Renal denervation improves cardiac function in rats with chronic heart failure: Effects on expression of β-adrenoceptors

PubMed Central

Zheng, Hong; Liu, Xuefei; Sharma, Neeru M.

2016-01-01

Chronic activation of the sympathetic drive contributes to cardiac remodeling and dysfunction during chronic heart failure (HF). The present study was undertaken to assess whether renal denervation (RDN) would abrogate the sympathoexcitation in HF and ameliorate the adrenergic dysfunction and cardiac damage. Ligation of the left coronary artery was used to induce HF in Sprague-Dawley rats. Four weeks after surgery, RDN was performed, 1 wk before the final measurements. At the end of the protocol, cardiac function was assessed by measuring ventricular hemodynamics. Rats with HF had an average infarct area >30% of the left ventricle and left ventricular end-diastolic pressure (LVEDP) >20 mmHg. β1- and β2-adrenoceptor proteins in the left ventricle were reduced by 37 and 49%, respectively, in the rats with HF. RDN lowered elevated levels of urinary excretion of norepinephrine and brain natriuretic peptide levels in the hearts of rats with HF. RDN also decreased LVEDP to 10 mmHg and improved basal dP/dt to within the normal range in rats with HF. RDN blunted loss of β1-adrenoceptor (by 47%) and β2-adrenoceptor (by 100%) protein expression and improved isoproterenol (0.5 μg/kg)-induced increase in +dP/dt (by 71%) and −dP/dt (by 62%) in rats with HF. RDN also attenuated the increase in collagen 1 expression in the left ventricles of rats with HF. These findings demonstrate that RDN initiated in chronic HF condition improves cardiac function mediated by adrenergic agonist and blunts β-adrenoceptor expression loss, providing mechanistic insights for RDN-induced improvements in cardiac function in the HF condition. PMID:27288440

Account for Clinical Heterogeneity in Assessment of Catheter-based Renal Denervation among Resistant Hypertension Patients: Subgroup Meta-analysis

PubMed Central

Chen, Xiao-Han; Kim, Sehee; Zeng, Xiao-Xi; Chen, Zhi-Bing; Cui, Tian-Lei; Hu, Zhang-Xue; Li, Yi; Fu, Ping

2017-01-01

Background: Catheter-based renal denervation (RDN) is a novel treatment for resistant hypertension (RH). A recent meta-analysis reported that RDN did not significantly reduce blood pressure (BP) based on the pooled effects with mild to severe heterogeneity. The aim of the present study was to identify and reduce clinical sources of heterogeneity and reassess the safety and efficacy of RDN within the identified homogeneous subpopulations. Methods: This was a meta-analysis of 9 randomized clinical trials (RCTs) among patients with RH up to June 2016. Sensitivity analyses and subgroup analyses were extensively conducted by baseline systolic blood pressure (SBP) level, antihypertensive medication change rates, and coronary heart disease (CHD). Results: In all patients with RH, no statistical differences were found in mortality, severe cardiovascular events rate, and changes in 24-h SBP and office SBP at 6 and 12 months. However, subgroup analyses showed significant differences between the RDN and control groups. In the subpopulations with baseline 24-h SBP ≥155 mmHg (1 mmHg = 0.133 kPa) and the infrequently changed medication, the use of RDN resulted in a significant reduction in 24-h SBP level at 6 months (P = 0.100 and P = 0.009, respectively). Subgrouping RCTs with a higher prevalent CHD in control showed that the control treatment was significantly better than RDN in office SBP reduction at 6 months (P < 0.001). Conclusions: In all patients with RH, the catheter-based RDN is not more effective in lowering ambulatory or office BP than an optimized antihypertensive drug treatment at 6 and 12 months. However, among RH patients with higher baseline SBP, RDN might be more effective in reducing SBP. PMID:28639575

Account for Clinical Heterogeneity in Assessment of Catheter-based Renal Denervation among Resistant Hypertension Patients: Subgroup Meta-analysis.

PubMed

Chen, Xiao-Han; Kim, Sehee; Zeng, Xiao-Xi; Chen, Zhi-Bing; Cui, Tian-Lei; Hu, Zhang-Xue; Li, Yi; Fu, Ping

2017-07-05

Catheter-based renal denervation (RDN) is a novel treatment for resistant hypertension (RH). A recent meta-analysis reported that RDN did not significantly reduce blood pressure (BP) based on the pooled effects with mild to severe heterogeneity. The aim of the present study was to identify and reduce clinical sources of heterogeneity and reassess the safety and efficacy of RDN within the identified homogeneous subpopulations. This was a meta-analysis of 9 randomized clinical trials (RCTs) among patients with RH up to June 2016. Sensitivity analyses and subgroup analyses were extensively conducted by baseline systolic blood pressure (SBP) level, antihypertensive medication change rates, and coronary heart disease (CHD). In all patients with RH, no statistical differences were found in mortality, severe cardiovascular events rate, and changes in 24-h SBP and office SBP at 6 and 12 months. However, subgroup analyses showed significant differences between the RDN and control groups. In the subpopulations with baseline 24-h SBP ≥155 mmHg (1 mmHg = 0.133 kPa) and the infrequently changed medication, the use of RDN resulted in a significant reduction in 24-h SBP level at 6 months (P = 0.100 and P= 0.009, respectively). Subgrouping RCTs with a higher prevalent CHD in control showed that the control treatment was significantly better than RDN in office SBP reduction at 6 months (P < 0.001). In all patients with RH, the catheter-based RDN is not more effective in lowering ambulatory or office BP than an optimized antihypertensive drug treatment at 6 and 12 months. However, among RH patients with higher baseline SBP, RDN might be more effective in reducing SBP.

Integrative Blood Pressure Response to Upright Tilt Post Renal Denervation

PubMed Central

Howden, Erin J.; East, Cara; Lawley, Justin S.; Stickford, Abigail S.L.; Verhees, Myrthe; Fu, Qi

2017-01-01

Abstract BACKGROUND Whether renal denervation (RDN) in patients with resistant hypertension normalizes blood pressure (BP) regulation in response to routine cardiovascular stimuli such as upright posture is unknown. We conducted an integrative study of BP regulation in patients with resistant hypertension who had received RDN to characterize autonomic circulatory control. METHODS Twelve patients (60 ± 9 [SD] years, n = 10 males) who participated in the Symplicity HTN-3 trial were studied and compared to 2 age-matched normotensive (Norm) and hypertensive (unmedicated, HTN) control groups. BP, heart rate (HR), cardiac output (Qc), muscle sympathetic nerve activity (MSNA), and neurohormonal variables were measured supine, and 30° (5 minutes) and 60° (20 minutes) head-up-tilt (HUT). Total peripheral resistance (TPR) was calculated from mean arterial pressure and Qc. RESULTS Despite treatment with RDN and 4.8 (range, 3–7) antihypertensive medications, the RDN had significantly higher supine systolic BP compared to Norm and HTN (149 ± 15 vs. 118 ± 6, 108 ± 8 mm Hg, P < 0.001). When supine, RDN had higher HR, TPR, MSNA, plasma norepinephrine, and effective arterial elastance compared to Norm. Plasma norepinephrine, Qc, and HR were also higher in the RDN vs. HTN. During HUT, BP remained higher in the RDN, due to increases in Qc, plasma norepinephrine, and aldosterone. CONCLUSION We provide evidence of a possible mechanism by which BP remains elevated post RDN, with the observation of increased Qc and arterial stiffness, as well as plasma norepinephrine and aldosterone levels at approximately 2 years post treatment. These findings may be the consequence of incomplete ablation of sympathetic renal nerves or be related to other factors. PMID:28338768

The Effects of Renal Denervation on Renal Hemodynamics and Renal Vasculature in a Porcine Model

PubMed Central

Verloop, Willemien L.; Hubens, Lisette E. G.; Spiering, Wilko; Doevendans, Pieter A.; Goldschmeding, Roel; Bleys, Ronald L. A. W.; Voskuil, Michiel

2015-01-01

Rationale Recently, the efficacy of renal denervation (RDN) has been debated. It is discussed whether RDN is able to adequately target the renal nerves. Objective We aimed to investigate how effective RDN was by means of functional hemodynamic measurements and nerve damage on histology. Methods and Results We performed hemodynamic measurements in both renal arteries of healthy pigs using a Doppler flow and pressure wire. Subsequently unilateral denervation was performed, followed by repeated bilateral hemodynamic measurements. Pigs were terminated directly after RDN or were followed for 3 weeks or 3 months after the procedure. After termination, both treated and control arteries were prepared for histology to evaluate vascular damage and nerve damage. Directly after RDN, resting renal blood flow tended to increase by 29±67% (P = 0.01). In contrast, renal resistance reserve increased from 1.74 (1.28) to 1.88 (1.17) (P = 0.02) during follow-up. Vascular histopathology showed that most nerves around the treated arteries were located outside the lesion areas (8±7 out of 55±25 (14%) nerves per pig were observed within a lesion area). Subsequently, a correlation was noted between a more impaired adventitia and a reduction in renal resistance reserve (β: -0.33; P = 0.05) at three weeks of follow-up. Conclusion Only a small minority of renal nerves was targeted after RDN. Furthermore, more severe adventitial damage was related to a reduction in renal resistance in the treated arteries at follow-up. These hemodynamic and histological observations may indicate that RDN did not sufficiently target the renal nerves. Potentially, this may explain the significant spread in the response after RDN. PMID:26587981

Renal Denervation: Intractable Hypertension and Beyond

PubMed Central

Ariyanon, Wassawon; Mao, Huijuan; Adýbelli, Zelal; Romano, Silvia; Rodighiero, Mariapia; Reimers, Bernhard; La Vecchia, Luigi; Ronco, Claudio

2014-01-01

Background Hypertension continues to be a major burden of public health concern despite the recent advances and proven benefit of pharmacological therapy. A certain subset of patients has hypertension resistant to maximal medical therapy and appropriate lifestyle measures. A novel catheter-based technique for renal denervation (RDN) as a new therapeutic avenue has great promise for the treatment of refractory hypertension. Summary This review included the physiology of the renal sympathetic nervous system and the renal nerve anatomy. Furthermore, the RDN procedure, technology systems, and RDN clinical trials as well as findings besides antihypertensive effects were discussed. Findings on safety and efficacy seem to suggest that renal sympathetic denervation could be of therapeutic benefit in refractory hypertensive patients. Despite the fast pace of development in RDN therapies, only initial and very limited clinical data are available. Large gaps in knowledge concerning the long-term effects and consequences of RDN still exist, and solid, randomized data are warranted. PMID:24847331

Renal denervation attenuates aldosterone expression and associated cardiovascular pathophysiology in angiotensin II-induced hypertension.

PubMed

Hong, Mo-Na; Li, Xiao-Dong; Chen, Dong-Rui; Ruan, Cheng-Chao; Xu, Jian-Zhong; Chen, Jing; Wu, Yong-Jie; Ma, Yu; Zhu, Ding-Liang; Gao, Ping-Jin

2016-10-18

The sympathetic nervous system interacts with the renin-angiotensin-aldosterone system (RAAS) contributing to cardiovascular diseases. In this study, we sought to determine if renal denervation (RDN) inhibits aldosterone expression and associated cardiovascular pathophysiological changes in angiotensin II (Ang II)-induced hypertension. Bilateral RDN or SHAM operation was performed before chronic 14-day Ang II subcutaneous infusion (200ng/kg/min) in male Sprague-Dawley rats. Bilateral RDN blunted Ang II-induced hypertension and ameliorated the mesenteric vascular dysfunction. Cardiovascular hypertrophy in response to Ang II was significantly attenuated by RDN as shown by histopathology and transthoracic echocardiography. Moreover, Ang II-induced vascular and myocardial inflammation and fibrosis were suppressed by RDN with concurrent decrease in fibronectin and collagen deposition, macrophage infiltration, and MCP-1 expression. Interestingly, RDN also inhibited Ang II-induced aldosterone expression in the plasma, kidney and heart. This was associated with the reduction of calcitonin gene-related peptide (CGRP) in the adrenal gland. Ang II promoted aldosterone secretion which was partly attenuated by CGRP in the adrenocortical cell line, suggesting a protective role of CGRP in this model. Activation of transforming growth factor-β (TGF-β)/Smad and mitogen-activated protein kinases (MAPKs) signaling pathway was both inhibited by RDN especially in the heart. These results suggest that the regulation of the renal sympathetic nerve in Ang II-induced hypertension and associated cardiovascular pathophysiological changes is likely mediated by aldosterone, with CGRP involvement.

Renal denervation attenuates aldosterone expression and associated cardiovascular pathophysiology in angiotensin II-induced hypertension

PubMed Central

Chen, Dong-Rui; Ruan, Cheng-Chao; Xu, Jian-Zhong; Chen, Jing; Wu, Yong-Jie; Ma, Yu; Zhu, Ding-Liang; Gao, Ping-Jin

2016-01-01

The sympathetic nervous system interacts with the renin-angiotensin-aldosterone system (RAAS) contributing to cardiovascular diseases. In this study, we sought to determine if renal denervation (RDN) inhibits aldosterone expression and associated cardiovascular pathophysiological changes in angiotensin II (Ang II)-induced hypertension. Bilateral RDN or SHAM operation was performed before chronic 14-day Ang II subcutaneous infusion (200ng/kg/min) in male Sprague-Dawley rats. Bilateral RDN blunted Ang II-induced hypertension and ameliorated the mesenteric vascular dysfunction. Cardiovascular hypertrophy in response to Ang II was significantly attenuated by RDN as shown by histopathology and transthoracic echocardiography. Moreover, Ang II-induced vascular and myocardial inflammation and fibrosis were suppressed by RDN with concurrent decrease in fibronectin and collagen deposition, macrophage infiltration, and MCP-1 expression. Interestingly, RDN also inhibited Ang II-induced aldosterone expression in the plasma, kidney and heart. This was associated with the reduction of calcitonin gene-related peptide (CGRP) in the adrenal gland. Ang II promoted aldosterone secretion which was partly attenuated by CGRP in the adrenocortical cell line, suggesting a protective role of CGRP in this model. Activation of transforming growth factor-β (TGF-β)/Smad and mitogen-activated protein kinases (MAPKs) signaling pathway was both inhibited by RDN especially in the heart. These results suggest that the regulation of the renal sympathetic nerve in Ang II-induced hypertension and associated cardiovascular pathophysiological changes is likely mediated by aldosterone, with CGRP involvement. PMID:27661131

Role of Adding Spironolactone and Renal Denervation in True Resistant Hypertension: One-Year Outcomes of Randomized PRAGUE-15 Study.

PubMed

Rosa, Ján; Widimský, Petr; Waldauf, Petr; Lambert, Lukáš; Zelinka, Tomáš; Táborský, Miloš; Branny, Marian; Toušek, Petr; Petrák, Ondřej; Čurila, Karol; Bednář, František; Holaj, Robert; Štrauch, Branislav; Václavík, Jan; Nykl, Igor; Krátká, Zuzana; Kociánová, Eva; Jiravský, Otakar; Rappová, Gabriela; Indra, Tomáš; Widimský, Jiří

2016-02-01

This randomized, multicenter study compared the relative efficacy of renal denervation (RDN) versus pharmacotherapy alone in patients with true resistant hypertension and assessed the effect of spironolactone addition. We present here the 12-month data. A total of 106 patients with true resistant hypertension were enrolled in this study: 52 patients were randomized to RDN and 54 patients to the spironolactone addition, with baseline systolic blood pressure of 159±17 and 155±17 mm Hg and average number of drugs 5.1 and 5.4, respectively. Twelve-month results are available in 101 patients. The intention-to-treat analysis found a comparable mean 24-hour systolic blood pressure decline of 6.4 mm Hg, P=0.001 in RDN versus 8.2 mm Hg, P=0.002 in the pharmacotherapy group. Per-protocol analysis revealed a significant difference of 24-hour systolic blood pressure decline between complete RDN (6.3 mm Hg, P=0.004) and the subgroup where spironolactone was added, and this continued within the 12 months (15 mm Hg, P= 0.003). Renal artery computed tomography angiograms before and after 1 year post-RDN did not reveal any relevant changes. This study shows that over a period of 12 months, RDN is safe, with no serious side effects and no major changes in the renal arteries. RDN in the settings of true resistant hypertension with confirmed compliance is not superior to intensified pharmacological treatment. Spironolactone addition (if tolerated) seems to be more effective in blood pressure reduction. © 2015 American Heart Association, Inc.

Blood Pressure and Renal Responses to Orthostatic Stress Before and After Radiofrequency Renal Denervation in Patients with Resistant Hypertension

PubMed Central

Vuignier, Yann; Grouzmann, Eric; Muller, Olivier; Vakilzadeh, Nima; Faouzi, Mohamed; Maillard, Marc P.; Qanadli, Salah D.; Burnier, Michel; Wuerzner, Grégoire

2018-01-01

Background/Aims In patients with resistant hypertension, renal denervation (RDN) studies have mainly focused their outcomes on blood pressure (BP). The aim of this study was to evaluate the long-term effect of RDN on neurohormonal profiles, renal hemodynamics and sodium excretion in a resting state and during stress induced by lower body negative pressure (LBNP). Materials and methods This was a single center prospective observational study. Norepinephrine, plasma renin activity (PRA), glomerular filtration rate (GFR), renal plasma flow (RPF) and sodium excretion were measured in unstimulated conditions (rest) and after one hour of LBNP at three different time points: before (M0), one (M1) and twelve months (M12) after RDN. Results Thirteen patients with resistant hypertension were included. In the resting state, no differences were observed in norepinephrine, PRA, sodium excretion and mean BP levels after RDN. GFR (78 ± 32 ml/min at M0 vs 66 ± 26 ml/min at M12 (p = 0.012) and filtration fraction (22.6 ±5.4% at M0 vs 15.1 ±5.3% at M12 (p = 0.002)) both decreased after RDN. During LBNP, the magnitude of the mean BP increase was reduced from +6.8 ± 6.6 mm Hg at M0 to +2.3 ± 1.3 mm Hg at M12 (p = 0.005). The LBNP-induced increase in norepinephrine and decrease in GFR and sodium excretion observed before RDN were blunted after the procedure. Conclusion A decrease in GFR and filtration fraction was observed one year after RDN. In addition, our results suggest that RDN blunts not only the norepinephrine but also the mean BP, the GFR and the sodium excretion responses to an orthostatic stress one year after the intervention. Registry number NCT01734096 PMID:29876358

Current Status of Renal Denervation in Hypertension.

PubMed

Briasoulis, Alexander; Bakris, George L

2016-11-01

Over the past 7 years, prospective cohorts and small randomized controlled studies showed that renal denervation therapy (RDN) in patients with resistant hypertension is safe but associated with variable effects on BP which are not substantially better than medical therapy alone. The failure of the most rigorously designed randomized sham-control study, SYMPLICITY HTN-3, to meet the efficacy endpoints has raised several methodological concerns. However, recently reported studies and ongoing trials with improved procedural characteristics, identification of patients with true treatment-resistant hypertension on appropriate antihypertensive regimens further explore potential benefits of RDN. The scope of this review is to summarize evidence from currently completed studies on RDN and discuss future perspectives of RDN therapy in patients with resistant hypertension.

Effects of percutaneous renal sympathetic denervation on cardiac function and exercise tolerance in patients with chronic heart failure.

PubMed

Gao, Jun-Qing; Xie, Yun; Yang, Wei; Zheng, Jian-Pu; Liu, Zong-Jun

2017-01-01

Sympathetic hyperactivity, a vital factor in the genesis and development of heart failure (HF), has been reported to be effectively reduced by percutaneous renal denervation (RDN), which may play an important role in HF treatment. To determine the effects of percutaneous RDN on cardiac function in patients with chronic HF (CHF). Fourteen patients (mean age 69.6 years; ejection fraction [EF] <45%) with CHF received bilateral RDN. Adverse cardiac events, blood pressure (BP), and biochemical parameters were assessed before and six months after percutaneous operation. Patients also underwent echocardiographic assessment of cardiac function and 6-min walk test before and at six months after percutaneous operation. The distance achieved by the 14 patients in the 6-min walk test increased significantly from 152.9±38.0 m before RDN to 334.3±94.4 m at six months after RDN (p<0.001), while EF increased from 36.0±4.1% to 43.8±7.9% (p=0.003) on echocardiography. No RDN-related complications were observed during the follow-up period. In 6-month follow-up, systolic BP decreased from 138.6±22.1 mmHg to 123.2±10.5 mmHg (p=0.026) and diastolic BP from 81.1±11.3 mmHg to 72.9±7.5 mmHg (p=0.032). Creatinine levels did not change significantly (1.3±0.65 mg/dl to 1.2±0.5 mg/dl, p=0.8856). RDN is potentially an effective technique for the treatment of severe HF that can significantly increase EF and improve exercise tolerance. Copyright © 2016 Sociedade Portuguesa de Cardiologia. Publicado por Elsevier España, S.L.U. All rights reserved.

[Safety and short-term efficacy of renal sympathetic denervation in the treatment of resistant hypertension].

PubMed

Jiang, Xiong-jing; Liang, Tuo; Dong, Hui; Peng, Meng; Ma, Wen-jun; Guan, Ting; Zhang, Hui-min; Bian, Jin; Xu, Bo; Gao, Run-lin

2012-12-11

Transcatheter renal sympathetic denervation (RDN) is a novel technology/therapy in treating resistant hypertension. The present study aims to evaluate the safety and short-term efficacy of RDN for the treatment of resistant hypertension in a Chinese population. This prospective single-center pilot study was the first one conducted in China with Medtronic Ardian Symplicity Catheter System. Eight patients (6 males and 2 females) with resistant hypertension underwent RDN at our hospital from February to April 2012. All patients were followed up at one month and three months post-RDN. Blood pressure, use of antihypertensive medications, renal function and complications were recorded and analyzed. At one month and three months post-RDN, 24-hour ambulatory blood pressure monitoring showed mean systolic blood pressure and diastolic blood pressure decreased 10 (0 - 18) 13 (3 - 19) and 8 (-2 - 15), 9 (2 - 16) mm Hg throughout 24 hours respectively (P < 0.05, vs baseline). The number of drugs decreased from 4.3 ± 0.5 to 2.8 ± 0.9 and 2.5 ± 0.7 post-RSD respectively (P < 0.01). There was no significant change of renal function (P > 0.05). No complications were observed. The preliminary results revealed that RDN was safe and effective for the treatment of resistant hypertension in the Chinese population during a 3-month follow-up. Further large and long-term studies are warranted.

Design and simulation of novel laparoscopic renal denervation system: a feasibility study.

PubMed

Ye, Eunbi; Baik, Jinhwan; Lee, Seunghyun; Ryu, Seon Young; Yang, Sunchoel; Choi, Eue-Keun; Song, Won Hoon; Yuk, Hyeong Dong; Jeong, Chang Wook; Park, Sung-Min

2018-05-18

In this study, we propose a novel laparoscopy-based renal denervation (RDN) system for treating patients with resistant hypertension. In this feasibility study, we investigated whether our proposed surgical instrument can ablate renal nerves from outside of the renal artery safely and effectively and can overcome the depth-related limitations of the previous catheter-based system with less damage to the arterial walls. We designed a looped bipolar electrosurgical instrument to be used with laparoscopy-based RDN system. The tip of instrument wraps around the renal artery and delivers the radio-frequency (RF) energy. We evaluated the thermal distribution via simulation study on a numerical model designed using histological data and validated the results by the in vitro study. Finally, to show the effectiveness of this system, we compared the performance of our system with that of catheter-based RDN system through simulations. Simulation results were within the 95% confidence intervals of the in vitro experimental results. The validated results demonstrated that the proposed laparoscopy-based RDN system produces an effective thermal distribution for the removal of renal sympathetic nerves without damaging the arterial wall and addresses the depth limitation of catheter-based RDN system. We developed a novel laparoscope-based electrosurgical RDN method for hypertension treatment. The feasibility of our system was confirmed through a simulation study as well as in vitro experiments. Our proposed method could be an effective treatment for resistant hypertension as well as central nervous system diseases.

Renal Denervation Findings on Cardiac and Renal Fibrosis in Rats with Isoproterenol Induced Cardiomyopathy

NASA Astrophysics Data System (ADS)

Liu, Qian; Zhang, Qi; Wang, Kai; Wang, Shengchan; Lu, Dasheng; Li, Zhenzhen; Geng, Jie; Fang, Ping; Wang, Ying; Shan, Qijun

2015-12-01

Cardio-renal fibrosis plays key roles in heart failure and chronic kidney disease. We sought to determine the effects of renal denervation (RDN) on cardiac and renal fibrosis in rats with isoproterenol induced cardiomyopathy. Sixty male Sprague Dawley rats were randomly assigned to Control (n = 10) and isoproterenol (ISO)-induced cardiomyopathy group (n = 50). At week 5, 31 survival ISO-induced cardiomyopathy rats were randomized to RDN (n = 15) and Sham group (n = 16). Compared with Control group, ejection fraction was decreased, diastolic interventricular septal thickness and left atrial dimension were increased in ISO-induced cardiomyopathy group at 5 week. After 10 weeks, cardio-renal pathophysiologic results demonstrated that the collagen volume fraction of left atrio-ventricular and kidney tissues reduced significantly in RDN group compared with Sham group. Moreover the pro-fibrosis factors (TGF-β1, MMP2 and Collagen I), inflammatory cytokines (CRP and TNF-α), and collagen synthesis biomarkers (PICP, PINP and PIIINP) concentration significantly decreased in RDN group. Compared with Sham group, RDN group showed that release of noradrenaline and aldosterone were reduced, angiotensin-converting enzyme (ACE)/angiotensin II (Ang II)/angiotensin II type-1 receptor (AT1R) axis was downregulated. Meanwhile, angiotensin-converting enzyme 2 (ACE2)/angiotensin-1-7 (Ang-(1-7))/mas receptor (Mas-R) axis was upregulated. RDN inhibits cardio-renal fibrogenesis through multiple pathways, including reducing SNS over-activity, rebalancing RAAS axis.

Diabetes: How and RDN Can Help with Diabetes

MedlinePlus

... that your daily cup (or three!) provides some health benefits. How an RDN Can Help with Diabetes Maintaining ... Keep Your Picnic Safe Food Safety Tips for Outdoor Dining Keep Your Picnic Safe Dads and Breast- ...

Renal denervation in comparison with intensified pharmacotherapy in true resistant hypertension: 2-year outcomes of randomized PRAGUE-15 study.

PubMed

Rosa, Ján; Widimský, Petr; Waldauf, Petr; Zelinka, Tomáš; Petrák, Ondřej; Táborský, Miloš; Branny, Marian; Toušek, Petr; Čurila, Karol; Lambert, Lukáš; Bednář, František; Holaj, Robert; Štrauch, Branislav; Václavík, Jan; Kociánová, Eva; Nykl, Igor; Jiravský, Otakar; Rappová, Gabriela; Indra, Tomáš; Krátká, Zuzana; Widimský, Jiří

2017-05-01

The randomized, multicentre study compared the efficacy of renal denervation (RDN) versus spironolactone addition in patients with true resistant hypertension. We present the 24-month data. A total of 106 patients with true resistant hypertension were enrolled in this study: 52 patients were randomized to RDN and 54 patients to the spironolactone addition, with baseline SBP of 159 ± 17 and 155 ± 17 mmHg and average number of drugs 5.1 and 5.4, respectively. Two-year data are available in 86 patients. Spironolactone addition, as crossover after 1 year, was performed in 23 patients after RDN, and spironolactone addition followed by RDN was performed in five patients. Similar and comparable reduction of 24-h SBP after RDN or spironolactone addition after randomization was observed, 9.1 mmHg (P = 0.001) and 10.9 mmHg (P = 0.001), respectively. Similar decrease of office blood pressure (BP) was observed, 17.7 mmHg (P < 0.001) versus 14.1 mmHg (P < 0.001), whereas the number of antihypertensive drugs did not differ significantly between groups. Crossover analysis showed nonsignificantly better efficacy of spironolactone addition in 24-h SBP and office SBP reduction than RDN (3.7 mmHg, P = 0.27 and 4.6 mmHg, P = 0.28 in favour of spironolactone addition, respectively). Meanwhile, the number of antihypertensive drugs was significantly increased after spironolactone addition (+0.7, P = 0.001). In the settings of true resistant hypertension, spironolactone addition (if tolerated) seems to be of better efficacy than RDN in BP reduction over a period of 24 months. However, by contrast to the 12-month results, BP changes were not significantly greater.

Effects of renal sympathetic denervation on blood pressure and glycaemic control in patients with true resistant hypertension: results of Polish Renal Denervation Registry (RDN-POL Registry).

PubMed

Kądziela, Jacek; Prejbisz, Aleksander; Kostka-Jeziorny, Katarzyna; Dudek, Dariusz; Narkiewicz, Krzysztof; Sadowski, Jerzy; Lekston, Andrzej; Gziut, Aneta; Więcek, Andrzej; Buszman, Paweł; Kleinrok, Andrzej; Kochman, Janusz; Czarnecka, Danuta; Januszewicz, Andrzej; Witkowski, Adam

2016-01-01

The assessment of percutaneous renal sympathetic denervation (RDN) efficacy in patients with true-resistant hypertension (true-RH) in a newly established net of Polish centres (RDN-POL Registry). Forty-four patients with true-RH (23 men, mean age 52.3 years) with daytime systolic blood pressure (SBP) in ambulatory blood pressure monitoring (ABPM) ≥ 135 mm Hg, on ≥ three antihypertensive agents, including diuretic, underwent RDN and completed 12-month follow-up. Mean reductions of office SBP/diastolic blood pressure were -23.8/-10.0, -12.5/-4.6, and -12.6/-6.1 mm Hg at 3, 6, and 12 months, respectively (all significant except diastolic at 6 months). Diabetes was the only predictor of office SBP reduction at 6 months (OR 9.6, 95% CI 1.4-66.5, p < 0.05). Mean 24-h SBP change was -8.3 mm Hg at 6 months and -4.6 mm Hg at 12 months. Increased 2 h-glucose in oral glucose tolerance test was the only predictor of 24-h SBP reduction at 6 months (OR 1.24 for 10 mg/dL glucose increase, 95% CI 1.04-1.48, p < 0.05). At 12 months, 24-h SBP change predictors were: baseline office SBP (OR 4.93 for 10 mm Hg SBP increment, 95% CI 1.01-24.1, p < 0.05) and 2 h-glucose (OR 1.47, 95% CI 1.08-2.00, p < 0.05). In ABPM responders, significant reduction of 2 h glucose was found as compared to the non-responders (-45.8 vs. -7.7 mg/dL, p < 0.005). The RDN-POL Registry demonstrated moderate blood pressure decrease after RDN. The predictors of blood pressure reduction were diabetes, 2 h-glucose, and baseline office SBP. Analysis of ABPM responders indicates a probable positive impact of RDN on glycaemic control.

Blood pressure response to renal denervation is correlated with baseline blood pressure variability: a patient-level meta-analysis.

PubMed

Persu, Alexandre; Gordin, Daniel; Jacobs, Lotte; Thijs, Lutgarde; Bots, Michiel L; Spiering, Wilko; Miroslawska, Atena; Spaak, Jonas; Rosa, Ján; de Jong, Mark R; Berra, Elena; Fadl Elmula, Fadl Elmula M; Wuerzner, Gregoire; Taylor, Alison H M; Olszanecka, Agnieszka; Czarnecka, Danuta; Mark, Patrick B; Burnier, Michel; Renkin, Jean; Kjeldsen, Sverre E; Widimský, Jiří; Elvan, Arif; Kahan, Thomas; Steigen, Terje K; Blankestijn, Peter J; Tikkanen, Ilkka; Staessen, Jan A

2018-02-01

Sympathetic tone is one of the main determinants of blood pressure (BP) variability and treatment-resistant hypertension. The aim of our study was to assess changes in BP variability after renal denervation (RDN). In addition, on an exploratory basis, we investigated whether baseline BP variability predicted the BP changes after RDN. We analyzed 24-h BP recordings obtained at baseline and 6 months after RDN in 167 treatment-resistant hypertension patients (40% women; age, 56.7 years; mean 24-h BP, 152/90 mmHg) recruited at 11 expert centers. BP variability was assessed by weighted SD [SD over time weighted for the time interval between consecutive readings (SDiw)], average real variability (ARV), coefficient of variation, and variability independent of the mean (VIM). Mean office and 24-h BP fell by 15.4/6.6 and 5.5/3.7 mmHg, respectively (P < 0.001). In multivariable-adjusted analyses, systolic/diastolic SDiw and VIM for 24-h SBP/DBP decreased by 1.18/0.63 mmHg (P ≤ 0.01) and 0.86/0.42 mmHg (P ≤ 0.05), respectively, whereas no significant changes in ARV or coefficient of variation occurred. Furthermore, baseline SDiw (P = 0.0006), ARV (P = 0.01), and VIM (P = 0.04) predicted the decrease in 24-h DBP but not 24-h SBP after RDN. RDN was associated with a decrease in BP variability independent of the BP level, suggesting that responders may derive benefits from the reduction in BP variability as well. Furthermore, baseline DBP variability estimates significantly correlated with mean DBP decrease after RDN. If confirmed in younger patients with less arterial damage, in the absence of the confounding effect of drugs and drug adherence, baseline BP variability may prove a good predictor of BP response to RDN.

Effects of catheter-based renal denervation on cardiac sympathetic activity and innervation in patients with resistant hypertension.

PubMed

Donazzan, Luca; Mahfoud, Felix; Ewen, Sebastian; Ukena, Christian; Cremers, Bodo; Kirsch, Carl-Martin; Hellwig, Dirk; Eweiwi, Tareq; Ezziddin, Samer; Esler, Murray; Böhm, Michael

2016-04-01

To investigate, whether renal denervation (RDN) has a direct effect on cardiac sympathetic activity and innervation density. RDN demonstrated its efficacy not only in reducing blood pressure (BP) in certain patients, but also in decreasing cardiac hypertrophy and arrhythmias. These pleiotropic effects occur partly independent from the observed BP reduction. Eleven patients with resistant hypertension (mean office systolic BP 180 ± 18 mmHg, mean antihypertensive medications 6.0 ± 1.5) underwent I-123-mIBG scintigraphy to exclude pheochromocytoma. We measured cardiac sympathetic innervation and activity before and 9 months after RDN. Cardiac sympathetic innervation was assessed by heart to mediastinum ratio (H/M) and sympathetic activity by wash out ratio (WOR). Effects on office BP, 24 h ambulatory BP monitoring, were documented. Office systolic BP and mean ambulatory systolic BP were significantly reduced from 180 to 141 mmHg (p = 0.006) and from 149 to 129 mmHg (p = 0.014), respectively. Cardiac innervation remained unchanged before and after RDN (H/M 2.5 ± 0.5 versus 2.6 ± 0.4, p = 0.285). Cardiac sympathetic activity was significantly reduced by 67 % (WOR decreased from 24.1 ± 12.7 to 7.9 ± 25.3 %, p = 0.047). Both, responders and non-responders experienced a reduction of cardiac sympathetic activity. RDN significantly reduced cardiac sympathetic activity thereby demonstrating a direct effect on the heart. These changes occurred independently from BP effects and provide a pathophysiological basis for studies, investigating the potential effect of RDN on arrhythmias and heart failure.

Efficacy and safety of catheter-based radiofrequency renal denervation in stented renal arteries.

PubMed

Mahfoud, Felix; Tunev, Stefan; Ruwart, Jennifer; Schulz-Jander, Daniel; Cremers, Bodo; Linz, Dominik; Zeller, Thomas; Bhatt, Deepak L; Rocha-Singh, Krishna; Böhm, Michael; Melder, Robert J

2014-12-01

In selected patients with hypertension, renal artery (RA) stenting is used to treat significant atherosclerotic stenoses. However, blood pressure often remains uncontrolled after the procedure. Although catheter-based renal denervation (RDN) can reduce blood pressure in certain patients with resistant hypertension, there are no data on the feasibility and safety of RDN in stented RA. We report marked blood pressure reduction after RDN in a patient with resistant hypertension who underwent previous stenting. Subsequently, radiofrequency ablation was investigated within the stented segment of porcine RA, distal to the stented segment, and in nonstented RA and compared with stent only and untreated controls. There were neither observations of thrombus nor gross or histological changes in the kidneys. After radiofrequency ablation of the nonstented RA, sympathetic nerves innervating the kidney were significantly reduced, as indicated by significant decreases in sympathetic terminal axons and reduction of norepinephrine in renal tissue. Similar denervation efficacy was found when RDN was performed distal to a renal stent. In contrast, when radiofrequency ablation was performed within the stented segment of the RA, significant sympathetic nerve ablation was not seen. Histological observation showed favorable healing in all arteries. Radiofrequency ablation of previously stented RA demonstrated that RDN provides equally safe experimental procedural outcomes in a porcine model whether the radiofrequency treatment is delivered within, adjacent, or without the stent struts being present in the RA. However, efficacious RDN is only achieved when radiofrequency ablation is delivered to the nonstented RA segment distal to the stent. © 2014 American Heart Association, Inc.

Catheter-Based Renal Denervation for Resistant Hypertension: Will It Ever Be Ready for "Prime Time"?

PubMed

Laffin, Luke J; Bakris, George L

2017-09-01

The year 2014 was a turning point for the field of renal denervation (RDN) and its potential use to treat resistant hypertension. Tremendous enthusiasm shifted to sober reflection on the efficacy of a technology once touted as the cure to resistant hypertension. The following review highlights 2 major questions: First, does catheter-based RDN lower blood pressure and, second, will RDN using catheter-directed therapy for the treatment of resistant hypertension ever become more than an investigational technology. © American Journal of Hypertension, Ltd 2016. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Accessory renal arteries: Prevalence in resistant hypertension and an important role in nonresponse to radiofrequency renal denervation.

PubMed

VonAchen, Paige; Hamann, Jason; Houghland, Thomas; Lesser, John R; Wang, Yale; Caye, David; Rosenthal, Kristi; Garberich, Ross F; Daniels, Mary; Schwartz, Robert S

The aim of this study was to understand the role of accessory renal arteries in resistant hypertension, and to establish their role in nonresponse to radiofrequency renal denervation (RDN) procedures. Prior studies suggest a role for accessory renal arteries in hypertensive syndromes, and recent clinical trials of renal denervation report that these anomalies are highly prevalent in resistant hypertension. This study evaluated the relationships among resistant hypertension, accessory renal arteries, and the response to radiofrequency (RF) renal denervation. Computed Tomography Angiography (CTA) and magnetic resonance imaging (MRI) scans from 58 patients with resistant hypertension undergoing RF renal denervation (RDN) were evaluated. Results were compared with CT scans in 57 healthy, normotensive subjects undergoing screening as possible renal transplant donors. All scans were carefully studied for accessory renal arteries, and were correlated with long term blood pressure reduction. Accessory renal arteries were markedly more prevalent in the hypertensive patients than normotensive renal donors (59% vs 32% respectively, p=0.004). RDN had an overall nonresponse rate of 29% (response rate 71%). Patients without accessory vessels had a borderline higher response rate to RDN than those with at least one accessory vessel (83% vs 62% respectively, p=0.076) and a higher RDN response than patients with untreated accessory arteries (83% vs 55%; p=0.040). For accessory renal arteries and nonresponse, the sensitivity was 76%, specificity 49%, with positive and negative predictive values 38% and 83% respectively. Accessory renal arteries were markedly over-represented in resistant hypertensives compared with healthy controls. While not all patients with accessory arteries were nonresponders, nonresponse was related to both the presence and non-treatment of accessory arteries. Addressing accessory renal arteries in future clinical trials may improve RDN therapeutic efficacy. Copyright © 2016 Elsevier Inc. All rights reserved.

How to perform a cost-effectiveness analysis with surrogate endpoint: renal denervation in patients with resistant hypertension (DENERHTN) trial as an example.

PubMed

Bulsei, Julie; Darlington, Meryl; Durand-Zaleski, Isabelle; Azizi, Michel

2018-04-01

Whilst much uncertainty exists as to the efficacy of renal denervation (RDN), the positive results of the DENERHTN study in France confirmed the interest of an economic evaluation in order to assess efficiency of RDN and inform local decision makers about the costs and benefits of this intervention. The uncertainty surrounding both the outcomes and the costs can be described using health economic methods such as the non-parametric bootstrap. Internationally, numerous health economic studies using a cost-effectiveness model to assess the impact of RDN in terms of cost and effectiveness compared to antihypertensive medical treatment have been conducted. The DENERHTN cost-effectiveness study was the first health economic evaluation specifically designed to assess the cost-effectiveness of RDN using individual data. Using the DENERHTN results as an example, we provide here a summary of the principle methods used to perform a cost-effectiveness analysis.

Abdominal Aortic Calcifications Influences the Systemic and Renal Hemodynamic Response to Renal Denervation in the DENERHTN (Renal Denervation for Hypertension) Trial.

PubMed

Courand, Pierre-Yves; Pereira, Helena; Del Giudice, Costantino; Gosse, Philippe; Monge, Matthieu; Bobrie, Guillaume; Delsart, Pascal; Mounier-Vehier, Claire; Lantelme, Pierre; Denolle, Thierry; Dourmap, Caroline; Halimi, Jean Michel; Girerd, Xavier; Rossignol, Patrick; Zannad, Faiez; Ormezzano, Olivier; Vaisse, Bernard; Herpin, Daniel; Ribstein, Jean; Bouhanick, Beatrice; Mourad, Jean-Jacques; Ferrari, Emile; Chatellier, Gilles; Sapoval, Marc; Azarine, Arshid; Azizi, Michel

2017-10-10

The DENERHTN (Renal Denervation for Hypertension) trial confirmed the efficacy of renal denervation (RDN) in lowering daytime ambulatory systolic blood pressure when added to standardized stepped-care antihypertensive treatment (SSAHT) for resistant hypertension at 6 months. This post hoc exploratory analysis assessed the impact of abdominal aortic calcifications (AAC) on the hemodynamic and renal response to RDN at 6 months. In total, 106 patients with resistant hypertension were randomly assigned to RDN plus SSAHT or to the same SSAHT alone (control group). Total AAC volume was measured, with semiautomatic software and blind to randomization, from the aortic hiatus to the iliac bifurcation using the prerandomization noncontrast abdominal computed tomography scans of 90 patients. Measurements were expressed as tertiles. The baseline-adjusted difference in the change in daytime ambulatory systolic blood pressure from baseline to 6 months between the RDN and control groups was -10.1 mm Hg ( P =0.0462) in the lowest tertile and -2.5 mm Hg ( P =0.4987) in the 2 highest tertiles of AAC volume. Estimated glomerular filtration rate remained stable at 6 months for the patients in the lowest tertile of AAC volume who underwent RDN (+2.5 mL/min per 1.73 m 2 ) but decreased in the control group (-8.0 mL/min per 1.73 m 2 , P =0.0148). In the 2 highest tertiles of AAC volume, estimated glomerular filtration rate decreased similarly in the RDN and control groups ( P =0.2640). RDN plus SSAHT resulted in a larger decrease in daytime ambulatory systolic blood pressure than SSAHT alone in patients with a lower AAC burden than in those with a higher AAC burden. This larger decrease in daytime ambulatory systolic blood pressure was not associated with a decrease in estimated glomerular filtration rate. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01570777. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Blood pressure response to catheter-based renal sympathetic denervation in severe resistant hypertension: data from the Greek Renal Denervation Registry.

PubMed

Tsioufis, C; Ziakas, A; Dimitriadis, K; Davlouros, P; Marketou, M; Kasiakogias, A; Thomopoulos, C; Petroglou, D; Tsiachris, D; Doumas, M; Skalidis, E; Karvounis, C; Alexopoulos, D; Vardas, P; Kallikazaros, I; Stefanadis, C; Papademetriou, V; Tousoulis, D

2017-05-01

The efficacy of catheter-based renal sympathetic denervation (RDN) in terms of blood pressure (BP) reduction has been questioned, while "real-world" data from registries are needed. In this study, we report the complete set of 12-month data on office and ambulatory BP changes as well as the predictors for BP response to RDN from a national registry. In 4 Greek hospital centers, 79 patients with severe drug-resistant hypertension (age 59 ± 10 years, 53 males, body mass index 33 ± 5 kg/m 2 ; office BP and 24-h ambulatory BP were 176 ± 15/95 ± 13 and 155 ± 14/90 ± 12 mmHg, respectively, 4.4 ± 0.9 antihypertensive drugs) underwent RDN and were followed-up for 12 months in the Greek Renal Denervation Registry. Bilateral RDN was performed using percutaneous femoral approach and standardized techniques. Reduction in office systolic/diastolic BP at 6 and 12 months from baseline was -30/-12 and -29/-12 mmHg, while the reduction in 24-h ambulatory BP was -16/-9 and -15/-9 mmHg, respectively (p < 0.05 for all). Patients that were RDN responders (85%, n = 58), defined as an at least 10-mmHg decrease in office systolic BP at 12 months, compared to non-responders were younger (57 ± 9 vs 65 ± 8 years, p < 0.05), had higher baseline office systolic BP (176 ± 17 vs 160 ± 11 mmHg, p < 0.05) and 24-h systolic BP (159 ± 13 vs 149 ± 11 mmHg, p < 0.05). Stepwise logistic regression analysis revealed that age, obesity parameters, and baseline office BP were independent predictors of RDN response (p < 0.05 for both), but not the type of RDN catheter or the use of aldosterone antagonists. At 12 months, there were no significant changes in renal function and any new serious device or procedure-related adverse events. In our "real-world" multicenter national registry, the efficacy of renal denervation in reducing BP as well as safety is confirmed during a 12-month follow-up. Moreover, younger age, obesity, and higher levels of baseline systolic BP are independently related to better BP response to RDN.

Renal sympathetic denervation in therapy resistant hypertension - pathophysiological aspects and predictors for treatment success

PubMed Central

Fengler, Karl; Rommel, Karl Philipp; Okon, Thomas; Schuler, Gerhard; Lurz, Philipp

2016-01-01

Many forms of human hypertension are associated with an increased systemic sympathetic activity. Especially the renal sympathetic nervous system has been found to play a prominent role in this context. Therefore, catheter-interventional renal sympathetic denervation (RDN) has been established as a treatment for patients suffering from therapy resistant hypertension in the past decade. The initial enthusiasm for this treatment was markedly dampened by the results of the Symplicity-HTN-3 trial, although the transferability of the results into clinical practice to date appears to be questionable. In contrast to the extensive use of RDN in treating hypertensive patients within or without clinical trial settings over the past years, its effects on the complex pathophysiological mechanisms underlying therapy resistant hypertension are only partly understood and are part of ongoing research. Effects of RDN have been described on many levels in human trials: From altered systemic sympathetic activity across cardiac and metabolic alterations down to changes in renal function. Most of these changes could sustainably change long-term morbidity and mortality of the treated patients, even if blood pressure remains unchanged. Furthermore, a number of promising predictors for a successful treatment with RDN have been identified recently and further trials are ongoing. This will certainly help to improve the preselection of potential candidates for RDN and thereby optimize treatment outcomes. This review summarizes important pathophysiologic effects of renal denervation and illustrates the currently known predictors for therapy success. PMID:27621771

Renal denervation in patients with resistant hypertension: six-month results.

PubMed

Dores, Hélder; de Sousa Almeida, Manuel; de Araújo Gonçalves, Pedro; Branco, Patrícia; Gaspar, Augusta; Sousa, Henrique; Canha Gomes, Angela; Andrade, Maria João; Carvalho, Maria Salomé; Campante Teles, Rui; Raposo, Luís; Mesquita Gabriel, Henrique; Pereira Machado, Francisco; Mendes, Miguel

2014-04-01

Increased activation of the sympathetic nervous system plays a central role in the pathophysiology of hypertension (HTN). Catheter-based renal denervation (RDN) was recently developed for the treatment of resistant HTN. To assess the safety and efficacy of RDN for blood pressure (BP) reduction at six months in patients with resistant HTN. In this prospective registry of patients with essential resistant HTN who underwent RDN between July 2011 and May 2013, the efficacy of RDN was defined as ≥ 10 mm Hg reduction in office systolic blood pressure (SBP) six months after the intervention. In a resistant HTN outpatient clinic, 177 consecutive patients were evaluated, of whom 34 underwent RDN (age 62.7 ± 7.6 years; 50.0% male). There were no vascular complications, either at the access site or in the renal arteries. Of the 22 patients with complete six-month follow-up, the response rate was 81.8% (n=18). The mean office SBP reduction was 22 mm Hg (174 ± 23 vs. 152 ± 22 mm Hg; p<0.001) and 9 mm Hg in diastolic BP (89 ± 16 vs. 80 ± 11 mm Hg; p=0.006). The number of antihypertensive drugs (5.5 ± 1.0 vs. 4.6 ± 1.1; p=0.010) and pharmacological classes (5.4 ± 0.7 vs. 4.6 ± 1.1; p=0.009) also decreased significantly. Of the 24-hour ambulatory BP monitoring and echocardiographic parameters analyzed, there were significant reductions in diastolic load (45 ± 29 vs. 27 ± 26%; p=0.049) and in left ventricular mass index (174 ± 56 vs. 158 ± 60 g/m(2); p=0.014). In this cohort of patients with resistant HTN, RDN was safe and effective, with a significant BP reduction at six-month follow-up. Copyright © 2013 Sociedade Portuguesa de Cardiologia. Published by Elsevier España. All rights reserved.

Adjusted drug treatment is superior to renal sympathetic denervation in patients with true treatment-resistant hypertension.

PubMed

Fadl Elmula, Fadl Elmula M; Hoffmann, Pavel; Larstorp, Anne C; Fossum, Eigil; Brekke, Magne; Kjeldsen, Sverre E; Gjønnæss, Eyvind; Hjørnholm, Ulla; Kjaer, Vibeke N; Rostrup, Morten; Os, Ingrid; Stenehjem, Aud; Høieggen, Aud

2014-05-01

We aimed to investigate for the first time the blood pressure (BP)-lowering effect of renal sympathetic denervation (RDN) versus clinically adjusted drug treatment in true treatment-resistant hypertension (TRH) after excluding patients with confounding poor drug adherence. Patients with apparent TRH (n=65) were referred for RDN, and those with secondary and spurious hypertension (n=26) were excluded. TRH was defined as office systolic BP (SBP) >140 mm Hg, despite maximally tolerated doses of ≥3 antihypertensive drugs including a diuretic. In addition, ambulatory daytime SBP >135 mm Hg after witnessed intake of antihypertensive drugs was required, after which 20 patients had normalized BP and were excluded. Patients with true TRH were randomized and underwent RDN (n=9) performed with Symplicity Catheter System versus clinically adjusted drug treatment (n=10). The study was stopped early for ethical reasons because RDN had uncertain BP-lowering effect. Office SBP and diastolic BP in the drug-adjusted group changed from 160±14/88±13 mm Hg (±SD) at baseline to 132±10/77±8 mm Hg at 6 months (P<0.0005 and P=0.02, SBP and diastolic BP, respectively) and in the RDN group from 156±13/91±15 to 148±7/89±8 mm Hg (P=0.42 and P=0.48, SBP and diastolic BP, respectively). SBP and diastolic BP were significantly lower in the drug-adjusted group at 6 months (P=0.002 and P=0.004, respectively), and absolute changes in SBP were larger in the drug-adjusted group (P=0.008). Ambulatory BPs changed in parallel to office BPs. Our data suggest that adjusted drug treatment has superior BP lowering effects compared with RDN in patients with true TRH. Clinical Trial Registration- URL: http://www.clinicaltrials.gov. Unique identifier: NCT01673516.

Accurate Depth of Radiofrequency-Induced Lesions in Renal Sympathetic Denervation Based on a Fine Histological Sectioning Approach in a Porcine Model

PubMed Central

Terao, Hisako; Nakamura, Shintaro; Hagiwara, Hitomi; Furukawa, Toshihito; Matsumura, Kiyoshi; Sakakura, Kenichi

2018-01-01

Background— Ablation lesion depth caused by radiofrequency-based renal denervation (RDN) was limited to <4 mm in previous animal studies, suggesting that radiofrequency-RDN cannot ablate a substantial percentage of renal sympathetic nerves. We aimed to define the true lesion depth achieved with radiofrequency-RDN using a fine sectioning method and to investigate biophysical parameters that could predict lesion depth. Methods and Results— Radiofrequency was delivered to 87 sites in 14 renal arteries from 9 farm pigs at various ablation settings: 2, 4, 6, and 9 W for 60 seconds and 6 W for 120 seconds. Electric impedance and electrode temperature were recorded during ablation. At 7 days, 2470 histological sections were obtained from the treated arteries. Maximum lesion depth increased at 2 to 6 W, peaking at 6.53 (95% confidence interval, 4.27–8.78) mm under the 6 W/60 s condition. It was not augmented by greater power (9 W) or longer duration (120 seconds). There were statistically significant tendencies at 6 and 9 W, with higher injury scores in the media, nerves, arterioles, and fat. Maximum lesion depth was positively correlated with impedance reduction and peak electrode temperature (Pearson correlation coefficients were 0.59 and 0.53, respectively). Conclusions— Lesion depth was 6.5 mm for radiofrequency-RDN at 6 W/60 s. The impedance reduction and peak electrode temperature during ablation were closely associated with lesion depth. Hence, these biophysical parameters could provide prompt feedback during radiofrequency-RDN procedures in the clinical setting. PMID:29440276

Renal denervation in the era of HTN-3. Comprehensive review and glimpse into the future.

PubMed

Silva, Joana Delgado; Costa, Marco; Gersh, Bernard J; Gonçalves, Lino

2016-08-01

The pathophysiological role of sympathetic overactivity in conditions such as hypertension has been well documented. Catheter-based renal denervation (RDN) is a minimally invasive percutaneous procedure which aims to disrupt sympathetic nerve afferent and efferent activity through the application of radiofrequency energy directly within the renal artery wall. This technique has emerged as a very promising treatment with dramatic effects on refractory hypertension but also in other conditions in which a sympathetic influence is present. Several studies have evaluated the safety and efficacy of this procedure, presently surrounded by controversy since the recent outcome of Symplicity HTN-3, the first randomized, sham-control trial, which failed to confirm RDN previous reported benefits on BP and cardiovascular risk lowering. Consequently, although some centers halted their RDN programs, research continues and both the concept of denervation and treatment strategies are being redefined to identify patients who can drive the most benefit from this technology. In the United States, the Food and Drug Administration (FDA) has appropriately mandated that RDN remains an investigative procedure and a new generation of sham-controlled trials are ongoing and aimed to assess not only its efficacy against pharmacotherapy but also trials in drug free patients with the objective of demonstrating once and for all whether the procedure actually does lower BP in comparison to a placebo arm. In this article, we present an overview of the sympathetic nervous system and its role in hypertension, examine the current data on RDN, and share some insights and future expectations. Copyright © 2016 American Society of Hypertension. All rights reserved.

Accurate Depth of Radiofrequency-Induced Lesions in Renal Sympathetic Denervation Based on a Fine Histological Sectioning Approach in a Porcine Model.

PubMed

Sakaoka, Atsushi; Terao, Hisako; Nakamura, Shintaro; Hagiwara, Hitomi; Furukawa, Toshihito; Matsumura, Kiyoshi; Sakakura, Kenichi

2018-02-01

Ablation lesion depth caused by radiofrequency-based renal denervation (RDN) was limited to <4 mm in previous animal studies, suggesting that radiofrequency-RDN cannot ablate a substantial percentage of renal sympathetic nerves. We aimed to define the true lesion depth achieved with radiofrequency-RDN using a fine sectioning method and to investigate biophysical parameters that could predict lesion depth. Radiofrequency was delivered to 87 sites in 14 renal arteries from 9 farm pigs at various ablation settings: 2, 4, 6, and 9 W for 60 seconds and 6 W for 120 seconds. Electric impedance and electrode temperature were recorded during ablation. At 7 days, 2470 histological sections were obtained from the treated arteries. Maximum lesion depth increased at 2 to 6 W, peaking at 6.53 (95% confidence interval, 4.27-8.78) mm under the 6 W/60 s condition. It was not augmented by greater power (9 W) or longer duration (120 seconds). There were statistically significant tendencies at 6 and 9 W, with higher injury scores in the media, nerves, arterioles, and fat. Maximum lesion depth was positively correlated with impedance reduction and peak electrode temperature (Pearson correlation coefficients were 0.59 and 0.53, respectively). Lesion depth was 6.5 mm for radiofrequency-RDN at 6 W/60 s. The impedance reduction and peak electrode temperature during ablation were closely associated with lesion depth. Hence, these biophysical parameters could provide prompt feedback during radiofrequency-RDN procedures in the clinical setting. © 2018 The Authors.

Does Renal Artery Supply Indicate Treatment Success of Renal Denervation?

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

Schmid, Axel, E-mail: axel.schmid@uk-erlangen.de; Ditting, Tilmann, E-mail: tilmann.ditting@uk-erlangen.de; Sobotka, Paul A., E-mail: sobotka@alumni.stanford.edu

PurposeRenal denervation (RDN) emerged as an innovative interventional antihypertensive therapy. With the exception of pretreatment blood pressure (BP) level, no other clear predictor for treatment efficacy is yet known. We analyzed whether the presence of multiple renal arteries has an impact on BP reduction after RDN.MethodsFifty-three patients with treatment-resistant hypertension (office BP {>=} 140/90 mmHg and 24-h ambulatory BP monitoring ({>=}130/80 mmHg) underwent bilateral catheter-based RDN. Patients were stratified into one-vessel (OV) (both sides) and at least multivessel (MV) supply at one side. Both groups were treated on one vessel at each side; in case of multiple arteries, only themore » dominant artery was treated on each side.ResultsBaseline clinical characteristics (including BP, age, and estimated glomerular filtration rate) did not differ between patients with OV (n = 32) and MV (n = 21). Office BP was significantly reduced in both groups at 3 months (systolic: OV -15 {+-} 23 vs. MV -16 {+-} 20 mmHg; diastolic: OV -10 {+-} 12 vs. MV -8 {+-} 11 mmHg, both p = NS) as well as 6 months (systolic: OV -18 {+-} 18 vs. MV -17 {+-} 22 mmHg; diastolic: OV -10 {+-} 10 vs. -10 {+-} 12 mmHg, both p = NS) after RDN. There was no difference in responder rate (rate of patients with office systolic BP reduction of at least 10 mmHg after 6 months) between the groups.ConclusionIn patients with multiple renal arteries, RDN of one renal artery-namely, the dominant one-is sufficient to induce BP reduction in treatment-resistant hypertension.« less

Renal artery anatomy affects the blood pressure response to renal denervation in patients with resistant hypertension.

PubMed

Hering, Dagmara; Marusic, Petra; Walton, Antony S; Duval, Jacqueline; Lee, Rebecca; Sata, Yusuke; Krum, Henry; Lambert, Elisabeth; Peter, Karlheinz; Head, Geoff; Lambert, Gavin; Esler, Murray D; Schlaich, Markus P

2016-01-01

Renal denervation (RDN) has been shown to reduce blood pressure (BP), muscle sympathetic nerve activity (MSNA) and target organ damage in patients with resistant hypertension (RH) and bilateral single renal arteries. The safety and efficacy of RDN in patients with multiple renal arteries remains unclear. We measured office and 24-hour BP at baseline, 3 and 6 months following RDN in 91 patients with RH, including 65 patients with single renal arteries bilaterally (group 1), 16 patients with dual renal arteries on either one or both sides (group 2) and 10 patients with other anatomical constellations or structural abnormalities (group 3). Thirty nine out of 91 patients completed MSNA at baseline and follow-up. RDN significantly reduced office and daytime SBP in group 1 at both 3 and 6 months follow-up (P<0.001) but not in groups 2 and 3. Similarly, a significant reduction in resting baseline MSNA was only observed in group 1 (P<0.05). There was no deterioration in kidney function in any group. While RDN can be performed safely irrespective of the underlying renal anatomy, the presence of single renal arteries with or without structural abnormalities is associated with a more pronounced BP and MSNA lowering effect than the presence of dual renal arteries in patients with RH. However, when patients with dual renal arteries received renal nerve ablation in all arteries there was trend towards a greater BP reduction. Insufficient renal sympathetic nerve ablation may account for these differences. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Atmospheric pressure fluctuations in the far infrasound range and emergency transport events coded as circulatory system diseases.

PubMed

Didyk, L A; Gorgo, Yu P; Dirckx, J J J; Bogdanov, V B; Buytaert, J A N; Lysenko, V A; Didyk, N P; Vershygora, A V; Erygina, V T

2008-09-01

This study examines whether a relation exists between rapid atmospheric pressure fluctuations, attributed to the far infrasound frequency range (APF), and a number of emergency transport events coded as circulatory system diseases (EEC). Over an entire year, the average integral amplitudes of APF in the range of periods from 3 s to 120 s over each hour (HA) were measured. Daily dynamics of HA averaged over the year revealed a wave shape with smooth increase from night to day followed by decrease from day to night. The total daily number of EEC within the city of Kiev, Ukraine, was related to the daily mean of HA (DHA) and to the ratio of HA averaged over the day time to HA averaged over the night time (Rdn), and was checked for confounding effects of classical meteorological variables through non-parametric regression algorithms. The number of EEC were significantly higher on days with high DHA (3.72-11.07 Pa, n = 87) compared to the low DHA (0.7-3.62 Pa, n = 260, p = 0.01), as well at days with low Rdn (0.21-1.64, n = 229) compared to the high Rdn (1.65-7.2, n = 118, p = 0.03). A difference between DHA and Rdn effects on the emergency events related to different categories of circulatory diseases points to a higher sensitivity of rheumatic and cerebro-vascular diseases to DHA, and ischaemic and hypertensive diseases to Rdn. Results suggest that APF could be considered as a meteorotropic factor capable of influencing circulatory system diseases.

Atmospheric pressure fluctuations in the far infrasound range and emergency transport events coded as circulatory system diseases

NASA Astrophysics Data System (ADS)

Didyk, L. A.; Gorgo, Yu. P.; Dirckx, J. J. J.; Bogdanov, V. B.; Buytaert, J. A. N.; Lysenko, V. A.; Didyk, N. P.; Vershygora, A. V.; Erygina, V. T.

2008-09-01

This study examines whether a relation exists between rapid atmospheric pressure fluctuations, attributed to the far infrasound frequency range (APF), and a number of emergency transport events coded as circulatory system diseases (EEC). Over an entire year, the average integral amplitudes of APF in the range of periods from 3 s to 120 s over each hour (HA) were measured. Daily dynamics of HA averaged over the year revealed a wave shape with smooth increase from night to day followed by decrease from day to night. The total daily number of EEC within the city of Kiev, Ukraine, was related to the daily mean of HA (DHA) and to the ratio of HA averaged over the day time to HA averaged over the night time (Rdn), and was checked for confounding effects of classical meteorological variables through non-parametric regression algorithms. The number of EEC were significantly higher on days with high DHA (3.72 11.07 Pa, n = 87) compared to the low DHA (0.7 3.62 Pa, n = 260, p = 0.01), as well at days with low Rdn (0.21 1.64, n = 229) compared to the high Rdn (1.65 7.2, n = 118, p = 0.03). A difference between DHA and Rdn effects on the emergency events related to different categories of circulatory diseases points to a higher sensitivity of rheumatic and cerebro-vascular diseases to DHA, and ischaemic and hypertensive diseases to Rdn. Results suggest that APF could be considered as a meteorotropic factor capable of influencing circulatory system diseases.

Normalizing rainfall/debris-flow thresholds along the U.S. Pacific coast for long-term variations in precipitation climate

USGS Publications Warehouse

Wilson, Raymond C.

1997-01-01

Broad-scale variations in long-term precipitation climate may influence rainfall/debris-flow threshold values along the U.S. Pacific coast, where both the mean annual precipitation (MAP) and the number of rainfall days (#RDs) are controlled by topography, distance from the coastline, and geographic latitude. Previous authors have proposed that rainfall thresholds are directly proportional to MAP, but this appears to hold only within limited areas (< 1?? latitude), where rainfall frequency (#RDs) is nearly constant. MAP-normalized thresholds underestimate the critical rainfall when applied to areas to the south, where the #RDs decrease, and overestimate threshold rainfall when applied to areas to the north, where the #RDs increase. For normalization between climates where both MAP and #RDs vary significantly, thresholds may best be described as multiples of the rainy-day normal, RDN = MAP/#RDs. Using data from several storms that triggered significant debris-flow activity in southern California, the San Francisco Bay region, and the Pacific Northwest, peak 24-hour rainfalls were plotted against RDN values, displaying a linear relationship with a lower bound at about 14 RDN. RDN ratios in this range may provide a threshold for broad-scale regional forecasting of debris-flow activity.

An analysis of the blood pressure and safety outcomes to renal denervation in African Americans and Non-African Americans in the SYMPLICITY HTN-3 trial.

PubMed

Flack, John M; Bhatt, Deepak L; Kandzari, David E; Brown, David; Brar, Sandeep; Choi, James W; D'Agostino, Ralph; East, Cara; Katzen, Barry T; Lee, Lilian; Leon, Martin B; Mauri, Laura; O'Neill, William W; Oparil, Suzanne; Rocha-Singh, Krishna; Townsend, Raymond R; Bakris, George

2015-10-01

SYMPLICITY HTN-3, the first trial of renal denervation (RDN) versus sham, enrolled 26% African Americans, a prospectively stratified cohort. Although the 6-month systolic blood pressure (SBP) reduction in African Americans (AAs) was similar in the RDN group (-15.5 ± 25.4 mm Hg, n = 85 vs. -17.8 ± 29.2, n = 49, P = .641), the sham SBP response was 9.2 mm Hg greater (P = .057) in AAs than non-AAs. In multivariate analyses, sham SBP response was predicted by an interaction between AA and a complex antihypertensive regimen (at least one antihypertensive medication prescribed ≥3 times daily), while in the RDN group, SBP response was predicted by an interaction between AA race and baseline BP ≥ 180 mm Hg. AA race did not independently predict SBP response in either sham or RDN. There appears to be effect modification by race with individual-level patient characteristics in both treatment arms that affect the observed pattern of SBP responses. Copyright © 2015 American Society of Hypertension. Published by Elsevier Inc. All rights reserved.

Effects of renal denervation on cardiac oxidative stress and local activity of the sympathetic nervous system and renin-angiotensin system in acute myocardial infracted dogs.

PubMed

Feng, Qiaoli; Lu, Chengzhi; Wang, Li; Song, Lijun; Li, Chao; Uppada, Ravi Chandra

2017-02-17

This study sought to evaluate the therapeutic effects of renal denervation (RDN) on acute myocardial infarction (MI) in canines and explore its possible mechanisms of action. Eighteen healthy mongrel dogs were randomly assigned to either the control group, the MI group or the MI + RDN group. To assess cardiac function, left ventricular ejection fraction (LVEF), left ventricular end-diastolic dimension (LVEDD), left ventricular end-systolic dimension (LVESD) and fraction shortening (FS) were recorded. Additionally, haemodynamic parameters such as left ventricular systolic pressure (LVSP), left ventricular end-diastolic pressure (LVEDP) and heart rate (HR) were measured. Cardiac oxidative stress levels were evaluated based on the expression of p47 phox mRNA, malondialdehyde (MDA), anti-superoxide anion free radical (ASAFR) and activity of superoxide dismutase (SOD). To measure the local activity of the sympathetic nervous system (SNS) and renin-angiotensin system (RAS), the levels of tyrosine hydroxylase (TH), angiotensin II (AngII), angiotensin-converting enzyme 2 (ACE2), angiotensin (1-7) [Ang(1-7)] and Mas receptor (MasR) in myocardial tissues were recorded. The expression of TH in renal tissue and serum creatinine were used to assess the effectiveness of the RDN procedure and renal function, respectively. We found that MI deteriorated heart function and activated cardiac oxidative stress and the local neurohumoral system, while RDN partially reversed these changes. Compared with the control group, parameters including LVEDD, LVESD, LVEDP and the levels of ASAFR, MDA, p47 phox ,ACE2, Ang(1-7), MasR, AngII and TH-positive nerves were increased (all P < 0.05) in myocardial infracted dogs; meanwhile, LVEF, FS, LVSP and SOD expression were decreased (all P < 0.05). However, after RDN therapy, these changes were significantly improved (P < 0.05), except that there were no significant differences observed in FS or LVSP between the two groups (P = 0.092 and 0.931, respectively). Importantly, the expression of TH, AngII and Ang(1-7) was positively correlated with MDA and negatively correlated with SOD. Between-group comparisons demonstrated no differences in serum creatinine (P = 0.706). RDN attenuated cardiac remodelling and improved heart function by decreasing the level of cardiac oxidative stress and the local activity of the SNS and RAS in cardiac tissues. Additionally, the safety of the RDN procedure was established, as no significant decrease in LVSP or rise in serum creatinine was observed in our study.

Rumen degradable protein supply affects microbial efficiency in continuous culture and growth in steers.

PubMed

Brooks, M A; Harvey, R M; Johnson, N F; Kerley, M S

2012-12-01

We hypothesized that microbial efficiency and output from fermentation in the rumen would be optimized when peptide supply was balanced with peptide requirement of ruminal microflora. This study was conducted to measure response of varying rumen degradable peptide (RDPep) supply on ruminal fermentation characteristics and steer growth. A continuous culture experiment was conducted with diets formulated to achieve a predicted RDPep balance (RDPep supplied above RDPep required) of -0.30 to 1.45% CP with rumen degradable N (RDN) balance (RDN supplied above RDN required) above dietary ammonia-N requirement of microbes. Two additional treatments had RDPep balances of -0.30 and 0.78% CP with insufficient ammonia-N supply to meet microbial requirements. Single-flow fermenters (N = 24; n = 6) were inoculated with rumen fluid and maintained anaerobically at 39°C with a 0.06 h(-1) dilution rate. Inadequate RDN decreased OM digestion and microbial N flow, and increased rumen undegradable N (P < 0.01). Microbial efficiency decreased in RDN-deficient diets and was greatest when RDPep balance did not excessively exceed microbial requirement of RDPep predicted (P < 0.01). A growth study was conducted with 49 yearling, crossbred, Angus steers (initial BW 370 ± 34 kg). Animals were assigned to 1 of 4 treatment groups by BW and further divided into 3 pens with 4 steers per pen to achieve similar initial pen weights. Treatments consisted of 4 isonitrogenous diets balanced for RDN but varying in predicted RDPep balance (0.55%, -0.02%, -0.25%, and -0.65% CP). Animals were maintained on treatment for 70 d with individual BW taken on d 0, 1, 21, 42, 70, and 71. Final BW decreased linearly with decreasing RDPep (P = 0.05). Average daily gain and G:F displayed a quadratic effect with greater ADG and G:F at greater and lesser RDPep levels (P = 0.02). We concluded that balancing RDPep supply to predicted requirement improved fermentation efficiency and microbial output, which in turn improved animal performance.

Spironolactone versus sympathetic renal denervation to treat true resistant hypertension: results from the DENERVHTA study – a randomized controlled trial

PubMed Central

Oliveras, Anna; Armario, Pedro; Clarà, Albert; Sans-Atxer, Laia; Vázquez, Susana; Pascual, Julio; De la Sierra, Alejandro

2016-01-01

Objective: Both renal denervation (RDN) and spironolactone have been proposed for the treatment of resistant hypertension. However, they have not been compared in a randomized clinical trial. We aimed to compare the efficacy of spironolactone versus RDN in patients with resistant hypertension. Methods: A total of 24 patients with office SBP at least 150 mmHg and 24-h SBP at least 140 mmHg despite receiving at least three full-dose antihypertensive drugs, one a diuretic, but without aldosterone antagonists, were randomized to receive RDN or spironolactone (50 mg) as add-on therapy. Primary endpoint was change in 24-h SBP at 6 months. Comparisons between treatment groups were performed using generalized linear models adjusted by age, sex, and baseline values. Results: Spironolactone was more effective than RDN in reducing 24-h SBP and 24-h DBP: mean baseline-adjusted differences between the two groups were −17.9 mmHg (95%CI −30.9 to −4.9); P = 0.010 and −6.6 mmHg (95%CI −12.9 to −0.3); P = 0.041, for 24-h SBP and 24-h DBP, respectively. As regards changes in office blood pressure, mean baseline-adjusted differences between the two groups were −12.1 mmHg (95%CI −29.1 to 5.1); P = 0.158 and of −5.3 mmHg (95%CI −16.3 to 5.8); P = 0.332, for office SBP and office DBP, respectively. Otherwise, the decrease of estimated glomerular filtration rate was greater in the spironolactone group; mean baseline-adjusted difference between the two groups was −10.7 ml/min per 1.73 m2 (95%CI −20.1 to −1.4); P = 0.027. Conclusion: We conclude that spironolactone is more effective than RDN to reduce 24-h SBP and 24-h DBP in patients with resistant hypertension. Therefore, spironolactone should be the fourth antihypertensive drug to prescribe if deemed well tolerated’ in all patients with resistant hypertension before considering RDN. PMID:27327441

Spironolactone versus sympathetic renal denervation to treat true resistant hypertension: results from the DENERVHTA study - a randomized controlled trial.

PubMed

Oliveras, Anna; Armario, Pedro; Clarà, Albert; Sans-Atxer, Laia; Vázquez, Susana; Pascual, Julio; De la Sierra, Alejandro

2016-09-01

Both renal denervation (RDN) and spironolactone have been proposed for the treatment of resistant hypertension. However, they have not been compared in a randomized clinical trial. We aimed to compare the efficacy of spironolactone versus RDN in patients with resistant hypertension. A total of 24 patients with office SBP at least 150 mmHg and 24-h SBP at least 140 mmHg despite receiving at least three full-dose antihypertensive drugs, one a diuretic, but without aldosterone antagonists, were randomized to receive RDN or spironolactone (50 mg) as add-on therapy. Primary endpoint was change in 24-h SBP at 6 months. Comparisons between treatment groups were performed using generalized linear models adjusted by age, sex, and baseline values. Spironolactone was more effective than RDN in reducing 24-h SBP and 24-h DBP: mean baseline-adjusted differences between the two groups were -17.9 mmHg (95%CI -30.9 to -4.9); P = 0.010 and -6.6 mmHg (95%CI -12.9 to -0.3); P = 0.041, for 24-h SBP and 24-h DBP, respectively. As regards changes in office blood pressure, mean baseline-adjusted differences between the two groups were -12.1 mmHg (95%CI -29.1 to 5.1); P = 0.158 and of -5.3 mmHg (95%CI -16.3 to 5.8); P = 0.332, for office SBP and office DBP, respectively. Otherwise, the decrease of estimated glomerular filtration rate was greater in the spironolactone group; mean baseline-adjusted difference between the two groups was -10.7 ml/min per 1.73 m (95%CI -20.1 to -1.4); P = 0.027. We conclude that spironolactone is more effective than RDN to reduce 24-h SBP and 24-h DBP in patients with resistant hypertension. Therefore, spironolactone should be the fourth antihypertensive drug to prescribe if deemed well tolerated' in all patients with resistant hypertension before considering RDN.

Effects of renal denervation on end organ damage in hypertensive patients.

PubMed

Verloop, Willemien L; Vink, Eva E; Spiering, Wilko; Blankestijn, Peter J; Doevendans, Pieter A; Bots, Michiel L; Vonken, Evert-jan; Voskuil, Michiel; Leiner, Tim

2015-05-01

Renal denervation (RDN) is believed to reduce sympathetic nerve activity and is a potential treatment for resistant hypertension. The present study investigated the effects of RDN on end organ damage (EOD). The present study was a prospective cohort study (registered as NCT01427049). Uncontrolled hypertensive patients underwent a work-up prior to and one year after RDN. Cardiac magnetic resonance (CMR) imaging was used to determine left ventricular (LV)-mass; pulse wave analysis and pulse wave velocity (PWV) were used for evaluation of central blood pressure (BP) and arterial stiffness and 24-hour urine was collected for assessment of urinary albumin excretion. The 24-hour ambulatory BP measurement (ABPM) was used to evaluate the effect of RDN on BP. Fifty-four patients gave informed consent for study participation. Mean age was 58 ± 10 years, 50% were male. One year after RDN, mean ABPM decreased by 7 ± 18/5 ± 11 mm Hg (p = 0.01/p < 0.01). In the patients followed-up in a standardised fashion ABPM decreased by 5 ± 18/4 ± 12 mm Hg (n = 34; p = 0.11/p = 0.09). Mean body surface area indexed LV-mass decreased by 3.3 ± 11.5 g/m(2) (corresponding to a 3 ± 11% reduction; p = 0.09). PWV increased by 2.9 (-2.2 to +6.1) m/s (p = 0.04). Augmentation index corrected for 75 beats per min did not change (median increase 3.0 (-7 to +17) mm Hg; p = 0.89). Urinary albumin excretion did not change during follow-up (mean decrease 10 ± 117 mg/24 hour; p = 0.61). In the current study, we observed a modest effect from renal denervation. Moreover, RDN did not result in a statistical significant effect on end organ damage 12 months after treatment. © The European Society of Cardiology 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Effects of renal sympathetic denervation on heart rate and atrioventricular conduction in patients with resistant hypertension.

PubMed

Ukena, Christian; Mahfoud, Felix; Spies, Aline; Kindermann, Ingrid; Linz, Dominik; Cremers, Bodo; Laufs, Ulrich; Neuberger, Hans-Ruprecht; Böhm, Michael

2013-09-10

Renal sympathetic denervation (RDN) reduces sympathetic activity and blood pressure (BP) in patients with resistant hypertension. The present study aimed to investigate the effects of RDN on HR and other electrocardiographic parameters. 136 patients aged 62.2 ± 0.8 years (58% male, BP 177 ± 2/93 ± 1 mmHg) with resistant hypertension underwent RDN. BP and a 12-lead electrocardiogram (ECG) were recorded before, 3 months (n=127), and 6 months (n=88) after RDN. After 3 months (3M) and 6 months (6M), systolic BP was reduced by 25.5 ± 2.4 mmHg (p<0.0001) and 28.1 ± 3 mmHg (p<0.0001). HR at baseline was 66.1 ± 1 beats per minute (bpm) and was reduced by 2.6 ± 0.8 bpm after 3 months (p=0.001) and 2.1 ± 1.1 bpm after 6 months (p=0.046). Patients with HR at baseline between 60-71 bpm and ≥ 71 bpm had a reduction of 2.9 ± 7.6 bpm (p=0.008) and 9.0 ± 8.6 bpm (p<0.0001), respectively, whereas in patients with baseline HR<60 bpm HR slightly increased after 3 months (2.7 ± 8.4 bpm; p=0.035). Neither baseline HR nor change of HR correlated with the reduction of systolic BP. The PR interval was prolonged by 11.3 ± 2.5 ms (p<0.0001) and 10.3 ± 2.5 ms (p<0.0001) at 3 and 6 months after RDN, respectively. Renal sympathetic denervation reduced heart rate and the PR interval as indicators of cardiac autonomic activity. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Rationale and design of the Investigator-Steered Project on Intravascular Renal Denervation for Management of Drug-Resistant Hypertension (INSPiRED) trial.

PubMed

Jin, Yu; Jacobs, Lotte; Baelen, Marie; Thijs, Lutgarde; Renkin, Jean; Hammer, Frank; Kefer, Joelle; Petit, Thibault; Verhamme, Peter; Janssens, Stefan; Sinnaeve, Peter; Lengelé, Jean-Philippe; Persu, Alexandre; Staessen, Jan A

2014-06-01

The SYMPLICITY studies showed that renal denervation (RDN) is feasible as novel treatment for resistant hypertension. However, RDN is a costly and invasive procedure, the long-term efficacy and safety of which has not yet been proven. Therefore, we designed the INSPiRED trial to compare the blood pressure lowering efficacy and safety of RDN vs usual medical therapy. INSPiRED is a randomized controlled trial enrolling 240 treatment-resistant hypertensive patients at 16 expert hypertension centres in Belgium. Eligible patients, aged 20-69 years old, have a 24-h ambulatory blood pressure of 130 mmHg systolic or 80 mmHg diastolic or more, while taking at least three antihypertensive drugs. They are randomized to RDN (EnligHTN(TM), SJM system) plus usual care (intervention group) or usual care alone (control group) in a ratio of 1:1. The primary endpoints for efficacy and safety, measured after 6 months, are the baseline-adjusted between-group differences in 24h systolic blood pressure and in glomerular filtration rate as estimated by the Chronic Kidney Disease Epidemiology Collaboration equation. Follow-up will continue up to 36 months after randomization. INSPiRED is powered to demonstrate a 10-mmHg difference in systolic blood pressure between randomized groups with a two-sided p-value of 0.01 and 90% power. It will generate long-term efficacy and safety data, identify the subset of treatment-resistant hypertensive patients responsive to RDN, provide information on cost-effectiveness, and by doing so INSPiRED will inform guideline committees and health policy makers. ClinicalTrials.gov Identifier: NCT 01505010.

Impact of Lesion Placement on Efficacy and Safety of Catheter-Based Radiofrequency Renal Denervation.

PubMed

Mahfoud, Felix; Tunev, Stefan; Ewen, Sebastian; Cremers, Bodo; Ruwart, Jennifer; Schulz-Jander, Daniel; Linz, Dominik; Davies, Justin; Kandzari, David E; Whitbourn, Robert; Böhm, Michael; Melder, Robert J

2015-10-20

Insufficient procedural efficacy has been proposed to explain nonresponse to renal denervation (RDN). The aim of this study was to examine the impact of different patterns of lesion placements on the efficacy and consistency of catheter-based radiofrequency RDN in pigs. The impact of increasing number of lesions versus location of RDN was investigated in a porcine model (Group 1; n = 51). The effect of treating the main artery, the branches, and the 2 combined was compared in Group 2 (n = 48). The durability of response and safety of combined treatment of the main artery plus branches was examined in Group 3 (n = 16). Renal norepinephrine (NE) tissue content and renal cortical axon density were assessed. Increasing the number of RF lesions (4, 8, and 12) in the main renal artery was not sufficient to yield a clear dose-response relationship on NE content and axon density. In contrast, targeted treatment of the renal artery branches or distal segment of the main renal artery resulted in markedly less variability of response and significantly greater reduction of both NE and axon density than conventional treatment of only the main renal artery. Combination treatment (main artery plus branches) produced the greatest change in renal NE and axon density with the least heterogeneity. The changes were durable through 28 days post-treatment. These data provide the rationale for investigation of an optimized approach for RDN in future clinical studies. This may have profound implications for the clinical application of RDN, as this approach may not only achieve greater reductions in sympathetic activity but also reduce treatment effect variability. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Rationale and design of the Investigator-Steered Project on intravascular Renal Denervation for Management of Drug-Resistant Hypertension (INSPiRED) trial

PubMed Central

Jin, Yu; Jacobs, Lotte; Baelen, Marie; Thijs, Lutgarde; Renkin, Jean; Hammer, Frank; Kefer, Joelle; Petit, Thibault; Verhamme, Peter; Janssens, Stefan; Sinnaeve, Peter; Lengelé, Jean-Philippe; Persu, Alexandre

2014-01-01

The SYMPLICITY studies showed that renal denervation (RDN) is feasible as novel treatment for resistant hypertension. However, RDN is a costly and invasive procedure, the long-term efficacy and safety of which has not yet been proven. Therefore, we designed the INSPiRED trial to compare the blood pressure lowering efficacy and safety of RDN vs usual medical therapy. INSPiRED is a randomized controlled trial enrolling 240 treatment-resistant hypertensive patients at 16 expert hypertension centres in Belgium. Eligible patients, aged 20–69 years old, have a 24-h ambulatory blood pressure of 130 mmHg systolic or 80 mmHg diastolic or more, while taking at least three antihypertensive drugs. They are randomized to RDN (EnligHTNTM, SJM system) plus usual care (intervention group) or usual care alone (control group) in a ratio of 1:1. The primary endpoints for efficacy and safety, measured after 6 months, are the baseline-adjusted between-group differences in 24h systolic blood pressure and in glomerular filtration rate as estimated by the Chronic Kidney Disease Epidemiology Collaboration equation. Follow-up will continue up to 36 months after randomization. INSPiRED is powered to demonstrate a 10-mmHg difference in systolic blood pressure between randomized groups with a two-sided p-value of 0.01 and 90% power. It will generate long-term efficacy and safety data, identify the subset of treatment-resistant hypertensive patients responsive to RDN, provide information on cost-effectiveness, and by doing so INSPiRED will inform guideline committees and health policy makers. Trial registration: ClinicalTrials.gov Identifier: NCT 01505010. PMID:24742341

The Drosophila Insulin Receptor Independently Modulates Lifespan and Locomotor Senescence

PubMed Central

Boylan, Michael; Achall, Rajesh; Shirras, Alan; Broughton, Susan J.

2015-01-01

The Insulin/IGF-like signalling (IIS) pathway plays an evolutionarily conserved role in ageing. In model organisms reduced IIS extends lifespan and ameliorates some forms of functional senescence. However, little is known about IIS in nervous system ageing and behavioural senescence. To investigate this role in Drosophila melanogaster, we measured the effect of reduced IIS on senescence of two locomotor behaviours, negative geotaxis and exploratory walking. Two long-lived fly models with systemic IIS reductions (daGAL4/UAS-InRDN (ubiquitous expression of a dominant negative insulin receptor) and d2GAL/UAS-rpr (ablation of insulin-like peptide producing cells)) showed an amelioration of negative geotaxis senescence similar to that previously reported for the long-lived IIS mutant chico. In contrast, exploratory walking in daGAL4/UAS-InRDN and d2GAL/UAS-rpr flies declined with age similarly to controls. To determine the contribution of IIS in the nervous system to these altered senescence patterns and lifespan, the InRDN was targeted to neurons (elavGAL4/UAS-InRDN), which resulted in extension of lifespan in females, normal negative geotaxis senescence in males and females, and detrimental effects on age-specific exploratory walking behaviour in males and females. These data indicate that the Drosophila insulin receptor independently modulates lifespan and age-specific function of different types of locomotor behaviour. The data suggest that ameliorated negative geotaxis senescence of long-lived flies with systemic IIS reductions is due to ageing related effects of reduced IIS outside the nervous system. The lifespan extension and coincident detrimental or neutral effects on locomotor function with a neuron specific reduction (elavGAL4/UAS-InRDN) indicates that reduced IIS is not beneficial to the neural circuitry underlying the behaviours despite increasing lifespan. PMID:26020640

Renal denervation in heart failure with normal left ventricular ejection fraction. Rationale and design of the DIASTOLE (DenervatIon of the renAl Sympathetic nerves in hearT failure with nOrmal Lv Ejection fraction) trial.

PubMed

Verloop, Willemien L; Beeftink, Martine M A; Nap, Alex; Bots, Michiel L; Velthuis, Birgitta K; Appelman, Yolande E; Cramer, Maarten-Jan; Agema, Willem R P; Scholtens, Asbjorn M; Doevendans, Pieter A; Allaart, Cor P; Voskuil, Michiel

2013-12-01

Aim Increasing evidence suggests an important role for hyperactivation of the sympathetic nervous system (SNS) in the clinical phenomena of heart failure with normal LVEF (HFNEF) and hypertension. Moreover, the level of renal sympathetic activation is directly related to the severity of heart failure. Since percutaneous renal denervation (pRDN) has been shown to be effective in modulating elevated SNS activity in patients with hypertension, it can be hypothesized that pRDN has a positive effect on HFNEF. The DIASTOLE trial will investigate whether renal sympathetic denervation influences parameters of HFNEF. Methods DIASTOLE is a multicentre, randomized controlled trial. Sixty patients, diagnosed with HFNEF and treated for hypertension, will be randomly allocated in a 1:1 ratio to undergo renal denervation on top of medical treatment (n = 30) or to maintain medical treatment alone (n = 30). The primary objective is to investigate the efficacy of pRDN by means of pulsed wave Doppler echocardiographic parameters. Secondary objectives include safety of pRDN and a comparison of changes in the following parameters after pRDN: LV mass, LV volume, LVEF, and left atrial volume as determined by magnetic resonance imaging. Also, MIBG (metaiodobenzylguanidine) uptake and washout, BNP levels, blood pressure, heart rate variability, exercise capacity, and quality of life will be assessed. Perspective DIASTOLE is a randomized controlled trial evaluating renal denervation as a treatment option for HFNEF. The results of the current trial will provide important information regarding the treatment of HFNEF, and therefore may have major impact on future therapeutic strategies. Trail registration NCT01583881.

Renal denervation reduces office and ambulatory heart rate in patients with uncontrolled hypertension: 12-month outcomes from the global SYMPLICITY registry.

PubMed

Böhm, Michael; Ukena, Christian; Ewen, Sebastian; Linz, Dominik; Zivanovic, Ina; Hoppe, Uta; Narkiewicz, Krzysztof; Ruilope, Luis; Schlaich, Markus; Negoita, Manuela; Schmieder, Roland; Williams, Bryan; Zeymer, Uwe; Zirlik, Andreas; Mancia, Giuseppe; Mahfoud, Felix

2016-12-01

Renal denervation (RDN) can reduce sympathetic activity and blood pressure (BP) in patients with hypertension. The effects on resting and ambulatory heart rate (HR), also regulated by the sympathetic nervous system, are not established. Herein, we report 12-month outcomes from the Global SYMPLICITY Registry on office and ambulatory HR and BP in patients with uncontrolled hypertension (n = 846). HR declined in correlation with the HR at baseline and at 12 months, in particular, in patients in the upper tertile of HR (>74 bpm). BP reduction was similar in the tertiles of HR at baseline. Similar effects were observed when 24-h ambulatory HR and SBP were determined. Office HR was similarly decreased when patients were on a β-blocker or not. Antihypertensive treatment remained unchanged during the 12-month period of the Global SYMPLICITY Registry. RDN reduces BP independent from HR. A HR reduction is dependent on baseline HR and unchanged by β-blocker treatment. The effects of RDN on SBP and HR are durable up to 1 year. HR reduction might be a target for RDN in patients with high HR at baseline, which needs to be scrutinized in prospective trials.

Perivascular radiofrequency renal denervation lowers blood pressure and ameliorates cardiorenal fibrosis in spontaneously hypertensive rats

PubMed Central

Zhang, Yan; Su, Linan; Zhang, Yunrong; Wang, Qiang; Yang, Dachun; Li, De; Yang, Yongjian; Ma, Shuangtao

2017-01-01

Background Catheter-based renal denervation (RDN) is a promising approach to treat hypertension, but innervation patterns limit the response to endovascular RDN and the post-procedural renal artery narrowing or stenosis questions the endovascular ablation strategy. This study was performed to investigate the anti-hypertensive and target organ protective effects of perivascular RDN in spontaneously hypertensive rats (SHR). Methods SHR and normotensive Wistar-Kyoto (WKY) rats were divided into sham group (n = 10), radiofrequency ablation group (n = 20) in which rats received bilateral perivascular ablation with radiofrequency energy (2 watts), and chemical (10% phenol in 95% ethanol) ablation group (n = 12). The tail-cuff blood pressure was measured before the ablation and on day 14 and day 28 after the procedure. The plasma levels of creatinine, urea nitrogen, and catecholamines, urinary excretion of electrolytes and protein, and myocardial and glomerular fibrosis were analyzed and compared among the groups on day 28 after the procedure. Results We identified that 2-watt is the optimal radiofrequency power for perivascular RDN in rats. Perivascular radiofrequency and chemical ablation achieved roughly comparable blood pressure reduction in SHR but not in WKY on day 14 and day 28 following the procedure. Radiofrequency-mediated ablation substantially destroyed the renal nerves surrounding the renal arteries of both SHR and WKY without damaging the renal arteries and diminished the expression of tyrosine hydroxylase, the enzyme marker for postganglionic sympathetic nerves. Additionally, perivascular radiofrequency ablation also decreased the plasma catecholamines of SHR. Interestingly, both radiofrequency and chemical ablation decreased the myocardial and glomerular fibrosis of SHR, while neither increased the plasma creatinine and blood urea nitrogen nor affected the urinary excretion of electrolytes and protein when compared to sham group. Conclusions Radiofrequency-mediated perivascular RDN may become a feasible procedure against hypertension, and provide similar anti-hypertensive and target organ protective effects as does the chemical ablation. PMID:28453557

Efficacy and safety of renal denervation for Chinese patients with resistant hypertension using a microirrigated catheter: study design and protocol for a prospective multicentre randomised controlled trial.

PubMed

Liu, Zongjun; Shen, Li; Huang, Weijian; Zhao, Xianxian; Fang, Weiyi; Wang, Changqian; Yin, Zhaofang; Wang, Jianan; Fu, Guosheng; Liu, Xuebo; Jiang, Jianjun; Zhang, Zhihui; Li, Jingbo; Lu, Yingmin; Ge, Junbo

2017-09-01

Available data show that approximately 8%-18% of patients with primary hypertension will develop resistant hypertension. In recent years, catheter-based renal denervation (RDN) has emerged as a potential treatment option for resistant hypertension. A number of observational studies and randomised controlled trials among non-Chinese patients have demonstrated its potential safety and efficacy. This is a multicentre, randomised, open-label, parallel-group, active controlled trial that will investigate the efficacy and safety of a 5F saline-irrigated radiofrequency ablation (RFA) used for RDN in the treatment of Chinese patients with resistant hypertension. A total of 254 patients who have failed pharmacological therapy will be enrolled. Eligible subjects will be randomised in a 1:1 ratio to undergo RDN using the RFA plus antihypertensive medication or to receive treatment with antihypertensive medication alone. The primary outcome measure is the change in 24 hours average ambulatory systolic blood pressure from baseline to 3 months, comparing the RDN-plus-medication group with the medication-alone group. Important secondary endpoints include the change in office blood pressure from baseline to 6 months after randomisation. Safety endpoints such as changes in renal function will also be evaluated. The full analysis set, according to the intent-to-treat principle, will be established as the primary analysis population. All participants will provide informed consent; the study protocol has been approved by the Independent Ethics Committee for each site. This study is designed to investigate the efficacy and safety of RDN using a 5F saline microirrigated RFA. Findings will be shared with participating hospitals, policymakers and the academic community to promote the clinical management of resistant hypertension in China. ClinicalTrials.gov ID: NCT02900729; pre-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Perivascular radiofrequency renal denervation lowers blood pressure and ameliorates cardiorenal fibrosis in spontaneously hypertensive rats.

PubMed

Wei, Shujie; Li, Dan; Zhang, Yan; Su, Linan; Zhang, Yunrong; Wang, Qiang; Yang, Dachun; Li, De; Yang, Yongjian; Ma, Shuangtao

2017-01-01

Catheter-based renal denervation (RDN) is a promising approach to treat hypertension, but innervation patterns limit the response to endovascular RDN and the post-procedural renal artery narrowing or stenosis questions the endovascular ablation strategy. This study was performed to investigate the anti-hypertensive and target organ protective effects of perivascular RDN in spontaneously hypertensive rats (SHR). SHR and normotensive Wistar-Kyoto (WKY) rats were divided into sham group (n = 10), radiofrequency ablation group (n = 20) in which rats received bilateral perivascular ablation with radiofrequency energy (2 watts), and chemical (10% phenol in 95% ethanol) ablation group (n = 12). The tail-cuff blood pressure was measured before the ablation and on day 14 and day 28 after the procedure. The plasma levels of creatinine, urea nitrogen, and catecholamines, urinary excretion of electrolytes and protein, and myocardial and glomerular fibrosis were analyzed and compared among the groups on day 28 after the procedure. We identified that 2-watt is the optimal radiofrequency power for perivascular RDN in rats. Perivascular radiofrequency and chemical ablation achieved roughly comparable blood pressure reduction in SHR but not in WKY on day 14 and day 28 following the procedure. Radiofrequency-mediated ablation substantially destroyed the renal nerves surrounding the renal arteries of both SHR and WKY without damaging the renal arteries and diminished the expression of tyrosine hydroxylase, the enzyme marker for postganglionic sympathetic nerves. Additionally, perivascular radiofrequency ablation also decreased the plasma catecholamines of SHR. Interestingly, both radiofrequency and chemical ablation decreased the myocardial and glomerular fibrosis of SHR, while neither increased the plasma creatinine and blood urea nitrogen nor affected the urinary excretion of electrolytes and protein when compared to sham group. Radiofrequency-mediated perivascular RDN may become a feasible procedure against hypertension, and provide similar anti-hypertensive and target organ protective effects as does the chemical ablation.

Catheter-based renal denervation as therapy for chronic severe kidney-related pain.

PubMed

de Jager, Rosa L; Casteleijn, Niek F; de Beus, Esther; Bots, Michiel L; Vonken, Evert-Jan E; Gansevoort, Ron T; Blankestijn, Peter J

2017-06-22

Loin pain haematuria syndrome (LPHS) and autosomal dominant polycystic kidney disease (ADPKD) are the most important non-urological conditions to cause chronic severe kidney-related pain. Multidisciplinary programmes and surgical methods have shown inconsistent results with respect to pain reduction. Percutaneous catheter-based renal denervation (RDN) could be a less invasive treatment option for these patients. Our aim was to explore the change in perceived pain and use of analgesic medication from baseline to 3, 6 and 12 months after RDN. Patients with LPHS or ADPKD, who experienced kidney-related pain ≥3 months with a visual analogue scale (VAS) score ≥ 50/100 could be included. Percutaneous RDN was performed with a single-electrode radiofrequency ablation catheter. RDN was performed in 11 patients (6 with LPHS and 5 with ADPKD). Perceived pain declined in the whole group by 23 mm (P = 0.012 for the total group). In patients with LPHS and ADPKD, the median daily defined dosage of analgesic medication decreased from 1.6 [interquartile range (IQR) 0.7-2.3] and 1.4 (IQR 0.0-7.4) at baseline to 0.3 (IQR 0.0-1.9; P = 0.138) and 0.0 (IQR 0.0-0.8; P = 0.285) at 12 months, respectively. Mean estimated glomerular filtration rate decreased in the whole group by 5.4 mL/min/1.73 m 2 at 6 months compared with baseline (P = 0.163). These results suggest that percutaneous catheter-based RDN reduces pain complaints and the use of analgesic medication in patients with LPHS or ADPKD. The present results can serve as the rationale for a larger, preferably randomized (sham) controlled study. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Non-invasive Renal Denervation: Update on External Ultrasound Approaches.

PubMed

Schmieder, Roland E; Ott, Christian; Bramlage, Peter

2016-06-01

In the last decade, intravenous renal denervation (RDN) has emerged as an alternative to pharmacological treatment in patients with resistant hypertension, but currently involves an invasive and technically challenging procedure. The Surround Sound™ system utilises externally delivered ultrasound to achieve RDN using a completely non-invasive, automated real-time tracking system coupled with a therapeutic delivery module thereby addressing these limitations. A brief history, technical overview and summary of preclinical and clinical studies of the KonaMedical Surround Sound™ system are presented. A literature search using the terms "renal denervation", "resistant hypertension" and "external ultrasound" was performed using PubMed, and references retrieved were selected based on relevancy and year of publication (date range 1991-2015). The Surround Sound™ system appears to be a promising approach to RDN which eliminates several of the factors currently limiting the intravenous approach. So far, it has demonstrated efficacy for reducing blood pressure in resistant hypertension patients with minimal adverse effects. Several double-blind, sham-controlled clinical trials are currently underway to confirm the validity of these findings.

Effective Treatment of Pediatric Eating Disorders.

PubMed

Ariail, Ashley; Carpenter, Elizabeth; Smith, Twyala; Sacco, Briana

2018-06-01

Eating disorders are prevalent in the pediatric population yet underdiagnosed by pediatric health care professionals. The gold standard of care consists of a multidisciplinary team approach including a therapist, registered dietitian nutritionist (RDN), and psychiatrist, combined with family-centered treatment. Although families do not cause eating disorders, they are essential to a child's recovery from an eating disorder. Psychoeducation, supportive limit setting, processing relational dynamics, and externalizing the eating disorder are therapeutic interventions used in the treatment of an eating disorder. The RDN provides assessment, education, and guidance with food and nutrition, as well as establishing goal weight and implementing meal plans. Over time, the RDN assists with integrating the patient and family into their normal lifestyle, including guiding adjustments in the meal plan for weight maintenance, increasing activity, dining out, and increasing the variety of foods consumed. Psychopharmacological interventions help target comorbid psychiatric conditions but should be used in conjunction with other therapeutic interventions to effectively treat pediatric eating disorders. [Pediatr Ann. 2018;47(6):e250-e253.]. Copyright 2018, SLACK Incorporated.

Catheter-based renal denervation for resistant hypertension: Twenty-four month results of the EnligHTN I first-in-human study using a multi-electrode ablation system.

PubMed

Tsioufis, Costas P; Papademetriou, Vasilios; Dimitriadis, Kyriakos S; Kasiakogias, Alexandros; Tsiachris, Dimitrios; Worthley, Matthew I; Sinhal, Ajay R; Chew, Derek P; Meredith, Ian T; Malaiapan, Yuvi; Thomopoulos, Costas; Kallikazaros, Ioannis; Tousoulis, Dimitrios; Worthley, Stephen G

2015-12-15

Long term safety and efficacy data of multi-electrode ablation system for renal denervation (RDN) in patients with drug resistant hypertension (dRHT) are limited. We studied 46 patients (age: 60 ± 10 years, 4.7 ± 1.0 antihypertensive drugs) with drug resistant hypertension (dRHT). Reduction in office BP at 24 months from baseline was -29/-13 mmHg, while the reduction in 24-hour ambulatory BP and in home BP at 24 months were -13/-7 mmHg and -11/-6 mmHg respectively (p<0.05 for all). A correlation analysis revealed that baseline office and ambulatory BP were related to the extent of office and ambulatory BP drop. Apart from higher body mass index (33.3 ± 4.7 vs 29.5 ± 6.2 kg/m(2), p<0.05), there were no differences in patients that were RDN responders defined as ≥10 mmHg decrease (74%, n=34) compared to non-responders. Stepwise logistic regression analysis revealed no prognosticators of RDN response (p=NS for all). At 24 months there were no new serious device or procedure related adverse events. The EnligHTN I study shows that the multi-electrode ablation system provides a safe method of RDN in dRHT accompanied by a clinically relevant and sustained BP reduction. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Results of a randomized controlled pilot trial of intravascular renal denervation for management of treatment-resistant hypertension.

PubMed

Jacobs, Lotte; Persu, Alexandre; Huang, Qi-Fang; Lengelé, Jean-Philippe; Thijs, Lutgarde; Hammer, Frank; Yang, Wen-Yi; Zhang, Zhen-Yu; Renkin, Jean; Sinnaeve, Peter; Wei, Fang-Fei; Pasquet, Agnès; Fadl Elmula, Fadl Elmula M; Carlier, Marc; Elvan, Arif; Wunder, Cora; Kjeldsen, Sverre E; Toennes, Stefan W; Janssens, Stefan; Verhamme, Peter; Staessen, Jan A

2017-12-01

Previous trials of catheter-based renal-artery denervation (RDN) as treatment modality in resistant hypertension (rHT) generated unconvincing results. In the Investigator-Steered Project on Intravascular Denervation for Management of Treatment-Resistant Hypertension (INSPiRED; NCT01505010), we optimized selection and management of rHT patients. With ethical clearance to randomize 18 patients, three Belgian hypertension centers screened 29 rHT patients on treatment with ≥3 drugs, of whom 17 after optimization of treatment (age <70 years; systolic/diastolic office blood pressure (BP) ≥ 140/90 mm Hg; 24-h BP ≥130/80 mm Hg; glomerular filtration rate [eGFR] ≥ 45 mL/min/1.73 m 2 ; body mass index <40kg/m 2 ) were randomized and 15 were analyzed 6 months later, while medical treatment was continued (n = 9) or combined with RDN by the EnligHTN™ multi-electrode system (n = 6). The baseline-adjusted between-group differences amounted to 19.5/10.4 mm Hg (change in control vs. intervention group, +7.6/+2.2 vs. -11.9/-8.2 mm Hg; P = .088) for office BP, 22.4/13.1 mm Hg (+0.7/+0.3 vs. -21.7/-12.8; mm Hg; P ≤ .049) for 24-h BP, the primary efficacy endpoint, and 2.5 mL/min/1.73 m 2 (+1.5 vs. -1.1 mL/min/1.73 m 2 ; P = .86) for eGFR, the primary safety endpoint. At 6 month, ECG voltages and the number of prescribed drugs (P ≤ .036) were lower in RDN patients, but quality of life and adherence, captured by questionnaire and urine analysis were similar in both groups. Changes in BP and adherence were unrelated. No major complications occurred. The INSPiRED pilot suggests that RDN with the EnligHTN ™ system is effective and safe and generated insights useful for the design of future RDN trials.

Renal Denervation Prevents Immune Cell Activation and Renal Inflammation in Angiotensin II–Induced Hypertension

PubMed Central

Xiao, Liang; Kirabo, Annet; Wu, Jing; Saleh, Mohamed A.; Zhu, Linjue; Wang, Feng; Takahashi, Takamune; Loperena, Roxana; Foss, Jason D.; Mernaugh, Raymond L.; Chen, Wei; Roberts, Jackson; Osborn, John W.; Itani, Hana A.; Harrison, David G.

2015-01-01

Rationale Inflammation and adaptive immunity plays a crucial role in the development of hypertension. Angiotensin II and likely other hypertensive stimuli activate the central nervous system and promote T cell activation and end-organ damage in peripheral tissues. Objective To determine if renal sympathetic nerves mediate renal inflammation and T cell activation in hypertension. Methods and Results Bilateral renal denervation (RDN) using phenol application to the renal arteries reduced renal norepinephrine (NE) levels and blunted angiotensin II induced hypertension. Bilateral RDN also reduced inflammation, as reflected by decreased accumulation of total leukocytes, T cells and both CD4+ and CD8+ T cells in the kidney. This was associated with a marked reduction in renal fibrosis, albuminuria and nephrinuria. Unilateral RDN, which partly attenuated blood pressure, only reduced inflammation in the denervated kidney, suggesting that this effect is pressure independent. Angiotensin II also increased immunogenic isoketal-protein adducts in renal dendritic cells (DCs) and increased surface expression of costimulation markers and production of IL-1α, IL-1β, and IL-6 from splenic dendritic cells. NE also dose dependently stimulated isoketal formation in cultured DCs. Adoptive transfer of splenic DCs from angiotensin II-treated mice primed T cell activation and hypertension in recipient mice. RDN prevented these effects of hypertension on DCs. In contrast to these beneficial effects of ablating all renal nerves, renal afferent disruption with capsaicin had no effect on blood pressure or renal inflammation. Conclusions Renal sympathetic nerves contribute to dendritic cell activation, subsequent T cell infiltration and end-organ damage in the kidney in the development of hypertension. PMID:26156232

Is the failure of SYMPLICITY HTN-3 trial to meet its efficacy endpoint the "end of the road" for renal denervation?

PubMed

Epstein, Murray; de Marchena, Eduardo

2015-02-01

Resistant hypertension is a common medical problem that is increasing with the advent of an increasingly older and heavier population. The etiology of resistant hypertension is almost always multifactorial, but the results of numerous studies indicate that renal sympathetic activation is a particularly common cause of resistance to antihypertensive treatment. Consistent with the belief in a pivotal role of renal sympathetic stimulation, there has been a growing interest in renal denervation (RDN) treatment strategies. The long-awaited results of SYMPLICITY HTN-3 study disclosed that the reduction in blood pressure by the SYMPLICITY device did not differ from that in the sham-procedure arm of the study. In the present article, we identify several factors that explain why the study failed to demonstrate any benefit from the intervention. The reasons are multifactorial and include inadequate screening at entry and frequent medication changes during the study. Additional problems include the lack of experience of many operators with the SYMPLICITY device and procedure variability, as attested to by a diminished number of ablation "quadrants." Also a factor was the inability of the first generation Medtronic device to allow four ablations to be performed simultaneously. We recommend that future RDN studies adhere to more rigorous screening procedures, and utilize newer multi-site denervation systems that facilitate four ablations simultaneously. Drug optimization should be achieved by monitoring adherence throughout the study. Nevertheless, we are optimistic about a future role of RDN. To optimize chances of success, increased efforts are necessary to identify the appropriate patients for RDN and investigators must use second and third generation denervation devices and techniques. Copyright © 2015 American Society of Hypertension. Published by Elsevier Inc. All rights reserved.

Anatomical and procedural determinants of ambulatory blood pressure lowering following catheter-based renal denervation using radiofrequency.

PubMed

Lauder, Lucas; Ewen, Sebastian; Tzafriri, Abraham R; Edelman, Elazer R; Cremers, Bodo; Kulenthiran, Saarraaken; Ukena, Christian; Linz, Dominik; Kindermann, Ingrid; Tsioufis, Costas; Scheller, Bruno; Böhm, Michael; Mahfoud, Felix

2018-03-02

Catheter-based renal sympathetic denervation (RDN) has been introduced to lower blood pressure (BP) and sympathetic activity in patients with uncontrolled hypertension with at best equivocal results. It has been postulated that anatomic and procedural elements introduce unaccounted variability and yet little is known of the impact of renal anatomy and procedural parameters on BP response to RDN. Anatomical parameters such as length and diameter were analyzed by quantitative vascular analysis and the prevalence of accessory renal arteries and renal artery disease were documented in 150 patients with resistant hypertension undergoing bilateral RDN using a mono-electrode radiofrequency catheter (Symplicity Flex, Medtronic). Accessory renal arteries and renal artery disease were present in 56 (37%) and 14 patients (9%), respectively. At 6-months, 24 h-ambulatory BP was reduced by 11/6 mm Hg (p < 0.001 for both). Change of systolic blood pressure (SBP) was not related to the presence of accessory renal arteries (p = 0.543) or renal artery disease (p = 0.598). Patients with at least one main renal artery diameter ≤ 4 mm had a more pronounced reduction of 24 h-ambulatory SBP compared to patients where both arteries were >4 mm (-19 vs. -10 mmHg; p = 0.038). Neither the length of the renal artery nor the number of RF ablations influenced 24 h-ambulatory BP reduction at 6 months. 24 h-ambulatory BP lowering was most pronounced in patients with smaller renal artery diameter but not related to renal artery length, accessory arteries or renal artery disease. Further, there was no dose-response relationship observed with increasing number of ablations. Because little is known of the impact of renal anatomy and procedural parameters on blood pressure (BP) response to renal denervation (RDN), anatomical and procedural data were analyzed in 150 patients undergoing bilateral RDN. BP lowering was most pronounced in patients with smaller renal artery diameter but not related to renal artery length, the presence of renal artery disease or accessory renal arteries. Further, there was no dose-response relationship observed with increasing number of ablations. Copyright © 2018. Published by Elsevier Inc.

A clinician's perspective of the role of renal sympathetic nerves in hypertension

PubMed Central

Briasoulis, Alexandros; Bakris, George L.

2015-01-01

The renal sympathetic nerves have significant contribution to the control of different aspects of kidney function. Early animal studies of renal denervation in a large number of different models of hypertension showed that that RDN improved BP control. Recently, data from prospective cohorts and randomized studies showed that renal denervation therapy (RDN) is a safe procedure but is associated with only modest reduction of ambulatory blood pressure (BP) in patients on intensive medical therapy. The main goal of this article is to review the results of preclinical and clinical studies on the contribution of the renal sympathetic nervous system to hypertension and the therapeutic applications of catheter-based renal denervation. PMID:25859218

Energy budgeting and carbon footprint of transgenic cotton-wheat production system through peanut intercropping and FYM addition.

PubMed

Singh, Raman Jeet; Ahlawat, I P S

2015-05-01

Two of the most pressing sustainability issues are the depletion of fossil energy resources and the emission of atmospheric green house gases like carbon dioxide to the atmosphere. The aim of this study was to assess energy budgeting and carbon footprint in transgenic cotton-wheat cropping system through peanut intercropping with using 25-50% substitution of recommended dose of nitrogen (RDN) of cotton through farmyard manure (FYM) along with 100% RDN through urea and control (0 N). To quantify the residual effects of previous crops and their fertility levels, a succeeding crop of wheat was grown with varying rates of nitrogen, viz. 0, 50, 100, and 150 kg ha(-1). Cotton + peanut-wheat cropping system recorded 21% higher system productivity which ultimately helped to maintain higher net energy return (22%), energy use efficiency (12%), human energy profitability (3%), energy productivity (7%), carbon outputs (20%), carbon efficiency (17%), and 11% lower carbon footprint over sole cotton-wheat cropping system. Peanut addition in cotton-wheat system increased the share of renewable energy inputs from 18 to 21%. With substitution of 25% RDN of cotton through FYM, share of renewable energy resources increased in the range of 21% which resulted into higher system productivity (4%), net energy return (5%), energy ratio (6%), human energy profitability (74%), energy productivity (6%), energy profitability (5%), and 5% lower carbon footprint over no substitution. The highest carbon footprint (0.201) was recorded under control followed by 50 % substitution of RDN through FYM (0.189). With each successive increase in N dose up to 150 kg N ha(-1) to wheat, energy productivity significantly reduced and share of renewable energy inputs decreased from 25 to 13%. Application of 100 kg N ha(-1) to wheat maintained the highest grain yield (3.71 t ha(-1)), net energy return (105,516 MJ ha(-1)), and human energy profitability (223.4) over other N doses applied to wheat. Application of 50 kg N ha(-1) to wheat maintained the least carbon footprint (0.091) followed by 100 kg N ha(-1) (0.100). Our study indicates that system productivity as well as energy and carbon use efficiencies of transgenic cotton-wheat production system can be enhanced by inclusion of peanut as an intercrop in cotton and substitution of 25% RDN of cotton through FYM, as well as application of 100 kg N ha(-1) to succeeding wheat crop.

Interventional Therapies for Resistant Hypertension: A Brief Update

PubMed Central

Brandon, Lisa

2016-01-01

Resistant hypertension remains a clinical challenge with few management options beyond maximisation of medications. Catheter- based renal artery denervation (RDN) was proposed in 2009 as a possible therapy for resistant hypertension and early feasibility trials caused excitement among cardiologists and antihypertensive specialists, showing remarkable and sustained blood pressure reductions. In 2014, enthusiasm for RDN dampened following the SYMPLICITY 3 trial results, which showed no statistically significant difference in blood pressure between the intervention and control arms. However, hope remains for the improved management of resistant hypertension; procedural understanding, technological advancements and alternative targets – such as baroreceptor activation therapy and arteriovenous shunts – may aid the identification of those patients for whom specific interventional therapies will be effective. PMID:29588709

The effect of two different renal denervation strategies on blood pressure in resistant hypertension: Comparison of full-length versus proximal renal artery ablation.

PubMed

Chen, Weijie; Ling, Zhiyu; Du, Huaan; Song, Wenxin; Xu, Yanping; Liu, Zengzhang; Su, Li; Xiao, Peilin; Yuan, Yuelong; Lu, Jiayi; Zhang, Jianhong; Li, Zhifeng; Shao, Jiang; Zhong, Bin; Zhou, Bei; Woo, Kamsang; Yin, Yuehui

2016-11-01

Renal denervation (RDN) is used to manage blood pressure (BP) in patients with resistant hypertension (rHT), but effectiveness is still a concern, and key arterial portion for successful RDN is not clear. The aim of this study was to investigate the efficacy and safety of proximal versus full-length renal artery ablation in patients with resistant hypertension (rHT). Forty-seven patients with rHT were randomly assigned to receive full-length ablation (n = 23) or proximal ablation (n = 24) of the renal arteries. All lesions were treated with radiofrequency energy via a saline-irrigated catheter. Office BP was measured during 12 months of follow-up and ambulatory BP at baseline and 6 months (n = 15 in each group). Compared with full-length ablation, proximal ablation reduced the number of ablation points in both the right (6.1 ± 0.7 vs. 3.3 ± 0.6, P < 0.001) and the left renal arteries (6.2 ± 0.7 vs. 3.3 ± 0.8, P < 0.001), with significantly shorter RF delivery time (P < 0.001), but higher RF power (P = 0.011). Baseline office BPs was 179.4 ± 13.7/102.8 ± 9.4 mm Hg in the full-length group and 181.9 ± 12.8/103.5 ± 8.9 mm Hg in the proximal group (P > 0.5). Similar office BPs was reduced by -39.4 ± 11.5/-20.9 ± 7.1 mm Hg at 6 months and -38.2 ± 10.3/-21.5 ± 5.8 mm Hg at 12 months in the full-length group (P < 0.001), -42.0 ± 11.6/-21.4 ± 7.9 mm Hg at 6 months and -40.9 ± 10.3/-22.1 ± 5.6 mm Hg at 12 months in the proximal group (P < 0.001), and progressive BP reductions were observed over the 6 months (P < 0.001) in both groups. The drop in ambulatory 24-hr SBP and DBP were significantly less than the drop in office BP (P < 0.001). No renovascular or other adverse complications were observed. The results indicate that proximal RDN has a similar efficacy and safety profile compared with full-length RDN, and propose the proximal artery as the key portion for RDN. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Supplements and Men's Health

MedlinePlus

... Supplements and Men's Health Print Email Supplements and Men's Health Reviewed by Taylor Wolfram, MS, RDN, LDN ... an individualized nutrition plan. There are several reasons men may need supplements. They include: Bone Health: Calcium ...

Sympathetic Response and Outcomes Following Renal Denervation in Patients With Chronic Heart Failure: 12-Month Outcomes From the Symplicity HF Feasibility Study.

PubMed

Hopper, Ingrid; Gronda, Edoardo; Hoppe, Uta C; Rundqvist, Bengt; Marwick, Thomas H; Shetty, Sharad; Hayward, Christopher; Lambert, Thomas; Hering, Dagmara; Esler, Murray; Schlaich, Markus; Walton, Antony; Airoldi, Flavio; Brandt, Mathias C; Cohen, Sidney A; Reiters, Pascalle; Krum, Henry

2017-09-01

Heart failure (HF) is associated with chronic sympathetic activation. Renal denervation (RDN) aims to reduce sympathetic activity by ablating the renal sympathetic nerves. We investigated the effect of RDN in patients with chronic HF and concurrent renal dysfunction in a prospective, multicenter, single-arm feasibility study. Thirty-nine patients with chronic systolic HF (left ventricular ejection fraction [LVEF] <40%, New York Heart Association class II-III,) and renal impairment (estimated glomerular filtration rate [eGFR; assessed with the use of the Modification of Diet in Renal Disease equation] < 75 mL • min -1  • 1.73 m -2 ) on stable medical therapy were enrolled. Mean age was 65 ± 11 years; 62% had ischemic HF. The average number of ablations per patient was 13 ± 3. No protocol-defined safety events were associated with the procedure. One subject experienced a renal artery occlusion that was possibly related to the denervation procedure. Statistically significant reductions in N-terminal pro-B-type natriuretic peptide (NT-proBNP; 1530 ± 1228 vs 1428 ± 1844 ng/mL; P = .006) and 120-minute glucose tolerance test (11.2 ± 5.1 vs 9.9 ± 3.6; P = .026) were seen at 12 months, but there was no significant change in LVEF (28 ± 9% vs 29 ± 11%; P= .536), 6-minute walk test (384 ± 96 vs 391 ± 97 m; P= .584), or eGFR (52.6 ± 15.3 vs 52.3 ± 18.5 mL • min -1  • 1.73 m -2 ; P= .700). RDN was associated with reductions in NT-proBNP and 120-minute glucose tolerance test in HF patients 12 months after RDN treatment. There was no deterioration in other indices of cardiac and renal function in this small feasibility study. Copyright © 2017 Elsevier Inc. All rights reserved.

Executive summary of the joint position paper on renal denervation of the Cardiovascular and Interventional Radiological Society of Europe and the European Society of Hypertension.

PubMed

Moss, Jonathan G; Belli, Anna-Maria; Coca, Antonio; Lee, Michael; Mancia, Giuseppe; Peregrin, Jan H; Redon, Josep; Reekers, Jim A; Tsioufis, Costas; Vorwerk, Dierk; Schmieder, Roland E

2016-12-01

Renal denervation (RDN) was reported as a novel exciting treatment for resistant hypertension in 2009. An initial randomized trial supported its efficacy and the technique gained rapid acceptance across the globe. However, a subsequent large blinded, sham arm randomized trial conducted in the USA (to gain Food and Drug Administration approval) failed to achieve its primary efficacy end point in reducing office blood pressure at 6 months. Published in 2014 this trial received both widespread praise and criticism. RDN has effectively stopped out with clinical trials pending further evidence. This joint consensus document representing the European Society of Hypertension and the Cardiovascular and Radiological Society of Europe attempts to distill the current evidence and provide future direction and guidance.

Chemical Profiling of Re-Du-Ning Injection by Ultra-Performance Liquid Chromatography Coupled with Electrospray Ionization Tandem Quadrupole Time-of-Flight Mass Spectrometry through the Screening of Diagnostic Ions in MSE Mode

PubMed Central

Wang, Zhenzhong; Geng, Jianliang; Dai, Yi; Xiao, Wei; Yao, Xinsheng

2015-01-01

The broad applications and mechanism explorations of traditional Chinese medicine prescriptions (TCMPs) require a clear understanding of TCMP chemical constituents. In the present study, we describe an efficient and universally applicable analytical approach based on ultra-performance liquid chromatography coupled to electrospray ionization tandem quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q/TOF-MS) with the MSE (E denotes collision energy) data acquisition mode, which allowed the rapid separation and reliable determination of TCMP chemical constituents. By monitoring diagnostic ions in the high energy function of MSE, target peaks of analogous compounds in TCMPs could be rapidly screened and identified. “Re-Du-Ning” injection (RDN), a eutherapeutic traditional Chinese medicine injection (TCMI) that has been widely used to reduce fever caused by viral infections in clinical practice, was studied as an example. In total, 90 compounds, including five new iridoids and one new sesquiterpene, were identified or tentatively characterized by accurate mass measurements within 5 ppm error. This analysis was accompanied by MS fragmentation and reference standard comparison analyses. Furthermore, the herbal sources of these compounds were unambiguously confirmed by comparing the extracted ion chromatograms (EICs) of RDN and ingredient herbal extracts. Our work provides a certain foundation for further studies of RDN. Moreover, the analytical approach developed herein has proven to be generally applicable for profiling the chemical constituents in TCMPs and other complicated mixtures. PMID:25875968

Resistant hypertension: Renal denervation or intensified medical treatment?

PubMed

Morganti, Alberto; Mancia, Giuseppe

2018-04-01

Resistant hypertension (RH) can be diagnosed if blood pressure (BP) is not controlled with the combination of three antihypertensive drugs, including a diuretic, all at effective doses. Patients affected by this condition exhibit a marked increase in the risk of cardiovascular and renal morbid and fatal events. They also exhibit an increased activity of the sympathetic nervous system which is likely to importantly contribute at the renal and other vascular levels to the hypertensive state. Almost 10years ago renal denervation (RDN) by radiofrequency thermal energy delivery to the walls of the renal arteries was proposed for the treatment of RH. Several uncontrolled studies initially reported that this procedure substantially reduced the elevated BP values but this conclusion has not been supported by a recent randomized control trial, which has almost marginalized this therapeutic approach. A revival, however, is under way because of recent positive findings and technical improvement that hold promise to make renal denervation more complete. The antihypertensive efficacy and overall validity of RDN will have to be tested against drug treatment of RH. Several studies indicate that an excess of aldosterone production contributes to RH and recent evidence documents indisputably that anti-aldosterone agents such as spironolactone can effectively control BP in many RH patients, although with some side effects that require close patients' monitoring. At present, it is advisable to treat RH with the addition of an anti-aldosterone agent. If BP control is not achieved or serious side effects become manifest RDN may then be considered. Copyright © 2017 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Reading Acquisition and Beyond: Decoding Includes Cognition.

ERIC Educational Resources Information Center

Perfetti, Charles A.

1984-01-01

Focuses on (1) the acquisition and use of word representations and (2) the acquisition of the alphabetic code. Urges that instruction provide conditions to promote the learning of three types of representation--word forms, letter patterns, and mapping. (RDN)

State Hegemony and the Schooling Process.

ERIC Educational Resources Information Center

Berman, Edward H.

1984-01-01

Examines some of the mechanisms whereby the state utilizes the schools to disseminate the ideology of state capitalism. Focuses particularly on the way influential foundations, often in conjunction with government agencies, have sponsored surveys on the direction American schooling should take. (Author/RDN)

Relative velocity changes using ambient seismic noise at Okmok and Redoubt volcanoes, Alaska

NASA Astrophysics Data System (ADS)

Bennington, N. L.; Haney, M. M.; De Angelis, S.; Thurber, C. H.

2013-12-01

Okmok and Redoubt are two of the most active volcanoes in the Aleutian Arc. Leading up to its most recent eruption, Okmok, a shield volcano on Umnak Island, showed precursors to volcanic activity only five hours before it erupted explosively in July 2008. Redoubt, a stratovolcano located along the Cook Inlet, displayed several months of precursory activity leading up to its March 2009 eruption. Frequent activity at both volcanoes poses a major hazard due to heavy traffic along the North Pacific air routes. Additionally, Okmok is adjacent to several of the world's most productive fisheries and Redoubt is located only 110 miles SW of Anchorage, the major population center of Alaska. For these reasons, it is imperative that we improve our ability to detect early signs of unrest, which could potentially lead to eruptive activity at these volcanoes. We take advantage of continuous waveforms recorded on seismic networks at Redoubt and Okmok in an attempt to identify seismic precursors to the recent eruptions at both volcanoes. We perform seismic interferometry using ambient noise, following Brenguier et al. (2008), in order to probe the subsurface and determine temporal changes in relative seismic velocity from pre- through post-eruption, for the 2008 Okmok and 2009 Redoubt eruptions. In a preliminary investigation, we analyzed 6 months of noise cross-correlation functions averaged over 10-day intervals leading up to the 2009 eruption at Redoubt. During February 2009, station pairs RSO-DFR and RDN-RSO showed a decrease in seismic velocity of ~0.02%. By the beginning of March, the relative velocity changes returned to background levels. Stations RSO and RDN are located within the summit breach, and station DFR is to the north. Although these results are preliminary, it is interesting to note that the decrease in seismic velocity at both station pairs overlaps with the time period when Grapenthin et al. (2012) hypothesize magma in the mid-to-deep crustal reservoir was reheated and migrated to a second shallow reservoir between 2 and 4.5 km depth. This hypothesized shallow magma reservoir is within the sensitivity depth of our ambient noise analysis, and thus the decrease in seismic velocity may be associated with magma movement at shallow depths underneath Redoubt. At the onset of eruption, the relative velocity change at station pair RDN-RSO decreased by ~0.03% while that at RSO-DFR remained at background levels. Notably, this decrease in seismic velocity is observed only at the station pair with a propagation path that traverses the summit breach. Our investigation continues as we search for time variations in the ambient seismic noise signal preceding and following the 2008 Okmok and 2009 Redoubt eruptions and endeavor to identify what those changes may represent.

Eligibility for Renal Denervation: Anatomical Classification and Results in Essential Resistant Hypertension

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

Okada, Takuya, E-mail: okabone@gmail.com; Pellerin, Olivier; Savard, Sébastien

PurposeTo classify the renal artery (RA) anatomy based on specific requirements for endovascular renal artery denervation (RDN) in patients with drug-resistant hypertension (RH).Materials and MethodsThe RA anatomy of 122 consecutive RH patients was evaluated by computed tomography angiography and classified as two types: A (main RA ≥20 mm in length and ≥4.0 mm in diameter) or B (main RA <20 mm in length or main RA <4.0 mm in diameter). The A type included three subtypes: A1 (without accessory RAs), A2 (with accessory RAs <3.0 mm in diameter), and A3 (with accessory RAs ≥3.0 mm in diameter]. A1 and A2 types were eligible for RDN withmore » the Simplicity Flex catheter. Type B included twi subtypes based on the main RA length and diameter. Patients were accordingly classified into three eligibility categories: complete (CE; both RAs were eligible), partial (PE; one eligible RA), and noneligibility (NE; no eligible RA).ResultsBilateral A1 type was the most prevalent and was observed in 48.4 % of the patients followed by the A1/A2 type (18 %). CE, PE, and NE were observed in 69.7, 22.9, and 7.4 % of patients, respectively. The prevalence of accessory RAs was 41 %.ConclusionsOf RH patients, 30.3 % were not eligible for bilateral RDN with the current Simplicity Flex catheter. This classification provides the basis for standardized reporting to allow for pooling of results of larger patient cohorts in the future.« less

Effects of Multi-Electrode Renal Denervation on Insulin Sensitivity and Glucose Metabolism in a Canine Model of Type 2 Diabetes Mellitus.

PubMed

Pan, Tao; Guo, Jin-He; Ling, Long; Qian, Yue; Dong, Yong-Hua; Yin, Hua-Qing; Zhu, Hai-Dong; Teng, Gao-Jun

2018-05-01

To evaluate the effects of multi-electrode catheter-based renal denervation (RDN) on insulin sensitivity and glucose metabolism in a type 2 diabetes mellitus (T2DM) canine model. Thirty-three dogs were divided equally into 3 groups: bilateral renal denervation (BRDN) group, left renal denervation (LRDN) group, and sham operation (SHAM) group. Body weight and blood biochemistry were measured at baseline, 20 weeks, and 32 weeks, and renal angiography and computerized tomographic (CT) angiography were determined before the procedure and 1 month, 2 months, and 3 months after the procedure. Western blot was used to identify the activities of gluconeogenic enzymes and insulin-signaling proteins. Fasting plasma glucose (9.64 ± 1.57 mmol/L vs 5.12 ± 1.08 mmol/L; P < .0001), fasting insulin (16.19 ± 1.43 mIU/mL vs 5.07 ± 1.13 mIU/mL; P < .0001), and homeostasis-model assessment of insulin resistance (HOMA-IR; 6.95 ± 1.33 vs 1.15 ± 0.33; P < .0001) in the BRDN group had significantly decreased at the 3-month follow-up compared with the SHAM group. Western blot analyses showed that RDN suppressed the gluconeogenetic genes, modulated insulin action, and activated insulin receptors-AKT signaling cascade in the liver. CT angiography and histopathologic analyses did not show any dissection, aneurysm, thrombus, or rupture in any of the renal arteries. These findings identified that multi-electrode catheter-based RDN could effectively decrease gluconeogenesis and glycogenolysis, resulting in improvements in insulin sensitivity and glucose metabolism in a T2DM canine model. Copyright © 2017 SIR. Published by Elsevier Inc. All rights reserved.

Renal denervation with standard radiofrequency ablation catheter is effective in 24-hour ambulatory blood pressure reduction - follow-up at 1/3/6/12 months.

PubMed

Prochnau, D; Otto, S; Figulla, H-R; Surber, R

2016-07-01

To examine the effect of renal denervation (RDN) on 24‑h ambulatory blood pressure (ABP) with a standard radiofrequency ablation catheter (RF catheter). Seventy-five patients with resistant hypertension received bilateral RDN with an RF catheter (6 RF applications, 1 minute each, 8-12 watts). Seventy patients fulfilled inclusion criteria with mean systolic ABP ≥140 mmHg (mean 165/89) despite treatment with ≥3 antihypertensive drugs (mean 5.9) including a diuretic, and were further analysed for ABP changes. Follow-up at 1/3/6/12 months comprised biochemical evaluations and ABP measurement. At 6/12 months, duplex sonography of the renal arteries was additionally performed. At 1/3/6/12 months we observed a significant reduction in systolic ABP of -15/-17/-18/-15 mmHg (n = 55/53/57/50; non-parametric Friedman test, p < 0.001) and diastolic ABP of -6/-9/-10/-7 mmHg (p < 0.001). Of the patients, 70 %/64 % showed a systolic ABP reduction of ≥10 mmHg, and 77 %/70 % of ≥5 mmHg at 6/12-month follow-up. Two patients (2.7 %) developed renal artery stenosis (>70 %) with subsequent stenting without complications. Logistic regression analysis with systolic ABP reduction ≥10 mmHg at 12 months follow-up as criterion revealed that only the mean baseline systolic ABP was significant, OR = 2.174. RDN with a standard RF catheter can be used safely to reduce mean ABP in resistant hypertension as shown in long-term follow-up.

Change the Joke and Slip the Yoke: Traditions of Afro-American Satire.

ERIC Educational Resources Information Center

Cook, William W.

1985-01-01

Cites and discusses various forms of satire from Black American, Caribbean, and African cultures. Forms considered include oral ballads ('toasts') antebellum sermons, praise poems, mother-rhyming, ritual insult and theater. Emphasizes the agonistic element and the impossibility of performing Afro-American satire in standard English. (RDN)

Effective Schooling in Desegregated Settings: What Do We Know About Learning Style and Linguistic Differences?

ERIC Educational Resources Information Center

Hare, Bruce R.; Levine, Daniel U.

1985-01-01

Argues that mismatches between home and classroom environments play an important part in accounting for the low academic performance of many low-status, especially minority, students. Discusses approaches emphasizing cooperative learning and individualized instruction for use with culturally different students in desegregated settings. (RDN)

Conflict and Protest in Israeli Society: The Case of the Black Panthers in Israel.

ERIC Educational Resources Information Center

Bernstein, Deborah

1984-01-01

Traces the emergence and development of an Israeli protest movement--the mainly slum-based Black Panthers--its politicization, and its decline. Looks at this development and the issues raised by the Panthers against the backdrop of the conflictual relations in Israeli society. (RDN)

Urbanism and Racial Attitudes: A Test of Some Urban Theories.

ERIC Educational Resources Information Center

Wilson, Thomas C.

1984-01-01

National survey data are used to test the relationship between urbanism and racial attitudes among Whites, and a liberalizing effect of urbanism is found. It appears that urbanism liberalizes racial attitudes by increasing equal-status, cooperative, and relatively personal contact between members of racial subcultures. (Author/RDN)

Marketing a Proprietary Child Care System.

ERIC Educational Resources Information Center

Owens, Buffy L.

1984-01-01

Argues that next to quality of care, location is the most important factor in the success of a child care center. Emphasizes site evaluation; the grand opening; creating awareness in the community through paid advertising; telephone techniques; school tours; a free week of enrollment; and maintaining good parent relations. (RDN)

A renaissance of neural networks in drug discovery.

PubMed

Baskin, Igor I; Winkler, David; Tetko, Igor V

2016-08-01

Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.

Cross-Layer Service Discovery Mechanism for OLSRv2 Mobile Ad Hoc Networks.

PubMed

Vara, M Isabel; Campo, Celeste

2015-07-20

Service discovery plays an important role in mobile ad hoc networks (MANETs). The lack of central infrastructure, limited resources and high mobility make service discovery a challenging issue for this kind of network. This article proposes a new service discovery mechanism for discovering and advertising services integrated into the Optimized Link State Routing Protocol Version 2 (OLSRv2). In previous studies, we demonstrated the validity of a similar service discovery mechanism integrated into the previous version of OLSR (OLSRv1). In order to advertise services, we have added a new type-length-value structure (TLV) to the OLSRv2 protocol, called service discovery message (SDM), according to the Generalized MANET Packet/Message Format defined in Request For Comments (RFC) 5444. Each node in the ad hoc network only advertises its own services. The advertisement frequency is a user-configurable parameter, so that it can be modified depending on the user requirements. Each node maintains two service tables, one to store information about its own services and another one to store information about the services it discovers in the network. We present simulation results, that compare our service discovery integrated into OLSRv2 with the one defined for OLSRv1 and with the integration of service discovery in Ad hoc On-demand Distance Vector (AODV) protocol, in terms of service discovery ratio, service latency and network overhead.

Cross-Layer Service Discovery Mechanism for OLSRv2 Mobile Ad Hoc Networks

PubMed Central

Vara, M. Isabel; Campo, Celeste

2015-01-01

Service discovery plays an important role in mobile ad hoc networks (MANETs). The lack of central infrastructure, limited resources and high mobility make service discovery a challenging issue for this kind of network. This article proposes a new service discovery mechanism for discovering and advertising services integrated into the Optimized Link State Routing Protocol Version 2 (OLSRv2). In previous studies, we demonstrated the validity of a similar service discovery mechanism integrated into the previous version of OLSR (OLSRv1). In order to advertise services, we have added a new type-length-value structure (TLV) to the OLSRv2 protocol, called service discovery message (SDM), according to the Generalized MANET Packet/Message Format defined in Request For Comments (RFC) 5444. Each node in the ad hoc network only advertises its own services. The advertisement frequency is a user-configurable parameter, so that it can be modified depending on the user requirements. Each node maintains two service tables, one to store information about its own services and another one to store information about the services it discovers in the network. We present simulation results, that compare our service discovery integrated into OLSRv2 with the one defined for OLSRv1 and with the integration of service discovery in Ad hoc On-demand Distance Vector (AODV) protocol, in terms of service discovery ratio, service latency and network overhead. PMID:26205272

Illegal Immigrants in Texas: Impact on Social Services and Related Considerations.

ERIC Educational Resources Information Center

Weintraub, Sidney

1984-01-01

The State of Texas receives more from taxes paid by undocumented persons than it costs the state to provide them with public services, such as education, health care, corrections, and welfare. However, six Texas cities together expended more to provide services to undocumented aliens than they received in taxes. (RDN)

Immigration, Ethnic Stratification, and Native Working Class Discipline: Comparisons of Documented and Undocumented Dominicans.

ERIC Educational Resources Information Center

Grasmuck, Sherri

1984-01-01

Compares working conditions of documented and undocumented Dominicans in New York City. Concludes that one of the most important functions served by the illegal alien population in a surplus labor region like New York City resides primarily in its greater controllability by employers in the secondary labor market. (RDN)

A Conversation with Paulo Freire at the University of Massachusetts at Boston.

ERIC Educational Resources Information Center

Bruss, Neal; Macedo, Donaldo P.

1984-01-01

Freire responds to questions about the relation between philosophy of language and pedagogy, the possible importance of a "magical way" of understanding material and political reality, his literacy experiments in Guinea-Bissau and Cape Verde, his impressions of the U.S., and the power of his methodology in practice. (RDN)

The National Commission Reports: Do the States Have the Fiscal Capacity to Respond?

ERIC Educational Resources Information Center

Geske, Terry G.; Hoke, Gordon A.

1985-01-01

States within certain regions (like the Great Lakes area) are probably incapable of financing any major educational reform. The declining Illinois public school system exemplifies this predicament. However, change in the form of a more efficient organization of Illinois school districts is both feasible and likely. (RDN)

Use of Social Welfare Programs and the Disintegration of the Black Nuclear Family.

ERIC Educational Resources Information Center

Jewell, K. Sue

1984-01-01

Social welfare programs contribute to Black women's decisions to terminate their marriages by modifying existing beliefs and values, giving rise to a perception of welfare as a more viable alternative to marital relationships. But welfare is insufficient to enable the maintenance of Black women's preseparation standard of living. (RDN)

First-in-Man Experience with a Novel Catheter-Based Renal Denervation System of Ultrasonic Ablation in Patients with Resistant Hypertension.

PubMed

Chernin, Gil; Szwarcfiter, Iris; Scheinert, Dierk; Blessing, Erwin; Diehm, Nicolas; Dens, Jo; Walton, Antony; Verheye, Stefan; Shetty, Sharad; Jonas, Michael

2018-06-16

To report results of renal denervation (RDN) with the first catheter-based, non-balloon occlusion ultrasonic system in patients with resistant hypertension. In a multicenter, single-arm trial, 39 patients with resistant hypertension (defined as uncontrolled hypertension while taking ≥ 3 antihypertensive medications) were treated. The cohort consisted of 4 groups: severe resistant hypertension (office systolic blood pressure [OSBP] ≥ 160 mm Hg) treated with a unidirectional catheter (group 1; n = 14); severe resistant hypertension treated with a multidirectional catheter (group 2; n = 18); moderate resistant hypertension (OSBP 140-159 mm Hg) treated with a multidirectional catheter (group 3; n = 5); and recurrent severe resistant hypertension, after an initial response to RF RDN (group 4; n = 2). Blood pressure monitoring was performed for 6 months. Severe adverse events were not noted immediately after the procedure or during follow-up. Treatment time was longer with unidirectional than with multidirectional catheters (36.7 min ± 9.6 vs 11.9 min ± 5.8; P < .001). Mean reductions in office blood pressure (systolic/diastolic) at 1, 3, and 6 months were -26.1/-9.6 mm Hg, -28.0/-9.9 mm Hg, and -30.6/-14.1 mm Hg (P < .01 for all). Per-group analysis showed significant OSBP reduction for groups 1 and 2. Patients with isolated systolic hypertension had a significantly smaller reduction in OSBP after 6 months compared with patients with combined systolic/diastolic hypertension (-16.2 mm Hg ± 18.5 vs -9.9 mm Hg ± 33.4; P < .005). Use of the RDN system was feasible and safe in this phase I study. Significant blood pressure reductions were observed over 6 months, although less in patients with isolated systolic hypertension. Copyright © 2018 SIR. All rights reserved.

Integration and Analysis of Neighbor Discovery and Link Quality Estimation in Wireless Sensor Networks

PubMed Central

Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Abd Razak, Shukor

2014-01-01

Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications. PMID:24678277

Privacy-Preserving Relationship Path Discovery in Social Networks

NASA Astrophysics Data System (ADS)

Mezzour, Ghita; Perrig, Adrian; Gligor, Virgil; Papadimitratos, Panos

As social networks sites continue to proliferate and are being used for an increasing variety of purposes, the privacy risks raised by the full access of social networking sites over user data become uncomfortable. A decentralized social network would help alleviate this problem, but offering the functionalities of social networking sites is a distributed manner is a challenging problem. In this paper, we provide techniques to instantiate one of the core functionalities of social networks: discovery of paths between individuals. Our algorithm preserves the privacy of relationship information, and can operate offline during the path discovery phase. We simulate our algorithm on real social network topologies.

Deterioration and Repair of Concrete in the Lower Monumental Navigation Lock Wall.

DTIC Science & Technology

1981-06-01

Lewiston , ID, along the Columbia and Snake Rivers. Because there are no alternate waterways or lock, the transportation system stops if the lock is...tem 3 Page 1 of 1 LOWER SNAKE RIVER PROJECT LOWERGRANITE 7.0 Lewiston LFliLE GOOSE C LOWER ~ MONUMENTAL ~rDN LCOWER SNAKE Tri-cites ICE HAROBO R ASH

Investigation of the Progressive Bi-Level Coding Technique for the High- Resolution Bi-Level Data Compression Standard

DTIC Science & Technology

1990-07-01

DOEdistribution categories from the Standard Distribution for Block 5. Funding Numbers. To include contract Unclassified Scientific and Technical and...a de -,sgee,-cnW iga danim -mihikihnct i "Erglit a n dr -. hviilchkan mi nu deike saitudwniesc rdn i u unLklvvrc mie rdrermlhe mSneahofc g~inttn ere

Research of Ad Hoc Networks Access Algorithm

NASA Astrophysics Data System (ADS)

Xiang, Ma

With the continuous development of mobile communication technology, Ad Hoc access network has become a hot research, Ad Hoc access network nodes can be used to expand capacity of multi-hop communication range of mobile communication system, even business adjacent to the community, improve edge data rates. When the ad hoc network is the access network of the internet, the gateway discovery protocol is very important to choose the most appropriate gateway to guarantee the connectivity between ad hoc network and IP based fixed networks. The paper proposes a QoS gateway discovery protocol which uses the time delay and stable route to the gateway selection conditions. And according to the gateway discovery protocol, it also proposes a fast handover scheme which can decrease the handover time and improve the handover efficiency.

Security Services Discovery by ATM Endsystems

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

Sholander, Peter; Tarman, Thomas

This contribution proposes strawman techniques for Security Service Discovery by ATM endsystems in ATM networks. Candidate techniques include ILMI extensions, ANS extensions and new ATM anycast addresses. Another option is a new protocol based on an IETF service discovery protocol, such as Service Location Protocol (SLP). Finally, this contribution provides strawman requirements for Security-Based Routing in ATM networks.

Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.

PubMed

Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C

2017-02-01

Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Study of Tools for Network Discovery and Network Mapping

DTIC Science & Technology

2003-11-01

connected to the switch. iv. Accessibility of historical data and event data In general, network discovery tools keep a history of the collected...has the following software dependencies: - Java Virtual machine 76 - Perl modules - RRD Tool - TomCat - PostgreSQL STRENGTHS AND...systems - provide a simple view of the current network status - generate alarms on status change - generate history of status change VISUAL MAP

Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus.

PubMed

Sohn, Elliott H; van Dijk, Hille W; Jiao, Chunhua; Kok, Pauline H B; Jeong, Woojin; Demirkaya, Nazli; Garmager, Allison; Wit, Ferdinand; Kucukevcilioglu, Murat; van Velthoven, Mirjam E J; DeVries, J Hans; Mullins, Robert F; Kuehn, Markus H; Schlingemann, Reinier Otto; Sonka, Milan; Verbraak, Frank D; Abràmoff, Michael David

2016-05-10

Diabetic retinopathy (DR) has long been recognized as a microvasculopathy, but retinal diabetic neuropathy (RDN), characterized by inner retinal neurodegeneration, also occurs in people with diabetes mellitus (DM). We report that in 45 people with DM and no to minimal DR there was significant, progressive loss of the nerve fiber layer (NFL) (0.25 μm/y) and the ganglion cell (GC)/inner plexiform layer (0.29 μm/y) on optical coherence tomography analysis (OCT) over a 4-y period, independent of glycated hemoglobin, age, and sex. The NFL was significantly thinner (17.3 μm) in the eyes of six donors with DM than in the eyes of six similarly aged control donors (30.4 μm), although retinal capillary density did not differ in the two groups. We confirmed significant, progressive inner retinal thinning in streptozotocin-induced "type 1" and B6.BKS(D)-Lepr(db)/J "type 2" diabetic mouse models on OCT; immunohistochemistry in type 1 mice showed GC loss but no difference in pericyte density or acellular capillaries. The results suggest that RDN may precede the established clinical and morphometric vascular changes caused by DM and represent a paradigm shift in our understanding of ocular diabetic complications.

discovery toolset for Emulytics v. 1.0

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

Fritz, David; Crussell, Jonathan

The discovery toolset for Emulytics enables the construction of high-fidelity emulation models of systems. The toolset consists of a set of tools and techniques to automatically go from network discovery of operational systems to emulating those complex systems. Our toolset combines data from host discovery and network mapping tools into an intermediate representation that can then be further refined. Once the intermediate representation reaches the desired state, our toolset supports emitting the Emulytics models with varying levels of specificity based on experiment needs.

An Adaptive Jitter Mechanism for Reactive Route Discovery in Sensor Networks

PubMed Central

Cordero, Juan Antonio; Yi, Jiazi; Clausen, Thomas

2014-01-01

This paper analyses the impact of jitter when applied to route discovery in reactive (on-demand) routing protocols. In multi-hop non-synchronized wireless networks, jitter—a small, random variation in the timing of message emission—is commonly employed, as a means to avoid collisions of simultaneous transmissions by adjacent routers over the same channel. In a reactive routing protocol for sensor and ad hoc networks, jitter is recommended during the route discovery process, specifically, during the network-wide flooding of route request messages, in order to avoid collisions. Commonly, a simple uniform jitter is recommended. Alas, this is not without drawbacks: when applying uniform jitter to the route discovery process, an effect called delay inversion is observed. This paper, first, studies and quantifies this delay inversion effect. Second, this paper proposes an adaptive jitter mechanism, designed to alleviate the delay inversion effect and thereby to reduce the route discovery overhead and (ultimately) allow the routing protocol to find more optimal paths, as compared to uniform jitter. This paper presents both analytical and simulation studies, showing that the proposed adaptive jitter can effectively decrease the cost of route discovery and increase the path quality. PMID:25111238

IndeCut evaluates performance of network motif discovery algorithms.

PubMed

Ansariola, Mitra; Megraw, Molly; Koslicki, David

2018-05-01

Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets-thus it was not possible to assess the validity of resulting network motifs. In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options. The open source software package is available at https://github.com/megrawlab/IndeCut. megrawm@science.oregonstate.edu or david.koslicki@math.oregonstate.edu. Supplementary data are available at Bioinformatics online.

Modeling Emergence in Neuroprotective Regulatory Networks

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

Sanfilippo, Antonio P.; Haack, Jereme N.; McDermott, Jason E.

2013-01-05

The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatorymore » networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.« less

A Security Strategy for Cyber Threats on Neighbor Discovery in 6Lowpan Networks

DTIC Science & Technology

2017-12-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release. Distribution is unlimited. A SECURITY...STRATEGY FOR CYBER THREATS ON NEIGHBOR DISCOVERY IN 6LOWPAN NETWORKS by Cheng Hai Ang December 2017 Thesis Advisor: Preetha Thulasiraman...REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE A SECURITY STRATEGY FOR CYBER THREATS ON NEIGHBOR DISCOVERY IN 6LOWPAN

Service Demand Discovery Mechanism for Mobile Social Networks.

PubMed

Wu, Dapeng; Yan, Junjie; Wang, Honggang; Wang, Ruyan

2016-11-23

In the last few years, the service demand for wireless data over mobile networks has continually been soaring at a rapid pace. Thereinto, in Mobile Social Networks (MSNs), users can discover adjacent users for establishing temporary local connection and thus sharing already downloaded contents with each other to offload the service demand. Due to the partitioned topology, intermittent connection and social feature in such a network, the service demand discovery is challenging. In particular, the service demand discovery is exploited to identify the best relay user through the service registration, service selection and service activation. In order to maximize the utilization of limited network resources, a hybrid service demand discovery architecture, such as a Virtual Dictionary User (VDU) is proposed in this paper. Based on the historical data of movement, users can discover their relationships with others. Subsequently, according to the users activity, VDU is selected to facilitate the service registration procedure. Further, the service information outside of a home community can be obtained through the Global Active User (GAU) to support the service selection. To provide the Quality of Service (QoS), the Service Providing User (SPU) is chosen among multiple candidates. Numerical results show that, when compared with other classical service algorithms, the proposed scheme can improve the successful service demand discovery ratio by 25% under reduced overheads.

Discovery of Information Diffusion Process in Social Networks

NASA Astrophysics Data System (ADS)

Kim, Kwanho; Jung, Jae-Yoon; Park, Jonghun

Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

Science of the science, drug discovery and artificial neural networks.

PubMed

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Renal sympathetic denervation in the treatment of resistant hypertension.

PubMed

Sánchez-Álvarez, Catalina; González-Vélez, Miguel; Stilp, Erik; Ward, Charisse; Mena-Hurtado, Carlos

2014-12-01

Arterial hypertension (HTN) is a major health problem worldwide. Treatment-resistant hypertension (trHTN) is defined as the failure to achieve target blood pressure despite the concomitant use of maximally tolerated doses of three different antihypertensive medications, including a diuretic. trHTN is associated with considerable morbidity and mortality. Renal sympathetic denervation (RDn) is available and implemented abroad as a strategy for the treatment of trHTN and is currently under clinical investigation in the United States. Selective renal sympathectomy via an endovascular approach effectively decreases renal sympathetic nerve hyperactivity leading to a decrease in blood pressure. The Symplicity catheter, currently under investigation in the United States, is a 6-French compatible system advanced under fluoroscopic guidance via percutaneous access of the common femoral artery to the distal lumen of each of the main renal arteries. Radiofrequency (RF) energy is then applied to the endoluminal surface of the renal arteries via an electrode located at the tip of the catheter. Two clinical trials (Symplicity HTN 1 and Symplicity HTN 2) have shown the efficacy of RDn with a post-procedure decline of 27/17 mmHg at 12 months and 32/12 mmHg at 6 months, respectively, with few minor adverse events. Symplicity HTN-3 study is a, multi-center, prospective, single-blind, randomized, controlled study currently under way and will provide further insights about the safety and efficacy of renal denervation in patients with trHTN.

CTD² Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network* | Office of Cancer Genomics

Cancer.gov

The Cancer Target Discovery and Development (CTD2) Network aims to use functional genomics to accelerate the translation of high-throughput and high-content genomic and small-molecule data towards use in precision oncology.

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

PubMed Central

Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

2010-01-01

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

Group Centric Networking: Large Scale Over the Air Testing of Group Centric Networking

DTIC Science & Technology

2016-11-01

protocol designed to support groups of devices in a local region [4]. It attempts to use the wireless medium to broadcast minimal control information...1) Group Discovery: The goal of the group discovery algo- rithm is to find group nodes without globally flooding control messages. To facilitate this...Large Scale Over-the-Air Testing of Group Centric Networking Logan Mercer, Greg Kuperman, Andrew Hunter, Brian Proulx MIT Lincoln Laboratory

Naval Ship Maintenance: An Analysis of the Dutch Shipbuilding Industry using the Knowledge Value Added, Systems Dynamics, and Integrated Risk Management Methodologies

DTIC Science & Technology

2013-04-01

and Integrated Risk Management Methodologies 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e...supply chains, risk management with real options, and sustainability . [dnford@nps.edu] Thomas J. Housel—Housel specializes in valuing intellectual...maintenance services for the RDN. Damen Schelde has used an ILS since 2002 to manage the shipbuilding process from project initiation through the

Neighbor Discovery Algorithm in Wireless Local Area Networks Using Multi-beam Directional Antennas

NASA Astrophysics Data System (ADS)

Wang, Jin; Peng, Wei; Liu, Song

2017-10-01

Neighbor discovery is an important step for Wireless Local Area Networks (WLAN) and the use of multi-beam directional antennas can greatly improve the network performance. However, most neighbor discovery algorithms in WLAN, based on multi-beam directional antennas, can only work effectively in synchronous system but not in asynchro-nous system. And collisions at AP remain a bottleneck for neighbor discovery. In this paper, we propose two asynchrono-us neighbor discovery algorithms: asynchronous hierarchical scanning (AHS) and asynchronous directional scanning (ADS) algorithm. Both of them are based on three-way handshaking mechanism. AHS and ADS reduce collisions at AP to have a good performance in a hierarchical way and directional way respectively. In the end, the performance of the AHS and ADS are tested on OMNeT++. Moreover, it is analyzed that different application scenarios and the factors how to affect the performance of these algorithms. The simulation results show that AHS is suitable for the densely populated scenes around AP while ADS is suitable for that most of the neighborhood nodes are far from AP.

Biological Networks for Cancer Candidate Biomarkers Discovery

PubMed Central

Yan, Wenying; Xue, Wenjin; Chen, Jiajia; Hu, Guang

2016-01-01

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. PMID:27625573

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

Making the Long Tail Visible: Social Networking Sites and Independent Music Discovery

ERIC Educational Resources Information Center

Gaffney, Michael; Rafferty, Pauline

2009-01-01

Purpose: The purpose of this paper is to investigate users' knowledge and use of social networking sites and folksonomies to discover if social tagging and folksonomies, within the area of independent music, aid in its information retrieval and discovery. The sites examined in this project are MySpace, Lastfm, Pandora and Allmusic. In addition,…

Blueprint for antimicrobial hit discovery targeting metabolic networks.

PubMed

Shen, Y; Liu, J; Estiu, G; Isin, B; Ahn, Y-Y; Lee, D-S; Barabási, A-L; Kapatral, V; Wiest, O; Oltvai, Z N

2010-01-19

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.

Network discovery with DCM

PubMed Central

Friston, Karl J.; Li, Baojuan; Daunizeau, Jean; Stephan, Klaas E.

2011-01-01

This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series. The implicit adjacency matrix specifies the form of the network (e.g., cyclic or acyclic) and its graph-theoretical attributes (e.g., degree distribution). The scheme is illustrated using functional magnetic resonance imaging (fMRI) time series to discover functional brain networks. Crucially, it can be applied to experimentally evoked responses (activation studies) or endogenous activity in task-free (resting state) fMRI studies. Unlike conventional approaches to network discovery, DCM permits the analysis of directed and cyclic graphs. Furthermore, it eschews (implausible) Markovian assumptions about the serial independence of random fluctuations. The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks. The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions. We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies. PMID:21182971

Renal Sympathetic Denervation in the Treatment of Resistant Hypertension

PubMed Central

Sánchez-Álvarez, Catalina; González-Vélez, Miguel; Stilp, Erik; Ward, Charisse; Mena-Hurtado, Carlos

2014-01-01

Arterial hypertension (HTN) is a major health problem worldwide. Treatment-resistant hypertension (trHTN) is defined as the failure to achieve target blood pressure despite the concomitant use of maximally tolerated doses of three different antihypertensive medications, including a diuretic. trHTN is associated with considerable morbidity and mortality. Renal sympathetic denervation (RDn) is available and implemented abroad as a strategy for the treatment of trHTN and is currently under clinical investigation in the United States. Selective renal sympathectomy via an endovascular approach effectively decreases renal sympathetic nerve hyperactivity leading to a decrease in blood pressure. The Symplicity catheter, currently under investigation in the United States, is a 6-French compatible system advanced under fluoroscopic guidance via percutaneous access of the common femoral artery to the distal lumen of each of the main renal arteries. Radiofrequency (RF) energy is then applied to the endoluminal surface of the renal arteries via an electrode located at the tip of the catheter. Two clinical trials (Symplicity HTN 1 and Symplicity HTN 2) have shown the efficacy of RDn with a post-procedure decline of 27/17mmHg at 12 months and 32/12 mmHg at 6 months, respectively, with few minor adverse events. Symplicity HTN-3 study is a, multi-center, prospective, single-blind, randomized, controlled study currently under way and will provide further insights about the safety and efficacy of renal denervation in patients with trHTN. PMID:25506285

Tertiary work-up of apparent treatment-resistant hypertension.

PubMed

Heimark, Sondre; Eskås, Per Anders; Mariampillai, Julian Eek; Larstorp, Anne Cecilie K; Høieggen, Aud; Fadl Elmula, Fadl Elmula M

2016-10-01

Treatment-resistant hypertension (TRH) has regained attention with development of new methods for treatment. However, the prevalence of TRH varies considerably from primary to secondary and tertiary care. We aimed to assess the prevalence of true TRH in a population of patients with apparent TRH in a university hospital setting of tertiary work-up and also investigate reasons for poor BP control and evaluate how work-up can be performed in general practice and secondary care. In this cohort study, we characterize a study population from Oslo Renal Denervation (RDN) Study. Patients (n = 83) were referred for RDN from secondary care. All patients underwent thorough medical investigation and 24-h ambulatory blood pressure measurements (24ABPM) after directly observed therapy (DOT). We then assessed reasons for lack of BP control. Fifty-three of 83 patients did not have true TRH. Main reasons for non-TRH were poor drug adherence (32%), secondary hypertension (30%) and white coat hypertension (15%). Forty-seven percent achieved blood pressure control after DOT with subsequent 24ABPM. There were otherwise no statistically significant differences in patient characteristics between the true TRH and the non-TRH group. Despite being a highly selected cohort referred for tertiary work-up of apparent TRH, BP control was achieved or secondary causes were identified in almost two thirds of the patients. Thorough investigation according to guidelines and DOT with subsequent 24ABPM is needed in work-up of apparent TRH.

Choosing experiments to accelerate collective discovery

PubMed Central

Rzhetsky, Andrey; Foster, Jacob G.; Foster, Ian T.

2015-01-01

A scientist’s choice of research problem affects his or her personal career trajectory. Scientists’ combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity’s importance corresponds to its degree centrality, and a problem’s difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies. PMID:26554009

Blueprint for antimicrobial hit discovery targeting metabolic networks

PubMed Central

Shen, Y.; Liu, J.; Estiu, G.; Isin, B.; Ahn, Y-Y.; Lee, D-S.; Barabási, A-L.; Kapatral, V.; Wiest, O.; Oltvai, Z. N.

2010-01-01

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy. PMID:20080587

Constructing a Graph Database for Semantic Literature-Based Discovery.

PubMed

Hristovski, Dimitar; Kastrin, Andrej; Dinevski, Dejan; Rindflesch, Thomas C

2015-01-01

Literature-based discovery (LBD) generates discoveries, or hypotheses, by combining what is already known in the literature. Potential discoveries have the form of relations between biomedical concepts; for example, a drug may be determined to treat a disease other than the one for which it was intended. LBD views the knowledge in a domain as a network; a set of concepts along with the relations between them. As a starting point, we used SemMedDB, a database of semantic relations between biomedical concepts extracted with SemRep from Medline. SemMedDB is distributed as a MySQL relational database, which has some problems when dealing with network data. We transformed and uploaded SemMedDB into the Neo4j graph database, and implemented the basic LBD discovery algorithms with the Cypher query language. We conclude that storing the data needed for semantic LBD is more natural in a graph database. Also, implementing LBD discovery algorithms is conceptually simpler with a graph query language when compared with standard SQL.

Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature.

PubMed

Poornima, Paramasivan; Kumar, Jothi Dinesh; Zhao, Qiaoli; Blunder, Martina; Efferth, Thomas

2016-09-01

Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer. Copyright © 2016 Elsevier Ltd. All rights reserved.

Energy Efficient Probabilistic Broadcasting for Mobile Ad-Hoc Network

NASA Astrophysics Data System (ADS)

Kumar, Sumit; Mehfuz, Shabana

2017-06-01

In mobile ad-hoc network (MANETs) flooding method is used for broadcasting route request (RREQ) packet from one node to another node for route discovery. This is the simplest method of broadcasting of RREQ packets but it often results in broadcast storm problem, originating collisions and congestion of packets in the network. A probabilistic broadcasting is one of the widely used broadcasting scheme for route discovery in MANETs and provides solution for broadcasting storm problem. But it does not consider limited energy of the battery of the nodes. In this paper, a new energy efficient probabilistic broadcasting (EEPB) is proposed in which probability of broadcasting RREQs is calculated with respect to remaining energy of nodes. The analysis of simulation results clearly indicate that an EEPB route discovery scheme in ad-hoc on demand distance vector (AODV) can increase the network lifetime with a decrease in the average power consumption and RREQ packet overhead. It also decreases the number of dropped packets in the network, in comparison to other EEPB schemes like energy constraint gossip (ECG), energy aware gossip (EAG), energy based gossip (EBG) and network lifetime through energy efficient broadcast gossip (NEBG).

Knowledge extraction from evolving spiking neural networks with rank order population coding.

PubMed

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Challenges of the information age: the impact of false discovery on pathway identification.

PubMed

Rog, Colin J; Chekuri, Srinivasa C; Edgerton, Mary E

2012-11-21

Pathways with members that have known relevance to a disease are used to support hypotheses generated from analyses of gene expression and proteomic studies. Using cancer as an example, the pitfalls of searching pathways databases as support for genes and proteins that could represent false discoveries are explored. The frequency with which networks could be generated from 100 instances each of randomly selected five and ten genes sets as input to MetaCore, a commercial pathways database, was measured. A PubMed search enumerated cancer-related literature published for any gene in the networks. Using three, two, and one maximum intervening step between input genes to populate the network, networks were generated with frequencies of 97%, 77%, and 7% using ten gene sets and 73%, 27%, and 1% using five gene sets. PubMed reported an average of 4225 cancer-related articles per network gene. This can be attributed to the richly populated pathways databases and the interest in the molecular basis of cancer. As information sources become enriched, they are more likely to generate plausible mechanisms for false discoveries.

A Bloom Filter-Powered Technique Supporting Scalable Semantic Discovery in Data Service Networks

NASA Astrophysics Data System (ADS)

Zhang, J.; Shi, R.; Bao, Q.; Lee, T. J.; Ramachandran, R.

2016-12-01

More and more Earth data analytics software products are published onto the Internet as a service, in the format of either heavyweight WSDL service or lightweight RESTful API. Such reusable data analytics services form a data service network, which allows Earth scientists to compose (mashup) services into value-added ones. Therefore, it is important to have a technique that is capable of helping Earth scientists quickly identify appropriate candidate datasets and services in the global data service network. Most existing services discovery techniques, however, mainly rely on syntax or semantics-based service matchmaking between service requests and available services. Since the scale of the data service network is increasing rapidly, the run-time computational cost will soon become a bottleneck. To address this issue, this project presents a way of applying network routing mechanism to facilitate data service discovery in a service network, featuring scalability and performance. Earth data services are automatically annotated in Web Ontology Language for Services (OWL-S) based on their metadata, semantic information, and usage history. Deterministic Annealing (DA) technique is applied to dynamically organize annotated data services into a hierarchical network, where virtual routers are created to represent semantic local network featuring leading terms. Afterwards Bloom Filters are generated over virtual routers. A data service search request is transformed into a network routing problem in order to quickly locate candidate services through network hierarchy. A neural network-powered technique is applied to assure network address encoding and routing performance. A series of empirical study has been conducted to evaluate the applicability and effectiveness of the proposed approach.

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

PubMed Central

Namasivayam, Aishwarya Alex; Morales, Alejandro Ferreiro; Lacave, Ángela María Fajardo; Tallam, Aravind; Simovic, Borislav; Alfaro, David Garrido; Bobbili, Dheeraj Reddy; Martin, Florian; Androsova, Ganna; Shvydchenko, Irina; Park, Jennifer; Calvo, Jorge Val; Hoeng, Julia; Peitsch, Manuel C.; Racero, Manuel González Vélez; Biryukov, Maria; Talikka, Marja; Pérez, Modesto Berraquero; Rohatgi, Neha; Díaz-Díaz, Noberto; Mandarapu, Rajesh; Ruiz, Rubén Amián; Davidyan, Sergey; Narayanasamy, Shaman; Boué, Stéphanie; Guryanova, Svetlana; Arbas, Susana Martínez; Menon, Swapna; Xiang, Yang

2016-01-01

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications. PMID:27429547

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes.

PubMed

Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku; Zhang, Wei; Kreisberg, Jason F; Tamayo, Pablo; Ideker, Trey

2018-04-25

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research. Copyright © 2018 Elsevier Inc. All rights reserved.

Systems biology impact on antiepileptic drug discovery.

PubMed

Margineanu, Doru Georg

2012-02-01

Systems biology (SB), a recent trend in bioscience research to consider the complex interactions in biological systems from a holistic perspective, sees the disease as a disturbed network of interactions, rather than alteration of single molecular component(s). SB-relying network pharmacology replaces the prevailing focus on specific drug-receptor interaction and the corollary of rational drug design of "magic bullets", by the search for multi-target drugs that would act on biological networks as "magic shotguns". Epilepsy being a multi-factorial, polygenic and dynamic pathology, SB approach appears particularly fit and promising for antiepileptic drug (AED) discovery. In fact, long before the advent of SB, AED discovery already involved some SB-like elements. A reported SB project aimed to find out new drug targets in epilepsy relies on a relational database that integrates clinical information, recordings from deep electrodes and 3D-brain imagery with histology and molecular biology data on modified expression of specific genes in the brain regions displaying spontaneous epileptic activity. Since hitting a single target does not treat complex diseases, a proper pharmacological promiscuity might impart on an AED the merit of being multi-potent. However, multi-target drug discovery entails the complicated task of optimizing multiple activities of compounds, while having to balance drug-like properties and to control unwanted effects. Specific design tools for this new approach in drug discovery barely emerge, but computational methods making reliable in silico predictions of poly-pharmacology did appear, and their progress might be quite rapid. The current move away from reductionism into network pharmacology allows expecting that a proper integration of the intrinsic complexity of epileptic pathology in AED discovery might result in literally anti-epileptic drugs. Copyright © 2011 Elsevier B.V. All rights reserved.

Network-based drug discovery by integrating systems biology and computational technologies

PubMed Central

Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

2013-01-01

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

PubMed Central

Zhang, Shihua; Zhang, Liang; Tai, Yuling; Wang, Xuewen; Ho, Chi-Tang; Wan, Xiaochun

2018-01-01

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant. PMID:29915604

The path to producing pharmaceuticals from natural products uncovered by academia-from the perspective of a science coordinator.

PubMed

Fujie, Akihiko

2017-01-01

To actualize the invention of all-Japanese medicines, the Department of Innovative Drug Discovery and Development (iD3) in the Japan Agency for Medical Research and Development (AMED) serves as the headquarters for the Drug Discovery Support Network. iD3 assists with creating research strategies for the seeds of medicines discovered by academia and provides technological support, intellectual property management, and aid for applying the seeds through industry-led efforts. In this review, from the perspective of a science coordinator, I will describe the current activities of the drug discovery support network and iD3 as well as the challenges and future developments of pharmaceutical research and development using the natural product drug discovery method.

Session Initiation Protocol Network Encryption Device Plain Text Domain Discovery Service

DTIC Science & Technology

2007-12-07

MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION / AVAILABILITY STATEMENT 13. SUPPLEMENTARY NOTES 14. ABSTRACT 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: a...such as the TACLANE, have developed unique discovery methods to establish Plain Text Domain (PTD) Security Associations (SA). All of these techniques...can include network and host Internet Protocol (IP) addresses, Information System Security Office (ISSO) point of contact information and PTD status

Handling Neighbor Discovery and Rendezvous Consistency with Weighted Quorum-Based Approach

PubMed Central

Own, Chung-Ming; Meng, Zhaopeng; Liu, Kehan

2015-01-01

Neighbor discovery and the power of sensors play an important role in the formation of Wireless Sensor Networks (WSNs) and mobile networks. Many asynchronous protocols based on wake-up time scheduling have been proposed to enable neighbor discovery among neighboring nodes for the energy saving, especially in the difficulty of clock synchronization. However, existing researches are divided two parts with the neighbor-discovery methods, one is the quorum-based protocols and the other is co-primality based protocols. Their distinction is on the arrangements of time slots, the former uses the quorums in the matrix, the latter adopts the numerical analysis. In our study, we propose the weighted heuristic quorum system (WQS), which is based on the quorum algorithm to eliminate redundant paths of active slots. We demonstrate the specification of our system: fewer active slots are required, the referring rate is balanced, and remaining power is considered particularly when a device maintains rendezvous with discovered neighbors. The evaluation results showed that our proposed method can effectively reschedule the active slots and save the computing time of the network system. PMID:26404297

In-Silico Identification Of Micro-Loops In Myelodysplastic Syndromes

NASA Astrophysics Data System (ADS)

Beck, Dominik; Brandl, Miriam; Pham, Tuan D.; Chang, Chung-Che; Zhou, Xiaobo

2011-06-01

Micro-loops are regulatory network motifs that leverage transcriptional and posttranscriptional control to effectively regulate the transcriptome. In this paper a regulatory network for Myelodysplastic Syndromes (MDSs) was constructed from the literature and publicly available data sources. The network was filtered using data from deep-sequencing of small RNAs, exon and microarrays. Motif discovery showed that micro-loops might exist in MDS. We further used the identified micro-loops and performed basic network analysis to identify the known disease gene RUNX1/AML, as well as miRNA family hsa-mir-181. This suggested that the concept of micro-loops can be applied to enhance disease gene identification and biomarker discovery.

Academy of Nutrition and Dietetics: Revised 2017 Standards of Practice and Standards of Professional Performance for Registered Dietitian Nutritionists (Competent, Proficient, and Expert) in Oncology Nutrition.

PubMed

Charuhas Macris, Paula; Schilling, Karen; Palko, Raymond

2018-04-01

Oncology nutrition encompasses nutrition care for individuals along the cancer care continuum. Nutrition is a vital component of prevention, treatment, and healthy survivorship. The practice of an oncology registered dietitian nutritionist (RDN) reflects the setting and population served with diverse cancer diagnoses, including expanded roles and responsibilities reflecting the RDN's interests and organization's activities. Provision of nutrition services in oncology requires that RDNs have advanced knowledge in the focus area of oncology nutrition. Thus, the Oncology Nutrition Dietetic Practice Group, with guidance from the Academy of Nutrition and Dietetics Quality Management Committee, has developed Standards of Practice and Standards of Professional Performance as tools for RDNs currently in practice or interested in working in oncology nutrition, to address their current skill level and to identify areas for additional professional development in this practice area. The Standards of Practice address and apply the Nutrition Care Process and workflow elements, which are screening, assessment, diagnosis, intervention, evaluation/monitoring, and discharge planning and transitions of care. The Standards of Professional Performance consist of the following six domains of professionalism including: Quality in Practice, Competence and Accountability, Provision of Services, Application of Research, Communication and Application of Knowledge, and Utilization and Management of Resources. Within each standard, specific indicators provide measurable action statements and describe three skill levels (competent, proficient, and expert) for RDNs working in oncology nutrition. Copyright © 2018 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Privacy Preserved and Secured Reliable Routing Protocol for Wireless Mesh Networks.

PubMed

Meganathan, Navamani Thandava; Palanichamy, Yogesh

2015-01-01

Privacy preservation and security provision against internal attacks in wireless mesh networks (WMNs) are more demanding than in wired networks due to the open nature and mobility of certain nodes in the network. Several schemes have been proposed to preserve privacy and provide security in WMNs. To provide complete privacy protection in WMNs, the properties of unobservability, unlinkability, and anonymity are to be ensured during route discovery. These properties can be achieved by implementing group signature and ID-based encryption schemes during route discovery. Due to the characteristics of WMNs, it is more vulnerable to many network layer attacks. Hence, a strong protection is needed to avoid these attacks and this can be achieved by introducing a new Cross-Layer and Subject Logic based Dynamic Reputation (CLSL-DR) mechanism during route discovery. In this paper, we propose a new Privacy preserved and Secured Reliable Routing (PSRR) protocol for WMNs. This protocol incorporates group signature, ID-based encryption schemes, and CLSL-DR mechanism to ensure strong privacy, security, and reliability in WMNs. Simulation results prove this by showing better performance in terms of most of the chosen parameters than the existing protocols.

Symmetric Link Key Management for Secure Neighbor Discovery in a Decentralized Wireless Sensor Network

DTIC Science & Technology

2017-09-01

and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT...KEY MANAGEMENT FOR SECURE NEIGHBOR DISCOVERY IN A DECENTRALIZED WIRELESS SENSOR NETWORK by Kelvin T. Chew September 2017 Thesis Advisor...DATE September 2017 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE SYMMETRIC LINK KEY MANAGEMENT FOR SECURE NEIGHBOR

A Discovery Process for Initializing Ad Hoc Underwater Acoustic Networks

DTIC Science & Technology

2008-12-01

the ping utility packet is set to global address 0, its function becomes a broadcast ping and it elicits echoes from all neighboring nodes within...destination. At the Seaweb server, a global neighbor table and a global routing table are maintained to support network configurability. 2. Cellular...aggregates the received peer discovery data in a global neighbor table and ultimately decides how routing to each branch node should be configured

Discovery and validation of a glioblastoma co-expressed gene module

PubMed Central

Dunwoodie, Leland J.; Poehlman, William L.; Ficklin, Stephen P.; Feltus, Frank Alexander

2018-01-01

Tumors exhibit complex patterns of aberrant gene expression. Using a knowledge-independent, noise-reducing gene co-expression network construction software called KINC, we created multiple RNAseq-based gene co-expression networks relevant to brain and glioblastoma biology. In this report, we describe the discovery and validation of a glioblastoma-specific gene module that contains 22 co-expressed genes. The genes are upregulated in glioblastoma relative to normal brain and lower grade glioma samples; they are also hypo-methylated in glioblastoma relative to lower grade glioma tumors. Among the proneural, neural, mesenchymal, and classical glioblastoma subtypes, these genes are most-highly expressed in the mesenchymal subtype. Furthermore, high expression of these genes is associated with decreased survival across each glioblastoma subtype. These genes are of interest to glioblastoma biology and our gene interaction discovery and validation workflow can be used to discover and validate co-expressed gene modules derived from any co-expression network. PMID:29541392

Discovery and validation of a glioblastoma co-expressed gene module.

PubMed

Dunwoodie, Leland J; Poehlman, William L; Ficklin, Stephen P; Feltus, Frank Alexander

2018-02-16

Tumors exhibit complex patterns of aberrant gene expression. Using a knowledge-independent, noise-reducing gene co-expression network construction software called KINC, we created multiple RNAseq-based gene co-expression networks relevant to brain and glioblastoma biology. In this report, we describe the discovery and validation of a glioblastoma-specific gene module that contains 22 co-expressed genes. The genes are upregulated in glioblastoma relative to normal brain and lower grade glioma samples; they are also hypo-methylated in glioblastoma relative to lower grade glioma tumors. Among the proneural, neural, mesenchymal, and classical glioblastoma subtypes, these genes are most-highly expressed in the mesenchymal subtype. Furthermore, high expression of these genes is associated with decreased survival across each glioblastoma subtype. These genes are of interest to glioblastoma biology and our gene interaction discovery and validation workflow can be used to discover and validate co-expressed gene modules derived from any co-expression network.

A resource management architecture based on complex network theory in cloud computing federation

NASA Astrophysics Data System (ADS)

Zhang, Zehua; Zhang, Xuejie

2011-10-01

Cloud Computing Federation is a main trend of Cloud Computing. Resource Management has significant effect on the design, realization, and efficiency of Cloud Computing Federation. Cloud Computing Federation has the typical characteristic of the Complex System, therefore, we propose a resource management architecture based on complex network theory for Cloud Computing Federation (abbreviated as RMABC) in this paper, with the detailed design of the resource discovery and resource announcement mechanisms. Compare with the existing resource management mechanisms in distributed computing systems, a Task Manager in RMABC can use the historical information and current state data get from other Task Managers for the evolution of the complex network which is composed of Task Managers, thus has the advantages in resource discovery speed, fault tolerance and adaptive ability. The result of the model experiment confirmed the advantage of RMABC in resource discovery performance.

How Are Television Networks Involved in Distance Learning?

ERIC Educational Resources Information Center

Bucher, Katherine

1996-01-01

Reviews the involvement of various television networks in distance learning, including public broadcasting stations, Cable in the Classroom, Arts and Entertainment Network, Black Entertainment Television, C-SPAN, CNN (Cable News Network), The Discovery Channel, The Learning Channel, Mind Extension University, The Weather Channel, National Teacher…

Impact of Network Activity Levels on the Performance of Passive Network Service Dependency Discovery

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

Carroll, Thomas E.; Chikkagoudar, Satish; Arthur-Durett, Kristine M.

Network services often do not operate alone, but instead, depend on other services distributed throughout a network to correctly function. If a service fails, is disrupted, or degraded, it is likely to impair other services. The web of dependencies can be surprisingly complex---especially within a large enterprise network---and evolve with time. Acquiring, maintaining, and understanding dependency knowledge is critical for many network management and cyber defense activities. While automation can improve situation awareness for network operators and cyber practitioners, poor detection accuracy reduces their confidence and can complicate their roles. In this paper we rigorously study the effects of networkmore » activity levels on the detection accuracy of passive network-based service dependency discovery methods. The accuracy of all except for one method was inversely proportional to network activity levels. Our proposed cross correlation method was particularly robust to the influence of network activity. The proposed experimental treatment will further advance a more scientific evaluation of methods and provide the ability to determine their operational boundaries.« less

Relay discovery and selection for large-scale P2P streaming

PubMed Central

Zhang, Chengwei; Wang, Angela Yunxian

2017-01-01

In peer-to-peer networks, application relays have been commonly used to provide various networking services. The service performance often improves significantly if a relay is selected appropriately based on its network location. In this paper, we studied the location-aware relay discovery and selection problem for large-scale P2P streaming networks. In these large-scale and dynamic overlays, it incurs significant communication and computation cost to discover a sufficiently large relay candidate set and further to select one relay with good performance. The network location can be measured directly or indirectly with the tradeoffs between timeliness, overhead and accuracy. Based on a measurement study and the associated error analysis, we demonstrate that indirect measurements, such as King and Internet Coordinate Systems (ICS), can only achieve a coarse estimation of peers’ network location and those methods based on pure indirect measurements cannot lead to a good relay selection. We also demonstrate that there exists significant error amplification of the commonly used “best-out-of-K” selection methodology using three RTT data sets publicly available. We propose a two-phase approach to achieve efficient relay discovery and accurate relay selection. Indirect measurements are used to narrow down a small number of high-quality relay candidates and the final relay selection is refined based on direct probing. This two-phase approach enjoys an efficient implementation using the Distributed-Hash-Table (DHT). When the DHT is constructed, the node keys carry the location information and they are generated scalably using indirect measurements, such as the ICS coordinates. The relay discovery is achieved efficiently utilizing the DHT-based search. We evaluated various aspects of this DHT-based approach, including the DHT indexing procedure, key generation under peer churn and message costs. PMID:28410384

Relay discovery and selection for large-scale P2P streaming.

PubMed

Zhang, Chengwei; Wang, Angela Yunxian; Hei, Xiaojun

2017-01-01

In peer-to-peer networks, application relays have been commonly used to provide various networking services. The service performance often improves significantly if a relay is selected appropriately based on its network location. In this paper, we studied the location-aware relay discovery and selection problem for large-scale P2P streaming networks. In these large-scale and dynamic overlays, it incurs significant communication and computation cost to discover a sufficiently large relay candidate set and further to select one relay with good performance. The network location can be measured directly or indirectly with the tradeoffs between timeliness, overhead and accuracy. Based on a measurement study and the associated error analysis, we demonstrate that indirect measurements, such as King and Internet Coordinate Systems (ICS), can only achieve a coarse estimation of peers' network location and those methods based on pure indirect measurements cannot lead to a good relay selection. We also demonstrate that there exists significant error amplification of the commonly used "best-out-of-K" selection methodology using three RTT data sets publicly available. We propose a two-phase approach to achieve efficient relay discovery and accurate relay selection. Indirect measurements are used to narrow down a small number of high-quality relay candidates and the final relay selection is refined based on direct probing. This two-phase approach enjoys an efficient implementation using the Distributed-Hash-Table (DHT). When the DHT is constructed, the node keys carry the location information and they are generated scalably using indirect measurements, such as the ICS coordinates. The relay discovery is achieved efficiently utilizing the DHT-based search. We evaluated various aspects of this DHT-based approach, including the DHT indexing procedure, key generation under peer churn and message costs.

Teaching Network Security with IP Darkspace Data

ERIC Educational Resources Information Center

Zseby, Tanja; Iglesias Vázquez, Félix; King, Alistair; Claffy, K. C.

2016-01-01

This paper presents a network security laboratory project for teaching network traffic anomaly detection methods to electrical engineering students. The project design follows a research-oriented teaching principle, enabling students to make their own discoveries in real network traffic, using data captured from a large IP darkspace monitor…

VISIONET: intuitive visualisation of overlapping transcription factor networks, with applications in cardiogenic gene discovery.

PubMed

Nim, Hieu T; Furtado, Milena B; Costa, Mauro W; Rosenthal, Nadia A; Kitano, Hiroaki; Boyd, Sarah E

2015-05-01

Existing de novo software platforms have largely overlooked a valuable resource, the expertise of the intended biologist users. Typical data representations such as long gene lists, or highly dense and overlapping transcription factor networks often hinder biologists from relating these results to their expertise. VISIONET, a streamlined visualisation tool built from experimental needs, enables biologists to transform large and dense overlapping transcription factor networks into sparse human-readable graphs via numerically filtering. The VISIONET interface allows users without a computing background to interactively explore and filter their data, and empowers them to apply their specialist knowledge on far more complex and substantial data sets than is currently possible. Applying VISIONET to the Tbx20-Gata4 transcription factor network led to the discovery and validation of Aldh1a2, an essential developmental gene associated with various important cardiac disorders, as a healthy adult cardiac fibroblast gene co-regulated by cardiogenic transcription factors Gata4 and Tbx20. We demonstrate with experimental validations the utility of VISIONET for expertise-driven gene discovery that opens new experimental directions that would not otherwise have been identified.

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks

PubMed Central

Valdivieso Caraguay, Ángel Leonardo; García Villalba, Luis Javier

2017-01-01

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors. PMID:28362346

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks.

PubMed

Caraguay, Ángel Leonardo Valdivieso; Villalba, Luis Javier García

2017-03-31

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors.

Cross-platform method for identifying candidate network biomarkers for prostate cancer.

PubMed

Jin, G; Zhou, X; Cui, K; Zhang, X-S; Chen, L; Wong, S T C

2009-11-01

Discovering biomarkers using mass spectrometry (MS) and microarray expression profiles is a promising strategy in molecular diagnosis. Here, the authors proposed a new pipeline for biomarker discovery that integrates disease information for proteins and genes, expression profiles in both genomic and proteomic levels, and protein-protein interactions (PPIs) to discover high confidence network biomarkers. Using this pipeline, a total of 474 molecules (genes and proteins) related to prostate cancer were identified and a prostate-cancer-related network (PCRN) was derived from the integrative information. Thus, a set of candidate network biomarkers were identified from multiple expression profiles composed by eight microarray datasets and one proteomics dataset. The network biomarkers with PPIs can accurately distinguish the prostate patients from the normal ones, which potentially provide more reliable hits of biomarker candidates than conventional biomarker discovery methods.

Symmetries and stability of chimera states in small, globally-coupled networks

NASA Astrophysics Data System (ADS)

Hart, Joseph D.; Bansal, Kanika; Murphy, Thomas E.; Roy, Rajarshi

It has recently been demonstrated that symmetries in a network's topology can help predict the patterns of synchronized clusters that can emerge in a network of coupled oscillators. This and related discoveries have led to increased interest in both network symmetries and cluster synchronization. In parallel with these discoveries, interest in chimera states-dynamical patterns in which a network separates into coherent and incoherent portions-has grown, and chimeras have now been observed in a variety of experimental systems. We present an opto-electronic experiment in which both chimera states and synchronized clusters are observed in a small, globally-coupled network. We show that the symmetries and sub-symmetries of the network permit the formation of the chimera and cluster states. A recently developed group theoretical approach enables us to predict the stability of the observed chimera and cluster states, and highlights the close relationship between chimera and cluster states as belonging to the broader phenomenon of partial synchronization.

Low Data Drug Discovery with One-Shot Learning.

PubMed

Altae-Tran, Han; Ramsundar, Bharath; Pappu, Aneesh S; Pande, Vijay

2017-04-26

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).

Network inference and network response identification: moving genome-scale data to the next level of biological discovery

PubMed Central

Veiga, Diogo F. T.; Dutta, Bhaskar; Balaźsi, Gábor

2011-01-01

The escalating amount of genome-scale data demands a pragmatic stance from the research community. How can we utilize this deluge of information to better understand biology, cure diseases, or engage cells in bioremediation or biomaterial production for various purposes? A research pipeline moving new sequence, expression and binding data towards practical end goals seems to be necessary. While most individual researchers are not motivated by such well-articulated pragmatic end goals, the scientific community has already self-organized itself to successfully convert genomic data into fundamentally new biological knowledge and practical applications. Here we review two important steps in this workflow: network inference and network response identification, applied to transcriptional regulatory networks. Among network inference methods, we concentrate on relevance networks due to their conceptual simplicity. We classify and discuss network response identification approaches as either data-centric or network-centric. Finally, we conclude with an outlook on what is still missing from these approaches and what may be ahead on the road to biological discovery. PMID:20174676

Service discovery with routing protocols for MANETs

NASA Astrophysics Data System (ADS)

Gu, Xuemai; Shi, Shuo

2005-11-01

Service discovery is becoming an important topic as its use throughout the Internet becomes more widespread. In Mobile Ad hoc Networks (MANETs), the routing protocol is very important because it is special network. To find a path for data, and destination nodes, nodes send packets to each node, creating substantial overhead traffic and consuming much time. Even though a variety of routing protocols have been developed for use in MANETs, they are insufficient for reducing overhead traffic and time. In this paper, we propose SDRP: a new service discovery protocol combined with routing policies in MANETs. The protocol is performed upon a distributed network. We describe a service by a unique ID number and use a group-cast routing policy in advertisement and request. The group-cast routing policy decreases the traffic in networks, and it is efficient to find destination node. In addition, the nodes included in the reply path also cache the advertisement information, and it means when each node finds a node next time, they can know where it is as soon as possible, so they minimize the time. Finally, we compare SDRP with both Flood and MAODV in terms of overload, and average delay. Simulation results show SDRP can spend less response time and accommodate even high mobility network environments.

Information flow through threespine stickleback networks without social transmission

PubMed Central

Atton, N.; Hoppitt, W.; Webster, M. M.; Galef, B. G.; Laland, K. N.

2012-01-01

Social networks can result in directed social transmission of learned information, thus influencing how innovations spread through populations. Here we presented shoals of threespine sticklebacks (Gasterosteous aculeatus) with two identical foraging tasks and applied network-based diffusion analysis (NBDA) to determine whether the order in which individuals in a social group contacted and solved the tasks was affected by the group's network structure. We found strong evidence for a social effect on discovery of the foraging tasks with individuals tending to discover a task sooner when others in their group had previously done so, and with the spread of discovery of the foraging tasks influenced by groups' social networks. However, the same patterns of association did not reliably predict spread of solution to the tasks, suggesting that social interactions affected the time at which the tasks were discovered, but not the latency to its solution following discovery. The present analysis, one of the first applications of NBDA to a natural animal system, illustrates how NBDA can lead to insight into the mechanisms supporting behaviour acquisition that more conventional statistical approaches might miss. Importantly, we provide the first compelling evidence that the spread of novel behaviours can result from social learning in the absence of social transmission, a phenomenon that we refer to as an untransmitted social effect on learning. PMID:22896644

Overview of artificial neural networks.

PubMed

Zou, Jinming; Han, Yi; So, Sung-Sau

2008-01-01

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

Electrical Properties of an m × n Hammock Network

NASA Astrophysics Data System (ADS)

Tan, Zhen; Tan, Zhi-Zhong; Zhou, Ling

2018-05-01

Electrical property is an important problem in the field of natural science and physics, which usually involves potential, current and resistance in the electric circuit. We investigate the electrical properties of an arbitrary hammock network, which has not been resolved before, and propose the exact potential formula of an arbitrary m × n hammock network by means of the Recursion-Transform method with current parameters (RT-I) pioneered by one of us [Z. Z. Tan, Phys. Rev. E 91 (2015) 052122], and the branch currents and equivalent resistance of the network are derived naturally. Our key technique is to setting up matrix equations and making matrix transformation, the potential formula derived is a meaningful discovery, which deduces many novel applications. The discovery of potential formula of the hammock network provides new theoretical tools and techniques for related scientific research. Supported by the Natural Science Foundation of Jiangsu Province under Grant No. BK20161278

Organization and scaling in water supply networks

NASA Astrophysics Data System (ADS)

Cheng, Likwan; Karney, Bryan W.

2017-12-01

Public water supply is one of the society's most vital resources and most costly infrastructures. Traditional concepts of these networks capture their engineering identity as isolated, deterministic hydraulic units, but overlook their physics identity as related entities in a probabilistic, geographic ensemble, characterized by size organization and property scaling. Although discoveries of allometric scaling in natural supply networks (organisms and rivers) raised the prospect for similar findings in anthropogenic supplies, so far such a finding has not been reported in public water or related civic resource supplies. Examining an empirical ensemble of large number and wide size range, we show that water supply networks possess self-organized size abundance and theory-explained allometric scaling in spatial, infrastructural, and resource- and emission-flow properties. These discoveries establish scaling physics for water supply networks and may lead to novel applications in resource- and jurisdiction-scale water governance.

Choosing experiments to accelerate collective discovery

DOE PAGES

Rzhetsky, Andrey; Foster, Jacob G.; Foster, Ian T.; ...

2015-11-24

Scientists perform a tiny subset of all possible experiments. What characterizes the experiments they choose? What are the consequences of those choices for the pace of scientific discovery? We model scientific knowledge as a network and science as a sequence of experiments designed to gradually uncover it. By analyzing millions of biomedical articles published over 30 y, we find that biomedical scientists pursue conservative research strategies exploring the local neighborhood of central, important molecules. Although such strategies probably serve scientific careers, we show that they slow scientific advance, especially in mature fields, where more risk and less redundant experimentation wouldmore » accelerate discovery of the network. Lastly, we also consider institutional arrangements that could help science pursue these more efficient strategies.« less

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

NASA Technical Reports Server (NTRS)

Crichton, Daniel; Srivastava, Sudhir; Johnsey, Donald

2003-01-01

Discovery of disease biomarkers for cancer is a leading focus of early detection. The National Cancer Institute created a network of collaborating institutions focused on the discovery and validation of cancer biomarkers called the Early Detection Research Network (EDRN). Informatics plays a key role in enabling a virtual knowledge environment that provides scientists real time access to distributed data sets located at research institutions across the nation. The distributed and heterogeneous nature of the collaboration makes data sharing across institutions very difficult. EDRN has developed a comprehensive informatics effort focused on developing a national infrastructure enabling seamless access, sharing and discovery of science data resources across all EDRN sites. This paper will discuss the EDRN knowledge system architecture, its objectives and its accomplishments.

Distinctive Behaviors of Druggable Proteins in Cellular Networks

PubMed Central

Workman, Paul; Al-Lazikani, Bissan

2015-01-01

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/. PMID:26699810

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

PubMed

Dobchev, Dimitar; Karelson, Mati

2016-07-01

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.

PubMed

Janet, Jon Paul; Chan, Lydia; Kulik, Heather J

2018-03-01

Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool. We introduce a strategy for error-aware ML-driven discovery by limiting how far the GA travels away from the nearest ANN training points while maximizing property (i.e., spin-splitting) fitness, leading to discovery of 80% of the leads from full chemical space enumeration. Over a 51-complex subset, average unsigned errors (4.5 kcal/mol) are close to the ANN's baseline 3 kcal/mol error. By obtaining leads from the trained ANN within seconds rather than days from a DFT-driven GA, this strategy demonstrates the power of ML for accelerating inorganic material discovery.

Treating resistant hypertension with new devices.

PubMed

Wienemann, H; Meincke, F; Kaiser, L; Heeger, C H; Bergmann, M W

2014-06-01

Arterial hypertension is a frequent, chronic disease, which is one of the main risk factor for cardiovascular and renal diseases such as heart failure, chronic kidney disease, hypertensive heart disease, stroke as well as cardiac arrhythmias. In the clinical setting it remains challenging to accomplish the thresholds of guideline blood pressure (BP) levels now defined as office based BP to be below <140 mmHg. Patients on three or more antihypertensive drugs, with systolic BP values above ≥160 mmHg (≥150 mmHg for patients with type 2 diabetes) are classified as having resistant hypertension. In the past six years the development of interventional sympathetic renal artery denervation (RDN) opened a new treatment option targeting the afferent and efferent sympathetic nerves of the kidney to reduce BP. A large variety of devices are available on the market. Newly developed devices try to focus on new strategies such as ultrasound or irrigated catheters, which might reduce the post-procedural complications and increase the success rate. The first generation SymplicityTM device (Medtronic, Palo Alto, CA, USA) was shown to be safe, with side effects rarely occurring. Clinical trials demonstrate that this procedure is successful in about 70% of patients. However current data from Simplicity HTN-3 with 25% african-americans and a massive BP-lowering effect in the control "sham" group was not able to find a significant effect in the overall patient cohort. Possibly devices which allow to safely destroy sympathetic renal innervation more efficiently might allow for a higher responder rate. Irrigated RDN and ultrasound devices could deliver more energy to deeper tissue levels. This article provides an overview of currently available data on devices.

Using Academy Standards of Excellence in Nutrition and Dietetics for organization self-assessment and quality improvement.

PubMed

Price, Joyce A; Kent, Sue; Cox, Sharon A; McCauley, Sharon M; Parekh, Janki; Klein, Catherine J

2014-08-01

Standards of Excellence in Nutrition and Dietetics for an Organization is a self-assessment tool to measure and evaluate an organization's program, services, and initiatives that identify and distinguish the Registered Dietitian Nutritionist (RDN) brand as the professional expert in food and nutrition. The Standards of Excellence will serve as a road map to recognize RDNs as leaders and collaborators. Standards of Excellence criteria apply to all practice segments of nutrition and dietetics: health care, education and research, business and industry, and community nutrition and public health. Given the membership's call to action to be recognized for their professional expertise, the Academy of Nutrition and Dietetics Quality Management Committee developed four Standards of Excellence in Nutrition and Dietetics for Organizations: Quality of Leadership, Quality of Organization, Quality of Practice, and Quality of Outcomes. Within each standard, specific indicators provide strategies for an organization to demonstrate excellence. The Academy will develop a self-evaluation scoring tool to assist the organization in applying and implementing one or more of the strategies in the Standards of Excellence indicators. The organization can use the self-assessment tool to establish itself as a Center of Excellence in Nutrition and Dietetics. The role examples illustrate initiatives RDNs and organizations can take to identify themselves as a Center of Excellence in Nutrition and Dietetics. Achieving the Excellence level is an important collaborative initiative between nutrition and dietetics organizations and the Academy to provide increased autonomy, supportive management, respect within peers and community, opportunities for professional development, support for further education, and compensation for the RDN. For purposes of the Standards, "organization" means workplace or practice setting. Copyright © 2014 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Position of the Academy of Nutrition and Dietetics: The Role of Medical Nutrition Therapy and Registered Dietitian Nutritionists in the Prevention and Treatment of Prediabetes and Type 2 Diabetes.

PubMed

Briggs Early, Kathaleen; Stanley, Kathleen

2018-02-01

It is the position of the Academy of Nutrition and Dietetics that for adults with prediabetes or type 2 diabetes, medical nutrition therapy (MNT) provided by registered dietitian nutritionists (RDNs) is effective in improving medical outcomes and quality of life, and is cost-effective. MNT provided by RDNs is also successful and essential to preventing progression of prediabetes and obesity to type 2 diabetes. It is essential that MNT provided by RDNs be integrated into health care systems and public health programs and be adequately reimbursed. The Academy's evidence-based nutrition practice guidelines for the prevention of diabetes and the management of diabetes document strong evidence supporting the clinical effectiveness of MNT provided by RDNs. Cost-effectiveness has also been documented. The nutrition practice guidelines recommend that as part of evidence-based health care, providers caring for individuals with prediabetes or type 2 diabetes should be referred to an RDN for individualized MNT upon diagnosis and at regular intervals throughout the lifespan as part of their treatment regimen. Standards of care for three levels of diabetes practice have been published by the Diabetes Care and Education Practice Group. RDNs are also qualified to provide additional services beyond MNT in diabetes care and management. Unfortunately, barriers to accessing RDN services exist. Reimbursement for services is essential. Major medical and health organizations have provided support for the essential role of MNT and RDNs for the prevention and treatment of type 2 diabetes. Copyright © 2018 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

New strategy for drug discovery by large-scale association analysis of molecular networks of different species.

PubMed

Zhang, Bo; Fu, Yingxue; Huang, Chao; Zheng, Chunli; Wu, Ziyin; Zhang, Wenjuan; Yang, Xiaoyan; Gong, Fukai; Li, Yuerong; Chen, Xiaoyu; Gao, Shuo; Chen, Xuetong; Li, Yan; Lu, Aiping; Wang, Yonghua

2016-02-25

The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.

Advanced systems biology methods in drug discovery and translational biomedicine.

PubMed

Zou, Jun; Zheng, Ming-Wu; Li, Gen; Su, Zhi-Guang

2013-01-01

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

The Fragment Network: A Chemistry Recommendation Engine Built Using a Graph Database.

PubMed

Hall, Richard J; Murray, Christopher W; Verdonk, Marcel L

2017-07-27

The hit validation stage of a fragment-based drug discovery campaign involves probing the SAR around one or more fragment hits. This often requires a search for similar compounds in a corporate collection or from commercial suppliers. The Fragment Network is a graph database that allows a user to efficiently search chemical space around a compound of interest. The result set is chemically intuitive, naturally grouped by substitution pattern and meaningfully sorted according to the number of observations of each transformation in medicinal chemistry databases. This paper describes the algorithms used to construct and search the Fragment Network and provides examples of how it may be used in a drug discovery context.

Systematic identification of latent disease-gene associations from PubMed articles.

PubMed

Zhang, Yuji; Shen, Feichen; Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

Discovery of Boolean metabolic networks: integer linear programming based approach.

PubMed

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

Systematic identification of latent disease-gene associations from PubMed articles

PubMed Central

Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research. PMID:29373609

Casimer Funk, nonconformist nomenclature, and networks surrounding the discovery of vitamins.

PubMed

Maltz, Alesia

2013-07-01

In the 2 decades between when the existence of vitamins was first postulated and when they were isolated, scientists and research physicians could produce no conclusive evidence for their existence from the laboratory or clinic. By the time the first vitamin was chemically isolated, vitamins were already widely accepted by scientists, clinicians, the public, and government agencies. In the period between when vitamins were postulated and the Nobel Prize was awarded for their discovery, a debate over nomenclature served as a substitute for a priority dispute. The most popular term "vitamine" was introduced by Casimer Funk in 1912 and was changed to "vitamin" by Cecil Drummond in 1920. Initial conditions surrounding the discovery of vitamins, including World War I, necessitated the creation of unusual networks for the dissemination of scientific information about vitamins. In Great Britain, research institutes, government agencies, and individual researchers were instrumental in creating a set of national and international networks for the dissemination of information from research laboratories to hospitals, physicians, pharmaceutical houses, and the public. These networks of dissemination still exert an influence on how scientific information about vitamins is communicated to the public today.

Developing integrated crop knowledge networks to advance candidate gene discovery.

PubMed

Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

2016-12-01

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

NCI’s Cooperative Human Tissue Network

Cancer.gov

Quality biospecimens are a foundational resource for cancer research. One of NCI’s longest running biospecimen programs is the Cooperative Human Tissue Network, a resource mainly for basic discovery and early translational research.

Discovery Mechanisms for the Sensor Web

PubMed Central

Jirka, Simon; Bröring, Arne; Stasch, Christoph

2009-01-01

This paper addresses the discovery of sensors within the OGC Sensor Web Enablement framework. Whereas services like the OGC Web Map Service or Web Coverage Service are already well supported through catalogue services, the field of sensor networks and the according discovery mechanisms is still a challenge. The focus within this article will be on the use of existing OGC Sensor Web components for realizing a discovery solution. After discussing the requirements for a Sensor Web discovery mechanism, an approach will be presented that was developed within the EU funded project “OSIRIS”. This solution offers mechanisms to search for sensors, exploit basic semantic relationships, harvest sensor metadata and integrate sensor discovery into already existing catalogues. PMID:22574038

Algorithms for Data Sharing, Coordination, and Communication in Dynamic Network Settings

DTIC Science & Technology

2007-12-03

problems in dynamic networks, focusing on mobile networks with wireless communication. Problems studied include data management, time synchronization ...The discovery of a fundamental limitation in capabilities for time synchronization in large networks. (2) The identification and development of the...Problems studied include data management, time synchronization , communication problems (broadcast, geocast, and point-to-point routing), distributed

Research synergy and drug development: Bright stars in neighboring constellations.

PubMed

Keserci, Samet; Livingston, Eric; Wan, Lingtian; Pico, Alexander R; Chacko, George

2017-11-01

Drug discovery and subsequent availability of a new breakthrough therapeutic or 'cure' is a compelling example of societal benefit from research advances. These advances are invariably collaborative, involving the contributions of many scientists to a discovery network in which theory and experiment are built upon. To document and understand such scientific advances, data mining of public and commercial data sources coupled with network analysis can be used as a digital methodology to assemble and analyze component events in the history of a therapeutic. This methodology is extensible beyond the history of therapeutics and its use more generally supports (i) efficiency in exploring the scientific history of a research advance (ii) documenting and understanding collaboration (iii) portfolio analysis, planning and optimization (iv) communication of the societal value of research. Building upon prior art, we have conducted a case study of five anti-cancer therapeutics to identify the collaborations that resulted in the successful development of these therapeutics both within and across their respective networks. We have linked the work of over 235,000 authors in roughly 106,000 scientific publications that capture the research crucial for the development of these five therapeutics. Applying retrospective citation discovery, we have identified a core set of publications cited in the networks of all five therapeutics and additional intersections in combinations of networks. We have enriched the content of these networks by annotating them with information on research awards from the US National Institutes of Health (NIH). Lastly, we have mapped these awards to their cognate peer review panels, identifying another layer of collaborative scientific activity that influenced the research represented in these networks.

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

Abercrombie, Robert K; Udoeyop, Akaninyene W; Schlicher, Bob G

This work examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, patents, news archives, andmore » online mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain we investigated.« less

Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology

PubMed Central

Gu, Jiangyong; Gui, Yuanshen; Chen, Lirong; Yuan, Gu; Lu, Hui-Zhe; Xu, Xiaojie

2013-01-01

Background Natural products have been an important source of lead compounds for drug discovery. How to find and evaluate bioactive natural products is critical to the achievement of drug/lead discovery from natural products. Methodology We collected 19,7201 natural products structures, reported biological activities and virtual screening results. Principal component analysis was employed to explore the chemical space, and we found that there was a large portion of overlap between natural products and FDA-approved drugs in the chemical space, which indicated that natural products had large quantity of potential lead compounds. We also explored the network properties of natural product-target networks and found that polypharmacology was greatly enriched to those compounds with large degree and high betweenness centrality. In order to make up for a lack of experimental data, high throughput virtual screening was employed. All natural products were docked to 332 target proteins of FDA-approved drugs. The most potential natural products for drug discovery and their indications were predicted based on a docking score-weighted prediction model. Conclusions Analysis of molecular descriptors, distribution in chemical space and biological activities of natural products was conducted in this article. Natural products have vast chemical diversity, good drug-like properties and can interact with multiple cellular target proteins. PMID:23638153

Detailed view inside the aft fuselage of the Orbiter Discovery ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Detailed view inside the aft fuselage of the Orbiter Discovery showing the network of supply, distribution and feed lines to deliver fuel, oxidizer and other vital gasses and fluids to the Space Shuttle Main Engines (SSMEs). This photograph was taken in the Orbiter Processing Facility at the Kennedy Space Center. - Space Transportation System, Orbiter Discovery (OV-103), Lyndon B. Johnson Space Center, 2101 NASA Parkway, Houston, Harris County, TX

Combat Failure: Nightmare of Armored Units Since World War II

DTIC Science & Technology

1991-12-18

Ditribution is Unlimited 92-32409 l ...... i.9 92 REPORT DOCUMENTATION PAGE Inftng frtssfin lot ths. (@(Iftlon of informs lion. "I’ a’ttd to A.If *QE I No u...8217, D ’ IVY04𔃾. IM4Iudinqth lb ifl lt I Of 0 - l .~w~ng nt411`1JClions. 14 Vthing 4 -iiin.s ICII to oul rft M r me-oniemfli the data needed. and to’p4...l.udllng sug"uitrnI lotr redujsrnq thstrurci r rdn to Washington itoidrlujgime, li ",r oiryl r~t lif-otI l lotntr 0oviellons and Iteoorfa. IIIS Jgtmnoin

Low Data Drug Discovery with One-Shot Learning

PubMed Central

2017-01-01

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. Model.2015, 55, 263–27425635324). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016). PMID:28470045

Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks

PubMed Central

Lim, Chiwoo; Kim, Sang-Hyo

2018-01-01

In this paper, a new non-orthogonal multiple-access scheme, trellis tone modulation multiple-access (TTMMA), is proposed for peer discovery of distributed device-to-device (D2D) communication. The range and capacity of discovery are important performance metrics in peer discovery. The proposed trellis tone modulation uses single-tone transmission and achieves a long discovery range due to its low Peak-to-Average Power Ratio (PAPR). The TTMMA also exploits non-orthogonal resource assignment to increase the discovery capacity. For the multi-user detection of superposed multiple-access signals, a message-passing algorithm with supplementary schemes are proposed. With TTMMA and its message-passing demodulation, approximately 1.5 times the number of devices are discovered compared to the conventional frequency division multiple-access (FDMA)-based discovery. PMID:29673167

Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks.

PubMed

Lim, Chiwoo; Jang, Min; Kim, Sang-Hyo

2018-04-17

In this paper, a new non-orthogonal multiple-access scheme, trellis tone modulation multiple-access (TTMMA), is proposed for peer discovery of distributed device-to-device (D2D) communication. The range and capacity of discovery are important performance metrics in peer discovery. The proposed trellis tone modulation uses single-tone transmission and achieves a long discovery range due to its low Peak-to-Average Power Ratio (PAPR). The TTMMA also exploits non-orthogonal resource assignment to increase the discovery capacity. For the multi-user detection of superposed multiple-access signals, a message-passing algorithm with supplementary schemes are proposed. With TTMMA and its message-passing demodulation, approximately 1.5 times the number of devices are discovered compared to the conventional frequency division multiple-access (FDMA)-based discovery.

Resource Discovery within the Networked "Hybrid" Library.

ERIC Educational Resources Information Center

Leigh, Sally-Anne

This paper focuses on the development, adoption, and integration of resource discovery, knowledge management, and/or knowledge sharing interfaces such as interactive portals, and the use of the library's World Wide Web presence to increase the availability and usability of information services. The introduction addresses changes in library…

Top-K Interesting Subgraph Discovery in Information Networks

DTIC Science & Technology

2014-03-03

Integrative Biomarker Discovery for Breast Cancer Metastasis from Gene Expression and Protein Interaction Data Using Error-tolerant Pattern Mining” at...Jiawei Han¶ ∗Microsoft, India . Email: gmanish@microsoft.com †State University of New York at Buffalo. Email: jing@buffalo.edu ‡University of California

Cognitive and Social Structure of the Elite Collaboration Network of Astrophysics: A Case Study on Shifting Network Structures

ERIC Educational Resources Information Center

Heidler, Richard

2011-01-01

Scientific collaboration can only be understood along the epistemic and cognitive grounding of scientific disciplines. New scientific discoveries in astrophysics led to a major restructuring of the elite network of astrophysics. To study the interplay of the epistemic grounding and the social network structure of a discipline, a mixed-methods…

Applied metabolomics in drug discovery.

PubMed

Cuperlovic-Culf, M; Culf, A S

2016-08-01

The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

Research and Simulation on Application of the Mobile IP Network

NASA Astrophysics Data System (ADS)

Yibing, Deng; Wei, Hu; Minghui, Li; Feng, Gao; Junyi, Shen

The paper analysed the mobile node, home agent, and foreign agent of mobile IP network firstly, some key technique, such as mobile IP network basical principle, protocol work principle, agent discovery, registration, and IP packet transmission, were discussed. Then a network simulation model was designed, validating the characteristic of mobile IP network, and some advantages, which were brought by mobile network, were testified. Finally, the conclusion is gained: mobile IP network could realize the expectation of consumer that they can communicate with others anywhere.

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

Abercrombie, Robert K; Udoeyop, Akaninyene W

This work examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, worldwide patents, news archives,more » and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain we investigated.« less

Integrated Approaches to Drug Discovery for Oxidative Stress-Related Retinal Diseases.

PubMed

Nishimura, Yuhei; Hara, Hideaki

2016-01-01

Excessive oxidative stress induces dysregulation of functional networks in the retina, resulting in retinal diseases such as glaucoma, age-related macular degeneration, and diabetic retinopathy. Although various therapies have been developed to reduce oxidative stress in retinal diseases, most have failed to show efficacy in clinical trials. This may be due to oversimplification of target selection for such a complex network as oxidative stress. Recent advances in high-throughput technologies have facilitated the collection of multilevel omics data, which has driven growth in public databases and in the development of bioinformatics tools. Integration of the knowledge gained from omics databases can be used to generate disease-related biological networks and to identify potential therapeutic targets within the networks. Here, we provide an overview of integrative approaches in the drug discovery process and provide simple examples of how the approaches can be exploited to identify oxidative stress-related targets for retinal diseases.

Integrated Approaches to Drug Discovery for Oxidative Stress-Related Retinal Diseases

PubMed Central

Hara, Hideaki

2016-01-01

Excessive oxidative stress induces dysregulation of functional networks in the retina, resulting in retinal diseases such as glaucoma, age-related macular degeneration, and diabetic retinopathy. Although various therapies have been developed to reduce oxidative stress in retinal diseases, most have failed to show efficacy in clinical trials. This may be due to oversimplification of target selection for such a complex network as oxidative stress. Recent advances in high-throughput technologies have facilitated the collection of multilevel omics data, which has driven growth in public databases and in the development of bioinformatics tools. Integration of the knowledge gained from omics databases can be used to generate disease-related biological networks and to identify potential therapeutic targets within the networks. Here, we provide an overview of integrative approaches in the drug discovery process and provide simple examples of how the approaches can be exploited to identify oxidative stress-related targets for retinal diseases. PMID:28053689

Scientometric methods for identifying emerging technologies

DOEpatents

Abercrombie, Robert K; Schlicher, Bob G; Sheldon, Frederick T

2015-11-03

Provided is a method of generating a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, worldwide patents, news archives, and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain.

Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure.

PubMed

Pacini, Clare; Ajioka, James W; Micklem, Gos

2017-04-12

Correlation matrices are important in inferring relationships and networks between regulatory or signalling elements in biological systems. With currently available technology sample sizes for experiments are typically small, meaning that these correlations can be difficult to estimate. At a genome-wide scale estimation of correlation matrices can also be computationally demanding. We develop an empirical Bayes approach to improve covariance estimates for gene expression, where we assume the covariance matrix takes a block diagonal form. Our method shows lower false discovery rates than existing methods on simulated data. Applied to a real data set from Bacillus subtilis we demonstrate it's ability to detecting known regulatory units and interactions between them. We demonstrate that, compared to existing methods, our method is able to find significant covariances and also to control false discovery rates, even when the sample size is small (n=10). The method can be used to find potential regulatory networks, and it may also be used as a pre-processing step for methods that calculate, for example, partial correlations, so enabling the inference of the causal and hierarchical structure of the networks.

Knowledge Discovery from Biomedical Ontologies in Cross Domains.

PubMed

Shen, Feichen; Lee, Yugyung

2016-01-01

In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.

Knowledge Discovery from Biomedical Ontologies in Cross Domains

PubMed Central

Shen, Feichen; Lee, Yugyung

2016-01-01

In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies. PMID:27548262

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.

PubMed

Nam, Seungyoon

2017-04-01

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

Assessing Robustness Properties in Dynamic Discovery of Ad Hoc Network Services (Briefing Charts)

DTIC Science & Technology

2001-10-04

JINI entities in directed -- discovery mode. It is part of the SCM_Discovery -- Module. Sends Unicast messages to SCMs on list of -- SCMS to be...discovered until all SCMS are found. -- Receives updates from SCM DB of discovered SCMs and -- removes SCMs accordingly -- NOTE: Failure and...For All (SM, SD, SCM ): (SM, SD) IsElementOf SCM registered-services (CC1) implies SCM IsElementOf SM discovered- SCMs For All

The Discovery of the Optical Transient For GRB 010222

NASA Astrophysics Data System (ADS)

Henden, Arne A.

2001-04-01

On February 22, 2001, a very bright gamma-ray burst was detected by the Italian BeppoSAX satellite. The localization was posted about 4 hours after the burst. Prompt notification by phone and by the AAVSO Gamma-Ray Burst Network pager alert system resulted in the discovery of the optical afterglow within the first hour after the locatlization posting. This paper gives a brief history of the event and how the AAVSO was essential to the discovery.

Finding overlapping communities in multilayer networks

PubMed Central

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks. PMID:29694387

Finding overlapping communities in multilayer networks.

PubMed

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. | Office of Cancer Genomics

Cancer.gov

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall.

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

The Analysis of Search Results for the Clarification and Identification of Technology Emergence (AR-CITE) computer code examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e. scholarly publications and citation, world patents, news archives, and on-line mapping networks) are assembled to become one collective networkmore » (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the subject domain to be clarified and identified.« less

Implementing a Discovery Layer: A Rookie's Season

ERIC Educational Resources Information Center

Brubaker, Noah; Leach-Murray, Susan; Parker, Sherri

2012-01-01

The year 2011 was the PALNI (Private Academic Library Network of Indiana) consortium's "rookie season" for the implementation of Primo, the 2010 Discovery Layer 500 race winner. In this article, the authors report on their transition to the cloud within Ex Libris Ltd.'s Primo TotalCare environment: their preparation, the steps involved…

Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks.

PubMed

Katiravan, Jeevaa; Sylvia, D; Rao, D Srinivasa

2015-01-01

In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network.

Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks

PubMed Central

Katiravan, Jeevaa; Sylvia, D.; Rao, D. Srinivasa

2015-01-01

In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network. PMID:26167529

Applying ADLs to Assess Emerging Industry Specifications for Dynamic Discovery of Ad Hoc Network Services

DTIC Science & Technology

2001-01-31

function of Jini, UPnP, SLP, Bluetooth , and HAVi • Projected specific UML models for Jini, UPnP, and SLP • Developed a Rapide Model of Jini...is used by all JINI entities in directed -- discovery mode. It is part of the SCM_Discovery -- Module. Sends Unicast messages to SCMs on list of... SCMS to be discovered until all SCMS are found. -- Receives updates from SCM DB of discovered SCMs and -- removes SCMs accordingly -- NOTE

A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.

PubMed

Jiang, Guoqian; Wang, Chen; Zhu, Qian; Chute, Christopher G

2013-01-01

Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

Directional Bias and Pheromone for Discovery and Coverage on Networks

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

Fink, Glenn A.; Berenhaut, Kenneth S.; Oehmen, Christopher S.

2012-09-11

Natural multi-agent systems often rely on “correlated random walks” (random walks that are biased toward a current heading) to distribute their agents over a space (e.g., for foraging, search, etc.). Our contribution involves creation of a new movement and pheromone model that applies the concept of heading bias in random walks to a multi-agent, digital-ants system designed for cyber-security monitoring. We examine the relative performance effects of both pheromone and heading bias on speed of discovery of a target and search-area coverage in a two-dimensional network layout. We found that heading bias was unexpectedly helpful in reducing search time andmore » that it was more influential than pheromone for improving coverage. We conclude that while pheromone is very important for rapid discovery, heading bias can also greatly improve both performance metrics.« less

Camera Network Topology Discovery Literature Review

DTIC Science & Technology

2011-11-01

essential for 21st century military, enviromental and surveillance applications [Devarajan, Cheng & Radke 2008]. Computer networks pose several research...starting and ending points of object trajectories into entry/exit regions [Makris & Ellis 2003]. 3LDA is a new standard for document analysis. The model

Global Landscape of a Co-Expressed Gene Network in Barley and its Application to Gene Discovery in Triticeae Crops

PubMed Central

Mochida, Keiichi; Uehara-Yamaguchi, Yukiko; Yoshida, Takuhiro; Sakurai, Tetsuya; Shinozaki, Kazuo

2011-01-01

Accumulated transcriptome data can be used to investigate regulatory networks of genes involved in various biological systems. Co-expression analysis data sets generated from comprehensively collected transcriptome data sets now represent efficient resources that are capable of facilitating the discovery of genes with closely correlated expression patterns. In order to construct a co-expression network for barley, we analyzed 45 publicly available experimental series, which are composed of 1,347 sets of GeneChip data for barley. On the basis of a gene-to-gene weighted correlation coefficient, we constructed a global barley co-expression network and classified it into clusters of subnetwork modules. The resulting clusters are candidates for functional regulatory modules in the barley transcriptome. To annotate each of the modules, we performed comparative annotation using genes in Arabidopsis and Brachypodium distachyon. On the basis of a comparative analysis between barley and two model species, we investigated functional properties from the representative distributions of the gene ontology (GO) terms. Modules putatively involved in drought stress response and cellulose biogenesis have been identified. These modules are discussed to demonstrate the effectiveness of the co-expression analysis. Furthermore, we applied the data set of co-expressed genes coupled with comparative analysis in attempts to discover potentially Triticeae-specific network modules. These results demonstrate that analysis of the co-expression network of the barley transcriptome together with comparative analysis should promote the process of gene discovery in barley. Furthermore, the insights obtained should be transferable to investigations of Triticeae plants. The associated data set generated in this analysis is publicly accessible at http://coexpression.psc.riken.jp/barley/. PMID:21441235

Optimal route discovery for soft QOS provisioning in mobile ad hoc multimedia networks

NASA Astrophysics Data System (ADS)

Huang, Lei; Pan, Feng

2007-09-01

In this paper, we propose an optimal routing discovery algorithm for ad hoc multimedia networks whose resource keeps changing, First, we use stochastic models to measure the network resource availability, based on the information about the location and moving pattern of the nodes, as well as the link conditions between neighboring nodes. Then, for a certain multimedia packet flow to be transmitted from a source to a destination, we formulate the optimal soft-QoS provisioning problem as to find the best route that maximize the probability of satisfying its desired QoS requirements in terms of the maximum delay constraints. Based on the stochastic network resource model, we developed three approaches to solve the formulated problem: A centralized approach serving as the theoretical reference, a distributed approach that is more suitable to practical real-time deployment, and a distributed dynamic approach that utilizes the updated time information to optimize the routing for each individual packet. Examples of numerical results demonstrated that using the route discovered by our distributed algorithm in a changing network environment, multimedia applications could achieve better QoS statistically.

Therapeutic target discovery using Boolean network attractors: improvements of kali

PubMed Central

Guziolowski, Carito

2018-01-01

In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modelling pathological mechanisms was introduced. In the present article, the improvements made on this algorithm, named kali, are described. These improvements are (i) the possibility to work on asynchronous Boolean networks, (ii) a finer assessment of therapeutic targets and (iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system, such as a Boolean network, are associated with the phenotypes of the modelled biological system. Given a logic-based model of pathological mechanisms, kali searches for therapeutic targets able to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing their likeliness. kali is illustrated on an example network and used on a biological case study. The case study is a published logic-based model of bladder tumorigenesis from which kali returns consistent results. However, like any computational tool, kali can predict but cannot replace human expertise: it is a supporting tool for coping with the complexity of biological systems in the field of drug discovery. PMID:29515890

Network Discovery Pipeline Elucidates Conserved Time-of-Day–Specific cis-Regulatory Modules

PubMed Central

McEntee, Connor; Byer, Amanda; Trout, Jonathan D; Hazen, Samuel P; Shen, Rongkun; Priest, Henry D; Sullivan, Christopher M; Givan, Scott A; Yanovsky, Marcelo; Hong, Fangxin; Kay, Steve A; Chory, Joanne

2008-01-01

Correct daily phasing of transcription confers an adaptive advantage to almost all organisms, including higher plants. In this study, we describe a hypothesis-driven network discovery pipeline that identifies biologically relevant patterns in genome-scale data. To demonstrate its utility, we analyzed a comprehensive matrix of time courses interrogating the nuclear transcriptome of Arabidopsis thaliana plants grown under different thermocycles, photocycles, and circadian conditions. We show that 89% of Arabidopsis transcripts cycle in at least one condition and that most genes have peak expression at a particular time of day, which shifts depending on the environment. Thermocycles alone can drive at least half of all transcripts critical for synchronizing internal processes such as cell cycle and protein synthesis. We identified at least three distinct transcription modules controlling phase-specific expression, including a new midnight specific module, PBX/TBX/SBX. We validated the network discovery pipeline, as well as the midnight specific module, by demonstrating that the PBX element was sufficient to drive diurnal and circadian condition-dependent expression. Moreover, we show that the three transcription modules are conserved across Arabidopsis, poplar, and rice. These results confirm the complex interplay between thermocycles, photocycles, and the circadian clock on the daily transcription program, and provide a comprehensive view of the conserved genomic targets for a transcriptional network key to successful adaptation. PMID:18248097

Defining Tolerance: Impacts of Delay and Disruption when Managing Challenged Networks

NASA Technical Reports Server (NTRS)

Birrane, Edward J. III; Burleigh, Scott C.; Cerf, Vint

2011-01-01

Challenged networks exhibit irregularities in their communication performance stemming from node mobility, power constraints, and impacts from the operating environment. These irregularities manifest as high signal propagation delay and frequent link disruption. Understanding those limits of link disruption and propagation delay beyond which core networking features fail is an ongoing area of research. Various wireless networking communities propose tools and techniques that address these phenomena. Emerging standardization activities within the Internet Research Task Force (IRTF) and the Consultative Committee for Space Data Systems (CCSDS) look to build upon both this experience and scalability analysis. Successful research in this area is predicated upon identifying enablers for common communication functions (notably node discovery, duplex communication, state caching, and link negotiation) and how increased disruptions and delays affect their feasibility within the network. Networks that make fewer assumptions relating to these enablers provide more universal service. Specifically, reliance on node discovery and link negotiation results in network-specific operational concepts rather than scalable technical solutions. Fundamental to this debate are the definitions, assumptions, operational concepts, and anticipated scaling of these networks. This paper presents the commonalities and differences between delay and disruption tolerance, including support protocols and critical enablers. We present where and how these tolerances differ. We propose a set of use cases that must be accommodated by any standardized delay-tolerant network and discuss the implication of these on existing tool development.

Antituberculosis activity of the molecular libraries screening center network library.

PubMed

Maddry, Joseph A; Ananthan, Subramaniam; Goldman, Robert C; Hobrath, Judith V; Kwong, Cecil D; Maddox, Clinton; Rasmussen, Lynn; Reynolds, Robert C; Secrist, John A; Sosa, Melinda I; White, E Lucile; Zhang, Wei

2009-09-01

There is an urgent need for the discovery and development of new antitubercular agents that target novel biochemical pathways and treat drug-resistant forms of the disease. One approach to addressing this need is through high-throughput screening of drug-like small molecule libraries against the whole bacterium in order to identify a variety of new, active scaffolds that will stimulate additional biological research and drug discovery. Through the Molecular Libraries Screening Center Network, the NIAID Tuberculosis Antimicrobial Acquisition and Coordinating Facility tested a 215,110-compound library against Mycobacterium tuberculosis strain H37Rv. A medicinal chemistry survey of the results from the screening campaign is reported herein.

Is Multitask Deep Learning Practical for Pharma?

PubMed

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems

PubMed Central

Liang, Junbin; Liu, Renping; Ni, Wei; Li, Yin; Li, Ran; Ma, Wenpeng; Qi, Chuanda

2018-01-01

In the post-Cloud era, the proliferation of Internet of Things (IoT) has pushed the horizon of Edge computing, which is a new computing paradigm with data processed at the edge of the network. As the important systems of Edge computing, wireless sensor and actuator networks (WSANs) play an important role in collecting and processing the sensing data from the surrounding environment as well as taking actions on the events happening in the environment. In WSANs, in-network data storage and information discovery schemes with high energy efficiency, high load balance and low latency are needed because of the limited resources of the sensor nodes and the real-time requirement of some specific applications, such as putting out a big fire in a forest. In this article, the existing schemes of WSANs on data storage and information discovery are surveyed with detailed analysis on their advancements and shortcomings, and possible solutions are proposed on how to achieve high efficiency, good load balance, and perfect real-time performances at the same time, hoping that it can provide a good reference for the future research of the WSANs-based Edge computing systems. PMID:29439442

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems.

PubMed

Ma, Xingpo; Liang, Junbin; Liu, Renping; Ni, Wei; Li, Yin; Li, Ran; Ma, Wenpeng; Qi, Chuanda

2018-02-10

In the post-Cloud era, the proliferation of Internet of Things (IoT) has pushed the horizon of Edge computing, which is a new computing paradigm with data are processed at the edge of the network. As the important systems of Edge computing, wireless sensor and actuator networks (WSANs) play an important role in collecting and processing the sensing data from the surrounding environment as well as taking actions on the events happening in the environment. In WSANs, in-network data storage and information discovery schemes with high energy efficiency, high load balance and low latency are needed because of the limited resources of the sensor nodes and the real-time requirement of some specific applications, such as putting out a big fire in a forest. In this article, the existing schemes of WSANs on data storage and information discovery are surveyed with detailed analysis on their advancements and shortcomings, and possible solutions are proposed on how to achieve high efficiency, good load balance, and perfect real-time performances at the same time, hoping that it can provide a good reference for the future research of the WSANs-based Edge computing systems.

Discovery in a World of Mashups

NASA Astrophysics Data System (ADS)

King, T. A.; Ritschel, B.; Hourcle, J. A.; Moon, I. S.

2014-12-01

When the first digital information was stored electronically, discovery of what existed was through file names and the organization of the file system. With the advent of networks, digital information was shared on a wider scale, but discovery remained based on file and folder names. With a growing number of information sources, named based discovery quickly became ineffective. The keyword based search engine was one of the first types of a mashup in the world of Web 1.0. Embedded links from one document to another with prescribed relationships between files and the world of Web 2.0 was formed. Search engines like Google used the links to improve search results and a worldwide mashup was formed. While a vast improvement, the need for semantic (meaning rich) discovery was clear, especially for the discovery of scientific data. In response, every science discipline defined schemas to describe their type of data. Some core schemas where shared, but most schemas are custom tailored even though they share many common concepts. As with the networking of information sources, science increasingly relies on data from multiple disciplines. So there is a need to bring together multiple sources of semantically rich information. We explore how harvesting, conceptual mapping, facet based search engines, search term promotion, and style sheets can be combined to create the next generation of mashups in the emerging world of Web 3.0. We use NASA's Planetary Data System and NASA's Heliophysics Data Environment to illustrate how to create a multi-discipline mash-up.

Network-Based Approaches in Drug Discovery and Early Development

PubMed Central

Harrold, JM; Ramanathan, M; Mager, DE

2015-01-01

Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target–drug combination with high potential for yielding clinical success within the efficacy–toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification. PMID:24025802

Scientific Knowledge Discovery in Complex Semantic Networks of Geophysical Systems

NASA Astrophysics Data System (ADS)

Fox, P.

2012-04-01

The vast majority of explorations of the Earth's systems are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. Recent successes in the application of complex network theory and algorithms to climate data, raise expectations that more general graph-based approaches offer the opportunity for new discoveries. In the past ~ 5 years in the natural sciences there has substantial progress in providing both specialists and non-specialists the ability to describe in machine readable form, geophysical quantities and relations among them in meaningful and natural ways, effectively breaking the prior syntax barrier. The corresponding open-world semantics and reasoning provide higher-level interconnections. That is, semantics provided around the data structures, using semantically-equipped tools, and semantically aware interfaces between science application components allowing for discovery at the knowledge level. More recently, formal semantic approaches to continuous and aggregate physical processes are beginning to show promise and are soon likely to be ready to apply to geoscientific systems. To illustrate these opportunities, this presentation presents two application examples featuring domain vocabulary (ontology) and property relations (named and typed edges in the graphs). First, a climate knowledge discovery pilot encoding and exploration of CMIP5 catalog information with the eventual goal to encode and explore CMIP5 data. Second, a multi-stakeholder knowledge network for integrated assessments in marine ecosystems, where the data is highly inter-disciplinary.

Comparison of branch and distally focused main renal artery denervation using two different radio-frequency systems in a porcine model.

PubMed

Mahfoud, Felix; Pipenhagen, Catherine A; Boyce Moon, L; Ewen, Sebastian; Kulenthiran, Saarraaken; Fish, Jeffrey M; Jensen, James A; Virmani, Renu; Joner, Michael; Yahagi, Kazuyuki; Tsioufis, Costas; Böhm, Michael

2017-08-15

Anatomic placement of lesions may impact efficacy of radio-frequency (RF) catheter renal denervation (RDN). However, it is unclear if it is necessary to perform treatments post bifurcation with systems that may provide deeper penetration to achieve successful RDN. Sixteen domestic swine (n=16) were randomly assigned to 4 groups: 1) 8 lesions created in the branch arteries using the Spyral catheter (SP8); 2) 8 lesions created in the branch arteries plus 4 lesions created in the main artery using the SP catheter (SP12); 3) 8 lesions created in the main artery using the EnligHTN catheter with the distal position as close as possible to the bifurcation (EN8); and 4) 12 lesions created in the main artery using the EN catheter with the distal position as close as possible to the bifurcation (EN12). Each arm showed statistically significant changes in kidney norepinephrine (NE, ng/g) between treated kidneys vs. untreated contralateral control. There were no statistically significant differences in tissue NE% reductions across each arm based on catheter, anatomic location, & number of lesions (p=0.563): EN8 -74±34%, EN12 -95±3%, SP8 -76±16%, SP12 -82±17% (p=0.496). A total of 46 lesions were measured for lesion depth: EN main (3.3±2.8mm) vs. SP branch (2.0±1.0mm, p=0.039), SP main (2.9±1.6mm) vs. SP branch (p=0.052), and EN main vs. SP main (p=0.337). Distally-focused main renal artery treatment using the EN system appears to be equally efficacious in reducing tissue NE levels compared with SP treatment in the branches plus main renal arteries, advocating for device-specific procedure execution. Copyright © 2017 Elsevier B.V. All rights reserved.

Community Discovery in Dynamic, Rich-Context Social Networks

ERIC Educational Resources Information Center

Lin, Yu-Ru

2010-01-01

My research interest has been in understanding the human communities formed through interpersonal social activities. Participation in online communities on social network sites such as Twitter has been observed to influence people's behavior in diverse ways including financial decision-making and political choices, suggesting the rich potential…

Evaluating Discovery Services Architectures in the Context of the Internet of Things

NASA Astrophysics Data System (ADS)

Polytarchos, Elias; Eliakis, Stelios; Bochtis, Dimitris; Pramatari, Katerina

As the "Internet of Things" is expected to grow rapidly in the following years, the need to develop and deploy efficient and scalable Discovery Services in this context is very important for its success. Thus, the ability to evaluate and compare the performance of different Discovery Services architectures is vital if we want to allege that a given design is better at meeting requirements of a specific application. The purpose of this chapter is to provide a paradigm for the evaluation of different Discovery Services for the Internet of Things in terms of efficiency, scalability and performance through the use of simulations. The methodology presented uses the application of Discovery Services to a supply chain with the Service Lookup Service Discovery Service using OMNeT++, an open source network simulation suite. Then, we delve into the simulation design and the details of our findings.

Assessing the State-of-the-Art in Dynamic Discovery of Ad Hoc Network Services

DTIC Science & Technology

2001-07-18

directed -- discovery mode. It is part of the SCM_Discovery -- Module. Sends Unicast messages to SCMs on list of -- SCMS to be discovered until all... SCMS are found. -- Receives updates from SCM DB of discovered SCMs and -- removes SCMs accordingly -- NOTE: Failure and recovery behavior are not...ALLFindService10 SM4 GROUP1GroupJoin10 SCM1 SM4LinkFail5 SM4NodeFail5 ParametersCommandTime TopologyScenario Execute with Rapide For All (SM, SD, SCM

Efficient Strategies for Active Interface-Level Network Topology Discovery

DTIC Science & Technology

2013-09-01

Network Information Centre API Application Programming Interface APNIC Asia-Pacific Network Information Centre ARIN American Registry for Internet Numbers...very convenient Application Programming Interface ( API ) for easy primitive implementation. Ark’s API facilitates easy development and rapid...prototyping – important attributes as the char- acteristics of our primitives evolve. The API allows a high-level of abstraction, which in turn leads to rapid

Intelligent routing protocol for ad hoc wireless network

NASA Astrophysics Data System (ADS)

Peng, Chaorong; Chen, Chang Wen

2006-05-01

A novel routing scheme for mobile ad hoc networks (MANETs), which combines hybrid and multi-inter-routing path properties with a distributed topology discovery route mechanism using control agents is proposed in this paper. In recent years, a variety of hybrid routing protocols for Mobile Ad hoc wireless networks (MANETs) have been developed. Which is proactively maintains routing information for a local neighborhood, while reactively acquiring routes to destinations beyond the global. The hybrid protocol reduces routing discovery latency and the end-to-end delay by providing high connectivity without requiring much of the scarce network capacity. On the other side the hybrid routing protocols in MANETs likes Zone Routing Protocol still need route "re-discover" time when a route between zones link break. Sine the topology update information needs to be broadcast routing request on local zone. Due to this delay, the routing protocol may not be applicable for real-time data and multimedia communication. We utilize the advantages of a clustering organization and multi-routing path in routing protocol to achieve several goals at the same time. Firstly, IRP efficiently saves network bandwidth and reduces route reconstruction time when a routing path fails. The IRP protocol does not require global periodic routing advertisements, local control agents will automatically monitor and repair broke links. Secondly, it efficiently reduces congestion and traffic "bottlenecks" for ClusterHeads in clustering network. Thirdly, it reduces significant overheads associated with maintaining clusters. Fourthly, it improves clusters stability due to dynamic topology changing frequently. In this paper, we present the Intelligent Routing Protocol. First, we discuss the problem of routing in ad hoc networks and the motivation of IRP. We describe the hierarchical architecture of IRP. We describe the routing process and illustrate it with an example. Further, we describe the control manage mechanisms, which are used to control active route and reduce the traffic amount in the route discovery procedure. Finial, the numerical experiments are given to show the effectiveness of IRP routing protocol.

The discovery of 9/8-ribbons, β/γ-peptides with curved shapes governed by a combined configuration-conformation code.

PubMed

Grison, Claire M; Robin, Sylvie; Aitken, David J

2015-11-21

The de novo design of a β/γ-peptidic foldamer motif has led to the discovery of an unprecedented 9/8-ribbon featuring an uninterrupted alternating C9/C8 hydrogen-bonding network. The ribbons adopt partially curved topologies determined synchronistically by the β-residue configuration and the γ-residue conformation sets.

From scientific discovery to cures: bright stars within a galaxy.

PubMed

Williams, R Sanders; Lotia, Samad; Holloway, Alisha K; Pico, Alexander R

2015-09-24

We propose that data mining and network analysis utilizing public databases can identify and quantify relationships between scientific discoveries and major advances in medicine (cures). Further development of such approaches could help to increase public understanding and governmental support for life science research and could enhance decision making in the quest for cures. Copyright © 2015 Elsevier Inc. All rights reserved.

System-level multi-target drug discovery from natural products with applications to cardiovascular diseases.

PubMed

Zheng, Chunli; Wang, Jinan; Liu, Jianling; Pei, Mengjie; Huang, Chao; Wang, Yonghua

2014-08-01

The term systems pharmacology describes a field of study that uses computational and experimental approaches to broaden the view of drug actions rooted in molecular interactions and advance the process of drug discovery. The aim of this work is to stick out the role that the systems pharmacology plays across the multi-target drug discovery from natural products for cardiovascular diseases (CVDs). Firstly, based on network pharmacology methods, we reconstructed the drug-target and target-target networks to determine the putative protein target set of multi-target drugs for CVDs treatment. Secondly, we reintegrated a compound dataset of natural products and then obtained a multi-target compounds subset by virtual-screening process. Thirdly, a drug-likeness evaluation was applied to find the ADME-favorable compounds in this subset. Finally, we conducted in vitro experiments to evaluate the reliability of the selected chemicals and targets. We found that four of the five randomly selected natural molecules can effectively act on the target set for CVDs, indicating the reasonability of our systems-based method. This strategy may serve as a new model for multi-target drug discovery of complex diseases.

Genomics and transcriptomics in drug discovery.

PubMed

Dopazo, Joaquin

2014-02-01

The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery. Copyright © 2013 Elsevier Ltd. All rights reserved.

Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.

PubMed

Benson, Neil

2015-08-01

Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review

PubMed Central

Csermely, Peter; Korcsmáros, Tamás; Kiss, Huba J.M.; London, Gábor; Nussinov, Ruth

2013-01-01

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach. PMID:23384594

Collecting and Using Networked Statistics: Current Status, Future Goals

ERIC Educational Resources Information Center

Hiott, Judith

2004-01-01

For more than five years the Houston Public Library has collected statistics for measuring networked collections and services based on emerging guidelines. While the guidelines have provided authority and stability to the process, the clarification process continues. The development of information discovery software, such as federated search tools…

Discovery Learning in Autonomous Agents Using Genetic Algorithms

DTIC Science & Technology

1993-12-01

Meyer and Wilson (47). 65. Roitblat , H. L., et al. "Biomimetic Sonar Processing: Prom Dolphin Echoloc-Ation to Artificial Neural Networks." In Meyer and...34 In Meyer and Wilson (47). 65. Roitblat , H. L., et al. "Biomimetic Sonar Processing: From Dolphin Echolocation to Artificial Neural Networks." In

Developing an intelligence analysis process through social network analysis

NASA Astrophysics Data System (ADS)

Waskiewicz, Todd; LaMonica, Peter

2008-04-01

Intelligence analysts are tasked with making sense of enormous amounts of data and gaining an awareness of a situation that can be acted upon. This process can be extremely difficult and time consuming. Trying to differentiate between important pieces of information and extraneous data only complicates the problem. When dealing with data containing entities and relationships, social network analysis (SNA) techniques can be employed to make this job easier. Applying network measures to social network graphs can identify the most significant nodes (entities) and edges (relationships) and help the analyst further focus on key areas of concern. Strange developed a model that identifies high value targets such as centers of gravity and critical vulnerabilities. SNA lends itself to the discovery of these high value targets and the Air Force Research Laboratory (AFRL) has investigated several network measures such as centrality, betweenness, and grouping to identify centers of gravity and critical vulnerabilities. Using these network measures, a process for the intelligence analyst has been developed to aid analysts in identifying points of tactical emphasis. Organizational Risk Analyzer (ORA) and Terrorist Modus Operandi Discovery System (TMODS) are the two applications used to compute the network measures and identify the points to be acted upon. Therefore, the result of leveraging social network analysis techniques and applications will provide the analyst and the intelligence community with more focused and concentrated analysis results allowing them to more easily exploit key attributes of a network, thus saving time, money, and manpower.

Molecular Networking and Pattern-Based Genome Mining Improves Discovery of Biosynthetic Gene Clusters and their Products from Salinispora Species

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

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna

Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. In this paper, we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains, including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated themore » identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. Finally, these efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.« less

Molecular Networking and Pattern-Based Genome Mining Improves Discovery of Biosynthetic Gene Clusters and their Products from Salinispora Species

DOE PAGES

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna; ...

2015-04-09

Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. In this paper, we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains, including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated themore » identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. Finally, these efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.« less

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

Molecular Networking and Pattern-Based Genome Mining Improves discovery of biosynthetic gene clusters and their products from Salinispora species

PubMed Central

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna; Sarkar, Anindita; Li, Jie; Ziemert, Nadine; Wang, Mingxun; Bandeira, Nuno; Moore, Bradley S.; Dorrestein, Pieter C.; Jensen, Paul R.

2015-01-01

Summary Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. Here we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated the identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. These efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches. PMID:25865308

Analytical challenges translating mass spectrometry-based phosphoproteomics from discovery to clinical applications

PubMed Central

Iliuk, Anton B.; Arrington, Justine V.; Tao, Weiguo Andy

2014-01-01

Phosphoproteomics is the systematic study of one of the most common protein modifications in high throughput with the aim of providing detailed information of the control, response, and communication of biological systems in health and disease. Advances in analytical technologies and strategies, in particular the contributions of high-resolution mass spectrometers, efficient enrichments of phosphopeptides, and fast data acquisition and annotation, have catalyzed dramatic expansion of signaling landscapes in multiple systems during the past decade. While phosphoproteomics is an essential inquiry to map high-resolution signaling networks and to find relevant events among the apparently ubiquitous and widespread modifications of proteome, it presents tremendous challenges in separation sciences to translate it from discovery to clinical practice. In this mini-review, we summarize the analytical tools currently utilized for phosphoproteomic analysis (with focus on MS), progresses made on deciphering clinically relevant kinase-substrate networks, MS uses for biomarker discovery and validation, and the potential of phosphoproteomics for disease diagnostics and personalized medicine. PMID:24890697

Overland flow erosion inferred from Martian channel network geometry

NASA Astrophysics Data System (ADS)

Seybold, Hansjörg; Kirchner, James

2016-04-01

The controversy about the origin of Mars' channel networks is almost as old as their discovery 150 years ago. Over the last few decades, new Mars probes have revealed more detailed structures in Martian The controversy about the origin of Mars' channel networks is almost as old as their discovery 150 years ago. Over the last few decades, new Mars probes have revealed more detailed structures in Martian drainage networks, and new studies suggest that Mars once had large volumes of surface water. But how this water flowed, and how it could have carved the channels, remains unclear. Simple scaling arguments show that networks formed by similar mechanisms should have similar branching angles on Earth and Mars, suggesting that Earth analogues can be informative here. A recent analysis of high-resolution data for the continental United States shows that climate leaves a characteristic imprint in the branching geometry of stream networks. Networks growing in humid regions have an average branching angle of α = 2π/5 = 72° [1], which is characteristic of network growth by groundwater sapping [2]. Networks in arid regions, where overland flow erosion is more dominant, show much smaller branching angles. Here we show that the channel networks on Mars have branching angles that resemble those created by surficial flows on Earth. This result implies that the growth of Martian channel networks was dominated by near-surface flow, and suggests that deeper infiltration was inhibited, potentially by permafrost or by impermeable weathered soils. [1] Climate's Watermark in the Geometry of River Networks, Seybold et al.; under review [2] Ramification of stream networks, Devauchelle et al.; PNAS (2012)

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts.

PubMed

Oguz, Cihan; Sen, Shurjo K; Davis, Adam R; Fu, Yi-Ping; O'Donnell, Christopher J; Gibbons, Gary H

2017-10-26

One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits. To this end, we employed random forests (RFs) and neural networks (NNs) for predictive modeling of coronary artery calcium (CAC), which is an intermediate endo-phenotype of coronary artery disease (CAD). Model inputs were derived from advanced cases in the ClinSeq®; discovery cohort (n=16) and the FHS replication cohort (n=36) from 89 th -99 th CAC score percentile range, and age-matched controls (ClinSeq®; n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables and genotypes of 56 single nucleotide polymorphisms (SNPs) ranked highest in terms of their nominal correlation with the advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristic curves (ROC-AUC). RF models trained and tested with clinical variables generated ROC-AUC values of 0.69 and 0.61 in the discovery and replication cohorts, respectively. In contrast, in both cohorts, the set of SNPs derived from the discovery cohort were highly predictive (ROC-AUC ≥0.85) with no significant change in predictive performance upon integration of clinical and genotype variables. Using the 21 SNPs that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq®; data and tested with FHS data and obtained high predictive accuracy (ROC-AUC=0.80-0.85) with several topologies. Several CAD and "vascular aging" related biological processes were enriched in the network of genes constructed from the predictive SNPs. We identified a molecular network predictive of advanced coronary calcium using genotype data from ClinSeq®; and FHS cohorts. Our results illustrate that machine learning tools, which utilize complex interactions between disease predictors intrinsic to the pathogenesis of polygenic disorders, hold promise for deriving predictive disease models and networks.

Learning to Predict Social Influence in Complex Networks

DTIC Science & Technology

2012-03-29

03/2010 – 17/03/2012 Abstract: First, we addressed the problem of analyzing information diffusion process in a social network using two kinds...algorithm which avoids the inner loop optimization during the search. We tested the performance using the structures of four real world networks, and...result of information diffusion that starts from the node. 2 We use “infected” and “activated” interchangeably. Efficient Discovery of Influential

Dynamic Testing and Automatic Repair of Reconfigurable Wiring Harnesses

DTIC Science & Technology

2006-11-27

Switch An M ×N grid of switches configured to provide a M -input, N -output routing network. Permutation Network A permutation network performs an...wiring reduces the effective advantage of their reduced switch count, particularly when considering that regular grids (crossbar switches being a...are connected to. The outline circuit shown in Fig. 20 shows how a suitable ‘discovery probe’ might be implemented. The circuit shows a UART

A Workflow-based Intelligent Network Data Movement Advisor with End-to-end Performance Optimization

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

Zhu, Michelle M.; Wu, Chase Q.

2013-11-07

Next-generation eScience applications often generate large amounts of simulation, experimental, or observational data that must be shared and managed by collaborative organizations. Advanced networking technologies and services have been rapidly developed and deployed to facilitate such massive data transfer. However, these technologies and services have not been fully utilized mainly because their use typically requires significant domain knowledge and in many cases application users are even not aware of their existence. By leveraging the functionalities of an existing Network-Aware Data Movement Advisor (NADMA) utility, we propose a new Workflow-based Intelligent Network Data Movement Advisor (WINDMA) with end-to-end performance optimization formore » this DOE funded project. This WINDMA system integrates three major components: resource discovery, data movement, and status monitoring, and supports the sharing of common data movement workflows through account and database management. This system provides a web interface and interacts with existing data/space management and discovery services such as Storage Resource Management, transport methods such as GridFTP and GlobusOnline, and network resource provisioning brokers such as ION and OSCARS. We demonstrate the efficacy of the proposed transport-support workflow system in several use cases based on its implementation and deployment in DOE wide-area networks.« less

Lightweight and confidential data discovery and dissemination for wireless body area networks.

PubMed

He, Daojing; Chan, Sammy; Zhang, Yan; Yang, Haomiao

2014-03-01

As a special sensor network, a wireless body area network (WBAN) provides an economical solution to real-time monitoring and reporting of patients' physiological data. After a WBAN is deployed, it is sometimes necessary to disseminate data into the network through wireless links to adjust configuration parameters of body sensors or distribute management commands and queries to sensors. A number of such protocols have been proposed recently, but they all focus on how to ensure reliability and overlook security vulnerabilities. Taking into account the unique features and application requirements of a WBAN, this paper presents the design, implementation, and evaluation of a secure, lightweight, confidential, and denial-of-service-resistant data discovery and dissemination protocol for WBANs to ensure the data items disseminated are not altered or tampered. Based on multiple one-way key hash chains, our protocol provides instantaneous authentication and can tolerate node compromise. Besides the theoretical analysis that demonstrates the security and performance of the proposed protocol, this paper also reports the experimental evaluation of our protocol in a network of resource-limited sensor nodes, which shows its efficiency in practice. In particular, extensive security analysis shows that our protocol is provably secure.

Utilizing Social Bookmarking Tag Space for Web Content Discovery: A Social Network Analysis Approach

ERIC Educational Resources Information Center

Wei, Wei

2010-01-01

Social bookmarking has gained popularity since the advent of Web 2.0. Keywords known as tags are created to annotate web content, and the resulting tag space composed of the tags, the resources, and the users arises as a new platform for web content discovery. Useful and interesting web resources can be located through searching and browsing based…

Enabling Service Discovery in a Federation of Systems: WS-Discovery Case Study

DTIC Science & Technology

2014-06-01

found that Pastry [3] coupled with SCRIBE [4] provides everything we require from the overlay network: Pastry nodes form a decentralized, self...application-independent manner. Furthermore, Pastry provides mechanisms that support and facilitate application-specific object replication, caching, and fault...recovery. Add SCRIBE to Pastry , and you get a generic, scalable and efficient group communication and event notification system providing

A fuzzy neural network for intelligent data processing

NASA Astrophysics Data System (ADS)

Xie, Wei; Chu, Feng; Wang, Lipo; Lim, Eng Thiam

2005-03-01

In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.

Analysis of latency performance of bluetooth low energy (BLE) networks.

PubMed

Cho, Keuchul; Park, Woojin; Hong, Moonki; Park, Gisu; Cho, Wooseong; Seo, Jihoon; Han, Kijun

2014-12-23

Bluetooth Low Energy (BLE) is a short-range wireless communication technology aiming at low-cost and low-power communication. The performance evaluation of classical Bluetooth device discovery have been intensively studied using analytical modeling and simulative methods, but these techniques are not applicable to BLE, since BLE has a fundamental change in the design of the discovery mechanism, including the usage of three advertising channels. Recently, there several works have analyzed the topic of BLE device discovery, but these studies are still far from thorough. It is thus necessary to develop a new, accurate model for the BLE discovery process. In particular, the wide range settings of the parameters introduce lots of potential for BLE devices to customize their discovery performance. This motivates our study of modeling the BLE discovery process and performing intensive simulation. This paper is focused on building an analytical model to investigate the discovery probability, as well as the expected discovery latency, which are then validated via extensive experiments. Our analysis considers both continuous and discontinuous scanning modes. We analyze the sensitivity of these performance metrics to parameter settings to quantitatively examine to what extent parameters influence the performance metric of the discovery processes.

Analysis of Latency Performance of Bluetooth Low Energy (BLE) Networks

PubMed Central

Cho, Keuchul; Park, Woojin; Hong, Moonki; Park, Gisu; Cho, Wooseong; Seo, Jihoon; Han, Kijun

2015-01-01

Bluetooth Low Energy (BLE) is a short-range wireless communication technology aiming at low-cost and low-power communication. The performance evaluation of classical Bluetooth device discovery have been intensively studied using analytical modeling and simulative methods, but these techniques are not applicable to BLE, since BLE has a fundamental change in the design of the discovery mechanism, including the usage of three advertising channels. Recently, there several works have analyzed the topic of BLE device discovery, but these studies are still far from thorough. It is thus necessary to develop a new, accurate model for the BLE discovery process. In particular, the wide range settings of the parameters introduce lots of potential for BLE devices to customize their discovery performance. This motivates our study of modeling the BLE discovery process and performing intensive simulation. This paper is focused on building an analytical model to investigate the discovery probability, as well as the expected discovery latency, which are then validated via extensive experiments. Our analysis considers both continuous and discontinuous scanning modes. We analyze the sensitivity of these performance metrics to parameter settings to quantitatively examine to what extent parameters influence the performance metric of the discovery processes. PMID:25545266

Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas.

PubMed

Liseron-Monfils, Christophe; Lewis, Tim; Ashlock, Daniel; McNicholas, Paul D; Fauteux, François; Strömvik, Martina; Raizada, Manish N

2013-03-15

The discovery of genetic networks and cis-acting DNA motifs underlying their regulation is a major objective of transcriptome studies. The recent release of the maize genome (Zea mays L.) has facilitated in silico searches for regulatory motifs. Several algorithms exist to predict cis-acting elements, but none have been adapted for maize. A benchmark data set was used to evaluate the accuracy of three motif discovery programs: BioProspector, Weeder and MEME. Analysis showed that each motif discovery tool had limited accuracy and appeared to retrieve a distinct set of motifs. Therefore, using the benchmark, statistical filters were optimized to reduce the false discovery ratio, and then remaining motifs from all programs were combined to improve motif prediction. These principles were integrated into a user-friendly pipeline for motif discovery in maize called Promzea, available at http://www.promzea.org and on the Discovery Environment of the iPlant Collaborative website. Promzea was subsequently expanded to include rice and Arabidopsis. Within Promzea, a user enters cDNA sequences or gene IDs; corresponding upstream sequences are retrieved from the maize genome. Predicted motifs are filtered, combined and ranked. Promzea searches the chosen plant genome for genes containing each candidate motif, providing the user with the gene list and corresponding gene annotations. Promzea was validated in silico using a benchmark data set: the Promzea pipeline showed a 22% increase in nucleotide sensitivity compared to the best standalone program tool, Weeder, with equivalent nucleotide specificity. Promzea was also validated by its ability to retrieve the experimentally defined binding sites of transcription factors that regulate the maize anthocyanin and phlobaphene biosynthetic pathways. Promzea predicted additional promoter motifs, and genome-wide motif searches by Promzea identified 127 non-anthocyanin/phlobaphene genes that each contained all five predicted promoter motifs in their promoters, perhaps uncovering a broader co-regulated gene network. Promzea was also tested against tissue-specific microarray data from maize. An online tool customized for promoter motif discovery in plants has been generated called Promzea. Promzea was validated in silico by its ability to retrieve benchmark motifs and experimentally defined motifs and was tested using tissue-specific microarray data. Promzea predicted broader networks of gene regulation associated with the historic anthocyanin and phlobaphene biosynthetic pathways. Promzea is a new bioinformatics tool for understanding transcriptional gene regulation in maize and has been expanded to include rice and Arabidopsis.

Chemical Genetic Screens for TDP-43 Modifiers and ALS Drug Discovery

DTIC Science & Technology

2015-03-01

Blackstone , 2012). Recently, a large network including many of these genes have been identify and this network is highly similar to Parkinson’s, ALS and...10.1186/1750- 1326-8-30 Blackstone , C. (2012). Cellular pathways of hereditary spastic paraplegia. Annu. Rev. Neurosci. 35, 25–47. doi: 10.1146/annurev

Early Detection Research Network (EDRN) | Division of Cancer Prevention

Cancer.gov

http://edrn.nci.nih.gov/EDRN is a collaborative network that maintains comprehensive infrastructure and resources critical to the discovery, development and validation of biomarkers for cancer risk and early detection. The program comprises a public/private sector consortium to accelerate the development of biomarkers that will change medical practice, ensure data

Cognitive Affordances of the Cyberinfrastructure for Science and Math Learning

ERIC Educational Resources Information Center

Martinez, Michael E.; Peters Burton, Erin E.

2011-01-01

The "cyberinfrastucture" is a broad informational network that entails connections to real-time data sensors as well as tools that permit visualization and other forms of analysis, and that facilitates access to vast scientific databases. This multifaceted network, already a major boon to scientific discovery, now shows exceptional promise in…

Design of a Covert RFID Tag Network for Target Discovery and Target Information Routing

PubMed Central

Pan, Qihe; Narayanan, Ram M.

2011-01-01

Radio frequency identification (RFID) tags are small electronic devices working in the radio frequency range. They use wireless radio communications to automatically identify objects or people without the need for line-of-sight or contact, and are widely used in inventory tracking, object location, environmental monitoring. This paper presents a design of a covert RFID tag network for target discovery and target information routing. In the design, a static or very slowly moving target in the field of RFID tags transmits a distinct pseudo-noise signal, and the RFID tags in the network collect the target information and route it to the command center. A map of each RFID tag’s location is saved at command center, which can determine where a RFID tag is located based on each RFID tag’s ID. We propose the target information collection method with target association and clustering, and we also propose the information routing algorithm within the RFID tag network. The design and operation of the proposed algorithms are illustrated through examples. Simulation results demonstrate the effectiveness of the design. PMID:22163693

Design of a covert RFID tag network for target discovery and target information routing.

PubMed

Pan, Qihe; Narayanan, Ram M

2011-01-01

Radio frequency identification (RFID) tags are small electronic devices working in the radio frequency range. They use wireless radio communications to automatically identify objects or people without the need for line-of-sight or contact, and are widely used in inventory tracking, object location, environmental monitoring. This paper presents a design of a covert RFID tag network for target discovery and target information routing. In the design, a static or very slowly moving target in the field of RFID tags transmits a distinct pseudo-noise signal, and the RFID tags in the network collect the target information and route it to the command center. A map of each RFID tag's location is saved at command center, which can determine where a RFID tag is located based on each RFID tag's ID. We propose the target information collection method with target association and clustering, and we also propose the information routing algorithm within the RFID tag network. The design and operation of the proposed algorithms are illustrated through examples. Simulation results demonstrate the effectiveness of the design.

The Cancer Target Discovery and Development Network Dashboard Allows Users to Search for Interesting Data and Results | Office of Cancer Genomics

Cancer.gov

The CTD2 Dashboard hosts analyzed data and other evidence generated by the CTD2 Network. It is a web interface for the research community to browse and search CTD2 Network data related to genes, proteins, and compounds from individual CTD2 Centers, or explore observations across multiple Centers.

CTD² Publication Guidelines | Office of Cancer Genomics

Cancer.gov

The Cancer Target Discovery and Development (CTD2) Network is a “community resource project” supported by the National Cancer Institute’s Office of Cancer Genomics. Members of the Network release data to the broader research community by depositing data into NCI-supported or public databases. Data deposition is NOT equivalent to publishing in a peer-reviewed journal. Unless there is a manuscript associated with a dataset, the Network considers data to be formally unpublished.

Formation of crystal-like structures and branched networks from nonionic spherical micelles

NASA Astrophysics Data System (ADS)

Cardiel, Joshua J.; Furusho, Hirotoshi; Skoglund, Ulf; Shen, Amy Q.

2015-12-01

Crystal-like structures at nano and micron scales have promise for purification and confined reactions, and as starting points for fabricating highly ordered crystals for protein engineering and drug discovery applications. However, developing controlled crystallization techniques from batch processes remain challenging. We show that neutrally charged nanoscale spherical micelles from biocompatible nonionic surfactant solutions can evolve into nano- and micro-sized branched networks and crystal-like structures. This occurs under simple combinations of temperature and flow conditions. Our findings not only suggest new opportunities for developing controlled universal crystallization and encapsulation procedures that are sensitive to ionic environments and high temperatures, but also open up new pathways for accelerating drug discovery processes, which are of tremendous interest to pharmaceutical and biotechnological industries.

Digital Social Network Mining for Topic Discovery

NASA Astrophysics Data System (ADS)

Moradianzadeh, Pooya; Mohi, Maryam; Sadighi Moshkenani, Mohsen

Networked computers are expanding more and more around the world, and digital social networks becoming of great importance for many people's work and leisure. This paper mainly focused on discovering the topic of exchanging information in digital social network. In brief, our method is to use a hierarchical dictionary of related topics and words that mapped to a graph. Then, with comparing the extracted keywords from the context of social network with graph nodes, probability of relation between context and desired topics will be computed. This model can be used in many applications such as advertising, viral marketing and high-risk group detection.

A network model of knowledge accumulation through diffusion and upgrade

NASA Astrophysics Data System (ADS)

Zhuang, Enyu; Chen, Guanrong; Feng, Gang

2011-07-01

In this paper, we introduce a model to describe knowledge accumulation through knowledge diffusion and knowledge upgrade in a multi-agent network. Here, knowledge diffusion refers to the distribution of existing knowledge in the network, while knowledge upgrade means the discovery of new knowledge. It is found that the population of the network and the number of each agent’s neighbors affect the speed of knowledge accumulation. Four different policies for updating the neighboring agents are thus proposed, and their influence on the speed of knowledge accumulation and the topology evolution of the network are also studied.

Network-based Approaches in Pharmacology.

PubMed

Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier

2017-10-01

In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Applying gene regulatory network logic to the evolution of social behavior.

PubMed

Baran, Nicole M; McGrath, Patrick T; Streelman, J Todd

2017-06-06

Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to study behavior. Applying the GRN approach to the brain and behavior provides a quantitative and manipulative framework for discovery. We illustrate features of this framework using the example of social behavior and the neural circuitry of aggression.

Inhibitory Behavioral Control: A Stochastic Dynamic Causal Modeling Study Using Network Discovery Analysis

PubMed Central

Steinberg, Joel L.; Cunningham, Kathryn A.; Lane, Scott D.; Kramer, Larry A.; Narayana, Ponnada A.; Kosten, Thomas R.; Bechara, Antoine; Moeller, F. Gerard

2015-01-01

Abstract This study employed functional magnetic resonance imaging (fMRI)-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity underlying inhibitory behavioral control. fMRI data were acquired from 15 healthy subjects while they performed a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard NoGo conditions) in distinguishing spatial patterns of lines. Based on the previous inhibitory control literature and the present fMRI activation results, 10 brain regions were postulated as nodes in the effective connectivity model. Due to the large number of potential interconnections among these nodes, the number of models for final analysis was reduced to a manageable level for the whole group by conducting DCM Network Discovery, which is a recently developed option within the Statistical Parametric Mapping software package. Given the optimum network model, the DCM Network Discovery analysis found that the locations of the driving input into the model from all the experimental stimuli in the Go/NoGo task were the amygdala and the hippocampus. The strengths of several cortico-subcortical connections were modulated (influenced) by the two NoGo conditions. Specifically, connectivity from the middle frontal gyrus (MFG) to hippocampus was enhanced by the Easy condition and further enhanced by the Hard NoGo condition, possibly suggesting that compared with the Easy NoGo condition, stronger control from MFG was needed for the hippocampus to discriminate/learn the spatial pattern in order to respond correctly (inhibit), during the Hard NoGo condition. PMID:25336321

Proposal and Evaluation of BLE Discovery Process Based on New Features of Bluetooth 5.0.

PubMed

Hernández-Solana, Ángela; Perez-Diaz-de-Cerio, David; Valdovinos, Antonio; Valenzuela, Jose Luis

2017-08-30

The device discovery process is one of the most crucial aspects in real deployments of sensor networks. Recently, several works have analyzed the topic of Bluetooth Low Energy (BLE) device discovery through analytical or simulation models limited to version 4.x. Non-connectable and non-scannable undirected advertising has been shown to be a reliable alternative for discovering a high number of devices in a relatively short time period. However, new features of Bluetooth 5.0 allow us to define a variant on the device discovery process, based on BLE scannable undirected advertising events, which results in higher discovering capacities and also lower power consumption. In order to characterize this new device discovery process, we experimentally model the real device behavior of BLE scannable undirected advertising events. Non-detection packet probability, discovery probability, and discovery latency for a varying number of devices and parameters are compared by simulations and experimental measurements. We demonstrate that our proposal outperforms previous works, diminishing the discovery time and increasing the potential user device density. A mathematical model is also developed in order to easily obtain a measure of the potential capacity in high density scenarios.

Proposal and Evaluation of BLE Discovery Process Based on New Features of Bluetooth 5.0

PubMed Central

2017-01-01

The device discovery process is one of the most crucial aspects in real deployments of sensor networks. Recently, several works have analyzed the topic of Bluetooth Low Energy (BLE) device discovery through analytical or simulation models limited to version 4.x. Non-connectable and non-scannable undirected advertising has been shown to be a reliable alternative for discovering a high number of devices in a relatively short time period. However, new features of Bluetooth 5.0 allow us to define a variant on the device discovery process, based on BLE scannable undirected advertising events, which results in higher discovering capacities and also lower power consumption. In order to characterize this new device discovery process, we experimentally model the real device behavior of BLE scannable undirected advertising events. Non-detection packet probability, discovery probability, and discovery latency for a varying number of devices and parameters are compared by simulations and experimental measurements. We demonstrate that our proposal outperforms previous works, diminishing the discovery time and increasing the potential user device density. A mathematical model is also developed in order to easily obtain a measure of the potential capacity in high density scenarios. PMID:28867786

Discovery of Action Patterns and User Correlations in Task-Oriented Processes for Goal-Driven Learning Recommendation

ERIC Educational Resources Information Center

Zhou, Xiaokang; Chen, Jian; Wu, Bo; Jin, Qun

2014-01-01

With the high development of social networks, collaborations in a socialized web-based learning environment has become increasing important, which means people can learn through interactions and collaborations in communities across social networks. In this study, in order to support the enhanced collaborative learning, two important factors, user…

An Information Theoretic Approach for Measuring Data Discovery and Utilization During Analytical and Decision Making Processes

DTIC Science & Technology

2015-07-31

and make the expected decision outcomes. The scenario is based around a scripted storyboard where an organized crime network is operating in a city to...interdicted by law enforcement to disrupt the network. The scenario storyboard was used to develop a probabilistic vehicle traffic model in order to

The Mind Research Network - Mental Illness Neuroscience Discovery Grant

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

Roberts, J.; Calhoun, V.

The scientific and technological programs of the Mind Research Network (MRN), reflect DOE missions in basic science and associated instrumentation, computational modeling, and experimental techniques. MRN's technical goals over the course of this project have been to develop and apply integrated, multi-modality functional imaging techniques derived from a decade of DOE-support research and technology development.

A Context-Aware Paradigm for Information Discovery and Dissemination in Mobile Environments

ERIC Educational Resources Information Center

Lundquist, Doug

2011-01-01

The increasing power and ubiquity of mobile wireless devices is enabling real-time information delivery for many diverse applications. A crucial question is how to allocate finite network resources efficiently and fairly despite the uncertainty common in highly dynamic mobile ad hoc networks. We propose a set of routing protocols, Self-Balancing…

Strategic plan : providing high precision search to NASA employees using the NASA engineering network

NASA Technical Reports Server (NTRS)

Dutra, Jayne E.; Smith, Lisa

2006-01-01

The goal of this plan is to briefly describe new technologies available to us in the arenas of information discovery and discuss the strategic value they have for the NASA enterprise with some considerations and suggestions for near term implementations using the NASA Engineering Network (NEN) as a delivery venue.

Analytics for Cyber Network Defense

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

Plantenga, Todd.; Kolda, Tamara Gibson

2011-06-01

This report provides a brief survey of analytics tools considered relevant to cyber network defense (CND). Ideas and tools come from elds such as statistics, data mining, and knowledge discovery. Some analytics are considered standard mathematical or statistical techniques, while others re ect current research directions. In all cases the report attempts to explain the relevance to CND with brief examples.

Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation

ERIC Educational Resources Information Center

Hinton, Geoffrey; Osindero, Simon; Welling, Max; Teh, Yee-Whye

2006-01-01

We describe a way of modeling high-dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron-like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of…

Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task

ERIC Educational Resources Information Center

Tabor, Whitney; Cho, Pyeong W.; Dankowicz, Harry

2013-01-01

Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the…

A Complex Systems Approach to Causal Discovery in Psychiatry.

PubMed

Saxe, Glenn N; Statnikov, Alexander; Fenyo, David; Ren, Jiwen; Li, Zhiguo; Prasad, Meera; Wall, Dennis; Bergman, Nora; Briggs, Ernestine C; Aliferis, Constantin

2016-01-01

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation.

PubMed

Meng, Hu; Liu, Ying; Lai, Luhua

2015-08-18

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

PubMed

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Khan, Muhammad Farhan; Naeem, Muhammad; Anpalagan, Alagan

2015-03-25

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

PubMed Central

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Farhan Khan, Muhammad; Naeem, Muhammad; Anpalagan, Alagan

2015-01-01

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed. PMID:25815444

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.

PubMed

Xi, Jianing; Wang, Minghui; Li, Ao

2018-06-05

Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Advancing cancer drug discovery towards more agile development of targeted combination therapies.

PubMed

Carragher, Neil O; Unciti-Broceta, Asier; Cameron, David A

2012-01-01

Current drug-discovery strategies are typically 'target-centric' and are based upon high-throughput screening of large chemical libraries against nominated targets and a selection of lead compounds with optimized 'on-target' potency and selectivity profiles. However, high attrition of targeted agents in clinical development suggest that combinations of targeted agents will be most effective in treating solid tumors if the biological networks that permit cancer cells to subvert monotherapies are identified and retargeted. Conventional drug-discovery and development strategies are suboptimal for the rational design and development of novel drug combinations. In this article, we highlight a series of emerging technologies supporting a less reductionist, more agile, drug-discovery and development approach for the rational design, validation, prioritization and clinical development of novel drug combinations.

Receiver-Based Ad Hoc On Demand Multipath Routing Protocol for Mobile Ad Hoc Networks

PubMed Central

Al-Nahari, Abdulaziz; Mohamad, Mohd Murtadha

2016-01-01

Decreasing the route rediscovery time process in reactive routing protocols is challenging in mobile ad hoc networks. Links between nodes are continuously established and broken because of the characteristics of the network. Finding multiple routes to increase the reliability is also important but requires a fast update, especially in high traffic load and high mobility where paths can be broken as well. The sender node keeps re-establishing path discovery to find new paths, which makes for long time delay. In this paper we propose an improved multipath routing protocol, called Receiver-based ad hoc on demand multipath routing protocol (RB-AOMDV), which takes advantage of the reliability of the state of the art ad hoc on demand multipath distance vector (AOMDV) protocol with less re-established discovery time. The receiver node assumes the role of discovering paths when finding data packets that have not been received after a period of time. Simulation results show the delay and delivery ratio performances are improved compared with AOMDV. PMID:27258013

Receiver-Based Ad Hoc On Demand Multipath Routing Protocol for Mobile Ad Hoc Networks.

PubMed

Al-Nahari, Abdulaziz; Mohamad, Mohd Murtadha

2016-01-01

Decreasing the route rediscovery time process in reactive routing protocols is challenging in mobile ad hoc networks. Links between nodes are continuously established and broken because of the characteristics of the network. Finding multiple routes to increase the reliability is also important but requires a fast update, especially in high traffic load and high mobility where paths can be broken as well. The sender node keeps re-establishing path discovery to find new paths, which makes for long time delay. In this paper we propose an improved multipath routing protocol, called Receiver-based ad hoc on demand multipath routing protocol (RB-AOMDV), which takes advantage of the reliability of the state of the art ad hoc on demand multipath distance vector (AOMDV) protocol with less re-established discovery time. The receiver node assumes the role of discovering paths when finding data packets that have not been received after a period of time. Simulation results show the delay and delivery ratio performances are improved compared with AOMDV.

DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

PubMed

Wang, QuanQiu; Xu, Rong

2015-01-01

Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

Transforming Big Data into cancer-relevant insight: An initial, multi-tier approach to assess reproducibility and relevance

PubMed Central

2016-01-01

The Cancer Target Discovery and Development (CTD2) Network was established to accelerate the transformation of “Big Data” into novel pharmacological targets, lead compounds, and biomarkers for rapid translation into improved patient outcomes. It rapidly became clear in this collaborative network that a key central issue was to define what constitutes sufficient computational or experimental evidence to support a biologically or clinically relevant finding. This manuscript represents a first attempt to delineate the challenges of supporting and confirming discoveries arising from the systematic analysis of large-scale data resources in a collaborative work environment and to provide a framework that would begin a community discussion to resolve these challenges. The Network implemented a multi-Tier framework designed to substantiate the biological and biomedical relevance as well as the reproducibility of data and insights resulting from its collaborative activities. The same approach can be used by the broad scientific community to drive development of novel therapeutic and biomarker strategies for cancer. PMID:27401613

Discovering latent commercial networks from online financial news articles

NASA Astrophysics Data System (ADS)

Xia, Yunqing; Su, Weifeng; Lau, Raymond Y. K.; Liu, Yi

2013-08-01

Unlike most online social networks where explicit links among individual users are defined, the relations among commercial entities (e.g. firms) may not be explicitly declared in commercial Web sites. One main contribution of this article is the development of a novel computational model for the discovery of the latent relations among commercial entities from online financial news. More specifically, a CRF model which can exploit both structural and contextual features is applied to commercial entity recognition. In addition, a point-wise mutual information (PMI)-based unsupervised learning method is developed for commercial relation identification. To evaluate the effectiveness of the proposed computational methods, a prototype system called CoNet has been developed. Based on the financial news articles crawled from Google finance, the CoNet system achieves average F-scores of 0.681 and 0.754 in commercial entity recognition and commercial relation identification, respectively. Our experimental results confirm that the proposed shallow natural language processing methods are effective for the discovery of latent commercial networks from online financial news.

Rational design of HIV vaccine and microbicides: report of the EUROPRISE annual conference.

PubMed

Wahren, Britta; Biswas, Priscilla; Borggren, Marie; Coleman, Adam; Da Costa, Kelly; De Haes, Winni; Dieltjens, Tessa; Dispinseri, Stefania; Grupping, Katrijn; Hallengärd, David; Hornig, Julia; Klein, Katja; Mainetti, Lara; Palma, Paolo; Reudelsterz, Marc; Seifried, Janna; Selhorst, Philippe; Sköld, Annette; Uchtenhagen, Hannes; van Gils, Marit J; Weber, Caroline; Shattock, Robin; Scarlatti, Gabriella

2010-07-26

EUROPRISE is a Network of Excellence sponsored from 2007 to 2011 by the European Commission within the 6th Framework Program. The Network encompasses a wide portfolio of activities ranging from an integrated research program in the field of HIV vaccines and microbicides to training, dissemination and advocacy. The research program covers the whole pipeline of vaccine and microbicide development from discovery to early clinical trials. The Network is composed of 58 partners representing more than 65 institutions from 13 European countries; it also includes three major pharmaceutical companies (GlaxoSmithKline, Novartis and Sanofi-Pasteur) involved in HIV microbicide and vaccine research. The Network displays a dedicated and informative web page: http://www.europrise.org. Finally, a distinguishing trait of EUROPRISE is its PhD School of students from across Europe, a unique example in the world of science aimed at spreading excellence through training. EUROPRISE held its second annual conference in Budapest in November, 2009. The conference had 143 participants and their presentations covered aspects of vaccine and microbicide research, development and discovery. Since training is a major task of the Network, the students of the EUROPRISE PhD program summarized certain presentations and their view of the conference in this paper.

Rational design of HIV vaccine and microbicides: report of the EUROPRISE annual conference

PubMed Central

2010-01-01

EUROPRISE is a Network of Excellence sponsored from 2007 to 2011 by the European Commission within the 6th Framework Program. The Network encompasses a wide portfolio of activities ranging from an integrated research program in the field of HIV vaccines and microbicides to training, dissemination and advocacy. The research program covers the whole pipeline of vaccine and microbicide development from discovery to early clinical trials. The Network is composed of 58 partners representing more than 65 institutions from 13 European countries; it also includes three major pharmaceutical companies (GlaxoSmithKline, Novartis and Sanofi-Pasteur) involved in HIV microbicide and vaccine research. The Network displays a dedicated and informative web page: http://www.europrise.org. Finally, a distinguishing trait of EUROPRISE is its PhD School of students from across Europe, a unique example in the world of science aimed at spreading excellence through training. EUROPRISE held its second annual conference in Budapest in November, 2009. The conference had 143 participants and their presentations covered aspects of vaccine and microbicide research, development and discovery. Since training is a major task of the Network, the students of the EUROPRISE PhD program summarized certain presentations and their view of the conference in this paper. PMID:20659333

Network-based approaches to climate knowledge discovery

NASA Astrophysics Data System (ADS)

Budich, Reinhard; Nyberg, Per; Weigel, Tobias

2011-11-01

Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.

CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network

PubMed Central

Aksoy, Bülent Arman; Dančík, Vlado; Smith, Kenneth; Mazerik, Jessica N.; Ji, Zhou; Gross, Benjamin; Nikolova, Olga; Jaber, Nadia; Califano, Andrea; Schreiber, Stuart L.; Gerhard, Daniela S.; Hermida, Leandro C.; Jagu, Subhashini

2017-01-01

Abstract The Cancer Target Discovery and Development (CTD2) Network aims to use functional genomics to accelerate the translation of high-throughput and high-content genomic and small-molecule data towards use in precision oncology. As part of this goal, and to share its conclusions with the research community, the Network developed the ‘CTD2 Dashboard’ [https://ctd2-dashboard.nci.nih.gov/], which compiles CTD2 Network-generated conclusions, termed ‘observations’, associated with experimental entities, collected by its member groups (‘Centers’). Any researcher interested in learning about a given gene, protein, or compound (a ‘subject’) studied by the Network can come to the CTD2 Dashboard to quickly and easily find, review, and understand Network-generated experimental results. In particular, the Dashboard allows visitors to connect experiments about the same target, biomarker, etc., carried out by multiple Centers in the Network. The Dashboard’s unique knowledge representation allows information to be compiled around a subject, so as to become greater than the sum of the individual contributions. The CTD2 Network has broadly defined levels of validation for evidence (‘Tiers’) pertaining to a particular finding, and the CTD2 Dashboard uses these Tiers to indicate the extent to which results have been validated. Researchers can use the Network’s insights and tools to develop a new hypothesis or confirm existing hypotheses, in turn advancing the findings towards clinical applications. Database URL: https://ctd2-dashboard.nci.nih.gov/ PMID:29220450

Forty years of secondhand smoke research: the gap between discovery and delivery.

PubMed

Harris, Jenine K; Luke, Douglas A; Zuckerman, Rachael B; Shelton, Sarah C

2009-06-01

Public health initiatives often focus on the discovery of risk factors associated with disease and death. Although this is an important step in protecting public health, recently the field has recognized that it is critical to move along the continuum from discovery of risk factors to delivery of interventions, and to improve the quality and speed of translating scientific discoveries into practice. To understand how public health problems move from discovery to delivery, citation network analysis was used to examine 1877 articles on secondhand smoke (SHS) published between 1965 and 2005. Data were collected and analyzed in 2006-2007. Citation patterns showed discovery and delivery to be distinct areas of SHS research. There was little cross-citation between discovery and delivery research, including only nine citation connections between the main paths. A discovery article was 83.5% less likely to cite a delivery article than to cite another discovery article (OR=0.165 [95% CI=0.139, 0.197]), and a delivery article was 64.3% less likely (OR=0.357 [95% CI=0.330, 0.386]) to cite a discovery article than to cite another delivery article. Research summaries, such as Surgeon General reports, were cited frequently and appear to bridge the discovery-delivery gap. There was a lack of cross-citation between discovery and delivery, even though they share the goal of understanding and reducing the impact of SHS. Reliance on research summaries, although they provide an important bridge between discovery and delivery, may slow the development of a field.

Ontology- and graph-based similarity assessment in biological networks.

PubMed

Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

2010-10-15

A standard systems-based approach to biomarker and drug target discovery consists of placing putative biomarkers in the context of a network of biological interactions, followed by different 'guilt-by-association' analyses. The latter is typically done based on network structural features. Here, an alternative analysis approach in which the networks are analyzed on a 'semantic similarity' space is reported. Such information is extracted from ontology-based functional annotations. We present SimTrek, a Cytoscape plugin for ontology-based similarity assessment in biological networks. http://rosalind.infj.ulst.ac.uk/SimTrek.html francisco.azuaje@crp-sante.lu Supplementary data are available at Bioinformatics online.

Wireless Distribution Systems To Support Medical Response to Disasters

PubMed Central

Arisoylu, Mustafa; Mishra, Rajesh; Rao, Ramesh; Lenert, Leslie A.

2005-01-01

We discuss the design of multi-hop access networks with multiple gateways that supports medical response to disasters. We examine and implement protocols to ensure high bandwidth, robust, self-healing and secure wireless multi-hop access networks for extreme conditions. Address management, path setup, gateway discovery and selection protocols are described. Future directions and plans are also considered. PMID:16779171

A Review of the Accomplishments of the CTD² Network | Office of Cancer Genomics

Cancer.gov

The Office of Cancer Genomics (OCG) Cancer Target Discovery and Development or CTD2 initiative was established by the National Cancer Institute (NCI) to accelerate the “translation” of high-throughput, high-content genomic data to the bedside through functional genomics. The CTD2 initiative is a collaborative network of 13 different research teams, or Centers.

Network medicine in disease analysis and therapeutics.

PubMed

Chen, B; Butte, A J

2013-12-01

Two parallel trends are occurring in drug discovery. The first is that we are moving away from a symptom-based disease classification system to a system based on molecules and molecular states. The second is that we are shifting from targeting a single molecule toward targeting multiple molecules, pathways, or networks. Network medicine is an approach to understanding disease and discovering therapeutics looking at many molecules and how they interrelate, and it may play a critical role in the adoption of both trends.

Synthesis and characterization of a series of rod-disc combined liquid crystals

NASA Astrophysics Data System (ADS)

Mansdorf, Bart Allan

This research was divided into two parts. The objective of the first part was to develop a scheme for synthesizing a series of compounds that contained six cyanobiphenyl mesogens attached to a triphenylene core with (variable length) methylene spacer groups (from 6--12). Seven compounds were synthesized through a four-step process to give the desired product: RDn (R, rod-shaped mesogen; D, discotic mesogen; n, number of methylene spacer groups) with an overall yield of approximately 5%. The purity of each product was confirmed using 1H and 13C NMR, FTIR, MALDI-TOF MS, and elemental analysis. The objective of the second part of this research was to determine the structure and phase behavior of these compounds as well as investigate the effects of an electric field on the structure of these materials. Using DSC, the clearing temperatures of the RDn samples increased with a decrease in the spacer length (from Ti = 173°C for RD6 to Ti = 121°C for RD11) while showing an odd-even effect. After studying the thermal transitions in DSC, a more in-depth analysis of the RD10 and RD12 samples was carried out. Using PLM (polarized optical microscope) and WAXD, a nematic phase and two different crystalline phases were identified in RD10 and RD12. In order to investigate the effect of an electric field on RD12, a LC cell was constructed. The cell was filled with the sample and heated to the nematic phase and placed under a PLM. A DC electric field was applied to the cell that resulted in a nearly linear change in birefringence of the sample with an increase of applied voltage up to a critical value of 150V, where the field of view became completely dark. We speculate that the cyanobiphenyl groups "aligned" in the electric field due to the strong dipole moment on the cyano group. We speculate that this molecule behaves like a closing "umbrella" with the cyanobiphenyl groups aligning in the electric field and the discotic cores remaining flat or perpendicular to the field. The result is a 3-dimensionally chiral structure from an achiral material.

Pollutant runoff yields in the Yamato-gawa River, Japan, to be applied for EAH books of municipal wastewater intending pollutant discharge reduction

NASA Astrophysics Data System (ADS)

Tsuzuki, Yoshiaki; Yoneda, Minoru

2011-04-01

SummaryA Social Experiment Program to decrease municipal wastewater pollutant discharge by "soft interventions" in households and to improve river water quality was conducted in the Yamato-gawa River Basin, Japan. Environmental accounting housekeeping (EAH) books of municipal wastewater were prepared mainly for dissemination purpose to be applied during the Social Experiment Program. The EAH books are table format spreadsheets to estimate pollutant discharges. Pollutant load per capita flowing into water body (PLC wb) and pollutant runoff yields from sub-river basins to the river mouth are indispensable parameters for their preparation. In order to estimate the pollutant runoff yields of the pollutants, BOD, TN and TP, a concept of pollutant runoff yield from upper monitoring point, MP n, to lower monitoring point, MP n+1 ( Rm n(n+1)), and that from corresponding sub-river basin ( Rd(n+1)(n+1)) was introduced in this paper. When proportion of the pollutant runoff yields, p n (= Rm n(n+1)/ Rd(n+1)(n+1)), was equal to 1.0 in all the river sections, which was determined based on the simulation results of Rm and Rd, pollutant runoff yield from sub-river basin n to the monitoring point nearest to the river mouth, Ry n7, were estimated to be 0.3-66.8% for BOD, 25.8-75.8% for TN and 18.9-78.5% for TP. The EAH books of municipal wastewater were prepared by adopting the estimated pollutant runoff yields, Ry n7. The EAH books were thought to be distributed widely, however, they did not seem to be used by many ordinary citizens in the Social Experiment Program in February, 2010, judging from the small number of website visitor counter and less responses from people. Possible reasons for less usage than expected were considered to be unsuccessful negotiation with the official organizations of the Social Experiment Program on the EAH books utilization as official tools and some difficulties in using the EAH books for ordinary people.

Emerging Concepts and Methodologies in Cancer Biomarker Discovery.

PubMed

Lu, Meixia; Zhang, Jinxiang; Zhang, Lanjing

2017-01-01

Cancer biomarker discovery is a critical part of cancer prevention and treatment. Despite the decades of effort, only a small number of cancer biomarkers have been identified for and validated in clinical settings. Conceptual and methodological breakthroughs may help accelerate the discovery of additional cancer biomarkers, particularly their use for diagnostics. In this review, we have attempted to review the emerging concepts in cancer biomarker discovery, including real-world evidence, open access data, and data paucity in rare or uncommon cancers. We have also summarized the recent methodological progress in cancer biomarker discovery, such as high-throughput sequencing, liquid biopsy, big data, artificial intelligence (AI), and deep learning and neural networks. Much attention has been given to the methodological details and comparison of the methodologies. Notably, these concepts and methodologies interact with each other and will likely lead to synergistic effects when carefully combined. Newer, more innovative concepts and methodologies are emerging as the current emerging ones became mainstream and widely applied to the field. Some future challenges are also discussed. This review contributes to the development of future theoretical frameworks and technologies in cancer biomarker discovery and will contribute to the discovery of more useful cancer biomarkers.

TCMSP: a database of systems pharmacology for drug discovery from herbal medicines.

PubMed

Ru, Jinlong; Li, Peng; Wang, Jinan; Zhou, Wei; Li, Bohui; Huang, Chao; Li, Pidong; Guo, Zihu; Tao, Weiyang; Yang, Yinfeng; Xu, Xue; Li, Yan; Wang, Yonghua; Yang, Ling

2014-01-01

Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed. The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski's rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development. The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.

Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

PubMed

Maes, Michael; Nowak, Gabriel; Caso, Javier R; Leza, Juan Carlos; Song, Cai; Kubera, Marta; Klein, Hans; Galecki, Piotr; Noto, Cristiano; Glaab, Enrico; Balling, Rudi; Berk, Michael

2016-07-01

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular networks or pathways. Drug discovery processes should delineate new drugs targeting the intracellular networks and immune-related pathways.

Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining

PubMed Central

2012-01-01

Background Fever is one of the most common adverse events of vaccines. The detailed mechanisms of fever and vaccine-associated gene interaction networks are not fully understood. In the present study, we employed a genome-wide, Centrality and Ontology-based Network Discovery using Literature data (CONDL) approach to analyse the genes and gene interaction networks associated with fever or vaccine-related fever responses. Results Over 170,000 fever-related articles from PubMed abstracts and titles were retrieved and analysed at the sentence level using natural language processing techniques to identify genes and vaccines (including 186 Vaccine Ontology terms) as well as their interactions. This resulted in a generic fever network consisting of 403 genes and 577 gene interactions. A vaccine-specific fever sub-network consisting of 29 genes and 28 gene interactions was extracted from articles that are related to both fever and vaccines. In addition, gene-vaccine interactions were identified. Vaccines (including 4 specific vaccine names) were found to directly interact with 26 genes. Gene set enrichment analysis was performed using the genes in the generated interaction networks. Moreover, the genes in these networks were prioritized using network centrality metrics. Making scientific discoveries and generating new hypotheses were possible by using network centrality and gene set enrichment analyses. For example, our study found that the genes in the generic fever network were more enriched in cell death and responses to wounding, and the vaccine sub-network had more gene enrichment in leukocyte activation and phosphorylation regulation. The most central genes in the vaccine-specific fever network are predicted to be highly relevant to vaccine-induced fever, whereas genes that are central only in the generic fever network are likely to be highly relevant to generic fever responses. Interestingly, no Toll-like receptors (TLRs) were found in the gene-vaccine interaction network. Since multiple TLRs were found in the generic fever network, it is reasonable to hypothesize that vaccine-TLR interactions may play an important role in inducing fever response, which deserves a further investigation. Conclusions This study demonstrated that ontology-based literature mining is a powerful method for analyzing gene interaction networks and generating new scientific hypotheses. PMID:23256563

Protein interactions in 3D: from interface evolution to drug discovery.

PubMed

Winter, Christof; Henschel, Andreas; Tuukkanen, Anne; Schroeder, Michael

2012-09-01

Over the past 10years, much research has been dedicated to the understanding of protein interactions. Large-scale experiments to elucidate the global structure of protein interaction networks have been complemented by detailed studies of protein interaction interfaces. Understanding the evolution of interfaces allows one to identify convergently evolved interfaces which are evolutionary unrelated but share a few key residues and hence have common binding partners. Understanding interaction interfaces and their evolution is an important basis for pharmaceutical applications in drug discovery. Here, we review the algorithms and databases on 3D protein interactions and discuss in detail applications in interface evolution, drug discovery, and interface prediction. Copyright © 2012 Elsevier Inc. All rights reserved.

Using cyber vulnerability testing techniques to expose undocumented security vulnerabilities in DCS and SCADA equipment

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

Pollet, J.

2006-07-01

This session starts by providing an overview of typical DCS (Distributed Control Systems) and SCADA (Supervisory Control and Data Acquisition) architectures, and exposes cyber security vulnerabilities that vendors never admit, but are found through a comprehensive cyber testing process. A complete assessment process involves testing all of the layers and components of a SCADA or DCS environment, from the perimeter firewall all the way down to the end devices controlling the process, including what to look for when conducting a vulnerability assessment of real-time control systems. The following systems are discussed: 1. Perimeter (isolation from corporate IT or other non-criticalmore » networks) 2. Remote Access (third Party access into SCADA or DCS networks) 3. Network Architecture (switch, router, firewalls, access controls, network design) 4. Network Traffic Analysis (what is running on the network) 5. Host Operating Systems Hardening 6. Applications (how they communicate with other applications and end devices) 7. End Device Testing (PLCs, RTUs, DCS Controllers, Smart Transmitters) a. System Discovery b. Functional Discovery c. Attack Methodology i. DoS Tests (at what point does the device fail) ii. Malformed Packet Tests (packets that can cause equipment failure) iii. Session Hijacking (do anything that the operator can do) iv. Packet Injection (code and inject your own SCADA commands) v. Protocol Exploitation (Protocol Reverse Engineering / Fuzzing) This paper will provide information compiled from over five years of conducting cyber security testing on control systems hardware, software, and systems. (authors)« less

Genome network medicine: innovation to overcome huge challenges in cancer therapy.

PubMed

Roukos, Dimitrios H

2014-01-01

The post-ENCODE era shapes now a new biomedical research direction for understanding transcriptional and signaling networks driving gene expression and core cellular processes such as cell fate, survival, and apoptosis. Over the past half century, the Francis Crick 'central dogma' of single n gene/protein-phenotype (trait/disease) has defined biology, human physiology, disease, diagnostics, and drugs discovery. However, the ENCODE project and several other genomic studies using high-throughput sequencing technologies, computational strategies, and imaging techniques to visualize regulatory networks, provide evidence that transcriptional process and gene expression are regulated by highly complex dynamic molecular and signaling networks. This Focus article describes the linear experimentation-based limitations of diagnostics and therapeutics to cure advanced cancer and the need to move on from reductionist to network-based approaches. With evident a wide genomic heterogeneity, the power and challenges of next-generation sequencing (NGS) technologies to identify a patient's personal mutational landscape for tailoring the best target drugs in the individual patient are discussed. However, the available drugs are not capable of targeting aberrant signaling networks and research on functional transcriptional heterogeneity and functional genome organization is poorly understood. Therefore, the future clinical genome network medicine aiming at overcoming multiple problems in the new fields of regulatory DNA mapping, noncoding RNA, enhancer RNAs, and dynamic complexity of transcriptional circuitry are also discussed expecting in new innovation technology and strong appreciation of clinical data and evidence-based medicine. The problematic and potential solutions in the discovery of next-generation, molecular, and signaling circuitry-based biomarkers and drugs are explored. © 2013 Wiley Periodicals, Inc.

Distribution of Information in Ad Hoc Networks

DTIC Science & Technology

2007-09-01

2.4. MACA Protocol...................................20 Figure 2.5. Route discovery in AODV (From [32]).............28 Figure 2.6. Creation of a...19 Figure 2.3. Exposed terminal Problem (From [20]) (3) MACA and MACAW Protocols. One of the first protocols conceived for wireless local area...networks is MACA [21] (Multiple Accesses with Collision Avoidance). The transmitter sends a small packet, or RTS (Request To Send), which has little

SFTP: A Secure and Fault-Tolerant Paradigm against Blackhole Attack in MANET

NASA Astrophysics Data System (ADS)

KumarRout, Jitendra; Kumar Bhoi, Sourav; Kumar Panda, Sanjaya

2013-02-01

Security issues in MANET are a challenging task nowadays. MANETs are vulnerable to passive attacks and active attacks because of a limited number of resources and lack of centralized authority. Blackhole attack is an attack in network layer which degrade the network performance by dropping the packets. In this paper, we have proposed a Secure Fault-Tolerant Paradigm (SFTP) which checks the Blackhole attack in the network. The three phases used in SFTP algorithm are designing of coverage area to find the area of coverage, Network Connection algorithm to design a fault-tolerant model and Route Discovery algorithm to discover the route and data delivery from source to destination. SFTP gives better network performance by making the network fault free.

Context-sensitive network-based disease genetics prediction and its implications in drug discovery

PubMed Central

Chen, Yang; Xu, Rong

2017-01-01

Abstract Motivation: Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. Results: We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach (p

Network-based approach to identify prognostic biomarkers for estrogen receptor-positive breast cancer treatment with tamoxifen.

PubMed

Liu, Rong; Guo, Cheng-Xian; Zhou, Hong-Hao

2015-01-01

This study aims to identify effective gene networks and prognostic biomarkers associated with estrogen receptor positive (ER+) breast cancer using human mRNA studies. Weighted gene coexpression network analysis was performed with a complex ER+ breast cancer transcriptome to investigate the function of networks and key genes in the prognosis of breast cancer. We found a significant correlation of an expression module with distant metastasis-free survival (HR = 2.25; 95% CI .21.03-4.88 in discovery set; HR = 1.78; 95% CI = 1.07-2.93 in validation set). This module contained genes enriched in the biological process of the M phase. From this module, we further identified and validated 5 hub genes (CDK1, DLGAP5, MELK, NUSAP1, and RRM2), the expression levels of which were strongly associated with poor survival. Highly expressed MELK indicated poor survival in luminal A and luminal B breast cancer molecular subtypes. This gene was also found to be associated with tamoxifen resistance. Results indicated that a network-based approach may facilitate the discovery of biomarkers for the prognosis of ER+ breast cancer and may also be used as a basis for establishing personalized therapies. Nevertheless, before the application of this approach in clinical settings, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed.

Enabling information management systems in tactical network environments

NASA Astrophysics Data System (ADS)

Carvalho, Marco; Uszok, Andrzej; Suri, Niranjan; Bradshaw, Jeffrey M.; Ceccio, Philip J.; Hanna, James P.; Sinclair, Asher

2009-05-01

Net-Centric Information Management (IM) and sharing in tactical environments promises to revolutionize forward command and control capabilities by providing ubiquitous shared situational awareness to the warfighter. This vision can be realized by leveraging the tactical and Mobile Ad hoc Networks (MANET) which provide the underlying communications infrastructure, but, significant technical challenges remain. Enabling information management in these highly dynamic environments will require multiple support services and protocols which are affected by, and highly dependent on, the underlying capabilities and dynamics of the tactical network infrastructure. In this paper we investigate, discuss, and evaluate the effects of realistic tactical and mobile communications network environments on mission-critical information management systems. We motivate our discussion by introducing the Advanced Information Management System (AIMS) which is targeted for deployment in tactical sensor systems. We present some operational requirements for AIMS and highlight how critical IM support services such as discovery, transport, federation, and Quality of Service (QoS) management are necessary to meet these requirements. Our goal is to provide a qualitative analysis of the impact of underlying assumptions of availability and performance of some of the critical services supporting tactical information management. We will also propose and describe a number of technologies and capabilities that have been developed to address these challenges, providing alternative approaches for transport, service discovery, and federation services for tactical networks.

Weighted Association Rule Mining for Item Groups with Different Properties and Risk Assessment for Networked Systems

NASA Astrophysics Data System (ADS)

Kim, Jungja; Ceong, Heetaek; Won, Yonggwan

In market-basket analysis, weighted association rule (WAR) discovery can mine the rules that include more beneficial information by reflecting item importance for special products. In the point-of-sale database, each transaction is composed of items with similar properties, and item weights are pre-defined and fixed by a factor such as the profit. However, when items are divided into more than one group and the item importance must be measured independently for each group, traditional weighted association rule discovery cannot be used. To solve this problem, we propose a new weighted association rule mining methodology. The items should be first divided into subgroups according to their properties, and the item importance, i.e. item weight, is defined or calculated only with the items included in the subgroup. Then, transaction weight is measured by appropriately summing the item weights from each subgroup, and the weighted support is computed as the fraction of the transaction weights that contains the candidate items relative to the weight of all transactions. As an example, our proposed methodology is applied to assess the vulnerability to threats of computer systems that provide networked services. Our algorithm provides both quantitative risk-level values and qualitative risk rules for the security assessment of networked computer systems using WAR discovery. Also, it can be widely used for new applications with many data sets in which the data items are distinctly separated.

Towards a privacy preserving cohort discovery framework for clinical research networks.

PubMed

Yuan, Jiawei; Malin, Bradley; Modave, François; Guo, Yi; Hogan, William R; Shenkman, Elizabeth; Bian, Jiang

2017-02-01

The last few years have witnessed an increasing number of clinical research networks (CRNs) focused on building large collections of data from electronic health records (EHRs), claims, and patient-reported outcomes (PROs). Many of these CRNs provide a service for the discovery of research cohorts with various health conditions, which is especially useful for rare diseases. Supporting patient privacy can enhance the scalability and efficiency of such processes; however, current practice mainly relies on policy, such as guidelines defined in the Health Insurance Portability and Accountability Act (HIPAA), which are insufficient for CRNs (e.g., HIPAA does not require encryption of data - which can mitigate insider threats). By combining policy with privacy enhancing technologies we can enhance the trustworthiness of CRNs. The goal of this research is to determine if searchable encryption can instill privacy in CRNs without sacrificing their usability. We developed a technique, implemented in working software to enable privacy-preserving cohort discovery (PPCD) services in large distributed CRNs based on elliptic curve cryptography (ECC). This technique also incorporates a block indexing strategy to improve the performance (in terms of computational running time) of PPCD. We evaluated the PPCD service with three real cohort definitions: (1) elderly cervical cancer patients who underwent radical hysterectomy, (2) oropharyngeal and tongue cancer patients who underwent robotic transoral surgery, and (3) female breast cancer patients who underwent mastectomy) with varied query complexity. These definitions were tested in an encrypted database of 7.1 million records derived from the publically available Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS). We assessed the performance of the PPCD service in terms of (1) accuracy in cohort discovery, (2) computational running time, and (3) privacy afforded to the underlying records during PPCD. The empirical results indicate that the proposed PPCD can execute cohort discovery queries in a reasonable amount of time, with query runtime in the range of 165-262s for the 3 use cases, with zero compromise in accuracy. We further show that the search performance is practical because it supports a highly parallelized design for secure evaluation over encrypted records. Additionally, our security analysis shows that the proposed construction is resilient to standard adversaries. PPCD services can be designed for clinical research networks. The security construction presented in this work specifically achieves high privacy guarantees by preventing both threats originating from within and beyond the network. Copyright © 2016 Elsevier Inc. All rights reserved.

Discovering the invisible universe

NASA Astrophysics Data System (ADS)

Friedman, Herbert

1991-02-01

The history of astronomical observations outside the visible range is surveyed in a review for general readers. Consideration is given to Jansky's discovery of cosmic radio emission, the pioneering radio observers of the 1940s, the larger radio telescopes built since 1950, aperture synthesis and the Very Large Array, terrestrial and space VLBI networks, ground-based and satellite observations in the IR band, the discovery and early laboratory characterization of X-rays, and X-ray observations from sounding rockets and satellites. Extensive photographs, drawings, diagrams, and sample images are provided.

The case for open-source software in drug discovery.

PubMed

DeLano, Warren L

2005-02-01

Widespread adoption of open-source software for network infrastructure, web servers, code development, and operating systems leads one to ask how far it can go. Will "open source" spread broadly, or will it be restricted to niches frequented by hopeful hobbyists and midnight hackers? Here we identify reasons for the success of open-source software and predict how consumers in drug discovery will benefit from new open-source products that address their needs with increased flexibility and in ways complementary to proprietary options.

An eMERGE Clinical Center at Partners Personalized Medicine

PubMed Central

Smoller, Jordan W.; Karlson, Elizabeth W.; Green, Robert C.; Kathiresan, Sekar; MacArthur, Daniel G.; Talkowski, Michael E.; Murphy, Shawn N.; Weiss, Scott T.

2016-01-01

The integration of electronic medical records (EMRs) and genomic research has become a major component of efforts to advance personalized and precision medicine. The Electronic Medical Records and Genomics (eMERGE) network, initiated in 2007, is an NIH-funded consortium devoted to genomic discovery and implementation research by leveraging biorepositories linked to EMRs. In its most recent phase, eMERGE III, the network is focused on facilitating implementation of genomic medicine by detecting and disclosing rare pathogenic variants in clinically relevant genes. Partners Personalized Medicine (PPM) is a center dedicated to translating personalized medicine into clinical practice within Partners HealthCare. One component of the PPM is the Partners Healthcare Biobank, a biorepository comprising broadly consented DNA samples linked to the Partners longitudinal EMR. In 2015, PPM joined the eMERGE Phase III network. Here we describe the elements of the eMERGE clinical center at PPM, including plans for genomic discovery using EMR phenotypes, evaluation of rare variant penetrance and pleiotropy, and a novel randomized trial of the impact of returning genetic results to patients and clinicians. PMID:26805891

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

PubMed

Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang-Qun Sean

2018-03-30

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

An eMERGE Clinical Center at Partners Personalized Medicine.

PubMed

Smoller, Jordan W; Karlson, Elizabeth W; Green, Robert C; Kathiresan, Sekar; MacArthur, Daniel G; Talkowski, Michael E; Murphy, Shawn N; Weiss, Scott T

2016-01-20

The integration of electronic medical records (EMRs) and genomic research has become a major component of efforts to advance personalized and precision medicine. The Electronic Medical Records and Genomics (eMERGE) network, initiated in 2007, is an NIH-funded consortium devoted to genomic discovery and implementation research by leveraging biorepositories linked to EMRs. In its most recent phase, eMERGE III, the network is focused on facilitating implementation of genomic medicine by detecting and disclosing rare pathogenic variants in clinically relevant genes. Partners Personalized Medicine (PPM) is a center dedicated to translating personalized medicine into clinical practice within Partners HealthCare. One component of the PPM is the Partners Healthcare Biobank, a biorepository comprising broadly consented DNA samples linked to the Partners longitudinal EMR. In 2015, PPM joined the eMERGE Phase III network. Here we describe the elements of the eMERGE clinical center at PPM, including plans for genomic discovery using EMR phenotypes, evaluation of rare variant penetrance and pleiotropy, and a novel randomized trial of the impact of returning genetic results to patients and clinicians.

The Localized Discovery and Recovery for Query Packet Losses in Wireless Sensor Networks with Distributed Detector Clusters

PubMed Central

Teng, Rui; Leibnitz, Kenji; Miura, Ryu

2013-01-01

An essential application of wireless sensor networks is to successfully respond to user queries. Query packet losses occur in the query dissemination due to wireless communication problems such as interference, multipath fading, packet collisions, etc. The losses of query messages at sensor nodes result in the failure of sensor nodes reporting the requested data. Hence, the reliable and successful dissemination of query messages to sensor nodes is a non-trivial problem. The target of this paper is to enable highly successful query delivery to sensor nodes by localized and energy-efficient discovery, and recovery of query losses. We adopt local and collective cooperation among sensor nodes to increase the success rate of distributed discoveries and recoveries. To enable the scalability in the operations of discoveries and recoveries, we employ a distributed name resolution mechanism at each sensor node to allow sensor nodes to self-detect the correlated queries and query losses, and then efficiently locally respond to the query losses. We prove that the collective discovery of query losses has a high impact on the success of query dissemination and reveal that scalability can be achieved by using the proposed approach. We further study the novel features of the cooperation and competition in the collective recovery at PHY and MAC layers, and show that the appropriate number of detectors can achieve optimal successful recovery rate. We evaluate the proposed approach with both mathematical analyses and computer simulations. The proposed approach enables a high rate of successful delivery of query messages and it results in short route lengths to recover from query losses. The proposed approach is scalable and operates in a fully distributed manner. PMID:23748172

The center for causal discovery of biomedical knowledge from big data

PubMed Central

Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-01-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. PMID:26138794

Using directed information for influence discovery in interconnected dynamical systems

NASA Astrophysics Data System (ADS)

Rao, Arvind; Hero, Alfred O.; States, David J.; Engel, James Douglas

2008-08-01

Structure discovery in non-linear dynamical systems is an important and challenging problem that arises in various applications such as computational neuroscience, econometrics, and biological network discovery. Each of these systems have multiple interacting variables and the key problem is the inference of the underlying structure of the systems (which variables are connected to which others) based on the output observations (such as multiple time trajectories of the variables). Since such applications demand the inference of directed relationships among variables in these non-linear systems, current methods that have a linear assumption on structure or yield undirected variable dependencies are insufficient. Hence, in this work, we present a methodology for structure discovery using an information-theoretic metric called directed time information (DTI). Using both synthetic dynamical systems as well as true biological datasets (kidney development and T-cell data), we demonstrate the utility of DTI in such problems.

The Matchmaker Exchange: a platform for rare disease gene discovery.

PubMed

Philippakis, Anthony A; Azzariti, Danielle R; Beltran, Sergi; Brookes, Anthony J; Brownstein, Catherine A; Brudno, Michael; Brunner, Han G; Buske, Orion J; Carey, Knox; Doll, Cassie; Dumitriu, Sergiu; Dyke, Stephanie O M; den Dunnen, Johan T; Firth, Helen V; Gibbs, Richard A; Girdea, Marta; Gonzalez, Michael; Haendel, Melissa A; Hamosh, Ada; Holm, Ingrid A; Huang, Lijia; Hurles, Matthew E; Hutton, Ben; Krier, Joel B; Misyura, Andriy; Mungall, Christopher J; Paschall, Justin; Paten, Benedict; Robinson, Peter N; Schiettecatte, François; Sobreira, Nara L; Swaminathan, Ganesh J; Taschner, Peter E; Terry, Sharon F; Washington, Nicole L; Züchner, Stephan; Boycott, Kym M; Rehm, Heidi L

2015-10-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow. © 2015 WILEY PERIODICALS, INC.

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

PubMed

Jurca, Gabriela; Addam, Omar; Aksac, Alper; Gao, Shang; Özyer, Tansel; Demetrick, Douglas; Alhajj, Reda

2016-04-26

Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

PubMed Central

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi; Brookes, Anthony J.; Brownstein, Catherine A.; Brudno, Michael; Brunner, Han G.; Buske, Orion J.; Carey, Knox; Doll, Cassie; Dumitriu, Sergiu; Dyke, Stephanie O.M.; den Dunnen, Johan T.; Firth, Helen V.; Gibbs, Richard A.; Girdea, Marta; Gonzalez, Michael; Haendel, Melissa A.; Hamosh, Ada; Holm, Ingrid A.; Huang, Lijia; Hurles, Matthew E.; Hutton, Ben; Krier, Joel B.; Misyura, Andriy; Mungall, Christopher J.; Paschall, Justin; Paten, Benedict; Robinson, Peter N.; Schiettecatte, François; Sobreira, Nara L.; Swaminathan, Ganesh J.; Taschner, Peter E.; Terry, Sharon F.; Washington, Nicole L.; Züchner, Stephan; Boycott, Kym M.; Rehm, Heidi L.

2015-01-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for “the needle in a haystack” to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can “match” these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow. PMID:26295439

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

DOE PAGES

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi; ...

2015-09-17

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be amore » reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. In conclusion, three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.« less

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

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

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be amore » reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. In conclusion, three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.« less

What Neural Substrates Trigger the Adept Scientific Pattern Discovery by Biologists?

NASA Astrophysics Data System (ADS)

Lee, Jun-Ki; Kwon, Yong-Ju

2011-04-01

This study investigated the neural correlates of experts and novices during biological object pattern detection using an fMRI approach in order to reveal the neural correlates of a biologist's superior pattern discovery ability. Sixteen healthy male participants (8 biologists and 8 non-biologists) volunteered for the study. Participants were shown fifteen series of organism pictures and asked to detect patterns amid stimulus pictures. Primary findings showed significant activations in the right middle temporal gyrus and inferior parietal lobule amongst participants in the biologist (expert) group. Interestingly, the left superior temporal gyrus was activated in participants from the non-biologist (novice) group. These results suggested that superior pattern discovery ability could be related to a functional facilitation of the parieto-temporal network, which is particularly driven by the right middle temporal gyrus and inferior parietal lobule in addition to the recruitment of additional brain regions. Furthermore, the functional facilitation of the network might actually pertain to high coherent processing skills and visual working memory capacity. Hence, study results suggested that adept scientific thinking ability can be detected by neuronal substrates, which may be used as criteria for developing and evaluating a brain-based science curriculum and test instrument.

Geographically distributed environmental sensor system

DOEpatents

French, Patrick; Veatch, Brad; O'Connor, Mike

2006-10-03

The present invention is directed to a sensor network that includes a number of sensor units and a base unit. The base station operates in a network discovery mode (in which network topology information is collected) in a data polling mode (in which sensed information is collected from selected sensory units). Each of the sensor units can include a number of features, including an anemometer, a rain gauge, a compass, a GPS receiver, a barometric pressure sensor, an air temperature sensor, a humidity sensor, a level, and a radiant temperature sensor.

Multi-target drugs: the trend of drug research and development.

PubMed

Lu, Jin-Jian; Pan, Wei; Hu, Yuan-Jia; Wang, Yi-Tao

2012-01-01

Summarizing the status of drugs in the market and examining the trend of drug research and development is important in drug discovery. In this study, we compared the drug targets and the market sales of the new molecular entities approved by the U.S. Food and Drug Administration from January 2000 to December 2009. Two networks, namely, the target-target and drug-drug networks, have been set up using the network analysis tools. The multi-target drugs have much more potential, as shown by the network visualization and the market trends. We discussed the possible reasons and proposed the rational strategies for drug research and development in the future.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

PubMed

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

System-Level Insights into the Cellular Interactome of a Non-Model Organism: Inferring, Modelling and Analysing Functional Gene Network of Soybean (Glycine max)

PubMed Central

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN. PMID:25423109

Army Technology

DTIC Science & Technology

2015-02-01

are conducting research in areas such as networked Soldier helmet sensors . For mobility, we have a large effort in establishing Degraded Visual...will allow Soldiers to access information that they don’t have a sensor for, but because they are on a network or shared architecture, they will be...something we control. No one seriously wakes up saying, “Today, I will discover something.” However, we can increase the likelihood of discovery through

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource.

PubMed

Shim, Hongseok; Kim, Ji Hyun; Kim, Chan Yeong; Hwang, Sohyun; Kim, Hyojin; Yang, Sunmo; Lee, Ji Eun; Lee, Insuk

2016-11-16

Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish (Danio rerio), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

First EURONEAR NEA discoveries from La Palma using the INT

NASA Astrophysics Data System (ADS)

Vaduvescu, O.; Hudin, L.; Tudor, V.; Char, F.; Mocnik, T.; Kwiatkowski, T.; de Leon, J.; Cabrera-Lavers, A.; Alvarez, C.; Popescu, M.; Cornea, R.; Díaz Alfaro, M.; Ordonez-Etxeberria, I.; Kamiński, K.; Stecklum, B.; Verdes-Montenegro, L.; Sota, A.; Casanova, V.; Martin Ruiz, S.; Duffard, R.; Zamora, O.; Gomez-Jimenez, M.; Micheli, M.; Koschny, D.; Busch, M.; Knofel, A.; Schwab, E.; Negueruela, I.; Dhillon, V.; Sahman, D.; Marchant, J.; Génova-Santos, R.; Rubiño-Martín, J. A.; Riddick, F. C.; Mendez, J.; Lopez-Martinez, F.; Gänsicke, B. T.; Hollands, M.; Kong, A. K. H.; Jin, R.; Hidalgo, S.; Murabito, S.; Font, J.; Bereciartua, A.; Abe, L.; Bendjoya, P.; Rivet, J. P.; Vernet, D.; Mihalea, S.; Inceu, V.; Gajdos, S.; Veres, P.; Serra-Ricart, M.; Abreu Rodriguez, D.

2015-05-01

Since 2006, the European Near Earth Asteroids Research (EURONEAR) project has been contributing to the research of near-Earth asteroids (NEAs) within a European network. One of the main aims is the amelioration of the orbits of NEAs, and starting in 2014 February we focus on the recovery of one-opposition NEAs using the Isaac Newton Telescope (INT) in La Palma in override mode. Part of this NEA recovery project, since 2014 June EURONEAR serendipitously started to discover and secure the first NEAs from La Palma and using the INT, thanks to the teamwork including amateurs and students who promptly reduce the data, report discoveries and secure new objects recovered with the INT and few other telescopes from the EURONEAR network. Five NEAs were discovered with the INT, including 2014 LU14, 2014 NL52 (one very fast rotator), 2014 OL339 (the fourth known Earth quasi-satellite), 2014 SG143 (a quite large NEA), and 2014 VP. Another very fast moving NEA was discovered but was unfortunately lost due to lack of follow-up time. Additionally, another 14 NEA candidates were identified based on two models, all being rapidly followed-up using the INT and another 11 telescopes within the EURONEAR network. They include one object discovered by Pan-STARRS, two Mars crossers, two Hungarias, one Jupiter trojan, and other few inner main belt asteroids (MBAs). Using the INT and Sierra Nevada 1.5 m for photometry, then the Gran Telescopio de Canarias for spectroscopy, we derived the very rapid rotation of 2014 NL52, then its albedo, magnitude, size, and its spectral class. Based on the total sky coverage in dark conditions, we evaluate the actual survey discovery rate using 2-m class telescopes. One NEA is possible to be discovered randomly within minimum 2.8 deg2 and maximum 5.5 deg2. These findings update our past statistics, being based on double sky coverage and taking into account the recent increase in discovery.

Efficient discovery of overlapping communities in massive networks

PubMed Central

Gopalan, Prem K.; Blei, David M.

2013-01-01

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks. PMID:23950224

The future of discovery chemistry: quo vadis? Academic to industrial--the maturation of medicinal chemistry to chemical biology.

PubMed

Hoffmann, Torsten; Bishop, Cheryl

2010-04-01

At Roche, we set out to think about the future role of medicinal chemistry in drug discovery in a project involving both Roche internal stakeholders and external experts in drug discovery chemistry. To derive a coherent strategy, selected scientists were asked to take extreme positions and to derive two orthogonal strategic options: chemistry as the traditional mainstream science and chemistry as the central entrepreneurial science. We believe today's role of medicinal chemistry in industry has remained too narrow. To provide the innovation that industry requires, medicinal chemistry must play its part and diversify at pace with our increasing understanding of chemical biology and network pharmacology. 2010 Elsevier Ltd. All rights reserved.

Mental Models of Invisible Logical Networks

NASA Technical Reports Server (NTRS)

Sanderson, P.

1984-01-01

Subjects were required to discover the structure of a logical network whose links were invisible. Network structure had to be inferred from the behavior of the components after a failure. It was hypothesized that since such failure diagnosis tasks often draw on spatial processes, a good deal of spatial complexity in the network should affect network discovery. Results show that the ability to discover the linkages in the network is directly related to the spatial complexity of the pathway described by the linkages. This effect was generally independent of the amount of evidence available to subjects about the existence of the link. These results raise the question of whether inferences about spatially complex pathways were simply not made, or whether they were made but not retained because of a high load on memory resources.

Community structure in networks

NASA Astrophysics Data System (ADS)

Newman, Mark

2004-03-01

Many networked systems, including physical, biological, social, and technological networks, appear to contain ``communities'' -- groups of nodes within which connections are dense, but between which they are sparser. The ability to find such communities in an automated fashion could be of considerable use. Communities in a web graph for instance might correspond to sets of web sites dealing with related topics, while communities in a biochemical network or an electronic circuit might correspond to functional units of some kind. We present a number of new methods for community discovery, including methods based on ``betweenness'' measures and methods based on modularity optimization. We also give examples of applications of these methods to both computer-generated and real-world network data, and show how our techniques can be used to shed light on the sometimes dauntingly complex structure of networked systems.

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

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

Weighill, Deborah; Jones, Piet; Shah, Manesh

Biological organisms are complex systems that are composed of functional networks of interacting molecules and macro-molecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plant's sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes usemore » of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to target functions. This new scoring system was applied to quantify the lines of evidence linking genes to lignin-related genes and phenotypes across the network layers, and allowed for the generation of new hypotheses surrounding potential new candidate genes involved in lignin biosynthesis in P. trichocarpa, including various AGAMOUS-LIKE genes. Lastly, the resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation, and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for complex phenotypes, such as recalcitrance.« less

Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics.

PubMed

Penrod, Nadia M; Moore, Jason H

2014-02-05

The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. We use this approach to prioritize genes as drug target candidates in a set of ER⁺ breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER⁺ breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use.

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

DOE PAGES

Weighill, Deborah; Jones, Piet; Shah, Manesh; ...

2018-05-11

Biological organisms are complex systems that are composed of functional networks of interacting molecules and macro-molecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plant's sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes usemore » of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to target functions. This new scoring system was applied to quantify the lines of evidence linking genes to lignin-related genes and phenotypes across the network layers, and allowed for the generation of new hypotheses surrounding potential new candidate genes involved in lignin biosynthesis in P. trichocarpa, including various AGAMOUS-LIKE genes. Lastly, the resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation, and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for complex phenotypes, such as recalcitrance.« less

Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics

PubMed Central

2014-01-01

Background The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. Results We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Conclusions Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use. PMID:24495353

TCGA researchers identify 4 subtypes of stomach cancer

Cancer.gov

Stomach cancers fall into four distinct molecular subtypes, researchers with The Cancer Genome Atlas (TCGA) Network have found. Scientists report that this discovery could change how researchers think about developing treatments for stomach cancer, also c

Knowledge Retrieval Solutions.

ERIC Educational Resources Information Center

Khan, Kamran

1998-01-01

Excalibur RetrievalWare offers true knowledge retrieval solutions. Its fundamental technologies, Adaptive Pattern Recognition Processing and Semantic Networks, have capabilities for knowledge discovery and knowledge management of full-text, structured and visual information. The software delivers a combination of accuracy, extensibility,…

Genome Neighborhood Network Reveals Insights into Enediyne Biosynthesis and Facilitates Prediction and Prioritization for Discovery

PubMed Central

Rudolf, Jeffrey D.; Yan, Xiaohui; Shen, Ben

2015-01-01

The enediynes are one of the most fascinating families of bacterial natural products given their unprecedented molecular architecture and extraordinary cytotoxicity. Enediynes are rare with only 11 structurally characterized members and four additional members isolated in their cycloaromatized form. Recent advances in DNA sequencing have resulted in an explosion of microbial genomes. A virtual survey of the GenBank and JGI genome databases revealed 87 enediyne biosynthetic gene clusters from 78 bacteria strains, implying enediynes are more common than previously thought. Here we report the construction and analysis of an enediyne genome neighborhood network (GNN) as a high-throughput approach to analyze secondary metabolite gene clusters. Analysis of the enediyne GNN facilitated rapid gene cluster annotation, revealed genetic trends in enediyne biosynthetic gene clusters resulting in a simple prediction scheme to determine 9- vs 10-membered enediyne gene clusters, and supported a genomic-based strain prioritization method for enediyne discovery. PMID:26318027

Oceans of Data : the Australian Ocean Data Network

NASA Astrophysics Data System (ADS)

Proctor, R.; Blain, P.; Mancini, S.

2012-04-01

The Australian Integrated Marine Observing System (IMOS, www.imos.org.au) is a research infrastructure project to establish an enduring marine observing system for Australian oceanic waters and shelf seas (in total, 4% of the world's oceans). Marine data and information are the main products and data management is therefore a central element to the project's success. A single integrative framework for data and information management has been developed which allows discovery and access of the data by scientists, managers and the public, based on standards and interoperability. All data is freely available. This information infrastructure has been further developed to form the Australian Ocean Data Network (AODN, www.aodn.org.au) which is rapidly becoming the 'one-stop-shop' for marine data in Australia. In response to requests from users, new features have recently been added to data discovery, visualization, and data access which move the AODN closer towards providing full integration of multi-disciplinary data.

Generalised power graph compression reveals dominant relationship patterns in complex networks

PubMed Central

Ahnert, Sebastian E.

2014-01-01

We introduce a framework for the discovery of dominant relationship patterns in complex networks, by compressing the networks into power graphs with overlapping power nodes. When paired with enrichment analysis of node classification terms, the most compressible sets of edges provide a highly informative sketch of the dominant relationship patterns that define the network. In addition, this procedure also gives rise to a novel, link-based definition of overlapping node communities in which nodes are defined by their relationships with sets of other nodes, rather than through connections within the community. We show that this completely general approach can be applied to undirected, directed, and bipartite networks, yielding valuable insights into the large-scale structure of real-world networks, including social networks and food webs. Our approach therefore provides a novel way in which network architecture can be studied, defined and classified. PMID:24663099

SOUNET: Self-Organized Underwater Wireless Sensor Network.

PubMed

Kim, Hee-Won; Cho, Ho-Shin

2017-02-02

In this paper, we propose an underwater wireless sensor network (UWSN) named SOUNET where sensor nodes form and maintain a tree-topological network for data gathering in a self-organized manner. After network topology discovery via packet flooding, the sensor nodes consistently update their parent node to ensure the best connectivity by referring to the timevarying neighbor tables. Such a persistent and self-adaptive method leads to high network connectivity without any centralized control, even when sensor nodes are added or unexpectedly lost. Furthermore, malfunctions that frequently happen in self-organized networks such as node isolation and closed loop are resolved in a simple way. Simulation results show that SOUNET outperforms other conventional schemes in terms of network connectivity, packet delivery ratio (PDR), and energy consumption throughout the network. In addition, we performed an experiment at the Gyeongcheon Lake in Korea using commercial underwater modems to verify that SOUNET works well in a real environment.

SOUNET: Self-Organized Underwater Wireless Sensor Network

PubMed Central

Kim, Hee-won; Cho, Ho-Shin

2017-01-01

In this paper, we propose an underwater wireless sensor network (UWSN) named SOUNET where sensor nodes form and maintain a tree-topological network for data gathering in a self-organized manner. After network topology discovery via packet flooding, the sensor nodes consistently update their parent node to ensure the best connectivity by referring to the time-varying neighbor tables. Such a persistent and self-adaptive method leads to high network connectivity without any centralized control, even when sensor nodes are added or unexpectedly lost. Furthermore, malfunctions that frequently happen in self-organized networks such as node isolation and closed loop are resolved in a simple way. Simulation results show that SOUNET outperforms other conventional schemes in terms of network connectivity, packet delivery ratio (PDR), and energy consumption throughout the network. In addition, we performed an experiment at the Gyeongcheon Lake in Korea using commercial underwater modems to verify that SOUNET works well in a real environment. PMID:28157164

Consistent visualizations of changing knowledge

PubMed Central

Tipney, Hannah J.; Schuyler, Ronald P.; Hunter, Lawrence

2009-01-01

Networks are increasingly used in biology to represent complex data in uncomplicated symbolic form. However, as biological knowledge is continually evolving, so must those networks representing this knowledge. Capturing and presenting this type of knowledge change over time is particularly challenging due to the intimate manner in which researchers customize those networks they come into contact with. The effective visualization of this knowledge is important as it creates insight into complex systems and stimulates hypothesis generation and biological discovery. Here we highlight how the retention of user customizations, and the collection and visualization of knowledge associated provenance supports effective and productive network exploration. We also present an extension of the Hanalyzer system, ReOrient, which supports network exploration and analysis in the presence of knowledge change. PMID:21347184

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

PubMed

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

PubMed Central

Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-01-01

Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking. PMID:21445339

Design Document for the Technology Demonstration of the Joint Network Defence and Management System (JNDMS) Project

DTIC Science & Technology

2012-02-06

Event Interface Custom ASCII JSS Client Y (Spectrum) 3.2 8 IT Infrastructure Performance Data/Vulnerability Assessment eHealth , Spectrum NSM...monitoring of infrastructure servers.) The Concord product line. Concord products ( eHealth and Spectrum) can provide both real-time and historical...Network and Systems Management (NSM) • Unicenter Asset Management • Spectrum • eHealth • Centennial Discovery Table 12 summarizes the the role of

Attractor Signaling Models for Discovery of Combinatorial Therapies

DTIC Science & Technology

2014-11-01

acquired!drug!resistance!still!makes!the!5-year!survival!rate!for!this! disease ! less!than!15%.!Over!the!years,!many!specific!mechanisms!associated!with!drug...Moreover, it has been suggested that a biological system in a chronic or therapy- resistant disease state can be seen as a network that has become...therapeutic methods for complex diseases such as cancer. Even if our knowledge of biological networks is incomplete, rapid progress is currently being

Globalization and WMD Proliferation Networks: The Policy Landscape

DTIC Science & Technology

2006-07-01

scientific advances, it moved to shut down this network by classifying all information relating to the Manhattan Project . This security action had only...As with the U.S. efforts during World War II to deny access to Manhattan Project Report Documentation Page Form ApprovedOMB No. 0704-0188 Public...the scientific discoveries paving the way for the atomic bomb, as well as of the U.S. government’s subsequent classification of Manhattan Project information

Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology.

PubMed

Bianconi, Fortunato; Baldelli, Elisa; Ludovini, Vienna; Luovini, Vienna; Petricoin, Emanuel F; Crinò, Lucio; Valigi, Paolo

2015-10-19

The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer. We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm based on moment independent analysis of input/output uncertainty. The framework utilizes novel computational methods which enable evaluating the model fragility with respect to quantitative performance measures and parameters such as reaction rate constants and initial conditions. The algorithm generates a small subset of parameters that can be used to act on complex networks and to obtain the desired behaviors. We have applied the proposed framework to the EGFR-IGF1R signal transduction network, a crucial pathway in lung cancer, as an example of Cancer Systems Biology application in drug discovery. Furthermore, we have tested our framework on a pulse generator network as an example of Synthetic Biology application, thus proving the suitability of our methodology to the characterization of the input/output synthetic circuits. The achieved results are of immediate practical application in computational biology, and while we demonstrate their use in two specific examples, they can in fact be used to study a wider class of biological systems.

Detecting dark-matter waves with a network of precision-measurement tools

NASA Astrophysics Data System (ADS)

Derevianko, Andrei

2018-04-01

Virialized ultralight fields (VULFs) are viable cold dark-matter candidates and include scalar and pseudoscalar bosonic fields, such as axions and dilatons. Direct searches for VULFs rely on low-energy precision-measurement tools. While previous proposals have focused on detecting coherent oscillations of the VULF signals at the VULF Compton frequencies for individual devices, here I consider a network of such devices. Virialized ultralight fields are essentially dark-matter waves and as such they carry both temporal and spatial phase information. Thereby, the discovery reach can be improved by using networks of precision-measurement tools. To formalize this idea, I derive a spatiotemporal two-point correlation function for the ultralight dark-matter fields in the framework of the standard halo model. Due to VULFs being Gaussian random fields, the derived two-point correlation function fully determines N -point correlation functions. For a network of ND devices within the coherence length of the field, the sensitivity compared to a single device can be improved by a factor of √{ND}. Further, I derive a VULF dark-matter signal profile for an individual device. The resulting line shape is strongly asymmetric due to the parabolic dispersion relation for massive nonrelativistic bosons. I discuss the aliasing effect that extends the discovery reach to VULF frequencies higher than the experimental sampling rate. I present sensitivity estimates and develop a stochastic field signal-to-noise ratio statistic. Finally, I consider an application of the formalism developed to atomic clocks and their networks.

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

PubMed Central

Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S.; Niu, Xufeng; Zhang, Jinfeng

2011-01-01

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. PMID:21738677

An Analysis of Automated Solutions for the Certification and Accreditation of Navy Medicine Information Assets

DTIC Science & Technology

2005-09-01

discovery of network security threats and vulnerabilities will be done by doing penetration testing during the C&A process. This can be done on a...2.1.1; Appendix E, J COBR -1 Protection of Backup and Restoration Assets Availability 1.3.1; 2.1.3; 2.1.7; 3.1; 4.3; Appendix J, M CODB-2 Data... discovery , inventory, scanning and loading of C&A information in its central database, (2) automatic generation of the SRTM , (3) automatic generation

Collaborative Research to Advance Precision Medicine in the Post-Genomic World | Office of Cancer Genomics

Cancer.gov

My name is Subhashini Jagu, and I am the Scientific Program Manager for the Cancer Target Discovery and Development (CTD2) Network at the Office of Cancer Genomics (OCG). In my new role, I help CTD2 work toward its mission, which is to develop new scientific approaches to accelerate the translation of genomic discoveries into new treatments. Collaborative efforts that bring together a variety of expertise and infrastructure are needed to understand and successfully treat cancer, a highly complex disease.

Researchers use Modified CRISPR Systems to Modulate Gene Expression on a Genomic Scale

Cancer.gov

Cancer Target Discovery and Development Network (CTD2) researchers at the University of California, San Francisco, developed a CRISPR system that can regulate both gene repression and activation with fewer off-target effects.

Feature Discovery by Competitive Learning.

ERIC Educational Resources Information Center

Rumelhart, David E.; Zipser, David

1985-01-01

Reports results of studies with an unsupervised learning paradigm called competitive learning which is examined using computer simulation and formal analysis. When competitive learning is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished. (Author)

Exploiting Recurring Structure in a Semantic Network

NASA Technical Reports Server (NTRS)

Wolfe, Shawn R.; Keller, Richard M.

2004-01-01

With the growing popularity of the Semantic Web, an increasing amount of information is becoming available in machine interpretable, semantically structured networks. Within these semantic networks are recurring structures that could be mined by existing or novel knowledge discovery methods. The mining of these semantic structures represents an interesting area that focuses on mining both for and from the Semantic Web, with surprising applicability to problems confronting the developers of Semantic Web applications. In this paper, we present representative examples of recurring structures and show how these structures could be used to increase the utility of a semantic repository deployed at NASA.

An Unprecedented Blue Chromophore Found in Nature using a "Chemistry First" and Molecular Networking Approach: Discovery of Dactylocyanines A-H.

PubMed

Bonneau, Natacha; Chen, Guanming; Lachkar, David; Boufridi, Asmaa; Gallard, Jean-François; Retailleau, Pascal; Petek, Sylvain; Debitus, Cécile; Evanno, Laurent; Beniddir, Mehdi A; Poupon, Erwan

2017-10-17

Guided by a "chemistry first" approach using molecular networking, eight new bright-blue colored natural compounds, namely dactylocyanines A-H (3-10), were isolated from the Polynesian marine sponge Dactylospongia metachromia. Starting from ilimaquinone (1), an hemisynthetic phishing probe (2) was prepared for annotating and matching structurally related natural substances in D. metachromia crude extract network. This strategy allowed characterizing for the first time in Nature the blue zwitterionic quinonoid chromophore. The solvatochromic properties of the latter are reported. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Open Access High Throughput Drug Discovery in the Public Domain: A Mount Everest in the Making

PubMed Central

Roy, Anuradha; McDonald, Peter R.; Sittampalam, Sitta; Chaguturu, Rathnam

2013-01-01

High throughput screening (HTS) facilitates screening large numbers of compounds against a biochemical target of interest using validated biological or biophysical assays. In recent years, a significant number of drugs in clinical trails originated from HTS campaigns, validating HTS as a bona fide mechanism for hit finding. In the current drug discovery landscape, the pharmaceutical industry is embracing open innovation strategies with academia to maximize their research capabilities and to feed their drug discovery pipeline. The goals of academic research have therefore expanded from target identification and validation to probe discovery, chemical genomics, and compound library screening. This trend is reflected in the emergence of HTS centers in the public domain over the past decade, ranging in size from modestly equipped academic screening centers to well endowed Molecular Libraries Probe Centers Network (MLPCN) centers funded by the NIH Roadmap initiative. These centers facilitate a comprehensive approach to probe discovery in academia and utilize both classical and cutting-edge assay technologies for executing primary and secondary screening campaigns. The various facets of academic HTS centers as well as their implications on technology transfer and drug discovery are discussed, and a roadmap for successful drug discovery in the public domain is presented. New lead discovery against therapeutic targets, especially those involving the rare and neglected diseases, is indeed a Mount Everestonian size task, and requires diligent implementation of pharmaceutical industry’s best practices for a successful outcome. PMID:20809896

Direct2Experts: a pilot national network to demonstrate interoperability among research-networking platforms.

PubMed

Weber, Griffin M; Barnett, William; Conlon, Mike; Eichmann, David; Kibbe, Warren; Falk-Krzesinski, Holly; Halaas, Michael; Johnson, Layne; Meeks, Eric; Mitchell, Donald; Schleyer, Titus; Stallings, Sarah; Warden, Michael; Kahlon, Maninder

2011-12-01

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the network's functionality and focus on the end users.

A hybrid network-based method for the detection of disease-related genes

NASA Astrophysics Data System (ADS)

Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

2018-02-01

Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

Network Analysis: Applications for the Developing Brain

PubMed Central

Chu-Shore, Catherine J.; Kramer, Mark A.; Bianchi, Matt T.; Caviness, Verne S.; Cash, Sydney S.

2011-01-01

Development of the human brain follows a complex trajectory of age-specific anatomical and physiological changes. The application of network analysis provides an illuminating perspective on the dynamic interregional and global properties of this intricate and complex system. Here, we provide a critical synopsis of methods of network analysis with a focus on developing brain networks. After discussing basic concepts and approaches to network analysis, we explore the primary events of anatomical cortical development from gestation through adolescence. Upon this framework, we describe early work revealing the evolution of age-specific functional brain networks in normal neurodevelopment. Finally, we review how these relationships can be altered in disease and perhaps even rectified with treatment. While this method of description and inquiry remains in early form, there is already substantial evidence that the application of network models and analysis to understanding normal and abnormal human neural development holds tremendous promise for future discovery. PMID:21303762

Percolation mechanism drives actin gels to the critically connected state

NASA Astrophysics Data System (ADS)

Lee, Chiu Fan; Pruessner, Gunnar

2016-05-01

Cell motility and tissue morphogenesis depend crucially on the dynamic remodeling of actomyosin networks. An actomyosin network consists of an actin polymer network connected by cross-linker proteins and motor protein myosins that generate internal stresses on the network. A recent discovery shows that for a range of experimental parameters, actomyosin networks contract to clusters with a power-law size distribution [J. Alvarado, Nat. Phys. 9, 591 (2013), 10.1038/nphys2715]. Here, we argue that actomyosin networks can exhibit a robust critical signature without fine-tuning because the dynamics of the system can be mapped onto a modified version of percolation with trapping (PT), which is known to show critical behavior belonging to the static percolation universality class without the need for fine-tuning of a control parameter. We further employ our PT model to generate experimentally testable predictions.

Systems biology-embedded target validation: improving efficacy in drug discovery.

PubMed

Vandamme, Drieke; Minke, Benedikt A; Fitzmaurice, William; Kholodenko, Boris N; Kolch, Walter

2014-01-01

The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment. © 2013 Wiley Periodicals, Inc.

Cafe Variome: general-purpose software for making genotype-phenotype data discoverable in restricted or open access contexts.

PubMed

Lancaster, Owen; Beck, Tim; Atlan, David; Swertz, Morris; Thangavelu, Dhiwagaran; Veal, Colin; Dalgleish, Raymond; Brookes, Anthony J

2015-10-01

Biomedical data sharing is desirable, but problematic. Data "discovery" approaches-which establish the existence rather than the substance of data-precisely connect data owners with data seekers, and thereby promote data sharing. Cafe Variome (http://www.cafevariome.org) was therefore designed to provide a general-purpose, Web-based, data discovery tool that can be quickly installed by any genotype-phenotype data owner, or network of data owners, to make safe or sensitive content appropriately discoverable. Data fields or content of any type can be accommodated, from simple ID and label fields through to extensive genotype and phenotype details based on ontologies. The system provides a "shop window" in front of data, with main interfaces being a simple search box and a powerful "query-builder" that enable very elaborate queries to be formulated. After a successful search, counts of records are reported grouped by "openAccess" (data may be directly accessed), "linkedAccess" (a source link is provided), and "restrictedAccess" (facilitated data requests and subsequent provision of approved records). An administrator interface provides a wide range of options for system configuration, enabling highly customized single-site or federated networks to be established. Current uses include rare disease data discovery, patient matchmaking, and a Beacon Web service. © 2015 WILEY PERIODICALS, INC.

Heterogeniety and Heterarchy: How far can network analyses in Earth and space sciences?

NASA Astrophysics Data System (ADS)

Prabhu, A.; Fox, P. A.; Eleish, A.; Li, C.; Pan, F.; Zhong, H.

2017-12-01

The vast majority of explorations of Earth systems are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or conceptual/ semantic, level. Recent successes in the application of complex network theory and algorithms to minerology, fossils and proteins over billions of years of Earth's history, raise expectations that more general graph-based approaches offer the opportunity for new discoveries = needles instead of haystacks. In the past 10 years in the natural sciences there has substantial progress in providing both specialists and non-specialists the ability to describe in machine readable form, geophysical quantities and relations among them in meaningful and natural ways, effectively breaking the prior syntax barrier. The corresponding open-world semantics and reasoning provide higher-level interconnections. That is, semantics provided around the data structures, using open-source tools, allow for discovery at the knowledge level. This presentation will cover the fundamentals of data-rich network analyses for geosciences, provide illustrative examples in mineral evolution and offer future paths for consideration.

Transforming Big Data into cancer-relevant insight: An initial, multi-tier approach to assess reproducibility and relevance* | Office of Cancer Genomics

Cancer.gov

The Cancer Target Discovery and Development (CTD^2) Network was established to accelerate the transformation of "Big Data" into novel pharmacological targets, lead compounds, and biomarkers for rapid translation into improved patient outcomes. It rapidly became clear in this collaborative network that a key central issue was to define what constitutes sufficient computational or experimental evidence to support a biologically or clinically relevant finding.

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

DTIC Science & Technology

2015-07-01

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data Guy Van den Broeck∗ and Karthika Mohan∗ and Arthur Choi and Adnan ...notwithstanding any other provision of law , no person shall be subject to a penalty for failing to comply with a collection of information if it does...Wasserman, L. (2011). All of Statistics. Springer Science & Business Media. Yaramakala, S., & Margaritis, D. (2005). Speculative markov blanket discovery for optimal feature selection. In Proceedings of ICDM.

Interactions Between Structure and Processing that Control Moisture Uptake in High-Performance Polycyanurates (Briefing Charts)

DTIC Science & Technology

2015-03-24

distribution is unlimited.  . Interactions Between Structure and Processing that Control Moisture Uptake in High-Performance Polycyanurates Presenter: Dr...Edwards AFB, CA 4 California State University, Long Beach, CA 90840 2 Outline: Basic Studies of Moisture Uptake in Cyanate Ester Networks • Background...Motivation • SOTA Theories of Moisture Uptake in Thermosetting Networks • New Tools and New Discoveries • Unresolved Issues and Ways to Address Them

Cyber Defence in the Armed Forces of the Czech Republic

DTIC Science & Technology

2010-11-01

undesirable action backward discovery. This solution is based on special tools using NetFlow protocol. Active network elements or specialized hardware...probes attached to the backbone network using a tap can be the sources of NetFlow data. The principal advantage of NetFlow protocol is the fact that it...provides primary data in the open form, which can be easily utilized in the subsequent operations. The FlowMon Probe 4000 is mostly used NetFlow

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.

PubMed

Huang, Xin; Zeng, Jun; Zhou, Lina; Hu, Chunxiu; Yin, Peiyuan; Lin, Xiaohui

2016-08-31

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma

NASA Astrophysics Data System (ADS)

Huang, Xin; Zeng, Jun; Zhou, Lina; Hu, Chunxiu; Yin, Peiyuan; Lin, Xiaohui

2016-08-01

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence-Function Space and Genome Context to Discover Novel Functions.

PubMed

Gerlt, John A

2017-08-22

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of "genomic enzymology" web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence-function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence–Function Space and Genome Context to Discover Novel Functions

PubMed Central

2017-01-01

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of “genomic enzymology” web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence–function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems. PMID:28826221

Semantic MEDLINE for Discovery Browsing: Using Semantic Predications and the Literature-Based Discovery Paradigm to Elucidate a Mechanism for the Obesity Paradox

PubMed Central

Cairelli, Michael J.; Miller, Christopher M.; Fiszman, Marcelo; Workman, T. Elizabeth; Rindflesch, Thomas C.

2013-01-01

Applying the principles of literature-based discovery (LBD), we elucidate the paradox that obesity is beneficial in critical care despite contributing to disease generally. Our approach enhances a previous extension to LBD, called “discovery browsing,” and is implemented using Semantic MEDLINE, which summarizes the results of a PubMed search into an interactive graph of semantic predications. The methodology allows a user to construct argumentation underpinning an answer to a biomedical question by engaging the user in an iterative process between system output and user knowledge. Components of the Semantic MEDLINE output graph identified as “interesting” by the user both contribute to subsequent searches and are constructed into a logical chain of relationships constituting an explanatory network in answer to the initial question. Based on this methodology we suggest that phthalates leached from plastic in critical care interventions activate PPAR gamma, which is anti-inflammatory and abundant in obese patients. PMID:24551329

What Does Galileo's Discovery of Jupiter's Moons Tell Us About the Process of Scientific Discovery?

NASA Astrophysics Data System (ADS)

Lawson, Anton E.

In 1610, Galileo Galilei discovered Jupiter''smoons with the aid of a new morepowerful telescope of his invention. Analysisof his report reveals that his discoveryinvolved the use of at least three cycles ofhypothetico-deductive reasoning. Galileofirst used hypothetico-deductive reasoning to generateand reject a fixed star hypothesis.He then generated and rejected an ad hocastronomers-made-a-mistake hypothesis.Finally, he generated, tested, and accepted a moonhypothesis. Galileo''s reasoningis modeled in terms of Piaget''s equilibration theory,Grossberg''s theory of neurologicalactivity, a neural network model proposed by Levine &Prueitt, and another proposedby Kosslyn & Koenig. Given that hypothetico-deductivereasoning has played a rolein other important scientific discoveries, thequestion is asked whether it plays a rolein all important scientific discoveries. In otherwords, is hypothetico-deductive reasoningthe essence of the scientific method? Possiblealternative scientific methods, such asBaconian induction and combinatorial analysis,are explored and rejected as viablealternatives. Educational implications of thishypothetico-deductive view of scienceare discussed.

Bioinformatics in protein kinases regulatory network and drug discovery.

PubMed

Chen, Qingfeng; Luo, Haiqiong; Zhang, Chengqi; Chen, Yi-Ping Phoebe

2015-04-01

Protein kinases have been implicated in a number of diseases, where kinases participate many aspects that control cell growth, movement and death. The deregulated kinase activities and the knowledge of these disorders are of great clinical interest of drug discovery. The most critical issue is the development of safe and efficient disease diagnosis and treatment for less cost and in less time. It is critical to develop innovative approaches that aim at the root cause of a disease, not just its symptoms. Bioinformatics including genetic, genomic, mathematics and computational technologies, has become the most promising option for effective drug discovery, and has showed its potential in early stage of drug-target identification and target validation. It is essential that these aspects are understood and integrated into new methods used in drug discovery for diseases arisen from deregulated kinase activity. This article reviews bioinformatics techniques for protein kinase data management and analysis, kinase pathways and drug targets and describes their potential application in pharma ceutical industry. Copyright © 2015 Elsevier Inc. All rights reserved.

Application of Combination High-Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product-Based Combination Drugs.

PubMed

Isgut, Monica; Rao, Mukkavilli; Yang, Chunhua; Subrahmanyam, Vangala; Rida, Padmashree C G; Aneja, Ritu

2018-03-01

Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery. Natural products consist of a multitude of constituents that can act on a variety of targets in the body to induce pharmacodynamic responses that may together culminate in an additive or synergistic therapeutic effect. Although natural products cannot be patented, they can be used as starting points in the discovery of potent combination therapeutics. The optimal mix of bioactive ingredients in natural products can be determined via phenotypic screening. The targets and molecular mechanisms of action of these active ingredients can then be determined using chemical proteomics, and by implementing a reverse pharmacokinetics approach. This review article provides evidence supporting the potential benefits of natural product-based combination drugs, and summarizes drug discovery methods that can be applied to this class of drugs. © 2017 Wiley Periodicals, Inc.

Context-sensitive network-based disease genetics prediction and its implications in drug discovery.

PubMed

Chen, Yang; Xu, Rong

2017-04-01

Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach ( p

InFlo: a novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer.

PubMed

Dimitrova, N; Nagaraj, A B; Razi, A; Singh, S; Kamalakaran, S; Banerjee, N; Joseph, P; Mankovich, A; Mittal, P; DiFeo, A; Varadan, V

2017-04-27

Characterizing the complex interplay of cellular processes in cancer would enable the discovery of key mechanisms underlying its development and progression. Published approaches to decipher driver mechanisms do not explicitly model tissue-specific changes in pathway networks and the regulatory disruptions related to genomic aberrations in cancers. We therefore developed InFlo, a novel systems biology approach for characterizing complex biological processes using a unique multidimensional framework integrating transcriptomic, genomic and/or epigenomic profiles for any given cancer sample. We show that InFlo robustly characterizes tissue-specific differences in activities of signalling networks on a genome scale using unique probabilistic models of molecular interactions on a per-sample basis. Using large-scale multi-omics cancer datasets, we show that InFlo exhibits higher sensitivity and specificity in detecting pathway networks associated with specific disease states when compared to published pathway network modelling approaches. Furthermore, InFlo's ability to infer the activity of unmeasured signalling network components was also validated using orthogonal gene expression signatures. We then evaluated multi-omics profiles of primary high-grade serous ovarian cancer tumours (N=357) to delineate mechanisms underlying resistance to frontline platinum-based chemotherapy. InFlo was the only algorithm to identify hyperactivation of the cAMP-CREB1 axis as a key mechanism associated with resistance to platinum-based therapy, a finding that we subsequently experimentally validated. We confirmed that inhibition of CREB1 phosphorylation potently sensitized resistant cells to platinum therapy and was effective in killing ovarian cancer stem cells that contribute to both platinum-resistance and tumour recurrence. Thus, we propose InFlo to be a scalable and widely applicable and robust integrative network modelling framework for the discovery of evidence-based biomarkers and therapeutic targets.

Historical Incidence of Spontaneous Lesions in Kidneys from Naïve Swine Utilized In Interventional Renal Denervation Studies.

PubMed

Rouselle, Serge D; Dillon, Krista N; Rousselle-Sabiac, Theo H; Brady, Dane A; Tunev, Stefan; Tellez, Armando

2016-08-01

The use of preclinical animal models is integral to the safety assessment, pathogenesis research, and testing of diagnostic technologies and therapeutic interventions. With inherent similarity to human anatomy and physiology, various porcine models have been the preferred preclinical model in some research areas such as medical devices, wound healing, and skin therapies. The porcine model has been the cornerstone for interventional cardiology for the evaluation and development of this catheter-based renal denervation (RDN) therapy. The porcine model provides similar vascular access and renal neurovascular anatomy to humans. In these preclinical studies, the downstream kidneys from treated arteries are assessed for possible histopathological changes in the vessel dependent territories. In assessing renal safety following RDN, it becomes critical to distinguish treatment-related changes from pre-existing background pathologies. The incidence of background pathological changes in porcine kidneys has not been previously established in normal clinically healthy. Samples from the cranial, middle, and caudal portion of 331 naïve kidneys from 181 swine were processed histologically to slides and evaluated microscopically. The most commonly encountered spontaneous changes were chronic pyelonephritis found in nearly half of the evaluated naïve kidneys (∼40 %; score 1 = 91 %, score 2 = 8.4 %, score 3 = 0.76 %) followed by chronic interstitial inflammation in 9.7 % of the kidneys (score 1 = 90.6 %, score 2 = 9.4 %). Interestingly, there were a few rare spontaneous vascular changes that could potentially affect data interpretation in interventional and toxicology studies: arteritis and arteriolar dissection. The presence of pelvic cysts was a common occurrence (6.3 %) in the kidney. The domestic swine is a widely used preclinical species in interventional research, namely in the emerging field of transcatheter renal denervation. This retrospective study presents the historical incidence of spontaneous lesions recorded in the kidneys from naive pigs enrolled in renal denervation studies. There were commonly encountered changes of little pathological consequence such as pyelonephritis or pelvic cysts and rare vascular changes such as arteritis and arteriolar dissection that were of greater potential impact on study data interpretation. These results offer a benchmark by which to gage the potential effect of a procedure or treatment on renal histopathology in swine and assist in data interpretation.

Validation of reference genes for quantitative expression analysis by real-time RT-PCR in Saccharomyces cerevisiae

PubMed Central

Teste, Marie-Ange; Duquenne, Manon; François, Jean M; Parrou, Jean-Luc

2009-01-01

Background Real-time RT-PCR is the recommended method for quantitative gene expression analysis. A compulsory step is the selection of good reference genes for normalization. A few genes often referred to as HouseKeeping Genes (HSK), such as ACT1, RDN18 or PDA1 are among the most commonly used, as their expression is assumed to remain unchanged over a wide range of conditions. Since this assumption is very unlikely, a geometric averaging of multiple, carefully selected internal control genes is now strongly recommended for normalization to avoid this problem of expression variation of single reference genes. The aim of this work was to search for a set of reference genes for reliable gene expression analysis in Saccharomyces cerevisiae. Results From public microarray datasets, we selected potential reference genes whose expression remained apparently invariable during long-term growth on glucose. Using the algorithm geNorm, ALG9, TAF10, TFC1 and UBC6 turned out to be genes whose expression remained stable, independent of the growth conditions and the strain backgrounds tested in this study. We then showed that the geometric averaging of any subset of three genes among the six most stable genes resulted in very similar normalized data, which contrasted with inconsistent results among various biological samples when the normalization was performed with ACT1. Normalization with multiple selected genes was therefore applied to transcriptional analysis of genes involved in glycogen metabolism. We determined an induction ratio of 100-fold for GPH1 and 20-fold for GSY2 between the exponential phase and the diauxic shift on glucose. There was no induction of these two genes at this transition phase on galactose, although in both cases, the kinetics of glycogen accumulation was similar. In contrast, SGA1 expression was independent of the carbon source and increased by 3-fold in stationary phase. Conclusion In this work, we provided a set of genes that are suitable reference genes for quantitative gene expression analysis by real-time RT-PCR in yeast biological samples covering a large panel of physiological states. In contrast, we invalidated and discourage the use of ACT1 as well as other commonly used reference genes (PDA1, TDH3, RDN18, etc) as internal controls for quantitative gene expression analysis in yeast. PMID:19874630

Validation of reference genes for quantitative expression analysis by real-time RT-PCR in Saccharomyces cerevisiae.

PubMed

Teste, Marie-Ange; Duquenne, Manon; François, Jean M; Parrou, Jean-Luc

2009-10-30

Real-time RT-PCR is the recommended method for quantitative gene expression analysis. A compulsory step is the selection of good reference genes for normalization. A few genes often referred to as HouseKeeping Genes (HSK), such as ACT1, RDN18 or PDA1 are among the most commonly used, as their expression is assumed to remain unchanged over a wide range of conditions. Since this assumption is very unlikely, a geometric averaging of multiple, carefully selected internal control genes is now strongly recommended for normalization to avoid this problem of expression variation of single reference genes. The aim of this work was to search for a set of reference genes for reliable gene expression analysis in Saccharomyces cerevisiae. From public microarray datasets, we selected potential reference genes whose expression remained apparently invariable during long-term growth on glucose. Using the algorithm geNorm, ALG9, TAF10, TFC1 and UBC6 turned out to be genes whose expression remained stable, independent of the growth conditions and the strain backgrounds tested in this study. We then showed that the geometric averaging of any subset of three genes among the six most stable genes resulted in very similar normalized data, which contrasted with inconsistent results among various biological samples when the normalization was performed with ACT1. Normalization with multiple selected genes was therefore applied to transcriptional analysis of genes involved in glycogen metabolism. We determined an induction ratio of 100-fold for GPH1 and 20-fold for GSY2 between the exponential phase and the diauxic shift on glucose. There was no induction of these two genes at this transition phase on galactose, although in both cases, the kinetics of glycogen accumulation was similar. In contrast, SGA1 expression was independent of the carbon source and increased by 3-fold in stationary phase. In this work, we provided a set of genes that are suitable reference genes for quantitative gene expression analysis by real-time RT-PCR in yeast biological samples covering a large panel of physiological states. In contrast, we invalidated and discourage the use of ACT1 as well as other commonly used reference genes (PDA1, TDH3, RDN18, etc) as internal controls for quantitative gene expression analysis in yeast.

Genetic variants associated with severe retinopathy of prematurity in extremely low birth weight infants.

PubMed

Hartnett, M Elizabeth; Morrison, Margaux A; Smith, Silvia; Yanovitch, Tammy L; Young, Terri L; Colaizy, Tarah; Momany, Allison; Dagle, John; Carlo, Waldemar A; Clark, Erin A S; Page, Grier; Murray, Jeff; DeAngelis, Margaret M; Cotten, C Michael

2014-08-12

To determine genetic variants associated with severe retinopathy of prematurity (ROP) in a candidate gene cohort study of US preterm infants. Preterm infants in the discovery cohort were enrolled through the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network, and those in the replication cohort were from the University of Iowa. All infants were phenotyped for ROP severity. Because of differences in the durations of enrollment between cohorts, severe ROP was defined as threshold disease in the discovery cohort and as threshold disease or type 1 ROP in the replication cohort. Whole genome amplified DNA from stored blood spot samples from the Neonatal Research Network biorepository was genotyped using an Illumina GoldenGate platform for candidate gene single nucleotide polymorphisms (SNPs) involving angiogenic, developmental, inflammatory, and oxidative pathways. Three analyses were performed to determine significant epidemiologic variables and SNPs associated with levels of ROP severity. Analyses controlled for multiple comparisons, ancestral eigenvalues, family relatedness, and significant epidemiologic variables. Single nucleotide polymorphisms significantly associated with ROP severity from the discovery cohort were analyzed in the replication cohort and in meta-analysis. Eight hundred seventeen infants in the discovery cohort and 543 in the replication cohort were analyzed. Severe ROP occurred in 126 infants in the discovery and in 14 in the replication cohort. In both cohorts, ventilation days and seizure occurrence were associated with severe ROP. After controlling for significant factors and multiple comparisons, two intronic SNPs in the gene BDNF (rs7934165 and rs2049046, P < 3.1 × 10(-5)) were associated with severe ROP in the discovery cohort and were not associated with severe ROP in the replication cohort. However, when the cohorts were analyzed together in an exploratory meta-analysis, rs7934165 increased in associated significance with severe ROP (P = 2.9 × 10(-7)). Variants in BDNF encoding brain-derived neurotrophic factor were associated with severe ROP in a large candidate gene study of infants with threshold ROP. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data

PubMed Central

Jupiter, Daniel; Chen, Hailin; VanBuren, Vincent

2009-01-01

Background Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult. Results STARNET 2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. STARNET 2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBI's Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new HEATSEEKER module. Conclusion STARNET 2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a STARNET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at , and does not require user registration. PMID:19828039

From pull-down data to protein interaction networks and complexes with biological relevance.

PubMed

Zhang, Bing; Park, Byung-Hoon; Karpinets, Tatiana; Samatova, Nagiza F

2008-04-01

Recent improvements in high-throughput Mass Spectrometry (MS) technology have expedited genome-wide discovery of protein-protein interactions by providing a capability of detecting protein complexes in a physiological setting. Computational inference of protein interaction networks and protein complexes from MS data are challenging. Advances are required in developing robust and seamlessly integrated procedures for assessment of protein-protein interaction affinities, mathematical representation of protein interaction networks, discovery of protein complexes and evaluation of their biological relevance. A multi-step but easy-to-follow framework for identifying protein complexes from MS pull-down data is introduced. It assesses interaction affinity between two proteins based on similarity of their co-purification patterns derived from MS data. It constructs a protein interaction network by adopting a knowledge-guided threshold selection method. Based on the network, it identifies protein complexes and infers their core components using a graph-theoretical approach. It deploys a statistical evaluation procedure to assess biological relevance of each found complex. On Saccharomyces cerevisiae pull-down data, the framework outperformed other more complicated schemes by at least 10% in F(1)-measure and identified 610 protein complexes with high-functional homogeneity based on the enrichment in Gene Ontology (GO) annotation. Manual examination of the complexes brought forward the hypotheses on cause of false identifications. Namely, co-purification of different protein complexes as mediated by a common non-protein molecule, such as DNA, might be a source of false positives. Protein identification bias in pull-down technology, such as the hydrophilic bias could result in false negatives.

Rethinking the learning of belief network probabilities

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

Musick, R.

Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rotemore » learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neutral networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.« less

Comparative mRNA analysis of behavioral and genetic mouse models of aggression.

PubMed

Malki, Karim; Tosto, Maria G; Pain, Oliver; Sluyter, Frans; Mineur, Yann S; Crusio, Wim E; de Boer, Sietse; Sandnabba, Kenneth N; Kesserwani, Jad; Robinson, Edward; Schalkwyk, Leonard C; Asherson, Philip

2016-04-01

Mouse models of aggression have traditionally compared strains, most notably BALB/cJ and C57BL/6. However, these strains were not designed to study aggression despite differences in aggression-related traits and distinct reactivity to stress. This study evaluated expression of genes differentially regulated in a stress (behavioral) mouse model of aggression with those from a recent genetic mouse model aggression. The study used a discovery-replication design using two independent mRNA studies from mouse brain tissue. The discovery study identified strain (BALB/cJ and C57BL/6J) × stress (chronic mild stress or control) interactions. Probe sets differentially regulated in the discovery set were intersected with those uncovered in the replication study, which evaluated differences between high and low aggressive animals from three strains specifically bred to study aggression. Network analysis was conducted on overlapping genes uncovered across both studies. A significant overlap was found with the genetic mouse study sharing 1,916 probe sets with the stress model. Fifty-one probe sets were found to be strongly dysregulated across both studies mapping to 50 known genes. Network analysis revealed two plausible pathways including one centered on the UBC gene hub which encodes ubiquitin, a protein well-known for protein degradation, and another on P38 MAPK. Findings from this study support the stress model of aggression, which showed remarkable molecular overlap with a genetic model. The study uncovered a set of candidate genes including the Erg2 gene, which has previously been implicated in different psychopathologies. The gene networks uncovered points at a Redox pathway as potentially being implicated in aggressive related behaviors. © 2016 Wiley Periodicals, Inc.

CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

2014-12-01

Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

Knowledge Discovery in Spectral Data by Means of Complex Networks

PubMed Central

Zanin, Massimiliano; Papo, David; Solís, José Luis González; Espinosa, Juan Carlos Martínez; Frausto-Reyes, Claudio; Anda, Pascual Palomares; Sevilla-Escoboza, Ricardo; Boccaletti, Stefano; Menasalvas, Ernestina; Sousa, Pedro

2013-01-01

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease. PMID:24957895

Knowledge discovery in spectral data by means of complex networks.

PubMed

Zanin, Massimiliano; Papo, David; Solís, José Luis González; Espinosa, Juan Carlos Martínez; Frausto-Reyes, Claudio; Anda, Pascual Palomares; Sevilla-Escoboza, Ricardo; Jaimes-Reategui, Rider; Boccaletti, Stefano; Menasalvas, Ernestina; Sousa, Pedro

2013-03-11

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

Advanced Scientific Computing Research Network Requirements: ASCR Network Requirements Review Final Report

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

Bacon, Charles; Bell, Greg; Canon, Shane

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In October 2012, ESnet and the Office of Advanced Scientific Computing Research (ASCR) of the DOE SCmore » organized a review to characterize the networking requirements of the programs funded by the ASCR program office. The requirements identified at the review are summarized in the Findings section, and are described in more detail in the body of the report.« less

Optimal forwarding ratio on dynamical networks with heterogeneous mobility

NASA Astrophysics Data System (ADS)

Gan, Yu; Tang, Ming; Yang, Hanxin

2013-05-01

Since the discovery of non-Poisson statistics of human mobility trajectories, more attention has been paid to understand the role of these patterns in different dynamics. In this study, we first introduce the heterogeneous mobility of mobile agents into dynamical networks, and then investigate packet forwarding strategy on the heterogeneous dynamical networks. We find that the faster speed and the higher proportion of high-speed agents can enhance the network throughput and reduce the mean traveling time in random forwarding. A hierarchical structure in the dependence of high-speed is observed: the network throughput remains unchanged at small and large high-speed value. It is also interesting to find that a slightly preferential forwarding to high-speed agents can maximize the network capacity. Through theoretical analysis and numerical simulations, we show that the optimal forwarding ratio stems from the local structural heterogeneity of low-speed agents.

NOW: A Workflow Language for Orchestration in Nomadic Networks

NASA Astrophysics Data System (ADS)

Philips, Eline; van der Straeten, Ragnhild; Jonckers, Viviane

Existing workflow languages for nomadic or mobile ad hoc networks do not offer adequate support for dealing with the volatile connections inherent to these environments. Services residing on mobile devices are exposed to (temporary) network failures, which should be considered the rule rather than the exception. This paper proposes a nomadic workflow language built on top of an ambient-oriented programming language which supports dynamic service discovery and communication primitives resilient to network failures. Our proposed language provides high level workflow abstractions for control flow and supports rich network and service failure detection and handling through compensating actions. Moreover, we introduce a powerful variable binding mechanism which enables dynamic data flow between services in a nomadic environment. By adding this extra layer of abstraction on top of an ambient-oriented programming language, the application programmer is offered a flexible way to develop applications for nomadic networks.

Autoconfiguration and Service Discovery

NASA Astrophysics Data System (ADS)

Manner, Jukka

To be useful, IP networking requires various parameters to be set up. A network node needs at least an IP address, routing information, and name services. In a fixed network this configuration is typically done with a centralized scheme, where a server hosts the configuration information and clients query the server with the Dynamic Host Configuration Protocol (DHCP). Companies, university campuses and even home broadband use the DHCP system to configure hosts. This signaling happens in the background, and users seldom need to think about it; only when things are not working properly, manual intervention is needed. The same protocol can be used in mobile networks, where the client device communicates with the access network provider and his DHCP service. The core information provided by DHCP includes a unique IP address for the host, the IP address of the closest IP router for routing messages to other networks, and the location of domain name servers.

The Science DMZ: A Network Design Pattern for Data-Intensive Science

DOE PAGES

Dart, Eli; Rotman, Lauren; Tierney, Brian; ...

2014-01-01

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers andmore » research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.« less

Distributed Load Shedding over Directed Communication Networks with Time Delays

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

Yang, Tao; Wu, Di

When generation is insufficient to support all loads under emergencies, effective and efficient load shedding needs to be deployed in order to maintain the supply-demand balance. This paper presents a distributed load shedding algorithm, which makes efficient decision based on the discovered global information. In the global information discovery process, each load only communicates with its neighboring load via directed communication links possibly with arbitrarily large but bounded time varying communication delays. We propose a novel distributed information discovery algorithm based on ratio consensus. Simulation results are used to validate the proposed method.

Neural network wavelet technology: A frontier of automation

NASA Technical Reports Server (NTRS)

Szu, Harold

1994-01-01

Neural networks are an outgrowth of interdisciplinary studies concerning the brain. These studies are guiding the field of Artificial Intelligence towards the, so-called, 6th Generation Computer. Enormous amounts of resources have been poured into R/D. Wavelet Transforms (WT) have replaced Fourier Transforms (FT) in Wideband Transient (WT) cases since the discovery of WT in 1985. The list of successful applications includes the following: earthquake prediction; radar identification; speech recognition; stock market forecasting; FBI finger print image compression; and telecommunication ISDN-data compression.

Discovery and Enumeration of Organic-Chemical and Biomimetic Reaction Cycles within the Network of Chemistry.

PubMed

Bajczyk, Michał D; Dittwald, Piotr; Wołos, Agnieszka; Szymkuć, Sara; Grzybowski, Bartosz A

2018-02-23

Analysis of the chemical-organic knowledge represented as a giant network reveals that it contains millions of reaction sequences closing into cycles. Without realizing it, independent chemists working at different times have jointly created examples of cyclic sequences that allow for the recovery of useful reagents and for the autoamplification of synthetically important molecules, those that mimic biological cycles, and those that can be operated one-pot. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Overcoming Challenges of Real-World Implementation

PubMed Central

Shuldiner, AR; Relling, MV; Peterson, JF; Hicks, JK; Freimuth, RR; Sadee, W; Pereira, NL; Roden, DM; Johnson, JA; Klein, TE

2013-01-01

The pace of discovery of potentially actionable pharmacogenetic variants has increased dramatically in recent years. However, the implementation of this new knowledge for individualized patient care has been slow. The Pharmacogenomics Research Network (PGRN) Translational Pharmacogenetics Program seeks to identify barriers and develop real-world solutions to implementation of evidence-based pharmacogenetic tests in diverse health-care settings. Dissemination of the resulting toolbox of “implementation best practices” will prove useful to a broad audience. PMID:23588301

Asynchronous updates can promote the evolution of cooperation on multiplex networks

NASA Astrophysics Data System (ADS)

Allen, James M.; Hoyle, Rebecca B.

2017-04-01

We study the importance to the frequency of cooperation of the choice of updating strategies in a game played asynchronously or synchronously across layers in a multiplex network. Updating asynchronously in the public goods game leads to higher frequencies of cooperation compared to synchronous updates. How large this effect is depends on the sensitivity of the game dynamics to changes in the number of cooperators surrounding a player, with the largest effect observed when players payoffs are small. The discovery of this effect enhances understanding of cooperation on multiplex networks, and demonstrates a new way to maintain cooperation in these systems.

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

MUFFINN: cancer gene discovery via network analysis of somatic mutation data.

PubMed

Cho, Ara; Shim, Jung Eun; Kim, Eiru; Supek, Fran; Lehner, Ben; Lee, Insuk

2016-06-23

A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.

Translational biomarkers: from discovery and development to clinical practice.

PubMed

Subramanyam, Meena; Goyal, Jaya

The refinement of disease taxonomy utilizing molecular phenotypes has led to significant improvements in the precision of disease diagnosis and customization of treatment options. This has also spurred efforts to identify novel biomarkers to understand the impact of therapeutically altering the underlying molecular network on disease course, and to support decision-making in drug discovery and development. However, gaps in knowledge regarding disease heterogeneity, combined with the inadequacies of surrogate disease model systems, make it challenging to demonstrate the unequivocal association of molecular and physiological biomarkers to disease pathology. This article will discuss the current landscape in biomarker research and highlight strategies being adopted to increase the likelihood of transitioning biomarkers from discovery to medical practice to enable more objective decision making, and to improve health outcome. Copyright © 2016 Elsevier Ltd. All rights reserved.

STS-70 Discovery launch before tower clear (fish eye view)

NASA Technical Reports Server (NTRS)

1995-01-01

The fourth Space Shuttle flight of 1995 is off to an all-but- perfect start, as the Shuttle Discovery surges skyward from Launch Pad 39B at 9:41:55.078 a.m. EDT, July 13, 1995. On board for Discovery's 21st spaceflight are a crew of five: Commander Terence 'Tom' Henricks; Pilot Kevin R. Kregel; and Mission Specialists Nancy Jane Currie, Donald A. Thomas and Mary Ellen Weber. Primary objective of Mission STS-70 is to assure the continued readiness of NASA's Tracking and Data Relay Satellite (TDRS) communications network which links Earth-orbiting spacecraft -- including the Shuttle -- with the ground. The 70th Shuttle flight overall also marks the maiden flight of the new Block I Space Shuttle Main Engine configuration designed to increase engine performance as well as safety and reliability.

76 FR 63312 - Center for Scientific Review; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-12

...: Biological Chemistry, Biophysics, and Drug Discovery. Date: November 3-4, 2011. Time: 7:30 a.m. to 12 p.m... Network Analysis and Health. Date: November 4, 2011. Time: 8 a.m. to 6 p.m. Agenda: To review and evaluate...

Multilingual Information Discovery and AccesS (MIDAS): A Joint ACM DL'99/ ACM SIGIR'99 Workshop.

ERIC Educational Resources Information Center

Oard, Douglas; Peters, Carol; Ruiz, Miguel; Frederking, Robert; Klavans, Judith; Sheridan, Paraic

1999-01-01

Discusses a multidisciplinary workshop that addressed issues concerning internationally distributed information networks. Highlights include multilingual information access in media other than character-coded text; cross-language information retrieval and multilingual metadata; and evaluation of multilingual systems. (LRW)

A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Artery Disease.

PubMed

Shameer, Khader; Dow, Garrett; Glicksberg, Benjamin S; Johnson, Kipp W; Ze, Yi; Tomlinson, Max S; Readhead, Ben; Dudley, Joel T; Kullo, Iftikhar J

2018-01-01

Currently, drug discovery approaches focus on the design of therapies that alleviate an index symptom by reengineering the underlying biological mechanism in agonistic or antagonistic fashion. For example, medicines are routinely developed to target an essential gene that drives the disease mechanism. Therapeutic overloading where patients get multiple medications to reduce the primary and secondary side effect burden is standard practice. This single-symptom based approach may not be scalable, as we understand that diseases are more connected than random and molecular interactions drive disease comorbidities. In this work, we present a proof-of-concept drug discovery strategy by combining network biology, disease comorbidity estimates, and computational drug repositioning, by targeting the risk factors and comorbidities of peripheral artery disease, a vascular disease associated with high morbidity and mortality. Individualized risk estimation and recommending disease sequelae based therapies may help to lower the mortality and morbidity of peripheral artery disease.

Integration, Networking, and Global Biobanking in the Age of New Biology.

PubMed

Karimi-Busheri, Feridoun; Rasouli-Nia, Aghdass

2015-01-01

Scientific revolution is changing the world forever. Many new disciplines and fields have emerged with unlimited possibilities and opportunities. Biobanking is one of many that is benefiting from revolutionary milestones in human genome, post-genomic, and computer and bioinformatics discoveries. The storage, management, and analysis of massive clinical and biological data sets cannot be achieved without a global collaboration and networking. At the same time, biobanking is facing many significant challenges that need to be addressed and solved including dealing with an ever increasing complexity of sample storage and retrieval, data management and integration, and establishing common platforms in a global context. The overall picture of the biobanking of the future, however, is promising. Many population-based biobanks have been formed, and more are under development. It is certain that amazing discoveries will emerge from this large-scale method of preserving and accessing human samples. Signs of a healthy collaboration between industry, academy, and government are encouraging.

Neural network-based QSAR and insecticide discovery: spinetoram

NASA Astrophysics Data System (ADS)

Sparks, Thomas C.; Crouse, Gary D.; Dripps, James E.; Anzeveno, Peter; Martynow, Jacek; DeAmicis, Carl V.; Gifford, James

2008-06-01

Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry.

Comparing the Consumption of CPU Hours with Scientific Output for the Extreme Science and Engineering Discovery Environment (XSEDE).

PubMed

Knepper, Richard; Börner, Katy

2016-01-01

This paper presents the results of a study that compares resource usage with publication output using data about the consumption of CPU cycles from the Extreme Science and Engineering Discovery Environment (XSEDE) and resulting scientific publications for 2,691 institutions/teams. Specifically, the datasets comprise a total of 5,374,032,696 central processing unit (CPU) hours run in XSEDE during July 1, 2011 to August 18, 2015 and 2,882 publications that cite the XSEDE resource. Three types of studies were conducted: a geospatial analysis of XSEDE providers and consumers, co-authorship network analysis of XSEDE publications, and bi-modal network analysis of how XSEDE resources are used by different research fields. Resulting visualizations show that a diverse set of consumers make use of XSEDE resources, that users of XSEDE publish together frequently, and that the users of XSEDE with the highest resource usage tend to be "traditional" high-performance computing (HPC) community members from astronomy, atmospheric science, physics, chemistry, and biology.

Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

PubMed Central

Samusik, Nikolay; Wang, Xiaowei; Guan, Leying; Nolan, Garry P.

2017-01-01

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject. PMID:29281633

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

PubMed

Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E

2016-08-01

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

Visualizing Dynamic Bitcoin Transaction Patterns.

PubMed

McGinn, Dan; Birch, David; Akroyd, David; Molina-Solana, Miguel; Guo, Yike; Knottenbelt, William J

2016-06-01

This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.

Visualizing Dynamic Bitcoin Transaction Patterns

PubMed Central

McGinn, Dan; Birch, David; Akroyd, David; Molina-Solana, Miguel; Guo, Yike; Knottenbelt, William J.

2016-01-01

Abstract This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network. PMID:27441715

A metabolomics guided exploration of marine natural product chemical space.

PubMed

Floros, Dimitrios J; Jensen, Paul R; Dorrestein, Pieter C; Koyama, Nobuhiro

2016-09-01

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections. Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs. In this work we utilize untargeted LC-MS/MS based metabolomics together with molecular networking to. This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B. Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Artery Disease

PubMed Central

Shameer, Khader; Dow, Garrett; Glicksberg, Benjamin S.; Johnson, Kipp W.; Ze, Yi; Tomlinson, Max S.; Readhead, Ben; Dudley, Joel T.; Kullo, Iftikhar J.

2018-01-01

Currently, drug discovery approaches focus on the design of therapies that alleviate an index symptom by reengineering the underlying biological mechanism in agonistic or antagonistic fashion. For example, medicines are routinely developed to target an essential gene that drives the disease mechanism. Therapeutic overloading where patients get multiple medications to reduce the primary and secondary side effect burden is standard practice. This single-symptom based approach may not be scalable, as we understand that diseases are more connected than random and molecular interactions drive disease comorbidities. In this work, we present a proof-of-concept drug discovery strategy by combining network biology, disease comorbidity estimates, and computational drug repositioning, by targeting the risk factors and comorbidities of peripheral artery disease, a vascular disease associated with high morbidity and mortality. Individualized risk estimation and recommending disease sequelae based therapies may help to lower the mortality and morbidity of peripheral artery disease. PMID:29888052

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

PubMed Central

2017-01-01

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery. PMID:29392184

Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach

PubMed Central

Song, Min

2016-01-01

In biomedicine, scientific literature is a valuable source for knowledge discovery. Mining knowledge from textual data has become an ever important task as the volume of scientific literature is growing unprecedentedly. In this paper, we propose a framework for examining a certain disease based on existing information provided by scientific literature. Disease-related entities that include diseases, drugs, and genes are systematically extracted and analyzed using a three-level network-based approach. A paper-entity network and an entity co-occurrence network (macro-level) are explored and used to construct six entity specific networks (meso-level). Important diseases, drugs, and genes as well as salient entity relations (micro-level) are identified from these networks. Results obtained from the literature-based literature mining can serve to assist clinical applications. PMID:27195695

Enhancing a Socio-technical Data Ecosystem for Societally Relevant, Sustained Arctic Observing

NASA Astrophysics Data System (ADS)

Pulsifer, P. L.

2017-12-01

In recent years, much has been learned about the state of data and related systems for the Arctic region, however work remains to be done to achieve an envisioned integrated and well-defined pan-Arctic observing and data network. The envisioned comprehensive network will enables access to high quality data, expertise and information in support of scientific understanding, stakeholder needs, and agency operations. In this paper we argue that priorities for establishing such a network are in the areas of better understanding the current system, machine-enhanced data discovery and mediation, and the human aspects of community building. The author has engaged extensively in international, Canadian and U.S.-based data coordination and system design efforts. This includes a series of meetings, workshops, systems design activities, and publications. The results of these efforts have been analyzed and a synthesis of these analyses are presented here. Analysis reveals that there are a large number of polar data resources interacting in a complex network that functions as a data ecosystem. Understanding this ecosystem is critical and required to guide design. Given the size and complexity of the network, achieving broad data discovery and access and meaningful data integration will require advanced techniques including machine learning, semantic mediation, and the use of highly connected virtual research environments. To achieve the aforementioned goal will require a community of engaged researchers, technologists, and stakeholders to establish requirements and the social and organizational context needed for effective approaches. The results imply that: i) an effective governance mechanism must be established that includes "bottom up" and "top down" control; ii) the established governance mechanism must include effective networking of actors in the system; iii) funders must adopt a long-term, sustainable infrastructure approach to systems development; iv) best practices will include service and application "chaining" to provide solutions for the diverse Arctic community. Establishing cyberinfrastructure for a sustained Arctic observing network that benefits society will require an innovative combination of emerging technologies and community-building across stakeholders.

Geosciences Information Network (GIN): A modular, distributed, interoperable data network for the geosciences

NASA Astrophysics Data System (ADS)

Allison, M.; Gundersen, L. C.; Richard, S. M.; Dickinson, T. L.

2008-12-01

A coalition of the state geological surveys (AASG), the U.S. Geological Survey (USGS), and partners will receive NSF funding over 3 years under the INTEROP solicitation to start building the Geoscience Information Network (www.geoinformatics.info/gin) a distributed, interoperable data network. The GIN project will develop standardized services to link existing and in-progress components using a few standards and protocols, and work with data providers to implement these services. The key components of this network are 1) catalog system(s) for data discovery; 2) service definitions for interfaces for searching catalogs and accessing resources; 3) shared interchange formats to encode information for transmission (e.g. various XML markup languages); 4) data providers that publish information using standardized services defined by the network; and 5) client applications adapted to use information resources provided by the network. The GIN will integrate and use catalog resources that currently exist or are in development. We are working with the USGS National Geologic Map Database's existing map catalog, with the USGS National Geological and Geophysical Data Preservation Program, which is developing a metadata catalog (National Digital Catalog) for geoscience information resource discovery, and with the GEON catalog. Existing interchange formats will be used, such as GeoSciML, ChemML, and Open Geospatial Consortium sensor, observation and measurement MLs. Client application development will be fostered by collaboration with industry and academic partners. The GIN project will focus on the remaining aspects of the system -- service definitions and assistance to data providers to implement the services and bring content online - and on system integration of the modules. Initial formal collaborators include the OneGeology-Europe consortium of 27 nations that is building a comparable network under the EU INSPIRE initiative, GEON, Earthchem, and GIS software company ESRI. OneGeology-Europe and GIN have agreed to integrate their networks, effectively adopting global standards among geological surveys that are available across the entire field. ESRI is creating a Geology Data Model for ArcGIS software to be compatible with GIN, and other companies are expressing interest in adapting their services, applications, and clients to take advantage of the large data resources planned to become available through GIN.

Direct2Experts: a pilot national network to demonstrate interoperability among research-networking platforms

PubMed Central

Barnett, William; Conlon, Mike; Eichmann, David; Kibbe, Warren; Falk-Krzesinski, Holly; Halaas, Michael; Johnson, Layne; Meeks, Eric; Mitchell, Donald; Schleyer, Titus; Stallings, Sarah; Warden, Michael; Kahlon, Maninder

2011-01-01

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the network's functionality and focus on the end users. PMID:22037890

Application of circular consensus sequencing and network analysis to characterize the bovine IgG repertoire

USDA-ARS?s Scientific Manuscript database

Background: Vertebrate immune systems generate diverse repertoires of antibodies capable of mediating response to a variety of antigens. Next generation sequencing methods provide unique approaches to a number of immuno-based research areas including antibody discovery and engineering, disease surve...

Genetic associations with micronutrient levels identified in immune and gastrointestinal networks

USDA-ARS?s Scientific Manuscript database

The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes ...

An Assessment, Survey, and Systems Engineering Design of Information Sharing and Discovery Systems in a Network-Centric Environment

DTIC Science & Technology

2009-12-01

type of information available through DISA search tools: Centralized Search, Federated Search , and Enterprise Search (Defense Information Systems... Federated Search , and Enterprise 41 Search services. Likewise, EFD and GCDS support COIs in discovering information by making information

Air Pollution Data for Model Evaluation and Application

EPA Science Inventory

One objective of designing an air pollution monitoring network is to obtain data for evaluating air quality models that are used in the air quality management process and scientific discovery.^1.2 A common use is to relate emissions to air quality, including assessing ...

Improving Performance and Predictability of Storage Arrays

ERIC Educational Resources Information Center

Altiparmak, Nihat

2013-01-01

Massive amount of data is generated everyday through sensors, Internet transactions, social networks, video, and all other digital sources available. Many organizations store this data to enable breakthrough discoveries and innovation in science, engineering, medicine, and commerce. Such massive scale of data poses new research problems called big…

Web-based services for drug design and discovery.

PubMed

Frey, Jeremy G; Bird, Colin L

2011-09-01

Reviews of the development of drug discovery through the 20(th) century recognised the importance of chemistry and increasingly bioinformatics, but had relatively little to say about the importance of computing and networked computing in particular. However, the design and discovery of new drugs is arguably the most significant single application of bioinformatics and cheminformatics to have benefitted from the increases in the range and power of the computational techniques since the emergence of the World Wide Web, commonly now referred to as simply 'the Web'. Web services have enabled researchers to access shared resources and to deploy standardized calculations in their search for new drugs. This article first considers the fundamental principles of Web services and workflows, and then explores the facilities and resources that have evolved to meet the specific needs of chem- and bio-informatics. This strategy leads to a more detailed examination of the basic components that characterise molecules and the essential predictive techniques, followed by a discussion of the emerging networked services that transcend the basic provisions, and the growing trend towards embracing modern techniques, in particular the Semantic Web. In the opinion of the authors, the issues that require community action are: increasing the amount of chemical data available for open access; validating the data as provided; and developing more efficient links between the worlds of cheminformatics and bioinformatics. The goal is to create ever better drug design services.

"The developmental and functional logic of neuronal circuits": commentary on the Kavli Prize in Neuroscience.

PubMed

Glover, J C

2009-11-10

The first Kavli Prize in Neuroscience recognizes a confluence of career achievements that together provide a fundamental understanding of how brain and spinal cord circuits are assembled during development and function in the adult. The members of the Kavli Neuroscience Prize Committee have decided to reward three scientists (Sten Grillner, Thomas Jessell, and Pasko Rakic) jointly "for discoveries on the developmental and functional logic of neuronal circuits". Pasko Rakic performed groundbreaking studies of the developing cerebral cortex, including the discovery of how radial glia guide the neuronal migration that establishes cortical layers and for the radial unit hypothesis and its implications for cortical connectivity and evolution. Thomas Jessell discovered molecular principles governing the specification and patterning of different neuron types and the development of their synaptic interconnection into sensorimotor circuits. Sten Grillner elucidated principles of network organization in the vertebrate locomotor central pattern generator, along with its command systems and sensory and higher order control. The discoveries of Rakic, Jessell and Grillner provide a framework for how neurons obtain their identities and ultimate locations, establish appropriate connections with each other, and how the resultant neuronal networks operate. Their work has significantly advanced our understanding of brain development and function and created new opportunities for the treatment of neurological disorders. Each has pioneered an important area of neuroscience research and left a legacy of exceptional scientific achievement, insight, communication, mentoring and leadership.

Nuclear jasmonate and salicylate signaling and crosstalk in defense against pathogens.

PubMed

Gimenez-Ibanez, Selena; Solano, Roberto

2013-01-01

An extraordinary progress has been made over the last two decades on understanding the components and mechanisms governing plant innate immunity. After detection of a pathogen, effective plant resistance depends on the activation of a complex signaling network integrated by small signaling molecules and hormonal pathways, and the balance of these hormone systems determines resistance to particular pathogens. The discovery of new components of hormonal signaling pathways, including plant nuclear hormone receptors, is providing a picture of complex crosstalk and induced hormonal changes that modulate disease and resistance through several protein families that perceive hormones within the nucleus and lead to massive gene induction responses often achieved by de-repression. This review highlights recent advances in our understanding of positive and negative regulators of these hormones signaling pathways that are crucial regulatory targets of hormonal crosstalk in disease and defense. We focus on the most recent discoveries on the jasmonate and salicylate pathway components that explain their crosstalk with other hormonal pathways in the nucleus. We discuss how these components fine-tune defense responses to build a robust plant immune system against a great number of different microbes and, finally, we summarize recent discoveries on specific nuclear hormonal manipulation by microbes which exemplify the ingenious ways by which pathogens can take control over the plant's hormone signaling network to promote disease.

Nuclear jasmonate and salicylate signaling and crosstalk in defense against pathogens

PubMed Central

Gimenez-Ibanez, Selena; Solano, Roberto

2013-01-01

An extraordinary progress has been made over the last two decades on understanding the components and mechanisms governing plant innate immunity. After detection of a pathogen, effective plant resistance depends on the activation of a complex signaling network integrated by small signaling molecules and hormonal pathways, and the balance of these hormone systems determines resistance to particular pathogens. The discovery of new components of hormonal signaling pathways, including plant nuclear hormone receptors, is providing a picture of complex crosstalk and induced hormonal changes that modulate disease and resistance through several protein families that perceive hormones within the nucleus and lead to massive gene induction responses often achieved by de-repression. This review highlights recent advances in our understanding of positive and negative regulators of these hormones signaling pathways that are crucial regulatory targets of hormonal crosstalk in disease and defense. We focus on the most recent discoveries on the jasmonate and salicylate pathway components that explain their crosstalk with other hormonal pathways in the nucleus. We discuss how these components fine-tune defense responses to build a robust plant immune system against a great number of different microbes and, finally, we summarize recent discoveries on specific nuclear hormonal manipulation by microbes which exemplify the ingenious ways by which pathogens can take control over the plant’s hormone signaling network to promote disease. PMID:23577014

Learning about learning: Mining human brain sub-network biomarkers from fMRI data

PubMed Central

Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Singh, Ambuj K.

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in “resting state” employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions. PMID:29016686

Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

PubMed

Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S; Wymbs, Nicholas F; Grafton, Scott T; Singh, Ambuj K

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

Finding community structure in very large networks

NASA Astrophysics Data System (ADS)

Clauset, Aaron; Newman, M. E. J.; Moore, Cristopher

2004-12-01

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(mdlogn) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with mtilde n and dtilde logn , in which case our algorithm runs in essentially linear time, O(nlog2n) . As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2×106 edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.

A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks.

PubMed

Wang, Hao; Wang, Shilian; Bu, Renfei; Zhang, Eryang

2017-08-08

Underwater wireless sensor networks (UWSNs) have attracted increasing attention in recent years because of their numerous applications in ocean monitoring, resource discovery and tactical surveillance. However, the design of reliable and efficient transmission and routing protocols is a challenge due to the low acoustic propagation speed and complex channel environment in UWSNs. In this paper, we propose a novel cross-layer routing protocol based on network coding (NCRP) for UWSNs, which utilizes network coding and cross-layer design to greedily forward data packets to sink nodes efficiently. The proposed NCRP takes full advantages of multicast transmission and decode packets jointly with encoded packets received from multiple potential nodes in the entire network. The transmission power is optimized in our design to extend the life cycle of the network. Moreover, we design a real-time routing maintenance protocol to update the route when detecting inefficient relay nodes. Substantial simulations in underwater environment by Network Simulator 3 (NS-3) show that NCRP significantly improves the network performance in terms of energy consumption, end-to-end delay and packet delivery ratio compared with other routing protocols for UWSNs.

Environmentally Friendly Procedure Based on Supercritical Fluid Chromatography and Tandem Mass Spectrometry Molecular Networking for the Discovery of Potent Antiviral Compounds from Euphorbia semiperfoliata.

PubMed

Nothias, Louis-Félix; Boutet-Mercey, Stéphanie; Cachet, Xavier; De La Torre, Erick; Laboureur, Laurent; Gallard, Jean-François; Retailleau, Pascal; Brunelle, Alain; Dorrestein, Pieter C; Costa, Jean; Bedoya, Luis M; Roussi, Fanny; Leyssen, Pieter; Alcami, José; Paolini, Julien; Litaudon, Marc; Touboul, David

2017-10-27

A supercritical fluid chromatography-based targeted purification procedure using tandem mass spectrometry and molecular networking was developed to analyze, annotate, and isolate secondary metabolites from complex plant extract mixture. This approach was applied for the targeted isolation of new antiviral diterpene esters from Euphorbia semiperfoliata whole plant extract. The analysis of bioactive fractions revealed that unknown diterpene esters, including jatrophane esters and phorbol esters, were present in the samples. The purification procedure using semipreparative supercritical fluid chromatography led to the isolation and identification of two new jatrophane esters (13 and 14) and one known (15) and three new 4-deoxyphorbol esters (16-18). The structure and absolute configuration of compound 16 were confirmed by X-ray crystallography. This compound was found to display antiviral activity against Chikungunya virus (EC 50 = 0.45 μM), while compound 15 proved to be a potent and selective inhibitor of HIV-1 replication in a recombinant virus assay (EC 50 = 13 nM). This study showed that a supercritical fluid chromatography-based protocol and molecular networking can facilitate and accelerate the discovery of bioactive small molecules by targeting molecules of interest, while minimizing the use of toxic solvents.

Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio)

PubMed Central

2012-01-01

Background Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions. PMID:22849515

Two Phase Admission Control for QoS Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Chen, Chien-Sheng; Su, Yi-Wen; Liu, Wen-Hsiung; Chi, Ching-Lung

In this paper a novel and effective two phase admission control (TPAC) for QoS mobile ad hoc networks is proposed that satisfies the real-time traffic requirements in mobile ad hoc networks. With a limited amount of extra overhead, TPAC can avoid network congestions by a simple and precise admission control which blocks most of the overloading flow-requests in the route discovery process. When compared with previous QoS routing schemes such as QoS-aware routing protocol and CACP protocols, it is shown from system simulations that the proposed scheme can increase the system throughput and reduce both the dropping rate and the end-to-end delay. Therefore, TPAC is surely an effective QoS-guarantee protocol to provide for real-time traffic.

The center for causal discovery of biomedical knowledge from big data.

PubMed

Cooper, Gregory F; Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-11-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Authentic Astronomical Discovery in Planetariums: Bringing Data to Domes

NASA Astrophysics Data System (ADS)

Wyatt, Ryan Jason; Subbarao, Mark; Christensen, Lars; Emmons, Ben; Hurt, Robert

2018-01-01

Planetariums offer a unique opportunity to disseminate astronomical discoveries using data visualization at all levels of complexity: the technical infrastructure to display data and a sizeable cohort of enthusiastic educators to interpret results. “Data to Dome” is an initiative the International Planetarium Society to develop our community’s capacity to integrate data in fulldome planetarium systems—including via open source software platforms such as WorldWide Telescope and OpenSpace. We are cultivating a network of planetarium professionals who integrate data into their presentations and share their content with others. Furthermore, we propose to shorten the delay between discovery and dissemination in planetariums. Currently, the “latest science” is often presented days or weeks after discoveries are announced, and we can shorten this to hours or even minutes. The Data2Dome (D2D) initiative, led by the European Southern Observatory, proposes technical infrastructure and data standards that will streamline content flow from research institutions to planetariums, offering audiences a unique opportunity to access to the latest astronomical data in near real time.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

Dereplication of peptidic natural products through database search of mass spectra

PubMed Central

Mohimani, Hosein; Gurevich, Alexey; Mikheenko, Alla; Garg, Neha; Nothias, Louis-Felix; Ninomiya, Akihiro; Takada, Kentaro; Dorrestein, Pieter C.; Pevzner, Pavel A.

2016-01-01

Peptidic Natural Products (PNPs) are widely used compounds that include many antibiotics and a variety of other bioactive peptides. While recent breakthroughs in PNP discovery raised the challenge of developing new algorithms for their analysis, identification of PNPs via database search of tandem mass spectra remains an open problem. To address this problem, natural product researchers utilize dereplication strategies that identify known PNPs and lead to the discovery of new ones even in cases when the reference spectra are not present in existing spectral libraries. DEREPLICATOR is a new dereplication algorithm that enabled high-throughput PNP identification and that is compatible with large-scale mass spectrometry-based screening platforms for natural product discovery. After searching nearly one hundred million tandem mass spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure, DEREPLICATOR identified an order of magnitude more PNPs (and their new variants) than any previous dereplication efforts. PMID:27820803

NGDS Final Report

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

Blackman, Harold; Moore, Joseph

2014-06-30

The ultimate goal of the National Geothermal Data System (NGDS) is to support the discovery and generation of geothermal sources of energy. The NGDS was designed and has been implemented to provide online access to important geothermal-related data from a network of data providers in order to: • Increase the efficiency of exploration, development and usage of geothermal energy by providing a basis for financial risk analysis of potential sites • Assist state and federal agencies in making land and resource management assessments • Foster the discovery of new geothermal resources by supporting ongoing and future geothermal-related research • Increasemore » public awareness of geothermal energy It is through the implementation of this distributed data system and its subsequent use that substantial increases to the general access and understanding of geothermal related data will result. NGDS provides a mechanism for the sharing of data thereby fostering the discovery of new resources and supporting ongoing geothermal research.« less

A Road Map for Precision Medicine in the Epilepsies

PubMed Central

2015-01-01

Summary Technological advances have paved the way for accelerated genomic discovery and are bringing precision medicine clearly into view. Epilepsy research in particular is well-suited to serve as a model for the development and deployment of targeted therapeutics in precision medicine because of the rapidly expanding genetic knowledge base in epilepsy, the availability of good in vitro and in vivo model systems to efficiently study the biological consequences of genetic mutations, the ability to turn these models into effective drug screening platforms, and the establishment of collaborative research groups. Moving forward, it is critical that we strengthen these collaborations, particularly through integrated research platforms to provide robust analyses both for accurate personal genome analysis and gene and drug discovery. Similarly, the implementation of clinical trial networks will allow the expansion of patient sample populations with genetically defined epilepsy so that drug discovery can be translated into clinical practice. PMID:26416172

Cytokines in Alzheimer's disease and multiple sclerosis.

PubMed

Patterson, P H

1995-10-01

Cytokines are well known as mediators of inflammation, and recent work has highlighted the role of these agents and inflammatory events in Alzheimer's disease and multiple sclerosis. The discovery of subclasses of T-helper cells has provided a critical framework to aid in understanding how the cytokine network regulates these diseases.

Ghost writer | ASCR Discovery

Science.gov Websites

the one illustrated here, the outer membrane protein OprF of Pseudomonas aeruginosa in its -1990s, NWChem was designed to run on networked processors, as in an HPC system, using one-sided communication, says Jeff Hammond of Intel Corp.'s Parallel Computing Laboratory. In one-sided communication, a

An Unsupervised Method for Uncovering Morphological Chains (Open Access, Publisher’s Version)

DTIC Science & Technology

2015-03-08

Consortium. Marco Baroni, Johannes Matiasek, and Harald Trost. 2002. Unsupervised discovery of morphologically re- lated words based on orthographic and...Better word representations with re- cursive neural networks for morphology. In CoNLL, Sofia, Bulgaria. Mohamed Maamouri, Ann Bies, Hubert Jin, and Tim

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

PubMed

Ziebarth, Jesse D; Cui, Yan

2017-01-01

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Exploring Wound-Healing Genomic Machinery with a Network-Based Approach

PubMed Central

Vitali, Francesca; Marini, Simone; Balli, Martina; Grosemans, Hanne; Sampaolesi, Maurilio; Lussier, Yves A.; Cusella De Angelis, Maria Gabriella; Bellazzi, Riccardo

2017-01-01

The molecular mechanisms underlying tissue regeneration and wound healing are still poorly understood despite their importance. In this paper we develop a bioinformatics approach, combining biology and network theory to drive experiments for better understanding the genetic underpinnings of wound healing mechanisms and for selecting potential drug targets. We start by selecting literature-relevant genes in murine wound healing, and inferring from them a Protein-Protein Interaction (PPI) network. Then, we analyze the network to rank wound healing-related genes according to their topological properties. Lastly, we perform a procedure for in-silico simulation of a treatment action in a biological pathway. The findings obtained by applying the developed pipeline, including gene expression analysis, confirms how a network-based bioinformatics method is able to prioritize candidate genes for in vitro analysis, thus speeding up the understanding of molecular mechanisms and supporting the discovery of potential drug targets. PMID:28635674

Fusion Energy Sciences Network Requirements

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

Dart, Eli; Tierney, Brian

2012-09-26

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy Office of Science, the single largest supporter of basic research in the physical sciences in the United States. In support of the Office of Science programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In December 2011, ESnet and the Office of Fusion Energy Sciences (FES), of the DOE Officemore » of Science (SC), organized a workshop to characterize the networking requirements of the programs funded by FES. The requirements identified at the workshop are summarized in the Findings section, and are described in more detail in the body of the report.« less

Nurses collaborating with cross disciplinary networks: starting to integrate genomics into practice.

PubMed

Adegbola, Maxine

2010-07-01

Nurses and other health-care providers are poised to include genetic discoveries into practice settings and to translate such knowledge for consumer benefit within culturally appropriate contexts. Nurses must seek collaboration with multi-disciplinary networks both locally and internationally. They must also capitalize on the expertise of other seasoned researchers in order to gain national and international exposure, recognition, and funding. Scholarly tailgating is using network relationships to achieve one's professional goals, and capitalizing on expert knowledge from seasoned researchers, educators, and practitioners from diverse international groups. By using scholarly tailgating principles, nurses can become important agents of change for multi-disciplinary networks, and thereby assist in decreasing health disparities. The purpose of this document is to encourage and inspire nurses to seek collaborative multi-disciplinary networks to enable genomic integration into health-care practice and education. Strategies for integrating genomics into practice settings are discussed.

A complex of meteorite-forming bodies (the Innisfree - Ridgedale family).

NASA Astrophysics Data System (ADS)

Shestaka, I. S.

1994-12-01

For the first time a swarm of meteorite-forming bodies was identified. Yearly this swarm's orbit approaches the Earth's orbit in early February. This swarm contains the Innisfree and Ridgedale fireballs, 9 small meteoric swarms, several asteroids and 12 fireballs photographed by the cameras of the Prairie Network and Canadian Meteorite Observation and Discovery Project. The discovery of this complex, intensive bombardments of the Moon's surface recorded by means of seismographs left on the Moon, the analysis of the time distributions of meteorite falls on the Earth and other established facts confirm the existence of swarms of meteorite-forming bodies which are crossing the Earth's orbit.

Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

PubMed Central

Hwang, Sohyun; Rhee, Seung Y; Marcotte, Edward M; Lee, Insuk

2012-01-01

AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests. PMID:21886106

Mammalian synthetic biology: emerging medical applications

PubMed Central

Kis, Zoltán; Pereira, Hugo Sant'Ana; Homma, Takayuki; Pedrigi, Ryan M.; Krams, Rob

2015-01-01

In this review, we discuss new emerging medical applications of the rapidly evolving field of mammalian synthetic biology. We start with simple mammalian synthetic biological components and move towards more complex and therapy-oriented gene circuits. A comprehensive list of ON–OFF switches, categorized into transcriptional, post-transcriptional, translational and post-translational, is presented in the first sections. Subsequently, Boolean logic gates, synthetic mammalian oscillators and toggle switches will be described. Several synthetic gene networks are further reviewed in the medical applications section, including cancer therapy gene circuits, immuno-regulatory networks, among others. The final sections focus on the applicability of synthetic gene networks to drug discovery, drug delivery, receptor-activating gene circuits and mammalian biomanufacturing processes. PMID:25808341

Integrating non-coding RNAs in JAK-STAT regulatory networks

PubMed Central

Witte, Steven; Muljo, Stefan A

2014-01-01

Being a well-characterized pathway, JAK-STAT signaling serves as a valuable paradigm for studying the architecture of gene regulatory networks. The discovery of untranslated or non-coding RNAs, namely microRNAs and long non-coding RNAs, provides an opportunity to elucidate their roles in such networks. In principle, these regulatory RNAs can act as downstream effectors of the JAK-STAT pathway and/or affect signaling by regulating the expression of JAK-STAT components. Examples of interactions between signaling pathways and non-coding RNAs have already emerged in basic cell biology and human diseases such as cancer, and can potentially guide the identification of novel biomarkers or drug targets for medicine. PMID:24778925

Membrane-trafficking sorting hubs: cooperation between PI4P and small GTPases at the trans-Golgi Network

PubMed Central

Santiago-Tirado, Felipe H.; Bretscher, Anthony

2011-01-01

Cell polarity in eukaryotes requires constant sorting, packaging, and transport of membrane-bound cargo within the cell. These processes occur in two sorting hubs: the recycling endosome for incoming material, and the trans-Golgi Network for outgoing. Phosphatidylinositol 3-phosphate and 4–5 phosphate are enriched at the endocytic and exocytic sorting hubs, respectively, where they act together with small GTPases to recruit factors to segregate cargo and regulate carrier formation and transport. In this review, we summarize the current understanding of how these lipids and GTPases directly regulate membrane trafficking, emphasizing the recent discoveries of phosphatidylinositol 4-phosphate functions at the trans-Golgi Network. PMID:21764313

Providing data science support for systems pharmacology and its implications to drug discovery.

PubMed

Hart, Thomas; Xie, Lei

2016-01-01

The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.

Using Content Distribution Networks for Astronomy Outreach

NASA Astrophysics Data System (ADS)

Jäger, M.; Christiansen, L. L.; André, M.

2015-09-01

Thousands of people from all over the world search the internet on a daily basis for the newest discoveries in astronomy: be it in the form of press releases, high resolution images, videos or even planetarium fulldome content. The growing amount of data available, combined with the increasing number of media files and users distributed across the globe, leads to a significant decrease in speed for those users located furthest from the server delivering the content. One solution for bringing astronomical content to users faster is to use a content delivery network.

Entitymetrics: Measuring the Impact of Entities

PubMed Central

Ding, Ying; Song, Min; Han, Jia; Yu, Qi; Yan, Erjia; Lin, Lili; Chambers, Tamy

2013-01-01

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD. PMID:24009660

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks.

PubMed

He, Jieyue; Wang, Chunyan; Qiu, Kunpu; Zhong, Wei

2014-01-01

Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. The algorithm of probability graph isomorphism evaluation based on circuit simulation method excludes most of subgraphs which are not probability isomorphism and reduces the search space of the probability isomorphism subgraphs using the mismatch values in the node voltage set. It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies.

An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks

PubMed Central

2014-01-01

Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism evaluation based on circuit simulation method excludes most of subgraphs which are not probability isomorphism and reduces the search space of the probability isomorphism subgraphs using the mismatch values in the node voltage set. It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies. PMID:25350277

Cluster Based Location-Aided Routing Protocol for Large Scale Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Wang, Yi; Dong, Liang; Liang, Taotao; Yang, Xinyu; Zhang, Deyun

Routing algorithms with low overhead, stable link and independence of the total number of nodes in the network are essential for the design and operation of the large-scale wireless mobile ad hoc networks (MANET). In this paper, we develop and analyze the Cluster Based Location-Aided Routing Protocol for MANET (C-LAR), a scalable and effective routing algorithm for MANET. C-LAR runs on top of an adaptive cluster cover of the MANET, which can be created and maintained using, for instance, the weight-based distributed algorithm. This algorithm takes into consideration the node degree, mobility, relative distance, battery power and link stability of mobile nodes. The hierarchical structure stabilizes the end-to-end communication paths and improves the networks' scalability such that the routing overhead does not become tremendous in large scale MANET. The clusterheads form a connected virtual backbone in the network, determine the network's topology and stability, and provide an efficient approach to minimizing the flooding traffic during route discovery and speeding up this process as well. Furthermore, it is fascinating and important to investigate how to control the total number of nodes participating in a routing establishment process so as to improve the network layer performance of MANET. C-LAR is to use geographical location information provided by Global Position System to assist routing. The location information of destination node is used to predict a smaller rectangle, isosceles triangle, or circle request zone, which is selected according to the relative location of the source and the destination, that covers the estimated region in which the destination may be located. Thus, instead of searching the route in the entire network blindly, C-LAR confines the route searching space into a much smaller estimated range. Simulation results have shown that C-LAR outperforms other protocols significantly in route set up time, routing overhead, mean delay and packet collision, and simultaneously maintains low average end-to-end delay, high success delivery ratio, low control overhead, as well as low route discovery frequency.

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts

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

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2011-02-18

Transcriptional regulatory networks are being determined using “reverse engineering” methods that infer connections based on correlations in gene state. Corroboration of such networks through independent means such as evidence from the biomedical literature is desirable. Here, we explore a novel approach, a bootstrapping version of our previous Cross-Ontological Analytic method (XOA) that can be used for semi-automated annotation and verification of inferred regulatory connections, as well as for discovery of additional functional relationships between the genes. First, we use our annotation and network expansion method on a biological network learned entirely from the literature. We show how new relevant linksmore » between genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. Second, we apply our method to annotation, verification, and expansion of a set of regulatory connections found by the Context Likelihood of Relatedness algorithm.« less

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

PubMed

Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

2015-01-01

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

The Role of Surface Water for the Branching Geometry of Mars' Channel Networks

NASA Astrophysics Data System (ADS)

Seybold, H. F.; Rothman, D.; Kirchner, J. W.

2016-12-01

The controversy over the origin of Mars' channel networks is almost as old as their discovery 150 years ago. In recent decades, new Mars probe missions have revealed detailed network structures, and new studies suggest that Mars once had an active hydrologic cycle. But how this water flowed and how it could have carved these huge channel networks remains unclear. A recent analysis of high-resolution data for the Continental United States suggests that climate leaves a characteristic imprint in the branching geometry of stream networks: arid regions dominated by overland or near-surface flows have much narrower branching angles than humid regions with greater groundwater recharge. Based on this result we analyze the channel networks of Mars, and find that their geometry resembles those created by near-surface and overland flows on Earth. This result gives additional support to the hypothesis that Mars once had a more active hydrologic cycle, with liquid water flowing over its surface.