm, Sarah; Ducher, Michel; Aussem, Alexandre; Schott, Anne-Marie

2015-01-01

Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach. EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences. Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density. Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.

Networks.

ERIC Educational Resources Information Center

Maughan, George R.; Petitto, Karen R.; McLaughlin, Don

2001-01-01

Describes the connectivity features and options of modern campus communication and information system networks, including signal transmission (wire-based and wireless), signal switching, convergence of networks, and network assessment variables, to enable campus leaders to make sound future-oriented decisions. (EV)

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks

PubMed Central

Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute new seed bank sources for species that are affected by the distance to natural lands and roads. PMID:29360857

Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups

PubMed Central

2017-01-01

Background Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. The success of a DHSN is based on the number of participants and their consistent creation of externalities through the generation of new content. To promote network growth, it would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities. Objective The aim of the study was to investigate the feasibility of developing predictive models that identify potential superusers in real time. This study examined associations between posting behavior, 4 demographic variables, and 20 indication-specific variables. Methods Data were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs. Of these, 2 were designed to assist in the treatment of addictions (problem drinking and smoking cessation), and 2 for mental health (depressive disorder, panic disorder). To analyze posting behavior, 10 models were developed, and negative binomial regressions were conducted to examine associations between number of posts, and demographic and indication-specific variables. Results The DHSNs varied in number of days active (3658-5210), number of registrants (5049-52,396), number of actors (1085-8452), and number of posts (16,231-521,997). In the sample, all 10 models had low R2 values (.013-.086) with limited statistically significant demographic and indication-specific variables. Conclusions Very few variables were associated with social network engagement. Although some variables were statistically significant, they did not appear to be practically significant. Based on the large number of study participants, variation in DHSN theme, and extensive time-period, we did not find strong evidence that demographic characteristics or indication severity sufficiently explain the variability in number of posts per actor. Researchers should investigate alternative models that identify superusers or other individuals who create social network externalities. PMID:28213340

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

PubMed

Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute new seed bank sources for species that are affected by the distance to natural lands and roads.

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

PubMed

Adalsteinsson, David; McMillen, David; Elston, Timothy C

2004-03-08

Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Pass-transistor very large scale integration

NASA Technical Reports Server (NTRS)

Maki, Gary K. (Inventor); Bhatia, Prakash R. (Inventor)

2004-01-01

Logic elements are provided that permit reductions in layout size and avoidance of hazards. Such logic elements may be included in libraries of logic cells. A logical function to be implemented by the logic element is decomposed about logical variables to identify factors corresponding to combinations of the logical variables and their complements. A pass transistor network is provided for implementing the pass network function in accordance with this decomposition. The pass transistor network includes ordered arrangements of pass transistors that correspond to the combinations of variables and complements resulting from the logical decomposition. The logic elements may act as selection circuits and be integrated with memory and buffer elements.

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

NASA Technical Reports Server (NTRS)

Kimes, Daniel S.; Nelson, Ross F.

1998-01-01

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

On the effect of networks of cycle-tracks on the risk of cycling. The case of Seville.

PubMed

Marqués, R; Hernández-Herrador, V

2017-05-01

We analyze the evolution of the risk of cycling in Seville before and after the implementation of a network of segregated cycle tracks in the city. Specifically, we study the evolution of the risk for cyclists of being involved in a collision with a motor vehicle, using data reported by the traffic police along the period 2000-2013, i.e. seven years before and after the network was built. A sudden drop of such risk was observed after the implementation of the network of bikeways. We study, through a multilinear regression analysis, the evolution of the risk by means of explanatory variables representing changes in the built environment, specifically the length of the bikeways and a stepwise jump variable taking the values 0/1 before/after the network was implemented. We found that this last variable has a high value as explanatory variable, even higher than the length of the network, thus suggesting that networking the bikeways has a substantial effect on cycling safety by itself and beyond the mere increase in the length of the bikeways. We also analyze safety in numbers through a non-linear regression analysis. Our results fully agree qualitatively and quantitatively with the results previously reported by Jacobsen (2003), thus providing an independent confirmation of Jacobsen's results. Finally, the mutual causal relationships between the increase in safety, the increase in the number of cyclists and the presence of the network of bikeways are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency

PubMed Central

Wang, Zengjian; Zhang, Delong; Liang, Bishan; Chang, Song; Pan, Jinghua; Huang, Ruiwang; Liu, Ming

2016-01-01

Biological motion perception (BMP) refers to the ability to perceive the moving form of a human figure from a limited amount of stimuli, such as from a few point lights located on the joints of a moving body. BMP is commonplace and important, but there is great inter-individual variability in this ability. This study used multiple regression model analysis to explore the association between BMP performance and intrinsic brain activity, in order to investigate the neural substrates underlying inter-individual variability of BMP performance. The resting-state functional magnetic resonance imaging (rs-fMRI) and BMP performance data were collected from 24 healthy participants, for whom intrinsic brain networks were constructed, and a graph-based network efficiency metric was measured. Then, a multiple linear regression model was used to explore the association between network regional efficiency and BMP performance. We found that the local and global network efficiency of many regions was significantly correlated with BMP performance. Further analysis showed that the local efficiency rather than global efficiency could be used to explain most of the BMP inter-individual variability, and the regions involved were predominately located in the Default Mode Network (DMN). Additionally, discrimination analysis showed that the local efficiency of certain regions such as the thalamus could be used to classify BMP performance across participants. Notably, the association pattern between network nodal efficiency and BMP was different from the association pattern of static directional/gender information perception. Overall, these findings show that intrinsic brain network efficiency may be considered a neural factor that explains BMP inter-individual variability. PMID:27853427

Informal Training in Staff Networks to Support Dissemination of Health Promotion Programs

PubMed Central

Ramanadhan, Shoba; Wiecha, Jean L.; Gortmaker, Steven L.; Emmons, Karen M.; Viswanath, Kasisomayajula

2011-01-01

Purpose To study informal skill transfer via staff networks as a complement to formal training among afterschool childcare providers implementing a health promotion program. Design Cross-sectional, sociometric network analysis. Setting Boston Young Men’s Christian Association (YMCA) afterschool programs implementing the iPLAY program. Participants All 91 staff members at 20 sites were eligible; 80 completed the survey (88% response rate). Measures At the network level, network density measured system-level connectedness. At the staff level, the independent variable was out degree, the number of individuals to whom respondents noted a program-related connection. The dependent variable was skill gains, the number of key implementation skills gained from the network. Analysis We mapped the staff program-related social network. We utilized multiple linear regression to estimate the relationship between out degree and skill gains, and we adjusted for clustering of staff in sites. Results Most staff (77%) reported gaining at least one skill from the network, but only 2% of potential network connections were established. The regression model showed that out degree (i.e., number of program-related contacts) was significantly associated with skill gains (β = .48, p < .01) independent of other variables. Conclusion Informal skill transfer in staff networks may be a useful complement to formal training for implementation of health promotion programs, but informal skill transfer was likely underutilized in this network. Future research employing longitudinal and/or multisite data should examine these findings in greater detail. PMID:20809826

Variability in personality expression across contexts: a social network approach.

PubMed

Clifton, Allan

2014-04-01

The current research investigated how the contextual expression of personality differs across interpersonal relationships. Two related studies were conducted with college samples (Study 1: N = 52, 38 female; Study 2: N = 111, 72 female). Participants in each study completed a five-factor measure of personality and constructed a social network detailing their 30 most important relationships. Participants used a brief Five-Factor Model scale to rate their personality as they experience it when with each person in their social network. Multiple informants selected from each social network then rated the target participant's personality (Study 1: N = 227, Study 2: N = 777). Contextual personality ratings demonstrated incremental validity beyond standard global self-report in predicting specific informants' perceptions. Variability in these contextualized personality ratings was predicted by the position of the other individuals within the social network. Across both studies, participants reported being more extraverted and neurotic, and less conscientious, with more central members of their social networks. Dyadic social network-based assessments of personality provide incremental validity in understanding personality, revealing dynamic patterns of personality variability unobservable with standard assessment techniques. © 2013 Wiley Periodicals, Inc.

Bayesian networks in neuroscience: a survey.

PubMed

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.

Bayesian networks in neuroscience: a survey

PubMed Central

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied. PMID:25360109

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

PubMed Central

Stevens, Alexander A.; Tappon, Sarah C.; Garg, Arun; Fair, Damien A.

2012-01-01

Background Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Methodology/Principal Findings Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. Conclusions/Significance The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise. PMID:22276205

Price of anarchy on heterogeneous traffic-flow networks

NASA Astrophysics Data System (ADS)

Rose, A.; O'Dea, R.; Hopcraft, K. I.

2016-09-01

The efficiency of routing traffic through a network, comprising nodes connected by links whose cost of traversal is either fixed or varies in proportion to volume of usage, can be measured by the "price of anarchy." This is the ratio of the cost incurred by agents who act to minimize their individual expenditure to the optimal cost borne by the entire system. As the total traffic load and the network variability—parameterized by the proportion of variable-cost links in the network—changes, the behaviors that the system presents can be understood with the introduction of a network of simpler structure. This is constructed from classes of nonoverlapping paths connecting source to destination nodes that are characterized by the number of variable-cost edges they contain. It is shown that localized peaks in the price of anarchy occur at critical traffic volumes at which it becomes beneficial to exploit ostensibly more expensive paths as the network becomes more congested. Simulation results verifying these findings are presented for the variation of the price of anarchy with the network's size, aspect ratio, variability, and traffic load.

Closed loop supply chain network design with fuzzy tactical decisions

NASA Astrophysics Data System (ADS)

Sherafati, Mahtab; Bashiri, Mahdi

2016-09-01

One of the most strategic and the most significant decisions in supply chain management is reconfiguration of the structure and design of the supply chain network. In this paper, a closed loop supply chain network design model is presented to select the best tactical and strategic decision levels simultaneously considering the appropriate transportation mode in activated links. The strategic decisions are made for a long term; thus, it is more satisfactory and more appropriate when the decision variables are considered uncertain and fuzzy, because it is more flexible and near to the real world. This paper is the first research which considers fuzzy decision variables in the supply chain network design model. Moreover, in this study a new fuzzy optimization approach is proposed to solve a supply chain network design problem with fuzzy tactical decision variables. Finally, the proposed approach and model are verified using several numerical examples. The comparison of the results with other existing approaches confirms efficiency of the proposed approach. Moreover the results confirms that by considering the vagueness of tactical decisions some properties of the supply chain network will be improved.

From molecular noise to behavioural variability in a single bacterium

NASA Astrophysics Data System (ADS)

Korobkova, Ekaterina; Emonet, Thierry; Vilar, Jose M. G.; Shimizu, Thomas S.; Cluzel, Philippe

2004-04-01

The chemotaxis network that governs the motion of Escherichia coli has long been studied to gain a general understanding of signal transduction. Although this pathway is composed of just a few components, it exhibits some essential characteristics of biological complexity, such as adaptation and response to environmental signals. In studying intracellular networks, most experiments and mathematical models have assumed that network properties can be inferred from population measurements. However, this approach masks underlying temporal fluctuations of intracellular signalling events. We have inferred fundamental properties of the chemotaxis network from a noise analysis of behavioural variations in individual bacteria. Here we show that certain properties established by population measurements, such as adapted states, are not conserved at the single-cell level: for timescales ranging from seconds to several minutes, the behaviour of non-stimulated cells exhibit temporal variations much larger than the expected statistical fluctuations. We find that the signalling network itself causes this noise and identify the molecular events that produce it. Small changes in the concentration of one key network component suppress temporal behavioural variability, suggesting that such variability is a selected property of this adaptive system.

Performance analysis of Aloha networks with power capture and near/far effect

NASA Astrophysics Data System (ADS)

McCartin, Joseph T.

1989-06-01

An analysis is presented for the throughput characteristics for several classes of Aloha packet networks. Specifically, the throughput for variable packet length Aloha utilizing multiple power levels to induce receiver capture is derived. The results are extended to an analysis of a selective-repeat ARQ Aloha network. Analytical results are presented which indicate a significant increase in throughput for a variable packet network implementing a random two power level capture scheme. Further research into the area of the near/far effect on Aloha networks is included. Improvements in throughput for mobile radio Aloha networks which are subject to the near/far effect are presented. Tactical Command, Control and Communications (C3) systems of the future will rely on Aloha ground mobile data networks. The incorporation of power capture and the near/far effect into future tactical networks will result in improved system analysis, design, and performance.

Time-dependent breakdown of fiber networks: Uncertainty of lifetime

NASA Astrophysics Data System (ADS)

Mattsson, Amanda; Uesaka, Tetsu

2017-05-01

Materials often fail when subjected to stresses over a prolonged period. The time to failure, also called the lifetime, is known to exhibit large variability of many materials, particularly brittle and quasibrittle materials. For example, a coefficient of variation reaches 100% or even more. Its distribution shape is highly skewed toward zero lifetime, implying a large number of premature failures. This behavior contrasts with that of normal strength, which shows a variation of only 4%-10% and a nearly bell-shaped distribution. The fundamental cause of this large and unique variability of lifetime is not well understood because of the complex interplay between stochastic processes taking place on the molecular level and the hierarchical and disordered structure of the material. We have constructed fiber network models, both regular and random, as a paradigm for general material structures. With such networks, we have performed Monte Carlo simulations of creep failure to establish explicit relationships among fiber characteristics, network structures, system size, and lifetime distribution. We found that fiber characteristics have large, sometimes dominating, influences on the lifetime variability of a network. Among the factors investigated, geometrical disorders of the network were found to be essential to explain the large variability and highly skewed shape of the lifetime distribution. With increasing network size, the distribution asymptotically approaches a double-exponential form. The implication of this result is that, so-called "infant mortality," which is often predicted by the Weibull approximation of the lifetime distribution, may not exist for a large system.

Social networks and well-being: a comparison of older people in Mediterranean and non-Mediterranean countries.

PubMed

Litwin, Howard

2010-09-01

This study examined whether the social networks of older persons in Mediterranean and non-Mediterranean countries were appreciably different and whether they functioned in similar ways in relation to well-being outcomes. The sample included family household respondents aged 60 years and older from the first wave of the Survey of Health, Ageing and Retirement in Europe in 5 Mediterranean (n = 3,583) and 7 non-Mediterranean (n = 5,471) countries. Region was regressed separately by gender on variables from 4 network domains: structure and interaction, exchange, engagement and relationship quality, and controlling for background and health characteristics. In addition, 2 well-being outcomes-depressive symptoms and perceived income inadequacy-were regressed on the study variables, including regional social network interaction terms. The results revealed differences across the 2 regional settings in each of the realms of social network, above and beyond the differences that exist in background characteristics and health status. The findings also showed that the social network variables had different effects on the well-being outcomes in the respective settings. The findings underscore that the social network phenomenon is contextually bound. The social networks of older people should be seen within their unique regional milieu and in relation to the values and social norms that prevail in different sets of societies.

Linking environmental variability to population and community dynamics: Chapter 7

USGS Publications Warehouse

Pantel, Jelena H.; Pendleton, Daniel E.; Walters, Annika W.; Rogers, Lauren A.

2014-01-01

Linking population and community responses to environmental variability lies at the heart of ecology, yet methodological approaches vary and existence of broad patterns spanning taxonomic groups remains unclear. We review the characteristics of environmental and biological variability. Classic approaches to link environmental variability to population and community variability are discussed as are the importance of biotic factors such as life history and community interactions. In addition to classic approaches, newer techniques such as information theory and artificial neural networks are reviewed. The establishment and expansion of observing networks will provide new long-term ecological time-series data, and with it, opportunities to incorporate environmental variability into research. This review can help guide future research in the field of ecological and environmental variability.

Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.

PubMed

Toni, Tina; Tidor, Bruce

2013-01-01

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA--for example, on the same transcript--was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.

Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

PubMed Central

Toni, Tina; Tidor, Bruce

2013-01-01

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology. PMID:23555205

Water Diplomacy: A Synthesis of Explicit and Tacit Water Information to Create Actionable Knowledge

NASA Astrophysics Data System (ADS)

Islam, S.; Moomaw, W.; Portney, K.; Reed, M.; Vogel, R. M.; Water Diplomacy

2011-12-01

Water issues are complex because they cross multiple boundaries and involve various stakeholders with competing needs. The origin of many water issues is a dynamic consequence of competition and feedback among variables in the natural, societal and political domains. Together, these interactions generate what we call water networks. As population growth, economic development and climate change impose pressures on finite water resources, management of these water networks becomes crucial. Science alone is not sufficient; nor can policy-making that does not take science into account yield sustainable management solutions. Rather, sustainable solutions may only be found through a diplomatic or negotiated approach that simultaneously takes science, policy, and politics into account. Water issues need to be understood as the product of competition, interconnection, and feedback among variables in the Natural and Societal Domains (NSDs). Within the natural domain: water quantity (Q), water quality (P), and ecosystem (E) constrain and define network dynamics. While in the societal domain, interactions among culture and values (V), assets (C), and governance and institutions (G) create complex contextual differences in the network. These six NSD variables constitute the nodes of a water network while interactions and feedback among natural, societal and political forces define the complexity of a network. The knowledge needed to resolve water conflicts and to manage water networks effectively must extend beyond scientific assessment that ignore societal variables (C, G, and V) or treat them as exogenous, and beyond policy analysis that does not consider the impact of natural variables (E, P, and Q) and the couplings among them. Many water conflicts arise when NSD variables, and the networks they define, are mismanaged. These networks are open-ended systems that cross boundaries (physical, disciplinary, and jurisdictional ) and change continuously; thus, efforts to manage them assuming that they have fixed boundaries , or can be optimized with scientific objectivity without properly accounting for contextual differences, are likely to fail. Once water conflicts are framed properly, the tools of joint fact-finding and collaborative problem-solving can be used to negotiate solutions that are both adaptive and enforceable. We will use AquaPedia - a growing knowledge base of water issues from across the world - to demonstrate the utility of this synthesis of explicit and tacit knowledge in addressing water problems and creating actionable knowledge.

Optimal information networks: Application for data-driven integrated health in populations

PubMed Central

Servadio, Joseph L.; Convertino, Matteo

2018-01-01

Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables, instead considering only individual correlations. In addition, a unified method for assessing integrated health statuses of populations is lacking, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator, representing integrated health in a city. PMID:29423440

Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches.

PubMed

Zounemat-Kermani, Mohammad; Ramezani-Charmahineh, Abdollah; Adamowski, Jan; Kisi, Ozgur

2018-06-13

Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R 2 , and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.

Social networks and self-rated health in two French-speaking Canadian community dwelling populations over 65.

PubMed

Zunzunegui, M V; Koné, A; Johri, M; Béland, F; Wolfson, C; Bergman, H

2004-05-01

The objective was to evaluate the associations between older persons' health status and their social integration and social networks (family, children, friends and community), in two French-speaking, Canadian community dwelling populations aged 65 years and over, using the conceptual framework proposed by Berkman and Thomas. Data were taken from two 1995 surveys conducted in the city of Moncton (n = 1518) and the Montreal neighbourhood of Hochelaga-Maisonneuve (n = 1500). Social engagement (a cumulative index of social activities), networks consisting of friends, family and children and social support were measured using validated scales. Multiple logistic regressions based on structured inclusion of potentially mediating variables were fitted to estimate the associations between health status and social networks. Self-rated health was better for those with a high level of social integration and a strong network of friends in both locations. In addition, in Hochelaga-Maisonneuve family and children networks were positively associated with good health, though the effect of friend networks was attenuated in the presence of disability, good social support from children was associated with good health. Age, sex and education were included as antecedent variables; smoking, alcohol consumption, exercise, locus of control and depressive symptoms were considered intermediary variables between social networks and health. In conclusion, social networks, integration and support demonstrated unique positive associations with health. The nature of these associations may vary between populations and cultures.

PeerShield: determining control and resilience criticality of collaborative cyber assets in networks

NASA Astrophysics Data System (ADS)

Cam, Hasan

2012-06-01

As attackers get more coordinated and advanced in cyber attacks, cyber assets are required to have much more resilience, control effectiveness, and collaboration in networks. Such a requirement makes it essential to take a comprehensive and objective approach for measuring the individual and relative performances of cyber security assets in network nodes. To this end, this paper presents four techniques as to how the relative importance of cyber assets can be measured more comprehensively and objectively by considering together the main variables of risk assessment (e.g., threats, vulnerabilities), multiple attributes (e.g., resilience, control, and influence), network connectivity and controllability among collaborative cyber assets in networks. In the first technique, a Bayesian network is used to include the random variables for control, recovery, and resilience attributes of nodes, in addition to the random variables of threats, vulnerabilities, and risk. The second technique shows how graph matching and coloring can be utilized to form collaborative pairs of nodes to shield together against threats and vulnerabilities. The third technique ranks the security assets of nodes by incorporating multiple weights and thresholds of attributes into a decision-making algorithm. In the fourth technique, the hierarchically well-separated tree is enhanced to first identify critical nodes of a network with respect to their attributes and network connectivity, and then selecting some nodes as driver nodes for network controllability.

Modeling the Components of an Economy as a Complex Adaptive System

DTIC Science & Technology

principles of constrained optimization and fails to see economic variables as part of an interconnected network. While tools for forecasting economic...data sets such as the stock market . This research portrays the stock market as one component of a networked system of economic variables, with the

Quantifying networks complexity from information geometry viewpoint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Felice, Domenico, E-mail: domenico.felice@unicam.it; Mancini, Stefano; INFN-Sezione di Perugia, Via A. Pascoli, I-06123 Perugia

We consider a Gaussian statistical model whose parameter space is given by the variances of random variables. Underlying this model we identify networks by interpreting random variables as sitting on vertices and their correlations as weighted edges among vertices. We then associate to the parameter space a statistical manifold endowed with a Riemannian metric structure (that of Fisher-Rao). Going on, in analogy with the microcanonical definition of entropy in Statistical Mechanics, we introduce an entropic measure of networks complexity. We prove that it is invariant under networks isomorphism. Above all, considering networks as simplicial complexes, we evaluate this entropy onmore » simplexes and find that it monotonically increases with their dimension.« less

Field demonstration of a continuous-variable quantum key distribution network.

PubMed

Huang, Duan; Huang, Peng; Li, Huasheng; Wang, Tao; Zhou, Yingming; Zeng, Guihua

2016-08-01

We report on what we believe is the first field implementation of a continuous-variable quantum key distribution (CV-QKD) network with point-to-point configuration. Four QKD nodes are deployed on standard communication infrastructures connected with commercial telecom optical fiber. Reliable key exchange is achieved in the wavelength-division-multiplexing CV-QKD network. The impact of a complex and volatile field environment on the excess noise is investigated, since excess noise controlling and reduction is arguably the major issue pertaining to distance and the secure key rate. We confirm the applicability and verify the maturity of the CV-QKD network in a metropolitan area, thus paving the way for a next-generation global secure communication network.

Systems and methods for modeling and analyzing networks

DOEpatents

Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W

2013-10-29

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Epilepsy and Neuromodulation—Randomized Controlled Trials

PubMed Central

Kwon, Churl-Su; Ripa, Valeria; Al-Awar, Omar; Panov, Fedor; Ghatan, Saadi; Jetté, Nathalie

2018-01-01

Neuromodulation is a treatment strategy that is increasingly being utilized in those suffering from drug-resistant epilepsy who are not appropriate for resective surgery. The number of double-blinded RCTs demonstrating the efficacy of neurostimulation in persons with epilepsy is increasing. Although reductions in seizure frequency is common in these trials, obtaining seizure freedom is rare. Invasive neuromodulation procedures (DBS, VNS, and RNS) have been approved as therapeutic measures. However, further investigations are necessary to delineate effective targeting, minimize side effects that are related to chronic implantation and to improve the cost effectiveness of these devices. The RCTs of non-invasive modes of neuromodulation whilst showing much promise (tDCS, eTNS, rTMS), require larger powered studies as well as studies that focus at better targeting techniques. We provide a review of double-blinded randomized clinical trials that have been conducted for neuromodulation in epilepsy. PMID:29670050

Dietary therapy is not the best option for refractory nonsurgical epilepsy.

PubMed

Vaccarezza, María Magdalena; Silva, Walter Horacio

2015-09-01

The ketogenic diet (KD) is currently a well-established treatment for patients with medically refractory, nonsurgical epilepsy. However, despite its efficacy, the KD is highly restrictive and constitutes a treatment with serious potential adverse effects, and often with difficulties in its implementation and compliance. Patients on the KD require strict follow-up and constant supervision by a medical team highly experienced in its management in order to prevent complications. Other alternative treatments for patients with refractory epilepsy include vagus nerve stimulation (VNS), new-generation antiepileptic drugs (AEDs), corpus callosotomy (CC), and responsive focal cortical stimulation (RNS). In this review, we explain not only the difficulties of the KD as a therapeutic option for refractory epilepsy but also the benefits of other therapeutic strategies, which, in many cases, have proven to have better efficacy than the KD itself. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Networks for image acquisition, processing and display

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.

1990-01-01

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a stochastic generator of synthetic data was developed. Synthetic basin characteristics were randomised, keeping the statistical properties of observed physical and climatic variables in the homogeneous region. The synthetic flood quantiles were stochastically generated taking the regression equation as basis. The learnt Bayesian network was validated by the reliability diagram, the Brier Score and the ROC diagram, which are common measures used in the validation of probabilistic forecasts. Summarising, the flood quantile estimations through Bayesian networks supply information about the prediction uncertainty as a probability distribution function of discharges is given as result. Therefore, the Bayesian network model has application as a decision support for water resources and planning management.

A strategic outlook for coordination of ground-based measurement networks of atmospheric state variables and atmospheric composition

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Thorne, P.; Braathen, G.; De Maziere, M.; Thompson, A. M.; Kurylo, M. J., III

2016-12-01

There are a number of ground-based global observing networks that collectively aim to make key measurements of atmospheric state variables and atmospheric chemical composition. These networks include, but are not limited to:NDACC: Network for the Detection of Atmospheric Composition Change GUAN: GCOS Upper Air Network GRUAN: GCOS Reference Upper Air Network EARLINET: the European Aerosol Research Lidar Network GAW: Global Atmosphere Watch SHADOZ: Southern Hemisphere ADditional OZonesondes TCCON: Total Carbon Column Observing Network BSRN: Baseline Surface Radiation Network While each network brings unique capabilities to the global observing system, there are many instances where the activities and capabilities of the networks overlap. These commonalities across multiple networks can confound funding agencies when allocating scarce financial resources. Overlaps between networks may also result in some duplication of effort and a resultant sub-optimal use of funding resource for the global observing system. While some degree of overlap is useful for quality assurance, it is essential to identify the degree to which one network can take on a specific responsibility on behalf of all other networks to avoid unnecessary duplication, to identify where expertise in any one network may serve other networks, and to develop a long-term strategy for the evolution of these networks that clarifies to funding agencies where new investment is required. This presentation will briefly summarise the key characteristics of each network listed above, adopt a matrix approach to identify commonalities and, in particular, where there may be a danger of duplication of effort, and where gaps between the networks may be compromising the services that these networks are expected to collectively deliver to the global atmospheric and climate science research communities. The presentation will also examine where sharing of data and tools between networks may result in a more efficient delivery of records of essential climate variables to the global research community. There are aspects of underpinning research that are needed across all of these networks, such as laboratory spectroscopy, that often do not receive the attention they deserve. The presentation will also seek to identify where that underpinning research is lacking.

Real-Time Alpine Measurement System Using Wireless Sensor Networks

PubMed Central

2017-01-01

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape. PMID:29120376

Real-Time Alpine Measurement System Using Wireless Sensor Networks.

PubMed

Malek, Sami A; Avanzi, Francesco; Brun-Laguna, Keoma; Maurer, Tessa; Oroza, Carlos A; Hartsough, Peter C; Watteyne, Thomas; Glaser, Steven D

2017-11-09

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

A Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies.

PubMed

Liang, Xiaoyun; Vaughan, David N; Connelly, Alan; Calamante, Fernando

2018-05-01

The conventional way to estimate functional networks is primarily based on Pearson correlation along with classic Fisher Z test. In general, networks are usually calculated at the individual-level and subsequently aggregated to obtain group-level networks. However, such estimated networks are inevitably affected by the inherent large inter-subject variability. A joint graphical model with Stability Selection (JGMSS) method was recently shown to effectively reduce inter-subject variability, mainly caused by confounding variations, by simultaneously estimating individual-level networks from a group. However, its benefits might be compromised when two groups are being compared, given that JGMSS is blinded to other groups when it is applied to estimate networks from a given group. We propose a novel method for robustly estimating networks from two groups by using group-fused multiple graphical-lasso combined with stability selection, named GMGLASS. Specifically, by simultaneously estimating similar within-group networks and between-group difference, it is possible to address inter-subject variability of estimated individual networks inherently related with existing methods such as Fisher Z test, and issues related to JGMSS ignoring between-group information in group comparisons. To evaluate the performance of GMGLASS in terms of a few key network metrics, as well as to compare with JGMSS and Fisher Z test, they are applied to both simulated and in vivo data. As a method aiming for group comparison studies, our study involves two groups for each case, i.e., normal control and patient groups; for in vivo data, we focus on a group of patients with right mesial temporal lobe epilepsy.

Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E

2012-01-01

A new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates thatmore » this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.« less

Clustering coefficients of protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Miller, Gerald A.; Shi, Yi Y.; Qian, Hong; Bomsztyk, Karol

2007-05-01

The properties of certain networks are determined by hidden variables that are not explicitly measured. The conditional probability (propagator) that a vertex with a given value of the hidden variable is connected to k other vertices determines all measurable properties. We study hidden variable models and find an averaging approximation that enables us to obtain a general analytical result for the propagator. Analytic results showing the validity of the approximation are obtained. We apply hidden variable models to protein-protein interaction networks (PINs) in which the hidden variable is the association free energy, determined by distributions that depend on biochemistry and evolution. We compute degree distributions as well as clustering coefficients of several PINs of different species; good agreement with measured data is obtained. For the human interactome two different parameter sets give the same degree distributions, but the computed clustering coefficients differ by a factor of about 2. This shows that degree distributions are not sufficient to determine the properties of PINs.

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.

PubMed

Zhang, Jie; Cheng, Wei; Liu, Zhaowen; Zhang, Kai; Lei, Xu; Yao, Ye; Becker, Benjamin; Liu, Yicen; Kendrick, Keith M; Lu, Guangming; Feng, Jianfeng

2016-08-01

SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be potentially useful as a predictor for learning and neural rehabilitation. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Part mutual information for quantifying direct associations in networks.

PubMed

Zhao, Juan; Zhou, Yiwei; Zhang, Xiujun; Chen, Luonan

2016-05-03

Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.

Increased tree-ring network density reveals more precise estimations of sub-regional hydroclimate variability and climate dynamics in the Midwest, USA

NASA Astrophysics Data System (ADS)

Maxwell, Justin T.; Harley, Grant L.

2017-08-01

Understanding the historic variability in the hydroclimate provides important information on possible extreme dry or wet periods that in turn inform water management plans. Tree rings have long provided historical context of hydroclimate variability of the U.S. However, the tree-ring network used to create these countrywide gridded reconstructions is sparse in certain locations, such as the Midwest. Here, we increase ( n = 20) the spatial resolution of the tree-ring network in southern Indiana and compare a summer (June-August) Palmer Drought Severity Index (PDSI) reconstruction to existing gridded reconstructions of PDSI for this region. We find both droughts and pluvials that were previously unknown that rival the most intense PDSI values during the instrumental period. Additionally, historical drought occurred in Indiana that eclipsed instrumental conditions with regard to severity and duration. During the period 1962-2004 CE, we find that teleconnections of drought conditions through the Atlantic Meridional Overturning Circulation have a strong influence ( r = -0.60, p < 0.01) on secondary tree growth in this region for the late spring-early summer season. These findings highlight the importance of continuing to increase the spatial resolution of the tree-ring network used to infer past climate dynamics to capture the sub-regional spatial variability. Increasing the spatial resolution of the tree-ring network for a given region can better identify sub-regional variability, improve the accuracy of regional tree-ring PDSI reconstructions, and provide better information for climatic teleconnections.

Neural network regulation driven by autonomous neural firings

NASA Astrophysics Data System (ADS)

Cho, Myoung Won

2016-07-01

Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

Social Networks and Well-being: A Comparison of Older People in Mediterranean and Non-Mediterranean Countries

PubMed Central

2010-01-01

Objectives. This study examined whether the social networks of older persons in Mediterranean and non-Mediterranean countries were appreciably different and whether they functioned in similar ways in relation to well-being outcomes. Methods. The sample included family household respondents aged 60 years and older from the first wave of the Survey of Health, Ageing and Retirement in Europe in 5 Mediterranean (n = 3,583) and 7 non-Mediterranean (n = 5,471) countries. Region was regressed separately by gender on variables from 4 network domains: structure and interaction, exchange, engagement and relationship quality, and controlling for background and health characteristics. In addition, 2 well-being outcomes—depressive symptoms and perceived income inadequacy—were regressed on the study variables, including regional social network interaction terms. Results. The results revealed differences across the 2 regional settings in each of the realms of social network, above and beyond the differences that exist in background characteristics and health status. The findings also showed that the social network variables had different effects on the well-being outcomes in the respective settings. Discussion. The findings underscore that the social network phenomenon is contextually bound. The social networks of older people should be seen within their unique regional milieu and in relation to the values and social norms that prevail in different sets of societies. PMID:20008485

A network-based approach for semi-quantitative knowledge mining and its application to yield variability

NASA Astrophysics Data System (ADS)

Schauberger, Bernhard; Rolinski, Susanne; Müller, Christoph

2016-12-01

Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

The association between neighborhood social capital and self-reported dentate status in elderly Japanese--the Ohsaki Cohort 2006 Study.

PubMed

Aida, J; Kuriyama, S; Ohmori-Matsuda, K; Hozawa, A; Osaka, K; Tsuji, I

2011-06-01

Little is known about the influence of social capital on dental health. The aim of the present cross-sectional study was to determine the association between neighborhood social capital, individual social networks and social support and the number of remaining teeth in elderly Japanese. In December 2006, self-administered questionnaires were sent to 31,237 eligible community-dwelling individuals (response rate: 73.9%). Included in the analysis were 21,736 participants. Five neighborhood social capital variables were calculated from individual civic networks, sports and hobby networks, volunteer networks, friendship networks and social support variables. We used multilevel logistic regression models to estimate the odds ratio (OR) of having 20 or more teeth according to neighborhood social capital variables with adjustment for sex, age, individual social networks and social support, educational attainment, neighborhood educational level, dental health behavior, smoking status, history of diabetes and self-rated health. The average age of the participants was 74.9 (standard deviation; 6.6) years, and 28.5% of them had 20 or more teeth. In the univariate multilevel model, there were statistically significant associations between neighborhood sports and hobby networks, friendship networks and self-reported dentate status. In the multivariable multilevel model, compared with participants living in lowest friendship network neighborhoods, those living in highest friendship network neighborhoods had an OR 1.17 (95% confidence interval, 1.04-1.30) times higher for having 20 or more teeth. There is a significant association between one network aspect of neighborhood social capital and individual dentate status regardless of individual social networks and social support. © 2010 John Wiley & Sons A/S.

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

PubMed Central

Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

2015-01-01

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. PMID:26714277

Systems effects on family planning innovativeness.

PubMed

Lee, S B

1983-12-01

Data from Korea were used to explore the importance of community level variables in explaining family planning adoption at the individual level. An open system concept was applied, assuming that individual family planning behavior is influenced by both environmental and individual factors. The environmental factors were measured at the village level and designated as community characteristics. The dimension of communication network variables was introduced. Each individual was characterized in terms of the degree of her involvement in family planning communication with others in her village. It was assumed that the nature of the communication network linking individuals with each other effects family planning adoption at the individual level. Specific objectives were to determine 1) the relative importance of the specific independent variables in explaining family planning adoption and 2) the relative importance of the community level variables in comparison with the individual level variables in explaining family planning adoption at the individual level. The data were originally gathered in a 1973 research project on Korea's mothers' clubs. 1047 respondents were interviewed, comprising all married women in 25 sample villages having mothers' clubs. The dependent variable was family planning adoption behavior, defined as current use of any of the modern methods of family planning. The independent variables were defined at 3 levels: individual, community, and at a level intermediate between them involving communication links between individuals. More of the individual level independent variables were significantly correlated with the dependent variables than the community level variables. Among those variables with statistically significant correlations, the correlation coefficients were consistently higher for the individual level than for the community level variables. More of the variance in the dependent variable was explained by individual level than by community level variables. Community level variables accounted for only about 2.5% of the total variance in the dependent variable, in marked contrast to the result showing individual level variables accounting for as much as 19% of the total variance. When both individual and community level variables were entered into a multiple correlation analysis, a multiple correlation coefficient of .4714 was obtained together they explained about 20% of the total variance. The 2 communication network variables--connectedness and integrativeness--were correlated with the dependent variable at much higher levels than most of the individual or community level variables. Connectedness accounted for the greatest amount of the total variance. The communication network variables as a group explained as much of the total variance in the dependent variable as the individual level variables and greatly more that the community level variables.

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

Using Social Network Analysis to Better Understand Compulsive Exercise Behavior Among a Sample of Sorority Members.

PubMed

Patterson, Megan S; Goodson, Patricia

2017-05-01

Compulsive exercise, a form of unhealthy exercise often associated with prioritizing exercise and feeling guilty when exercise is missed, is a common precursor to and symptom of eating disorders. College-aged women are at high risk of exercising compulsively compared with other groups. Social network analysis (SNA) is a theoretical perspective and methodology allowing researchers to observe the effects of relational dynamics on the behaviors of people. SNA was used to assess the relationship between compulsive exercise and body dissatisfaction, physical activity, and network variables. Descriptive statistics were conducted using SPSS, and quadratic assignment procedure (QAP) analyses were conducted using UCINET. QAP regression analysis revealed a statistically significant model (R 2 = .375, P < .0001) predicting compulsive exercise behavior. Physical activity, body dissatisfaction, and network variables were statistically significant predictor variables in the QAP regression model. In our sample, women who are connected to "important" or "powerful" people in their network are likely to have higher compulsive exercise scores. This result provides healthcare practitioners key target points for intervention within similar groups of women. For scholars researching eating disorders and associated behaviors, this study supports looking into group dynamics and network structure in conjunction with body dissatisfaction and exercise frequency.

Characterizing individual differences in reward sensitivity from the brain networks involved in response inhibition.

PubMed

Fuentes-Claramonte, Paola; Ávila, César; Rodríguez-Pujadas, Aina; Costumero, Víctor; Ventura-Campos, Noelia; Bustamante, Juan Carlos; Rosell-Negre, Patricia; Barrós-Loscertales, Alfonso

2016-01-01

A "disinhibited" cognitive profile has been proposed for individuals with high reward sensitivity, characterized by increased engagement in goal-directed responses and reduced processing of negative or unexpected cues, which impairs adequate behavioral regulation after feedback in these individuals. This pattern is manifested through deficits in inhibitory control and/or increases in RT variability. In the present work, we aimed to test whether this profile is associated with the activity of functional networks during a stop-signal task using independent component analysis (ICA). Sixty-one participants underwent fMRI while performing a stop-signal task, during which a manual response had to be inhibited. ICA was used to mainly replicate the functional networks involved in the task (Zhang and Li, 2012): two motor networks involved in the go response, the left and right fronto-parietal networks for stopping, a midline error-processing network, and the default-mode network (DMN), which was further subdivided into its anterior and posterior parts. Reward sensitivity was mainly associated with greater activity of motor networks, reduced activity in the midline network during correct stop trials and, behaviorally, increased RT variability. All these variables explained 36% of variance of the SR scores. This pattern of associations suggests that reward sensitivity involves greater motor engagement in the dominant response, more distractibility and reduced processing of salient or unexpected events, which may lead to disinhibited behavior. Copyright © 2015 Elsevier Inc. All rights reserved.

Effects of non-invasive vagus nerve stimulation on attack frequency over time and expanded response rates in patients with chronic cluster headache: a post hoc analysis of the randomised, controlled PREVA study.

PubMed

Gaul, Charly; Magis, Delphine; Liebler, Eric; Straube, Andreas

2017-12-01

In the PREVention and Acute treatment of chronic cluster headache (PREVA) study, attack frequency reductions from baseline were significantly more pronounced with non-invasive vagus nerve stimulation plus standard of care (nVNS + SoC) than with SoC alone. Given the intensely painful and frequent nature of chronic cluster headache attacks, additional patient-centric outcomes, including the time to and level of therapeutic response, were evaluated in a post hoc analysis of the PREVA study. After a 2-week baseline phase, 97 patients with chronic cluster headache entered a 4-week randomised phase to receive nVNS + SoC (n = 48) or SoC alone (n = 49). All 92 patients who continued into a 4-week extension phase received nVNS + SoC. Compared with SoC alone, nVNS + SoC led to a significantly lower mean weekly attack frequency by week 2 of the randomised phase; the attack frequency remained significantly lower in the nVNS + SoC group through week 3 of the extension phase (P < 0.02). Attack frequencies in the nVNS + SoC group were significantly lower at all study time points than they were at baseline (P < 0.05). Response rates were significantly greater with nVNS + SoC than with SoC alone when response was defined as attack frequency reductions of ≥25%, ≥50%, and ≥75% from baseline (≥25% and ≥50%, P < 0.001; ≥75%, P = 0.009). The 100% response rate was 8% with nVNS + SoC and 0% with SoC alone. Prophylactic nVNS led to rapid, significant, and sustained reductions in chronic cluster headache attack frequency within 2 weeks after its addition to SoC and was associated with significantly higher ≥25%, ≥50%, and ≥75% response rates than SoC alone. The rapid decrease in weekly attack frequency justifies a 4-week trial period to identify responders to nVNS, with a high degree of confidence, among patients with chronic cluster headache.

Neural Networks for Readability Analysis.

ERIC Educational Resources Information Center

McEneaney, John E.

This paper describes and reports on the performance of six related artificial neural networks that have been developed for the purpose of readability analysis. Two networks employ counts of linguistic variables that simulate a traditional regression-based approach to readability. The remaining networks determine readability from "visual…

Real-time Data Display System of the Korean Neonatal Network

PubMed Central

Lee, Byong Sop; Moon, Wi Hwan

2015-01-01

Real-time data reporting in clinical research networks can provide network members through interim analyses of the registered data, which can facilitate further studies and quality improvement activities. The aim of this report was to describe the building process of the data display system (DDS) of the Korean Neonatal Network (KNN) and its basic structure. After member verification at the KNN member's site, users can choose a variable of interest that is listed in the in-hospital data statistics (for 90 variables) or in the follow-up data statistics (for 54 variables). The statistical results of the outcome variables are displayed on the HyperText Markup Language 5-based chart graphs and tables. Participating hospitals can compare their performance to those of KNN as a whole and identify the trends over time. Ranking of each participating hospital is also displayed in terms of key outcome variables such as mortality and major neonatal morbidities with the names of other centers blinded. The most powerful function of the DDS is the ability to perform 'conditional filtering' which allows users to exclusively review the records of interest. Further collaboration is needed to upgrade the DDS to a more sophisticated analytical system and to provide a more user-friendly interface. PMID:26566352

Network reconstructions with partially available data

NASA Astrophysics Data System (ADS)

Zhang, Chaoyang; Chen, Yang; Hu, Gang

2017-06-01

Many practical systems in natural and social sciences can be described by dynamical networks. Day by day we have measured and accumulated huge amounts of data from these networks, which can be used by us to further our understanding of the world. The structures of the networks producing these data are often unknown. Consequently, understanding the structures of these networks from available data turns to be one of the central issues in interdisciplinary fields, which is called the network reconstruction problem. In this paper, we considered problems of network reconstructions using partially available data and some situations where data availabilities are not sufficient for conventional network reconstructions. Furthermore, we proposed to infer subnetwork with data of the subnetwork available only and other nodes of the entire network hidden; to depict group-group interactions in networks with averages of groups of node variables available; and to perform network reconstructions with known data of node variables only when networks are driven by both unknown internal fast-varying noises and unknown external slowly-varying signals. All these situations are expected to be common in practical systems and the methods and results may be useful for real world applications.

Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups.

PubMed

van Mierlo, Trevor; Li, Xinlong; Hyatt, Douglas; Ching, Andrew T

2017-02-17

Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. The success of a DHSN is based on the number of participants and their consistent creation of externalities through the generation of new content. To promote network growth, it would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities. The aim of the study was to investigate the feasibility of developing predictive models that identify potential superusers in real time. This study examined associations between posting behavior, 4 demographic variables, and 20 indication-specific variables. Data were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs. Of these, 2 were designed to assist in the treatment of addictions (problem drinking and smoking cessation), and 2 for mental health (depressive disorder, panic disorder). To analyze posting behavior, 10 models were developed, and negative binomial regressions were conducted to examine associations between number of posts, and demographic and indication-specific variables. The DHSNs varied in number of days active (3658-5210), number of registrants (5049-52,396), number of actors (1085-8452), and number of posts (16,231-521,997). In the sample, all 10 models had low R 2 values (.013-.086) with limited statistically significant demographic and indication-specific variables. Very few variables were associated with social network engagement. Although some variables were statistically significant, they did not appear to be practically significant. Based on the large number of study participants, variation in DHSN theme, and extensive time-period, we did not find strong evidence that demographic characteristics or indication severity sufficiently explain the variability in number of posts per actor. Researchers should investigate alternative models that identify superusers or other individuals who create social network externalities. ©Trevor van Mierlo, Xinlong Li, Douglas Hyatt, Andrew T Ching. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.02.2017.

Drainage networks after wildfire

USGS Publications Warehouse

Kinner, D.A.; Moody, J.A.

2005-01-01

Predicting runoff and erosion from watersheds burned by wildfires requires an understanding of the three-dimensional structure of both hillslope and channel drainage networks. We investigate the small-and large-scale structures of drainage networks using field studies and computer analysis of 30-m digital elevation model. Topologic variables were derived from a composite 30-m DEM, which included 14 order 6 watersheds within the Pikes Peak batholith. Both topologic and hydraulic variables were measured in the field in two smaller burned watersheds (3.7 and 7.0 hectares) located within one of the order 6 watersheds burned by the 1996 Buffalo Creek Fire in Central Colorado. Horton ratios of topologic variables (stream number, drainage area, stream length, and stream slope) for small-scale and large-scale watersheds are shown to scale geometrically with stream order (i.e., to be scale invariant). However, the ratios derived for the large-scale drainage networks could not be used to predict the rill and gully drainage network structure. Hydraulic variables (width, depth, cross-sectional area, and bed roughness) for small-scale drainage networks were found to be scale invariant across 3 to 4 stream orders. The relation between hydraulic radius and cross-sectional area is similar for rills and gullies, suggesting that their geometry can be treated similarly in hydraulic modeling. Additionally, the rills and gullies have relatively small width-to-depth ratios, implying sidewall friction may be important to the erosion and evolutionary process relative to main stem channels.

Elementary Students' Affective Variables in a Networked Learning Environment Supported by a Blog: A Case Study

ERIC Educational Resources Information Center

Allaire, Stéphane; Thériault, Pascale; Gagnon, Vincent; Lalancette, Evelyne

2013-01-01

This study documents to what extent writing on a blog in a networked learning environment could influence the affective variables of elementary-school students' writing. The framework is grounded more specifically in theory of self-determination (Deci & Ryan, 1985), relationship to writing (Chartrand & Prince, 2009) and the transactional…

The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

ERIC Educational Resources Information Center

Metz, Dale Evan; And Others

1992-01-01

A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…

Reduced linear noise approximation for biochemical reaction networks with time-scale separation: The stochastic tQSSA+

NASA Astrophysics Data System (ADS)

Herath, Narmada; Del Vecchio, Domitilla

2018-03-01

Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

PubMed

Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

2013-11-01

The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

Discrete-time bidirectional associative memory neural networks with variable delays

NASA Astrophysics Data System (ADS)

Liang, variable delays [rapid communication] J.; Cao, J.; Ho, D. W. C.

2005-02-01

Based on the linear matrix inequality (LMI), some sufficient conditions are presented in this Letter for the existence, uniqueness and global exponential stability of the equilibrium point of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Some of the stability criteria obtained in this Letter are delay-dependent, and some of them are delay-independent, they are less conservative than the ones reported so far in the literature. Furthermore, the results provide one more set of easily verified criteria for determining the exponential stability of discrete-time BAM neural networks.

Multifractal cross-correlation effects in two-variable time series of complex network vertex observables

NASA Astrophysics Data System (ADS)

OświÈ©cimka, Paweł; Livi, Lorenzo; DroŻdŻ, Stanisław

2016-10-01

We investigate the scaling of the cross-correlations calculated for two-variable time series containing vertex properties in the context of complex networks. Time series of such observables are obtained by means of stationary, unbiased random walks. We consider three vertex properties that provide, respectively, short-, medium-, and long-range information regarding the topological role of vertices in a given network. In order to reveal the relation between these quantities, we applied the multifractal cross-correlation analysis technique, which provides information about the nonlinear effects in coupling of time series. We show that the considered network models are characterized by unique multifractal properties of the cross-correlation. In particular, it is possible to distinguish between Erdös-Rényi, Barabási-Albert, and Watts-Strogatz networks on the basis of fractal cross-correlation. Moreover, the analysis of protein contact networks reveals characteristics shared with both scale-free and small-world models.

A support network typology for application in older populations with a preponderance of multigenerational households.

PubMed

Burholt, Vanessa; Dobbs, Christine

2014-08-01

This paper considers the support networks of older people in populations with a preponderance of multigenerational households and examines the most vulnerable network types in terms of loneliness and isolation. Current common typologies of support networks may not be sensitive to differences within and between different cultures. This paper uses cross-sectional data drawn from 590 elders (Gujaratis, Punjabis and Sylhetis) living in the United Kingdom and South Asia. Six variables were used in K-means cluster analysis to establish a new network typology. Two logistic regression models using loneliness and isolation as dependent variables assessed the contribution of the new network type to wellbeing. Four support networks were identified: 'Multigenerational Households: Older Integrated Networks', 'Multigenerational Households: Younger Family Networks', 'Family and Friends Integrated Networks' and 'Non-kin Restricted Networks'. Older South Asians with 'Non-kin Restricted Networks' were more likely to be lonely and isolated compared to others. Using network typologies developed with individualistically oriented cultures, distributions are skewed towards more robust network types and could underestimate the support needs of older people from familistic cultures, who may be isolated and lonely and with limited informal sources of help. The new typology identifies different network types within multigenerational households, identifies a greater proportion of older people with vulnerable networks and could positively contribute to service planning.

Time-frequency dynamics of resting-state brain connectivity measured with fMRI.

PubMed

Chang, Catie; Glover, Gary H

2010-03-01

Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time-frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the "anticorrelated" ("task-positive") network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks. Copyright (c) 2009 Elsevier Inc. All rights reserved.

A Dynamic Bayesian Network Model for the Production and Inventory Control

NASA Astrophysics Data System (ADS)

Shin, Ji-Sun; Takazaki, Noriyuki; Lee, Tae-Hong; Kim, Jin-Il; Lee, Hee-Hyol

In general, the production quantities and delivered goods are changed randomly and then the total stock is also changed randomly. This paper deals with the production and inventory control using the Dynamic Bayesian Network. Bayesian Network is a probabilistic model which represents the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of a lower limit and a ceiling of the total stock to certain values is shown.

A Noachian/Hesperian Hiatus and Erosive Reactivation of Martian Valley Networks

NASA Technical Reports Server (NTRS)

Irwin, R. P., III.; Maxwell, T. A.; Howard, A. D.; Craddock, R. A.; Moore, J. M.

2005-01-01

Despite new evidence for persistent flow and sedimentation on early Mars, it remains unclear whether valley networks were active over long geologic timescales (10(exp 5)-10(exp 8) yr), or if flows were persistent only during multiple discrete episodes of moderate (approx. 10(exp 4) yr) to short (<10 yr) duration. Understanding the long-term stability/variability of valley network hydrology would provide an important control on paleoclimate and groundwater models. Here we describe geologic evidence for a hiatus in highland valley network activity while the fretted terrain formed, followed by a discrete reactivation of persistent (but possibly variable) erosive flows. Additional information is included in the original extended abstract.

Shortwave surface radiation network for observing small-scale cloud inhomogeneity fields

NASA Astrophysics Data System (ADS)

Lakshmi Madhavan, Bomidi; Kalisch, John; Macke, Andreas

2016-03-01

As part of the High Definition Clouds and Precipitation for advancing Climate Prediction Observational Prototype Experiment (HOPE), a high-density network of 99 silicon photodiode pyranometers was set up around Jülich (10 km × 12 km area) from April to July 2013 to capture the small-scale variability of cloud-induced radiation fields at the surface. In this paper, we provide the details of this unique setup of the pyranometer network, data processing, quality control, and uncertainty assessment under variable conditions. Some exemplary days with clear, broken cloudy, and overcast skies were explored to assess the spatiotemporal observations from the network along with other collocated radiation and sky imager measurements available during the HOPE period.

Bayesian Network Structure Learning for Urban Land Use Classification from Landsat ETM+ and Ancillary Data

NASA Astrophysics Data System (ADS)

Park, M.; Stenstrom, M. K.

2004-12-01

Recognizing urban information from the satellite imagery is problematic due to the diverse features and dynamic changes of urban landuse. The use of Landsat imagery for urban land use classification involves inherent uncertainty due to its spatial resolution and the low separability among land uses. To resolve the uncertainty problem, we investigated the performance of Bayesian networks to classify urban land use since Bayesian networks provide a quantitative way of handling uncertainty and have been successfully used in many areas. In this study, we developed the optimized networks for urban land use classification from Landsat ETM+ images of Marina del Rey area based on USGS land cover/use classification level III. The networks started from a tree structure based on mutual information between variables and added the links to improve accuracy. This methodology offers several advantages: (1) The network structure shows the dependency relationships between variables. The class node value can be predicted even with particular band information missing due to sensor system error. The missing information can be inferred from other dependent bands. (2) The network structure provides information of variables that are important for the classification, which is not available from conventional classification methods such as neural networks and maximum likelihood classification. In our case, for example, bands 1, 5 and 6 are the most important inputs in determining the land use of each pixel. (3) The networks can be reduced with those input variables important for classification. This minimizes the problem without considering all possible variables. We also examined the effect of incorporating ancillary data: geospatial information such as X and Y coordinate values of each pixel and DEM data, and vegetation indices such as NDVI and Tasseled Cap transformation. The results showed that the locational information improved overall accuracy (81%) and kappa coefficient (76%), and lowered the omission and commission errors compared with using only spectral data (accuracy 71%, kappa coefficient 62%). Incorporating DEM data did not significantly improve overall accuracy (74%) and kappa coefficient (66%) but lowered the omission and commission errors. Incorporating NDVI did not much improve the overall accuracy (72%) and k coefficient (65%). Including Tasseled Cap transformation reduced the accuracy (accuracy 70%, kappa 61%). Therefore, additional information from the DEM and vegetation indices was not useful as locational ancillary data.

Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach.

PubMed

Zaqoot, Hossam Adel; Ansari, Abdul Khalique; Unar, Mukhtiar Ali; Khan, Shaukat Hyat

2009-01-01

Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.

The prediction of nonlinear dynamic loads on helicopters from flight variables using artificial neural networks

NASA Technical Reports Server (NTRS)

Cook, A. B.; Fuller, C. R.; O'Brien, W. F.; Cabell, R. H.

1992-01-01

A method of indirectly monitoring component loads through common flight variables is proposed which requires an accurate model of the underlying nonlinear relationships. An artificial neural network (ANN) model learns relationships through exposure to a database of flight variable records and corresponding load histories from an instrumented military helicopter undergoing standard maneuvers. The ANN model, utilizing eight standard flight variables as inputs, is trained to predict normalized time-varying mean and oscillatory loads on two critical components over a range of seven maneuvers. Both interpolative and extrapolative capabilities are demonstrated with agreement between predicted and measured loads on the order of 90 percent to 95 percent. This work justifies pursuing the ANN method of predicting loads from flight variables.

A Framework for Dynamic Constraint Reasoning Using Procedural Constraints

NASA Technical Reports Server (NTRS)

Jonsson, Ari K.; Frank, Jeremy D.

1999-01-01

Many complex real-world decision and control problems contain an underlying constraint reasoning problem. This is particularly evident in a recently developed approach to planning, where almost all planning decisions are represented by constrained variables. This translates a significant part of the planning problem into a constraint network whose consistency determines the validity of the plan candidate. Since higher-level choices about control actions can add or remove variables and constraints, the underlying constraint network is invariably highly dynamic. Arbitrary domain-dependent constraints may be added to the constraint network and the constraint reasoning mechanism must be able to handle such constraints effectively. Additionally, real problems often require handling constraints over continuous variables. These requirements present a number of significant challenges for a constraint reasoning mechanism. In this paper, we introduce a general framework for handling dynamic constraint networks with real-valued variables, by using procedures to represent and effectively reason about general constraints. The framework is based on a sound theoretical foundation, and can be proven to be sound and complete under well-defined conditions. Furthermore, the framework provides hybrid reasoning capabilities, as alternative solution methods like mathematical programming can be incorporated into the framework, in the form of procedures.

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation.

PubMed

Gratton, Caterina; Laumann, Timothy O; Nielsen, Ashley N; Greene, Deanna J; Gordon, Evan M; Gilmore, Adrian W; Nelson, Steven M; Coalson, Rebecca S; Snyder, Abraham Z; Schlaggar, Bradley L; Dosenbach, Nico U F; Petersen, Steven E

2018-04-18

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation

NASA Astrophysics Data System (ADS)

Tulbure, Mirela G.; Kininmonth, Stuart; Broich, Mark

2014-11-01

The concept of habitat networks represents an important tool for landscape conservation and management at regional scales. Previous studies simulated degradation of temporally fixed networks but few quantified the change in network connectivity from disintegration of key features that undergo naturally occurring spatiotemporal dynamics. This is particularly of concern for aquatic systems, which typically show high natural spatiotemporal variability. Here we focused on the Swan Coastal Plain, a bioregion that encompasses a global biodiversity hotspot in Australia with over 1500 water bodies of high biodiversity. Using graph theory, we conducted a temporal analysis of water body connectivity over 13 years of variable climate. We derived large networks of surface water bodies using Landsat data (1999-2011). We generated an ensemble of 278 potential networks at three dispersal distances approximating the maximum dispersal distance of different water dependent organisms. We assessed network connectivity through several network topology metrics and quantified the resilience of the network topology during wet and dry phases. We identified ‘stepping stone’ water bodies across time and compared our networks with theoretical network models with known properties. Results showed a highly dynamic seasonal pattern of variability in network topology metrics. A decline in connectivity over the 13 years was noted with potential negative consequences for species with limited dispersal capacity. The networks described here resemble theoretical scale-free models, also known as ‘rich get richer’ algorithm. The ‘stepping stone’ water bodies are located in the area around the Peel-Harvey Estuary, a Ramsar listed site, and some are located in a national park. Our results describe a powerful approach that can be implemented when assessing the connectivity for a particular organism with known dispersal distance. The approach of identifying the surface water bodies that act as ‘stepping stone’ over time may help prioritize surface water bodies that are essential for maintaining regional scale connectivity.

Inferring general relations between network characteristics from specific network ensembles.

PubMed

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

Linking Inter-Individual Variability in Functional Brain Connectivity to Cognitive Ability in Elderly Individuals

PubMed Central

Li, Rui; Yin, Shufei; Zhu, Xinyi; Ren, Weicong; Yu, Jing; Wang, Pengyun; Zheng, Zhiwei; Niu, Ya-Nan; Huang, Xin; Li, Juan

2017-01-01

Increasing evidence suggests that functional brain connectivity is an important determinant of cognitive aging. However, the fundamental concept of inter-individual variations in functional connectivity in older individuals is not yet completely understood. It is essential to evaluate the extent to which inter-individual variability in connectivity impacts cognitive performance at an older age. In the current study, we aimed to characterize individual variability of functional connectivity in the elderly and to examine its significance to individual cognition. We mapped inter-individual variability of functional connectivity by analyzing whole-brain functional connectivity magnetic resonance imaging data obtained from a large sample of cognitively normal older adults. Our results demonstrated a gradual increase in variability in primary regions of the visual, sensorimotor, and auditory networks to specific subcortical structures, particularly the hippocampal formation, and the prefrontal and parietal cortices, which largely constitute the default mode and fronto-parietal networks, to the cerebellum. Further, the inter-individual variability of the functional connectivity correlated significantly with the degree of cognitive relevance. Regions with greater connectivity variability demonstrated more connections that correlated with cognitive performance. These results also underscored the crucial function of the long-range and inter-network connections in individual cognition. Thus, individual connectivity–cognition variability mapping findings may provide important information for future research on cognitive aging and neurocognitive diseases. PMID:29209203

Encoding dependence in Bayesian causal networks

USDA-ARS?s Scientific Manuscript database

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...

Relating brain signal variability to knowledge representation.

PubMed

Heisz, Jennifer J; Shedden, Judith M; McIntosh, Anthony R

2012-11-15

We assessed the hypothesis that brain signal variability is a reflection of functional network reconfiguration during memory processing. In the present experiments, we use multiscale entropy to capture the variability of human electroencephalogram (EEG) while manipulating the knowledge representation associated with faces stored in memory. Across two experiments, we observed increased variability as a function of greater knowledge representation. In Experiment 1, individuals with greater familiarity for a group of famous faces displayed more brain signal variability. In Experiment 2, brain signal variability increased with learning after multiple experimental exposures to previously unfamiliar faces. The results demonstrate that variability increases with face familiarity; cognitive processes during the perception of familiar stimuli may engage a broader network of regions, which manifests as higher complexity/variability in spatial and temporal domains. In addition, effects of repetition suppression on brain signal variability were observed, and the pattern of results is consistent with a selectivity model of neural adaptation. Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.

Measuring Networking as an Outcome Variable in Undergraduate Research Experiences

PubMed Central

Hanauer, David I.; Hatfull, Graham

2015-01-01

The aim of this paper is to propose, present, and validate a simple survey instrument to measure student conversational networking. The tool consists of five items that cover personal and professional social networks, and its basic principle is the self-reporting of degrees of conversation, with a range of specific discussion partners. The networking instrument was validated in three studies. The basic psychometric characteristics of the scales were established by conducting a factor analysis and evaluating internal consistency using Cronbach’s alpha. The second study used a known-groups comparison and involved comparing outcomes for networking scales between two different undergraduate laboratory courses (one involving a specific effort to enhance networking). The final study looked at potential relationships between specific networking items and the established psychosocial variable of project ownership through a series of binary logistic regressions. Overall, the data from the three studies indicate that the networking scales have high internal consistency (α = 0.88), consist of a unitary dimension, can significantly differentiate between research experiences with low and high networking designs, and are related to project ownership scales. The ramifications of the networking instrument for student retention, the enhancement of public scientific literacy, and the differentiation of laboratory courses are discussed. PMID:26538387

Application of neural networks and sensitivity analysis to improved prediction of trauma survival.

PubMed

Hunter, A; Kennedy, L; Henry, J; Ferguson, I

2000-05-01

The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.

Use of a Real-Time Remote Monitoring Network (RTRM) to Characterize the Guadalquivir Estuary (Spain)

PubMed Central

Navarro, Gabriel; Huertas, Isabel Emma; Costas, Eduardo; Flecha, Susana; Díez-Minguito, Manuel; Caballero, Isabel; López-Rodas, Victoria; Prieto, Laura; Ruiz, Javier

2012-01-01

The temporal variability of hydrological variables in the Guadalquivir estuary was examined during three years through a real-time remote monitoring network (RTRM). The network was developed with the aim of studying the influence of hydrodynamical and hydrological features within the estuary on the functioning of the pelagic ecosystem. Completing this data-gathering network, monthly cruises were performed in order to measure biogeochemical variables that are indicative of the trophic status of the aquatic environment. The results showed that several sources of physical forcing, such as wind, tide-associated currents and river discharge were responsible for the spatio-temporal patterns of dissolved oxygen, salinity and turbidity in the estuary. The analysis was conducted under tidal and flood regime, which allowed us to identify river discharge as the main forcing agent of the hydrology inside the estuary. In particular, episodes of elevated turbidity detected by the network, together with episodes of low salinity and dissolved oxygen were closely related to the increase in water supply from a dam located upstream. The network installed provided accurate data that can be rapidly used for research or educational applications and by policy-makers or agencies in charge of the management of the coastal area. PMID:22438716

Application of neural networks to prediction of fish diversity and salmonid production in the Lake Ontario basin

USGS Publications Warehouse

McKenna, James E.

2005-01-01

Diversity and fish productivity are important measures of the health and status of aquatic systems. Being able to predict the values of these indices as a function of environmental variables would be valuable to management. Diversity and productivity have been related to environmental conditions by multiple linear regression and discriminant analysis, but such methods have several shortcomings. In an effort to predict fish species diversity and estimate salmonid production for streams in the eastern basin of Lake Ontario, I constructed neural networks and trained them on a data set containing abiotic information and either fish diversity or juvenile salmonid abundance. Twenty percent of the original data were retained as a test data set and used in the training. The ability to extend these neural networks to conditions throughout the streams was tested with data not involved in the network training. The resulting neural networks were able to predict the number of salmonids with more than 84% accuracy and diversity with more than 73% accuracy, which was far superior to the performance of multiple regression. The networks also identified the environmental variables with the greatest predictive power, namely, those describing water movement, stream size, and water chemistry. Thirteen input variables were used to predict diversity and 17 to predict salmonid abundance.

Constructing general partial differential equations using polynomial and neural networks.

PubMed

Zjavka, Ladislav; Pedrycz, Witold

2016-01-01

Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

Relationship between Social Networks Adoption and Social Intelligence

ERIC Educational Resources Information Center

Gunduz, Semseddin

2017-01-01

The purpose of this study was to set forth the relationship between the individuals' states to adopt social networks and social intelligence and analyze both concepts according to various variables. Research data were collected from 1145 social network users in the online media by using the Adoption of Social Network Scale and Social Intelligence…

Social Networks, Engagement and Resilience in University Students.

PubMed

Fernández-Martínez, Elena; Andina-Díaz, Elena; Fernández-Peña, Rosario; García-López, Rosa; Fulgueiras-Carril, Iván; Liébana-Presa, Cristina

2017-12-01

Analysis of social networks may be a useful tool for understanding the relationship between resilience and engagement, and this could be applied to educational methodologies, not only to improve academic performance, but also to create emotionally sustainable networks. This descriptive study was carried out on 134 university students. We collected the network structural variables, degree of resilience (CD-RISC 10), and engagement (UWES-S). The computer programs used were excel, UCINET for network analysis, and SPSS for statistical analysis. The analysis revealed results of means of 28.61 for resilience, 2.98 for absorption, 4.82 for dedication, and 3.13 for vigour. The students had two preferred places for sharing information: the classroom and WhatsApp. The greater the value for engagement, the greater the degree of centrality in the friendship network among students who are beginning their university studies. This relationship becomes reversed as the students move to later academic years. In terms of resilience, the highest values correspond to greater centrality in the friendship networks. The variables of engagement and resilience influenced the university students' support networks.

Social Networks, Engagement and Resilience in University Students

PubMed Central

García-López, Rosa; Fulgueiras-Carril, Iván

2017-01-01

Analysis of social networks may be a useful tool for understanding the relationship between resilience and engagement, and this could be applied to educational methodologies, not only to improve academic performance, but also to create emotionally sustainable networks. This descriptive study was carried out on 134 university students. We collected the network structural variables, degree of resilience (CD-RISC 10), and engagement (UWES-S). The computer programs used were excel, UCINET for network analysis, and SPSS for statistical analysis. The analysis revealed results of means of 28.61 for resilience, 2.98 for absorption, 4.82 for dedication, and 3.13 for vigour. The students had two preferred places for sharing information: the classroom and WhatsApp. The greater the value for engagement, the greater the degree of centrality in the friendship network among students who are beginning their university studies. This relationship becomes reversed as the students move to later academic years. In terms of resilience, the highest values correspond to greater centrality in the friendship networks. The variables of engagement and resilience influenced the university students’ support networks. PMID:29194361

A Skilful Marine Sclerochronological Network Based Reconstruction of North Atlantic Subpolar Gyre Dynamics

NASA Astrophysics Data System (ADS)

Reynolds, D.; Hall, I. R.; Slater, S. M.; Scourse, J. D.; Wanamaker, A. D.; Halloran, P. R.; Garry, F. K.

2017-12-01

Spatial network analyses of precisely dated, and annually resolved, tree-ring proxy records have facilitated robust reconstructions of past atmospheric climate variability and the associated mechanisms and forcings that drive it. In contrast, a lack of similarly dated marine archives has constrained the use of such techniques in the marine realm, despite the potential for developing a more robust understanding of the role basin scale ocean dynamics play in the global climate system. Here we show that a spatial network of marine molluscan sclerochronological oxygen isotope (δ18Oshell) series spanning the North Atlantic region provides a skilful reconstruction of basin scale North Atlantic sea surface temperatures (SSTs). Our analyses demonstrate that the composite marine series (referred to as δ18Oproxy_PC1) is significantly sensitive to inter-annual variability in North Atlantic SSTs (R=-0.61 P<0.01) and surface air temperatures (SATs; R=-0.67, P<0.01) over the 20th century. Subpolar gyre (SPG) SSTs dominates variability in the δ18Oproxy_PC1 series at sub-centennial frequencies (R=-0.51, P<0.01). Comparison of the δ18Oproxy_PC1 series against variability in the strength of the European Slope Current and maximum North Atlantic meridional overturning circulation derived from numeric climate models (CMIP5), indicates that variability in the SPG region, associated with the strength of the surface currents of the North Atlantic, are playing a significant role in shaping the multi-decadal scale SST variability over the industrial era. These analyses demonstrate that spatial networks developed from sclerochronological archives can provide powerful baseline archives of past ocean variability that can facilitate the development of a quantitative understanding for the role the oceans play in the global climate systems and constraining uncertainties in numeric climate models.

Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data

NASA Astrophysics Data System (ADS)

Afrand, Masoud; Hemmat Esfe, Mohammad; Abedini, Ehsan; Teimouri, Hamid

2017-03-01

The current paper first presents an empirical correlation based on experimental results for estimating thermal conductivity enhancement of MgO-water nanofluid using curve fitting method. Then, artificial neural networks (ANNs) with various numbers of neurons have been assessed by considering temperature and MgO volume fraction as the inputs variables and thermal conductivity enhancement as the output variable to select the most appropriate and optimized network. Results indicated that the network with 7 neurons had minimum error. Eventually, the output of artificial neural network was compared with the results of the proposed empirical correlation and those of the experiments. Comparisons revealed that ANN modeling was more accurate than curve-fitting method in the predicting the thermal conductivity enhancement of the nanofluid.

Deploying temporary networks for upscaling of sparse network stations

USDA-ARS?s Scientific Manuscript database

Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, busin...

Immigrant maternal depression and social networks. A multilevel Bayesian spatial logistic regression in South Western Sydney, Australia.

PubMed

Eastwood, John G; Jalaludin, Bin B; Kemp, Lynn A; Phung, Hai N; Barnett, Bryanne E W

2013-09-01

The purpose is to explore the multilevel spatial distribution of depressive symptoms among migrant mothers in South Western Sydney and to identify any group level associations that could inform subsequent theory building and local public health interventions. Migrant mothers (n=7256) delivering in 2002 and 2003 were assessed at 2-3 weeks after delivery for risk factors for depressive symptoms. The binary outcome variables were Edinburgh Postnatal Depression Scale scores (EPDS) of >9 and >12. Individual level variables included were: financial income, self-reported maternal health, social support network, emotional support, practical support, baby trouble sleeping, baby demanding and baby not content. The group level variable reported here is aggregated social support networks. We used Bayesian hierarchical multilevel spatial modelling with conditional autoregression. Migrant mothers were at higher risk of having depressive symptoms if they lived in a community with predominantly Australian-born mothers and strong social capital as measured by aggregated social networks. These findings suggest that migrant mothers are socially isolated and current home visiting services should be strengthened for migrant mothers living in communities where they may have poor social networks. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

The ASAC Flight Segment and Network Cost Models

NASA Technical Reports Server (NTRS)

Kaplan, Bruce J.; Lee, David A.; Retina, Nusrat; Wingrove, Earl R., III; Malone, Brett; Hall, Stephen G.; Houser, Scott A.

1997-01-01

To assist NASA in identifying research art, with the greatest potential for improving the air transportation system, two models were developed as part of its Aviation System Analysis Capability (ASAC). The ASAC Flight Segment Cost Model (FSCM) is used to predict aircraft trajectories, resource consumption, and variable operating costs for one or more flight segments. The Network Cost Model can either summarize the costs for a network of flight segments processed by the FSCM or can be used to independently estimate the variable operating costs of flying a fleet of equipment given the number of departures and average flight stage lengths.

A performance study of unmanned aerial vehicle-based sensor networks under cyber attack

NASA Astrophysics Data System (ADS)

Puchaty, Ethan M.

In UAV-based sensor networks, an emerging area of interest is the performance of these networks under cyber attack. This study seeks to evaluate the performance trade-offs from a System-of-Systems (SoS) perspective between various UAV communications architecture options in the context two missions: tracking ballistic missiles and tracking insurgents. An agent-based discrete event simulation is used to model a sensor communication network consisting of UAVs, military communications satellites, ground relay stations, and a mission control center. Network susceptibility to cyber attack is modeled with probabilistic failures and induced data variability, with performance metrics focusing on information availability, latency, and trustworthiness. Results demonstrated that using UAVs as routers increased network availability with a minimal latency penalty and communications satellite networks were best for long distance operations. Redundancy in the number of links between communication nodes helped mitigate cyber-caused link failures and add robustness in cases of induced data variability by an adversary. However, when failures were not independent, redundancy and UAV routing were detrimental in some cases to network performance. Sensitivity studies indicated that long cyber-caused downtimes and increasing failure dependencies resulted in build-ups of failures and caused significant degradations in network performance.

Gossip spread in social network Models

NASA Astrophysics Data System (ADS)

Johansson, Tobias

2017-04-01

Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

Robust nonlinear variable selective control for networked systems

NASA Astrophysics Data System (ADS)

Rahmani, Behrooz

2016-10-01

This paper is concerned with the networked control of a class of uncertain nonlinear systems. In this way, Takagi-Sugeno (T-S) fuzzy modelling is used to extend the previously proposed variable selective control (VSC) methodology to nonlinear systems. This extension is based upon the decomposition of the nonlinear system to a set of fuzzy-blended locally linearised subsystems and further application of the VSC methodology to each subsystem. To increase the applicability of the T-S approach for uncertain nonlinear networked control systems, this study considers the asynchronous premise variables in the plant and the controller, and then introduces a robust stability analysis and control synthesis. The resulting optimal switching-fuzzy controller provides a minimum guaranteed cost on an H2 performance index. Simulation studies on three nonlinear benchmark problems demonstrate the effectiveness of the proposed method.

Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality

PubMed Central

Hu, Yanzhu; Ai, Xinbo

2016-01-01

Complex network methodology is very useful for complex system explorer. However, the relationships among variables in complex system are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a synthetic method, named small-shuffle partial symbolic transfer entropy spectrum (SSPSTES), for inferring association network from multivariate time series. The method synthesizes surrogate data, partial symbolic transfer entropy (PSTE) and Granger causality. A proper threshold selection is crucial for common correlation identification methods and it is not easy for users. The proposed method can not only identify the strong correlation without selecting a threshold but also has the ability of correlation quantification, direction identification and temporal relation identification. The method can be divided into three layers, i.e. data layer, model layer and network layer. In the model layer, the method identifies all the possible pair-wise correlation. In the network layer, we introduce a filter algorithm to remove the indirect weak correlation and retain strong correlation. Finally, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pair-wise variables, and then get the weighted directed association network. Two numerical simulated data from linear system and nonlinear system are illustrated to show the steps and performance of the proposed approach. The ability of the proposed method is approved by an application finally. PMID:27832153

EUV brightness variations in the quiet Sun

NASA Astrophysics Data System (ADS)

Brković, A.; Rüedi, I.; Solanki, S. K.; Fludra, A.; Harrison, R. A.; Huber, M. C. E.; Stenflo, J. O.; Stucki, K.

2000-01-01

The Coronal Diagnostic Spectrometer (CDS) onboard the SOHO satellite has been used to obtain movies of quiet Sun regions at disc centre. These movies were used to study brightness variations of solar features at three different temperatures sampled simultaneously in the chromospheric He I 584.3 Ä (2 * 104 K), the transition region O V 629.7 Ä (2.5 * 105 K) and coronal Mg IX 368.1 Ä (106 K) lines. In all parts of the quiet Sun, from darkest intranetwork to brightest network, we find significant variability in the He I and O V line, while the variability in the Mg IX line is more marginal. The relative variability, defined by rms of intensity normalised to the local intensity, is independent of brightness and strongest in the transition region line. Thus the relative variability is the same in the network and the intranetwork. More than half of the points on the solar surface show a relative variability, determined over a period of 4 hours, greater than 15.5% for the O V line, but only 5% of the points exhibit a variability above 25%. Most of the variability appears to take place on time-scales between 5 and 80 minutes for the He I and O V lines. Clear signs of ``high variability'' events are found. For these events the variability as a function of time seen in the different lines shows a good correlation. The correlation is higher for more variable events. These events coincide with the (time averaged) brightest points on the solar surface, i.e. they occur in the network. The spatial positions of the most variable points are identical in all the lines.

MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data.

PubMed

Sella, Nadir; Verny, Louis; Uguzzoni, Guido; Affeldt, Séverine; Isambert, Hervé

2018-07-01

We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction. MIIC online can be freely accessed at https://miic.curie.fr. Supplementary data are available at Bioinformatics online.

The effects of types of social networks, perceived social support, and loneliness on the health of older people: accounting for the social context.

PubMed

Stephens, Christine; Alpass, Fiona; Towers, Andy; Stevenson, Brendan

2011-09-01

To use an ecological model of ageing (Berkman, Glass, Brissette, & Seeman, 2000) which includes upstream social context factors and downstream social support factors to examine the effects of social networks on health. Postal survey responses from a representative population sample of New Zealanders aged 55 to 70 years (N = 6,662). Correlations and multiple regression analyses provided support for a model in which social context contributes to social network type, which affects perceived social support and loneliness, and consequent mental and physical health. Ethnicity was related to social networks and health but this was largely accounted for by other contextual variables measuring socioeconomic status. Gender and age were also significant variables in the model. Social network type is a useful way to assess social integration within this model of cascading effects. More detailed information could be gained through the development of our network assessment instruments for older people.

Mediating effect of Adult Day Health Care (ADHC) and family network on Quality of Life among low-income older Korean immigrants.

PubMed

Kim, Bum Jung

2014-05-01

The purpose of this study is to examine the direct and indirect effects of Adult Day Health Care (ADHC) and family network on Quality of Life (QOL) for low-income older Korean immigrants in Los Angeles County, CA. A cross-sectional survey of low-income older Korean immigrants who use ADHC programs was conducted. Self-reported measures included sociocultural characteristics, acculturation, cognitive function, family network, utilization of ADHC, and QOL. The study found that for QOL, two variables had only direct effects: years in ADHC and acculturation. Family network was directly associated with QOL and indirectly associated with it through the variable "years in ADHC." Our findings indicate that a strong family network is positively associated with more years of attendance in ADHC, and with higher QOL scores. Thus, policy makers and practitioners should be aware of the positive association among social networks, attendance in ADHC, and higher QOL among low-income older Korean immigrants. © The Author(s) 2013.

Collective decision dynamics in the presence of external drivers

NASA Astrophysics Data System (ADS)

Bassett, Danielle S.; Alderson, David L.; Carlson, Jean M.

2012-09-01

We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision making. Our results indicate that (1) social networks lead to clustering and cohesive action among individuals, (2) binary information introduces high temporal variability and stagnation, and (3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.

Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method.

PubMed

Li, Xuanying; Li, Xiaotong; Hu, Cheng

2017-12-01

In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained. To reduce the control cost, an adaptive control scheme is designed to realize the asymptotic synchronization. It is noted that the dynamical behaviors of inertial neural networks are directly analyzed in this paper by constructing some new Lyapunov functionals, this is totally different from the traditional reduced-order variable substitution method. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Probabilistic estimation of dune retreat on the Gold Coast, Australia

USGS Publications Warehouse

Palmsten, Margaret L.; Splinter, Kristen D.; Plant, Nathaniel G.; Stockdon, Hilary F.

2014-01-01

Sand dunes are an important natural buffer between storm impacts and development backing the beach on the Gold Coast of Queensland, Australia. The ability to forecast dune erosion at a prediction horizon of days to a week would allow efficient and timely response to dune erosion in this highly populated area. Towards this goal, we modified an existing probabilistic dune erosion model for use on the Gold Coast. The original model was trained using observations of dune response from Hurricane Ivan on Santa Rosa Island, Florida, USA (Plant and Stockdon 2012. Probabilistic prediction of barrier-island response to hurricanes, Journal of Geophysical Research, 117(F3), F03015). The model relates dune position change to pre-storm dune elevations, dune widths, and beach widths, along with storm surge and run-up using a Bayesian network. The Bayesian approach captures the uncertainty of inputs and predictions through the conditional probabilities between variables. Three versions of the barrier island response Bayesian network were tested for use on the Gold Coast. One network has the same structure as the original and was trained with the Santa Rosa Island data. The second network has a modified design and was trained using only pre- and post-storm data from 1988-2009 for the Gold Coast. The third version of the network has the same design as the second version of the network and was trained with the combined data from the Gold Coast and Santa Rosa Island. The two networks modified for use on the Gold Coast hindcast dune retreat with equal accuracy. Both networks explained 60% of the observed dune retreat variance, which is comparable to the skill observed by Plant and Stockdon (2012) in the initial Bayesian network application at Santa Rosa Island. The new networks improved predictions relative to application of the original network on the Gold Coast. Dune width was the most important morphologic variable in hindcasting dune retreat, while hydrodynamic variables, surge and run-up elevation, were also important

Framework for a U.S. Geological Survey Hydrologic Climate-Response Program in Maine

USGS Publications Warehouse

Hodgkins, Glenn A.; Lent, Robert M.; Dudley, Robert W.; Schalk, Charles W.

2009-01-01

This report presents a framework for a U.S. Geological Survey (USGS) hydrologic climate-response program designed to provide early warning of changes in the seasonal water cycle of Maine. Climate-related hydrologic changes on Maine's rivers and lakes in the winter and spring during the last century are well documented, and several river and lake variables have been shown to be sensitive to air-temperature changes. Monitoring of relevant hydrologic data would provide important baseline information against which future climate change can be measured. The framework of the hydrologic climate-response program presented here consists of four major parts: (1) identifying homogeneous climate-response regions; (2) identifying hydrologic components and key variables of those components that would be included in a hydrologic climate-response data network - as an example, streamflow has been identified as a primary component, with a key variable of streamflow being winter-spring streamflow timing; the data network would be created by maintaining existing USGS data-collection stations and establishing new ones to fill data gaps; (3) regularly updating historical trends of hydrologic data network variables; and (4) establishing basins for process-based studies. Components proposed for inclusion in the hydrologic climate-response data network have at least one key variable for which substantial historical data are available. The proposed components are streamflow, lake ice, river ice, snowpack, and groundwater. The proposed key variables of each component have extensive historical data at multiple sites and are expected to be responsive to climate change in the next few decades. These variables are also important for human water use and (or) ecosystem function. Maine would be divided into seven climate-response regions that follow major river-basin boundaries (basins subdivided to hydrologic units with 8-digit codes or larger) and have relatively homogeneous climates. Key hydrologic variables within each climate-response region would be analyzed regularly to maintain up-to-date analyses of year-to-year variability, decadal variability, and longer term trends. Finally, one basin in each climate-response region would be identified for process-based hydrologic and ecological studies.

A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System.

PubMed

Zhang, Zhen; Ma, Cheng; Zhu, Rong

2017-08-23

Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas.

A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System

PubMed Central

Zhang, Zhen; Zhu, Rong

2017-01-01

Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas. PMID:28832522

Neighborhood adversity, ethnic diversity, and weak social cohesion and social networks predict high rates of maternal depressive symptoms: a critical realist ecological study in South Western Sydney, Australia.

PubMed

Eastwood, John Graeme; Kemp, Lynn Ann; Jalaludin, Bin Badrudin; Phung, Hai Ngoc

2013-01-01

The aim of the study reported here is to explore ecological covariate and latent variable associations with perinatal depressive symptoms in South Western Sydney for the purpose of informing subsequent theory generation of perinatal context, depression, and the developmental origins of health and disease. Mothers (n = 15,389) delivering in 2002 and 2003 were assessed at two to three weeks after delivery for risk factors for depressive symptoms. The binary outcome variables were Edinburgh Postnatal Depression Scale (EPDS)> 9 and > 12. Aggregated EPDS > 9 was analyzed for 101 suburbs. Suburb-level variables were drawn from the 2001 Australian Census, New South Wales Crime Statistics, and aggregated individual-level risk factors. Analysis included exploratory factor analysis, univariate and multivariate likelihood, and Bayesian linear regression with conditional autoregressive components. The exploratory factor analysis identified six factors: neighborhood adversity, social cohesion, health behaviors, housing quality, social services, and support networks. Variables associated with neighborhood adversity, social cohesion, social networks, and ethnic diversity were consistently associated with aggregated depressive symptoms. The findings support the theoretical proposition that neighborhood adversity causes maternal psychological distress and depression within the context of social buffers including social networks, social cohesion, and social services.

Transformational leadership and group interaction as climate antecedents: a social network analysis.

PubMed

Zohar, Dov; Tenne-Gazit, Orly

2008-07-01

In order to test the social mechanisms through which organizational climate emerges, this article introduces a model that combines transformational leadership and social interaction as antecedents of climate strength (i.e., the degree of within-unit agreement about climate perceptions). Despite their longstanding status as primary variables, both antecedents have received limited empirical research. The sample consisted of 45 platoons of infantry soldiers from 5 different brigades, using safety climate as the exemplar. Results indicate a partially mediated model between transformational leadership and climate strength, with density of group communication network as the mediating variable. In addition, the results showed independent effects for group centralization of the communication and friendship networks, which exerted incremental effects on climate strength over transformational leadership. Whereas centralization of the communication network was found to be negatively related to climate strength, centralization of the friendship network was positively related to it. Theoretical and practical implications are discussed.

An opinion-driven behavioral dynamics model for addictive behaviors

NASA Astrophysics Data System (ADS)

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; Ambrose, Bridget K.; Brodsky, Nancy S.; Brown, Theresa J.; Husten, Corinne; Glass, Robert J.

2015-04-01

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual's behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

Examining the Relationships Between Education, Social Networks and Democratic Support With ABM

NASA Technical Reports Server (NTRS)

Drucker, Nick; Campbell, Kenyth

2011-01-01

This paper introduces an agent-based model that explores the relationships between education, social networks, and support for democratic ideals. This study examines two factors thai affect democratic support, education, and social networks. Current theory concerning these two variables suggests that positive relationships exist between education and democratic support and between social networks and the spread of ideas. The model contains multiple variables of democratic support, two of which are evaluated through experimentation. The model allows individual entities within the system to make "decisions" about their democratic support independent of one another. The agent based approach also allows entities to utilize their social networks to spread ideas. Current theory supports experimentation results. In addion , these results show the model is capable of reproducing real world outcomes. This paper addresses the model creation process and the experimentation procedure, as well as future research avenues and potential shortcomings of the model

A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

NASA Astrophysics Data System (ADS)

Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

2017-04-01

The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of land-use/land-cover changes and river regulation on network-scale connectivity.

Equivalence between entanglement and the optimal fidelity of continuous variable teleportation.

PubMed

Adesso, Gerardo; Illuminati, Fabrizio

2005-10-07

We devise the optimal form of Gaussian resource states enabling continuous-variable teleportation with maximal fidelity. We show that a nonclassical optimal fidelity of N-user teleportation networks is necessary and sufficient for N-party entangled Gaussian resources, yielding an estimator of multipartite entanglement. The entanglement of teleportation is equivalent to the entanglement of formation in a two-user protocol, and to the localizable entanglement in a multiuser one. Finally, we show that the continuous-variable tangle, quantifying entanglement sharing in three-mode Gaussian states, is defined operationally in terms of the optimal fidelity of a tripartite teleportation network.

Paleoclimate networks: a concept meeting central challenges in the reconstruction of paleoclimate dynamics

NASA Astrophysics Data System (ADS)

Rehfeld, Kira; Goswami, Bedartha; Marwan, Norbert; Breitenbach, Sebastian; Kurths, Jürgen

2013-04-01

Statistical analysis of dependencies amongst paleoclimate data helps to infer on the climatic processes they reflect. Three key challenges have to be addressed, however: the datasets are heterogeneous in time (i) and space (ii), and furthermore time itself is a variable that needs to be reconstructed, which (iii) introduces additional uncertainties. To address these issues in a flexible way we developed the paleoclimate network framework, inspired by the increasing application of complex networks in climate research. Nodes in the paleoclimate network represent a paleoclimate archive, and an associated time series. Links between these nodes are assigned, if these time series are significantly similar. Therefore, the base of the paleoclimate network is formed by linear and nonlinear estimators for Pearson correlation, mutual information and event synchronization, which quantify similarity from irregularly sampled time series. Age uncertainties are propagated into the final network analysis using time series ensembles which reflect the uncertainty. We discuss how spatial heterogeneity influences the results obtained from network measures, and demonstrate the power of the approach by inferring teleconnection variability of the Asian summer monsoon for the past 1000 years.

Population coding in sparsely connected networks of noisy neurons.

PubMed

Tripp, Bryan P; Orchard, Jeff

2012-01-01

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Climate Change and Conservation Planning in California: The San Francisco Bay Area Upland Habitat Goals Approach

NASA Astrophysics Data System (ADS)

Branciforte, R.; Weiss, S. B.; Schaefer, N.

2008-12-01

Climate change threatens California's vast and unique biodiversity. The Bay Area Upland Habitat Goals is a comprehensive regional biodiversity assessment of the 9 counties surrounding San Francisco Bay, and is designing conservation land networks that will serve to protect, manage, and restore that biodiversity. Conservation goals for vegetation, rare plants, mammals, birds, fish, amphibians, reptiles, and invertebrates are set, and those goals are met using the optimization algorithm MARXAN. Climate change issues are being considered in the assessment and network design in several ways. The high spatial variability at mesoclimatic and topoclimatic scales in California creates high local biodiversity, and provides some degree of local resiliency to macroclimatic change. Mesoclimatic variability from 800 m scale PRISM climatic norms is used to assess "mesoclimate spaces" in distinct mountain ranges, so that high mesoclimatic variability, especially local extremes that likely support range limits of species and potential climatic refugia, can be captured in the network. Quantitative measures of network resiliency to climate change include the spatial range of key temperature and precipitation variables within planning units. Topoclimatic variability provides a finer-grained spatial patterning. Downscaling to the topoclimatic scale (10-50 m scale) includes modeling solar radiation across DEMs for predicting maximum temperature differentials, and topographic position indices for modeling minimum temperature differentials. PRISM data are also used to differentiate grasslands into distinct warm and cool types. The overall conservation strategy includes local and regional connectivity so that range shifts can be accommodated.

Technical aspects of neurostimulation: Focus on equipment, electric field modeling, and stimulation protocols.

PubMed

Klooster, D C W; de Louw, A J A; Aldenkamp, A P; Besseling, R M H; Mestrom, R M C; Carrette, S; Zinger, S; Bergmans, J W M; Mess, W H; Vonck, K; Carrette, E; Breuer, L E M; Bernas, A; Tijhuis, A G; Boon, P

2016-06-01

Neuromodulation is a field of science, medicine, and bioengineering that encompasses implantable and non-implantable technologies for the purpose of improving quality of life and functioning of humans. Brain neuromodulation involves different neurostimulation techniques: transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS), which are being used both to study their effects on cognitive brain functions and to treat neuropsychiatric disorders. The mechanisms of action of neurostimulation remain incompletely understood. Insight into the technical basis of neurostimulation might be a first step towards a more profound understanding of these mechanisms, which might lead to improved clinical outcome and therapeutic potential. This review provides an overview of the technical basis of neurostimulation focusing on the equipment, the present understanding of induced electric fields, and the stimulation protocols. The review is written from a technical perspective aimed at supporting the use of neurostimulation in clinical practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluation of a “Field Cage” for Electric Field Control in GaN-Based HEMTs That Extends the Scalability of Breakdown Into the kV Regime

DOE PAGES

Tierney, Brian D.; Choi, Sukwon; DasGupta, Sandeepan; ...

2017-08-16

A distributed impedance “field cage” structure is proposed and evaluated for electric field control in GaN-based, lateral high electron mobility transistors (HEMTs) operating as kilovolt-range power devices. In this structure, a resistive voltage divider is used to control the electric field throughout the active region. The structure complements earlier proposals utilizing floating field plates that did not employ resistively connected elements. Transient results, not previously reported for field plate schemes using either floating or resistively connected field plates, are presented for ramps of dV ds /dt = 100 V/ns. For both DC and transient results, the voltage between the gatemore » and drain is laterally distributed, ensuring the electric field profile between the gate and drain remains below the critical breakdown field as the source-to-drain voltage is increased. Our scheme indicates promise for achieving breakdown voltage scalability to a few kV.« less

Use of Vagus Nerve Stimulator on Children With Primary Generalized Epilepsy.

PubMed

Welch, William P; Sitwat, Bilal; Sogawa, Yoshimi

2018-06-01

To describe the response to vagus nerve stimulator (VNS) in otherwise neurotypical children with medically intractable primary generalized epilepsy. Retrospective chart review of patients who underwent vagus nerve stimulator surgery between January 2011 and December 2015. Eleven patients were identified. Median follow-up duration was 2.5 years (1.2-8.4 years). Prior to vagus nerve stimulator surgery, all patients had at least 1 seizure per week, and 7/11 (64%) had daily seizures. At 1-year follow-up after vagus nerve stimulator, 7/11 (64%) reported improved seizure frequency and 6/11 (55%) reported fewer than 1 seizure per month. Three patients (27%) reported complications related to vagus nerve stimulator surgery, and no patients required device removal. In children with medically intractable primary generalized epilepsy, vagus nerve stimulator is well tolerated and appears to lead to improvement in seizure frequency. Improvement was not attributable to epilepsy classification, age at vagus nerve stimulator implantation, output current, duty cycle, or follow-up duration.

Evaluation of a “Field Cage” for Electric Field Control in GaN-Based HEMTs That Extends the Scalability of Breakdown Into the kV Regime

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tierney, Brian D.; Choi, Sukwon; DasGupta, Sandeepan

A distributed impedance “field cage” structure is proposed and evaluated for electric field control in GaN-based, lateral high electron mobility transistors (HEMTs) operating as kilovolt-range power devices. In this structure, a resistive voltage divider is used to control the electric field throughout the active region. The structure complements earlier proposals utilizing floating field plates that did not employ resistively connected elements. Transient results, not previously reported for field plate schemes using either floating or resistively connected field plates, are presented for ramps of dV ds /dt = 100 V/ns. For both DC and transient results, the voltage between the gatemore » and drain is laterally distributed, ensuring the electric field profile between the gate and drain remains below the critical breakdown field as the source-to-drain voltage is increased. Our scheme indicates promise for achieving breakdown voltage scalability to a few kV.« less

Distributed Network and Multiprocessing Minicomputer State-of-the-Art Capabilities.

ERIC Educational Resources Information Center

Theis, Douglas J.

An examination of the capabilities of minicomputers and midicomputers now on the market reveals two basic items which users should evaluate when selecting computers for their own applications: distributed networking systems and multiprocessing architectures. Variables which should be considered in evaluating a distributed networking system…

Analysis of continuous-time switching networks

NASA Astrophysics Data System (ADS)

Edwards, R.

2000-11-01

Models of a number of biological systems, including gene regulation and neural networks, can be formulated as switching networks, in which the interactions between the variables depend strongly on thresholds. An idealized class of such networks in which the switching takes the form of Heaviside step functions but variables still change continuously in time has been proposed as a useful simplification to gain analytic insight. These networks, called here Glass networks after their originator, are simple enough mathematically to allow significant analysis without restricting the range of dynamics found in analogous smooth systems. A number of results have been obtained before, particularly regarding existence and stability of periodic orbits in such networks, but important cases were not considered. Here we present a coherent method of analysis that summarizes previous work and fills in some of the gaps as well as including some new results. Furthermore, we apply this analysis to a number of examples, including surprising long and complex limit cycles involving sequences of hundreds of threshold transitions. Finally, we show how the above methods can be extended to investigate aperiodic behaviour in specific networks, though a complete analysis will have to await new results in matrix theory and symbolic dynamics.

Network collaboration of organisations for homeless individuals in the Montreal region

PubMed Central

Fleury, Marie-Josée; Grenier, Guy; Lesage, Alain; Ma, Nan; Ngui, André Ngamini

2014-01-01

Introduction We know little about the intensity and determinants of interorganisational collaboration within the homeless network. This study describes the characteristics and relationships (along with the variables predicting their degree of interorganisational collaboration) of 68 organisations of such a network in Montreal (Quebec, Canada). Theory and methods Data were collected primarily through a self-administered questionnaire. Descriptive analyses were conducted followed by social network and multivariate analyses. Results The Montreal homeless network has a high density (50.5%) and a decentralised structure and maintains a mostly informal collaboration with the public and cross-sectorial sectors. The network density showed more frequent contacts among four types of organisations which could point to the existence of cliques. Four variables predicted interorganisational collaboration: organisation type, number of services offered, volume of referrals and satisfaction with the relationships with public organisations. Conclusions and discussion The Montreal homeless network seems adequate to address non-complex homelessness problems. Considering, however, that most homeless individuals present chronic and complex profiles, it appears necessary to have a more formal and better integrated network of homeless organisations, particularly in the health and social service sectors, in order to improve services. PMID:24520216

Dynamic Network Communication in the Human Functional Connectome Predicts Perceptual Variability in Visual Illusion.

PubMed

Wang, Zhiwei; Zeljic, Kristina; Jiang, Qinying; Gu, Yong; Wang, Wei; Wang, Zheng

2018-01-01

Ubiquitous variability between individuals in visual perception is difficult to standardize and has thus essentially been ignored. Here we construct a quantitative psychophysical measure of illusory rotary motion based on the Pinna-Brelstaff figure (PBF) in 73 healthy volunteers and investigate the neural circuit mechanisms underlying perceptual variation using functional magnetic resonance imaging (fMRI). We acquired fMRI data from a subset of 42 subjects during spontaneous and 3 stimulus conditions: expanding PBF, expanding modified-PBF (illusion-free) and expanding modified-PBF with physical rotation. Brain-wide graph analysis of stimulus-evoked functional connectivity patterns yielded a functionally segregated architecture containing 3 discrete hierarchical networks, commonly shared between rest and stimulation conditions. Strikingly, communication efficiency and strength between 2 networks predominantly located in visual areas robustly predicted individual perceptual differences solely in the illusory stimulus condition. These unprecedented findings demonstrate that stimulus-dependent, not spontaneous, dynamic functional integration between distributed brain networks contributes to perceptual variability in humans. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Self-discharge analysis and characterization of supercapacitors for environmentally powered wireless sensor network applications

NASA Astrophysics Data System (ADS)

Yang, Hengzhao; Zhang, Ying

2011-10-01

A new approach is presented to characterize the variable leakage resistance, a parameter in the variable leakage resistance model we developed to model supercapacitors used in environmentally powered wireless sensor network applications. Based on an analysis of the supercapacitor terminal behavior during the self-discharge, the variable leakage resistance is modeled as a function of the supercapacitor terminal voltage instead of the self-discharge time, which is more practical for an environmentally powered wireless sensor node. The new characterization approach is implemented and validated using MATLAB Simulink with a 10 F supercapacitor as an example. In addition, effects of initial voltages and temperatures on the supercapacitor self-discharge rate and the variable leakage resistance value are explored.

Reformulated Neural Network (ReNN): a New Alternative for Data-driven Modelling in Hydrology and Water Resources Engineering

NASA Astrophysics Data System (ADS)

Razavi, S.; Tolson, B.; Burn, D.; Seglenieks, F.

2012-04-01

Reformulated Neural Network (ReNN) has been recently developed as an efficient and more effective alternative to feedforward multi-layer perceptron (MLP) neural networks [Razavi, S., and Tolson, B. A. (2011). "A new formulation for feedforward neural networks." IEEE Transactions on Neural Networks, 22(10), 1588-1598, DOI: 1510.1109/TNN.2011.2163169]. This presentation initially aims to introduce the ReNN to the water resources community and then demonstrates ReNN applications to water resources related problems. ReNN is essentially equivalent to a single-hidden-layer MLP neural network but defined on a new set of network variables which is more effective than the traditional set of network weights and biases. The main features of the new network variables are that they are geometrically interpretable and each variable has a distinct role in forming the network response. ReNN is more efficiently trained as it has a less complex error response surface. In addition to the ReNN training efficiency, the interpretability of the ReNN variables enables the users to monitor and understand the internal behaviour of the network while training. Regularization in the ReNN response can be also directly measured and controlled. This feature improves the generalization ability of the network. The appeal of the ReNN is demonstrated with two ReNN applications to water resources engineering problems. In the first application, the ReNN is used to model the rainfall-runoff relationships in multiple watersheds in the Great Lakes basin located in northeastern North America. Modelling inflows to the Great Lakes are of great importance to the management of the Great Lakes system. Due to the lack of some detailed physical data about existing control structures in many subwatersheds of this huge basin, the data-driven approach to modelling such as the ReNN are required to replace predictions from a physically-based rainfall runoff model. Unlike traditional MLPs, the ReNN does not necessarily require an independent set of data for validation as the ReNN has the capability to control and verify the network degree of regularization. As such, the ReNN can be very beneficial in this case study as the data available in this case study is limited. In the second application, ReNN is fitted on the response function of the SWAT hydrologic model to act as a fast-to-run response surface surrogate (i.e., metamodel) of the original computationally intensive SWAT model. Besides the training efficiency gains, the ReNN applications demonstrate how the ReNN interpretability could help users develop more reliable networks which perform predictably better in terms of generalization.

Incorporation of varying types of temporal data in a neural network

NASA Technical Reports Server (NTRS)

Cohen, M. E.; Hudson, D. L.

1992-01-01

Most neural network models do not specifically deal with temporal data. Handling of these variables is complicated by the different uses to which temporal data are put, depending on the application. Even within the same application, temporal variables are often used in a number of different ways. In this paper, types of temporal data are discussed, along with their implications for approximate reasoning. Methods for integrating approximate temporal reasoning into existing neural network structures are presented. These methods are illustrated in a medical application for diagnosis of graft-versus-host disease which requires the use of several types of temporal data.

Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era.

PubMed

Zador, Zsolt; Huang, Wendy; Sperrin, Matthew; Lawton, Michael T

2018-06-01

Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. We reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

PubMed

Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks. The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

PubMed

Kumar, Gautam; Kothare, Mayuresh V

2013-12-01

We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

Common quandaries and their practical solutions in Bayesian network modeling

Treesearch

Bruce G. Marcot

2017-01-01

Use and popularity of Bayesian network (BN) modeling has greatly expanded in recent years, but many common problems remain. Here, I summarize key problems in BN model construction and interpretation,along with suggested practical solutions. Problems in BN model construction include parameterizing probability values, variable definition, complex network structures,...

Path integrals and large deviations in stochastic hybrid systems.

PubMed

Bressloff, Paul C; Newby, Jay M

2014-04-01

We construct a path-integral representation of solutions to a stochastic hybrid system, consisting of one or more continuous variables evolving according to a piecewise-deterministic dynamics. The differential equations for the continuous variables are coupled to a set of discrete variables that satisfy a continuous-time Markov process, which means that the differential equations are only valid between jumps in the discrete variables. Examples of stochastic hybrid systems arise in biophysical models of stochastic ion channels, motor-driven intracellular transport, gene networks, and stochastic neural networks. We use the path-integral representation to derive a large deviation action principle for a stochastic hybrid system. Minimizing the associated action functional with respect to the set of all trajectories emanating from a metastable state (assuming that such a minimization scheme exists) then determines the most probable paths of escape. Moreover, evaluating the action functional along a most probable path generates the so-called quasipotential used in the calculation of mean first passage times. We illustrate the theory by considering the optimal paths of escape from a metastable state in a bistable neural network.

Water management in the Roman world

NASA Astrophysics Data System (ADS)

Dermody, Brian J.; van Beek, Rens L. P. H.; Meeks, Elijah; Klein Goldewijk, Kees; Bierkens, Marc F. P.; Scheidel, Walter; Wassen, Martin J.; van der Velde, Ype; Dekker, Stefan C.

2014-05-01

Climate variability can have extreme impacts on societies in regions that are water-limited for agriculture. A society's ability to manage its water resources in such environments is critical to its long-term viability. Water management can involve improving agricultural yields through in-situ irrigation or redistributing water resources through trade in food. Here, we explore how such water management strategies affected the resilience of the Roman Empire to climate variability in the water-limited region of the Mediterranean. Using the large-scale hydrological model PCR-GLOBWB and estimates of landcover based on the Historical Database of the Global Environment (HYDE) we generate potential agricultural yield maps under variable climate. HYDE maps of population density in conjunction with potential yield estimates are used to develop maps of agricultural surplus and deficit. The surplus and deficit regions are abstracted to nodes on a water redistribution network based on the Stanford Geospatial Network Model of the Roman World (ORBIS). This demand-driven, water redistribution network allows us to quantitatively explore how water management strategies such as irrigation and food trade improved the resilience of the Roman Empire to climate variability.

Networks of Interacting Processes: Relationships Between Drivers and Deltaic Variables to Understand Water and Sediment Transport in Wax Lake Delta, Coastal Louisiana

NASA Astrophysics Data System (ADS)

Sendrowski, A.; Passalacqua, P.; Wagner, W.; Mohrig, D. C.; Meselhe, E. A.; Sadid, K. M.; Castañeda-Moya, E.; Twilley, R.

2017-12-01

Studying distributary channel networks in river deltaic systems provides important insight into deltaic functioning and evolution. This view of networks highlights the physical connection along channels and can also encompass the structural link between channels and deltaic islands (termed structural connectivity). An alternate view of the deltaic network is one composed of interacting processes, such as relationships between external drivers (e.g., river discharge, tides, and wind) and internal deltaic response variables (e.g., water level and sediment concentration). This network, also referred to as process connectivity, is dynamic across space and time, often comprises nonlinear relationships, and contributes to the development of complex channel networks and ecologically rich island platforms. The importance of process connectivity has been acknowledged, however, few studies have directly quantified these network interactions. In this work, we quantify process connections in Wax Lake Delta (WLD), coastal Louisiana. WLD is a naturally prograding delta that serves as an analogue for river diversion projects, thus it provides an excellent setting for understanding the influence of river discharge, tides, and wind on water and sediment in a delta. Time series of water level and sediment concentration were collected in three channels from November 2013 to February 2014, while water level and turbidity were collected on an island from April 2014 to August 2015. Additionally, a model run on WLD bathymetry generated two years of sediment concentration time series in multiple channels. River discharge, tide, and wind measurements were collected from the USGS and NOAA, respectively. We analyze this data with information theory (IT), a set of statistics that measure uncertainty in signals and communication between signals. Using IT, the timescale, strength, and direction of network links are quantified by measuring the synchronization and direct influence from one variable to another. We compare channel and island process connections, which show distinct differences. Our study captures the temporal evolution of variable transport at multiple locations. While WLD is river dominated, tides and wind show unique transport signatures related to tidal spring and neap transitions and wind events.

Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density

NASA Astrophysics Data System (ADS)

Nooruddin, Hasan A.; Anifowose, Fatai; Abdulraheem, Abdulazeez

2014-03-01

Soft computing techniques are recently becoming very popular in the oil industry. A number of computational intelligence-based predictive methods have been widely applied in the industry with high prediction capabilities. Some of the popular methods include feed-forward neural networks, radial basis function network, generalized regression neural network, functional networks, support vector regression and adaptive network fuzzy inference system. A comparative study among most popular soft computing techniques is presented using a large dataset published in literature describing multimodal pore systems in the Arab D formation. The inputs to the models are air porosity, grain density, and Thomeer parameters obtained using mercury injection capillary pressure profiles. Corrected air permeability is the target variable. Applying developed permeability models in recent reservoir characterization workflow ensures consistency between micro and macro scale information represented mainly by Thomeer parameters and absolute permeability. The dataset was divided into two parts with 80% of data used for training and 20% for testing. The target permeability variable was transformed to the logarithmic scale as a pre-processing step and to show better correlations with the input variables. Statistical and graphical analysis of the results including permeability cross-plots and detailed error measures were created. In general, the comparative study showed very close results among the developed models. The feed-forward neural network permeability model showed the lowest average relative error, average absolute relative error, standard deviations of error and root means squares making it the best model for such problems. Adaptive network fuzzy inference system also showed very good results.

[Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological Endocrinology].

PubMed

Milewski, Robert; Jamiołkowski, Jacek; Milewska Anna, Justyna; Domitrz, Jan; Szamatowicz, Jacek; Wołczyński, Sławomir

2009-12-01

Prognosis of pregnancy for patients treated with IVF ICSI/ET methods, using artificial neural networks. Retrospective study of 1007 cycles of infertility treatment of 899 patients of Department of Reproduction and Gynecological Endocrinology in Bialystok. The subjects were treated with IVF ICSI/ET method from August 2005 to September 2008. Classifying artificial neural network is described in the paper Architecture of the network is three-layered perceptron consisting of 45 neurons in the input layer 14 neurons in the hidden layer and a single output neuron. The source data for the network are 36 variables. 24 of them are nominal variables and the rest are quantitative variables. Among non-pregnancy cases only 59 prognosis of the network were incorrect. The results of treatment were correctly forecast in 68.5% of cases. The pregnancy was accurately confirmed in 49.1% of cases and lack of pregnancy in 86.5% of cases. Treatment of infertility with the use of in vitro fertilization methods continues to have too low efficiency per one treatment cycle. To improve this indicator it is necessary to find dependencies, which describe the model of IVF treatment. The application of advanced methods of bioinformatics allows to predict the result of the treatment more effectively With the help of artificial neural networks, we are able to forecast the failure of the treatment using IFV ICSI/ET procedure with almost 90% probability of certainty These possibilities can be used to predict negative cases.

High pressure air compressor valve fault diagnosis using feedforward neural networks

NASA Astrophysics Data System (ADS)

James Li, C.; Yu, Xueli

1995-09-01

Feedforward neural networks (FNNs) are developed and implemented to classify a four-stage high pressure air compressor into one of the following conditions: baseline, suction or exhaust valve faults. These FNNs are used for the compressor's automatic condition monitoring and fault diagnosis. Measurements of 39 variables are obtained under different baseline conditions and third-stage suction and exhaust valve faults. These variables include pressures and temperatures at all stages, voltage between phase aand phase b, voltage between phase band phase c, total three-phase real power, cooling water flow rate, etc. To reduce the number of variables, the amount of their discriminatory information is quantified by scattering matrices to identify statistical significant ones. Measurements of the selected variables are then used by a fully automatic structural and weight learning algorithm to construct three-layer FNNs to classify the compressor's condition. This learning algorithm requires neither guesses of initial weight values nor number of neurons in the hidden layer of an FNN. It takes an incremental approach in which a hidden neuron is trained by exemplars and then augmented to the existing network. These exemplars are then made orthogonal to the newly identified hidden neuron. They are subsequently used for the training of the next hidden neuron. The betterment continues until a desired accuracy is reached. After the neural networks are established, novel measurements from various conditions that haven't been previously seen by the FNNs are then used to evaluate their ability in fault diagnosis. The trained neural networks provide very accurate diagnosis for suction and discharge valve defects.

Resolving Structural Variability in Network Models and the Brain

PubMed Central

Klimm, Florian; Bassett, Danielle S.; Carlson, Jean M.; Mucha, Peter J.

2014-01-01

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data. PMID:24675546

Headache and temporo mandibular disorders: epidemiological assessment.

PubMed

D'Urso, Anna; Serritella, Emanuela; Tolevski Meshkova, Doria; Falisi, Giovanni; Di Paolo, Carlo

2016-04-01

Temporo mandibular disorders (TMDs) and headache are closely related pathologies. The aim of this study was to determine the prevalence, incidence, and the intensity of headache in 3304 dysfunctional patients (G1) at the Service of Clinical Gnathology of the Head-Neck Assistance Department of Umberto I Polyclinic at Sapienza University of Rome. G1 is composed by two subgroups of patient S1 (N.=2375) and S2 (N.=929) analyzed in different periods, respectively 1996-2006 and 2011-2013. The findings were compared with those of a control group of subjects from the general Italian population recently reported elsewhere. The prevalence of headache in the dysfunctional population was analyzed by calculating the proportion of that population who tested positive to cephalic pain for the entire study period. The incidence of headache has been calculated by determining the proportion cases of headache in TMD population, during the period analyzed among those considered at risk at the beginning of the examination period. The intensity of cephalic pain was evaluated using the Verbal Numeric Scale. Confidence Intervals (CI) at the 95% confidence level were calculated to get a precise estimate of research data. Comparison of G1 and control sample did not reveal many important differences, respectively with a headache prevalence of 49.5% (95%CI 47.8%-51.2%) and 42.8%(95%CI 46.8-38.8%). However, comparison of S1, which constituted a sample similar in numbers to the control sample and was observed during a similar time period, revealed a clearly greater prevalence of headache in the dysfunctional population 67.3% (95%CI 64.3-70.3%) than in the general representative population of Italian people 42.8% (95%CI 46.8-38.8%). The incidence of headache in the dysfunctional population was 39.49% (95%CI 37.79-41.19%). Headache incidence in the first subgroup (S1) was 32.7%(95%CI 34.6-30.8%), while in S2 was 59.24%(95%CI 56.24-62.24%), demonstrating an increase of incidence in the second subgroup analyzed. In G1 the average intensity of headache was severe (VNS>50). Headache is more intense in dysfunctional patients of the second subgroups (S2) than the first subgroup (S1) VNS 24.91 (95%CI 23.11-26.71) vs. 70.00 (95%CI 67.0-73.0). These findings confirm the existence of a relationship between headache and TMDs, showing that a dysfunctional patient has a greater predisposition to headache than a non-dysfunctional subject.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors.

PubMed

Ali, Hany S M; Blagden, Nicholas; York, Peter; Amani, Amir; Brook, Toni

2009-06-28

This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flow rates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs software was used to analyse a set of data obtained by random selection of the variables. The developed model was then assessed using a separate set of validation data and provided good agreement with the observed results. The antisolvent flow rate was found to have the dominant role on determining final particle size.

Correlation Between Fracture Network Properties and Stress Variability in Geological Media

NASA Astrophysics Data System (ADS)

Lei, Qinghua; Gao, Ke

2018-05-01

We quantitatively investigate the stress variability in fractured geological media under tectonic stresses. The fracture systems studied include synthetic fracture networks following power law length scaling and natural fracture patterns based on outcrop mapping. The stress field is derived from a finite-discrete element model, and its variability is analyzed using a set of mathematical formulations that honor the tensorial nature of stress data. We show that local stress perturbation, quantified by the Euclidean distance of a local stress tensor to the mean stress tensor, has a positive, linear correlation with local fracture intensity, defined as the total fracture length per unit area within a local sampling window. We also evaluate the stress dispersion of the entire stress field using the effective variance, that is, a scalar-valued measure of the overall stress variability. The results show that a well-connected fracture system under a critically stressed state exhibits strong local and global stress variabilities.

Structural Variability within Frontoparietal Networks and Individual Differences in Attentional Functions: An Approach Using the Theory of Visual Attention.

PubMed

Chechlacz, Magdalena; Gillebert, Celine R; Vangkilde, Signe A; Petersen, Anders; Humphreys, Glyn W

2015-07-29

Visuospatial attention allows us to select and act upon a subset of behaviorally relevant visual stimuli while ignoring distraction. Bundesen's theory of visual attention (TVA) (Bundesen, 1990) offers a quantitative analysis of the different facets of attention within a unitary model and provides a powerful analytic framework for understanding individual differences in attentional functions. Visuospatial attention is contingent upon large networks, distributed across both hemispheres, consisting of several cortical areas interconnected by long-association frontoparietal pathways, including three branches of the superior longitudinal fasciculus (SLF I-III) and the inferior fronto-occipital fasciculus (IFOF). Here we examine whether structural variability within human frontoparietal networks mediates differences in attention abilities as assessed by the TVA. Structural measures were based on spherical deconvolution and tractography-derived indices of tract volume and hindrance-modulated orientational anisotropy (HMOA). Individual differences in visual short-term memory (VSTM) were linked to variability in the microstructure (HMOA) of SLF II, SLF III, and IFOF within the right hemisphere. Moreover, VSTM and speed of information processing were linked to hemispheric lateralization within the IFOF. Differences in spatial bias were mediated by both variability in microstructure and volume of the right SLF II. Our data indicate that the microstructural and macrostrucutral organization of white matter pathways differentially contributes to both the anatomical lateralization of frontoparietal attentional networks and to individual differences in attentional functions. We conclude that individual differences in VSTM capacity, processing speed, and spatial bias, as assessed by TVA, link to variability in structural organization within frontoparietal pathways. Copyright © 2015 Chechlacz et al.

Empirical and Theoretical Aspects of Generation and Transfer of Information in a Neuromagnetic Source Network

PubMed Central

Vakorin, Vasily A.; Mišić, Bratislav; Krakovska, Olga; McIntosh, Anthony Randal

2011-01-01

Variability in source dynamics across the sources in an activated network may be indicative of how the information is processed within a network. Information-theoretic tools allow one not only to characterize local brain dynamics but also to describe interactions between distributed brain activity. This study follows such a framework and explores the relations between signal variability and asymmetry in mutual interdependencies in a data-driven pipeline of non-linear analysis of neuromagnetic sources reconstructed from human magnetoencephalographic (MEG) data collected as a reaction to a face recognition task. Asymmetry in non-linear interdependencies in the network was analyzed using transfer entropy, which quantifies predictive information transfer between the sources. Variability of the source activity was estimated using multi-scale entropy, quantifying the rate of which information is generated. The empirical results are supported by an analysis of synthetic data based on the dynamics of coupled systems with time delay in coupling. We found that the amount of information transferred from one source to another was correlated with the difference in variability between the dynamics of these two sources, with the directionality of net information transfer depending on the time scale at which the sample entropy was computed. The results based on synthetic data suggest that both time delay and strength of coupling can contribute to the relations between variability of brain signals and information transfer between them. Our findings support the previous attempts to characterize functional organization of the activated brain, based on a combination of non-linear dynamics and temporal features of brain connectivity, such as time delay. PMID:22131968

Spontaneous default network activity reflects behavioral variability independent of mind-wandering.

PubMed

Kucyi, Aaron; Esterman, Michael; Riley, Clay S; Valera, Eve M

2016-11-29

The brain's default mode network (DMN) is highly active during wakeful rest when people are not overtly engaged with a sensory stimulus or externally oriented task. In multiple contexts, increased spontaneous DMN activity has been associated with self-reported episodes of mind-wandering, or thoughts that are unrelated to the present sensory environment. Mind-wandering characterizes much of waking life and is often associated with error-prone, variable behavior. However, increased spontaneous DMN activity has also been reliably associated with stable, rather than variable, behavior. We aimed to address this seeming contradiction and to test the hypothesis that single measures of attentional states, either based on self-report or on behavior, are alone insufficient to account for DMN activity fluctuations. Thus, we simultaneously measured varying levels of self-reported mind-wandering, behavioral variability, and brain activity with fMRI during a unique continuous performance task optimized for detecting attentional fluctuations. We found that even though mind-wandering co-occurred with increased behavioral variability, highest DMN signal levels were best explained by intense mind-wandering combined with stable behavior simultaneously, compared with considering either single factor alone. These brain-behavior-experience relationships were highly consistent within known DMN subsystems and across DMN subregions. In contrast, such relationships were absent or in the opposite direction for other attention-relevant networks (salience, dorsal attention, and frontoparietal control networks). Our results suggest that the cognitive processes that spontaneous DMN activity specifically reflects are only partially related to mind-wandering and include also attentional state fluctuations that are not captured by self-report.

Spontaneous default network activity reflects behavioral variability independent of mind-wandering

PubMed Central

Kucyi, Aaron; Esterman, Michael; Riley, Clay S.; Valera, Eve M.

2016-01-01

The brain’s default mode network (DMN) is highly active during wakeful rest when people are not overtly engaged with a sensory stimulus or externally oriented task. In multiple contexts, increased spontaneous DMN activity has been associated with self-reported episodes of mind-wandering, or thoughts that are unrelated to the present sensory environment. Mind-wandering characterizes much of waking life and is often associated with error-prone, variable behavior. However, increased spontaneous DMN activity has also been reliably associated with stable, rather than variable, behavior. We aimed to address this seeming contradiction and to test the hypothesis that single measures of attentional states, either based on self-report or on behavior, are alone insufficient to account for DMN activity fluctuations. Thus, we simultaneously measured varying levels of self-reported mind-wandering, behavioral variability, and brain activity with fMRI during a unique continuous performance task optimized for detecting attentional fluctuations. We found that even though mind-wandering co-occurred with increased behavioral variability, highest DMN signal levels were best explained by intense mind-wandering combined with stable behavior simultaneously, compared with considering either single factor alone. These brain–behavior–experience relationships were highly consistent within known DMN subsystems and across DMN subregions. In contrast, such relationships were absent or in the opposite direction for other attention-relevant networks (salience, dorsal attention, and frontoparietal control networks). Our results suggest that the cognitive processes that spontaneous DMN activity specifically reflects are only partially related to mind-wandering and include also attentional state fluctuations that are not captured by self-report. PMID:27856733

Back to Pupillometry: How Cortical Network State Fluctuations Tracked by Pupil Dynamics Could Explain Neural Signal Variability in Human Cognitive Neuroscience

PubMed Central

2017-01-01

Abstract The mammalian thalamocortical system generates intrinsic activity reflecting different states of excitability, arising from changes in the membrane potentials of underlying neuronal networks. Fluctuations between these states occur spontaneously, regularly, and frequently throughout awake periods and influence stimulus encoding, information processing, and neuronal and behavioral responses. Changes of pupil size have recently been identified as a reliable marker of underlying neuronal membrane potential and thus can encode associated network state changes in rodent cortex. This suggests that pupillometry, a ubiquitous measure of pupil dilation in cognitive neuroscience, could be used as an index for network state fluctuations also for human brain signals. Considering this variable may explain task-independent variance in neuronal and behavioral signals that were previously disregarded as noise. PMID:29379876

Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

PubMed

Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

2008-12-01

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

Network Analysis to Risk Stratify Patients With Exercise Intolerance.

PubMed

Oldham, William M; Oliveira, Rudolf K F; Wang, Rui-Sheng; Opotowsky, Alexander R; Rubins, David M; Hainer, Jon; Wertheim, Bradley M; Alba, George A; Choudhary, Gaurav; Tornyos, Adrienn; MacRae, Calum A; Loscalzo, Joseph; Leopold, Jane A; Waxman, Aaron B; Olschewski, Horst; Kovacs, Gabor; Systrom, David M; Maron, Bradley A

2018-03-16

Current methods assessing clinical risk because of exercise intolerance in patients with cardiopulmonary disease rely on a small subset of traditional variables. Alternative strategies incorporating the spectrum of factors underlying prognosis in at-risk patients may be useful clinically, but are lacking. Use unbiased analyses to identify variables that correspond to clinical risk in patients with exercise intolerance. Data from 738 consecutive patients referred for invasive cardiopulmonary exercise testing at a single center (2011-2015) were analyzed retrospectively (derivation cohort). A correlation network of invasive cardiopulmonary exercise testing parameters was assembled using |r|>0.5. From an exercise network of 39 variables (ie, nodes) and 98 correlations (ie, edges) corresponding to P <9.5e -46 for each correlation, we focused on a subnetwork containing peak volume of oxygen consumption (pVo 2 ) and 9 linked nodes. K-mean clustering based on these 10 variables identified 4 novel patient clusters characterized by significant differences in 44 of 45 exercise measurements ( P <0.01). Compared with a probabilistic model, including 23 independent predictors of pVo 2 and pVo 2 itself, the network model was less redundant and identified clusters that were more distinct. Cluster assignment from the network model was predictive of subsequent clinical events. For example, a 4.3-fold ( P <0.0001; 95% CI, 2.2-8.1) and 2.8-fold ( P =0.0018; 95% CI, 1.5-5.2) increase in hazard for age- and pVo 2 -adjusted all-cause 3-year hospitalization, respectively, were observed between the highest versus lowest risk clusters. Using these data, we developed the first risk-stratification calculator for patients with exercise intolerance. When applying the risk calculator to patients in 2 independent invasive cardiopulmonary exercise testing cohorts (Boston and Graz, Austria), we observed a clinical risk profile that paralleled the derivation cohort. Network analyses were used to identify novel exercise groups and develop a point-of-care risk calculator. These data expand the range of useful clinical variables beyond pVo 2 that predict hospitalization in patients with exercise intolerance. © 2018 American Heart Association, Inc.

Optimization Techniques for Analysis of Biological and Social Networks

DTIC Science & Technology

2012-03-28

analyzing a new metaheuristic technique, variable objective search. 3. Experimentation and application: Implement the proposed algorithms , test and fine...alternative mathematical programming formulations, their theoretical analysis, the development of exact algorithms , and heuristics. Originally, clusters...systematic fashion under a unifying theoretical and algorithmic framework. Optimization, Complex Networks, Social Network Analysis, Computational

Network Structural Influences on the Adoption of Evidence-Based Prevention in Communities

ERIC Educational Resources Information Center

Fujimoto, Kayo; Valente, Thomas W.; Pentz, Mary Ann

2009-01-01

This study examined the impact of key variables in coalition communication networks, centralization and density, on the adoption of evidence-based substance abuse prevention. Data were drawn from a network survey and a corresponding community leader survey that measured leader attitudes and practices toward substance abuse prevention programs. Two…

Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

ERIC Educational Resources Information Center

Perkins, Kyle; And Others

1995-01-01

This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

A Wireless Platform for Energy Efficient Building Control Retrofits

DTIC Science & Technology

2012-08-01

University of Illinois at Urbana Champaign UTRC United Technologies Research Center VFD variable frequency drive WSN wireless sensor network ...demonstration area. .............................................................. 16 Table 4. Cost model for wireless sensor network ...buildings with MPC-based whole-building optimal control and (2) reduction in first costs achievable with a wireless sensor network (WSN)-based

Analytic Networks in Music Task Definition.

ERIC Educational Resources Information Center

Piper, Richard M.

For a student to acquire the conceptual systems of a discipline, the designer must reflect that structure or analytic network in his curriculum. The four networks identified for music and used in the development of the Southwest Regional Laboratory (SWRL) Music Program are the variable-value, the whole-part, the process-stage, and the class-member…

Mild traumatic brain injury: graph-model characterization of brain networks for episodic memory.

PubMed

Tsirka, Vasso; Simos, Panagiotis G; Vakis, Antonios; Kanatsouli, Kassiani; Vourkas, Michael; Erimaki, Sofia; Pachou, Ellie; Stam, Cornelis Jan; Micheloyannis, Sifis

2011-02-01

Episodic memory is among the cognitive functions that can be affected in the acute phase following mild traumatic brain injury (MTBI). The present study used EEG recordings to evaluate global synchronization and network organization of rhythmic activity during the encoding and recognition phases of an episodic memory task varying in stimulus type (kaleidoscope images, pictures, words, and pseudowords). Synchronization of oscillatory activity was assessed using a linear and nonlinear connectivity estimator and network analyses were performed using algorithms derived from graph theory. Twenty five MTBI patients (tested within days post-injury) and healthy volunteers were closely matched on demographic variables, verbal ability, psychological status variables, as well as on overall task performance. Patients demonstrated sub-optimal network organization, as reflected by changes in graph parameters in the theta and alpha bands during both encoding and recognition. There were no group differences in spectral energy during task performance or on network parameters during a control condition (rest). Evidence of less optimally organized functional networks during memory tasks was more prominent for pictorial than for verbal stimuli. Copyright © 2010 Elsevier B.V. All rights reserved.

Application of neural networks to software quality modeling of a very large telecommunications system.

PubMed

Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

1997-01-01

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

An Intelligent Monitoring Network for Detection of Cracks in Anvils of High-Press Apparatus.

PubMed

Tian, Hao; Yan, Zhaoli; Yang, Jun

2018-04-09

Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.

Origins of extrinsic variability in eukaryotic gene expression

NASA Astrophysics Data System (ADS)

Volfson, Dmitri; Marciniak, Jennifer; Blake, William J.; Ostroff, Natalie; Tsimring, Lev S.; Hasty, Jeff

2006-02-01

Variable gene expression within a clonal population of cells has been implicated in a number of important processes including mutation and evolution, determination of cell fates and the development of genetic disease. Recent studies have demonstrated that a significant component of expression variability arises from extrinsic factors thought to influence multiple genes simultaneously, yet the biological origins of this extrinsic variability have received little attention. Here we combine computational modelling with fluorescence data generated from multiple promoter-gene inserts in Saccharomyces cerevisiae to identify two major sources of extrinsic variability. One unavoidable source arising from the coupling of gene expression with population dynamics leads to a ubiquitous lower limit for expression variability. A second source, which is modelled as originating from a common upstream transcription factor, exemplifies how regulatory networks can convert noise in upstream regulator expression into extrinsic noise at the output of a target gene. Our results highlight the importance of the interplay of gene regulatory networks with population heterogeneity for understanding the origins of cellular diversity.

Origins of extrinsic variability in eukaryotic gene expression

NASA Astrophysics Data System (ADS)

Volfson, Dmitri; Marciniak, Jennifer; Blake, William J.; Ostroff, Natalie; Tsimring, Lev S.; Hasty, Jeff

2006-03-01

Variable gene expression within a clonal population of cells has been implicated in a number of important processes including mutation and evolution, determination of cell fates and the development of genetic disease. Recent studies have demonstrated that a significant component of expression variability arises from extrinsic factors thought to influence multiple genes in concert, yet the biological origins of this extrinsic variability have received little attention. Here we combine computational modeling with fluorescence data generated from multiple promoter-gene inserts in Saccharomyces cerevisiae to identify two major sources of extrinsic variability. One unavoidable source arising from the coupling of gene expression with population dynamics leads to a ubiquitous noise floor in expression variability. A second source which is modeled as originating from a common upstream transcription factor exemplifies how regulatory networks can convert noise in upstream regulator expression into extrinsic noise at the output of a target gene. Our results highlight the importance of the interplay of gene regulatory networks with population heterogeneity for understanding the origins of cellular diversity.

Some comparisons of complexity in dictionary-based and linear computational models.

PubMed

Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello

2011-03-01

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.

Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant

NASA Astrophysics Data System (ADS)

Li, Ming-Xia; Jiang, Zhi-Qiang; Xie, Wen-Jie; Xiong, Xiong; Zhang, Wei; Zhou, Wei-Xing

2015-02-01

Traders develop and adopt different trading strategies attempting to maximize their profits in financial markets. These trading strategies not only result in specific topological structures in trading networks, which connect the traders with the pairwise buy-sell relationships, but also have potential impacts on market dynamics. Here, we present a detailed analysis on how the market behaviors are correlated with the structures of traders in trading networks based on audit trail data for the Baosteel stock and its warrant at the transaction level from 22 August 2005 to 23 August 2006. In our investigation, we divide each trade day into 48 rolling time windows with a length of 5 min, construct a trading network within each window, and obtain a time series of over 11,600 trading networks. We find that there are strongly simultaneous correlations between the topological metrics (including network centralization, assortative index, and average path length) of trading networks that characterize the patterns of order execution and the financial variables (including return, volatility, intertrade duration, and trading volume) for the stock and its warrant. Our analysis may shed new lights on how the microscopic interactions between elements within complex system affect the system's performance.

Continuous-variable Measurement-device-independent Quantum Relay Network with Phase-sensitive Amplifiers

NASA Astrophysics Data System (ADS)

Li, Fei; Zhao, Wei; Guo, Ying

2018-01-01

Continuous-variable (CV) measurement-device-independent (MDI) quantum cryptography is now heading towards solving the practical problem of implementing scalable quantum networks. In this paper, we show that a solution can come from deploying an optical amplifier in the CV-MDI system, aiming to establish a high-rate quantum network. We suggest an improved CV-MDI protocol using the EPR states coupled with optical amplifiers. It can implement a practical quantum network scheme, where the legal participants create the secret correlations by using EPR states connecting to an untrusted relay via insecure links and applying the multi-entangled Greenberger-Horne-Zeilinger (GHZ) state analysis at relay station. Despite the possibility that the relay could be completely tampered with and imperfect links are subject to the powerful attacks, the legal participants are still able to extract a secret key from network communication. The numerical simulation indicates that the quantum network communication can be achieved in an asymmetric scenario, fulfilling the demands of a practical quantum network. Furthermore, we show that the use of optical amplifiers can compensate the inherent imperfections and improve the secret key rate of the CV-MDI system.

Monitoring air quality in mountains: Designing an effective network

USGS Publications Warehouse

Peterson, D.L.

2000-01-01

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.

The social support and social network characteristics of smokers in methadone maintenance treatment.

PubMed

de Dios, Marcel Alejandro; Stanton, Cassandra A; Caviness, Celeste M; Niaura, Raymond; Stein, Michael

2013-01-01

Previous studies have shown social support and social network variables to be important factors in smoking cessation treatment. Tobacco use is highly prevalent among individuals in methadone maintenance treatment (MMT). However, smoking cessation treatment outcomes in this vulnerable subpopulation have been poor and social support and social network variables may contribute. The current study examined the social support and social network characteristics of 151 MMT smokers involved in a randomized clinical trial of smoking cessation treatments. Participants were 50% women and 78% Caucasian. A high proportion (57%) of MMT smokers had spouses or partners who smoke and over two-thirds of households (68.5%) included at least one smoker. Our sample was characterized by relatively small social networks, but high levels of general social support and quitting support. The number of cigarettes per day was found to be positively associated with the number of smokers in the social network (r = .239, p < .05) and quitting self-efficacy was negatively associated with partner smoking (r = -.217, p < .001). Findings are discussed in the context of developing smoking cessation interventions that address the influential role of social support and social networks of smokers in MMT.

Variable Star Observing in Hungary

NASA Astrophysics Data System (ADS)

Mizser, Attila

1986-12-01

Astronomy and variable star observing has a long history in Hungary, dating back to the private observatories erected by the Hungarian nobility in the late 19th Century. The first organized network of amateur variable star observers, the Variable Star Section of the new Hungarian Astronomical Association, was organized around the Urania Observatory in Budapest in 1948. Other groups, dedicated to various types of variables, have since been organized.

An opinion-driven behavioral dynamics model for addictive behaviors

DOE PAGES

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; ...

2015-04-08

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Additionally, individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters providemore » targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. Furthermore, this has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.« less

Deployment strategy for battery energy storage system in distribution network based on voltage violation regulation

NASA Astrophysics Data System (ADS)

Wu, H.; Zhou, L.; Xu, T.; Fang, W. L.; He, W. G.; Liu, H. M.

2017-11-01

In order to improve the situation of voltage violation caused by the grid-connection of photovoltaic (PV) system in a distribution network, a bi-level programming model is proposed for battery energy storage system (BESS) deployment. The objective function of inner level programming is to minimize voltage violation, with the power of PV and BESS as the variables. The objective function of outer level programming is to minimize the comprehensive function originated from inner layer programming and all the BESS operating parameters, with the capacity and rated power of BESS as the variables. The differential evolution (DE) algorithm is applied to solve the model. Based on distribution network operation scenarios with photovoltaic generation under multiple alternative output modes, the simulation results of IEEE 33-bus system prove that the deployment strategy of BESS proposed in this paper is well adapted to voltage violation regulation invariable distribution network operation scenarios. It contributes to regulating voltage violation in distribution network, as well as to improve the utilization of PV systems.

Identifying PHM market and network opportunities.

PubMed

Grube, Mark E; Krishnaswamy, Anand; Poziemski, John; York, Robert W

2015-11-01

Two key processes for healthcare organizations seeking to assume a financially sustainable role in population health management (PHM), after laying the groundwork for the effort, are to identify potential PHM market opportunities and determine the scope of the PHM network. Key variables organizations should consider with respect to market opportunities include the patient population, the overall insurance/employer market, and available types of insurance products. Regarding the network's scope, organizations should consider both traditional strategic criteria for a viable network and at least five additional criteria: network essentiality and PHM care continuum, network adequacy, service distribution right-sizing, network growth strategy, and organizational agility.

Substance Abuse Treatment Stage and Personal Networks of Women in Substance Abuse Treatment

PubMed Central

Tracy, Elizabeth M.; Kim, HyunSoo; Brown, Suzanne; Min, Meeyoung O.; Jun, Min Kyoung; McCarty, Christopher

2012-01-01

This study examines the relationship among 4 treatment stages (i.e., engagement, persuasion, active treatment, relapse prevention) and the composition, social support, and structural characteristics of personal networks. The study sample includes 242 women diagnosed with substance dependence who were interviewed within their first month of intensive outpatient treatment. Using EgoNet software, the women reported on their 25 alter personal networks and the characteristics of each alter. With one exception, few differences were found in the network compositions at different stages of substance abuse treatment. The exception was the network composition of women in the active treatment stage, which included more network members from treatment programs or 12-Step meetings. Although neither the type nor amount of social support differed across treatment stages, reciprocity differed between women in active treatment and those in the engagement stage. Networks of women in active treatment were less connected, as indicated by a higher number of components, whereas networks of women in the persuasion stage had a higher degree of centralization, as indicated by networks dominated by people with the most ties. Overall, we find social network structural variables to relate to the stage of treatment, whereas network composition, type of social support, and sociodemographic variables (with a few exceptions) do not relate to treatment stage. Results suggest that social context, particularly how social contacts are arranged around clients, should be incorporated into treatment programs, regardless of demographic background. PMID:22639705

Predictors of Condom Use among Peer Social Networks of Men Who Have Sex with Men in Ghana, West Africa

PubMed Central

Nelson, LaRon E.; Wilton, Leo; Agyarko-Poku, Thomas; Zhang, Nanhua; Zou, Yuanshu; Aluoch, Marilyn; Apea, Vanessa; Hanson, Samuel Owiredu; Adu-Sarkodie, Yaw

2015-01-01

Ghanaian men who have sex with men (MSM) have high rates of HIV infection. A first step in designing culturally relevant prevention interventions for MSM in Ghana is to understand the influence that peer social networks have on their attitudes and behaviors. We aimed to examine whether, in a sample of Ghanaian MSM, mean scores on psychosocial variables theorized to influence HIV/STI risk differed between peer social networks and to examine whether these variables were associated with condom use. We conducted a formative, cross-sectional survey with 22 peer social networks of MSM (n = 137) in Ghana. We assessed basic psychological-needs satisfaction, HIV/STI knowledge, sense of community, HIV and gender non-conformity stigmas, gender equitable norms, sexual behavior and condom use. Data were analyzed using analysis of variance, generalized estimating equations, and Wilcoxon two sample tests. All models were adjusted for age and income, ethnicity, education, housing and community of residence. Mean scores for all psychosocial variables differed significantly by social network. Men who reported experiencing more autonomy support by their healthcare providers had higher odds of condom use for anal (AOR = 3.29, p<0.01), oral (AOR = 5.06, p<0.01) and vaginal (AOR = 1.8, p<0.05) sex. Those with a stronger sense of community also had higher odds of condom use for anal sex (AOR = 1.26, p<0.001). Compared to networks with low prevalence of consistent condom users, networks with higher prevalence of consistent condom users had higher STD and HIV knowledge, had norms that were more supportive of gender equity, and experienced more autonomy support in their healthcare encounters. Healthcare providers and peer social networks can have an important influence on safer-sex behaviors in Ghanaian MSM. More research with Ghanaian MSM is needed that considers knowledge, attitudes, and norms of their social networks in the development and implementation of culturally relevant HIV/STI prevention intervention strategies. PMID:25635774

Dynamic hydro-climatic networks in pristine and regulated rivers

NASA Astrophysics Data System (ADS)

Botter, G.; Basso, S.; Lazzaro, G.; Doulatyari, B.; Biswal, B.; Schirmer, M.; Rinaldo, A.

2014-12-01

Flow patterns observed at-a-station are the dynamical byproduct of a cascade of processes involving different compartments of the hydro-climatic network (e.g., climate, rainfall, soil, vegetation) that regulates the transformation of rainfall into streamflows. In complex branching rivers, flow regimes result from the heterogeneous arrangement around the stream network of multiple hydrologic cascades that simultaneously occur within distinct contributing areas. As such, flow regimes are seen as the integrated output of a complex "network of networks", which can be properly characterized by its degree of temporal variability and spatial heterogeneity. Hydrologic networks that generate river flow regimes are dynamic in nature. In pristine rivers, the time-variance naturally emerges at multiple timescales from climate variability (namely, seasonality and inter-annual fluctuations), implying that the magnitude (and the features) of the water flow between two nodes may be highly variable across different seasons and years. Conversely, the spatial distribution of river flow regimes within pristine rivers involves scale-dependent transport features, as well as regional climatic and soil use gradients, which in small and meso-scale catchments (A < 103 km2) are usually mild enough to guarantee quite uniform flow regimes and high spatial correlations. Human-impacted rivers, instead, constitute hybrid networks where observed spatio-temporal patterns are dominated by anthropogenic shifts, such as landscape alterations and river regulation. In regulated rivers, the magnitude and the features of water flows from node to node may change significantly through time due to damming and withdrawals. However, regulation may impact river regimes in a spatially heterogeneous manner (e.g. in localized river reaches), with a significant decrease of spatial correlations and network connectivity. Provided that the spatial and temporal dynamics of flow regimes in complex rivers may strongly impact important biotic processes involved in the river food web (e.g. biofilm and riparian vegetation dynamics), the study of rivers as dynamic networks provides important clues to water management strategies and freshwater ecosystem studies.

FTP Extensions for Variable Protocol Specification

NASA Technical Reports Server (NTRS)

Allman, Mark; Ostermann, Shawn

2000-01-01

The specification for the File Transfer Protocol (FTP) assumes that the underlying network protocols use a 32-bit network address and a 16-bit transport address (specifically IP version 4 and TCP). With the deployment of version 6 of the Internet Protocol, network addresses will no longer be 32-bits. This paper species extensions to FTP that will allow the protocol to work over a variety of network and transport protocols.

Spiking neural network simulation: memory-optimal synaptic event scheduling.

PubMed

Stewart, Robert D; Gurney, Kevin N

2011-06-01

Spiking neural network simulations incorporating variable transmission delays require synaptic events to be scheduled prior to delivery. Conventional methods have memory requirements that scale with the total number of synapses in a network. We introduce novel scheduling algorithms for both discrete and continuous event delivery, where the memory requirement scales instead with the number of neurons. Superior algorithmic performance is demonstrated using large-scale, benchmarking network simulations.

Network Design for Reliability and Resilience to Attack

DTIC Science & Technology

2014-03-01

attacker can destroy n arcs in the network SPNI Shortest-Path Network-Interdiction problem TSP Traveling Salesman Problem UB upper bound UKR Ukraine...elimination from the traveling salesman problem (TSP). Literature calls a walk that does not contain a cycle a path [19]. The objective function in...arc lengths as random variables with known probability distributions. The m-median problem seeks to design a network with minimum average travel cost

Determinants of successful clinical networks: the conceptual framework and study protocol.

PubMed

Haines, Mary; Brown, Bernadette; Craig, Jonathan; D'Este, Catherine; Elliott, Elizabeth; Klineberg, Emily; McInnes, Elizabeth; Middleton, Sandy; Paul, Christine; Redman, Sally; Yano, Elizabeth M

2012-03-13

Clinical networks are increasingly being viewed as an important strategy for increasing evidence-based practice and improving models of care, but success is variable and characteristics of networks with high impact are uncertain. This study takes advantage of the variability in the functioning and outcomes of networks supported by the Australian New South Wales (NSW) Agency for Clinical Innovation's non-mandatory model of clinical networks to investigate the factors that contribute to the success of clinical networks. The objective of this retrospective study is to examine the association between external support, organisational and program factors, and indicators of success among 19 clinical networks over a three-year period (2006-2008). The outcomes (health impact, system impact, programs implemented, engagement, user perception, and financial leverage) and explanatory factors will be collected using a web-based survey, interviews, and record review. An independent expert panel will provide judgements about the impact or extent of each network's initiatives on health and system impacts. The ratings of the expert panel will be the outcome used in multivariable analyses. Following the rating of network success, a qualitative study will be conducted to provide a more in-depth examination of the most successful networks. This is the first study to combine quantitative and qualitative methods to examine the factors that contribute to the success of clinical networks and, more generally, is the largest study of clinical networks undertaken. The adaptation of expert panel methods to rate the impacts of networks is the methodological innovation of this study. The proposed project will identify the conditions that should be established or encouraged by agencies developing clinical networks and will be of immediate use in forming strategies and programs to maximise the effectiveness of such networks.

Reconstructing the leading mode of multi-decadal North Atlantic variability over the last two millenia using functional paleoclimate networks

NASA Astrophysics Data System (ADS)

Franke, Jasper G.; Werner, Johannes; Donner, Reik V.

2017-04-01

The increasing availability of high-resolution North Atlantic paleoclimate proxies allows to not only study local climate variations in time, but also temporal changes in spatial variability patterns across the entire region possibly controlled by large-scale coherent variability modes such as the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation. In this study, we use functional paleoclimate network analysis [1,2] to investigate changes in the statistical similarity patterns among an ensemble of high-resolution terrestrial paleoclimate records from Northern Europe included in the Arctic 2k data base. Specifically, we construct complex networks capturing the mutual statistical similarity of inter-annual temperature variability recorded in tree ring records, ice cores and lake sediments for multidecadal time windows covering the last two millenia. The observed patterns of co-variability are ultimately connected to the North Atlantic atmospheric circulation and most prominently to multidecadal variations of the NAO. Based on the inferred networks, we study the dynamical similarity between regional clusters of archives defined according to present-day inter-annual temperature variations across the study region. This analysis identifies those time-dependent inter-regional linkages that are most informative about the leading-order North Atlantic climate variability according to a recent NAO reconstruction for the last millenium [3]. Based on these linkages, we extend the existing reconstruction to obtain qualitative information on multidecadal to centennial scale North Atlantic climate variability over the last two millenia. In general, we find a tendency towards a dominating positive NAO phase interrupted by pronounced and extended intervals of negative NAO. Relatively rapid transitions between both types of behaviour are present during distinct periods including the Little Ice Age, the Medieval Climate Anomaly and for the Dark Ages Little Ice Age. [1] K. Rehfeld, N. Marwan, S.F.M. Breitenbach, J. Kurths: Late Holocene Asian summer monsoon dynamics from small but complex networks of paleoclimate data. Climate Dynamics 41, 3-19, 2013 [2] J.L. Oster, N.P. Kelley: Tracking regional and global teleconnections recorded by western North American speleothem records. Quaternary Science Reviews 149, 18-33, 2016 [3] P. Ortega, F. Lehner, D. Swingedouw, V. Masson-Delmotte, C.C. Raible, M. Casado, P. Yiou: A model-tested North Atlantic Oscillation reconstruction for the past millenium. Nature 523, 71-74, 2015

Observability and synchronization of neuron models.

PubMed

Aguirre, Luis A; Portes, Leonardo L; Letellier, Christophe

2017-10-01

Observability is the property that enables recovering the state of a dynamical system from a reduced number of measured variables. In high-dimensional systems, it is therefore important to make sure that the variable recorded to perform the analysis conveys good observability of the system dynamics. The observability of a network of neuron models depends nontrivially on the observability of the node dynamics and on the topology of the network. The aim of this paper is twofold. First, to perform a study of observability using four well-known neuron models by computing three different observability coefficients. This not only clarifies observability properties of the models but also shows the limitations of applicability of each type of coefficients in the context of such models. Second, to study the emergence of phase synchronization in networks composed of neuron models. This is done performing multivariate singular spectrum analysis which, to the best of the authors' knowledge, has not been used in the context of networks of neuron models. It is shown that it is possible to detect phase synchronization: (i) without having to measure all the state variables, but only one (that provides greatest observability) from each node and (ii) without having to estimate the phase.

Modeling of bromate formation by ozonation of surface waters in drinking water treatment.

PubMed

Legube, Bernard; Parinet, Bernard; Gelinet, Karine; Berne, Florence; Croue, Jean-Philippe

2004-04-01

The main objective of this paper is to try to develop statistically and chemically rational models for bromate formation by ozonation of clarified surface waters. The results presented here show that bromate formation by ozonation of natural waters in drinking water treatment is directly proportional to the "Ct" value ("Ctau" in this study). Moreover, this proportionality strongly depends on many parameters: increasing of pH, temperature and bromide level leading to an increase of bromate formation; ammonia and dissolved organic carbon concentrations causing a reverse effect. Taking into account limitation of theoretical modeling, we proposed to predict bromate formation by stochastic simulations (multi-linear regression and artificial neural networks methods) from 40 experiments (BrO(3)(-) vs. "Ctau") carried out with three sand filtered waters sampled on three different waterworks. With seven selected variables we used a simple architecture of neural networks, optimized by "neural connection" of SPSS Inc./Recognition Inc. The bromate modeling by artificial neural networks gives better result than multi-linear regression. The artificial neural networks model allowed us classifying variables by decreasing order of influence (for the studied cases in our variables scale): "Ctau", [N-NH(4)(+)], [Br(-)], pH, temperature, DOC, alkalinity.

The SIMRAND methodology: Theory and application for the simulation of research and development projects

NASA Technical Reports Server (NTRS)

Miles, R. F., Jr.

1986-01-01

A research and development (R&D) project often involves a number of decisions that must be made concerning which subset of systems or tasks are to be undertaken to achieve the goal of the R&D project. To help in this decision making, SIMRAND (SIMulation of Research ANd Development Projects) is a methodology for the selection of the optimal subset of systems or tasks to be undertaken on an R&D project. Using alternative networks, the SIMRAND methodology models the alternative subsets of systems or tasks under consideration. Each path through an alternative network represents one way of satisfying the project goals. Equations are developed that relate the system or task variables to the measure of reference. Uncertainty is incorporated by treating the variables of the equations probabilistically as random variables, with cumulative distribution functions assessed by technical experts. Analytical techniques of probability theory are used to reduce the complexity of the alternative networks. Cardinal utility functions over the measure of preference are assessed for the decision makers. A run of the SIMRAND Computer I Program combines, in a Monte Carlo simulation model, the network structure, the equations, the cumulative distribution functions, and the utility functions.

Effects of training strategies implemented in a complex videogame on functional connectivity of attentional networks.

PubMed

Voss, Michelle W; Prakash, Ruchika Shaurya; Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

2012-01-02

We used the Space Fortress videogame, originally developed by cognitive psychologists to study skill acquisition, as a platform to examine learning-induced plasticity of interacting brain networks. Novice videogame players learned Space Fortress using one of two training strategies: (a) focus on all aspects of the game during learning (fixed priority), or (b) focus on improving separate game components in the context of the whole game (variable priority). Participants were scanned during game play using functional magnetic resonance imaging (fMRI), both before and after 20 h of training. As expected, variable priority training enhanced learning, particularly for individuals who initially performed poorly. Functional connectivity analysis revealed changes in brain network interaction reflective of more flexible skill learning and retrieval with variable priority training, compared to procedural learning and skill implementation with fixed priority training. These results provide the first evidence for differences in the interaction of large-scale brain networks when learning with different training strategies. Our approach and findings also provide a foundation for exploring the brain plasticity involved in transfer of trained abilities to novel real-world tasks such as driving, sport, or neurorehabilitation. Copyright © 2011 Elsevier Inc. All rights reserved.

A recurrent neural network for classification of unevenly sampled variable stars

NASA Astrophysics Data System (ADS)

Naul, Brett; Bloom, Joshua S.; Pérez, Fernando; van der Walt, Stéfan

2018-02-01

Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time (`light curves'). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally due to intranight cadence choices as well as diurnal and seasonal constraints1-5. With nightly observations of millions of variable stars and transients from upcoming surveys4,6, efficient and accurate discovery and classification techniques on noisy, irregularly sampled data must be employed with minimal human-in-the-loop involvement. Machine learning for inference tasks on such data traditionally requires the laborious hand-coding of domain-specific numerical summaries of raw data (`features')7. Here, we present a novel unsupervised autoencoding recurrent neural network8 that makes explicit use of sampling times and known heteroskedastic noise properties. When trained on optical variable star catalogues, this network produces supervised classification models that rival other best-in-class approaches. We find that autoencoded features learned in one time-domain survey perform nearly as well when applied to another survey. These networks can continue to learn from new unlabelled observations and may be used in other unsupervised tasks, such as forecasting and anomaly detection.

Abrupt and unexpected stressful life events are followed with increased disease activity in spondyloarthritis: a two years web-based cohort study.

PubMed

Luo, Guanguan; Boelle, Pierre Yves; Turbelin, Clément; Costantino, Félicie; Kerneis, Solen; Nahal, Roula Said; Breban, Maxime; Hanslik, Thomas

2018-06-05

The contribution of environmental factors to spondyloarthritis (SpA) course remains poorly characterized. We previously reported a possible triggering of disease flares by stressful life events and vaccination. The objective of the present study was to specify the types of vaccine and life event that may influence disease activity. A prospective cohort of adult SpA was followed for two years. Patients logged on to a secured website every month to complete a standardized auto-questionnaire. They reported whether they had been exposed to stressful life events, vaccinations or other environmental factors. Patients were asked to rate the distress resulting from exposure to life events on a numerical rating scale (NRS: 0-10). Primary outcome variable was the variation of Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) measured on two consecutive connections. Months where an event occurred were compared to months without events. The cutoff value of 1 is defined as the minimal clinically important variation for the BASDAI. The 272 enrolled SpA patients returned 3,388 questionnaires. Months where an abrupt and unexpected traumatic event occurred were associated with a significant increase of BASDAI of 0.57 [95%CI: 0.29; 0.85] (p<0.001). The higher the rating of distress, the larger the impact on BASDAI, reaching a clinically meaningful increase of 0.99 [0.17; 1.82] for a VNS ≥9. The effect of stressful events on BASDAI persisted during a median of 3 months. No other environmental factor was significantly associated with BASDAI variations. Among stressful life events, abrupt and unexpected events were associated with transient worsening of disease activity in SpA, which reached a clinically meaningful increase for the highest rating of distress. Association between vaccines and disease flare was not confirmed. Copyright © 2018. Published by Elsevier Masson SAS.

Inference of directed climate networks: role of instability of causality estimation methods

NASA Astrophysics Data System (ADS)

Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

2013-04-01

Climate data are increasingly analyzed by complex network analysis methods, including graph-theoretical approaches [1]. For such analysis, links between localized nodes of climate network are typically quantified by some statistical measures of dependence (connectivity) between measured variables of interest. To obtain information on the directionality of the interactions in the networks, a wide range of methods exists. These can be broadly divided into linear and nonlinear methods, with some of the latter having the theoretical advantage of being model-free, and principally a generalization of the former [2]. However, as a trade-off, this generality comes together with lower accuracy - in particular if the system was close to linear. In an overall stationary system, this may potentially lead to higher variability in the nonlinear network estimates. Therefore, with the same control of false alarms, this may lead to lower sensitivity for detection of real changes in the network structure. These problems are discussed on the example of daily SAT and SLP data from the NCEP/NCAR reanalysis dataset. We first reduce the dimensionality of data using PCA with VARIMAX rotation to detect several dozens of components that together explain most of the data variability. We further construct directed climate networks applying a selection of most widely used methods - variants of linear Granger causality and conditional mutual information. Finally, we assess the stability of the detected directed climate networks by computing them in sliding time windows. To understand the origin of the observed instabilities and their range, we also apply the same procedure to two types of surrogate data: either with non-stationarity in network structure removed, or imposed in a controlled way. In general, the linear methods show stable results in terms of overall similarity of directed climate networks inferred. For instance, for different decades of SAT data, the Spearman correlation of edge weights in the networks is ~ 0.6. The networks constructed using nonlinear measures were in general less stable both in real data and stationarized surrogates. Interestingly, when the nonlinear method parameters are optimized with respect to temporal stability of the networks, the networks seem to converge close to those detected by linear Granger causality. This provides further evidence for the hypothesis of overall sparsity and weakness of nonlinear coupling in climate networks on this spatial and temporal scale [3] and sufficient support for the use of linear methods in this context, unless specific clearly detectable nonlinear phenomena are targeted. Acknowledgement: This study is supported by the Czech Science Foundation, Project No. P103/11/J068. [1] Boccaletti, S.; Latora, V.; Moreno, Y.; Chavez, M. & Hwang, D. U.: Complex networks: Structure and dynamics, Physics Reports, 2006, 424, 175-308 [2] Barnett, L.; Barrett, A. B. & Seth, A. K.: Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables, Physical Review Letters, 2009, 103, 238701 [3] Hlinka, J.; Hartman, D.; Vejmelka, M.; Novotná, D.; Paluš, M.: Non-linear dependence and teleconnections in climate data: sources, relevance, nonstationarity, submitted preprint (http://arxiv.org/abs/1211.6688)

A Low Cost High Density Sensor Network for Air Quality at London Heathrow Airport

NASA Astrophysics Data System (ADS)

Bright, V.; Mead, M. I.; Popoola, O. A.; Baron, R. P.; Saffell, J.; Stewart, G.; Kaye, P.; Jones, R.

2012-12-01

Atmospheric composition within urban areas has a direct effect on the air quality of an environment in which a large majority of people live and work. Atmospheric pollutants including ozone (O3), nitrogen dioxide (NO2), volatile organic compounds (VOCs) and particulate matter (PM) can have a significant effect on human health. As such it is important to determine the potential exposure of individuals to these atmospheric constituents and investigate the processes that lead to the degradation of air quality within the urban environment. Whilst modelled pollutant levels on the local scale often suggest high degrees of spatial and temporal variability, the relatively sparse fixed site automated urban networks only provide low spatial resolution data that do not appear adequate in detecting such small scale variability. In this paper we demonstrate that measurements can now be made using networks of low-cost sensors that utilise a variety of techniques, including electrochemical and optical, to measure concentrations of atmospheric species. Once equipped with GPS and GPRS to determine position and transmit data respectively, these networks have the potential to provide valuable insights into pollutant variability inherent on the local or micro-scale. The methodology has been demonstrated successfully in field campaigns carried out in cities including London and Valencia, and is now being deployed as part of the Sensor Networks for Air Quality currently deployed at London Heathrow airport (SNAQ-Heathrow) which is outlined in the partner paper presented by Mead et al. (this conference). The SNAQ-Heathrow network of 50 sensor nodes will provide an unprecedented data set that includes measurements of O3, NO, NO2, CO, CO2, SO2, total VOCs, size-speciated PM as well as meteorological variables that include temperature, relative humidity, wind speed and direction. This network will provide high temporal (20 second intervals) and spatial (50 sites within the airport area) resolution data over a 12 month period with data transmitted back to a server every 2 hours. In this paper we present the data capture and storage, data accessibility, data mining and visualisation techniques applied to the measurements of the SNAQ Heathrow high density sensor network, the preliminary results of which provide an insight into the potential use of such networks in characterising air quality, emissions and validating dispersion models on local scales. We also present a web based interface developed for the sensor network that allows users to access archived data and assess meteorological conditions, atmospheric dispersion, pollutant levels and emission rates.

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

PubMed Central

Weiss, Scott T.

2014-01-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com. PMID:24922310

CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

PubMed

McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

2014-06-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

Modifications of resting state networks in spinocerebellar ataxia type 2.

PubMed

Cocozza, Sirio; Saccà, Francesco; Cervo, Amedeo; Marsili, Angela; Russo, Cinzia Valeria; Giorgio, Sara Maria Delle Acque; De Michele, Giuseppe; Filla, Alessandro; Brunetti, Arturo; Quarantelli, Mario

2015-09-01

We aimed to investigate the integrity of the Resting State Networks in spinocerebellar ataxia type 2 (SCA2) and the correlations between the modification of these networks and clinical variables. Resting-state functional magnetic resonance imaging (RS-fMRI) data from 19 SCA2 patients and 29 healthy controls were analyzed using an independent component analysis and dual regression, controlling at voxel level for the effect of atrophy by co-varying for gray matter volume. Correlations between the resting state networks alterations and disease duration, age at onset, number of triplets, and clinical score were assessed by Spearman's coefficient, for each cluster which was significantly different in SCA2 patients compared with healthy controls. In SCA2 patients, disruption of the cerebellar components of all major resting state networks was present, with supratentorial involvement only for the default mode network. When controlling at voxel level for gray matter volume, the reduction in functional connectivity in supratentorial regions of the default mode network, and in cerebellar regions within the default mode, executive and right fronto-parietal networks, was still significant. No correlations with clinical variables were found for any of the investigated resting state networks. The SCA2 patients show significant alterations of the resting state networks, only partly explained by the atrophy. The default mode network is the only resting state network that shows also supratentorial changes, which appear unrelated to the cortical gray matter volume. Further studies are needed to assess the clinical significance of these changes. © 2015 International Parkinson and Movement Disorder Society.

Hydrologic controls on basin-scale distribution of benthic macroinvertebrates

NASA Astrophysics Data System (ADS)

Bertuzzo, E.; Ceola, S.; Singer, G. A.; Battin, T. J.; Montanari, A.; Rinaldo, A.

2013-12-01

The presentation deals with the role of streamflow variability on basin-scale distributions of benthic macroinvertebrates. Specifically, we present a probabilistic analysis of the impacts of the variability along the river network of relevant hydraulic variables on the density of benthic macroinvertebrate species. The relevance of this work is based on the implications of the predictability of macroinvertebrate patterns within a catchment on fluvial ecosystem health, being macroinvertebrates commonly used as sensitive indicators, and on the effects of anthropogenic activity. The analytical tools presented here outline a novel procedure of general nature aiming at a spatially-explicit quantitative assessment of how near-bed flow variability affects benthic macroinvertebrate abundance. Moving from the analytical characterization of the at-a-site probability distribution functions (pdfs) of streamflow and bottom shear stress, a spatial extension to a whole river network is performed aiming at the definition of spatial maps of streamflow and bottom shear stress. Then, bottom shear stress pdf, coupled with habitat suitability curves (e.g., empirical relations between species density and bottom shear stress) derived from field studies are used to produce maps of macroinvertebrate suitability to shear stress conditions. Thus, moving from measured hydrologic conditions, possible effects of river streamflow alterations on macroinvertebrate densities may be fairly assessed. We apply this framework to an Austrian river network, used as benchmark for the analysis, for which rainfall and streamflow time-series and river network hydraulic properties and macroinvertebrate density data are available. A comparison between observed vs "modeled" species' density in three locations along the examined river network is also presented. Although the proposed approach focuses on a single controlling factor, it shows important implications with water resources management and fluvial ecosystem protection.

Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture and Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Water

DTIC Science & Technology

2000-10-01

Purpose: To combine clinical, serum, pathologic and computer derived information into an artificial neural network to develop/validate a model to...Development of an artificial neural network (year 02). Prospective validation of this model (projected year 03). All models will be tested and

Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture & Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Material

DTIC Science & Technology

1999-10-01

THE PURPOSE OF THIS REPORT IS TO COMBINE CLINICAL, SERUM, PATHOLOGICAL AND COMPUTER DERIVED INFORMATION INTO AN ARTIFICIAL NEURAL NETWORK TO DEVELOP...01). Development of a artificial neural network model (year 02). Prospective validation of this model (projected year 03). All models will be tested

Place-Based Social Network Quality and Correlates of Substance Use among Urban Adolescents

ERIC Educational Resources Information Center

Mason, Michael J.; Valente, Thomas W.; Coatsworth, J. Douglas; Mennis, Jeremy; Lawrence, Frank; Zelenak, Patricia

2010-01-01

A sample of 301 Philadelphia adolescents were assessed for substance use and place-based social network quality, a weighted variable based upon risky and protective behaviors of alters. The network measure was anchored in routine locations identified as safe, risky, important, or favorite. Results show young females' (13-16) substance use was…

Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways

PubMed Central

Burgess, Stephen; Daniel, Rhian M; Butterworth, Adam S; Thompson, Simon G

2015-01-01

Background: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. Methods: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. Results: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. Conclusions: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes. PMID:25150977

Relations that affect the probability and prediction of nitrate concentration in private wells in the glacial aquifer system in the United States

USGS Publications Warehouse

Warner, Kelly L.; Arnold, Terri L.

2010-01-01

Nitrate in private wells in the glacial aquifer system is a concern for an estimated 17 million people using private wells because of the proximity of many private wells to nitrogen sources. Yet, less than 5 percent of private wells sampled in this study contained nitrate in concentrations that exceeded the U.S. Environmental Protection Agency (USEPA) Maximum Contaminant Level (MCL) of 10 mg/L (milligrams per liter) as N (nitrogen). However, this small group with nitrate concentrations above the USEPA MCL includes some of the highest nitrate concentrations detected in groundwater from private wells (77 mg/L). Median nitrate concentration measured in groundwater from private wells in the glacial aquifer system (0.11 mg/L as N) is lower than that in water from other unconsolidated aquifers and is not strongly related to surface sources of nitrate. Background concentration of nitrate is less than 1 mg/L as N. Although overall nitrate concentration in private wells was low relative to the MCL, concentrations were highly variable over short distances and at various depths below land surface. Groundwater from wells in the glacial aquifer system at all depths was a mixture of old and young water. Oxidation and reduction potential changes with depth and groundwater age were important influences on nitrate concentrations in private wells. A series of 10 logistic regression models was developed to estimate the probability of nitrate concentration above various thresholds. The threshold concentration (1 to 10 mg/L) affected the number of variables in the model. Fewer explanatory variables are needed to predict nitrate at higher threshold concentrations. The variables that were identified as significant predictors for nitrate concentration above 4 mg/L as N included well characteristics such as open-interval diameter, open-interval length, and depth to top of open interval. Environmental variables in the models were mean percent silt in soil, soil type, and mean depth to saturated soil. The 10-year mean (1992-2001) application rate of nitrogen fertilizer applied to farms was included as the potential source variable. A linear regression model also was developed to predict mean nitrate concentrations in well networks. The model is based on network averages because nitrate concentrations are highly variable over short distances. Using values for each of the predictor variables averaged by network (network mean value) from the logistic regression models, the linear regression model developed in this study predicted the mean nitrate concentration in well networks with a 95 percent confidence in predictions.

Bandwidth variable transceivers with artificial neural network-aided provisioning and capacity improvement capabilities in meshed optical networks with cascaded ROADM filtering

NASA Astrophysics Data System (ADS)

Zhou, Xingyu; Zhuge, Qunbi; Qiu, Meng; Xiang, Meng; Zhang, Fangyuan; Wu, Baojian; Qiu, Kun; Plant, David V.

2018-02-01

We investigate the capacity improvement achieved by bandwidth variable transceivers (BVT) in meshed optical networks with cascaded ROADM filtering at fixed channel spacing, and then propose an artificial neural network (ANN)-aided provisioning scheme to select optimal symbol rate and modulation format for the BVTs in this scenario. Compared with a fixed symbol rate transceiver with standard QAMs, it is shown by both experiments and simulations that BVTs can increase the average capacity by more than 17%. The ANN-aided BVT provisioning method uses parameters monitored from a coherent receiver and then employs a trained ANN to transform these parameters into the desired configuration. It is verified by simulation that the BVT with the proposed provisioning method can approach the upper limit of the system capacity obtained by brute-force search under various degrees of flexibilities.

Variable speed limit strategies analysis with mesoscopic traffic flow model based on complex networks

NASA Astrophysics Data System (ADS)

Li, Shu-Bin; Cao, Dan-Ni; Dang, Wen-Xiu; Zhang, Lin

As a new cross-discipline, the complexity science has penetrated into every field of economy and society. With the arrival of big data, the research of the complexity science has reached its summit again. In recent years, it offers a new perspective for traffic control by using complex networks theory. The interaction course of various kinds of information in traffic system forms a huge complex system. A new mesoscopic traffic flow model is improved with variable speed limit (VSL), and the simulation process is designed, which is based on the complex networks theory combined with the proposed model. This paper studies effect of VSL on the dynamic traffic flow, and then analyzes the optimal control strategy of VSL in different network topologies. The conclusion of this research is meaningful to put forward some reasonable transportation plan and develop effective traffic management and control measures to help the department of traffic management.

Analysis of a utility-interactive wind-photovoltaic hybrid system with battery storage using neural network

NASA Astrophysics Data System (ADS)

Giraud, Francois

1999-10-01

This dissertation investigates the application of neural network theory to the analysis of a 4-kW Utility-interactive Wind-Photovoltaic System (WPS) with battery storage. The hybrid system comprises a 2.5-kW photovoltaic generator and a 1.5-kW wind turbine. The wind power generator produces power at variable speed and variable frequency (VSVF). The wind energy is converted into dc power by a controlled, tree-phase, full-wave, bridge rectifier. The PV power is maximized by a Maximum Power Point Tracker (MPPT), a dc-to-dc chopper, switching at a frequency of 45 kHz. The whole dc power of both subsystems is stored in the battery bank or conditioned by a single-phase self-commutated inverter to be sold to the utility at a predetermined amount. First, the PV is modeled using Artificial Neural Network (ANN). To reduce model uncertainty, the open-circuit voltage VOC and the short-circuit current ISC of the PV are chosen as model input variables of the ANN. These input variables have the advantage of incorporating the effects of the quantifiable and non-quantifiable environmental variants affecting the PV power. Then, a simplified way to predict accurately the dynamic responses of the grid-linked WPS to gusty winds using a Recurrent Neural Network (RNN) is investigated. The RNN is a single-output feedforward backpropagation network with external feedback, which allows past responses to be fed back to the network input. In the third step, a Radial Basis Functions (RBF) Network is used to analyze the effects of clouds on the Utility-Interactive WPS. Using the irradiance as input signal, the network models the effects of random cloud movement on the output current, the output voltage, the output power of the PV system, as well as the electrical output variables of the grid-linked inverter. Fourthly, using RNN, the combined effects of a random cloud and a wind gusts on the system are analyzed. For short period intervals, the wind speed and the solar radiation are considered as the sole sources of power, whose variations influence the system variables. Since both subsystems have different dynamics, their respective responses are expected to impact differently the whole system behavior. The dispatchability of the battery-supported system as well as its stability and reliability during gusts and/or cloud passage is also discussed. In the fifth step, the goal is to determine to what extent the overall power quality of the grid would be affected by a proliferation of Utility-interactive hybrid system and whether recourse to bulky or individual filtering and voltage controller is necessary. The final stage of the research includes a steady-state analysis of two-year operation (May 96--Apr 98) of the system, with a discussion on system reliability, on any loss of supply probability, and on the effects of the randomness in the wind and solar radiation upon the system design optimization.

Formation of Community-Based Hypertension Practice Networks: Success, Obstacles, and Lessons Learned

PubMed Central

Dart, Richard A.; Egan, Brent M.

2014-01-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper we review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O’QUIN) project, located at the Medical University of South Carolina. We highlight key lessons learned and new directions to be explored. PMID:24666425

Soil Bacterial and Fungal Communities Show Distinct Recovery Patterns during Forest Ecosystem Restoration

PubMed Central

Li, Song; Avera, Bethany N.; Strahm, Brian D.; Badgley, Brian D.

2017-01-01

ABSTRACT Bacteria and fungi are important mediators of biogeochemical processes and play essential roles in the establishment of plant communities, which makes knowledge about their recovery after extreme disturbances valuable for understanding ecosystem development. However, broad ecological differences between bacterial and fungal organisms, such as growth rates, stress tolerance, and substrate utilization, suggest they could follow distinct trajectories and show contrasting dynamics during recovery. In this study, we analyzed both the intra-annual variability and decade-scale recovery of bacterial and fungal communities in a chronosequence of reclaimed mined soils using next-generation sequencing to quantify their abundance, richness, β-diversity, taxonomic composition, and cooccurrence network properties. Bacterial communities shifted gradually, with overlapping β-diversity patterns across chronosequence ages, while shifts in fungal communities were more distinct among different ages. In addition, the magnitude of intra-annual variability in bacterial β-diversity was comparable to the changes across decades of chronosequence age, while fungal communities changed minimally across months. Finally, the complexity of bacterial cooccurrence networks increased with chronosequence age, while fungal networks did not show clear age-related trends. We hypothesize that these contrasting dynamics of bacteria and fungi in the chronosequence result from (i) higher growth rates for bacteria, leading to higher intra-annual variability; (ii) higher tolerance to environmental changes for fungi; and (iii) stronger influence of vegetation on fungal communities. IMPORTANCE Both bacteria and fungi play essential roles in ecosystem functions, and information about their recovery after extreme disturbances is important for understanding whole-ecosystem development. Given their many differences in phenotype, phylogeny, and life history, a comparison of different bacterial and fungal recovery patterns improves the understanding of how different components of the soil microbiota respond to ecosystem recovery. In this study, we highlight key differences between soil bacteria and fungi during the restoration of reclaimed mine soils in the form of long-term diversity patterns, intra-annual variability, and potential interaction networks. Cooccurrence networks revealed increasingly complex bacterial community interactions during recovery, in contrast to much simpler and more isolated fungal network patterns. This study compares bacterial and fungal cooccurrence networks and reveals cooccurrences persisting through successional ages. PMID:28476769

Soil Bacterial and Fungal Communities Show Distinct Recovery Patterns during Forest Ecosystem Restoration.

PubMed

Sun, Shan; Li, Song; Avera, Bethany N; Strahm, Brian D; Badgley, Brian D

2017-07-15

Bacteria and fungi are important mediators of biogeochemical processes and play essential roles in the establishment of plant communities, which makes knowledge about their recovery after extreme disturbances valuable for understanding ecosystem development. However, broad ecological differences between bacterial and fungal organisms, such as growth rates, stress tolerance, and substrate utilization, suggest they could follow distinct trajectories and show contrasting dynamics during recovery. In this study, we analyzed both the intra-annual variability and decade-scale recovery of bacterial and fungal communities in a chronosequence of reclaimed mined soils using next-generation sequencing to quantify their abundance, richness, β-diversity, taxonomic composition, and cooccurrence network properties. Bacterial communities shifted gradually, with overlapping β-diversity patterns across chronosequence ages, while shifts in fungal communities were more distinct among different ages. In addition, the magnitude of intra-annual variability in bacterial β-diversity was comparable to the changes across decades of chronosequence age, while fungal communities changed minimally across months. Finally, the complexity of bacterial cooccurrence networks increased with chronosequence age, while fungal networks did not show clear age-related trends. We hypothesize that these contrasting dynamics of bacteria and fungi in the chronosequence result from (i) higher growth rates for bacteria, leading to higher intra-annual variability; (ii) higher tolerance to environmental changes for fungi; and (iii) stronger influence of vegetation on fungal communities. IMPORTANCE Both bacteria and fungi play essential roles in ecosystem functions, and information about their recovery after extreme disturbances is important for understanding whole-ecosystem development. Given their many differences in phenotype, phylogeny, and life history, a comparison of different bacterial and fungal recovery patterns improves the understanding of how different components of the soil microbiota respond to ecosystem recovery. In this study, we highlight key differences between soil bacteria and fungi during the restoration of reclaimed mine soils in the form of long-term diversity patterns, intra-annual variability, and potential interaction networks. Cooccurrence networks revealed increasingly complex bacterial community interactions during recovery, in contrast to much simpler and more isolated fungal network patterns. This study compares bacterial and fungal cooccurrence networks and reveals cooccurrences persisting through successional ages. Copyright © 2017 American Society for Microbiology.

Age-related Multiscale Changes in Brain Signal Variability in Pre-task versus Post-task Resting-state EEG.

PubMed

Wang, Hongye; McIntosh, Anthony R; Kovacevic, Natasa; Karachalios, Maria; Protzner, Andrea B

2016-07-01

Recent empirical work suggests that, during healthy aging, the variability of network dynamics changes during task performance. Such variability appears to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into resting-state dynamics. We recorded EEG in young, middle-aged, and older adults during a "rest-task-rest" design and investigated if aging modifies the interaction between resting-state activity and external stimulus-induced activity. Using multiscale entropy as our measure of variability, we found that, with increasing age, resting-state dynamics shifts from distributed to more local neural processing, especially at posterior sources. In the young group, resting-state dynamics also changed from pre- to post-task, where fine-scale entropy increased in task-positive regions and coarse-scale entropy increased in the posterior cingulate, a key region associated with the default mode network. Lastly, pre- and post-task resting-state dynamics were linked to performance on the intervening task for all age groups, but this relationship became weaker with increasing age. Our results suggest that age-related changes in resting-state dynamics occur across different spatial and temporal scales and have consequences for information processing capacity.

Genes under weaker stabilizing selection increase network evolvability and rapid regulatory adaptation to an environmental shift.

PubMed

Laarits, T; Bordalo, P; Lemos, B

2016-08-01

Regulatory networks play a central role in the modulation of gene expression, the control of cellular differentiation, and the emergence of complex phenotypes. Regulatory networks could constrain or facilitate evolutionary adaptation in gene expression levels. Here, we model the adaptation of regulatory networks and gene expression levels to a shift in the environment that alters the optimal expression level of a single gene. Our analyses show signatures of natural selection on regulatory networks that both constrain and facilitate rapid evolution of gene expression level towards new optima. The analyses are interpreted from the standpoint of neutral expectations and illustrate the challenge to making inferences about network adaptation. Furthermore, we examine the consequence of variable stabilizing selection across genes on the strength and direction of interactions in regulatory networks and in their subsequent adaptation. We observe that directional selection on a highly constrained gene previously under strong stabilizing selection was more efficient when the gene was embedded within a network of partners under relaxed stabilizing selection pressure. The observation leads to the expectation that evolutionarily resilient regulatory networks will contain optimal ratios of genes whose expression is under weak and strong stabilizing selection. Altogether, our results suggest that the variable strengths of stabilizing selection across genes within regulatory networks might itself contribute to the long-term adaptation of complex phenotypes. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Missed connections: A case study of the social networks of physics doctoral students in a single department

NASA Astrophysics Data System (ADS)

Knaub, Alexis Victoria

Gender disparity is an issue among the many science, technology, engineering, and mathematics (STEM) fields. Although many previous studies examine gender issues in STEM as an aggregate discipline, there are unique issues to each of the fields that are considered STEM fields. Some fields, such as physics, have fewer women graduating with degrees than other fields. This suggests that women's experiences vary by STEM field. The majority of previous research also examines gender and other disparities at either the nationwide or individual level. This project entailed social network analysis through survey and interview data to examine a single physics department's doctoral students in order to provide a comprehensive look at student social experiences. In addition to examining gender, other demographic variables were studied to see if the results are truly associated with gender; these variables include race/ethnicity, year in program, student type, relationship status, research type, undergraduate institute, and subfield. Data were examined to determine if there are relationships to social connections and outcome variables such as persistence in completing the degree and the time to degree. Data collected on faculty were used to rank faculty members; data such as h-indices and number of students graduate over the past 5 years were collected. Fifty-five (55) of 110 possible participants completed the survey; forty-three are male, and twelve are female. Twenty-eight of the fifty-five survey participants were interview; twenty-three are male, and five are female. Findings for peer networks include that peer networks are established during the first year and do not change drastically as one progresses in the program. Geographic location within the campus affects socializing with peers. Connections to fellow students are not necessarily reciprocated; the maximum percentage of reciprocated connections is 60%. The number of connections one has varies by network purpose, with students having more connections for the more social purposes. Students are isolated when working on their research, even in their early years. Research discussion does not occur, unless one is providing casual updates to a peer. Findings for student-faculty networks indicate that these relationships are important but complicated. Advisor selection is often done casually, even when one is switching advisors. Faculty have a lot of influence on the doctoral students such as motivating research collaborations among students or aiding in the job search. Most doctoral students feel as though there is a power dynamic that hinders them from socializing with faculty and thus, are not close to the faculty. Opportunities to develop stronger relationships and for professional development are often missed. The total number of peer and faculty ties has significant relationships to whether a student considers leaving the program. Analyzing the qualitative and quantitative data through demographic variables showed how complex these experiences are. All demographic variables indicated there are statistically significant differences in social experience among the groups, though the extent varies. The year in program variable showed the most differences among cohort years, primarily with those in the fifth year. While gender showed few differences, women tended to have more homophilous peer networks than men and women tended to have more connections to higher prestige faculty. The race/ethnicity, student type, undergraduate institute, subfield, and relationship status variables produced few statistically significant results. Peer networks have statistically significant differences in homophily when examining research type. The regression model suggests that being female, having a higher year in the program, and/or completing undergraduate studies from a liberal arts college increases the time to degree. Being in a relationship (dating or married) and/or working on experimental research decreases the time to degree. Only research peer network and departmental information network variables remain in this model. Suggestions for further research for both physics/STEM education and social network analysis are included. Suggestions for ways in which the Jonas University physics department can improve its climate are also included. Although these suggestions are written based upon the Jonas University data, they may be applicable to other physics/STEM graduate programs.

Effective prediction of biodiversity in tidal flat habitats using an artificial neural network.

PubMed

Yoo, Jae-Won; Lee, Yong-Woo; Lee, Chang-Gun; Kim, Chang-Soo

2013-02-01

Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991-2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007-2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hooman, A.; Mohammadzadeh, M

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curvemore » (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions.« less

Rain rate and modeled fade distributions at 20 GHz and 30 GHz derived from five years of network rain gauge measurements

NASA Technical Reports Server (NTRS)

Goldhirsh, Julius; Krichevsky, Vladimir; Gebo, Norman

1992-01-01

Five years of rain rate and modeled slant path attenuation distributions at 20 GHz and 30 GHz derived from a network of 10 tipping bucket rain gages was examined. The rain gage network is located within a grid 70 km north-south and 47 km east-west in the Mid-Atlantic coast of the United States in the vicinity of Wallops Island, Virginia. Distributions were derived from the variable integration time data and from one minute averages. It was demonstrated that for realistic fade margins, the variable integration time results are adequate to estimate slant path attenuations at frequencies above 20 GHz using models which require one minute averages. An accurate empirical formula was developed to convert the variable integration time rain rates to one minute averages. Fade distributions at 20 GHz and 30 GHz were derived employing Crane's Global model because it was demonstrated to exhibit excellent accuracy with measured COMSTAR fades at 28.56 GHz.

Prediction of municipal solid waste generation using nonlinear autoregressive network.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A

2015-12-01

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

Class identity assignment for amphetamines using neural networks and GC-FTIR data

NASA Astrophysics Data System (ADS)

Gosav, S.; Praisler, M.; Van Bocxlaer, J.; De Leenheer, A. P.; Massart, D. L.

2006-08-01

An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC-FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.

Epidemic spreading on activity-driven networks with attractiveness.

PubMed

Pozzana, Iacopo; Sun, Kaiyuan; Perra, Nicola

2017-10-01

We study SIS epidemic spreading processes unfolding on a recent generalization of the activity-driven modeling framework. In this model of time-varying networks, each node is described by two variables: activity and attractiveness. The first describes the propensity to form connections, while the second defines the propensity to attract them. We derive analytically the epidemic threshold considering the time scale driving the evolution of contacts and the contagion as comparable. The solutions are general and hold for any joint distribution of activity and attractiveness. The theoretical picture is confirmed via large-scale numerical simulations performed considering heterogeneous distributions and different correlations between the two variables. We find that heterogeneous distributions of attractiveness alter the contagion process. In particular, in the case of uncorrelated and positive correlations between the two variables, heterogeneous attractiveness facilitates the spreading. On the contrary, negative correlations between activity and attractiveness hamper the spreading. The results presented contribute to the understanding of the dynamical properties of time-varying networks and their effects on contagion phenomena unfolding on their fabric.

Sparse short-distance connections enhance calcium wave propagation in a 3D model of astrocyte networks

PubMed Central

Lallouette, Jules; De Pittà, Maurizio; Ben-Jacob, Eshel; Berry, Hugues

2014-01-01

Traditionally, astrocytes have been considered to couple via gap-junctions into a syncytium with only rudimentary spatial organization. However, this view is challenged by growing experimental evidence that astrocytes organize as a proper gap-junction mediated network with more complex region-dependent properties. On the other hand, the propagation range of intercellular calcium waves (ICW) within astrocyte populations is as well highly variable, depending on the brain region considered. This suggests that the variability of the topology of gap-junction couplings could play a role in the variability of the ICW propagation range. Since this hypothesis is very difficult to investigate with current experimental approaches, we explore it here using a biophysically realistic model of three-dimensional astrocyte networks in which we varied the topology of the astrocyte network, while keeping intracellular properties and spatial cell distribution and density constant. Computer simulations of the model suggest that changing the topology of the network is indeed sufficient to reproduce the distinct ranges of ICW propagation reported experimentally. Unexpectedly, our simulations also predict that sparse connectivity and restriction of gap-junction couplings to short distances should favor propagation while long–distance or dense connectivity should impair it. Altogether, our results provide support to recent experimental findings that point toward a significant functional role of the organization of gap-junction couplings into proper astroglial networks. Dynamic control of this topology by neurons and signaling molecules could thus constitute a new type of regulation of neuron-glia and glia-glia interactions. PMID:24795613

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China.

PubMed

Wen, Xiaohu; Fang, Jing; Diao, Meina; Zhang, Chuanqi

2013-05-01

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl(-)), calcium (Ca(2+)), total alkalinity, total hardness, nitrate nitrogen (NO3-N), and ammonical nitrogen (NH4-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO3-N, NH4-N, and Ca(2+). Cl(-) was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.

Aberrant functional connectivity of default-mode network in type 2 diabetes patients.

PubMed

Cui, Ying; Jiao, Yun; Chen, Hua-Jun; Ding, Jie; Luo, Bing; Peng, Cheng-Yu; Ju, Sheng-Hong; Teng, Gao-Jun

2015-11-01

Type 2 diabetes mellitus is associated with increased risk for dementia. Patients with impaired cognition often show default-mode network disruption. We aimed to investigate the integrity of a default-mode network in diabetic patients by using independent component analysis, and to explore the relationship between network abnormalities, neurocognitive performance and diabetic variables. Forty-two patients with type 2 diabetes and 42 well-matched healthy controls were included and underwent resting-state functional MRI in a 3 Tesla unit. Independent component analysis was adopted to extract the default-mode network, including its anterior and posterior components. Z-maps of both sub-networks were compared between the two groups and correlated with each clinical variable. Patients showed increased connectivity around the medial prefrontal cortex in the anterior sub-network, but decreased connectivity around the posterior cingulate cortex in the posterior sub-network. The decreased connectivity in the posterior part was significantly correlated with the score on Complex Figure Test-delay recall test (r = 0.359, p = 0.020), the time spent on Trail-Making Test-part B (r = -0.346, p = 0.025) and the insulin resistance level (r = -0.404, p = 0.024). Dissociation pattern in the default-mode network was found in diabetic patients, which might provide powerful new insights into the neural mechanisms that underlie the diabetes-related cognitive decline. • Type 2 diabetes mellitus is associated with impaired cognition • Default- mode network plays a central role in maintaining normal cognition • Network connectivity within the default mode was disrupted in type 2 diabetes patients • Decreased network connectivity was correlated with cognitive performance and insulin resistance level • Disrupted default-mode network might explain the impaired cognition in diabetic population.

Recent developments in VSD imaging of small neuronal networks

PubMed Central

Hill, Evan S.; Bruno, Angela M.

2014-01-01

Voltage-sensitive dye (VSD) imaging is a powerful technique that can provide, in single experiments, a large-scale view of network activity unobtainable with traditional sharp electrode recording methods. Here we review recent work using VSDs to study small networks and highlight several results from this approach. Topics covered include circuit mapping, network multifunctionality, the network basis of decision making, and the presence of variably participating neurons in networks. Analytical tools being developed and applied to large-scale VSD imaging data sets are discussed, and the future prospects for this exciting field are considered. PMID:25225295

Harmonising and semantically linking key variables from in-situ observing networks of an Integrated Atlantic Ocean Observing System, AtlantOS

NASA Astrophysics Data System (ADS)

Darroch, Louise; Buck, Justin

2017-04-01

Atlantic Ocean observation is currently undertaken through loosely-coordinated, in-situ observing networks, satellite observations and data management arrangements at regional, national and international scales. The EU Horizon 2020 AtlantOS project aims to deliver an advanced framework for the development of an Integrated Atlantic Ocean Observing System that strengthens the Global Ocean Observing System (GOOS) and contributes to the aims of the Galway Statement on Atlantic Ocean Cooperation. One goal is to ensure that data from different and diverse in-situ observing networks are readily accessible and useable to a wider community, including the international ocean science community and other stakeholders in this field. To help achieve this goal, the British Oceanographic Data Centre (BODC) produced a parameter matrix to harmonise data exchange, data flow and data integration for the key variables acquired by multiple in-situ AtlantOS observing networks such as ARGO, Seafloor Mapping and OceanSITES. Our solution used semantic linking of controlled vocabularies and metadata for parameters that were "mappable" to existing EU and international standard vocabularies. An AtlantOS Essential Variables list of terms (aggregated level) based on Global Climate Observing System (GCOS) Essential Climate Variables (ECV), GOOS Essential Ocean Variables (EOV) and other key network variables was defined and published on the Natural Environment Research Council (NERC) Vocabulary Server (version 2.0) as collection A05 (http://vocab.nerc.ac.uk/collection/A05/current/). This new vocabulary was semantically linked to standardised metadata for observed properties and units that had been validated by the AtlantOS community: SeaDataNet parameters (P01), Climate and Forecast (CF) Standard Names (P07) and SeaDataNet units (P06). Observed properties were mapped to biological entities from the internationally assured AphiaID from the WOrld Register of Marine Species (WoRMS), http://www.marinespecies.org/aphia.php?p=webservice. The AtlantOS parameter matrix offers a way to harmonise the globally important variables (such as ECVs and EOVs) from in-situ observing networks that use different flavours of exchange formats based on SeaDataNet and CF parameter metadata. It also offers a way to standardise data in the wider Integrated Ocean Observing System. It uses sustainable and trusted standardised vocabularies that are governed by internationally renowned and long-standing organisations and is interoperable through the use of persistent resource identifiers, such as URNs and PURLs. It is the first step to integrating and serving data in a variety of international exchange formats using Application programming interfaces (API) improving both data discoverability and utility for users.

DNET: A communications facility for distributed heterogeneous computing

NASA Technical Reports Server (NTRS)

Tole, John; Nagappan, S.; Clayton, J.; Ruotolo, P.; Williamson, C.; Solow, H.

1989-01-01

This document describes DNET, a heterogeneous data communications networking facility. DNET allows programs operating on hosts on dissimilar networks to communicate with one another without concern for computer hardware, network protocol, or operating system differences. The overall DNET network is defined as the collection of host machines/networks on which the DNET software is operating. Each underlying network is considered a DNET 'domain'. Data communications service is provided between any two processes on any two hosts on any of the networks (domains) that may be reached via DNET. DNET provides protocol transparent, reliable, streaming data transmission between hosts (restricted, initially to DECnet and TCP/IP networks). DNET also provides variable length datagram service with optional return receipts.

Prediction of road accidents: A Bayesian hierarchical approach.

PubMed

Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H

2013-03-01

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any road network provided that the required data are available. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimization of Turbine Blade Design for Reusable Launch Vehicles

NASA Technical Reports Server (NTRS)

Shyy, Wei

1998-01-01

To facilitate design optimization of turbine blade shape for reusable launching vehicles, appropriate techniques need to be developed to process and estimate the characteristics of the design variables and the response of the output with respect to the variations of the design variables. The purpose of this report is to offer insight into developing appropriate techniques for supporting such design and optimization needs. Neural network and polynomial-based techniques are applied to process aerodynamic data obtained from computational simulations for flows around a two-dimensional airfoil and a generic three- dimensional wing/blade. For the two-dimensional airfoil, a two-layered radial-basis network is designed and trained. The performances of two different design functions for radial-basis networks, one based on the accuracy requirement, whereas the other one based on the limit on the network size. While the number of neurons needed to satisfactorily reproduce the information depends on the size of the data, the neural network technique is shown to be more accurate for large data set (up to 765 simulations have been used) than the polynomial-based response surface method. For the three-dimensional wing/blade case, smaller aerodynamic data sets (between 9 to 25 simulations) are considered, and both the neural network and the polynomial-based response surface techniques improve their performance as the data size increases. It is found while the relative performance of two different network types, a radial-basis network and a back-propagation network, depends on the number of input data, the number of iterations required for radial-basis network is less than that for the back-propagation network.

Use of neural networks to model complex immunogenetic associations of disease: human leukocyte antigen impact on the progression of human immunodeficiency virus infection.

PubMed

Ioannidis, J P; McQueen, P G; Goedert, J J; Kaslow, R A

1998-03-01

Complex immunogenetic associations of disease involving a large number of gene products are difficult to evaluate with traditional statistical methods and may require complex modeling. The authors evaluated the performance of feed-forward backpropagation neural networks in predicting rapid progression to acquired immunodeficiency syndrome (AIDS) for patients with human immunodeficiency virus (HIV) infection on the basis of major histocompatibility complex variables. Networks were trained on data from patients from the Multicenter AIDS Cohort Study (n = 139) and then validated on patients from the DC Gay cohort (n = 102). The outcome of interest was rapid disease progression, defined as progression to AIDS in <6 years from seroconversion. Human leukocyte antigen (HLA) variables were selected as network inputs with multivariate regression and a previously described algorithm selecting markers with extreme point estimates for progression risk. Network performance was compared with that of logistic regression. Networks with 15 HLA inputs and a single hidden layer of five nodes achieved a sensitivity of 87.5% and specificity of 95.6% in the training set, vs. 77.0% and 76.9%, respectively, achieved by logistic regression. When validated on the DC Gay cohort, networks averaged a sensitivity of 59.1% and specificity of 74.3%, vs. 53.1% and 61.4%, respectively, for logistic regression. Neural networks offer further support to the notion that HIV disease progression may be dependent on complex interactions between different class I and class II alleles and transporters associated with antigen processing variants. The effect in the current models is of moderate magnitude, and more data as well as other host and pathogen variables may need to be considered to improve the performance of the models. Artificial intelligence methods may complement linear statistical methods for evaluating immunogenetic associations of disease.

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

First-Year Students' Use of Social Network Sites to Reduce the Uncertainty of Anticipatory Socialization

ERIC Educational Resources Information Center

Anderson, Isolde K.; Lerstrom, Alan; Tintle, Nathan

2014-01-01

This study surveyed 399 incoming first-year students at two colleges in the Midwest on their use of social network sites before college entry and its impact on various dimensions of the first-year experience. Significant correlations were found for two pairs of variables: (a) students who used social network sites before arriving on campus…

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

PubMed

Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny

2018-04-16

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

The QSE-Reduced Nuclear Reaction Network for Silicon Burning

NASA Astrophysics Data System (ADS)

Hix, W. Raphael; Parete-Koon, Suzanne T.; Freiburghaus, Christian; Thielemann, Friedrich-Karl

2007-09-01

Iron and neighboring nuclei are formed in massive stars shortly before core collapse and during their supernova outbursts, as well as during thermonuclear supernovae. Complete and incomplete silicon burning are responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. Because of the large number of nuclei involved, accurate modeling of silicon burning is computationally expensive. However, examination of the physics of silicon burning has revealed that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present a new hybrid equilibrium-network scheme which takes advantage of this quasi-equilibrium in order to reduce the number of independent variables calculated. This allows accurate prediction of the nuclear abundance evolution, deleptonization, and energy generation at a greatly reduced computational cost when compared to a conventional nuclear reaction network. During silicon burning, the resultant QSE-reduced network is approximately an order of magnitude faster than the full network it replaces and requires the tracking of less than a third as many abundance variables, without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multidimensional applications.

Embedding recurrent neural networks into predator-prey models.

PubMed

Moreau, Yves; Louiès, Stephane; Vandewalle, Joos; Brenig, Leon

1999-03-01

We study changes of coordinates that allow the embedding of ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models-also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form (Brenig, L. (1988). Complete factorization and analytic solutions of generalized Lotka-Volterra equations. Physics Letters A, 133(7-8), 378-382), where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoid. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network. We expect that this transformation will permit the application of existing techniques for the analysis of Lotka-Volterra systems to recurrent neural networks. Furthermore, our results show that Lotka-Volterra systems are universal approximators of dynamical systems, just as are continuous-time neural networks.

Prediction of outcome in multiorgan resections for cancer using a bayes-network.

PubMed

Udelnow, Andrej; Leinung, Steffen; Grochola, Lukasz Filipp; Henne-Bruns, Doris; Wfcrl, Peter

2013-01-01

The long-term success of multivisceral resections for cancer is difficult to forecast due to the complexity of factors influencing the prognosis. The aim of our study was to assess the predictivity of a Bayes network for the postoperative outcome and survival. We included each oncologic patient undergoing resection of 4 or more organs from 2002 till 2005 at the Ulm university hospital. Preoperative data were assessed as well as the tumour classification, the resected organs, intra- and postoperative complications and overall survival. Using the Genie 2.0 software we developed a Bayes network. Multivisceral tumour resections were performed in 22 patients. The receiver operating curve areas of the variables "survival >12 months" and "hospitalisation >28 days" as predicted by the Bayes network were 0.81 and 0.77 and differed significantly from 0.5 (p: 0.019 and 0.028, respectively). The positive predictive values of the Bayes network for these variables were 1 and 0.8 and the negative ones 0.71 and 0.88, respectively. Bayes networks are useful for the prognosis estimation of individual patients and can help to decide whether to perform a multivisceral resection for cancer.

Variable neighborhood search for reverse engineering of gene regulatory networks.

PubMed

Nicholson, Charles; Goodwin, Leslie; Clark, Corey

2017-01-01

A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time. Copyright © 2016 Elsevier Inc. All rights reserved.

Synchronization in node of complex networks consist of complex chaotic system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Qiang, E-mail: qiangweibeihua@163.com; Digital Images Processing Institute of Beihua University, BeiHua University, Jilin, 132011, Jilin; Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024

2014-07-15

A new synchronization method is investigated for node of complex networks consists of complex chaotic system. When complex networks realize synchronization, different component of complex state variable synchronize up to different scaling complex function by a designed complex feedback controller. This paper change synchronization scaling function from real field to complex field for synchronization in node of complex networks with complex chaotic system. Synchronization in constant delay and time-varying coupling delay complex networks are investigated, respectively. Numerical simulations are provided to show the effectiveness of the proposed method.

Zinc oxide nanowire networks for macroelectronic devices

NASA Astrophysics Data System (ADS)

Unalan, Husnu Emrah; Zhang, Yan; Hiralal, Pritesh; Dalal, Sharvari; Chu, Daping; Eda, Goki; Teo, K. B. K.; Chhowalla, Manish; Milne, William I.; Amaratunga, Gehan A. J.

2009-04-01

Highly transparent zinc oxide (ZnO) nanowire networks have been used as the active material in thin film transistors (TFTs) and complementary inverter devices. A systematic study on a range of networks of variable density and TFT channel length was performed. ZnO nanowire networks provide a less lithographically intense alternative to individual nanowire devices, are always semiconducting, and yield significantly higher mobilites than those achieved from currently used amorphous Si and organic TFTs. These results suggest that ZnO nanowire networks could be ideal for inexpensive large area electronics.

Matching network for RF plasma source

DOEpatents

Pickard, Daniel S.; Leung, Ka-Ngo

2007-11-20

A compact matching network couples an RF power supply to an RF antenna in a plasma generator. The simple and compact impedance matching network matches the plasma load to the impedance of a coaxial transmission line and the output impedance of an RF amplifier at radio frequencies. The matching network is formed of a resonantly tuned circuit formed of a variable capacitor and an inductor in a series resonance configuration, and a ferrite core transformer coupled to the resonantly tuned circuit. This matching network is compact enough to fit in existing compact focused ion beam systems.