Improving Dengue Virus Capture Rates in Humans and Vectors in Kamphaeng Phet Province, Thailand, Using an Enhanced Spatiotemporal Surveillance Strategy

PubMed Central

Thomas, Stephen J.; Aldstadt, Jared; Jarman, Richard G.; Buddhari, Darunee; Yoon, In-Kyu; Richardson, Jason H.; Ponlawat, Alongkot; Iamsirithaworn, Sopon; Scott, Thomas W.; Rothman, Alan L.; Gibbons, Robert V.; Lambrechts, Louis; Endy, Timothy P.

2015-01-01

Dengue is of public health importance in tropical and sub-tropical regions. Dengue virus (DENV) transmission dynamics was studied in Kamphaeng Phet Province, Thailand, using an enhanced spatiotemporal surveillance of 93 hospitalized subjects with confirmed dengue (initiates) and associated cluster individuals (associates) with entomologic sampling. A total of 438 associates were enrolled from 208 houses with household members with a history of fever, located within a 200-m radius of an initiate case. Of 409 associates, 86 (21%) had laboratory-confirmed DENV infection. A total of 63 (1.8%) of the 3,565 mosquitoes collected were dengue polymerase chain reaction positive (PCR+). There was a significant relationship between spatial proximity to the initiate case and likelihood of detecting DENV from associate cases and Aedes mosquitoes. The viral detection rate from human hosts and mosquito vectors in this study was higher than previously observed by the study team in the same geographic area using different methodologies. We propose that the sampling strategy used in this study could support surveillance of DENV transmission and vector interactions. PMID:25986580

Distribution and abundance of host-seeking Culex species at three proximate locations with different levels of West Nile virus activity

USGS Publications Warehouse

Rochlin, I.; Ginsberg, H.S.; Campbell, S.R.

2009-01-01

Culex species were monitored at three proximate sites with historically different West Nile virus (WNV) activities. The site with human WNV transmission (epidemic) had the lowest abundance of the putative bridge vectors, Culex pipiens and Cx. salinarius. The site with horse cases but not human cases (epizootic) had the highest percent composition of Cx. salinarius, whereas the site with WNV-positive birds only (enzootic) had the highest Cx. pipiens abundance and percent composition. A total of 29 WNV-positive Culex pools were collected at the enzootic site, 17 at the epidemic site, and 14 at the epizootic site. Published models of human risk using Cx. pipiens and Cx. salinarius as the primary bridge vectors did not explain WNV activity at our sites. Other variables, such as additional vector species, environmental components, and socioeconomic factors, need to be examined to explain the observed patterns of WNV epidemic activity.

Mapping Neglected Swimming Pools from Satellite Data for Urban Vector Control

NASA Astrophysics Data System (ADS)

Barker, C. M.; Melton, F. S.; Reisen, W. K.

2010-12-01

Neglected swimming pools provide suitable breeding habit for mosquitoes, can contain thousands of mosquito larvae, and present both a significant nuisance and public health risk due to their inherent proximity to urban and suburban populations. The rapid increase and sustained rate of foreclosures in California associated with the recent recession presents a challenge for vector control districts seeking to identify, treat, and monitor neglected pools. Commercial high resolution satellite imagery offers some promise for mapping potential neglected pools, and for mapping pools for which routine maintenance has been reestablished. We present progress on unsupervised classification techniques for mapping both neglected pools and clean pools using high resolution commercial satellite data and discuss the potential uses and limitations of this data source in support of vector control efforts. An unsupervised classification scheme that utilizes image segmentation, band thresholds, and a change detection approach was implemented for sample regions in Coachella Valley, CA and the greater Los Angeles area. Comparison with field data collected by vector control personal was used to assess the accuracy of the estimates. The results suggest that the current system may provide some utility for early detection, or cost effective and time efficient annual monitoring, but additional work is required to address spectral and spatial limitations of current commercial satellite sensors for this purpose.

Characteristics of lentiviral vectors harboring the proximal promoter of the vav proto-oncogene: a weak and efficient promoter for gene therapy.

PubMed

Almarza, Elena; Río, Paula; Meza, Nestor W; Aldea, Montserrat; Agirre, Xabier; Guenechea, Guillermo; Segovia, José C; Bueren, Juan A

2007-08-01

Recent published data have shown the efficacy of gene therapy treatments of certain monogenic diseases. Risks of insertional oncogenesis, however, indicate the necessity of developing new vectors with weaker or cell-restricted promoters to minimize the trans-activation activity of integrated proviruses. We have inserted the proximal promoter of the vav proto-oncogene into self-inactivating lentiviral vectors (vav-LVs) and investigated the expression pattern and therapeutic efficacy of these vectors. Compared with other LVs frequently used in gene therapy, vav-LVs mediated a weak, though homogeneous and stable, expression in in vitro-cultured cells. Transplantation experiments using transduced mouse bone marrow and human CD34(+) cells confirmed the stable activity of the promoter in vivo. To investigate whether the weak activity of this promoter was compatible with a therapeutic effect, a LV expressing the Fanconi anemia A (FANCA) gene was constructed (vav-FANCA LV). Although this vector induced a low expression of FANCA, compared to the expression induced by a LV harboring the spleen focus-forming virus (SFFV) promoter, the two vectors corrected the phenotype of cells from a patient with FA-A with the same efficacy. We propose that self-inactivating vectors harboring weak promoters, such as the vav promoter, will improve the safety of gene therapy and will be of particular interest for the treatment of diseases where a high expression of the transgene is not required.

Analysis of proximal contact loss between implant-supported fixed dental prostheses and adjacent teeth in relation to influential factors and effects. A cross-sectional study.

PubMed

Byun, Soo-Jung; Heo, Seok-Mo; Ahn, Seung-Geun; Chang, Moontaek

2015-06-01

The aim was to analyze influential factors and effects of proximal contact loss between implant-supported fixed dental prostheses (FDP) and adjacent teeth. Ninety-four subjects treated with 135 FDPs supported by 188 implants were included. Degree of proximal contact tightness, food impaction, and periodontal/peri-implant tissue conditions were assessed in 191 proximal embrasures between implant-supported FDPs and adjacent teeth. Potential factors influencing proximal contact loss were estimated with the generalized estimation equation (GEE) procedure. Thirty-four percent of the proximal embrasures between implant-supported FDPs and teeth lost a proximal contact. The proximal contact loss rate continuously increased over the follow-up periods. A longer follow-up period, splinted implants, and mesial aspect of proximal contact were significant factors influencing the proximal contact loss in the univariate GEE analysis, whereas a longer follow-up period was the only significant factor in the multivariate GEE analysis. Food impaction was more frequently reported in the proximal contact loss group than the proximal contact group (odds ratio: 2.2). However, the proximal contact loss did not appear to affect the periodontal/peri-implant tissue conditions. Proximal contact loss between implant-supported FDPs and teeth occurred frequently and increased continuously over the follow-up period. The proximal contact loss significantly affected food impaction, but not the periodontal/peri-implant tissue conditions. Proximal contact loss should be carefully monitored during follow-up examinations in relation to food impaction. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging.

PubMed

Wang, Shuihua; Zhang, Yudong; Liu, Ge; Phillips, Preetha; Yuan, Ti-Fei

2016-01-01

Within the past decade, computer scientists have developed many methods using computer vision and machine learning techniques to detect Alzheimer's disease (AD) in its early stages. However, some of these methods are unable to achieve excellent detection accuracy, and several other methods are unable to locate AD-related regions. Hence, our goal was to develop a novel AD brain detection method. In this study, our method was based on the three-dimensional (3D) displacement-field (DF) estimation between subjects in the healthy elder control group and AD group. The 3D-DF was treated with AD-related features. The three feature selection measures were used in the Bhattacharyya distance, Student's t-test, and Welch's t-test (WTT). Two non-parallel support vector machines, i.e., generalized eigenvalue proximal support vector machine and twin support vector machine (TSVM), were then used for classification. A 50 × 10-fold cross validation was implemented for statistical analysis. The results showed that "3D-DF+WTT+TSVM" achieved the best performance, with an accuracy of 93.05 ± 2.18, a sensitivity of 92.57 ± 3.80, a specificity of 93.18 ± 3.35, and a precision of 79.51 ± 2.86. This method also exceled in 13 state-of-the-art approaches. Additionally, we were able to detect 17 regions related to AD by using the pure computer-vision technique. These regions include sub-gyral, inferior parietal lobule, precuneus, angular gyrus, lingual gyrus, supramarginal gyrus, postcentral gyrus, third ventricle, superior parietal lobule, thalamus, middle temporal gyrus, precentral gyrus, superior temporal gyrus, superior occipital gyrus, cingulate gyrus, culmen, and insula. These regions were reported in recent publications. The 3D-DF is effective in AD subject and related region detection.

Human Bocavirus Type-1 Capsid Facilitates the Transduction of Ferret Airways by Adeno-Associated Virus Genomes.

PubMed

Yan, Ziying; Feng, Zehua; Sun, Xingshen; Zhang, Yulong; Zou, Wei; Wang, Zekun; Jensen-Cody, Chandler; Liang, Bo; Park, Soo-Yeun; Qiu, Jianming; Engelhardt, John F

2017-08-01

Human bocavirus type-1 (HBoV1) has a high tropism for the apical membrane of human airway epithelia. The packaging of a recombinant adeno-associated virus 2 (rAAV2) genome into HBoV1 capsid produces a chimeric vector (rAAV2/HBoV1) that also efficiently transduces human airway epithelia. As such, this vector is attractive for use in gene therapies to treat lung diseases such as cystic fibrosis. However, preclinical development of rAAV2/HBoV1 vectors has been hindered by the fact that humans are the only known host for HBoV1 infection. This study reports that rAAV2/HBoV1 vector is capable of efficiently transducing the lungs of both newborn (3- to 7-day-old) and juvenile (29-day-old) ferrets, predominantly in the distal airways. Analyses of in vivo, ex vivo, and in vitro models of the ferret proximal airway demonstrate that infection of this particular region is less effective than it is in humans. Studies of vector binding and endocytosis in polarized ferret proximal airway epithelial cultures revealed that a lack of effective vector endocytosis is the main cause of inefficient transduction in vitro. While transgene expression declined proportionally with growth of the ferrets following infection at 7 days of age, reinfection of ferrets with rAAV2/HBoV1 at 29 days gave rise to approximately 5-fold higher levels of transduction than observed in naive infected 29-day-old animals. The findings presented here lay the foundation for clinical development of HBoV1 capsid-based vectors for lung gene therapy in cystic fibrosis using ferret models.

Exploring the broader health and well-being outcomes of mining communities in low- and middle-income countries: A systematic review.

PubMed

Mactaggart, Fiona; McDermott, Liane; Tynan, Anna; Whittaker, Maxine

2018-07-01

Health and well-being outcomes in communities living in proximity to mining activity may be influenced by a broad spectrum of factors including population growth, economic instability or land degradation. This review aims to synthesise broader outcomes associated with mining activity and in doing so, further explore possible determinants in communities of low- and middle-income countries. Four databases were systematically searched and articles were included if the study targeted adults residing in proximity to mining activity, and measured individual or community-level health or well-being outcomes. Narrative synthesis was conducted. Twelve articles were included. Mining was perceived to influence health behaviours, employment conditions, livelihoods and socio-political factors, which were linked to poorer health outcomes. Family relationships, mental health and community cohesion were negatively associated with mining activity. High-risk health behaviours, population growth and changes in vector ecology from environmental modification were associated with increased infectious disease prevalence. This review presents the broader health and well-being outcomes and their determinants, and strengthens the evidence to improve measurement and management of the public health implications of mining. This will support the mining sector to make sustainable investments, and support governments to maximise community development and minimise negative impacts.

Orbiter/payload proximity operations SES Postsim report. Lateral approach and other techniques

NASA Technical Reports Server (NTRS)

Olszewski, O.

1978-01-01

Various approach and stationkeeping simulations (proximity operations) were conducted in the Shuttle engineering simulator (SES). This simulator is the first to dynamically include the Orbiter reaction control system (RCS) plume effects on a payload being recovered after rendezvous operations. A procedure for braking, using the simultaneous firing of both jets, was evaluated and found very useful for proximity operations. However this procedure is very inefficient in the RCS usage and requires modifications to the digital autopilot (DAP) software. A new final approach, the lateral approach technique (LAT), or the momentum vector proximity approach, was also evaluated in the simulations. The LAT, which included a tailfirst approach for braking, was evaluated successfully with both inertial and gravity stabilized payloads.

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

NMR-based urine analysis in rats: prediction of proximal tubule kidney toxicity and phospholipidosis.

PubMed

Lienemann, Kai; Plötz, Thomas; Pestel, Sabine

2008-01-01

The aim of safety pharmacology is early detection of compound-induced side-effects. NMR-based urine analysis followed by multivariate data analysis (metabonomics) identifies efficiently differences between toxic and non-toxic compounds; but in most cases multiple administrations of the test compound are necessary. We tested the feasibility of detecting proximal tubule kidney toxicity and phospholipidosis with metabonomics techniques after single compound administration as an early safety pharmacology approach. Rats were treated orally, intravenously, inhalatively or intraperitoneally with different test compounds. Urine was collected at 0-8 h and 8-24 h after compound administration, and (1)H NMR-patterns were recorded from the samples. Variation of post-processing and feature extraction methods led to different views on the data. Support Vector Machines were trained on these different data sets and then aggregated as experts in an Ensemble. Finally, validity was monitored with a cross-validation study using a training, validation, and test data set. Proximal tubule kidney toxicity could be predicted with reasonable total classification accuracy (85%), specificity (88%) and sensitivity (78%). In comparison to alternative histological studies, results were obtained quicker, compound need was reduced, and very importantly fewer animals were needed. In contrast, the induction of phospholipidosis by the test compounds could not be predicted using NMR-based urine analysis or the previously published biomarker PAG. NMR-based urine analysis was shown to effectively predict proximal tubule kidney toxicity after single compound administration in rats. Thus, this experimental design allows early detection of toxicity risks with relatively low amounts of compound in a reasonably short period of time.

Avian pox in Magellanic Penguins (Spheniscus magellanicus).

PubMed

Kane, Olivia J; Uhart, Marcela M; Rago, Virginia; Pereda, Ariel J; Smith, Jeffrey R; Van Buren, Amy; Clark, J Alan; Boersma, P Dee

2012-07-01

Avian pox is an enveloped double-stranded DNA virus that is mechanically transmitted via arthropod vectors or mucosal membrane contact with infectious particles or birds. Magellanic Penguins (Spheniscus magellanicus) from two colonies (Punta Tombo and Cabo Dos Bahías) in Argentina showed sporadic, nonepidemic signs of avian pox during five and two of 29 breeding seasons (1982-2010), respectively. In Magellanic Penguins, avian pox expresses externally as wart-like lesions around the beak, flippers, cloaca, feet, and eyes. Fleas (Parapsyllus longicornis) are the most likely arthropod vectors at these colonies. Three chicks with cutaneous pox-like lesions were positive for Avipoxvirus and revealed phylogenetic proximity with an Avipoxvirus found in Black-browed Albatross (Thalassarche melanophrys) from the Falkland Islands in 1987. This proximity suggests a long-term circulation of seabird Avipoxviruses in the southwest Atlantic. Avian pox outbreaks in these colonies primarily affected chicks, often resulted in death, and were not associated with handling, rainfall, or temperature.

Modeling the effects of palm-house proximity on the theoretical risk of Chagas disease transmission in a rural locality of the Orinoco basin, Colombia.

PubMed

Erazo, Diana; Cordovez, Juan

2016-11-18

Chagas disease is a major public health concern in Latin America and it is transmitted by insects of the subfamily Triatominae, including Rhodnius spp. Since palm trees are ubiquitous in Colombia and a habitat for Rhodnius spp., the presence of palms near villages could increase contact rates between vectors and humans. Therefore, knowing whether a relationship exists between the proximity of palms to villages and the abundance and distribution of vectors therein, may be critical for Chagas disease prevention programs. Adapting a mathematical model for R. prolixus population dynamics in a small village, we model the implications of changing distances between palms and dwellings, to the risk of Chagas disease infection. We implemented a mathematical model that reflects R. prolixus population dynamics in a small village located in the department of Casanare (Colombia) to study the role of palm-house proximity. We varied the distance between palms and houses by monitoring the network global efficiency metric. We constructed 1,000 hypothetical villages varying distances and each one was run 100 times. According to the model, as palm-house proximity increases, houses were more likely to be visited by triatomine bugs. The number of bugs per unit time increased progressively in a non-linear fashion with high variability. We stress the importance of village configuration on the model output. From a theoretical perspective, palm-house proximity may have a positive effect on the incidence of Chagas disease. The model predicts a 1% increase in new human cases per year when houses and palms are brought closer by 75%.

A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis

PubMed Central

Rahman, M. M.; Antani, S. K.; Thoma, G. R.

2011-01-01

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall. PMID:21822350

Risk analysis and prediction of visceral leishmaniasis dispersion in São Paulo State, Brazil.

PubMed

Sevá, Anaiá da Paixão; Mao, Liang; Galvis-Ovallos, Fredy; Tucker Lima, Joanna Marie; Valle, Denis

2017-02-01

Visceral leishmaniasis (VL) is an important neglected disease caused by a protozoan parasite, and represents a serious public health problem in many parts of the world. It is zoonotic in Europe and Latin America, where infected dogs constitute the main domestic reservoir for the parasite and play a key role in VL transmission to humans. In Brazil this disease is caused by the protozoan Leishmania infantum chagasi, and is transmitted by the sand fly Lutzomyia longipalpis. Despite programs aimed at eliminating infection sources, the disease continues to spread throughout the Country. VL in São Paulo State, Brazil, first appeared in the northwestern region, spreading in a southeasterly direction over time. We integrate data on the VL vector, infected dogs and infected human dispersion from 1999 to 2013 through an innovative spatial temporal Bayesian model in conjunction with geographic information system. This model is used to infer the drivers of the invasion process and predict the future progression of VL through the State. We found that vector dispersion was influenced by vector presence in nearby municipalities at the previous time step, proximity to the Bolívia-Brazil gas pipeline, and high temperatures (i.e., annual average between 20 and 23°C). Key factors affecting infected dog dispersion included proximity to the Marechal Rondon Highway, high temperatures, and presence of the competent vector within the same municipality. Finally, vector presence, presence of infected dogs, and rainfall (approx. 270 to 540mm/year) drove the dispersion of human VL cases. Surprisingly, economic factors exhibited no noticeable influence on disease dispersion. Based on these drivers and stochastic simulations, we identified which municipalities are most likely to be invaded by vectors and infected hosts in the future. Prioritizing prevention and control strategies within the identified municipalities may help halt the spread of VL while reducing monitoring costs. Our results contribute important knowledge to public and animal health policy planning, and suggest that prevention and control strategies should focus on vector control and on blocking contact between vectors and hosts in the priority areas identified to be at risk.

Risk analysis and prediction of visceral leishmaniasis dispersion in São Paulo State, Brazil

PubMed Central

Mao, Liang; Galvis-Ovallos, Fredy; Tucker Lima, Joanna Marie; Valle, Denis

2017-01-01

Visceral leishmaniasis (VL) is an important neglected disease caused by a protozoan parasite, and represents a serious public health problem in many parts of the world. It is zoonotic in Europe and Latin America, where infected dogs constitute the main domestic reservoir for the parasite and play a key role in VL transmission to humans. In Brazil this disease is caused by the protozoan Leishmania infantum chagasi, and is transmitted by the sand fly Lutzomyia longipalpis. Despite programs aimed at eliminating infection sources, the disease continues to spread throughout the Country. VL in São Paulo State, Brazil, first appeared in the northwestern region, spreading in a southeasterly direction over time. We integrate data on the VL vector, infected dogs and infected human dispersion from 1999 to 2013 through an innovative spatial temporal Bayesian model in conjunction with geographic information system. This model is used to infer the drivers of the invasion process and predict the future progression of VL through the State. We found that vector dispersion was influenced by vector presence in nearby municipalities at the previous time step, proximity to the Bolívia-Brazil gas pipeline, and high temperatures (i.e., annual average between 20 and 23°C). Key factors affecting infected dog dispersion included proximity to the Marechal Rondon Highway, high temperatures, and presence of the competent vector within the same municipality. Finally, vector presence, presence of infected dogs, and rainfall (approx. 270 to 540mm/year) drove the dispersion of human VL cases. Surprisingly, economic factors exhibited no noticeable influence on disease dispersion. Based on these drivers and stochastic simulations, we identified which municipalities are most likely to be invaded by vectors and infected hosts in the future. Prioritizing prevention and control strategies within the identified municipalities may help halt the spread of VL while reducing monitoring costs. Our results contribute important knowledge to public and animal health policy planning, and suggest that prevention and control strategies should focus on vector control and on blocking contact between vectors and hosts in the priority areas identified to be at risk. PMID:28166251

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

E4orf1 Enhances Glucose Uptake Independent of Proximal Insulin Signaling

PubMed Central

Na, Ha-Na; Hegde, Vijay; Dubuisson, Olga; Dhurandhar, Nikhil V.

2016-01-01

Impaired proximal insulin signaling is often present in diabetes. Hence, approaches to enhance glucose disposal independent of proximal insulin signaling are desirable. Evidence indicates that Adenovirus-derived E4orf1 protein may offer such an approach. This study determined if E4orf1 improves insulin sensitivity and downregulates proximal insulin signaling in vivo and enhances cellular glucose uptake independent of proximal insulin signaling in vitro. High fat fed mice were injected with a retrovirus plasmid expressing E4orf1, or a null vector. E4orf1 significantly improved insulin sensitivity in response to a glucose load. Yet, their proximal insulin signaling in fat depots was impaired, as indicated by reduced tyrosine phosphorylation of insulin receptor (IR), and significantly increased abundance of ectonucleotide pyrophosphatase/phosphodiesterase-1 (ENPP1). In 3T3-L1 pre-adipocytes E4orf1 expression impaired proximal insulin signaling. Whereas, treatment with rosiglitazone reduced ENPP1 abundance. Unaffected by IR-KD (insulin receptor knockdown) with siRNA, E4orf1 significantly up-regulated distal insulin signaling pathway and enhanced cellular glucose uptake. In vivo, E4orf1 impairs proximal insulin signaling in fat depots yet improves glycemic control. This is probably explained by the ability of E4orf1 to promote cellular glucose uptake independent of proximal insulin signaling. E4orf1 may provide a therapeutic template to enhance glucose disposal in the presence of impaired proximal insulin signaling. PMID:27537838

E4orf1 Enhances Glucose Uptake Independent of Proximal Insulin Signaling.

PubMed

Na, Ha-Na; Hegde, Vijay; Dubuisson, Olga; Dhurandhar, Nikhil V

2016-01-01

Impaired proximal insulin signaling is often present in diabetes. Hence, approaches to enhance glucose disposal independent of proximal insulin signaling are desirable. Evidence indicates that Adenovirus-derived E4orf1 protein may offer such an approach. This study determined if E4orf1 improves insulin sensitivity and downregulates proximal insulin signaling in vivo and enhances cellular glucose uptake independent of proximal insulin signaling in vitro. High fat fed mice were injected with a retrovirus plasmid expressing E4orf1, or a null vector. E4orf1 significantly improved insulin sensitivity in response to a glucose load. Yet, their proximal insulin signaling in fat depots was impaired, as indicated by reduced tyrosine phosphorylation of insulin receptor (IR), and significantly increased abundance of ectonucleotide pyrophosphatase/phosphodiesterase-1 (ENPP1). In 3T3-L1 pre-adipocytes E4orf1 expression impaired proximal insulin signaling. Whereas, treatment with rosiglitazone reduced ENPP1 abundance. Unaffected by IR-KD (insulin receptor knockdown) with siRNA, E4orf1 significantly up-regulated distal insulin signaling pathway and enhanced cellular glucose uptake. In vivo, E4orf1 impairs proximal insulin signaling in fat depots yet improves glycemic control. This is probably explained by the ability of E4orf1 to promote cellular glucose uptake independent of proximal insulin signaling. E4orf1 may provide a therapeutic template to enhance glucose disposal in the presence of impaired proximal insulin signaling.

Comparison of organs' shapes with geometric and Zernike 3D moments.

PubMed

Broggio, D; Moignier, A; Ben Brahim, K; Gardumi, A; Grandgirard, N; Pierrat, N; Chea, M; Derreumaux, S; Desbrée, A; Boisserie, G; Aubert, B; Mazeron, J-J; Franck, D

2013-09-01

The morphological similarity of organs is studied with feature vectors based on geometric and Zernike 3D moments. It is particularly investigated if outliers and average models can be identified. For this purpose, the relative proximity to the mean feature vector is defined, principal coordinate and clustering analyses are also performed. To study the consistency and usefulness of this approach, 17 livers and 76 hearts voxel models from several sources are considered. In the liver case, models with similar morphological feature are identified. For the limited amount of studied cases, the liver of the ICRP male voxel model is identified as a better surrogate than the female one. For hearts, the clustering analysis shows that three heart shapes represent about 80% of the morphological variations. The relative proximity and clustering analysis rather consistently identify outliers and average models. For the two cases, identification of outliers and surrogate of average models is rather robust. However, deeper classification of morphological feature is subject to caution and can only be performed after cross analysis of at least two kinds of feature vectors. Finally, the Zernike moments contain all the information needed to re-construct the studied objects and thus appear as a promising tool to derive statistical organ shapes. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Risk Transmission Indicator of Schistosomiasis Japonicum Considering Human Activities in Lake and Marshland Regions- A Case Study of Poyang Lake, Jiangxi Province, P.R. China

NASA Astrophysics Data System (ADS)

Marie, Tiphanie; Yesou, Herve; Huber, Claire; De Fraipont, Paul; Uribe, Carlos; Lacaux, Jean-Pierre; Lafaye, Murielle; Lai, Xijun; Desnos, Yves-Louis

2013-01-01

This paper present the method used to determine the areas where schistosomiasis transmission is the higher. A primary work was necessary to this study: identification of potential presence of schistosomiasis japonicum’s vector in Poyang lakeshore area (Jiangxi Province, P.R. China). Results obtained from its first work were crossing with the most risky human activities and with villages to elaborate a level of transmission risk. The first parameter determined concern fishing, which was identified like the most risky activity for schistosomiasis transmission, and fish traps were digitalized using a very high resolution ALOS data. The second parameter is about the risky areas for buffalo grazing, and vector potential presence areas were crossed with village proximity to determine the most risky areas for human transmission. The third parameter built is a level of risk for each village digitalized around Poyang Lake, taking into account the proximity and level of potential presence of vector’s areas.

Meissner effect in normal-superconducting proximity-contact double layers

NASA Astrophysics Data System (ADS)

Higashitani, Seiji; Nagai, Katsuhiko

1995-02-01

The Meissner effect in normal-superconducting proximity-contact double layers is discussed in the clean limit. The diamagnetic current is calculated using the quasi-classical Green's function. We obtain the quasi-classical Green's function linear in the vector potential in the proximity-contact double layers with a finite reflection coefficient at the interface. It is found that the diamagnetic current in the clean normal layer is constant in space, therefore, the magnetic field linearly decreases in the clean normal layer. We give an explicit expression for the screening length in the clean normal layer and study its temperature dependence. We show that the temperature dependence in the clean normal layer is considerably different from that in the dirty normal layer and agrees with a recent experiment in Au-Nb system.

Spatial distribution of an infectious disease in a small mammal community

NASA Astrophysics Data System (ADS)

Correa, Juana P.; Bacigalupo, Antonella; Fontúrbel, Francisco E.; Oda, Esteban; Cattan, Pedro E.; Solari, Aldo; Botto-Mahan, Carezza

2015-10-01

Chagas disease is a zoonosis caused by the parasite Trypanosoma cruzi and transmitted by insect vectors to several mammals, but little is known about its spatial epidemiology. We assessed the spatial distribution of T. cruzi infection in vectors and small mammals to test if mammal infection status is related to the proximity to vector colonies. During four consecutive years we captured and georeferenced the locations of mammal species and colonies of Mepraia spinolai, a restricted-movement vector. Infection status on mammals and vectors was evaluated by molecular techniques. To examine the effect of vector colonies on mammal infection status, we constructed an infection distance index using the distance between the location of each captured mammal to each vector colony and the average T. cruzi prevalence of each vector colony, weighted by the number of colonies assessed. We collected and evaluated T. cruzi infection in 944 mammals and 1976 M. spinolai. We found a significant effect of the infection distance index in explaining their infection status, when considering all mammal species together. By examining the most abundant species separately, we found this effect only for the diurnal and gregarious rodent Octodon degus. Spatially explicit models involving the prevalence and location of infected vectors and hosts had not been reported previously for a wild disease.

Systemic errors in quantitative polymerase chain reaction titration of self-complementary adeno-associated viral vectors and improved alternative methods.

PubMed

Fagone, Paolo; Wright, J Fraser; Nathwani, Amit C; Nienhuis, Arthur W; Davidoff, Andrew M; Gray, John T

2012-02-01

Self-complementary AAV (scAAV) vector genomes contain a covalently closed hairpin derived from a mutated inverted terminal repeat that connects the two monomer single-stranded genomes into a head-to-head or tail-to-tail dimer. We found that during quantitative PCR (qPCR) this structure inhibits the amplification of proximal amplicons and causes the systemic underreporting of copy number by as much as 10-fold. We show that cleavage of scAAV vector genomes with restriction endonuclease to liberate amplicons from the covalently closed terminal hairpin restores quantitative amplification, and we implement this procedure in a simple, modified qPCR titration method for scAAV vectors. In addition, we developed and present an AAV genome titration procedure based on gel electrophoresis that requires minimal sample processing and has low interassay variability, and as such is well suited for the rigorous quality control demands of clinical vector production facilities.

Adaptive proxy map server for efficient vector spatial data rendering

NASA Astrophysics Data System (ADS)

Sayar, Ahmet

2013-01-01

The rapid transmission of vector map data over the Internet is becoming a bottleneck of spatial data delivery and visualization in web-based environment because of increasing data amount and limited network bandwidth. In order to improve both the transmission and rendering performances of vector spatial data over the Internet, we propose a proxy map server enabling parallel vector data fetching as well as caching to improve the performance of web-based map servers in a dynamic environment. Proxy map server is placed seamlessly anywhere between the client and the final services, intercepting users' requests. It employs an efficient parallelization technique based on spatial proximity and data density in case distributed replica exists for the same spatial data. The effectiveness of the proposed technique is proved at the end of the article by the application of creating map images enriched with earthquake seismic data records.

Verifying the equivalence of representations of the knee joint moment vector from a drop vertical jump task.

PubMed

Nichols, Julia K; O'Reilly, Oliver M

2017-03-01

Biomechanics software programs, such as Visual3D, Nexus, Cortex, and OpenSim, have the capability of generating several distinct component representations for joint moments and forces from motion capture data. These representations include those for orthonormal proximal and distal coordinate systems and a non-orthogonal joint coordinate system. In this article, a method is presented to address the challenging problem of evaluating and verifying the equivalence of these representations. The method accommodates the difficulty that there are two possible sets of non-orthogonal basis vectors that can be used to express a vector in the joint coordinate system and is illuminated using motion capture data from a drop vertical jump task. Copyright © 2016 Elsevier B.V. All rights reserved.

Geographic distribution, evolution, and disease importance of species within the Neotropical Anopheles albitarsis Group (Diptera, Culicidae).

PubMed

Foley, Desmond H; Linton, Yvonne-Marie; Ruiz-Lopez, J Freddy; Conn, Jan E; Sallum, Maria Anice M; Póvoa, Marinete M; Bergo, Eduardo S; Oliveira, Tatiane M P; Sucupira, Izis; Wilkerson, Richard C

2014-06-01

The Anopheles albitarsis group of mosquitoes comprises eight recognized species and one mitochondrial lineage. Our knowledge of malaria vectorial importance and the distribution and evolution of these taxa is incomplete. We constructed ecological niche models (ENMs) for these taxa and used hypothesized phylogenetic relationships and ENMs to investigate environmental and ecological divergence associated with speciation events. Two major clades were identified, one north (Clade 1) and one south (Clade 2) of the Amazon River that likely is or was a barrier to mosquito movement. Clade 1 species occur more often in higher average temperature locations than Clade 2 species, and taxon splits within Clade 1 corresponded with a greater divergence of variables related to precipitation than was the case within Clade 2. Comparison of the ecological profiles of sympatric species and sister species support the idea that phylogenetic proximity is related to ecological similarity. Anopheles albitarsis I, An. janconnae, and An. marajoara ENMs had the highest percentage of their predicted suitable habitat overlapping distribution models of Plasmodium falciparum and P. vivax, and warrant additional studies of the transmission potential of these species. Phylogenetic proximity may be related to malaria vectorial importance within the Albitarsis Group. © 2014 The Society for Vector Ecology.

Method and apparatus for identification of hazards along an intended travel route

NASA Technical Reports Server (NTRS)

Kronfeld, Kevin M. (Inventor); Lapis, Mary Beth (Inventor); Walling, Karen L. (Inventor); Chackalackal, Mathew S. (Inventor)

2003-01-01

Targets proximate to a travel route plan were evaluated to determine hazardousness. Projected geometric representation of a vehicle determines intrusion of hazardous targets along travel route plan. Geometric representation of hazardous targets projected along motion vector to determine intrusion upon travel route plan. Intrusion assessment presented on user display.

Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont Region, Italy

PubMed Central

2011-01-01

Background West Nile Virus (WNV) transmission in Italy was first reported in 1998 as an equine outbreak near the swamps of Padule di Fucecchio, Tuscany. No other cases were identified during the following decade until 2008, when horse and human outbreaks were reported in Emilia Romagna, North Italy. Since then, WNV outbreaks have occurred annually, spreading from their initial northern foci throughout the country. Following the outbreak in 1998 the Italian public health authority defined a surveillance plan to detect WNV circulation in birds, horses and mosquitoes. By applying spatial statistical analysis (spatial point pattern analysis) and models (Bayesian GLMM models) to a longitudinal dataset on the abundance of the three putative WNV vectors [Ochlerotatus caspius (Pallas 1771), Culex pipiens (Linnaeus 1758) and Culex modestus (Ficalbi 1890)] in eastern Piedmont, we quantified their abundance and distribution in space and time and generated prediction maps outlining the areas with the highest vector productivity and potential for WNV introduction and amplification. Results The highest abundance and significant spatial clusters of Oc. caspius and Cx. modestus were in proximity to rice fields, and for Cx. pipiens, in proximity to highly populated urban areas. The GLMM model showed the importance of weather conditions and environmental factors in predicting mosquito abundance. Distance from the preferential breeding sites and elevation were negatively associated with the number of collected mosquitoes. The Normalized Difference Vegetation Index (NDVI) was positively correlated with mosquito abundance in rice fields (Oc. caspius and Cx. modestus). Based on the best models, we developed prediction maps for the year 2010 outlining the areas where high abundance of vectors could favour the introduction and amplification of WNV. Conclusions Our findings provide useful information for surveillance activities aiming to identify locations where the potential for WNV introduction and local transmission are highest. Such information can be used by vector control offices to stratify control interventions in areas prone to the invasion of WNV and other mosquito-transmitted pathogens. PMID:22152822

Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont Region, Italy.

PubMed

Bisanzio, Donal; Giacobini, Mario; Bertolotti, Luigi; Mosca, Andrea; Balbo, Luca; Kitron, Uriel; Vazquez-Prokopec, Gonzalo M

2011-12-09

West Nile Virus (WNV) transmission in Italy was first reported in 1998 as an equine outbreak near the swamps of Padule di Fucecchio, Tuscany. No other cases were identified during the following decade until 2008, when horse and human outbreaks were reported in Emilia Romagna, North Italy. Since then, WNV outbreaks have occurred annually, spreading from their initial northern foci throughout the country. Following the outbreak in 1998 the Italian public health authority defined a surveillance plan to detect WNV circulation in birds, horses and mosquitoes. By applying spatial statistical analysis (spatial point pattern analysis) and models (Bayesian GLMM models) to a longitudinal dataset on the abundance of the three putative WNV vectors [Ochlerotatus caspius (Pallas 1771), Culex pipiens (Linnaeus 1758) and Culex modestus (Ficalbi 1890)] in eastern Piedmont, we quantified their abundance and distribution in space and time and generated prediction maps outlining the areas with the highest vector productivity and potential for WNV introduction and amplification. The highest abundance and significant spatial clusters of Oc. caspius and Cx. modestus were in proximity to rice fields, and for Cx. pipiens, in proximity to highly populated urban areas. The GLMM model showed the importance of weather conditions and environmental factors in predicting mosquito abundance. Distance from the preferential breeding sites and elevation were negatively associated with the number of collected mosquitoes. The Normalized Difference Vegetation Index (NDVI) was positively correlated with mosquito abundance in rice fields (Oc. caspius and Cx. modestus). Based on the best models, we developed prediction maps for the year 2010 outlining the areas where high abundance of vectors could favour the introduction and amplification of WNV. Our findings provide useful information for surveillance activities aiming to identify locations where the potential for WNV introduction and local transmission are highest. Such information can be used by vector control offices to stratify control interventions in areas prone to the invasion of WNV and other mosquito-transmitted pathogens.

Eco-bio-social determinants for house infestation by non-domiciliated Triatoma dimidiata in the Yucatan Peninsula, Mexico.

PubMed

Dumonteil, Eric; Nouvellet, Pierre; Rosecrans, Kathryn; Ramirez-Sierra, Maria Jesus; Gamboa-León, Rubi; Cruz-Chan, Vladimir; Rosado-Vallado, Miguel; Gourbière, Sébastien

2013-01-01

Chagas disease is a vector-borne disease of major importance in the Americas. Disease prevention is mostly limited to vector control. Integrated interventions targeting ecological, biological and social determinants of vector-borne diseases are increasingly used for improved control. We investigated key factors associated with transient house infestation by T. dimidiata in rural villages in Yucatan, Mexico, using a mixed modeling approach based on initial null-hypothesis testing followed by multimodel inference and averaging on data from 308 houses from three villages. We found that the presence of dogs, chickens and potential refuges, such as rock piles, in the peridomicile as well as the proximity of houses to vegetation at the periphery of the village and to public light sources are major risk factors for infestation. These factors explain most of the intra-village variations in infestation. These results underline a process of infestation distinct from that of domiciliated triatomines and may be used for risk stratification of houses for both vector surveillance and control. Combined integrated vector interventions, informed by an Ecohealth perspective, should aim at targeting several of these factors to effectively reduce infestation and provide sustainable vector control.

A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

Soil quality in anthropized ecosystem located in two biomes in Campinas city / SP-Brazil

NASA Astrophysics Data System (ADS)

Márcia Longo, Regina; Corcovia, Marina; Gomes, Raissa Caroline; Bettine, Sueli C.; Demamboro, Antonio Carlos; Fengler, Felipe H.; Irio Ribeiro, Admilson

2017-04-01

The rapid growth of large urban centers and the expansion of agricultural activities promote direct pressures on natural ecosystems. These actions have led to constant discussions by researchers and society as a whole in relation to preservation and quality of terrestrial ecosystems, and soil and vegetation components of vital importance to maintain these. In this context, the present study was to evaluate the anthropogenic interferences on soil properties in areas in two forest fragments located in the remaining urban areas in different biomes of Campinas-SP , Brazil. Both have their edges significantly disturbed by the proximity to urban centers , highways, sugarcane cultivation, among others. The remnant of the Atlantic Forest has an area of 250.36 ha is found in a so called protected area of ecological interest (A.R.I.E). This site access is restricted and has conservation measures, but is near major highways. The remnant of savanna has an approximate area of 40 ha there and has no conservation measure, finding it quite degraded. The physical properties and chemical soil in the two situations were collected throughout the border area totaling 28 points in the remaining savanna and 40 in the Atlantic Forest. The results were analyzed using Principal Component Analysis (PCA) to determine the main soil properties that reflect the quality of the ecosystems studied. It can be seen that most of the physical-chemical soil parameters were impacted in some way related to each other and in two ecosystems that is, the size of the vectors and the distance between them are studied in corresponding situations. The bulk density parameter has different behavior between the two biomes, since the particle density is presented close to each other but have different vector sizes. Some of the parameters have been identified with strong relationship between biomes: the Exchange Capacity Cationic (ECC) and the amounts of copper (Cu) by its close proximity of the vectors and the aluminum content (Al) and iron (Fe) for having sizes similar vectors and an appropriate distance between them. The group attributes: pH, calcium (Ca), sum of bases (SB), magnesium (Mg) and base saturation (V%) were found grouped in the two biomes but in different quadrants, reaffirming the great relationship between these parameters. Regarding the organic matter content of these they were similar in the two biomes. The higher nickel content (Ni), chromium (Cr) and lead (Pb) were found in the remaining forest, probably due to the proximity to large vehicle traffic roads. In this context, it can be concluded that the areas under study are impacted negatively in their edge areas, and components such as proximity to agricultural areas and urban sprawl, as well as proximity to major roads may be interfering with the direct mode environmental quality of forest remaining soil from two different biomes located in urban areas, and changes in levels of heavy metals and bulk density were the most affected. 1 Part of the project funded by the Foundation of the State of São Paulo Research - Brazil (FAPESP - process 2012 / 14423-8)

Quasiclassical description of a superconductor with a spin density wave

NASA Astrophysics Data System (ADS)

Moor, A.; Volkov, A. F.; Efetov, K. B.

2011-04-01

We derive equations for the quasiclassical Green’s functions ǧ within a simple model of a two-band superconductor with a spin density wave (SDW). The elements of the matrix ǧ are the retarded, advanced, and Keldysh functions, each of which is an 8×8 matrix in the Gor’kov-Nambu, the spin, and the band space. In equilibrium, these equations are a generalization of the Eilenberger equation. On the basis of the derived equations, we analyze the Knight shift, the proximity, and the dc Josephson effects in the superconductors under consideration. The Knight shift is shown to depend on the orientation of the external magnetic field with respect to the direction of the vector of the magnetization of the SDW. The proximity effect is analyzed for an interface between a superconductor with the SDW and a normal metal. The function describing both superconducting and magnetic correlations is shown to penetrate the normal metal or a metal with the SDW due to the proximity effect. The dc Josephson current in an SSDW/N/SSDW junction is also calculated as a function of the phase difference φ. It is shown that in our model, the Josephson current does not depend on the mutual orientation of the magnetic moments in the superconductors SSDW and is proportional to sinφ. The dissipationless spin current jsp depends on the angle α between the magnetization vectors in the same way (jsp~sinα) and is not zero above the superconducting transition temperature.

Ub-ISAP: a streamlined UNIX pipeline for mining unique viral vector integration sites from next generation sequencing data.

PubMed

Kamboj, Atul; Hallwirth, Claus V; Alexander, Ian E; McCowage, Geoffrey B; Kramer, Belinda

2017-06-17

The analysis of viral vector genomic integration sites is an important component in assessing the safety and efficiency of patient treatment using gene therapy. Alongside this clinical application, integration site identification is a key step in the genetic mapping of viral elements in mutagenesis screens that aim to elucidate gene function. We have developed a UNIX-based vector integration site analysis pipeline (Ub-ISAP) that utilises a UNIX-based workflow for automated integration site identification and annotation of both single and paired-end sequencing reads. Reads that contain viral sequences of interest are selected and aligned to the host genome, and unique integration sites are then classified as transcription start site-proximal, intragenic or intergenic. Ub-ISAP provides a reliable and efficient pipeline to generate large datasets for assessing the safety and efficiency of integrating vectors in clinical settings, with broader applications in cancer research. Ub-ISAP is available as an open source software package at https://sourceforge.net/projects/ub-isap/ .

A model for predicting field-directed particle transport in the magnetofection process.

PubMed

Furlani, Edward P; Xue, Xiaozheng

2012-05-01

To analyze the magnetofection process in which magnetic carrier particles with surface-bound gene vectors are attracted to target cells for transfection using an external magnetic field and to obtain a fundamental understanding of the impact of key factors such as particle size and field strength on the gene delivery process. A numerical model is used to study the field-directed transport of the carrier particle-gene vector complex to target cells in a conventional multiwell culture plate system. The model predicts the transport dynamics and the distribution of particle accumulation at the target cells. The impact of several factors that strongly influence gene vector delivery is assessed including the properties of the carrier particles, the strength of the field source, and its extent and proximity relative to the target cells. The study demonstrates that modeling can be used to predict and optimize gene vector delivery in the magnetofection process for novel and conventional in vitro systems.

Manipulating the cell differentiation through lentiviral vectors.

PubMed

Coppola, Valeria; Galli, Cesare; Musumeci, Maria; Bonci, Désirée

2010-01-01

The manipulation of cell differentiation is important to create new sources for the treatment of degenerative diseases or solve cell depletion after aggressive therapy against cancer. In this chapter, the use of a tissue-specific promoter lentiviral vector to obtain a myocardial pure lineage from murine embryonic stem cells (mES) is described in detail. Since the cardiac isoform of troponin I gene product is not expressed in skeletal or other muscle types, short mouse cardiac troponin proximal promoter is used to drive reporter genes. Cells are infected simultaneously with two lentiviral vectors, the first expressing EGFP to monitor the transduction efficiency, and the other expressing a puromycin resistance gene to select the specific cells of interest. This technical approach describes a method to obtain a pure cardiomyocyte population and can be applied to other lineages of interest.

A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM

PubMed Central

Li, Ke; Liu, Yi; Wang, Quanxin; Wu, Yalei; Song, Shimin; Sun, Yi; Liu, Tengchong; Wang, Jun; Li, Yang; Du, Shaoyi

2015-01-01

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively. PMID:26544549

An ensemble of dissimilarity based classifiers for Mackerel gender determination

NASA Astrophysics Data System (ADS)

Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.

2014-03-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

Robust support vector regression networks for function approximation with outliers.

PubMed

Chuang, Chen-Chia; Su, Shun-Feng; Jeng, Jin-Tsong; Hsiao, Chih-Ching

2002-01-01

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. In this paper, a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR. In the approach, traditional robust learning approaches are employed to improve the learning performance for any selected parameters. From the simulation results, our RSVR can always improve the performance of the learned systems for all cases. Besides, it can be found that even the training lasted for a long period, the testing errors would not go up. In other words, the overfitting phenomenon is indeed suppressed.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Currency crisis indication by using ensembles of support vector machine classifiers

NASA Astrophysics Data System (ADS)

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

2014-07-01

There are many methods that had been experimented in the analysis of currency crisis. However, not all methods could provide accurate indications. This paper introduces an ensemble of classifiers by using Support Vector Machine that's never been applied in analyses involving currency crisis before with the aim of increasing the indication accuracy. The proposed ensemble classifiers' performances are measured using percentage of accuracy, root mean squared error (RMSE), area under the Receiver Operating Characteristics (ROC) curve and Type II error. The performances of an ensemble of Support Vector Machine classifiers are compared with the single Support Vector Machine classifier and both of classifiers are tested on the data set from 27 countries with 12 macroeconomic indicators for each country. From our analyses, the results show that the ensemble of Support Vector Machine classifiers outperforms single Support Vector Machine classifier on the problem involving indicating a currency crisis in terms of a range of standard measures for comparing the performance of classifiers.

The place of proximity: social support in mother-adult daughter relationships.

PubMed

Scelza, Brooke A

2011-07-01

The mother-adult daughter relationship has been highlighted in both the social sciences and the public health literature as an important facet of social support networks, particularly as they pertain to maternal and child health. Evolutionary anthropologists also have shown positive associations between support from maternal grandmothers and various outcomes related to reproductive success; however, many of these studies rely on proximity as a surrogate measure of support. Here I present data from the Puerto Rican Maternal and Infant Health Survey (PRMIHS) comparing geographic proximity of mother and daughter with a self-reported measure of mother-to-daughter support. These two measures were used to predict infant health outcomes as well as various measures of instrumental and emotional aid provided during pregnancy and after birth. Primary support was shown to have a positive effect across the analyses, whereas geographic proximity was associated with an increased risk of infant mortality and low birth weight as well as reduced odds of receiving support. This paradox was then examined using a combination variable that teased out the interactions of maternal support and proximity. Women who were geographically close to their mothers but who did not consider them a primary source of support had increased odds of infant death and low birth weight, and were less likely to receive either tangible or intangible forms of aid, while women whose mothers were both close and primary showed uniformly positive outcomes. These results place the role of propinquity within the larger context of social support and highlight the need for more detailed studies of social support within evolutionary anthropology.

A Comprehensive Reanalysis of the Distal Iliotibial Band: Quantitative Anatomy, Radiographic Markers, and Biomechanical Properties.

PubMed

Godin, Jonathan A; Chahla, Jorge; Moatshe, Gilbert; Kruckeberg, Bradley M; Muckenhirn, Kyle J; Vap, Alexander R; Geeslin, Andrew G; LaPrade, Robert F

2017-09-01

The qualitative anatomy of the distal iliotibial band (ITB) has previously been described. However, a comprehensive characterization of the quantitative anatomic, radiographic, and biomechanical properties of the Kaplan fibers of the deep distal ITB has not yet been established. It is paramount to delineate these characteristics to fully understand the distal ITB's contribution to rotational knee stability. Purpose/Hypothesis: There were 2 distinct purposes for this study: (1) to perform a quantitative anatomic and radiographic evaluation of the distal ITB's attachment sites and their relationships to pertinent osseous and soft tissue landmarks, and (2) to quantify the biomechanical properties of the deep (Kaplan) fibers of the distal ITB. It was hypothesized that the distal ITB has definable parameters concerning its anatomic attachments and consistent relationships to surgically pertinent landmarks with correlating plain radiographic findings. In addition, it was hypothesized that the biomechanical properties of the Kaplan fibers would support their role as important restraints against internal rotation. Descriptive laboratory study. Ten nonpaired, fresh-frozen human cadaveric knees (mean age, 61.1 years; range, 54-65 years) were dissected for anatomic and radiographic purposes. A coordinate measuring device quantified the attachment areas of the distal ITB to the distal femur, patella, and proximal tibia and their relationships to pertinent bony landmarks. A radiographic analysis was performed by inserting pins into the attachment sites of relevant anatomic structures to assess their location relative to pertinent bony landmarks with fluoroscopic guidance. A further biomechanical assessment of 10 cadaveric knees quantified the load to failure and stiffness of the Kaplan fibers' insertion on the distal femur after a preconditioning protocol. Two separate deep (Kaplan) fiber bundles were identified with attachments to 2 newly identified femoral bony prominences (ridges). The proximal and distal bundles inserted on the distal femur 53.6 mm (95% CI, 50.7-56.6 mm) and 31.4 mm (95% CI, 27.3-35.5 mm) proximal to the lateral epicondyle, respectively. The centers of the bundle insertions were 22.5 mm (95% CI, 19.1-25.9 mm) apart. The total insertion area of the distal ITB on the proximal tibia was 429.1 mm 2 (95% CI, 349.2-509.1 mm 2 ). A distinct capsulo-osseous layer of the distal ITB was also identified that was intimately related to the lateral knee capsule. Its origin was in close proximity to the lateral gastrocnemius tubercle, and it inserted on the proximal tibia at the lateral tibial tubercle between the fibular head and the Gerdy tubercle. Radiographic analysis supported the quantitative anatomic findings. The mean maximum load during pull-to-failure testing was 71.3 N (95% CI, 41.2-101.4 N) and 170.2 N (95% CI, 123.6-216.8 N) for the proximal and distal Kaplan bundles, respectively. The most important finding of this study was that 2 distinct deep bundles (Kaplan fibers) of the distal ITB were identified. Each bundle of the deep layer of the ITB was associated with a newly identified distinct bony ridge. Radiographic analysis confirmed the measurements previously recorded and established reproducible landmarks for the newly described structures. Biomechanical testing revealed that the Kaplan fibers had a strong attachment to the distal femur, thereby supporting a role in rotational knee stability. The identification of 2 distinct deep fiber (Kaplan) attachments clarifies the function of the ITB more definitively. The results also support the role of the ITB in rotatory knee stability because of the fibers' vectors and their identified maximum loads. These findings provide the anatomic and biomechanical foundation needed for the development of reconstruction or repair techniques to anatomically address these deficiencies in knee ligament injuries.

TWSVR: Regression via Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh

2016-02-01

Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

Management of arthropod pathogen vectors in North America: Minimizing adverse effects on pollinators

USGS Publications Warehouse

Ginsberg, Howard; Bargar, Timothy A.; Hladik, Michelle L.; Lubelczyk, Charles

2017-01-01

Tick and mosquito management is important to public health protection. At the same time, growing concerns about declines of pollinator species raise the question of whether vector control practices might affect pollinator populations. We report the results of a task force of the North American Pollinator Protection Campaign (NAPPC) that examined potential effects of vector management practices on pollinators, and how these programs could be adjusted to minimize negative effects on pollinating species. The main types of vector control practices that might affect pollinators are landscape manipulation, biocontrol, and pesticide applications. Some current practices already minimize effects of vector control on pollinators (e.g., short-lived pesticides and application-targeting technologies). Nontarget effects can be further diminished by taking pollinator protection into account in the planning stages of vector management programs. Effects of vector control on pollinator species often depend on specific local conditions (e.g., proximity of locations with abundant vectors to concentrations of floral resources), so planning is most effective when it includes collaborations of local vector management professionals with local experts on pollinators. Interventions can then be designed to avoid pollinators (e.g., targeting applications to avoid blooming times and pollinator nesting habitats), while still optimizing public health protection. Research on efficient targeting of interventions, and on effects on pollinators of emerging technologies, will help mitigate potential deleterious effects on pollinators in future management programs. In particular, models that can predict effects of integrated pest management on vector-borne pathogen transmission, along with effects on pollinator populations, would be useful for collaborative decision-making.

Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients.

PubMed

Park, Eunjeong; Chang, Hyuk-Jae; Nam, Hyo Suk

2017-04-18

The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients. The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and signal processing. We extracted features of drift and pronation from accelerometer signals of wearable devices on the inner wrists of 16 stroke patients and 10 healthy controls. Signal processing and feature selection approach were applied to discriminate PDT features used to classify stroke patients. A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and random forest (RF), were implemented to discriminate stroke patients from controls with leave-one-out cross-validation. Signal processing by the PDT tool extracted a total of 12 PDT features from sensors. Feature selection abstracted the major attributes from the 12 PDT features to elucidate the dominant characteristics of proximal weakness of stroke patients using machine learning classification. Our proposed PDT classifiers had an area under the receiver operating characteristic curve (AUC) of .806 (SVM), .769 (RBFN), and .900 (RF) without feature selection, and feature selection improves the AUCs to .913 (SVM), .956 (RBFN), and .975 (RF), representing an average performance enhancement of 15.3%. Sensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients. ©Eunjeong Park, Hyuk-Jae Chang, Hyo Suk Nam. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.04.2017.

A Code Generation Approach for Auto-Vectorization in the Spade Compiler

NASA Astrophysics Data System (ADS)

Wang, Huayong; Andrade, Henrique; Gedik, Buğra; Wu, Kun-Lung

We describe an auto-vectorization approach for the Spade stream processing programming language, comprising two ideas. First, we provide support for vectors as a primitive data type. Second, we provide a C++ library with architecture-specific implementations of a large number of pre-vectorized operations as the means to support language extensions. We evaluate our approach with several stream processing operators, contrasting Spade's auto-vectorization with the native auto-vectorization provided by the GNU gcc and Intel icc compilers.

Fatigue fracture of a proximally modular, distally tapered fluted implant with diaphyseal fixation.

PubMed

Buttaro, Martín A; Mayor, Michael B; Van Citters, Douglas; Piccaluga, Francisco

2007-08-01

We report and analyze the causes of a fracture in a proximally modular, distally tapered fluted MP stem in a 48-year-old woman (168 cm, 67 kg) with severe proximal bone deficiency. Evidence of fatigue failure with striations initiated laterally was observed in the laser etching of the tensile aspect of the prosthesis. However, metallurgical analysis suggested that laser engraving did not alter the microstructure of the stem. Stress due to the absence of proximal femoral bone support may have been sufficiently high to put this particular stem at risk for fatigue fracture. This important complication should be addressed when choosing this therapeutic option in cases with substantial proximal femoral bone loss. Strut allograft support should be recommended in such cases.

Eco-Bio-Social Determinants for House Infestation by Non-domiciliated Triatoma dimidiata in the Yucatan Peninsula, Mexico

PubMed Central

Dumonteil, Eric; Nouvellet, Pierre; Rosecrans, Kathryn; Ramirez-Sierra, Maria Jesus; Gamboa-León, Rubi; Cruz-Chan, Vladimir; Rosado-Vallado, Miguel; Gourbière, Sébastien

2013-01-01

Background Chagas disease is a vector-borne disease of major importance in the Americas. Disease prevention is mostly limited to vector control. Integrated interventions targeting ecological, biological and social determinants of vector-borne diseases are increasingly used for improved control. Methodology/principal findings We investigated key factors associated with transient house infestation by T. dimidiata in rural villages in Yucatan, Mexico, using a mixed modeling approach based on initial null-hypothesis testing followed by multimodel inference and averaging on data from 308 houses from three villages. We found that the presence of dogs, chickens and potential refuges, such as rock piles, in the peridomicile as well as the proximity of houses to vegetation at the periphery of the village and to public light sources are major risk factors for infestation. These factors explain most of the intra-village variations in infestation. Conclusions/significance These results underline a process of infestation distinct from that of domiciliated triatomines and may be used for risk stratification of houses for both vector surveillance and control. Combined integrated vector interventions, informed by an Ecohealth perspective, should aim at targeting several of these factors to effectively reduce infestation and provide sustainable vector control. PMID:24086790

Eco-bio-social research on community-based approaches for Chagas disease vector control in Latin America.

PubMed

Gürtler, Ricardo E; Yadon, Zaida E

2015-02-01

This article provides an overview of three research projects which designed and implemented innovative interventions for Chagas disease vector control in Bolivia, Guatemala and Mexico. The research initiative was based on sound principles of community-based ecosystem management (ecohealth), integrated vector management, and interdisciplinary analysis. The initial situational analysis achieved a better understanding of ecological, biological and social determinants of domestic infestation. The key factors identified included: housing quality; type of peridomestic habitats; presence and abundance of domestic dogs, chickens and synanthropic rodents; proximity to public lights; location in the periphery of the village. In Bolivia, plastering of mud walls with appropriate local materials and regular cleaning of beds and of clothes next to the walls, substantially decreased domestic infestation and abundance of the insect vector Triatoma infestans. The Guatemalan project revealed close links between house infestation by rodents and Triatoma dimidiata, and vector infection with Trypanosoma cruzi. A novel community-operated rodent control program significantly reduced rodent infestation and bug infection. In Mexico, large-scale implementation of window screens translated into promising reductions in domestic infestation. A multi-pronged approach including community mobilisation and empowerment, intersectoral cooperation and adhesion to integrated vector management principles may be the key to sustainable vector and disease control in the affected regions. © World Health Organization 2015. The World Health Organization has granted Oxford University Press permission for the reproduction of this article.

Altered environment and risk of malaria outbreak in South Andaman, Andaman & Nicobar Islands, India affected by tsunami disaster

PubMed Central

Krishnamoorthy, Kaliannagoun; Jambulingam, Purushothaman; Natarajan, R; Shriram, AN; Das, Pradeep K; Sehgal, SC

2005-01-01

Background Pools of salt water and puddles created by giant waves from the sea due to the tsunami that occurred on 26th December 2004 would facilitate increased breeding of brackish water malaria vector, Anopheles sundaicus. Land uplifts in North Andaman and subsidence in South Andaman have been reported and subsidence may lead to environmental disturbances and vector proliferation. This warrants a situation analysis and vector surveillance in the tsunami hit areas endemic for malaria transmitted by brackish water mosquito, An. sundaicus to predict the risk of outbreak. Methods An extensive survey was carried out in the tsunami-affected areas in Andaman district of the Andaman and Nicobar Islands, India to assess the extent of breeding of malaria vectors in the habitats created by seawater flooding. Types of habitats in relation to source of seawater inundation and frequency were identified. The salinity of the water samples and the mosquito species present in the larval samples collected from these habitats were recorded. The malaria situation in the area was also analysed. Results South Andaman, covering Port Blair and Ferrargunj sub districts, is still under the recurring phenomenon of seawater intrusion either directly from the sea or through a network of creeks. Both daily cycles of high tides and periodical spring tides continue to cause flooding. Low-lying paddy fields and fallow land, with a salinity ranging from 3,000 to 42,505 ppm, were found to support profuse breeding of An. sundaicus, the local malaria vector, and Anopheles subpictus, a vector implicated elsewhere. This area is endemic for both vivax and falciparum malaria. Malaria slide positivity rate has started increasing during post-tsunami period, which can be considered as an indication of risk of malaria outbreak. Conclusion Paddy fields and fallow land with freshwater, hitherto not considered as potential sites for An. sundaicus, are now major breeding sites due to saline water. Consequently, there is a risk of vector abundance with enhanced malaria transmission potential, due to the vastness of these tsunami-created breeding grounds and likelihood of them becoming permanent due to continued flooding in view of land subsidence. The close proximity of the houses and paucity of cattle may lead to a higher degree of man/vector contact causing a threat of malaria outbreak in this densely populated area. Measures to prevent the possible outbreak of malaria in this tsunami-affected area are discussed. PMID:16029514

Non-Fermi surface nesting driven commensurate magnetic ordering in Fe-doped S r 2 Ru O 4

DOE PAGES

Zhu, M.; Shanavas, K. V.; Wang, Y.; ...

2017-02-10

Sr 2RuO 4, an unconventional superconductor, is known to possess an incommensurate spin-density wave instability driven by Fermi surface nesting. Here we report a static spin-density wave ordering with a commensurate propagation vector q c = (0.250.250) in Fe-doped Sr 2RuO 4, despite the magnetic fluctuations persisting at the incommensurate wave vectors q ic = (0.30.3L) as in the parent compound. The latter feature is corroborated by the first-principles calculations, which show that Fe substitution barely changes the nesting vector of the Fermi surface. Finally, these results suggest that in addition to the known incommensurate magnetic instability, Sr 2RuO 4more » is also in proximity to a commensurate magnetic tendency that can be stabilized via Fe doping.« less

Signal detection using support vector machines in the presence of ultrasonic speckle

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine L.; Pitas, Ioannis

2002-04-01

Support Vector Machines are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy.

PubMed

Wang, Zhi-Long; Zhou, Zhi-Guo; Chen, Ying; Li, Xiao-Ting; Sun, Ying-Shi

The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography. A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis. Support vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively. The support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy.

The Effect of Geographic Proximity to Unconventional Oil and Gas Development on Public Support for Hydraulic Fracturing.

PubMed

Boudet, Hilary S; Zanocco, Chad M; Howe, Peter D; Clarke, Christopher E

2018-04-10

With the rapid growth of unconventional oil and natural gas development transforming the U.S. economic and physical landscape, social scientists have increasingly explored the spatial dynamics of public support for this issue-that is, whether people closer to unconventional oil and gas development are more supportive or more opposed. While theoretical frameworks like construal-level theory and the "Not in My Backyard" (or NIMBY) moniker provide insight into these spatial dynamics, case studies in specific locations experiencing energy development reveal substantial variation in community responses. Larger-scale studies exploring the link between proximity and support have been hampered by data quality and availability. We draw on a unique data set that includes geo-coded data from national surveys (nine waves; n = 19,098) and high-resolution well location data to explore the relationship between proximity and both familiarity with and support for hydraulic fracturing. We use two different measures of proximity-respondent distance to the nearest well and the density of wells within a certain radius of the respondent's location. We find that both types of proximity to new development are linked to more familiarity with hydraulic fracturing, even after controlling for various individual and contextual factors, but only distance-based proximity is linked to more support for the practice. When significant, these relationships are similar to or exceed the effects of race, income, gender, and age. We discuss the implications of these findings for effective risk communication as well as the importance of incorporating spatial analysis into public opinion research on perceptions of energy development. © 2018 Society for Risk Analysis.

VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases

PubMed Central

Giraldo-Calderón, Gloria I.; Emrich, Scott J.; MacCallum, Robert M.; Maslen, Gareth; Dialynas, Emmanuel; Topalis, Pantelis; Ho, Nicholas; Gesing, Sandra; Madey, Gregory; Collins, Frank H.; Lawson, Daniel

2015-01-01

VectorBase is a National Institute of Allergy and Infectious Diseases supported Bioinformatics Resource Center (BRC) for invertebrate vectors of human pathogens. Now in its 11th year, VectorBase currently hosts the genomes of 35 organisms including a number of non-vectors for comparative analysis. Hosted data range from genome assemblies with annotated gene features, transcript and protein expression data to population genetics including variation and insecticide-resistance phenotypes. Here we describe improvements to our resource and the set of tools available for interrogating and accessing BRC data including the integration of Web Apollo to facilitate community annotation and providing Galaxy to support user-based workflows. VectorBase also actively supports our community through hands-on workshops and online tutorials. All information and data are freely available from our website at https://www.vectorbase.org/. PMID:25510499

Proximity effects in ferromagnet-superconductor structures

NASA Astrophysics Data System (ADS)

Halterman, Klaus Byron

I present an extensive theoretical investigation of the proximity effects that occur in ferromagnet/superconductor systems. I use a numerical method to solve self consistently the Bogoliubov-de Gennes equations in the continuum. I obtain the pair amplitude and the local density of states (DOS), and use these results to extract the relevant lengths characterizing both the leakage of superconductivity into the magnet and to study spin splitting induced in the superconductor. These phenomena are investigated as a function of parameters such as temperature, magnet polarization, interfacial scattering, sample size and Fermi wave vector mismatch, all of which turn out to have an important influence on the results. These comprehensive results should help characterize and analyze future data, and are shown to be in agreement with existing experiments.

A Regional Decision Support Scheme for Pest Risk Analysis in Southeast Asia.

PubMed

Soliman, T; MacLeod, A; Mumford, J D; Nghiem, T P L; Tan, H T W; Papworth, S K; Corlett, R T; Carrasco, L R

2016-05-01

A key justification to support plant health regulations is the ability of quarantine services to conduct pest risk analyses (PRA). Despite the supranational nature of biological invasions and the close proximity and connectivity of Southeast Asian countries, PRAs are conducted at the national level. Furthermore, some countries have limited experience in the development of PRAs, which may result in inadequate phytosanitary responses that put their plant resources at risk to pests vectored via international trade. We review existing decision support schemes for PRAs and, following international standards for phytosanitary measures, propose new methods that adapt existing practices to suit the unique characteristics of Southeast Asia. Using a formal written expert elicitation survey, a panel of regional scientific experts was asked to identify and rate unique traits of Southeast Asia with respect to PRA. Subsequently, an expert elicitation workshop with plant protection officials was conducted to verify the potential applicability of the developed methods. Rich biodiversity, shortage of trained personnel, social vulnerability, tropical climate, agriculture-dependent economies, high rates of land-use change, and difficulties in implementing risk management options were identified as challenging Southeast Asian traits. The developed methods emphasize local Southeast Asian conditions and could help support authorities responsible for carrying out PRAs within the region. These methods could also facilitate the creation of other PRA schemes in low- and middle-income tropical countries. © 2016 Society for Risk Analysis.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

Multiclass Reduced-Set Support Vector Machines

NASA Technical Reports Server (NTRS)

Tang, Benyang; Mazzoni, Dominic

2006-01-01

There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

A Subdivision-Based Representation for Vector Image Editing.

PubMed

Liao, Zicheng; Hoppe, Hugues; Forsyth, David; Yu, Yizhou

2012-11-01

Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

Washington Geothermal Play Fairway Analysis Heat, Permeability, and Fracture Model Data

DOE Data Explorer

Steely, Alex; Forson, Corina; Cladouhos, Trenton; Swyer, Mike; Davatzes, Nicholas; Anderson, Megan; Ritzinger, Brent; Glen, Jonathan; Peacock, Jared; Schermerhorn, William

2017-12-07

This submission contains raster and vector data for the entire state of Washington, with specific emphasis on the three geothermal play fairway sites: Mount St. Helens seismic zone (MSHSZ), Wind River valley (WRV), and Mount Baker (MB). Data are provided for 3 major geothermal models: heat, permeability, and fluid-filled fractures, and an additional infrastructure model. Both of the permeability and fluid-filled-fracture models are produced at 200 m and at 2 km depths; the heat model is only produced at the 200 m depth. Values are provided for both model favorability and model confidence. A combined model at 200m and 2 km depths is provided for favorability, confidence, and exploration risk. Raster data are provided in GeoTiff format and have a statewide coverage. Cell size is 104.355 ft; file type is unsigned 8-bit integer (0-255); 0 represents no favorability or confidence; 255 represents maximum favorability or confidence. The NAD83(HARN)/Washington South (ftUS) projection is used (EPSG:2927). Vector data are provided in shapefile or comma-delimited text file formats. Geographic coordinates, where provided, are in WGS84. A readme file accompanies each folder and provides an overview and description of the enclosed data. The heat model combines 5 intermediate raster layers (which are included in the download package): temperature gradient wells, young volcanic vents, hot springs, young intrusive volcanic rocks, and geothermometry. The permeability model combines 8 intermediate raster layers: density of mapped faults, 2D dilation tendency of mapped faults, 2D slip tendency of mapped faults, seismicity, 3D dilation tendency, 3D slip tendency, 3D maximum coulomb shear stress, and 3D slip gradients. The fluid-filled fracture model combines up to 4 intermediate rasters: resistivity from magneto-telluric 3D inversions, seismicity, Vp/Vs anomalies from passive seismic tomography, and Vs anomalies from ambient-noise tomography. A statewide infrastructure model is also provided that formalizes land-use constraints and restrictions relevant for geothermal prospecting and development. This model combines 10 intermediate rasters: areas off limits to drilling, existing or proposed geothermal leases, DNR-owned land, land-use restrictions along the Columbia River Gorge, areas inundated by water, availability of potential process water, proximity to existing roads, proximity to transmission lines, distance from urban areas, and snow-related elevation restrictions. Supporting vector data for the development of each raster layer is provided. For details on the areas of interest and modeling process please see the 'WA_State_Play_Fairway_Phase_2_Technical_Report' in the download package.

Distance Threshold for the Effect of Urban Agriculture on Elevated Self-reported Malaria Prevalence in Accra, Ghana

PubMed Central

Stoler, Justin; Weeks, John R.; Getis, Arthur; Hill, Allan G.

2009-01-01

Irrigated urban agriculture (UA), which has helped alleviate poverty and increase food security in rapidly urbanizing sub-Saharan Africa, may inadvertently support malaria vectors. Previous studies have not identified a variable distance effect on malaria prevalence from UA. This study examines the relationships between self-reported malaria information for 3,164 women surveyed in Accra, Ghana, in 2003, and both household characteristics and proximity to sites of UA. Malaria self-reports are associated with age, education, overall health, socioeconomic status, and solid waste disposal method. The odds of self-reported malaria are significantly higher for women living within 1 km of UA compared with all women living near an irrigation source, the association disappearing beyond this critical distance. Malaria prevalence is often elevated in communities within 1 km of UA despite more favorable socio-economic characteristics than communities beyond 1 km. Neighborhoods within 1 km of UA should be reconsidered as a priority for malaria-related care. PMID:19346373

Distance threshold for the effect of urban agriculture on elevated self-reported malaria prevalence in Accra, Ghana.

PubMed

Stoler, Justin; Weeks, John R; Getis, Arthur; Hill, Allan G

2009-04-01

Irrigated urban agriculture (UA), which has helped alleviate poverty and increase food security in rapidly urbanizing sub-Saharan Africa, may inadvertently support malaria vectors. Previous studies have not identified a variable distance effect on malaria prevalence from UA. This study examines the relationships between self-reported malaria information for 3,164 women surveyed in Accra, Ghana, in 2003, and both household characteristics and proximity to sites of UA. Malaria self-reports are associated with age, education, overall health, socioeconomic status, and solid waste disposal method. The odds of self-reported malaria are significantly higher for women living within 1 km of UA compared with all women living near an irrigation source, the association disappearing beyond this critical distance. Malaria prevalence is often elevated in communities within 1 km of UA despite more favorable socio-economic characteristics than communities beyond 1 km. Neighborhoods within 1 km of UA should be reconsidered as a priority for malaria-related care.

Diffusion of Lexical Change in Social Media

PubMed Central

Eisenstein, Jacob; O'Connor, Brendan; Smith, Noah A.; Xing, Eric P.

2014-01-01

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity – especially with regard to race – plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified “netspeak” dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English. PMID:25409166

Primary Stability Recognition of the Newly Designed Cementless Femoral Stem Using Digital Signal Processing

PubMed Central

Salleh, Sh-Hussain; Hamedi, Mahyar; Zulkifly, Ahmad Hafiz; Lee, Muhammad Hisyam; Mohd Noor, Alias; Harris, Arief Ruhullah A.; Abdul Majid, Norazman

2014-01-01

Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing. PMID:24800230

Primary stability recognition of the newly designed cementless femoral stem using digital signal processing.

PubMed

Baharuddin, Mohd Yusof; Salleh, Sh-Hussain; Hamedi, Mahyar; Zulkifly, Ahmad Hafiz; Lee, Muhammad Hisyam; Mohd Noor, Alias; Harris, Arief Ruhullah A; Abdul Majid, Norazman

2014-01-01

Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing.

Pathological brain detection based on wavelet entropy and Hu moment invariants.

PubMed

Zhang, Yudong; Wang, Shuihua; Sun, Ping; Phillips, Preetha

2015-01-01

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

Dengue Fever as an Emerging Infection in Southeast Iran.

PubMed

Heydari, Mostafa; Metanat, Maliheh; Rouzbeh-Far, Mohammad-Ali; Tabatabaei, Seyed Mehdi; Rakhshani, Mohammad; Sepehri-Rad, Nahid; Keshtkar-Jahromi, Maryam

2018-05-01

Dengue fever (DF) is a mosquito-borne acute viral disease presenting with hemorrhagic manifestations in severe cases. Southeast Iran is in close proximity to Pakistan, an endemic country for DF. This cross-sectional study was conducted in the Sistan and Baluchestan province in the southeast of Iran to investigate possibility of DF (immunoglobulin M [IgM], immunoglobulin G [IgG], and nonstructural protein 1 [NS1] antigen tests) in 60 clinically suspected patients (April 2013 to August 2015). NS1 protein was detected in 5% ( N = 3), at least one of the antibodies (IgM and/ or IgG) was detected in 11% ( N = 7) of the samples. Five patients identified as of acutely infected. There was a simultaneous presence of NS1 protein and IgG or IgM antibodies in 4% ( N = 2) of patients. Previous studies show establishment of potential vectors in this area. These evidences support the hypothesis that DF can be a health concern in Southeast Iran with potential future outbreaks.

Insect cell transformation vectors that support high level expression and promoter assessment in insect cell culture

USDA-ARS?s Scientific Manuscript database

A somatic transformation vector, pDP9, was constructed that provides a simplified means of producing permanently transformed cultured insect cells that support high levels of protein expression of foreign genes. The pDP9 plasmid vector incorporates DNA sequences from the Junonia coenia densovirus th...

Psychological responses to the proximity of climate change

NASA Astrophysics Data System (ADS)

Brügger, Adrian; Dessai, Suraje; Devine-Wright, Patrick; Morton, Thomas A.; Pidgeon, Nicholas F.

2015-12-01

A frequent suggestion to increase individuals' willingness to take action on climate change and to support relevant policies is to highlight its proximal consequences, that is, those that are close in space and time. But previous studies that have tested this proximizing approach have not revealed the expected positive effects on individual action and support for addressing climate change. We present three lines of psychological reasoning that provide compelling arguments as to why highlighting proximal impacts of climate change might not be as effective a way to increase individual mitigation and adaptation efforts as is often assumed. Our contextualization of the proximizing approach within established psychological research suggests that, depending on the particular theoretical perspective one takes on this issue, and on specific individual characteristics suggested by these perspectives, proximizing can bring about the intended positive effects, can have no (visible) effect or can even backfire. Thus, the effects of proximizing are much more complex than is commonly assumed. Revealing this complexity contributes to a refined theoretical understanding of the role that psychological distance plays in the context of climate change and opens up further avenues for future research and for interventions.

Adaptive relative pose control for autonomous spacecraft rendezvous and proximity operations with thrust misalignment and model uncertainties

NASA Astrophysics Data System (ADS)

Sun, Liang; Zheng, Zewei

2017-04-01

An adaptive relative pose control strategy is proposed for a pursue spacecraft in proximity operations on a tumbling target. Relative position vector between two spacecraft is required to direct towards the docking port of the target while the attitude of them must be synchronized. With considering the thrust misalignment of pursuer, an integrated controller for relative translational and relative rotational dynamics is developed by using norm-wise adaptive estimations. Parametric uncertainties, unknown coupled dynamics, and bounded external disturbances are compensated online by adaptive update laws. It is proved via Lyapunov stability theory that the tracking errors of relative pose converge to zero asymptotically. Numerical simulations including six degrees-of-freedom rigid body dynamics are performed to demonstrate the effectiveness of the proposed controller.

Movement coordination patterns between the foot joints during walking.

PubMed

Arnold, John B; Caravaggi, Paolo; Fraysse, François; Thewlis, Dominic; Leardini, Alberto

2017-01-01

In 3D gait analysis, kinematics of the foot joints are usually reported via isolated time histories of joint rotations and no information is provided on the relationship between rotations at different joints. The aim of this study was to identify movement coordination patterns in the foot during walking by expanding an existing vector coding technique according to an established multi-segment foot and ankle model. A graphical representation is also described to summarise the coordination patterns of joint rotations across multiple patients. Three-dimensional multi-segment foot kinematics were recorded in 13 adults during walking. A modified vector coding technique was used to identify coordination patterns between foot joints involving calcaneus, midfoot, metatarsus and hallux segments. According to the type and direction of joints rotations, these were classified as in-phase (same direction), anti-phase (opposite directions), proximal or distal joint dominant. In early stance, 51 to 75% of walking trials showed proximal-phase coordination between foot joints comprising the calcaneus, midfoot and metatarsus. In-phase coordination was more prominent in late stance, reflecting synergy in the simultaneous inversion occurring at multiple foot joints. Conversely, a distal-phase coordination pattern was identified for sagittal plane motion of the ankle relative to the midtarsal joint, highlighting the critical role of arch shortening to locomotor function in push-off. This study has identified coordination patterns between movement of the calcaneus, midfoot, metatarsus and hallux by expanding an existing vector cording technique for assessing and classifying coordination patterns of foot joints rotations during walking. This approach provides a different perspective in the analysis of multi-segment foot kinematics, and may be used for the objective quantification of the alterations in foot joint coordination patterns due to lower limb pathologies or following injuries.

Superconducting proximity in three-dimensional Dirac materials: Odd-frequency, pseudoscalar, pseudovector, and tensor-valued superconducting orders

NASA Astrophysics Data System (ADS)

Faraei, Zahra; Jafari, S. A.

2017-10-01

We find that a conventional s -wave superconductor in proximity to a three-dimensional Dirac material (3DDM), to all orders of perturbation in tunneling, induces a combination of s - and p -wave pairing only. We show that the Lorentz invariance of the superconducting pairing prevents the formation of Cooper pairs with higher orbital angular momenta in the 3DDM. This no-go theorem acquires stronger form when the probability of tunneling from the conventional superconductor to positive and negative energy states of 3DDM are equal. In this case, all the p -wave contribution except for the lowest order, identically vanish and hence we obtain an exact result for the induced p -wave superconductivity in 3DDM. Fierz decomposing the superconducting matrix we find that the temporal component of the vector superconducting order and the spatial components of the pseudovector order have odd-frequency pairing symmetry. We find that the latter is odd with respect to exchange of position and chirality of the electrons in the Cooper pair and is a spin-triplet, which is necessary for NMR detection of such an exotic pseudovector pairing. Moreover, we show that the tensorial order breaks into a polar vector and an axial vector and both of them have conventional pairing symmetry except for being a spin triplet. According to our study, for gapless 3DDM, the tensorial superconducting order will be the only order that is odd with respect to the chemical potential μ . Therefore we predict that a transverse p -n junction binds Majorana fermions. This effect can be used to control the neutral Majorana fermions with electric fields.

Orbiter/payload proximity operations: Lateral approach technique

NASA Technical Reports Server (NTRS)

Bell, J. A.; Jones, H. L.; Mcadoo, S. F.

1977-01-01

The lateral approach is presented for proximity operations associated with the retrieval of free flying payloads. An out of plane final approach emphasizing onboard software support is recommended for all except the latter segment of the final approach in which manual control is considered mandatory. An overall assessment of various candidate proximity operations techniques are made.

[Support vector machine?assisted diagnosis of human malignant gastric tissues based on dielectric properties].

PubMed

Zhang, Sa; Li, Zhou; Xin, Xue-Gang

2017-12-20

To achieve differential diagnosis of normal and malignant gastric tissues based on discrepancies in their dielectric properties using support vector machine. The dielectric properties of normal and malignant gastric tissues at the frequency ranging from 42.58 to 500 MHz were measured by coaxial probe method, and the Cole?Cole model was used to fit the measured data. Receiver?operating characteristic (ROC) curve analysis was used to evaluate the discrimination capability with respect to permittivity, conductivity, and Cole?Cole fitting parameters. Support vector machine was used for discriminating normal and malignant gastric tissues, and the discrimination accuracy was calculated using k?fold cross? The area under the ROC curve was above 0.8 for permittivity at the 5 frequencies at the lower end of the measured frequency range. The combination of the support vector machine with the permittivity at all these 5 frequencies combined achieved the highest discrimination accuracy of 84.38% with a MATLAB runtime of 3.40 s. The support vector machine?assisted diagnosis is feasible for human malignant gastric tissues based on the dielectric properties.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Vectorial mask optimization methods for robust optical lithography

NASA Astrophysics Data System (ADS)

Ma, Xu; Li, Yanqiu; Guo, Xuejia; Dong, Lisong; Arce, Gonzalo R.

2012-10-01

Continuous shrinkage of critical dimension in an integrated circuit impels the development of resolution enhancement techniques for low k1 lithography. Recently, several pixelated optical proximity correction (OPC) and phase-shifting mask (PSM) approaches were developed under scalar imaging models to account for the process variations. However, the lithography systems with larger-NA (NA>0.6) are predominant for current technology nodes, rendering the scalar models inadequate to describe the vector nature of the electromagnetic field that propagates through the optical lithography system. In addition, OPC and PSM algorithms based on scalar models can compensate for wavefront aberrations, but are incapable of mitigating polarization aberrations in practical lithography systems, which can only be dealt with under the vector model. To this end, we focus on developing robust pixelated gradient-based OPC and PSM optimization algorithms aimed at canceling defocus, dose variation, wavefront and polarization aberrations under a vector model. First, an integrative and analytic vector imaging model is applied to formulate the optimization problem, where the effects of process variations are explicitly incorporated in the optimization framework. A steepest descent algorithm is then used to iteratively optimize the mask patterns. Simulations show that the proposed algorithms can effectively improve the process windows of the optical lithography systems.

Essential and Dispensable Virus-Encoded Replication Elements Revealed by Efforts To Develop Hypoviruses as Gene Expression Vectors

PubMed Central

Suzuki, Nobuhiro; Geletka, Lynn M.; Nuss, Donald L.

2000-01-01

We have investigated whether hypoviruses, viral agents responsible for virulence attenuation (hypovirulence) of the chestnut blight fungus Cryphonectria parasitica, could serve as gene expression vectors. The infectious cDNA clone of the prototypic hypovirus CHV1-EP713 was modified to generate 20 different vector candidates. Although transient expression was achieved for a subset of vectors that contained the green fluorescent protein gene from Aequorea victoria, long-term expression (past day 8) was not observed for any vector construct. Analysis of viral RNAs recovered from transfected fungal colonies revealed that the foreign genes were readily deleted from the replicating virus, although small portions of foreign sequences were retained by some vectors after months of replication. However, the results of vector viability and progeny characterization provided unexpected new insights into essential and dispensable elements of hypovirus replication. The N-terminal portion (codons 1 to 24) of the 5′-proximal open reading frame (ORF), ORF A, was found to be required for virus replication, while the remaining 598 codons of this ORF were completely dispensable. Substantial alterations were tolerated in the pentanucleotide UAAUG that contains the ORF A termination codon and the overlapping putative initiation codon of the second of the two hypovirus ORFs, ORF B. Replication competence was maintained following either a frameshift mutation that caused a two-codon extension of ORF A or a modification that produced a single-ORF genomic organization. These results are discussed in terms of determinants of hypovirus replication, the potential utility of hypoviruses as gene expression vectors, and possible mechanisms by which hypoviruses recognize and delete foreign sequences. PMID:10906211

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

NASA Astrophysics Data System (ADS)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

The Design of a Templated C++ Small Vector Class for Numerical Computing

NASA Technical Reports Server (NTRS)

Moran, Patrick J.

2000-01-01

We describe the design and implementation of a templated C++ class for vectors. The vector class is templated both for vector length and vector component type; the vector length is fixed at template instantiation time. The vector implementation is such that for a vector of N components of type T, the total number of bytes required by the vector is equal to N * size of (T), where size of is the built-in C operator. The property of having a size no bigger than that required by the components themselves is key in many numerical computing applications, where one may allocate very large arrays of small, fixed-length vectors. In addition to the design trade-offs motivating our fixed-length vector design choice, we review some of the C++ template features essential to an efficient, succinct implementation. In particular, we highlight some of the standard C++ features, such as partial template specialization, that are not supported by all compilers currently. This report provides an inventory listing the relevant support currently provided by some key compilers, as well as test code one can use to verify compiler capabilities.

An Efficient Wait-Free Vector

DOE PAGES

Feldman, Steven; Valera-Leon, Carlos; Dechev, Damian

2016-03-01

The vector is a fundamental data structure, which provides constant-time access to a dynamically-resizable range of elements. Currently, there exist no wait-free vectors. The only non-blocking version supports only a subset of the sequential vector API and exhibits significant synchronization overhead caused by supporting opposing operations. Since many applications operate in phases of execution, wherein each phase only a subset of operations are used, this overhead is unnecessary for the majority of the application. To address the limitations of the non-blocking version, we present a new design that is wait-free, supports more of the operations provided by the sequential vector,more » and provides alternative implementations of key operations. These alternatives allow the developer to balance the performance and functionality of the vector as requirements change throughout execution. Compared to the known non-blocking version and the concurrent vector found in Intel’s TBB library, our design outperforms or provides comparable performance in the majority of tested scenarios. Over all tested scenarios, the presented design performs an average of 4.97 times more operations per second than the non-blocking vector and 1.54 more than the TBB vector. In a scenario designed to simulate the filling of a vector, performance improvement increases to 13.38 and 1.16 times. This work presents the first ABA-free non-blocking vector. Finally, unlike the other non-blocking approach, all operations are wait-free and bounds-checked and elements are stored contiguously in memory.« less

The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables.

PubMed

Yin, Kedong; Wang, Pengyu; Li, Xuemei

2017-12-13

With respect to multi-attribute group decision-making (MAGDM) problems, where attribute values take the form of interval grey trapezoid fuzzy linguistic variables (IGTFLVs) and the weights (including expert and attribute weight) are unknown, improved grey relational MAGDM methods are proposed. First, the concept of IGTFLV, the operational rules, the distance between IGTFLVs, and the projection formula between the two IGTFLV vectors are defined. Second, the expert weights are determined by using the maximum proximity method based on the projection values between the IGTFLV vectors. The attribute weights are determined by the maximum deviation method and the priorities of alternatives are determined by improved grey relational analysis. Finally, an example is given to prove the effectiveness of the proposed method and the flexibility of IGTFLV.

Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

NASA Astrophysics Data System (ADS)

Li, Tao

2018-06-01

The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

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

Feldman, Steven; Valera-Leon, Carlos; Dechev, Damian

The vector is a fundamental data structure, which provides constant-time access to a dynamically-resizable range of elements. Currently, there exist no wait-free vectors. The only non-blocking version supports only a subset of the sequential vector API and exhibits significant synchronization overhead caused by supporting opposing operations. Since many applications operate in phases of execution, wherein each phase only a subset of operations are used, this overhead is unnecessary for the majority of the application. To address the limitations of the non-blocking version, we present a new design that is wait-free, supports more of the operations provided by the sequential vector,more » and provides alternative implementations of key operations. These alternatives allow the developer to balance the performance and functionality of the vector as requirements change throughout execution. Compared to the known non-blocking version and the concurrent vector found in Intel’s TBB library, our design outperforms or provides comparable performance in the majority of tested scenarios. Over all tested scenarios, the presented design performs an average of 4.97 times more operations per second than the non-blocking vector and 1.54 more than the TBB vector. In a scenario designed to simulate the filling of a vector, performance improvement increases to 13.38 and 1.16 times. This work presents the first ABA-free non-blocking vector. Finally, unlike the other non-blocking approach, all operations are wait-free and bounds-checked and elements are stored contiguously in memory.« less

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Proximity operations concept design study, task 6

NASA Technical Reports Server (NTRS)

Williams, A. N.

1990-01-01

The feasibility of using optical technology to perform the mission of the proximity operations communications subsystem on Space Station Freedom was determined. Proximity operations mission requirements are determined and the relationship to the overall operational environment of the space station is defined. From this information, the design requirements of the communication subsystem are derived. Based on these requirements, a preliminary design is developed and the feasibility of implementation determined. To support the Orbital Maneuvering Vehicle and National Space Transportation System, the optical system development is straightforward. The requirements on extra-vehicular activity are such as to allow large fields of uncertainty, thus exacerbating the acquisition problem; however, an approach is given that could mitigate this problem. In general, it is found that such a system could indeed perform the proximity operations mission requirement, with some development required to support extra-vehicular activity.

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

PubMed

Bisenius, Sandrine; Mueller, Karsten; Diehl-Schmid, Janine; Fassbender, Klaus; Grimmer, Timo; Jessen, Frank; Kassubek, Jan; Kornhuber, Johannes; Landwehrmeyer, Bernhard; Ludolph, Albert; Schneider, Anja; Anderl-Straub, Sarah; Stuke, Katharina; Danek, Adrian; Otto, Markus; Schroeter, Matthias L

2017-01-01

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

Support vector machines

NASA Technical Reports Server (NTRS)

Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

2004-01-01

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

Lysine acetylation sites prediction using an ensemble of support vector machine classifiers.

PubMed

Xu, Yan; Wang, Xiao-Bo; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang

2010-05-07

Lysine acetylation is an essentially reversible and high regulated post-translational modification which regulates diverse protein properties. Experimental identification of acetylation sites is laborious and expensive. Hence, there is significant interest in the development of computational methods for reliable prediction of acetylation sites from amino acid sequences. In this paper we use an ensemble of support vector machine classifiers to perform this work. The experimentally determined acetylation lysine sites are extracted from Swiss-Prot database and scientific literatures. Experiment results show that an ensemble of support vector machine classifiers outperforms single support vector machine classifier and other computational methods such as PAIL and LysAcet on the problem of predicting acetylation lysine sites. The resulting method has been implemented in EnsemblePail, a web server for lysine acetylation sites prediction available at http://www.aporc.org/EnsemblePail/. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Product Quality Modelling Based on Incremental Support Vector Machine

NASA Astrophysics Data System (ADS)

Wang, J.; Zhang, W.; Qin, B.; Shi, W.

2012-05-01

Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

Role of geospatial technology in identifying natural habitat of malarial vectors in South Andaman, India.

PubMed

Shankar, Shiva; Agrawal, Deepak Kumar

2016-03-01

Malaria is a serious disease which has repeatedly threatened Andaman, an island territory of India. Uncharted dense vegetation and inaccessibility are the salient features of the area and the major areas are covered by remotely sensed data to identify the malaria vector's natural habitat. The present investigation appraises the role of geospatial technologies in identifying the natural habitat of malarial vectors. The base map was prepared from Survey of India's toposheets, the landuse map was prepared from indices techniques like normalised difference vegetation index (NDVI), normalised difference water index (NDWI), modified normalised difference water index (MNDWI), normalised difference pond index (NDPI), and normalized difference turbidity index (NDTI) in conjugation with visual interpretation. The soil moisture content map was reproduced from the soil atlas of Andaman and Nicobar Islands followed by generation of an aspect profile from ASTER-GDEM satellite data. Both the landuse map and aspect profile were validated for accuracy in the field. A weighted overlay analysis of the classes like landuse, soil moisture and aspect profile of the study area resulted in identification of the potential natural habitat map of malaria vector surrounding the areas of Tushnabad, Garacharma, Manglutan, Chouldari, Ferrargunj and Wimberlygunj hamlets. The natural habitat of malaria vector indicated that Tushnabad, Garacharma, Manglutan, Chouldari, Ferrargunj and Wimberlygunj hamlets are within the proximity of 2.5 km from the prime habitat location with more number of malaria positive cases. Also these hamlets are surrounded by dense evergreen forest and the land surface is draped by clay loam and clay soil texture exhibiting high soil moisture content warranting high rates of survival and proliferation of the vector ensuring resurgence of malaria every year. It is thus concluded that application of geospatial technologies plays an important role in identifying the natural habitat of malaria vector.

Neighborhood support network, perceived proximity to community facilities and depressive symptoms among low socioeconomic status Chinese elders.

PubMed

Chen, Yan-Yan; Wong, Gloria H Y; Lum, Terry Y; Lou, Vivian W Q; Ho, Andy H Y; Luo, Hao; Tong, Tracy L W

2016-01-01

Depressive symptoms are common in older people; most previous research on elderly depression focused on individual-level characteristics or neighborhood socioeconomic status. Modifiable neighborhood characteristics of older people dwelling in low-income communities are under-studied. This study aims to identify potentially modifiable social and physical neighborhood characteristics that influence depressive symptoms independent of individual-level characteristics among older Chinese. Data came from a cross-sectional survey conducted in four low-income public rental housing estates in Hong Kong in 2012. We interviewed a total of 400 elderly residents. The structured questionnaire covered demographics, activities of daily living, recent fall history, neighborhood support networks, and perceived proximity by walk to community facilities. Multiple regression was used to test whether inclusion of neighborhood factors in addition to individual characteristics increases model fit in explaining depressive symptoms in elders with low socioeconomic status. At individual level, activities of daily living and income significantly predicted depressive symptoms. Receiving support from friends or neighbors is associated with fewer depressive symptoms. However, participants who received organizational support had a 1.17 points of increase on the 15-item Geriatric Depression Scale (GDS-15). At-ease walkable proximity to medical facilities was positively associated with a better GDS score. Neighborhood support networks and perceived proximity by walk to community facilities contribute significantly to depressive symptoms among low-income elders. Programs and policies that facilitate neighborhood support and commuting or promote facility accessibility may help ameliorate depressive symptoms common among low-income elders.

Tube support for moisture separator reheater

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

Sabatino, R.A.

1987-08-11

In combination with a moisture separator reheater for a nuclear steam generating power plant, a reheater is described comprising: a sealed elongated substantially horizontal tubular shell member, a cycle fluid inlet passing through the shell member in predetermined position, mositure separator means positioned within the shell member proximate the bottom portion thereof, heat exchanger means comprising a plurality of elongated metallic U-shaped members disposed substantially within the shell member, a tube sheet member supporing the U-shaped tube members at one end thereof. The improvement consists of: the tube support member means proximate the U-bend portion of the U-shaped tube membersmore » each comprising an upper movable tube support member and a lower immovable tube support member, the remainder of the tube support means being immovable, the upper movable tube support member spacing and supporting the top leg portions of the U-shaped tube members, the lower immovable tube support member spacing and supporting the bottom leg portions of the U-shaped tube members, whereby the top leg portions of the U-shaped tube members proximate the U-bend are permitted to move to compensate for any increase in radius in the U-bend portion of the U-shaped tube member due to thermal expansion.« less

Ikonos-derived malaria transmission risk in northwestern Thailand.

PubMed

Sithiprasasna, Ratana; Ugsang, Donald M; Honda, Kiyoshi; Jones, James W; Singhasivanon, Pratap

2005-01-01

We mapped overall malaria cases and located each field observed major malaria vector breeding habitat using Global Positioning System (GPS) instruments from September 2000 to October 2003 around the three malaria-endemic villages of Ban Khun Huay, Ban Pa Dae, and Ban Tham Seau, Mae Sod district, Tak Province, Thailand. The land-use/land-cover classifications of the three villages and surrounding areas were performed on IKONOS satellite images acquired on 12 November 2001 with a spatial resolution of 1 x 1 m. Stream network was delineated and displayed. Proximity analysis was performed on the locations of the houses with and without malaria cases within a 1.5 km buffer from An. minimus immature mosquito breeding habitats, mainly stream margins. The 1.5 km used in our proximity analysis was arbitrarily estimated based on the An. minimus flight range. A statistical t-test at 5% significance level was performed to evaluate whether houses with malaria cases have higher proximities to streams than houses without malaria cases. The result shows no significant difference between proximity to streams between houses with malaria cases and houses without malaria cases. We suspect that the actual flight range of An. minimus may be greater than 1.5 km. The An. minimus larval habitat deserves more detailed investigation. Further studies on human behavior contrary to that required for adequate malaria control among these three villages are also recommended.

Understanding Karma Police: The Perceived Plausibility of Noun Compounds as Predicted by Distributional Models of Semantic Representation

PubMed Central

Günther, Fritz; Marelli, Marco

2016-01-01

Noun compounds, consisting of two nouns (the head and the modifier) that are combined into a single concept, differ in terms of their plausibility: school bus is a more plausible compound than saddle olive. The present study investigates which factors influence the plausibility of attested and novel noun compounds. Distributional Semantic Models (DSMs) are used to obtain formal (vector) representations of word meanings, and compositional methods in DSMs are employed to obtain such representations for noun compounds. From these representations, different plausibility measures are computed. Three of those measures contribute in predicting the plausibility of noun compounds: The relatedness between the meaning of the head noun and the compound (Head Proximity), the relatedness between the meaning of modifier noun and the compound (Modifier Proximity), and the similarity between the head noun and the modifier noun (Constituent Similarity). We find non-linear interactions between Head Proximity and Modifier Proximity, as well as between Modifier Proximity and Constituent Similarity. Furthermore, Constituent Similarity interacts non-linearly with the familiarity with the compound. These results suggest that a compound is perceived as more plausible if it can be categorized as an instance of the category denoted by the head noun, if the contribution of the modifier to the compound meaning is clear but not redundant, and if the constituents are sufficiently similar in cases where this contribution is not clear. Furthermore, compounds are perceived to be more plausible if they are more familiar, but mostly for cases where the relation between the constituents is less clear. PMID:27732599

Group prioritisation with unknown expert weights in incomplete linguistic context

NASA Astrophysics Data System (ADS)

Cheng, Dong; Cheng, Faxin; Zhou, Zhili; Wang, Juan

2017-09-01

In this paper, we study a group prioritisation problem in situations when the expert weights are completely unknown and their judgement preferences are linguistic and incomplete. Starting from the theory of relative entropy (RE) and multiplicative consistency, an optimisation model is provided for deriving an individual priority vector without estimating the missing value(s) of an incomplete linguistic preference relation. In order to address the unknown expert weights in the group aggregating process, we define two new kinds of expert weight indicators based on RE: proximity entropy weight and similarity entropy weight. Furthermore, a dynamic-adjusting algorithm (DAA) is proposed to obtain an objective expert weight vector and capture the dynamic properties involved in it. Unlike the extant literature of group prioritisation, the proposed RE approach does not require pre-allocation of expert weights and can solve incomplete preference relations. An interesting finding is that once all the experts express their preference relations, the final expert weight vector derived from the DAA is fixed irrespective of the initial settings of expert weights. Finally, an application example is conducted to validate the effectiveness and robustness of the RE approach.

Pure state consciousness and its local reduction to neuronal space

NASA Astrophysics Data System (ADS)

Duggins, A. J.

2013-01-01

The single neuronal state can be represented as a vector in a complex space, spanned by an orthonormal basis of integer spike counts. In this model a scalar element of experience is associated with the instantaneous firing rate of a single sensory neuron over repeated stimulus presentations. Here the model is extended to composite neural systems that are tensor products of single neuronal vector spaces. Depiction of the mental state as a vector on this tensor product space is intended to capture the unity of consciousness. The density operator is introduced as its local reduction to the single neuron level, from which the firing rate can again be derived as the objective correlate of a subjective element. However, the relational structure of perceptual experience only emerges when the non-local mental state is considered. A metric of phenomenal proximity between neuronal elements of experience is proposed, based on the cross-correlation function of neurophysiology, but constrained by the association of theoretical extremes of correlation/anticorrelation in inseparable 2-neuron states with identical and opponent elements respectively.

Chameleon vector bosons

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

Nelson, Ann E.; Instituto de Fisica Teorica UAM/CSIC, Facultad de Ciencias, C-XVI Universidad Autonoma de Madrid Cantoblanco, Madrid 28049; Walsh, Jonathan

2008-05-01

We show that for a force mediated by a vector particle coupled to a conserved U(1) charge, the apparent range and strength can depend on the size and density of the source, and the proximity to other sources. This chameleon effect is due to screening from a light charged scalar. Such screening can weaken astrophysical constraints on new gauge bosons. As an example we consider the constraints on chameleonic gauged B-L. We show that although Casimir measurements greatly constrain any B-L force much stronger than gravity with range longer than 0.1 {mu}m, there remains an experimental window for a long-rangemore » chameleonic B-L force. Such a force could be much stronger than gravity, and long or infinite range in vacuum, but have an effective range near the surface of the earth which is less than a micron.« less

Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems

DTIC Science & Technology

2013-05-28

those of the support vector machine and relevance vector machine, and the model runs more quickly than the other algorithms . When one class occurs...incremental support vector machine algorithm for online learning when fewer than 50 data points are available. (a) Papers published in peer-reviewed journals...learning environments, where data processing occurs one observation at a time and the classification algorithm improves over time with new

Detection of a chikungunya outbreak in Central Italy, August to September 2017.

PubMed

Venturi, Giulietta; Di Luca, Marco; Fortuna, Claudia; Remoli, Maria Elena; Riccardo, Flavia; Severini, Francesco; Toma, Luciano; Del Manso, Martina; Benedetti, Eleonora; Caporali, Maria Grazia; Amendola, Antonello; Fiorentini, Cristiano; De Liberato, Claudio; Giammattei, Roberto; Romi, Roberto; Pezzotti, Patrizio; Rezza, Giovanni; Rizzo, Caterina

2017-09-01

An autochthonous chikungunya outbreak is ongoing near Anzio, a coastal town in the province of Rome. The virus isolated from one patient and mosquitoes lacks the A226V mutation and belongs to an East Central South African strain. As of 20 September, 86 cases are laboratory-confirmed. The outbreak proximity to the capital, its late summer occurrence, and diagnostic delays, are favouring transmission. Vector control, enhanced surveillance and restricted blood donations are being implemented in affected areas.

A trial assessing N-3 as treatment for injury-induced cachexia (ATLANTIC trial): does a moderate dose fish oil intervention improve outcomes in older adults recovering from hip fracture?

PubMed

Miller, Michelle D; Yaxley, Alison; Villani, Anthony; Cobiac, Lynne; Fraser, Robert; Cleland, Leslie; James, Michael; Crotty, Maria

2010-10-22

Proximal femoral fractures are associated with increased morbidity and mortality. Pre-existing malnutrition and weight loss amongst this patient group is of primary concern, with conventional nutrition support being largely ineffective. The inflammatory response post proximal femoral fracture surgery and the subsequent risk of cachexia may explain the inability of conventional high energy high protein management to produce an anabolic response amongst these patients. Omega-3 fatty acids derived from fish oils have been extensively studied for their anti-inflammatory benefits. Due to their anti-inflammatory properties, the benefit of fish oil combined with individualized nutrition support amongst proximal femoral fracture patients post surgery is an attractive potential therapeutic strategy. The aim of the ATLANTIC trial is to assess the potential benefits of an anti-inflammatory dose of fish oil within the context of a 12 week individualised nutrition program, commencing seven days post proximal femoral fracture surgery. This randomized controlled, double blinded trial, will recruit 150 community dwelling elderly patients aged ≥65 years, within seven days of surgery for proximal femoral fracture. Participants will be randomly allocated to receive either a 12 week individualized nutrition support program complemented with 20 ml/day anti-inflammatory dose fish oil (~3.6 g eicosapentaenoic acid, ~2.4 g docosahexanoic acid; intervention), or, a 12 week individualized nutrition support program complemented with 20 ml/day low dose fish oil (~0.36 g eicosapentaenoic acid, ~0.24 g docosahexanoic acid; control). The ATLANTIC trial is the first of its kind to provide fish oil combined with individualized nutrition therapy as an intervention to address the inflammatory response experienced post proximal femoral fracture surgery amongst elderly patients. The final outcomes of this trial will assist clinicians in the development of effective and alternative treatment methods post proximal femoral fracture surgery which may ultimately result in a reduction in systemic inflammation, loss of weight and lean muscle and improvements in nutritional status, mobility, independence and quality of life among elderly patients. ACTRN12609000241235.

A trial assessing N-3 as treatment for injury-induced cachexia (ATLANTIC trial): does a moderate dose fish oil intervention improve outcomes in older adults recovering from hip fracture?

PubMed Central

2010-01-01

Background Proximal femoral fractures are associated with increased morbidity and mortality. Pre-existing malnutrition and weight loss amongst this patient group is of primary concern, with conventional nutrition support being largely ineffective. The inflammatory response post proximal femoral fracture surgery and the subsequent risk of cachexia may explain the inability of conventional high energy high protein management to produce an anabolic response amongst these patients. Omega-3 fatty acids derived from fish oils have been extensively studied for their anti-inflammatory benefits. Due to their anti-inflammatory properties, the benefit of fish oil combined with individualized nutrition support amongst proximal femoral fracture patients post surgery is an attractive potential therapeutic strategy. The aim of the ATLANTIC trial is to assess the potential benefits of an anti-inflammatory dose of fish oil within the context of a 12 week individualised nutrition program, commencing seven days post proximal femoral fracture surgery. Methods/Design This randomized controlled, double blinded trial, will recruit 150 community dwelling elderly patients aged ≥65 years, within seven days of surgery for proximal femoral fracture. Participants will be randomly allocated to receive either a 12 week individualized nutrition support program complemented with 20 ml/day anti-inflammatory dose fish oil (~3.6 g eicosapentaenoic acid, ~2.4 g docosahexanoic acid; intervention), or, a 12 week individualized nutrition support program complemented with 20 ml/day low dose fish oil (~0.36 g eicosapentaenoic acid, ~0.24 g docosahexanoic acid; control). Discussion The ATLANTIC trial is the first of its kind to provide fish oil combined with individualized nutrition therapy as an intervention to address the inflammatory response experienced post proximal femoral fracture surgery amongst elderly patients. The final outcomes of this trial will assist clinicians in the development of effective and alternative treatment methods post proximal femoral fracture surgery which may ultimately result in a reduction in systemic inflammation, loss of weight and lean muscle and improvements in nutritional status, mobility, independence and quality of life among elderly patients. Trial Registration ACTRN12609000241235 PMID:20964865

Vector-model-supported approach in prostate plan optimization

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

Liu, Eva Sau Fan; Department of Health Technology and Informatics, The Hong Kong Polytechnic University; Wu, Vincent Wing Cheung

Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100more » previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number without compromising the plan quality.« less

A helper virus-free HSV-1 vector containing the vesicular glutamate transporter-1 promoter supports expression preferentially in VGLUT1-containing glutamatergic neurons.

PubMed

Zhang, Guo-rong; Geller, Alfred I

2010-05-17

Multiple potential uses of direct gene transfer into neurons require restricting expression to specific classes of glutamatergic neurons. Thus, it is desirable to develop vectors containing glutamatergic class-specific promoters. The three vesicular glutamate transporters (VGLUTs) are expressed in distinct populations of neurons, and VGLUT1 is the predominant VGLUT in the neocortex, hippocampus, and cerebellar cortex. We previously reported a plasmid (amplicon) Herpes Simplex Virus (HSV-1) vector that placed the Lac Z gene under the regulation of the VGLUT1 promoter (pVGLUT1lac). Using helper virus-free vector stocks, we showed that this vector supported approximately 90% glutamatergic neuron-specific expression in postrhinal (POR) cortex, in rats sacrificed at either 4 days or 2 months after gene transfer. We now show that pVGLUT1lac supports expression preferentially in VGLUT1-containing glutamatergic neurons. pVGLUT1lac vector stock was injected into either POR cortex, which contains primarily VGLUT1-containing glutamatergic neurons, or into the ventral medial hypothalamus (VMH), which contains predominantly VGLUT2-containing glutamatergic neurons. Rats were sacrificed at 4 days after gene transfer, and the types of cells expressing ss-galactosidase were determined by immunofluorescent costaining. Cell counts showed that pVGLUT1lac supported expression in approximately 10-fold more cells in POR cortex than in the VMH, whereas a control vector supported expression in similar numbers of cells in these two areas. Further, in POR cortex, pVGLUT1lac supported expression predominately in VGLUT1-containing neurons, and, in the VMH, pVGLUT1lac showed an approximately 10-fold preference for the rare VGLUT1-containing neurons. VGLUT1-specific expression may benefit specific experiments on learning or specific gene therapy approaches, particularly in the neocortex. Copyright 2010 Elsevier B.V. All rights reserved.

Mechanisms of double-strand-break repair during gene targeting in mammalian cells.

PubMed Central

Ng, P; Baker, M D

1999-01-01

In the present study, the mechanism of double-strand-break (DSB) repair during gene targeting at the chromosomal immunoglobulin mu-locus in a murine hybridoma was examined. The gene-targeting assay utilized specially designed insertion vectors genetically marked in the region of homology to the chromosomal mu-locus by six diagnostic restriction enzyme site markers. The restriction enzyme markers permitted the contribution of vector-borne and chromosomal mu-sequences in the recombinant product to be determined. The use of the insertion vectors in conjunction with a plating procedure in which individual integrative homologous recombination events were retained for analysis revealed several important features about the mammalian DSB repair process:The presence of the markers within the region of shared homology did not affect the efficiency of gene targeting.In the majority of recombinants, the vector-borne marker proximal to the DSB was absent, being replaced with the corresponding chromosomal restriction enzyme site. This result is consistent with either formation and repair of a vector-borne gap or an "end" bias in mismatch repair of heteroduplex DNA (hDNA) that favored the chromosomal sequence. Formation of hDNA was frequently associated with gene targeting and, in most cases, began approximately 645 bp from the DSB and could encompass a distance of at least 1469 bp.The hDNA was efficiently repaired prior to DNA replication.The repair of adjacent mismatches in hDNA occurred predominantly on the same strand, suggesting the involvement of a long-patch repair mechanism. PMID:10049929

A heterologous prime-boosting strategy with replicating Vaccinia virus vectors and plant-produced HIV-1 Gag/dgp41 virus-like particles

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

Meador, Lydia R.

Showing modest efficacy, the RV144 HIV-1 vaccine clinical trial utilized a non-replicating canarypox viral vector and a soluble gp120 protein boost. Here we built upon the RV144 strategy by developing a novel combination of a replicating, but highly-attenuated Vaccinia virus vector, NYVAC-KC, and plant-produced HIV-1 virus-like particles (VLPs). Both components contained the full-length Gag and a membrane anchored truncated gp41 presenting the membrane proximal external region with its conserved broadly neutralizing epitopes in the pre-fusion conformation. We tested different prime/boost combinations of these components in mice and showed that the group primed with NYVAC-KC and boosted with both the viralmore » vectors and plant-produced VLPs have the most robust Gag-specific CD8 T cell responses, at 12.7% of CD8 T cells expressing IFN-γ in response to stimulation with five Gag epitopes. The same immunization group elicited the best systemic and mucosal antibody responses to Gag and dgp41 with a bias towards IgG1. - Highlights: • We devised a prime/boost anti HIV-1 vaccination strategy modeled after RV144. • We used plant-derived virus-like particles (VLPs) consisting of Gag and dgp41. • We used attenuated, replicating vaccinia virus vectors expressing the same antigens. • The immunogens elicited strong cellular and humoral immune responses.« less

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

NASA Astrophysics Data System (ADS)

Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

2017-07-01

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

Gene Flow within and between Catchments in the Threatened Riparian Plant Myricaria germanica

PubMed Central

Werth, Silke; Scheidegger, Christoph

2014-01-01

One of the major distinctions of riparian habitats is their linearity. In linear habitats, gene flow is predicted to follow a one-dimensional stepping stone model, characterized by bidirectional gene flow between neighboring populations. Here, we studied the genetic structure of Myricaria germanica, a threatened riparian shrub which is capable of both wind and water dispersal. Our data led us to reject the ‘one catchment – one gene pool’ hypothesis as we found support for two gene pools, rather than four as expected in a study area including four catchments. This result also implies that in the history of the studied populations, dispersal across catchments has occurred. Two contemporary catchment-crossing migration events were detected, albeit between spatially proximate catchments. Allelic richness and inbreeding coefficients differed substantially between gene pools. There was significant isolation by distance, and our data confirmed the one-dimensional stepping-stone model of gene flow. Contemporary migration was bidirectional within the studied catchments, implying that dispersal vectors other than water are important for M. germanica. PMID:24932520

Deadpool: A how-to-build guide

USDA-ARS?s Scientific Manuscript database

An easy-to-customize, low-cost, low disturbance proximal sensing cart for field-based high-throughput phenotyping is described. General dimensions and build guidelines are provided. The cart, named Deadpool, supports mounting multiple proximal sensors and cameras for characterizing plant traits grow...

Professor: A motorized field-based phenotyping cart

USDA-ARS?s Scientific Manuscript database

An easy-to-customize, low-cost, low disturbance, motorized proximal sensing cart for field-based high-throughput phenotyping is described. General dimensions, motor specifications, and a remote operation application are given. The cart, named Professor, supports mounting multiple proximal sensors an...

A hybrid approach to select features and classify diseases based on medical data

NASA Astrophysics Data System (ADS)

AbdelLatif, Hisham; Luo, Jiawei

2018-03-01

Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms

Alpharetroviral Self-inactivating Vectors: Long-term Transgene Expression in Murine Hematopoietic Cells and Low Genotoxicity

PubMed Central

Suerth, Julia D; Maetzig, Tobias; Brugman, Martijn H; Heinz, Niels; Appelt, Jens-Uwe; Kaufmann, Kerstin B; Schmidt, Manfred; Grez, Manuel; Modlich, Ute; Baum, Christopher; Schambach, Axel

2012-01-01

Comparative integrome analyses have highlighted alpharetroviral vectors with a relatively neutral, and thus favorable, integration spectrum. However, previous studies used alpharetroviral vectors harboring viral coding sequences and intact long-terminal repeats (LTRs). We recently developed self-inactivating (SIN) alpharetroviral vectors with an advanced split-packaging design. In a murine bone marrow (BM) transplantation model we now compared alpharetroviral, gammaretroviral, and lentiviral SIN vectors and showed that all vectors transduced hematopoietic stem cells (HSCs), leading to comparable, sustained multilineage transgene expression in primary and secondary transplanted mice. Alpharetroviral integrations were decreased near transcription start sites, CpG islands, and potential cancer genes compared with gammaretroviral, and decreased in genes compared with lentiviral integrations. Analyzing the transcriptome and intragenic integrations in engrafting cells, we observed stronger correlations between in-gene integration targeting and transcriptional activity for gammaretroviral and lentiviral vectors than for alpharetroviral vectors. Importantly, the relatively “extragenic” alpharetroviral integration pattern still supported long-term transgene expression upon serial transplantation. Furthermore, sensitive genotoxicity studies revealed a decreased immortalization incidence compared with gammaretroviral and lentiviral SIN vectors. We conclude that alpharetroviral SIN vectors have a favorable integration pattern which lowers the risk of insertional mutagenesis while supporting long-term transgene expression in the progeny of transplanted HSCs. PMID:22334016

Identifying saltcedar with hyperspectral data and support vector machines

USDA-ARS?s Scientific Manuscript database

Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Reversible Lifting of Surface Supported Lipid Bilayers with a Membrane-Spanning Nonionic Triblock Copolymer

DOE PAGES

Hayden, Steven C.; Junghans, Ann; Majewski, Jaroslaw; ...

2017-02-22

Neutron reflectometry was used to monitor structural variations in surface supported DMPC bilayers induced by the addition of Triton X-100, a surfactant commonly used to aid solubilization of membrane proteins, and the co-addition of a membrane spanning non-ionic amphiphilic triblock copolymer, (PEO 117-PPO 47-PE O117, Pluronic F98). Surfactant addition causes slight compression of the bilayer thickness and the creation of a distinct EO layer that increases the hydrophilic layer proximal to the supporting substrate (i.e., a water and EO gap between the lipid bilayer and quartz) to 6.8 ± 0.4 Å. Addition of the triblock copolymer into the DMPC: Tritonmore » X-100 bilayer increases the complexity (broadens) the lipid phase transition, further compresses the bilayer, and continues to expand the proximal hydrophilic layer thickness. The observed structural changes are temperature dependent with transmembrane polymer insertion achieved at 37 °C leading to a compressed membrane thickness of 39.2 ± 0.2 Å and proximal gap of 45.2 ± 0.2 Å. Temperature driven exclusion of the polymer at 15 °C causes partitioning of the polymer into the proximal space generating a large hydrogel cushion 162 ± 16 Å thick. An intermediate gap width (10 – 27 Å) is achieved at room temperature (22 – 25 °C). The temperature-driven changes in the proximal hydrophilic gap dimensions are shown to be reversible but thermal history causes variation in magnitude. Temperature-driven changes in polymer association with a supported lipid bilayer offer a facile means to reversibly control both the membrane characteristics as well as the separation between membrane and solid substrate.« less

Reversible Lifting of Surface Supported Lipid Bilayers with a Membrane-Spanning Nonionic Triblock Copolymer

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

Hayden, Steven C.; Junghans, Ann; Majewski, Jaroslaw

Neutron reflectometry was used to monitor structural variations in surface supported DMPC bilayers induced by the addition of Triton X-100, a surfactant commonly used to aid solubilization of membrane proteins, and the co-addition of a membrane spanning non-ionic amphiphilic triblock copolymer, (PEO 117-PPO 47-PE O117, Pluronic F98). Surfactant addition causes slight compression of the bilayer thickness and the creation of a distinct EO layer that increases the hydrophilic layer proximal to the supporting substrate (i.e., a water and EO gap between the lipid bilayer and quartz) to 6.8 ± 0.4 Å. Addition of the triblock copolymer into the DMPC: Tritonmore » X-100 bilayer increases the complexity (broadens) the lipid phase transition, further compresses the bilayer, and continues to expand the proximal hydrophilic layer thickness. The observed structural changes are temperature dependent with transmembrane polymer insertion achieved at 37 °C leading to a compressed membrane thickness of 39.2 ± 0.2 Å and proximal gap of 45.2 ± 0.2 Å. Temperature driven exclusion of the polymer at 15 °C causes partitioning of the polymer into the proximal space generating a large hydrogel cushion 162 ± 16 Å thick. An intermediate gap width (10 – 27 Å) is achieved at room temperature (22 – 25 °C). The temperature-driven changes in the proximal hydrophilic gap dimensions are shown to be reversible but thermal history causes variation in magnitude. Temperature-driven changes in polymer association with a supported lipid bilayer offer a facile means to reversibly control both the membrane characteristics as well as the separation between membrane and solid substrate.« less

Nephron segment-specific gene expression using AAV vectors.

PubMed

Asico, Laureano D; Cuevas, Santiago; Ma, Xiaobo; Jose, Pedro A; Armando, Ines; Konkalmatt, Prasad R

2018-02-26

AAV9 vector provides efficient gene transfer in all segments of the renal nephron, with minimum expression in non-renal cells, when administered retrogradely via the ureter. It is important to restrict the transgene expression to the desired cell type within the kidney, so that the physiological endpoints represent the function of the transgene expressed in that specific cell type within kidney. We hypothesized that segment-specific gene expression within the kidney can be accomplished using the highly efficient AAV9 vectors carrying the promoters of genes that are expressed exclusively in the desired segment of the nephron in combination with administration by retrograde infusion into the kidney via the ureter. We constructed AAV vectors carrying eGFP under the control of: kidney-specific cadherin (KSPC) gene promoter for expression in the entire nephron; Na + /glucose co-transporter (SGLT2) gene promoter for expression in the S1 and S2 segments of the proximal tubule; sodium, potassium, 2 chloride co-transporter (NKCC2) gene promoter for expression in the thick ascending limb of Henle's loop (TALH); E-cadherin (ECAD) gene promoter for expression in the collecting duct (CD); and cytomegalovirus (CMV) early promoter that provides expression in most of the mammalian cells, as control. We tested the specificity of the promoter constructs in vitro for cell type-specific expression in mouse kidney cells in primary culture, followed by retrograde infusion of the AAV vectors via the ureter in the mouse. Our data show that AAV9 vector, in combination with the segment-specific promoters administered by retrograde infusion via the ureter, provides renal nephron segment-specific gene expression. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

An investigation of reports of Controlled Flight Toward Terrain (CFTT)

NASA Technical Reports Server (NTRS)

Porter, R. F.; Loomis, J. P.

1981-01-01

Some 258 reports from more than 23,000 documents in the files of the Aviation Safety Reporting System (ASRS) were found to be to the hazard of flight into terrain with no prior awareness by the crew of impending disaster. Examination of the reports indicate that human error was a casual factor in 64% of the incidents in which some threat of terrain conflict was experienced. Approximately two-thirds of the human errors were attributed to controllers, the most common discrepancy being a radar vector below the Minimum Vector Altitude (MVA). Errors by pilots were of a much diverse nature and include a few instances of gross deviations from their assigned altitudes. The ground proximity warning system and the minimum safe altitude warning equipment were the initial recovery factor in some 18 serious incidents and were apparently the sole warning in six reported instances which otherwise would most probably have ended in disaster.

A high-level prokaryotic expression system: synthesis of human interleukin 1 alpha and its receptor antagonist.

PubMed

Birikh, K R; Lebedenko, E N; Boni, I V; Berlin, Y A

1995-10-27

Synthetic intronless genes, coding for human interleukin 1 alpha (IL 1 alpha) and interleukin 1 receptor antagonist (IL1ra), have been expressed efficiently in a specially designed prokaryotic vector, pGMCE (a pGEM1 derivative), where the target gene forms the second part of a two-cistron system. The first part of the system is a translation enhancer-containing mini-cistron, whose termination codon overlaps the start codon of the target gene. In the case of the IL1 alpha gene, the high expression level is largely due to the direct efficient translation initiation at the second cistron, whereas with the IL1ra gene in the same system, the proximal translation initiation region (TIR) provides a high level of coupled expression of the target gene. Thus, pGMCE is a potentially versatile vector for direct prokaryotic expression.

The construction of a synthetic Escherichia coli trp promoter and its use in the expression of a synthetic interferon gene.

PubMed Central

Windass, J D; Newton, C R; De Maeyer-Guignard, J; Moore, V E; Markham, A F; Edge, M D

1982-01-01

An 82 base pair DNA fragment has been synthesised which contains the E. coli trp promoter and operator sequences and also encodes the first Shine Dalgarno sequence of the trp operon. This DNA fragment is flanked by EcoRI and ClaI/TaqI cohesive ends and is thus easy to clone, transfer between vector systems and couple to genes to drive their expression. It has been cloned into plasmid pAT153, producing a convenient trp promoter vector. We have also joined the fragment to a synthetic IFN-alpha 1 gene, using synthetic oligonucleotides to generate a completely natural, highly efficient bacterial translation initiation signal on the promoter proximal side of the IFN gene. Plasmids carrying this construction enable E. coli cells to express IFN-alpha 1 almost constitutively and with significantly higher efficiency than from a lacUV5 promoter based system. Images PMID:6184675

How does climate change cause extinction?

PubMed Central

Cahill, Abigail E.; Aiello-Lammens, Matthew E.; Fisher-Reid, M. Caitlin; Hua, Xia; Karanewsky, Caitlin J.; Yeong Ryu, Hae; Sbeglia, Gena C.; Spagnolo, Fabrizio; Waldron, John B.; Warsi, Omar; Wiens, John J.

2013-01-01

Anthropogenic climate change is predicted to be a major cause of species extinctions in the next 100 years. But what will actually cause these extinctions? For example, will it be limited physiological tolerance to high temperatures, changing biotic interactions or other factors? Here, we systematically review the proximate causes of climate-change related extinctions and their empirical support. We find 136 case studies of climatic impacts that are potentially relevant to this topic. However, only seven identified proximate causes of demonstrated local extinctions due to anthropogenic climate change. Among these seven studies, the proximate causes vary widely. Surprisingly, none show a straightforward relationship between local extinction and limited tolerances to high temperature. Instead, many studies implicate species interactions as an important proximate cause, especially decreases in food availability. We find very similar patterns in studies showing decreases in abundance associated with climate change, and in those studies showing impacts of climatic oscillations. Collectively, these results highlight our disturbingly limited knowledge of this crucial issue but also support the idea that changing species interactions are an important cause of documented population declines and extinctions related to climate change. Finally, we briefly outline general research strategies for identifying these proximate causes in future studies. PMID:23075836

How does climate change cause extinction?

PubMed

Cahill, Abigail E; Aiello-Lammens, Matthew E; Fisher-Reid, M Caitlin; Hua, Xia; Karanewsky, Caitlin J; Ryu, Hae Yeong; Sbeglia, Gena C; Spagnolo, Fabrizio; Waldron, John B; Warsi, Omar; Wiens, John J

2013-01-07

Anthropogenic climate change is predicted to be a major cause of species extinctions in the next 100 years. But what will actually cause these extinctions? For example, will it be limited physiological tolerance to high temperatures, changing biotic interactions or other factors? Here, we systematically review the proximate causes of climate-change related extinctions and their empirical support. We find 136 case studies of climatic impacts that are potentially relevant to this topic. However, only seven identified proximate causes of demonstrated local extinctions due to anthropogenic climate change. Among these seven studies, the proximate causes vary widely. Surprisingly, none show a straightforward relationship between local extinction and limited tolerances to high temperature. Instead, many studies implicate species interactions as an important proximate cause, especially decreases in food availability. We find very similar patterns in studies showing decreases in abundance associated with climate change, and in those studies showing impacts of climatic oscillations. Collectively, these results highlight our disturbingly limited knowledge of this crucial issue but also support the idea that changing species interactions are an important cause of documented population declines and extinctions related to climate change. Finally, we briefly outline general research strategies for identifying these proximate causes in future studies.

Applying spectral unmixing and support vector machine to airborne hyperspectral imagery for detecting giant reed

USDA-ARS?s Scientific Manuscript database

This study evaluated linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and support vector machine (SVM) techniques for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern ...

Support vector machines classifiers of physical activities in preschoolers

USDA-ARS?s Scientific Manuscript database

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier

USDA-ARS?s Scientific Manuscript database

This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and an optimized support-vector-machine (SVM) classification scheme to characterize and classify wrinkle appearance of fabric. Fabric images were decomposed with the wavelet transform (WT), and five parame...

Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

EPA Science Inventory

Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis

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

Liu, Eva Sau Fan; Department of Health Technology and Informatics, The Hong Kong Polytechnic University; Wu, Vincent Wing Cheung

Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, followingmore » the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality.« less

PlasmoGEM, a database supporting a community resource for large-scale experimental genetics in malaria parasites.

PubMed

Schwach, Frank; Bushell, Ellen; Gomes, Ana Rita; Anar, Burcu; Girling, Gareth; Herd, Colin; Rayner, Julian C; Billker, Oliver

2015-01-01

The Plasmodium Genetic Modification (PlasmoGEM) database (http://plasmogem.sanger.ac.uk) provides access to a resource of modular, versatile and adaptable vectors for genome modification of Plasmodium spp. parasites. PlasmoGEM currently consists of >2000 plasmids designed to modify the genome of Plasmodium berghei, a malaria parasite of rodents, which can be requested by non-profit research organisations free of charge. PlasmoGEM vectors are designed with long homology arms for efficient genome integration and carry gene specific barcodes to identify individual mutants. They can be used for a wide array of applications, including protein localisation, gene interaction studies and high-throughput genetic screens. The vector production pipeline is supported by a custom software suite that automates both the vector design process and quality control by full-length sequencing of the finished vectors. The PlasmoGEM web interface allows users to search a database of finished knock-out and gene tagging vectors, view details of their designs, download vector sequence in different formats and view available quality control data as well as suggested genotyping strategies. We also make gDNA library clones and intermediate vectors available for researchers to produce vectors for themselves. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Habitat suitability and ecological niche profile of major malaria vectors in Cameroon

PubMed Central

2009-01-01

Background Suitability of environmental conditions determines a species distribution in space and time. Understanding and modelling the ecological niche of mosquito disease vectors can, therefore, be a powerful predictor of the risk of exposure to the pathogens they transmit. In Africa, five anophelines are responsible for over 95% of total malaria transmission. However, detailed knowledge of the geographic distribution and ecological requirements of these species is to date still inadequate. Methods Indoor-resting mosquitoes were sampled from 386 villages covering the full range of ecological settings available in Cameroon, Central Africa. Using a predictive species distribution modeling approach based only on presence records, habitat suitability maps were constructed for the five major malaria vectors Anopheles gambiae, Anopheles funestus, Anopheles arabiensis, Anopheles nili and Anopheles moucheti. The influence of 17 climatic, topographic, and land use variables on mosquito geographic distribution was assessed by multivariate regression and ordination techniques. Results Twenty-four anopheline species were collected, of which 17 are known to transmit malaria in Africa. Ecological Niche Factor Analysis, Habitat Suitability modeling and Canonical Correspondence Analysis revealed marked differences among the five major malaria vector species, both in terms of ecological requirements and niche breadth. Eco-geographical variables (EGVs) related to human activity had the highest impact on habitat suitability for the five major malaria vectors, with areas of low population density being of marginal or unsuitable habitat quality. Sunlight exposure, rainfall, evapo-transpiration, relative humidity, and wind speed were among the most discriminative EGVs separating "forest" from "savanna" species. Conclusions The distribution of major malaria vectors in Cameroon is strongly affected by the impact of humans on the environment, with variables related to proximity to human settings being among the best predictors of habitat suitability. The ecologically more tolerant species An. gambiae and An. funestus were recorded in a wide range of eco-climatic settings. The other three major vectors, An. arabiensis, An. moucheti, and An. nili, were more specialized. Ecological niche and species distribution modelling should help improve malaria vector control interventions by targeting places and times where the impact on vector populations and disease transmission can be optimized. PMID:20028559

Habitat suitability and ecological niche profile of major malaria vectors in Cameroon.

PubMed

Ayala, Diego; Costantini, Carlo; Ose, Kenji; Kamdem, Guy C; Antonio-Nkondjio, Christophe; Agbor, Jean-Pierre; Awono-Ambene, Parfait; Fontenille, Didier; Simard, Frédéric

2009-12-23

Suitability of environmental conditions determines a species distribution in space and time. Understanding and modelling the ecological niche of mosquito disease vectors can, therefore, be a powerful predictor of the risk of exposure to the pathogens they transmit. In Africa, five anophelines are responsible for over 95% of total malaria transmission. However, detailed knowledge of the geographic distribution and ecological requirements of these species is to date still inadequate. Indoor-resting mosquitoes were sampled from 386 villages covering the full range of ecological settings available in Cameroon, Central Africa. Using a predictive species distribution modeling approach based only on presence records, habitat suitability maps were constructed for the five major malaria vectors Anopheles gambiae, Anopheles funestus, Anopheles arabiensis, Anopheles nili and Anopheles moucheti. The influence of 17 climatic, topographic, and land use variables on mosquito geographic distribution was assessed by multivariate regression and ordination techniques. Twenty-four anopheline species were collected, of which 17 are known to transmit malaria in Africa. Ecological Niche Factor Analysis, Habitat Suitability modeling and Canonical Correspondence Analysis revealed marked differences among the five major malaria vector species, both in terms of ecological requirements and niche breadth. Eco-geographical variables (EGVs) related to human activity had the highest impact on habitat suitability for the five major malaria vectors, with areas of low population density being of marginal or unsuitable habitat quality. Sunlight exposure, rainfall, evapo-transpiration, relative humidity, and wind speed were among the most discriminative EGVs separating "forest" from "savanna" species. The distribution of major malaria vectors in Cameroon is strongly affected by the impact of humans on the environment, with variables related to proximity to human settings being among the best predictors of habitat suitability. The ecologically more tolerant species An. gambiae and An. funestus were recorded in a wide range of eco-climatic settings. The other three major vectors, An. arabiensis, An. moucheti, and An. nili, were more specialized. Ecological niche and species distribution modelling should help improve malaria vector control interventions by targeting places and times where the impact on vector populations and disease transmission can be optimized.

Robust resolution enhancement optimization methods to process variations based on vector imaging model

NASA Astrophysics Data System (ADS)

Ma, Xu; Li, Yanqiu; Guo, Xuejia; Dong, Lisong

2012-03-01

Optical proximity correction (OPC) and phase shifting mask (PSM) are the most widely used resolution enhancement techniques (RET) in the semiconductor industry. Recently, a set of OPC and PSM optimization algorithms have been developed to solve for the inverse lithography problem, which are only designed for the nominal imaging parameters without giving sufficient attention to the process variations due to the aberrations, defocus and dose variation. However, the effects of process variations existing in the practical optical lithography systems become more pronounced as the critical dimension (CD) continuously shrinks. On the other hand, the lithography systems with larger NA (NA>0.6) are now extensively used, rendering the scalar imaging models inadequate to describe the vector nature of the electromagnetic field in the current optical lithography systems. In order to tackle the above problems, this paper focuses on developing robust gradient-based OPC and PSM optimization algorithms to the process variations under a vector imaging model. To achieve this goal, an integrative and analytic vector imaging model is applied to formulate the optimization problem, where the effects of process variations are explicitly incorporated in the optimization framework. The steepest descent algorithm is used to optimize the mask iteratively. In order to improve the efficiency of the proposed algorithms, a set of algorithm acceleration techniques (AAT) are exploited during the optimization procedure.

THE BURDEN OF DENGUE AND CHIKUNGUNYA WORLDWIDE: IMPLICATIONS FOR THE SOUTHERN UNITED STATES AND CALIFORNIA

PubMed Central

Fredericks, Anthony C.; Fernandez-Sesma, Ana

2015-01-01

Dengue virus (DENV) spreads to humans through the bite of an infected Aedes aegypti or Aedes albopictus mosquito and is a growing public health threat to both industrialized and developing nations worldwide. Outbreaks of autochthonous dengue in the United States occurred extensively in the past but over the past three decades have again taken place in Florida, Hawai’i, and Texas as well as in American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and the US Virgin Islands. As the Aedes vectors spread worldwide it is anticipated that DENV as well as other viruses also transmitted by these vectors, such as Chikungunya virus (CHKV), will invade new areas of the world, including the US. In this review, we describe the current burden of dengue disease worldwide and the potential introduction of DENV and CHKV into different areas of the US. Of these areas, the state of California saw the arrival and spread of the Aedes aegypti vector beginning in 2013. This invasion presents a developing situation when considering the state’s number of imported dengue cases and proximity to northern Mexico as well as the rising specter of chikungunya in the Western hemisphere. The distribution of Aedes vectors in California as well as a discussion of several factors contributing to the risk of dengue importation are discussed and evaluated. PMID:25960096

1-norm support vector novelty detection and its sparseness.

PubMed

Zhang, Li; Zhou, WeiDa

2013-12-01

This paper proposes a 1-norm support vector novelty detection (SVND) method and discusses its sparseness. 1-norm SVND is formulated as a linear programming problem and uses two techniques for inducing sparseness, or the 1-norm regularization and the hinge loss function. We also find two upper bounds on the sparseness of 1-norm SVND, or exact support vector (ESV) and kernel Gram matrix rank bounds. The ESV bound indicates that 1-norm SVND has a sparser representation model than SVND. The kernel Gram matrix rank bound can loosely estimate the sparseness of 1-norm SVND. Experimental results show that 1-norm SVND is feasible and effective. Copyright © 2013 Elsevier Ltd. All rights reserved.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

PubMed Central

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. PMID:29275361

Newborn screening for proximal urea cycle disorders: Current evidence supporting recommendations for newborn screening.

PubMed

Merritt, J Lawrence; Brody, Linnea L; Pino, Gisele; Rinaldo, Piero

2018-04-20

Current newborn screening (NBS) for urea cycle disorders (UCD) is incomplete as only distal UCDs are included in most NBS programs by measuring elevated amino acid concentrations. NBS for the proximal UCDs involves the detection in NBS spots of low citrulline values, a finding which is often overlooked because it is considered to be inadequate. We retrospectively analyzed NBS blood spots from known UCD patients comparing the utility of the Region 4 Stork (R4S) interpretive tools to conventional cutoff based interpretation. This study shows the utility of R4S tools in detecting all UCDs, and provides evidence to support the nomination to add proximal UCDs to the recommended uniform screening panel. Copyright © 2018 Elsevier Inc. All rights reserved.

Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates

USDA-ARS?s Scientific Manuscript database

Methods based on sequence data analysis facilitate the tracking of disease outbreaks, allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are used postfactum after an outbreak has happened. Here, we show that support vector machine a...

Support vector machine incremental learning triggered by wrongly predicted samples

NASA Astrophysics Data System (ADS)

Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

2018-05-01

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

NASA Astrophysics Data System (ADS)

Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

2010-01-01

In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

Quantum Support Vector Machine for Big Data Classification

NASA Astrophysics Data System (ADS)

Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

2014-09-01

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

Habitat suitability mapping of Anopheles darlingi in the surroundings of the Manso hydropower plant reservoir, Mato Grosso, Central Brazil

PubMed Central

Zeilhofer, Peter; Santos, Emerson Soares dos; Ribeiro, Ana LM; Miyazaki, Rosina D; Santos, Marina Atanaka dos

2007-01-01

Background Hydropower plants provide more than 78 % of Brazil's electricity generation, but the country's reservoirs are potential new habitats for main vectors of malaria. In a case study in the surroundings of the Manso hydropower plant in Mato Grosso state, Central Brazil, habitat suitability of Anopheles darlingi was studied. Habitat profile was characterized by collecting environmental data. Remote sensing and GIS techniques were applied to extract additional spatial layers of land use, distance maps, and relief characteristics for spatial model building. Results Logistic regression analysis and ROC curves indicate significant relationships between the environment and presence of An. darlingi. Probabilities of presence strongly vary as a function of land cover and distance from the lake shoreline. Vector presence was associated with spatial proximity to reservoir and semi-deciduous forests followed by Cerrado woodland. Vector absence was associated with open vegetation formations such as grasslands and agricultural areas. We suppose that non-significant differences of vector incidences between rainy and dry seasons are associated with the availability of anthropogenic breeding habitat of the reservoir throughout the year. Conclusion Satellite image classification and multitemporal shoreline simulations through DEM-based GIS-analyses consist in a valuable tool for spatial modeling of A. darlingi habitats in the studied hydropower reservoir area. Vector presence is significantly increased in forested areas near reservoirs in bays protected from wind and wave action. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics. PMID:17343728

Habitat suitability mapping of Anopheles darlingi in the surroundings of the Manso hydropower plant reservoir, Mato Grosso, Central Brazil.

PubMed

Zeilhofer, Peter; dos Santos, Emerson Soares; Ribeiro, Ana L M; Miyazaki, Rosina D; dos Santos, Marina Atanaka

2007-03-07

Hydropower plants provide more than 78 % of Brazil's electricity generation, but the country's reservoirs are potential new habitats for main vectors of malaria. In a case study in the surroundings of the Manso hydropower plant in Mato Grosso state, Central Brazil, habitat suitability of Anopheles darlingi was studied. Habitat profile was characterized by collecting environmental data. Remote sensing and GIS techniques were applied to extract additional spatial layers of land use, distance maps, and relief characteristics for spatial model building. Logistic regression analysis and ROC curves indicate significant relationships between the environment and presence of An. darlingi. Probabilities of presence strongly vary as a function of land cover and distance from the lake shoreline. Vector presence was associated with spatial proximity to reservoir and semi-deciduous forests followed by Cerrado woodland. Vector absence was associated with open vegetation formations such as grasslands and agricultural areas. We suppose that non-significant differences of vector incidences between rainy and dry seasons are associated with the availability of anthropogenic breeding habitat of the reservoir throughout the year. Satellite image classification and multitemporal shoreline simulations through DEM-based GIS-analyses consist in a valuable tool for spatial modeling of A. darlingi habitats in the studied hydropower reservoir area. Vector presence is significantly increased in forested areas near reservoirs in bays protected from wind and wave action. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics.

Vygotsky's Zone of Proximal Development: Implications for Gifted Education.

ERIC Educational Resources Information Center

Shaughnessy, Michael F.

This paper reviews Lev Vygotsky's theories concerning optimizing of potential through assistance, support, or instruction. The paper notes that there is a "zone of proximal development" or a band around intelligence quotient (IQ) scores reflecting one's true potential. IQ tests are generally well-standardized and "static,"…

Health impacts of large dams

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

Lerer, L.B.; Scudder, T.

1999-03-01

Large dams have been criticized because of their negative environmental and social impacts. Public health interest largely has focused on vector-borne diseases, such as schistosomiasis, associated with reservoirs and irrigation projects. Large dams also influence health through changes in water and food security, increases in communicable diseases, and the social disruption caused by construction and involuntary resettlement. Communities living in close proximity to large dams often do not benefit from water transfer and electricity generation revenues. A comprehensive health component is required in environmental and social impact assessments for large dam projects.

Predicting complications of percutaneous coronary intervention using a novel support vector method.

PubMed

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases). The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Predicting complications of percutaneous coronary intervention using a novel support vector method

PubMed Central

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

Objective To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Materials and methods Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. Results The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer–Lemeshow χ2 value (seven cases) and the mean cross-entropy error (eight cases). Conclusions The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains. PMID:23599229

A support vector machine approach for classification of welding defects from ultrasonic signals

NASA Astrophysics Data System (ADS)

Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

2014-07-01

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

PubMed

Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

2012-09-01

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Euclidean chemical spaces from molecular fingerprints: Hamming distance and Hempel's ravens.

PubMed

Martin, Eric; Cao, Eddie

2015-05-01

Molecules are often characterized by sparse binary fingerprints, where 1s represent the presence of substructures and 0s represent their absence. Fingerprints are especially useful for similarity calculations, such as database searching or clustering, generally measuring similarity as the Tanimoto coefficient. In other cases, such as visualization, design of experiments, or latent variable regression, a low-dimensional Euclidian "chemical space" is more useful, where proximity between points reflects chemical similarity. A temptation is to apply principal components analysis (PCA) directly to these fingerprints to obtain a low dimensional continuous chemical space. However, Gower has shown that distances from PCA on bit vectors are proportional to the square root of Hamming distance. Unlike Tanimoto similarity, Hamming similarity (HS) gives equal weight to shared 0s as to shared 1s, that is, HS gives as much weight to substructures that neither molecule contains, as to substructures which both molecules contain. Illustrative examples show that proximity in the corresponding chemical space reflects mainly similar size and complexity rather than shared chemical substructures. These spaces are ill-suited for visualizing and optimizing coverage of chemical space, or as latent variables for regression. A more suitable alternative is shown to be Multi-dimensional scaling on the Tanimoto distance matrix, which produces a space where proximity does reflect structural similarity.

Bayesian data assimilation provides rapid decision support for vector-borne diseases.

PubMed

Jewell, Chris P; Brown, Richard G

2015-07-06

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Support Vector Machines: Relevance Feedback and Information Retrieval.

ERIC Educational Resources Information Center

Drucker, Harris; Shahrary, Behzad; Gibbon, David C.

2002-01-01

Compares support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred. Includes nine tables. (Contains 24…

Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression

Treesearch

Jeffrey T. Walton

2008-01-01

Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (...

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

PubMed

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

nu-Anomica: A Fast Support Vector Based Novelty Detection Technique

NASA Technical Reports Server (NTRS)

Das, Santanu; Bhaduri, Kanishka; Oza, Nikunj C.; Srivastava, Ashok N.

2009-01-01

In this paper we propose nu-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In -Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5 - 20 times.

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Design of 2D time-varying vector fields.

PubMed

Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D; Zhang, Eugene

2012-10-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects.

Techniques utilized in the simulated altitude testing of a 2D-CD vectoring and reversing nozzle

NASA Technical Reports Server (NTRS)

Block, H. Bruce; Bryant, Lively; Dicus, John H.; Moore, Allan S.; Burns, Maureen E.; Solomon, Robert F.; Sheer, Irving

1988-01-01

Simulated altitude testing of a two-dimensional, convergent-divergent, thrust vectoring and reversing exhaust nozzle was accomplished. An important objective of this test was to develop test hardware and techniques to properly operate a vectoring and reversing nozzle within the confines of an altitude test facility. This report presents detailed information on the major test support systems utilized, the operational performance of the systems and the problems encountered, and test equipment improvements recommended for future tests. The most challenging support systems included the multi-axis thrust measurement system, vectored and reverse exhaust gas collection systems, and infrared temperature measurement systems used to evaluate and monitor the nozzle. The feasibility of testing a vectoring and reversing nozzle of this type in an altitude chamber was successfully demonstrated. Supporting systems performed as required. During reverser operation, engine exhaust gases were successfully captured and turned downstream. However, a small amount of exhaust gas spilled out the collector ducts' inlet openings when the reverser was opened more than 60 percent. The spillage did not affect engine or nozzle performance. The three infrared systems which viewed the nozzle through the exhaust collection system worked remarkably well considering the harsh environment.

Adenovirus Vectors Target Several Cell Subtypes of Mammalian Inner Ear In Vivo

PubMed Central

Li, Wenyan; Shen, Jun

2016-01-01

Mammalian inner ear harbors diverse cell types that are essential for hearing and balance. Adenovirus is one of the major vectors to deliver genes into the inner ear for functional studies and hair cell regeneration. To identify adenovirus vectors that target specific cell subtypes in the inner ear, we studied three adenovirus vectors, carrying a reporter gene encoding green fluorescent protein (GFP) from two vendors or with a genome editing gene Cre recombinase (Cre), by injection into postnatal days 0 (P0) and 4 (P4) mouse cochlea through scala media by cochleostomy in vivo. We found three adenovirus vectors transduced mouse inner ear cells with different specificities and expression levels, depending on the type of adenoviral vectors and the age of mice. The most frequently targeted region was the cochlear sensory epithelium, including auditory hair cells and supporting cells. Adenovirus with GFP transduced utricular supporting cells as well. This study shows that adenovirus vectors are capable of efficiently and specifically transducing different cell types in the mammalian inner ear and provides useful tools to study inner ear gene function and to evaluate gene therapy to treat hearing loss and vestibular dysfunction. PMID:28116172

Bayesian data assimilation provides rapid decision support for vector-borne diseases

PubMed Central

Jewell, Chris P.; Brown, Richard G.

2015-01-01

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

Stable Local Volatility Calibration Using Kernel Splines

NASA Astrophysics Data System (ADS)

Coleman, Thomas F.; Li, Yuying; Wang, Cheng

2010-09-01

We propose an optimization formulation using L1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances the calibration accuracy with the model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a kernel function generating splines and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of the support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.

Distal and Proximal Vision: A Multi-Perspective Research in Sociology of Education

ERIC Educational Resources Information Center

Giancola, Orazio; Viteritti, Assunta

2014-01-01

Drawing inspiration from the research conducted in Italian schools involved in the reform process, the article proposes to investigate two visions in the research on Sociology of Education: one distal and the other proximal. The distal vision is offered by quantitative research nowadays supported by extensive public funding and framed as…

Evaluation of a proximity card authentication system for health care settings.

PubMed

Fontaine, Jacqueline; Zheng, Kai; Van De Ven, Cosmas; Li, Huiyang; Hiner, James; Mitchell, Kathy; Gendler, Stephen; Hanauer, David A

2016-08-01

Multiple users access computer workstations in busy clinical settings, requiring many logins throughout the day as users switch from one computer to another. This can lead to workflow inefficiencies as well as security concerns resulting from users sharing login sessions to save time. Proximity cards and readers have the potential to improve efficiency and security by allowing users to access clinical workstations simply by bringing the card near the reader, without the need for manual entry of a username and password. To assess the perceived impact of proximity cards and readers for rapid user authentication to clinical workstations in the setting of an existing electronic health record with single sign-on software already installed. Questionnaires were administered to clinical faculty and staff five months before and three months after the installation of proximity card readers in an inpatient birthing center and an outpatient obstetrics clinic. Open-ended feedback was also collected and qualitatively analyzed. There were 71 and 33 responses to the pre- and post-implementation surveys, respectively. There was a significant increase in the perceived speed of login with the proximity cards, and a significant decrease in the self-reported occurrence of shared login sessions between users. Feedback regarding the system was mostly positive, although several caveats were noted, including minimal benefit when used with an obstetric application that did not support single sign-on. Proximity cards and readers, along with single sign-on software, have the potential to enhance workflow efficiency by allowing for faster login times and diminish security concerns by reducing shared logins on clinical workstations. The positive feedback was used by our health system leadership to support the expanded implementation of the proximity card readers throughout the clinical setting. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Longitudinal effects of contextual and proximal factors on mother-infant interactions among Brazilian adolescent mothers.

PubMed

Diniz, Eva; DeSousa, Diogo; Koller, Silvia H; Volling, Brenda L

2016-05-01

Adolescent mothers often come from vulnerable backgrounds which might impact the quality of both maternal and infant behavior. Despite the negative impact of adolescent motherhood for maternal and infant behavior, social support may decrease the risks and promote maternal behavior toward the infant. The aim of this study was to investigate longitudinally the effects of proximal (maternal behavior) and distal (mother's perceived social support) variables on infant development in a sample of Brazilian adolescent mothers and their infants. Thirty-nine adolescent mothers (Mage=17.26years; SD=1.71) were observed interacting with their infants at 3 and 6 months postpartum and reported on social support. Results revealed that maternal and infant behavior were associated within and across times. Mothers' perceived social support at 3 months had an indirect effect on infant behavior at 6 months, totally mediated by maternal behavior at 6 months. Our findings revealed the mutual influence between maternal and infant behavior, revealing a proximal process. The results also underscored the importance of the passage of time in the interplay between mother-infant interactions and their developmental context. Copyright © 2016 Elsevier Inc. All rights reserved.

Inhibition effect of small interfering RNA of connective tissue growth factor on the expression of extracellular matrix molecules in cultured human renal proximal tubular cells.

PubMed

Liu, Yuyuan; Li, Weiwei; Liu, Hong; Peng, Youming; Yang, Qiu; Xiao, Li; Liu, Yinghong; Liu, Fuyou

2014-03-01

In this study, we investigated the effect of small interfering RNA (siRNA) of connective tissue growth factor (CTGF) by pRetro-Super (PRS) retrovirus vector on the expression of CTGF and related extracellular matrix molecules in human renal proximal tubular cells (HKCs) induced by high glucose, to provide help for renal tubulointerstitial fibrosis therapy. HKCs were exposed to d-glucose to observe their dose and time effect, while the mannitol as osmotic control. Retrovirus producing CTGF siRNA were constructed from the inverted oligonucleotides and transferred into packaging cell line PT67 with lipofectamine, and the virus supernatant was used to infect HKC. The expression of CTGF, fibronectin (FN) and collagen-type I (col1) were measured by semi-quantitative RT-PCR and Western blot. In response to high glucose, CTGF expression in HKCs was increased in a dose- and time-dependent manner, whereas the increase did not occur in the osmotic control. Introduction of PRS-CTGF-siRNA resulted in the significant reduction of CTGF, FN, col1 mRNA (p < 0.01, respectively) and CTGF, col1 protein (p < 0.05, respectively) expression, while PRS void vector group did not have these effects (p > 0.05). CTGF siRNA therapy can effectively reduce the levels of CTGF, FN and col1 induced by high glucose in cultured HKCs, which suggested that it may be a potential therapeutic strategy to prevent the renal interstitial fibrosis in the future.

Estimation of Teacher Practices Based on Text Transcripts of Teacher Speech Using a Support Vector Machine Algorithm

ERIC Educational Resources Information Center

Araya, Roberto; Plana, Francisco; Dartnell, Pablo; Soto-Andrade, Jorge; Luci, Gina; Salinas, Elena; Araya, Marylen

2012-01-01

Teacher practice is normally assessed by observers who watch classes or videos of classes. Here, we analyse an alternative strategy that uses text transcripts and a support vector machine classifier. For each one of the 710 videos of mathematics classes from the 2005 Chilean National Teacher Assessment Programme, a single 4-minute slice was…

Resonances in a Chaotic Attractor Crisis of the Lorenz Flow

NASA Astrophysics Data System (ADS)

Tantet, Alexis; Lucarini, Valerio; Dijkstra, Henk A.

2018-02-01

Local bifurcations of stationary points and limit cycles have successfully been characterized in terms of the critical exponents of these solutions. Lyapunov exponents and their associated covariant Lyapunov vectors have been proposed as tools for supporting the understanding of critical transitions in chaotic dynamical systems. However, it is in general not clear how the statistical properties of dynamical systems change across a boundary crisis during which a chaotic attractor collides with a saddle. This behavior is investigated here for a boundary crisis in the Lorenz flow, for which neither the Lyapunov exponents nor the covariant Lyapunov vectors provide a criterion for the crisis. Instead, the convergence of the time evolution of probability densities to the invariant measure, governed by the semigroup of transfer operators, is expected to slow down at the approach of the crisis. Such convergence is described by the eigenvalues of the generator of this semigroup, which can be divided into two families, referred to as the stable and unstable Ruelle-Pollicott resonances, respectively. The former describes the convergence of densities to the attractor (or escape from a repeller) and is estimated from many short time series sampling the state space. The latter is responsible for the decay of correlations, or mixing, and can be estimated from a long times series, invoking ergodicity. It is found numerically for the Lorenz flow that the stable resonances do approach the imaginary axis during the crisis, as is indicative of the loss of global stability of the attractor. On the other hand, the unstable resonances, and a fortiori the decay of correlations, do not flag the proximity of the crisis, thus questioning the usual design of early warning indicators of boundary crises of chaotic attractors and the applicability of response theory close to such crises.

Resonances in a Chaotic Attractor Crisis of the Lorenz Flow

NASA Astrophysics Data System (ADS)

Tantet, Alexis; Lucarini, Valerio; Dijkstra, Henk A.

2017-12-01

Local bifurcations of stationary points and limit cycles have successfully been characterized in terms of the critical exponents of these solutions. Lyapunov exponents and their associated covariant Lyapunov vectors have been proposed as tools for supporting the understanding of critical transitions in chaotic dynamical systems. However, it is in general not clear how the statistical properties of dynamical systems change across a boundary crisis during which a chaotic attractor collides with a saddle. This behavior is investigated here for a boundary crisis in the Lorenz flow, for which neither the Lyapunov exponents nor the covariant Lyapunov vectors provide a criterion for the crisis. Instead, the convergence of the time evolution of probability densities to the invariant measure, governed by the semigroup of transfer operators, is expected to slow down at the approach of the crisis. Such convergence is described by the eigenvalues of the generator of this semigroup, which can be divided into two families, referred to as the stable and unstable Ruelle-Pollicott resonances, respectively. The former describes the convergence of densities to the attractor (or escape from a repeller) and is estimated from many short time series sampling the state space. The latter is responsible for the decay of correlations, or mixing, and can be estimated from a long times series, invoking ergodicity. It is found numerically for the Lorenz flow that the stable resonances do approach the imaginary axis during the crisis, as is indicative of the loss of global stability of the attractor. On the other hand, the unstable resonances, and a fortiori the decay of correlations, do not flag the proximity of the crisis, thus questioning the usual design of early warning indicators of boundary crises of chaotic attractors and the applicability of response theory close to such crises.

Fast support vector data descriptions for novelty detection.

PubMed

Liu, Yi-Hung; Liu, Yan-Chen; Chen, Yen-Jen

2010-08-01

Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors. For applications where fast real-time response is needed, how to speed up the decision function is crucial. This paper aims at dealing with the issue of reducing the testing time complexity of SVDD. A method called fast SVDD (F-SVDD) is proposed. Unlike the traditional methods which all try to compress a kernel expansion into one with fewer terms, the proposed F-SVDD directly finds the preimage of a feature vector, and then uses a simple relationship between this feature vector and the SVDD sphere center to re-express the center with a single vector. The decision function of F-SVDD contains only one kernel term, and thus the decision boundary of F-SVDD is only spherical in the original space. Hence, the run-time complexity of the F-SVDD decision function is no longer linear in the support vectors, but is a constant, no matter how large the training set size is. In this paper, we also propose a novel direct preimage-finding method, which is noniterative and involves no free parameters. The unique preimage can be obtained in real time by the proposed direct method without taking trial-and-error. For demonstration, several real-world data sets and a large-scale data set, the extended MIT face data set, are used in experiments. In addition, a practical industry example regarding liquid crystal display micro-defect inspection is also used to compare the applicability of SVDD and our proposed F-SVDD when faced with mass data input. The results are very encouraging.

Early warning of West Nile virus mosquito vector: climate and land use models successfully explain phenology and abundance of Culex pipiens mosquitoes in north-western Italy.

PubMed

Rosà, Roberto; Marini, Giovanni; Bolzoni, Luca; Neteler, Markus; Metz, Markus; Delucchi, Luca; Chadwick, Elizabeth A; Balbo, Luca; Mosca, Andrea; Giacobini, Mario; Bertolotti, Luigi; Rizzoli, Annapaola

2014-06-12

West Nile Virus (WNV) is an emerging global health threat. Transmission risk is strongly related to the abundance of mosquito vectors, typically Culex pipiens in Europe. Early-warning predictors of mosquito population dynamics would therefore help guide entomological surveillance and thereby facilitate early warnings of transmission risk. We analysed an 11-year time series (2001 to 2011) of Cx. pipiens mosquito captures from the Piedmont region of north-western Italy to determine the principal drivers of mosquito population dynamics. Linear mixed models were implemented to examine the relationship between Cx. pipiens population dynamics and environmental predictors including temperature, precipitation, Normalized Difference Water Index (NDWI) and the proximity of mosquito traps to urban areas and rice fields. Warm temperatures early in the year were associated with an earlier start to the mosquito season and increased season length, and later in the year, with decreased abundance. Early precipitation delayed the start and shortened the length of the mosquito season, but increased total abundance. Conversely, precipitation later in the year was associated with a longer season. Finally, higher NDWI early in the year was associated with an earlier start to the season and increased season length, but was not associated with abundance. Proximity to rice fields predicted higher total abundance when included in some models, but was not a significant predictor of phenology. Proximity to urban areas was not a significant predictor in any of our models. Predicted variations in start of the season and season length ranged from one to three weeks, across the measured range of variables. Predicted mosquito abundance was highly variable, with numbers in excess of 1000 per trap per year when late season temperatures were low (average 21°C) to only 150 when late season temperatures were high (average 30°C). Climate data collected early in the year, in conjunction with local land use, can be used to provide early warning of both the timing and magnitude of mosquito outbreaks. This potentially allows targeted mosquito control measures to be implemented, with implications for prevention and control of West Nile Virus and other mosquito borne diseases.

Spatial and temporal analyses of citrus sudden death as a tool to generate hypotheses concerning its etiology.

PubMed

Bassanezi, Renato B; Bergamin Filho, Armando; Amorim, Lilian; Gimenes-Fernandes, Nelson; Gottwald, Tim R; Bové, Joseph M

2003-04-01

ABSTRACT Citrus sudden death (CSD), a new disease of unknown etiology that affects sweet orange grafted on Rangpur lime, was visually monitored for 14 months in 41 groves in Brazil. Ordinary runs analysis of CSD-symptomatic trees indicated a departure from randomness of symptomatic trees status among immediately adjacent trees mainly within rows. The binomial index of dispersion (D) and the intraclass correlation (k) for various quadrat sizes suggested aggregation of CSD-symptomatic trees for almost all plots within the quadrat sizes tested. Estimated parameters of the binary form of Taylor's power law provided an overall measure of aggregation of CSD-symptomatic trees for all quadrat sizes tested. Aggregation in each plot was dependent on disease incidence. Spatial autocorrelation analysis of proximity patterns suggested that aggregation often existed among quadrats of various sizes up to three lag distances; however, significant lag positions discontinuous from main proximity patterns were rare, indicating a lack of spatial association among discrete foci. Some asymmetry was also detected for some spatial autocorrelation proximity patterns, indicating that within-row versus across-row distributions are not necessarily equivalent. These results were interpreted to mean that the cause of the disease was most likely biotic and its dissemination was common within a local area of influence that extended to approximately six trees in all directions, including adjacent trees. Where asymmetry was indicated, this area of influence was somewhat elliptical. Longer-distance patterns were not detected within the confines of the plot sizes tested. Annual rates of CSD progress based on the Gompertz model ranged from 0.37 to 2.02. Numerous similarities were found between the spatial patterns of CSD and Citrus tristeza virus (CTV) described in the literature, both in the presence of the aphid vector, Toxoptera citricida. CSD differs from CTV in that symptoms occur in sweet orange grafted on Rangpur lime. Based on the symptoms of CSD and on its spatial and temporal patterns, our hypothesis is that CSD may be caused by a similar but undescribed pathogen such as a virus and probably vectored by insects such as aphids by similar spatial processes to those affecting CTV.

Support Vector Machine-Based Endmember Extraction

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

Filippi, Anthony M; Archibald, Richard K

Introduced in this paper is the utilization of Support Vector Machines (SVMs) to automatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality, and hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this Support Vector Machine-Based Endmembermore » Extraction (SVM-BEE) algorithm has the capability of autonomously determining endmembers from multiple clusters with computational speed and accuracy, while maintaining a robust tolerance to noise.« less

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

On the use of RADARSAT-1 for monitoring malaria risk in Kenya

NASA Astrophysics Data System (ADS)

Ross, S. G.; Thomson, M. C.; Pultz, T.; Mbogo, C. M.; Regens, J. L.; Swalm, C.; Githure, J.; Yan, G.; Gu, W.; Beier, J. C.

2002-01-01

The incidence and spread of vector-borne infectious diseases are increasing concerns in many parts of the world. Earth obervation techniques provide a recognised means for monitoring and mapping disease risk as well as correlating environmental indicators with various disease vectors. Because the areas most impacted by vector-borne disease are remote and not easily monitored using traditional, labor intensive survey techniques, high spatial and temporal coverage provided by spaceborne sensors allows for the investigation of large areas in a timely manner. However, since the majority of infectious diseases occur in tropical areas, one of the main barriers to earth observation techniques is persistent cloud-cover. Synthetic Aperture Radar (SAR) technology offers a solution to this problem by providing all-weather, day and night imaging capability. Based on SAR's sensitivity to target moisture conditions, sensors such as RADARSAT-1 can be readily used to map wetland and swampy areas that are conducive to functioning as aquatic larval habitats. Irrigation patterns, deforestation practises and the effects of local flooding can be monitored using SAR imagery, and related to potential disease vector abundance and proximity to populated areas. This paper discusses the contribution of C-band radar remote sensing technology to monitoring and mapping malaria. Preliminary results using RADARSAT-1 for identifying areas of high mosquito (Anopheles gambiae s.l.) abundance along the Kenya coast will be discussed. The authors consider the potential of RADARSAT-1 data based on SAR sensor characteristics and the preliminary results obtained. Further potential of spaceborne SAR data for monitoring vector-borne disease is discussed with respect to future advanced SAR sensors such as RADARSAT-2.

SHIP, a novel factor to ameliorate extracellular matrix accumulation via suppressing PI3K/Akt/CTGF signaling in diabetic kidney disease.

PubMed

Li, Fan; Li, Lisha; Cheng, Meijuan; Wang, Xiumin; Hao, Jun; Liu, Shuxia; Duan, Huijun

2017-01-22

Tubular interstitial extracellular matrix accumulation, which plays a key role in the pathogenesis and progression of diabetic kidney disease (DKD), is believed to be mediated by activation of PI3K/Akt signal pathway. However, it is still not clear whether SH2 domain-containing inositol 5'-phosphatase (SHIP), known as a negative regulator of PI3K/Akt pathway is also involved in extracellular matrix metabolism of diabetic kidney. In the present study, decreased SHIP and increased phospho-Akt (Ser 473, Thr 308) were found in renal tubular cells of diabetic mice accompanied by overexpression of connective tissue growth factor (CTGF) and extracellular matrix deposition versus normal mice. Again, high glucose attenuated SHIP expression in a time-dependent manner, concomitant with activation of PI3K/Akt signaling and extracellular matrix production in human renal proximal tubular epithelial cells (HK2) cultured in vitro, which was significantly prevented by transfection of M90-SHIP vector. Furthermore, in vivo delivery of rAd-INPP5D vector (SHIP expression vector) via intraperitoneal injection in diabetic mice increased SHIP expression by 3.36 times followed by 65.26%, 70.38% and 46.71% decreases of phospho-Akt (Ser 473), phospho-Akt (Thr 308) and CTGF expression versus diabetic mice receiving rAd-EGFP vector. Meanwhile, increased renal extracellular matrix accumulation of diabetic mice was also inhibited with intraperitoneal injection of rAd-INPP5D vector. These above data suggested that overexpression of SHIP might be a potent method to lessen renal extracellular matrix accumulation via inactivation of PI3K/Akt pathway and suppression of CTGF expression in DKD. Copyright © 2016 Elsevier Inc. All rights reserved.

Constraining primordial vector mode from B-mode polarization

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

Saga, Shohei; Ichiki, Kiyotomo; Shiraishi, Maresuke, E-mail: saga.shohei@nagoya-u.jp, E-mail: maresuke.shiraishi@pd.infn.it, E-mail: ichiki@a.phys.nagoya-u.ac.jp

The B-mode polarization spectrum of the Cosmic Microwave Background (CMB) may be the smoking gun of not only the primordial tensor mode but also of the primordial vector mode. If there exist nonzero vector-mode metric perturbations in the early Universe, they are known to be supported by anisotropic stress fluctuations of free-streaming particles such as neutrinos, and to create characteristic signatures on both the CMB temperature, E-mode, and B-mode polarization anisotropies. We place constraints on the properties of the primordial vector mode characterized by the vector-to-scalar ratio r{sub v} and the spectral index n{sub v} of the vector-shear power spectrum,more » from the Planck and BICEP2 B-mode data. We find that, for scale-invariant initial spectra, the ΛCDM model including the vector mode fits the data better than the model including the tensor mode. The difference in χ{sup 2} between the vector and tensor models is Δχ{sup 2} = 3.294, because, on large scales the vector mode generates smaller temperature fluctuations than the tensor mode, which is preferred for the data. In contrast, the tensor mode can fit the data set equally well if we allow a significantly blue-tilted spectrum. We find that the best-fitting tensor mode has a large blue tilt and leads to an indistinct reionization bump on larger angular scales. The slightly red-tilted vector mode supported by the current data set can also create O(10{sup -22})-Gauss magnetic fields at cosmological recombination. Our constraints should motivate research that considers models of the early Universe that involve the vector mode.« less

Self-entanglement of long linear DNA vectors using transient non-B-DNA attachment points: a new concept for improvement of non-viral therapeutic gene delivery.

PubMed

Tolmachov, Oleg E

2012-05-01

The cell-specific and long-term expression of therapeutic transgenes often requires a full array of native gene control elements including distal enhancers, regulatory introns and chromatin organisation sequences. The delivery of such extended gene expression modules to human cells can be accomplished with non-viral high-molecular-weight DNA vectors, in particular with several classes of linear DNA vectors. All high-molecular-weight DNA vectors are susceptible to damage by shear stress, and while for some of the vectors the harmful impact of shear stress can be minimised through the transformation of the vectors to compact topological configurations by supercoiling and/or knotting, linear DNA vectors with terminal loops or covalently attached terminal proteins cannot be self-compacted in this way. In this case, the only available self-compacting option is self-entangling, which can be defined as the folding of single DNA molecules into a configuration with mutual restriction of molecular motion by the individual segments of bent DNA. A negatively charged phosphate backbone makes DNA self-repulsive, so it is reasonable to assume that a certain number of 'sticky points' dispersed within DNA could facilitate the entangling by bringing DNA segments into proximity and by interfering with the DNA slipping away from the entanglement. I propose that the spontaneous entanglement of vector DNA can be enhanced by the interlacing of the DNA with sites capable of mutual transient attachment through the formation of non-B-DNA forms, such as interacting cruciform structures, inter-segment triplexes, slipped-strand DNA, left-handed duplexes (Z-forms) or G-quadruplexes. It is expected that the non-B-DNA based entanglement of the linear DNA vectors would consist of the initial transient and co-operative non-B-DNA mediated binding events followed by tight self-ensnarement of the vector DNA. Once in the nucleoplasm of the target human cells, the DNA can be disentangled by type II topoisomerases. The technology for such self-entanglement can be an avenue for the improvement of gene delivery with high-molecular-weight naked DNA using therapeutically important methods associated with considerable shear stress. Priority applications include in vivo muscle electroporation and sonoporation for Duchenne muscular dystrophy patients, aerosol inhalation to reach the target lung cells of cystic fibrosis patients and bio-ballistic delivery to skin melanomas with the vector DNA adsorbed on gold or tungsten projectiles. Copyright © 2012 Elsevier Ltd. All rights reserved.

[Comparison of clinical effect on locking plate for proximal humeral fracture with or without application of inferomedial screws].

PubMed

Guo, Xiu-wu; Fan, Jian; Yuan, Feng

2016-06-01

To compare clinical outcomes of locking plate for proximal humeral fracture whether application of inferomedial screws. From January 2012 to July 2013, 46 patients with proximal humeral fracture underwent locking plates were retrospectively analyzed. There were 25 males and 21 females aged from 29 to 80 years old with an average of 55.1 years old. Among them, 25 patients were treated with inferomedial screws (support group), including 13 males and 12 females aged from 38 to 80 years old with an average of (55.8 ± 11.8) years old; 8 cases were part two fracture,10 cases were part three fracture and 7 cases were part four fracture according to Neer classification. Twenty-one patients were treated without inferomedial screws (non-support group), including 12 males and 9 females aged from 29 to 79 years old with an average of (54.2 ± 14.8)years old; 6 cases were part two fracture, 9 cases were part three fracture and 6 cases were part four fracture according to Neer classification. Operative time, fracture healing time and complications were observed and compared, Neer scoring of shoulder joint were used to evaluate clinical effect. All patients were followed up from 12 to 41 months with an average of 15.6 months. Operative time and fracture healing time in support group was (1.6 ± 0.4) h and (3.0 ± 0.6) months, and (1.5 ± 0.4) h and (3.1 ± 0.6) months in non-support group, while there was no statistical difference in operative time and fracture healing time between two groups. There was significant differences in Neer score between support group (89.7± 4.9) and non-support group (83.1 ± 7.1). No complication occurred in support group,while 4 cases occurred complications in non-support group. Locking plate with inferomedial screws for proximal humeral fracture has advantages of stable fixation, less complications, quick recovery of function and satisfied clinical effect.

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

Progressive Classification Using Support Vector Machines

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Kocurek, Michael

2009-01-01

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Computation of fluid and particle motion from a time-sequenced image pair: a global outlier identification approach.

PubMed

Ray, Nilanjan

2011-10-01

Fluid motion estimation from time-sequenced images is a significant image analysis task. Its application is widespread in experimental fluidics research and many related areas like biomedical engineering and atmospheric sciences. In this paper, we present a novel flow computation framework to estimate the flow velocity vectors from two consecutive image frames. In an energy minimization-based flow computation, we propose a novel data fidelity term, which: 1) can accommodate various measures, such as cross-correlation or sum of absolute or squared differences of pixel intensities between image patches; 2) has a global mechanism to control the adverse effect of outliers arising out of motion discontinuities, proximity of image borders; and 3) can go hand-in-hand with various spatial smoothness terms. Further, the proposed data term and related regularization schemes are both applicable to dense and sparse flow vector estimations. We validate these claims by numerical experiments on benchmark flow data sets. © 2011 IEEE

Lightweight filter architecture for energy efficient mobile vehicle localization based on a distributed acoustic sensor network.

PubMed

Kim, Keonwook

2013-08-23

The generic properties of an acoustic signal provide numerous benefits for localization by applying energy-based methods over a deployed wireless sensor network (WSN). However, the signal generated by a stationary target utilizes a significant amount of bandwidth and power in the system without providing further position information. For vehicle localization, this paper proposes a novel proximity velocity vector estimator (PVVE) node architecture in order to capture the energy from a moving vehicle and reject the signal from motionless automobiles around the WSN node. A cascade structure between analog envelope detector and digital exponential smoothing filter presents the velocity vector-sensitive output with low analog circuit and digital computation complexity. The optimal parameters in the exponential smoothing filter are obtained by analytical and mathematical methods for maximum variation over the vehicle speed. For stationary targets, the derived simulation based on the acoustic field parameters demonstrates that the system significantly reduces the communication requirements with low complexity and can be expected to extend the operation time considerably.

Ecologic Factors Associated with West Nile Virus Transmission, Northeastern United States

PubMed Central

Brown, Heidi E.; Childs, James E.; Diuk-Wasser, Maria A.

2008-01-01

Since 1999, West Nile virus (WNV) disease has affected the northeastern United States. To describe the spatial epidemiology and identify risk factors for disease incidence, we analyzed 8 years (1999–2006) of county-based human WNV disease surveillance data. Among the 56.6 million residents in 8 northeastern states sharing primary enzootic vectors, we found 977 cases. We controlled for population density and potential bias from surveillance and spatial proximity. Analyses demonstrated significant spatial spreading from 1999 through 2004 (p<0.01, r2 = 0.16). A significant trend was apparent among increasingly urban counties; county quartiles with the least (<38%) forest cover had 4.4-fold greater odds (95% confidence interval [CI] 1.4–13.2, p = 0.01) of having above-median disease incidence (>0.75 cases/100,000 residents) than counties with the most (>70%) forest cover. These results quantify urbanization as a risk factor for WNV disease incidence and are consistent with knowledge of vector species in this area. PMID:18826816

Hypothesis: Impregnated school uniforms reduce the incidence of dengue infections in school children.

PubMed

Wilder-Smith, A; Lover, A; Kittayapong, P; Burnham, G

2011-06-01

Dengue infection causes a significant economic, social and medical burden in affected populations in over 100 countries in the tropics and sub-tropics. Current dengue control efforts have generally focused on vector control but have not shown major impact. School-aged children are especially vulnerable to infection, due to sustained human-vector-human transmission in the close proximity environments of schools. Infection in children has a higher rate of complications, including dengue hemorrhagic fever and shock syndromes, than infections in adults. There is an urgent need for integrated and complementary population-based strategies to protect vulnerable children. We hypothesize that insecticide-treated school uniforms will reduce the incidence of dengue in school-aged children. The hypothesis would need to be tested in a community based randomized trial. If proven to be true, insecticide-treated school uniforms would be a cost-effective and scalable community based strategy to reduce the burden of dengue in children. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spread of the Tiger: Global Risk of Invasion by the Mosquito Aedes albopictus

PubMed Central

BENEDICT, MARK Q.; LEVINE, REBECCA S.; HAWLEY, WILLIAM A.; LOUNIBOS, L. PHILIP

2008-01-01

Aedes albopictus, commonly known as the Asian tiger mosquito, is currently the most invasive mosquito in the world. It is of medical importance due to its aggressive daytime human-biting behavior and ability to vector many viruses, including dengue, LaCrosse, and West Nile. Invasions into new areas of its potential range are often initiated through the transportation of eggs via the international trade in used tires. We use a genetic algorithm, Genetic Algorithm for Rule Set Production (GARP), to determine the ecological niche of Ae. albopictus and predict a global ecological risk map for the continued spread of the species. We combine this analysis with risk due to importation of tires from infested countries and their proximity to countries that have already been invaded to develop a list of countries most at risk for future introductions and establishments. Methods used here have potential for predicting risks of future invasions of vectors or pathogens. PMID:17417960

Self-powered thin-film motion vector sensor

PubMed Central

Jing, Qingshen; Xie, Yannan; Zhu, Guang; Han, Ray P. S.; Wang, Zhong Lin

2015-01-01

Harnessing random micromeso-scale ambient energy is not only clean and sustainable, but it also enables self-powered sensors and devices to be realized. Here we report a robust and self-powered kinematic vector sensor fabricated using highly pliable organic films that can be bent to spread over curved and uneven surfaces. The device derives its operational energy from a close-proximity triboelectrification of two surfaces: a polytetrafluoroethylene film coated with a two-column array of copper electrodes that constitutes the mover and a polyimide film with the top and bottom surfaces coated with a two-column aligned array of copper electrodes that comprises the stator. During relative reciprocations, the electrodes in the mover generate electric signals of ±5 V to attain a peak power density of ≥65 mW m−2 at a speed of 0.3 ms−1. From our 86,000 sliding motion tests of kinematic measurements, the sensor exhibits excellent stability, repeatability and strong signal durability. PMID:26271603

Automated image segmentation using support vector machines

NASA Astrophysics Data System (ADS)

Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

2007-03-01

Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

Quantitative analysis of the plain radiographic appearance of nonossifying fibroma.

PubMed

Friedland, J A; Reinus, W R; Fisher, A J; Wilson, A J

1995-08-01

To quantitate radiographic features that distinguish the plain radiographic appearance of nonossifying fibroma (NOF) from other solitary lesions of bone. Seven hundred nine cases of focal bone lesions, including 34 NOFs, were analyzed according to demographic, anatomic, and plain radiographic features. Vector analysis of groups of features was performed to determine those that are most sensitive and specific for the appearance of NOF in contrast to other lesions in the data base. The radiographic appearance of NOFs was most consistently a medullary based (97%), lytic lesion (100%) with geographic bone destruction (100%), marginal sclerosis (97%), and well-defined edges (94%). A statistically significant number of lesions were located in the distal aspect of long bones. Unicameral bone cyst shared the most radiographic features with the NOF. Vector analysis showed a large degree of overlap between NOF and other lesions such as aneurysmal bone cyst, chondromyxoid fibroma, and eosinophilic granuloma. The description that optimized sensitivity and prevalence for detection of NOF is a medullary based, ovoid lesion in the distal or proximal portions of a long bone with well-defined edges, a partial or complete rind of sclerosis, and absence of fallen fragment, periosteal reaction, and cortical disruption. The radiographic appearance of NOF is relatively nonspecific but, using vector analysis, can be better elucidated over current textbook descriptions.

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C.

PubMed

Stoean, Ruxandra; Stoean, Catalin; Lupsor, Monica; Stefanescu, Horia; Badea, Radu

2011-01-01

Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance. The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis). Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the evolutionary method is viewed in comparison to the traditional one. The multifaceted discrimination into all five degrees of fibrosis and the slightly less difficult common separation into solely three related stages are both investigated. The resulting performance proves the superiority over the standard support vector classification and the attained formula is helpful in providing an immediate calculation of the liver stage for new cases, while establishing the presence/absence and comprehending the weight of each medical factor with respect to a certain fibrosis level. The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage. Perhaps most importantly, it significantly surpasses the classical support vector machines, which are both widely used and technically sound. All these therefore confirm the promise of the new methodology towards a dependable support within the medical decision-making. Copyright © 2010 Elsevier B.V. All rights reserved.

Interpreting linear support vector machine models with heat map molecule coloring

PubMed Central

2011-01-01

Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

NASA Astrophysics Data System (ADS)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Support vector machine for the diagnosis of malignant mesothelioma

NASA Astrophysics Data System (ADS)

Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

2018-04-01

Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

The optional selection of micro-motion feature based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Bo; Ren, Hongmei; Xiao, Zhi-he; Sheng, Jing

2017-11-01

Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis

PubMed Central

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-01-01

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. PMID:26403370

The Graft Bending Angle Can Affect Early Graft Healing After Anterior Cruciate Ligament Reconstruction: In Vivo Analysis With 2 Years' Follow-up.

PubMed

Tashiro, Yasutaka; Gale, Tom; Sundaram, Vani; Nagai, Kanto; Irrgang, James J; Anderst, William; Nakashima, Yasuharu; Tashman, Scott; Fu, Freddie H

2017-07-01

A high graft bending angle (GBA) after anterior cruciate ligament (ACL) reconstruction has been suggested to cause stress on the graft. Nevertheless, evidence about its effect on graft healing in vivo is limited. The signal intensity on magnetic resonance imaging (MRI) would be higher in the proximal region of the ACL graft, and higher signals would be correlated to a higher GBA. Descriptive laboratory study. Anatomic single-bundle ACL reconstruction was performed on 24 patients (mean age, 20 ± 4 years) using the transportal technique. A quadriceps tendon autograft with a bone plug was harvested. To evaluate graft healing, the signal/noise quotient (SNQ) was measured in 3 regions of interest (ROIs) of the proximal, midsubstance, and distal ACL graft using high-resolution MRI (0.45 × 0.45 × 0.70 mm), with decreased signals suggesting improved healing. Dynamic knee motion was examined during treadmill walking and running to assess the in vivo GBA. The GBA was calculated from the 3-dimensional angle between the graft and femoral tunnel vectors at each motion frame, based on tibiofemoral kinematics determined from dynamic stereo X-ray analysis. Graft healing and GBAs were assessed at 6 and 24 months postoperatively. Repeated-measures analysis of variance was used to compare the SNQ in the 3 ROIs at 2 time points. Pearson correlations were used to analyze the relationship between the SNQ and mean GBA during 0% to 15% of the gait cycle. The SNQ of the ACL graft in the proximal region was significantly higher than in the midsubstance ( P = .022) and distal regions ( P < .001) at 6 months. The SNQ in the proximal region was highly correlated with the GBA during standing ( R = 0.64, P < .001), walking ( R = 0.65, P = .002), and running ( R = 0.54, P = .015) but not in the other regions. At 24 months, signals in the proximal and midsubstance regions decreased significantly compared with 6 months ( P < .001 and P = .008, respectively), with no difference across the graft area. The signal intensity was highest in the proximal region and lowest in the distal region of the reconstructed graft at 6 months postoperatively. A steep GBA was significantly correlated with high signal intensities of the proximal graft in this early period. A steep GBA may negatively affect proximal graft healing after ACL reconstruction.

Early problem behavior among children from low-income, mother-headed families: a multiple risk perspective.

PubMed

Olson, Sheryl L; Ceballo, Rosario; Park, Curie

2002-12-01

Examined proximal and contextual factors most strongly related to externalizing behavior among young children growing up in low-income, mother-headed families. Participants were 50 low-income single mothers and their preschool-age children who were visited twice in the home setting. Measures of proximal (low levels of supportive parenting, high levels of punitive disciplinary practices, low levels of maternal emotional well-being) and contextual (low maternal support, high levels of family stress) risk were assessed in relation to maternal reports of child externalizing behavior and an index of negative child behavior during a clean-up task. Child defiance during the clean-up task was highly associated with punitive maternal control in the same situation but had no other direct correlates. However, multiple risk factors representing both proximal and contextual variables were associated with variations in children's behavior problem scores. Mothers of children with high behavior problem scores reported lower feelings of self-efficacy in handling child care and emotional stressors, more frequent use of punitive child disciplinary practices, and lower feelings of satisfaction with the quality of their supportive resources than others. Maternal self-evaluations of coping efficacy mediated the relation between perceived support and child behavior problems, suggesting that constructs of personal control are important to represent in future studies of highly stressed parents.

Convoys of social support in Mexico: Examining socio-demographic variation.

PubMed

Fuller-Iglesias, Heather R; Antonucci, Toni

2016-07-01

The Convoy Model suggests that at different stages of the lifespan the makeup of the social support network varies in step with developmental and contextual needs. Cultural norms may shape the makeup of social convoys as well as denote socio-demographic differences in social support. This study examines the social convoys of adults in Mexico. Specifically, it examines whether social network structure varies by age, gender, and education level, thus addressing the paucity of research on interpersonal relations in Mexico. A sample of 1,202 adults (18-99 years of age) was drawn from the Study of Social Relations and Well-being in Mexico. Hierarchical regression analyses indicated older adults had larger, more geographically proximate networks with a greater proportion of kin but less frequent contact. Women had larger, less geographically proximate networks with less frequent contact. Less educated individuals had smaller, more geographically proximate networks with more frequent contact and a greater proportion of kin. Age moderated gender and education effects indicated that younger women have more diverse networks and less educated older adults have weaker social ties. This study highlights socio-demographic variation in social convoys within the Mexican context, and suggests implications for fostering intergenerational relationships, policy, and interventions. Future research on Mexican convoys should further explore sources of support, and specifically address implications for well-being.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

On the sparseness of 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, Weida

2010-04-01

There is some empirical evidence available showing that 1-norm Support Vector Machines (1-norm SVMs) have good sparseness; however, both how good sparseness 1-norm SVMs can reach and whether they have a sparser representation than that of standard SVMs are not clear. In this paper we take into account the sparseness of 1-norm SVMs. Two upper bounds on the number of nonzero coefficients in the decision function of 1-norm SVMs are presented. First, the number of nonzero coefficients in 1-norm SVMs is at most equal to the number of only the exact support vectors lying on the +1 and -1 discriminating surfaces, while that in standard SVMs is equal to the number of support vectors, which implies that 1-norm SVMs have better sparseness than that of standard SVMs. Second, the number of nonzero coefficients is at most equal to the rank of the sample matrix. A brief review of the geometry of linear programming and the primal steepest edge pricing simplex method are given, which allows us to provide the proof of the two upper bounds and evaluate their tightness by experiments. Experimental results on toy data sets and the UCI data sets illustrate our analysis. Copyright 2009 Elsevier Ltd. All rights reserved.

Weighted K-means support vector machine for cancer prediction.

PubMed

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

PubMed

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

Multi-kernel aggregation of local and global features in long-wave infrared for detection of SWAT teams in challenging environments

NASA Astrophysics Data System (ADS)

Arya, Ankit S.; Anderson, Derek T.; Bethel, Cindy L.; Carruth, Daniel

2013-05-01

A vision system was designed for people detection to provide support to SWAT team members operating in challenging environments such as low-to-no light, smoke, etc. When the vision system is mounted on a mobile robot platform: it will enable the robot to function as an effective member of the SWAT team; to provide surveillance information; to make first contact with suspects; and provide safe entry for team members. The vision task is challenging because SWAT team members are typically concealed, carry various equipment such as shields, and perform tactical and stealthy maneuvers. Occlusion is a particular challenge because team members operate in close proximity to one another. An uncooled electro-opticaljlong wav e infrared (EO/ LWIR) camera, 7.5 to 13.5 m, was used. A unique thermal dataset was collected of SWAT team members from multiple teams performing tactical maneuvers during monthly training exercises. Our approach consisted of two stages: an object detector trained on people to find candidate windows, and a secondary feature extraction, multi-kernel (MK) aggregation and classification step to distinguish between SWAT team members and civilians. Two types of thermal features, local and global, are presented based on ma ximally stable extremal region (MSER) blob detection. Support vector machine (SVM) classification results of approximately [70, 93]% for SWAT team member detection are reported based on the exploration of different combinations of visual information in terms of training data.

A Novel, Modified Reverse Controlled Antegrade and Retrograde Subintimal Tracking Technique for Bypassing the Calcified Proximal Cap of Coronary Total Occlusions

PubMed Central

Ochiai, Masahiko; Munehisa, Masato; Ootomo, Tatsushi

2017-01-01

Antegrade crossing is the most common approach to chronic total occlusions (CTOs). However, it is sometimes difficult to penetrate the proximal hard cap with guidewires, especially in the case of CTOs of anomalous coronary arteries because of a lack of support. Herein, we describe a novel, modified reverse controlled antegrade and retrograde subintimal tracking (CART) technique in which the dissection reentry was intentionally created in the proximal segment of the vessel, not within the occluded segment, using retrograde guidewire and the aid of an antegrade balloon. This technique facilitated retrograde crossing of CTOs by avoiding the proximal hard cap and may provide a viable option for patients in which conventional reverse CART is not possible. PMID:28529807

Filial Responsibility and Informal Support among Family Caregivers of the Elderly in Jerusalem: A Path Analysis.

ERIC Educational Resources Information Center

Litwin, Howard

1994-01-01

Surveyed family caregivers of 110 hospitalized elderly Jews regarding filial responsibility and supports they provide their parent(s). Found future expectations of support explained by perceptions of filial responsibility that were explained by caregiver religiosity. Current support was influenced by proximity to care recipient, activities of…

T-ray relevant frequencies for osteosarcoma classification

NASA Astrophysics Data System (ADS)

Withayachumnankul, W.; Ferguson, B.; Rainsford, T.; Findlay, D.; Mickan, S. P.; Abbott, D.

2006-01-01

We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

Recombinase-Mediated Cassette Exchange Using Adenoviral Vectors.

PubMed

Kolb, Andreas F; Knowles, Christopher; Pultinevicius, Patrikas; Harbottle, Jennifer A; Petrie, Linda; Robinson, Claire; Sorrell, David A

2017-01-01

Site-specific recombinases are important tools for the modification of mammalian genomes. In conjunction with viral vectors, they can be utilized to mediate site-specific gene insertions in animals and in cell lines which are difficult to transfect. Here we describe a method for the generation and analysis of an adenovirus vector supporting a recombinase-mediated cassette exchange reaction and discuss the advantages and limitations of this approach.

What caused the 2012 dengue outbreak in Pucallpa, Peru? A socio-ecological autopsy.

PubMed

Charette, Margot; Berrang-Ford, Lea; Llanos-Cuentas, Elmer Alejandro; Cárcamo, César; Kulkarni, Manisha

2017-02-01

Dengue is highly endemic in Peru, with increases in transmission particularly since vector re-infestation of the country in the 1980s. Pucallpa, the second largest city in the Peruvian Amazon, experienced a large outbreak in 2012 that caused more than 10,000 cases and 13 deaths. To date, there has been limited research on dengue in the Peruvian Amazon outside of Iquitos, and no published review or critical analysis of the 2012 Pucallpa dengue outbreak. This study describes the incidence, surveillance, and control of dengue in Ucayali to understand the factors that contributed to the 2012 Pucallpa outbreak. We employed a socio-ecological autopsy approach to consider distal and proximal contributing factors, drawing on existing literature and interviews with key personnel involved in dengue control, surveillance and treatment in Ucayali. Spatio-temporal analysis showed that relative risk of dengue was higher in the northern districts of Calleria (RR = 2.18), Manantay (RR = 1.49) and Yarinacocha (RR = 1.25) compared to all other districts between 2004 and 2014. The seasonal occurrence of the 2012 outbreak is consistent with typical seasonal patterns for dengue incidence in the region. Our assessment suggests that the outbreak was proximally triggered by the introduction of a new virus serotype (DENV-2 Asian/America) to the region. Increased travel, rapid urbanization, and inadequate water management facilitated the potential for virus spread and transmission, both within Pucallpa and regionally. These triggers occurred within the context of failures in surveillance and control programming, including underfunded and ad hoc vector control. These findings have implications for future prevention and control of dengue in Ucayali as new diseases such as chikungunya and Zika threaten the region. Copyright © 2016 Elsevier Ltd. All rights reserved.

Early results for treatment of two- and three-part fractures of the proximal humerus using Contours PHP (proximal humeral plate).

PubMed

Biazzo, Alessio; Cardile, Carlo; Brunelli, Luca; Ragni, Paolo; Clementi, Daniele

2017-04-28

The management of displaced 2- and 3-part fractures of the proximal humerus is controversial, both in younger and in elderly patients. The purpose of this paper is to evaluate the functional results of the Contours Proximal Humerus Plate (OrthofixR, Bussolengo,Verona, Italy), for the treatment of displaced 2- and 3-part fractures of the proximal humerus. We retrospectively reviewed 55 patients with proximal humerus fractures, who underwent osteosynthesis with Contours Proximal Humerus Plate from December 2011 to March 2015. We had 21 patients with 2-part fractures and with an average age of 67.1 years and 34 patients with 3-part fractures, with average age of 63.6 years. The average union time was 3 months. The mean Constant score was 67 for 2-part fracture group and 64.9 for 3-part fracture group. The difference was not statistically significant (p = 0.18). The overall complication rate was 14.5 %. Six patients underwent additional surgery (10.9%). The most frequent major complication was secondary loss of reduction following varus collapse of the fracture (2 cases). In these patients, there was loss of medial hinge integrity due to impaction and osteoporosis. The placement of the main locking screw in the calcar area to provide inferomedial support is the rational of the Contours Proximal Humerus Plate. Osteosynthesis with Contours Proximal Humerus Plate is a safe system for treating displaced 2- and 3-part fractures of the proximal humerus, with good functional results and complication rates comparable to those reported in the literature.

Endoscopic hip osteotomies: less invasive approaches to peri-acetabular, proximal femoral and pubic symphyseal procedures

PubMed Central

Matsuda, Dean K.; Matsuda, Nicole A.

2015-01-01

Beyond the recent expansion of extra-articular hip arthroscopy into the peri-trochanteric and subgluteal space, this instructional course lecture introduces three innovative procedures: endoscopy-assisted periacetabular osteotomy, closed derotational proximal femoral osteotomy and endoscopic pubic symphysectomy. Supportive rationale, evolving indications, key surgical techniques and emerging outcomes are presented for these innovative less invasive procedures. PMID:27011827

Reentry Vehicle On-Site Inspection Technology Study

DTIC Science & Technology

1994-11-01

masking Image Additional required* generated information required/wquied Pasive radiation Neutron scanning Neutron dose rate PNS Yes No SRS/BMO Yes...considerations for RVOSI techbologles. Inspection methods Instrument Setup Data Host support proximity time collection (m) (hr) time (hir) Pasive radiation...The unwillingness of our own services to allow equipment in close proximity to missile front ends is a matter which will have to be considered if a

Proximal thoracic aorta dimensions after continuous-flow left ventricular assist device implantation: Longitudinal changes and relation to aortic valve insufficiency.

PubMed

Fine, Nowell M; Park, Soon J; Stulak, John M; Topilsky, Yan; Daly, Richard C; Joyce, Lyle D; Pereira, Naveen L; Schirger, John A; Edwards, Brooks S; Lin, Grace; Kushwaha, Sudhir S

2016-04-01

In this study we examined the impact of continuous-flow left ventricular assist device (CF-LVAD) support on proximal thoracic aorta dimensions. Aortic root and ascending aorta diameter were measured from serial echocardiograms before and after CF-LVAD implantation in patients with ≥6 months of support, and correlated with the development of >mild aortic valve insufficiency (AI). Of 162 patients included, mean age was 58 ± 11 years and 128 (79%) were male. Seventy-nine (63%) were destination therapy patients. Mean aortic root and ascending aorta diameters at baseline, 1 month, 6 months, 12 months and long-term follow-up (mean 2.0 ± 1.4 years) were 3.5 ± 0.4, 3.5 ± 0.3, 3.9 ± 0.3, 3.9 ± 0.2 and 4.0 ± 0.3, and 3.3 ± 0.2, 3.3 ± 0.3, 3.6 ± 0.2, 3.6 ± 0.3 and 3.6 ± 0.3 cm, respectively. Only change in aortic root diameter from 1-month to 6-month follow-up reached statistical significance (p = 0.03). Nine (6%) patients had accelerated proximal thoracic aorta expansion (>0.5 cm/year), occurring predominantly in the first 6 months after implantation. These patients were older and more likely to have hypertension and baseline proximal thoracic aorta dilation. Forty-five (28%) patients developed >mild AI at long-term follow-up, including 7 of 9 (78%) of those with accelerated proximal thoracic aorta expansion. All 7 had aortic valves that remained closed throughout the cardiac cycle, and this, along with duration of CF-LVAD support and increase in aortic root diameter, were significantly associated with developing >mild AI. CF-LVAD patients have small increases in proximal thoracic aorta dimensions that predominantly occur within the first 6 months after implantation and then stabilize. Increasing aortic root diameter was associated with AI development. Copyright © 2016 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis.

PubMed

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-11-27

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fuzzy support vector machines for adaptive Morse code recognition.

PubMed

Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

2006-11-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

PubMed

Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

2009-07-21

Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

NASA Astrophysics Data System (ADS)

Wu, Qi

2010-03-01

Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

Evaluation and recognition of skin images with aging by support vector machine

NASA Astrophysics Data System (ADS)

Hu, Liangjun; Wu, Shulian; Li, Hui

2016-10-01

Aging is a very important issue not only in dermatology, but also cosmetic science. Cutaneous aging involves both chronological and photoaging aging process. The evaluation and classification of aging is an important issue with the medical cosmetology workers nowadays. The purpose of this study is to assess chronological-age-related and photo-age-related of human skin. The texture features of skin surface skin, such as coarseness, contrast were analyzed by Fourier transform and Tamura. And the aim of it is to detect the object hidden in the skin texture in difference aging skin. Then, Support vector machine was applied to train the texture feature. The different age's states were distinguished by the support vector machine (SVM) classifier. The results help us to further understand the mechanism of different aging skin from texture feature and help us to distinguish the different aging states.

Classification of Stellar Spectra with Fuzzy Minimum Within-Class Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhong-bao, Liu; Wen-ai, Song; Jing, Zhang; Wen-juan, Zhao

2017-06-01

Classification is one of the important tasks in astronomy, especially in spectra analysis. Support Vector Machine (SVM) is a typical classification method, which is widely used in spectra classification. Although it performs well in practice, its classification accuracies can not be greatly improved because of two limitations. One is it does not take the distribution of the classes into consideration. The other is it is sensitive to noise. In order to solve the above problems, inspired by the maximization of the Fisher's Discriminant Analysis (FDA) and the SVM separability constraints, fuzzy minimum within-class support vector machine (FMWSVM) is proposed in this paper. In FMWSVM, the distribution of the classes is reflected by the within-class scatter in FDA and the fuzzy membership function is introduced to decrease the influence of the noise. The comparative experiments with SVM on the SDSS datasets verify the effectiveness of the proposed classifier FMWSVM.

Geographical traceability of Marsdenia tenacissima by Fourier transform infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Li, Chao; Yang, Sheng-Chao; Guo, Qiao-Sheng; Zheng, Kai-Yan; Wang, Ping-Li; Meng, Zhen-Gui

2016-01-01

A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine-genetic algorithms, support vector machine-particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine-grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way.

Object recognition of real targets using modelled SAR images

NASA Astrophysics Data System (ADS)

Zherdev, D. A.

2017-12-01

In this work the problem of recognition is studied using SAR images. The algorithm of recognition is based on the computation of conjugation indices with vectors of class. The support subspaces for each class are constructed by exception of the most and the less correlated vectors in a class. In the study we examine the ability of a significant feature vector size reduce that leads to recognition time decrease. The images of targets form the feature vectors that are transformed using pre-trained convolutional neural network (CNN).

Understanding the fluid mechanics behind transverse wall shear stress.

PubMed

Mohamied, Yumnah; Sherwin, Spencer J; Weinberg, Peter D

2017-01-04

The patchy distribution of atherosclerosis within arteries is widely attributed to local variation in haemodynamic wall shear stress (WSS). A recently-introduced metric, the transverse wall shear stress (transWSS), which is the average over the cardiac cycle of WSS components perpendicular to the temporal mean WSS vector, correlates particularly well with the pattern of lesions around aortic branch ostia. Here we use numerical methods to investigate the nature of the arterial flows captured by transWSS and the sensitivity of transWSS to inflow waveform and aortic geometry. TransWSS developed chiefly in the acceleration, peak systolic and deceleration phases of the cardiac cycle; the reverse flow phase was too short, and WSS in diastole was too low, for these periods to have a significant influence. Most of the spatial variation in transWSS arose from variation in the angle by which instantaneous WSS vectors deviated from the mean WSS vector rather than from variation in the magnitude of the vectors. The pattern of transWSS was insensitive to inflow waveform; only unphysiologically high Womersley numbers produced substantial changes. However, transWSS was sensitive to changes in geometry. The curvature of the arch and proximal descending aorta were responsible for the principal features, the non-planar nature of the aorta produced asymmetries in the location and position of streaks of high transWSS, and taper determined the persistence of the streaks down the aorta. These results reflect the importance of the fluctuating strength of Dean vortices in generating transWSS. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sugar epitopes as potential universal disease transmission blocking targets.

PubMed

Dinglasan, Rhoel R; Valenzuela, Jesús G; Azad, Abdu F

2005-01-01

One promising method to prevent vector-borne diseases is through the use of transmission blocking vaccines (TBVs). However, developing several anti-pathogen TBVs may be impractical. In this study, we have identified a conserved candidate carbohydrate target in the midguts of several Arthropod vectors. A screen of the novel GlycoChip glycan array found that the anti-carbohydrate malaria transmission blocking monoclonal antibody (MG96) preferentially recognized D-mannose (alpha) and the type II lactosamine disaccharide. The specificity for D-mannose was confirmed by competition ELISA using alpha-methyl mannoside as inhibitor. Con A, which identifies terminal mannose residues, did not inhibit MG96 reactivity with mosquito midgut lysates, suggesting that Con A has differential recognition of this monosaccharide. However, the jack bean lectin, Jacalin, which recognizes D-mannose (alpha), d-galactose (alpha/beta) and the T antigen, not only displays a similar banding profile to that recognized by MG96 on immunoblot but was also shown to effectively inhibit MG96. Wheat-germ agglutinin, which recognizes N-acetyllactosamine units, only partially inhibited MG96 reactivity. This highlights the contribution of both glycan moieties to the MG96 epitope or glycotope. Enzyme deglycosylation results suggest that MG96 recognizes a mannose alpha1-6 substitution on an O-linked oligosaccharide. Taken together, the data suggest that MG96 recognizes a discontinuous glycotope composed of Manalpha1-6 proximal to Galbeta1-4GlcNAc-alpha-O-R glycans on arthropod vector midguts. As such, these glycotopes may represent potential transmission blocking vaccine targets for a wide range of vector-borne pathogens.

AAV-mediated targeting of gene expression to the peri-infarct region in rat cortical stroke model.

PubMed

Mätlik, Kert; Abo-Ramadan, Usama; Harvey, Brandon K; Arumäe, Urmas; Airavaara, Mikko

2014-10-30

For stroke patients the recovery of cognitive and behavioral functions is often incomplete. Functional recovery is thought to be mediated largely by connectivity rearrangements in the peri-infarct region. A method for manipulating gene expression in this region would be useful for identifying new recovery-enhancing treatments. We have characterized a way of targeting adeno-associated virus (AAV) vectors to the peri-infarct region of cortical ischemic lesion in rats 2days after middle cerebral artery occlusion (MCAo). We used magnetic resonance imaging (MRI) to show that the altered properties of post-ischemic brain tissue facilitate the spreading of intrastriatally injected nanoparticles toward the infarct. We show that subcortical injection of green fluorescent protein-encoding dsAAV7-GFP resulted in transduction of cells in and around the white matter tract underlying the lesion, and in the cortex proximal to the lesion. A similar result was achieved with dsAAV7 vector encoding the cerebral dopamine neurotrophic factor (CDNF), a protein with therapeutic potential. Viral vector-mediated intracerebral gene delivery has been used before in rodent models of ischemic injury. However, the method of targeting gene expression to the peri-infarct region, after the initial phase of ischemic cell death, has not been described before. We demonstrate a straightforward and robust way to target AAV vector-mediated over-expression of genes to the peri-infarct region in a rat stroke model. This method will be useful for studying the action of specific proteins in peri-infarct region during the recovery process. Copyright © 2014 Elsevier B.V. All rights reserved.

Simulated Transmission of the Dengue Virus Across the US-Mexico Border Using Remotely Sensed and Ground Based Weather Data

NASA Technical Reports Server (NTRS)

Morin, Cory; Quattrochi, Dale A.

2015-01-01

Incidence of dengue fever, caused by a mosquito transmitted virus, have increased in the Americas during recent decades. In the US, local transmission has been reported in southern Texas and Florida. However, despite its close proximity to dengue endemic areas in Mexico and the presence of a primary mosquito vector, there are no reports of local transmission in Arizona. Many studies have demonstrated that weather influences dengue virus transmission by regulating vector development rates, vector habitat availability, and the duration of the virus extrinsic incubation period (EIP). The EIP, the period between mosquito infection and the ability for it to retransmit the virus, is especially important given its high sensitivity to temperature and the short lifespan of mosquitoes. Other studies, however, have suggested that human related factors such as socioeconomic status and herd immunity may explain much of the disparity in dengue incidence in the US-Mexico border region. Using a meteorologically driven model of vector population dynamics and virus transmission we compare simulations of dengue fever cases in southern Arizona and northern Mexico. A Monte Carlo approach is employed to select parameter values by evaluating simulations in Hermosillo Mexico with reported dengue fever case data. Simulations that replicate the case data best are retained and rerun using remotely sensed climate data from other Arizona and Mexico locations to determine the relative influence of weather on virus transmission. Although human and environmental factors undoubtedly influence dengue transmission in the US-Mexico border regions, weather is a major facilitator of the transmission process.

Reptiles as potential vectors and hosts of the amphibian pathogen Batrachochytrium dendrobatidis in Panama.

PubMed

Kilburn, Vanessa L; Ibáñez, Roberto; Green, David M

2011-12-06

Chytridiomycosis, the disease caused by Batrachochytrium dendrobatidis, is considered to be a disease exclusively of amphibians. However, B. dendrobatidis may also be capable of persisting in the environment, and non-amphibian vectors or hosts may contribute to disease transmission. Reptiles living in close proximity to amphibians and sharing similar ecological traits could serve as vectors or reservoir hosts for B. dendrobatidis, harbouring the organism on their skin without succumbing to disease. We surveyed for the presence of B. dendrobatidis DNA among 211 lizards and 8 snakes at 8 sites at varying elevations in Panama where the syntopic amphibians were at pre-epizootic, epizootic or post-epizootic stages of chytridiomycosis. Detection of B. dendrobatidis DNA was done using qPCR analysis. Evidence of the amphibian pathogen was present at varying intensities in 29 of 79 examined Anolis humilis lizards (32%) and 9 of 101 A. lionotus lizards (9%), and in one individual each of the snakes Pliocercus euryzonus, Imantodes cenchoa, and Nothopsis rugosus. In general, B. dendrobatidis DNA prevalence among reptiles was positively correlated with the infection prevalence among co-occurring anuran amphibians at any particular site (r = 0.88, p = 0.004). These reptiles, therefore, may likely be vectors or reservoir hosts for B. dendrobatidis and could serve as disease transmission agents. Although there is no evidence of B. dendrobatidis disease-induced declines in reptiles, cases of coincidence of reptile and amphibian declines suggest this potentiality. Our study is the first to provide evidence of non-amphibian carriers for B. dendrobatidis in a natural Neotropical environment.

Manual aspiration thrombectomy through proximal and distal supporting technique for the treatment of procedural distal A2 emboli: A technical case report.

PubMed

Kwak, Hyo Sung; Park, Jung Soo

Disrupted clots that form during endovascular treatment for acute ischemic stroke can cause distal embolization. It is not easy to recanalize occluded vessels resulting from distal emboli. In particular, endovascular treatment of distal A2 emboli is very challenging because it is difficult to access such a distal location and maintain microcatheter stability throughout the procedure. We report a case of successful recanalization of A2 occlusion caused by procedural-induced distal emboli through a proximal and distal supporting technique. Copyright © 2017. Published by Elsevier Urban & Partner Sp. z o.o.

Buruli ulcer disease prevalence in Benin, West Africa: Associations with land use/cover and the identification of disease clusters

USGS Publications Warehouse

Wagner, T.; Benbow, M.E.; Brenden, T.O.; Qi, J.; Johnson, R.C.

2008-01-01

Background: Buruli ulcer (BU) disease, caused by infection with the environmental mycobacterium M. ulcerans, is an emerging infectious disease in many tropical and sub-tropical countries. Although vectors and modes of transmission remain unknown, it is hypothesized that the transmission of BU disease is associated with human activities in or around aquatic environments, and that characteristics of the landscape (e.g., land use/cover) play a role in mediating BU disease. Several studies performed at relatively small spatial scales (e.g., within a single village or region of a country) support these hypotheses; however, if BU disease is associated with land use/cover characteristics, either through spatial constraints on vector-host dynamics or by mediating human activities, then large-scale (i.e., country-wide) associations should also emerge. The objectives of this study were to (1) investigate associations between BU disease prevalence in villages in Benin, West Africa and surrounding land use/cover patterns and other map-based characteristics, and (2) identify areas with greater and lower than expected prevalence rates (i.e., disease clusters) to assist with the development of prevention and control programs. Results: Our landscape-based models identified low elevation, rural villages surrounded by forest land cover, and located in drainage basins with variable wetness patterns as being associated with higher BU disease prevalence rates. We also identified five spatial disease clusters. Three of the five clusters contained villages with greater than expected prevalence rates and two clusters contained villages with lower than expected prevalence rates. Those villages with greater than expected BU disease prevalence rates spanned a fairly narrow region of south-central Benin. Conclusion: Our analyses suggest that interactions between natural land cover and human alterations to the landscape likely play a role in the dynamics of BU disease. For example, urbanization, potentially by providing access to protected water sources, may reduce the likelihood of becoming infected with BU disease. Villages located at low elevations may have higher BU disease prevalence rates due to their close spatial proximity to high risk environments. In addition, forest land cover and drainage basins with variable wetness patterns may be important for providing suitable growth conditions for M. ulcerans, influencing the distribution and abundance of vectors, or mediating vector-human interactions. The identification of disease clusters in this study provides direction for future research aimed at better understanding these and other environmental and social determinants involved in BU disease outbreaks. ?? 2008 Wagner et al; licensee BioMed Central Ltd.

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters

PubMed Central

Wagner, Tyler; Benbow, M Eric; Brenden, Travis O; Qi, Jiaguo; Johnson, R Christian

2008-01-01

Background Buruli ulcer (BU) disease, caused by infection with the environmental mycobacterium M. ulcerans, is an emerging infectious disease in many tropical and sub-tropical countries. Although vectors and modes of transmission remain unknown, it is hypothesized that the transmission of BU disease is associated with human activities in or around aquatic environments, and that characteristics of the landscape (e.g., land use/cover) play a role in mediating BU disease. Several studies performed at relatively small spatial scales (e.g., within a single village or region of a country) support these hypotheses; however, if BU disease is associated with land use/cover characteristics, either through spatial constraints on vector-host dynamics or by mediating human activities, then large-scale (i.e., country-wide) associations should also emerge. The objectives of this study were to (1) investigate associations between BU disease prevalence in villages in Benin, West Africa and surrounding land use/cover patterns and other map-based characteristics, and (2) identify areas with greater and lower than expected prevalence rates (i.e., disease clusters) to assist with the development of prevention and control programs. Results Our landscape-based models identified low elevation, rural villages surrounded by forest land cover, and located in drainage basins with variable wetness patterns as being associated with higher BU disease prevalence rates. We also identified five spatial disease clusters. Three of the five clusters contained villages with greater than expected prevalence rates and two clusters contained villages with lower than expected prevalence rates. Those villages with greater than expected BU disease prevalence rates spanned a fairly narrow region of south-central Benin. Conclusion Our analyses suggest that interactions between natural land cover and human alterations to the landscape likely play a role in the dynamics of BU disease. For example, urbanization, potentially by providing access to protected water sources, may reduce the likelihood of becoming infected with BU disease. Villages located at low elevations may have higher BU disease prevalence rates due to their close spatial proximity to high risk environments. In addition, forest land cover and drainage basins with variable wetness patterns may be important for providing suitable growth conditions for M. ulcerans, influencing the distribution and abundance of vectors, or mediating vector-human interactions. The identification of disease clusters in this study provides direction for future research aimed at better understanding these and other environmental and social determinants involved in BU disease outbreaks. PMID:18505567

Regional Distribution Models with Lack of Proximate Predictors: Africanized Honeybees Expanding North

NASA Technical Reports Server (NTRS)

Jarnevich, Catherine S.; Esaias, Wayne E.; Ma, Peter L. A.; Morisette, Jeffery T.; Nickeson, Jaime E.; Stohlgren, Thomas J.; Holcombe, Tracy R.; Nightingale, Joanne M.; Wolfe, Robert E.; Tan, Bin

2014-01-01

Species distribution models have often been hampered by poor local species data, reliance on coarse-scale climate predictors and the assumption that species-environment relationships, even with non-proximate predictors, are consistent across geographical space. Yet locally accurate maps of invasive species, such as the Africanized honeybee (AHB) in North America, are needed to support conservation efforts. Current AHB range maps are relatively coarse and are inconsistent with observed data. Our aim was to improve distribution maps using more proximate predictors (phenology) and using regional models rather than one across the entire range of interest to explore potential differences in drivers.

Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north

USGS Publications Warehouse

Jarnevich, Catherine S.; Esaias, Wayne E.; Ma, Peter L.A.; Morisette, Jeffery T.; Nickeson, Jaime E.; Stohlgren, Thomas J.; Holcombe, Tracy R.; Nightingale, Joanne M.; Wolfe, Robert E.; Tan, Bin

2014-01-01

Species distribution models have often been hampered by poor local species data, reliance on coarse-scale climate predictors and the assumption that species–environment relationships, even with non-proximate predictors, are consistent across geographical space. Yet locally accurate maps of invasive species, such as the Africanized honeybee (AHB) in North America, are needed to support conservation efforts. Current AHB range maps are relatively coarse and are inconsistent with observed data. Our aim was to improve distribution maps using more proximate predictors (phenology) and using regional models rather than one across the entire range of interest to explore potential differences in drivers.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Sparse kernel methods for high-dimensional survival data.

PubMed

Evers, Ludger; Messow, Claudia-Martina

2008-07-15

Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be 'kernelized'. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, depending only on a small fraction of the training data. We propose two methods. One is based on a geometric idea, where-akin to support vector classification-the margin between the failed observation and the observations currently at risk is maximised. The other approach is based on obtaining a sparse model by adding observations one after another akin to the Import Vector Machine (IVM). Data examples studied suggest that both methods can outperform competing approaches. Software is available under the GNU Public License as an R package and can be obtained from the first author's website http://www.maths.bris.ac.uk/~maxle/software.html.

Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression

NASA Astrophysics Data System (ADS)

Dougherty, Andrew W.

Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor responses in the time, gas and temperature domains, and the dual representation of the support vector regression solution is shown to provide insight into the sensor's sensitivity and potential orthogonality. Finally, the dual weights of the support vector regression solution to the sensor's response are suggested as a fitness function for a genetic algorithm, or some other method for efficiently searching large parameter spaces.

Standard Anatomic Terminologies: Comparison for Use in a Health Information Exchange–Based Prior Computed Tomography (CT) Alerting System

PubMed Central

Lowry, Tina; Vreeman, Daniel J; Loo, George T; Delman, Bradley N; Thum, Frederick L; Slovis, Benjamin H; Shapiro, Jason S

2017-01-01

Background A health information exchange (HIE)–based prior computed tomography (CT) alerting system may reduce avoidable CT imaging by notifying ordering clinicians of prior relevant studies when a study is ordered. For maximal effectiveness, a system would alert not only for prior same CTs (exams mapped to the same code from an exam name terminology) but also for similar CTs (exams mapped to different exam name terminology codes but in the same anatomic region) and anatomically proximate CTs (exams in adjacent anatomic regions). Notification of previous same studies across an HIE requires mapping of local site CT codes to a standard terminology for exam names (such as Logical Observation Identifiers Names and Codes [LOINC]) to show that two studies with different local codes and descriptions are equivalent. Notifying of prior similar or proximate CTs requires an additional mapping of exam codes to anatomic regions, ideally coded by an anatomic terminology. Several anatomic terminologies exist, but no prior studies have evaluated how well they would support an alerting use case. Objective The aim of this study was to evaluate the fitness of five existing standard anatomic terminologies to support similar or proximate alerts of an HIE-based prior CT alerting system. Methods We compared five standard anatomic terminologies (Foundational Model of Anatomy, Systematized Nomenclature of Medicine Clinical Terms, RadLex, LOINC, and LOINC/Radiological Society of North America [RSNA] Radiology Playbook) to an anatomic framework created specifically for our use case (Simple ANatomic Ontology for Proximity or Similarity [SANOPS]), to determine whether the existing terminologies could support our use case without modification. On the basis of an assessment of optimal terminology features for our purpose, we developed an ordinal anatomic terminology utility classification. We mapped samples of 100 random and the 100 most frequent LOINC CT codes to anatomic regions in each terminology, assigned utility classes for each mapping, and statistically compared each terminology’s utility class rankings. We also constructed seven hypothetical alerting scenarios to illustrate the terminologies’ differences. Results Both RadLex and the LOINC/RSNA Radiology Playbook anatomic terminologies ranked significantly better (P<.001) than the other standard terminologies for the 100 most frequent CTs, but no terminology ranked significantly better than any other for 100 random CTs. Hypothetical scenarios illustrated instances where no standard terminology would support appropriate proximate or similar alerts, without modification. Conclusions LOINC/RSNA Radiology Playbook and RadLex’s anatomic terminologies appear well suited to support proximate or similar alerts for commonly ordered CTs, but for less commonly ordered tests, modification of the existing terminologies with concepts and relations from SANOPS would likely be required. Our findings suggest SANOPS may serve as a framework for enhancing anatomic terminologies in support of other similar use cases. PMID:29242174

Vectoring of parallel synthetic jets: A parametric study

NASA Astrophysics Data System (ADS)

Berk, Tim; Gomit, Guillaume; Ganapathisubramani, Bharathram

2016-11-01

The vectoring of a pair of parallel synthetic jets can be described using five dimensionless parameters: the aspect ratio of the slots, the Strouhal number, the Reynolds number, the phase difference between the jets and the spacing between the slots. In the present study, the influence of the latter four on the vectoring behaviour of the jets is examined experimentally using particle image velocimetry. Time-averaged velocity maps are used to study the variations in vectoring behaviour for a parametric sweep of each of the four parameters independently. A topological map is constructed for the full four-dimensional parameter space. The vectoring behaviour is described both qualitatively and quantitatively. A vectoring mechanism is proposed, based on measured vortex positions. We acknowledge the financial support from the European Research Council (ERC Grant Agreement No. 277472).

Determination of Anti-Adeno-Associated Virus Vector Neutralizing Antibody Titer with an In Vitro Reporter System

PubMed Central

Meliani, Amine; Leborgne, Christian; Triffault, Sabrina; Jeanson-Leh, Laurence; Veron, Philippe

2015-01-01

Abstract Adeno-associated virus (AAV) vectors are a platform of choice for in vivo gene transfer applications. However, neutralizing antibodies (NAb) to AAV can be found in humans and some animal species as a result of exposure to the wild-type virus, and high-titer NAb develop following AAV vector administration. In some conditions, anti-AAV NAb can block transduction with AAV vectors even when present at low titers, thus requiring prescreening before vector administration. Here we describe an improved in vitro, cell-based assay for the determination of NAb titer in serum or plasma samples. The assay is easy to setup and sensitive and, depending on the purpose, can be validated to support clinical development of gene therapy products based on AAV vectors. PMID:25819687

Matrix Multiplication Algorithm Selection with Support Vector Machines

DTIC Science & Technology

2015-05-01

libraries that could intelligently choose the optimal algorithm for a particular set of inputs. Users would be oblivious to the underlying algorithmic...SAT.” J. Artif . Intell. Res.(JAIR), vol. 32, pp. 565–606, 2008. [9] M. G. Lagoudakis and M. L. Littman, “Algorithm selection using reinforcement...Artificial Intelligence , vol. 21, no. 05, pp. 961–976, 2007. [15] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM

Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set.

PubMed

Zhang, Jinshui; Yuan, Zhoumiqi; Shuai, Guanyuan; Pan, Yaozhong; Zhu, Xiufang

2017-04-26

This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM) method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient ( C ) and kernel width ( s ), in mapping homogeneous specific land cover.

Reference equations of motion for automatic rendezvous and capture

NASA Technical Reports Server (NTRS)

Henderson, David M.

1992-01-01

The analysis presented in this paper defines the reference coordinate frames, equations of motion, and control parameters necessary to model the relative motion and attitude of spacecraft in close proximity with another space system during the Automatic Rendezvous and Capture phase of an on-orbit operation. The relative docking port target position vector and the attitude control matrix are defined based upon an arbitrary spacecraft design. These translation and rotation control parameters could be used to drive the error signal input to the vehicle flight control system. Measurements for these control parameters would become the bases for an autopilot or feedback control system (FCS) design for a specific spacecraft.

Supersonic quasi-axisymmetric vortex breakdown

NASA Technical Reports Server (NTRS)

Kandil, Osama A.; Kandil, Hamdy A.; Liu, C. H.

1991-01-01

An extensive computational study of supersonic quasi-axisymmetric vortex breakdown in a configured circular duct is presented. The unsteady, compressible, full Navier-Stokes (NS) equations are used. The NS equations are solved for the quasi-axisymmetric flows using an implicit, upwind, flux difference splitting, finite volume scheme. The quasi-axisymmetric solutions are time accurate and are obtained by forcing the components of the flowfield vector to be equal on two axial planes, which are in close proximity of each other. The effect of Reynolds number, for laminar flows, on the evolution and persistence of vortex breakdown, is studied. Finally, the effect of swirl ration at the duct inlet is investigated.

Complex interactions between cis-regulatory modules in native conformation are critical for Drosophila snail expression

PubMed Central

Dunipace, Leslie; Ozdemir, Anil; Stathopoulos, Angelike

2011-01-01

It has been shown in several organisms that multiple cis-regulatory modules (CRMs) of a gene locus can be active concurrently to support similar spatiotemporal expression. To understand the functional importance of such seemingly redundant CRMs, we examined two CRMs from the Drosophila snail gene locus, which are both active in the ventral region of pre-gastrulation embryos. By performing a deletion series in a ∼25 kb DNA rescue construct using BAC recombineering and site-directed transgenesis, we demonstrate that the two CRMs are not redundant. The distal CRM is absolutely required for viability, whereas the proximal CRM is required only under extreme conditions such as high temperature. Consistent with their distinct requirements, the CRMs support distinct expression patterns: the proximal CRM exhibits an expanded expression domain relative to endogenous snail, whereas the distal CRM exhibits almost complete overlap with snail except at the anterior-most pole. We further show that the distal CRM normally limits the increased expression domain of the proximal CRM and that the proximal CRM serves as a `damper' for the expression levels driven by the distal CRM. Thus, the two CRMs interact in cis in a non-additive fashion and these interactions may be important for fine-tuning the domains and levels of gene expression. PMID:21813571

Vectoring of parallel synthetic jets

NASA Astrophysics Data System (ADS)

Berk, Tim; Ganapathisubramani, Bharathram; Gomit, Guillaume

2015-11-01

A pair of parallel synthetic jets can be vectored by applying a phase difference between the two driving signals. The resulting jet can be merged or bifurcated and either vectored towards the actuator leading in phase or the actuator lagging in phase. In the present study, the influence of phase difference and Strouhal number on the vectoring behaviour is examined experimentally. Phase-locked vorticity fields, measured using Particle Image Velocimetry (PIV), are used to track vortex pairs. The physical mechanisms that explain the diversity in vectoring behaviour are observed based on the vortex trajectories. For a fixed phase difference, the vectoring behaviour is shown to be primarily influenced by pinch-off time of vortex rings generated by the synthetic jets. Beyond a certain formation number, the pinch-off timescale becomes invariant. In this region, the vectoring behaviour is determined by the distance between subsequent vortex rings. We acknowledge the financial support from the European Research Council (ERC grant agreement no. 277472).

Convoys of social support in Mexico: Examining socio-demographic variation

PubMed Central

Fuller-Iglesias, Heather R.; Antonucci, Toni

2015-01-01

The Convoy Model suggests that at different stages of the lifespan the makeup of the social support network varies in step with developmental and contextual needs. Cultural norms may shape the makeup of social convoys as well as denote socio-demographic differences in social support. This study examines the social convoys of adults in Mexico. Specifically, it examines whether social network structure varies by age, gender, and education level, thus addressing the paucity of research on interpersonal relations in Mexico. A sample of 1,202 adults (18–99 years of age) was drawn from the Study of Social Relations and Well-being in Mexico. Hierarchical regression analyses indicated older adults had larger, more geographically proximate networks with a greater proportion of kin but less frequent contact. Women had larger, less geographically proximate networks with less frequent contact. Less educated individuals had smaller, more geographically proximate networks with more frequent contact and a greater proportion of kin. Age moderated gender and education effects indicated that younger women have more diverse networks and less educated older adults have weaker social ties. This study highlights socio-demographic variation in social convoys within the Mexican context, and suggests implications for fostering intergenerational relationships, policy, and interventions. Future research on Mexican convoys should further explore sources of support, and specifically address implications for well-being. PMID:27340310

Power line identification of millimeter wave radar based on PCA-GS-SVM

NASA Astrophysics Data System (ADS)

Fang, Fang; Zhang, Guifeng; Cheng, Yansheng

2017-12-01

Aiming at the problem that the existing detection method can not effectively solve the security of UAV's ultra low altitude flight caused by power line, a power line recognition method based on grid search (GS) and the principal component analysis and support vector machine (PCA-SVM) is proposed. Firstly, the candidate line of Hough transform is reduced by PCA, and the main feature of candidate line is extracted. Then, upport vector machine (SVM is) optimized by grid search method (GS). Finally, using support vector machine classifier optimized parameters to classify the candidate line. MATLAB simulation results show that this method can effectively identify the power line and noise, and has high recognition accuracy and algorithm efficiency.

Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

PubMed Central

Li, Pengfei; Jiang, Yongying; Xiang, Jiawei

2014-01-01

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples. PMID:24688361

Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires.

PubMed

Cinelli, Mattia; Sun, Yuxin; Best, Katharine; Heather, James M; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-04-01

Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone. The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases. The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund's Adjuvant. The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893 . The Decombinator package is available at github.com/innate2adaptive/Decombinator . The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html . b.chain@ucl.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

The quantitative role of flexor sheath incision in correcting Dupuytren proximal interphalangeal joint contractures.

PubMed

Blazar, P E; Floyd, E W; Earp, B E

2016-07-01

Controversy exists regarding intra-operative treatment of residual proximal interphalangeal joint contractures after Dupuytren's fasciectomy. We test the hypothesis that a simple release of the digital flexor sheath can correct residual fixed flexion contracture after subtotal fasciectomy. We prospectively enrolled 19 patients (22 digits) with Dupuytren's contracture of the proximal interphalangeal joint. The average pre-operative extension deficit of the proximal interphalangeal joints was 58° (range 30-90). The flexion contracture of the joint was corrected to an average of 28° after fasciectomy. In most digits (20 of 21), subsequent incision of the flexor sheath further corrected the contracture by an average of 23°, resulting in correction to an average flexion contracture of 4.7° (range 0-40). Our results support that contracture of the tendon sheath is a contributor to Dupuytren's contracture of the joint and that sheath release is a simple, low morbidity addition to correct Dupuytren's contractures of the proximal interphalangeal joint. Additional release of the proximal interphalangeal joint after fasciectomy, after release of the flexor sheath, is not necessary in many patients. IV (Case Series, Therapeutic). © The Author(s) 2015.

Earth observation in support of malaria control and epidemiology: MALAREO monitoring approaches.

PubMed

Franke, Jonas; Gebreslasie, Michael; Bauwens, Ides; Deleu, Julie; Siegert, Florian

2015-06-03

Malaria affects about half of the world's population, with the vast majority of cases occuring in Africa. National malaria control programmes aim to reduce the burden of malaria and its negative, socioeconomic effects by using various control strategies (e.g. vector control, environmental management and case tracking). Vector control is the most effective transmission prevention strategy, while environmental factors are the key parameters affecting transmission. Geographic information systems (GIS), earth observation (EO) and spatial modelling are increasingly being recognised as valuable tools for effective management and malaria vector control. Issues previously inhibiting the use of EO in epidemiology and malaria control such as poor satellite sensor performance, high costs and long turnaround times, have since been resolved through modern technology. The core goal of this study was to develop and implement the capabilities of EO data for national malaria control programmes in South Africa, Swaziland and Mozambique. High- and very high resolution (HR and VHR) land cover and wetland maps were generated for the identification of potential vector habitats and human activities, as well as geoinformation on distance to wetlands for malaria risk modelling, population density maps, habitat foci maps and VHR household maps. These products were further used for modelling malaria incidence and the analysis of environmental factors that favour vector breeding. Geoproducts were also transferred to the staff of national malaria control programmes in seven African countries to demonstrate how EO data and GIS can support vector control strategy planning and monitoring. The transferred EO products support better epidemiological understanding of environmental factors related to malaria transmission, and allow for spatio-temporal targeting of malaria control interventions, thereby improving the cost-effectiveness of interventions.

Seminal quality prediction using data mining methods.

PubMed

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of fertility rate. In this paper, eight feature selection methods are applied on fertility dataset to find out a set of good features. The investigational results shows that childish diseases (0.079) and high fever features (0.057) has less impact on fertility rate while age (0.8685), season (0.843), surgical intervention (0.7683), alcohol consumption (0.5992), smoking habit (0.575), number of hours spent on setting (0.4366) and accident (0.5973) features have more impact. It is also observed that feature selection methods increase the accuracy of above mentioned techniques (multilayer perceptron 92%, support vector machine 91%, SVM+PSO 94%, Navie Bayes (Kernel) 89% and decision tree 89%) as compared to without feature selection methods (multilayer perceptron 86%, support vector machine 86%, SVM+PSO 85%, Navie Bayes (Kernel) 83% and decision tree 84%) which shows the applicability of feature selection methods in prediction. This paper lightens the application of artificial techniques in medical domain. From this paper, it can be concluded that data mining methods can be used to predict a person with or without disease based on environmental and lifestyle parameters/features rather than undergoing various medical test. In this paper, five data mining techniques are used to predict the fertility rate and among which SVM+PSO provide more accurate results than support vector machine and decision tree.

A Collaborative Framework for Distributed Privacy-Preserving Support Vector Machine Learning

PubMed Central

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates “privacy-insensitive” intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner. PMID:23304414

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

PubMed

Alcaide-Leon, P; Dufort, P; Geraldo, A F; Alshafai, L; Maralani, P J; Spears, J; Bharatha, A

2017-06-01

Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( P = .021), reader 2 ( P = .035), and reader 3 ( P = .007). Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma. © 2017 by American Journal of Neuroradiology.

Effect of DSS on Bacterial Growth in Gastrointestinal Tract.

PubMed

Hlinková, J; Svobodová, H; Brachtlová, T; Gardlík, R

2016-01-01

Inflammatory bowel disease is an idiopathic autoimmune disorder that is mainly divided into ulcerative colitis and Crohn's disease. Probiotics are known for their beneficial effect and used as a treatment option in different gastrointestinal problems. The aim of our study was to find suitable bacterial vectors for gene therapy of inflammatory bowel disease. Salmonella enterica serovar Typhimurium SL7207 and Escherichia coli Nissle 1917 were investigated as potential vectors. Our results show that the growth of Escherichia coli Nissle 1917 was inhibited in the majority of samples collected from dextran sodium sulphate-treated animals compared with control growth in phosphate-buffered saline. The growth of Salmonella enterica serovar Typhimurium SL7207 in all investigated samples was enhanced or unaffected in comparison with phosphate-buffered saline; however, it did not reach the growth rates of Escherichia coli Nissle 1917. Dextran sodium sulphate treatment had a stimulating effect on the growth of both strains in homogenates of distant small intestine and proximal colon samples. The gastrointestinal tract contents and tissue homogenates did not inhibit growth of Salmonella enterica serovar Typhimurium SL7207 in comparison with the negative control, and provided more suitable environment for growth compared to Escherichia coli Nissle 1917. We therefore conclude that Salmonella enterica serovar Typhimurium SL7207 is a more suitable candidate for a potential bacterial vector, even though it has no known probiotic properties.

River-specific macrogenomic diversity in Simulium guianense s. l. (Diptera: Simuliidae), a complex of tropical American vectors associated with human onchocerciasis

PubMed Central

Hamada, Neusa; Cavalcante do Nascimento, Jeane Marcelle; Grillet, Maria Eugenia

2017-01-01

Simulium guianense Wise is a Latin American vector complex of black flies associated with transmission of the causal agent of human onchocerciasis (river blindness). An analysis of the chromosomal banding patterns of 607 larvae of S. guianense s. l. revealed a high level of variation involving 83 macrogenomic rearrangements across 25 populations in Brazil, French Guiana, and Venezuela. The 25 populations were assigned to 13 cytoforms (A1, A2, B1–B4, C, D, E1–E4, and F), some of which are probably valid species. Based on geographical proximity, a member of the B group of cytoforms probably represents the name-bearing type specimen of S. guianense and the primary vector in the last-remaining onchocerciasis foci in the Western Hemisphere. Cytoform B3 in Amapá State is implicated as an anthropophilic simuliid in an area currently and historically free of onchocerciasis. Distributions of cytoforms are associated with geography, elevation, and drainage basin, and are largely congruent with ecoregions. Despite extraordinarily large larval populations of S. guianense s. l. in big rivers and consequent production of female flies for dispersal, the cytoforms maintain their chromosomal distinction within individual rivers, suggesting a high degree of fidelity to the specialized breeding habitats—rocky shoals—of the natal rivers. PMID:28727841

River-specific macrogenomic diversity in Simulium guianense s. l. (Diptera: Simuliidae), a complex of tropical American vectors associated with human onchocerciasis.

PubMed

Adler, Peter H; Hamada, Neusa; Cavalcante do Nascimento, Jeane Marcelle; Grillet, Maria Eugenia

2017-01-01

Simulium guianense Wise is a Latin American vector complex of black flies associated with transmission of the causal agent of human onchocerciasis (river blindness). An analysis of the chromosomal banding patterns of 607 larvae of S. guianense s. l. revealed a high level of variation involving 83 macrogenomic rearrangements across 25 populations in Brazil, French Guiana, and Venezuela. The 25 populations were assigned to 13 cytoforms (A1, A2, B1-B4, C, D, E1-E4, and F), some of which are probably valid species. Based on geographical proximity, a member of the B group of cytoforms probably represents the name-bearing type specimen of S. guianense and the primary vector in the last-remaining onchocerciasis foci in the Western Hemisphere. Cytoform B3 in Amapá State is implicated as an anthropophilic simuliid in an area currently and historically free of onchocerciasis. Distributions of cytoforms are associated with geography, elevation, and drainage basin, and are largely congruent with ecoregions. Despite extraordinarily large larval populations of S. guianense s. l. in big rivers and consequent production of female flies for dispersal, the cytoforms maintain their chromosomal distinction within individual rivers, suggesting a high degree of fidelity to the specialized breeding habitats-rocky shoals-of the natal rivers.

Numerical assessment of low-frequency dosimetry from sampled magnetic fields

NASA Astrophysics Data System (ADS)

Freschi, Fabio; Giaccone, Luca; Cirimele, Vincenzo; Canova, Aldo

2018-01-01

Low-frequency dosimetry is commonly assessed by evaluating the electric field in the human body using the scalar potential finite difference method. This method is effective only when the sources of the magnetic field are completely known and the magnetic vector potential can be analytically computed. The aim of the paper is to present a rigorous method to characterize the source term when only the magnetic flux density is available at discrete points, e.g. in case of field measurements. The method is based on the solution of the discrete magnetic curl equation. The system is restricted to the independent set of magnetic fluxes and circulations of magnetic vector potential using the topological information of the computational mesh. The solenoidality of the magnetic flux density is preserved using a divergence-free interpolator based on vector radial basis functions. The analysis of a benchmark problem shows that the complexity of the proposed algorithm is linearly dependent on the number of elements with a controllable accuracy. The method proposed in this paper also proves to be useful and effective when applied to a real world scenario, where the magnetic flux density is measured in proximity of a power transformer. A 8 million voxel body model is then used for the numerical dosimetric analysis. The complete assessment is completed in less than 5 min, that is more than acceptable for these problems.

Numerical assessment of low-frequency dosimetry from sampled magnetic fields.

PubMed

Freschi, Fabio; Giaccone, Luca; Cirimele, Vincenzo; Canova, Aldo

2017-12-29

Low-frequency dosimetry is commonly assessed by evaluating the electric field in the human body using the scalar potential finite difference method. This method is effective only when the sources of the magnetic field are completely known and the magnetic vector potential can be analytically computed. The aim of the paper is to present a rigorous method to characterize the source term when only the magnetic flux density is available at discrete points, e.g. in case of field measurements. The method is based on the solution of the discrete magnetic curl equation. The system is restricted to the independent set of magnetic fluxes and circulations of magnetic vector potential using the topological information of the computational mesh. The solenoidality of the magnetic flux density is preserved using a divergence-free interpolator based on vector radial basis functions. The analysis of a benchmark problem shows that the complexity of the proposed algorithm is linearly dependent on the number of elements with a controllable accuracy. The method proposed in this paper also proves to be useful and effective when applied to a real world scenario, where the magnetic flux density is measured in proximity of a power transformer. A 8 million voxel body model is then used for the numerical dosimetric analysis. The complete assessment is completed in less than 5 min, that is more than acceptable for these problems.

Enhanced Co-Worker Social Support in Isolated Work Groups and Its Mitigating Role on the Work-Family Conflict-Depression Loss Spiral.

PubMed

McTernan, Wesley P; Dollard, Maureen F; Tuckey, Michelle R; Vandenberg, Robert J

2016-03-29

This paper examines a loss spiral model (i.e., reciprocal relationships) between work-family conflict and depression, moderated by co-worker support. We expected that the moderation effect due to co-worker support would be evident among those working in isolation (i.e., mining workers) due to a greater level of intragroup attraction and saliency attributable to the proximity effects. We used a two wave panel study and data from a random population sample of Australian employees (n = 2793, [n = 112 mining, n = 2681 non-mining]). Using structural equation modelling we tested the reciprocal three way interaction effects. In line with our theory, co-worker support buffered the reciprocal relationship between WFC and depression, showing a protective effect in both pathways. These moderation effects were found in the mining industry only suggesting a proximity component moderates the social support buffer hypothesis (i.e., a three way interaction effect). The present paper integrates previous theoretical perspectives of stress and support, and provides insight into the changing dynamics of workplace relationships.

Enhanced Co-Worker Social Support in Isolated Work Groups and Its Mitigating Role on the Work-Family Conflict-Depression Loss Spiral

PubMed Central

McTernan, Wesley P.; Dollard, Maureen F.; Tuckey, Michelle R.; Vandenberg, Robert J.

2016-01-01

This paper examines a loss spiral model (i.e., reciprocal relationships) between work-family conflict and depression, moderated by co-worker support. We expected that the moderation effect due to co-worker support would be evident among those working in isolation (i.e., mining workers) due to a greater level of intragroup attraction and saliency attributable to the proximity effects. We used a two wave panel study and data from a random population sample of Australian employees (n = 2793, [n = 112 mining, n = 2681 non-mining]). Using structural equation modelling we tested the reciprocal three way interaction effects. In line with our theory, co-worker support buffered the reciprocal relationship between WFC and depression, showing a protective effect in both pathways. These moderation effects were found in the mining industry only suggesting a proximity component moderates the social support buffer hypothesis (i.e., a three way interaction effect). The present paper integrates previous theoretical perspectives of stress and support, and provides insight into the changing dynamics of workplace relationships. PMID:27043592

Preparation for a first-in-man lentivirus trial in patients with cystic fibrosis

PubMed Central

Alton, Eric W F W; Beekman, Jeffery M; Boyd, A Christopher; Brand, June; Carlon, Marianne S; Connolly, Mary M; Chan, Mario; Conlon, Sinead; Davidson, Heather E; Davies, Jane C; Davies, Lee A; Dekkers, Johanna F; Doherty, Ann; Gea-Sorli, Sabrina; Gill, Deborah R; Griesenbach, Uta; Hasegawa, Mamoru; Higgins, Tracy E; Hironaka, Takashi; Hyndman, Laura; McLachlan, Gerry; Inoue, Makoto; Hyde, Stephen C; Innes, J Alastair; Maher, Toby M; Moran, Caroline; Meng, Cuixiang; Paul-Smith, Michael C; Pringle, Ian A; Pytel, Kamila M; Rodriguez-Martinez, Andrea; Schmidt, Alexander C; Stevenson, Barbara J; Sumner-Jones, Stephanie G; Toshner, Richard; Tsugumine, Shu; Wasowicz, Marguerite W; Zhu, Jie

2017-01-01

We have recently shown that non-viral gene therapy can stabilise the decline of lung function in patients with cystic fibrosis (CF). However, the effect was modest, and more potent gene transfer agents are still required. Fuson protein (F)/Hemagglutinin/Neuraminidase protein (HN)-pseudotyped lentiviral vectors are more efficient for lung gene transfer than non-viral vectors in preclinical models. In preparation for a first-in-man CF trial using the lentiviral vector, we have undertaken key translational preclinical studies. Regulatory-compliant vectors carrying a range of promoter/enhancer elements were assessed in mice and human air–liquid interface (ALI) cultures to select the lead candidate; cystic fibrosis transmembrane conductance receptor (CFTR) expression and function were assessed in CF models using this lead candidate vector. Toxicity was assessed and ‘benchmarked’ against the leading non-viral formulation recently used in a Phase IIb clinical trial. Integration site profiles were mapped and transduction efficiency determined to inform clinical trial dose-ranging. The impact of pre-existing and acquired immunity against the vector and vector stability in several clinically relevant delivery devices was assessed. A hybrid promoter hybrid cytosine guanine dinucleotide (CpG)- free CMV enhancer/elongation factor 1 alpha promoter (hCEF) consisting of the elongation factor 1α promoter and the cytomegalovirus enhancer was most efficacious in both murine lungs and human ALI cultures (both at least 2-log orders above background). The efficacy (at least 14% of airway cells transduced), toxicity and integration site profile supports further progression towards clinical trial and pre-existing and acquired immune responses do not interfere with vector efficacy. The lead rSIV.F/HN candidate expresses functional CFTR and the vector retains 90–100% transduction efficiency in clinically relevant delivery devices. The data support the progression of the F/HN-pseudotyped lentiviral vector into a first-in-man CF trial in 2017. PMID:27852956

An optical approach to proximity-operations communications for Space Station Freedom

NASA Technical Reports Server (NTRS)

Marshalek, Robert G.

1991-01-01

An optical communications system is described that supports bi-directional interconnections between Space Station Freedom (SSF) and a host of attached and co-orbiting platforms. These proximity-operations (Prox-Ops) platforms are categorized by their maximum distance from SSF, with several remaining inside 1-km range and several extending out to 37-km and 2000-km ranges in the initial and growth phases, respectively. Two distinct Prox-Ops optical terminals are described. A 1-cm-aperture system is used on the short-range platforms to reduce payload mass, and a 10-cm-aperture system is used on the long-range platforms and on SSF to support the optical link budgets. The system supports up to four simultaneous user links, by assigning wavelengths to the various platforms and by using separate SSF terminals for each link.

Recognition and Classification of Road Condition on the Basis of Friction Force by Using a Mobile Robot

NASA Astrophysics Data System (ADS)

Watanabe, Tatsuhito; Katsura, Seiichiro

A person operating a mobile robot in a remote environment receives realistic visual feedback about the condition of the road on which the robot is moving. The categorization of the road condition is necessary to evaluate the conditions for safe and comfortable driving. For this purpose, the mobile robot should be capable of recognizing and classifying the condition of the road surfaces. This paper proposes a method for recognizing the type of road surfaces on the basis of the friction between the mobile robot and the road surfaces. This friction is estimated by a disturbance observer, and a support vector machine is used to classify the surfaces. The support vector machine identifies the type of the road surface using feature vector, which is determined using the arithmetic average and variance derived from the torque values. Further, these feature vectors are mapped onto a higher dimensional space by using a kernel function. The validity of the proposed method is confirmed by experimental results.

Protein Kinase Classification with 2866 Hidden Markov Models and One Support Vector Machine

NASA Technical Reports Server (NTRS)

Weber, Ryan; New, Michael H.; Fonda, Mark (Technical Monitor)

2002-01-01

The main application considered in this paper is predicting true kinases from randomly permuted kinases that share the same length and amino acid distributions as the true kinases. Numerous methods already exist for this classification task, such as HMMs, motif-matchers, and sequence comparison algorithms. We build on some of these efforts by creating a vector from the output of thousands of structurally based HMMs, created offline with Pfam-A seed alignments using SAM-T99, which then must be combined into an overall classification for the protein. Then we use a Support Vector Machine for classifying this large ensemble Pfam-Vector, with a polynomial and chisquared kernel. In particular, the chi-squared kernel SVM performs better than the HMMs and better than the BLAST pairwise comparisons, when predicting true from false kinases in some respects, but no one algorithm is best for all purposes or in all instances so we consider the particular strengths and weaknesses of each.

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

DTIC Science & Technology

2014-03-27

and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth...International Workshop on Frontiers in Handwriting Recognition, pp. 49–54, IEEE, 2002. [12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine

Algorithm for detection the QRS complexes based on support vector machine

NASA Astrophysics Data System (ADS)

Van, G. V.; Podmasteryev, K. V.

2017-11-01

The efficiency of computer ECG analysis depends on the accurate detection of QRS-complexes. This paper presents an algorithm for QRS complex detection based of support vector machine (SVM). The proposed algorithm is evaluated on annotated standard databases such as MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity Se = 98.32% and specificity Sp = 95.46% for MIT-BIH Arrhythmia database. This algorithm can be used as the basis for the software to diagnose electrical activity of the heart.

The CCAAT box in the proximal SERCA2 gene promoter regulates basal and stress-induced transcription in cardiomyocytes.

PubMed

Fragoso-Medina, Jorge; Rodriguez, Gabriela; Zarain-Herzberg, Angel

2018-05-01

The cardiac sarco/endoplasmic reticulum Ca 2+ -ATPase-2a (SERCA2a) is vital for the correct handling of calcium concentration in cardiomyocytes. Recent studies showed that the induction of endoplasmic reticulum (ER) stress (ERS) with the SERCA2 inhibitor Thapsigargin (Tg) increases the mRNA and protein levels of SERCA2a. The SERCA2 gene promoter contains an ERS response element (ERSE) at position -78 bp that is conserved among species and might transcriptionally regulate SERCA2 gene expression. However, its involvement in SERCA2 basal and calcium-mediated transcriptional activation has not been elucidated. In this work, we show that in cellular cultures of neonatal rat ventricular myocytes, the treatment with Tg or the calcium ionophore A23187 increases the SERCA2a mRNA and protein abundance, as well as the transcriptional activity of two chimeric human SERCA2 gene constructs, containing -254 and -2579 bp of 5'-regulatory region cloned in the pGL3-basic vector and transiently transfected in cultured cardiomyocytes. We found that the ERSE present in the SERCA2 proximal promoter contains a CCAAT box that is involved in basal and ERS-mediated hSERCA2 transcriptional activation. The EMSA results showed that the CCAAT box present in the ERSE recruits the NF-Y transcription factor. Additionally, by ChIP assays, we confirmed in vivo binding of NF-Y and C/EBPβ transcription factors to the SERCA2 gene proximal promoter.

A portable approach for PIC on emerging architectures

NASA Astrophysics Data System (ADS)

Decyk, Viktor

2016-03-01

A portable approach for designing Particle-in-Cell (PIC) algorithms on emerging exascale computers, is based on the recognition that 3 distinct programming paradigms are needed. They are: low level vector (SIMD) processing, middle level shared memory parallel programing, and high level distributed memory programming. In addition, there is a memory hierarchy associated with each level. Such algorithms can be initially developed using vectorizing compilers, OpenMP, and MPI. This is the approach recommended by Intel for the Phi processor. These algorithms can then be translated and possibly specialized to other programming models and languages, as needed. For example, the vector processing and shared memory programming might be done with CUDA instead of vectorizing compilers and OpenMP, but generally the algorithm itself is not greatly changed. The UCLA PICKSC web site at http://www.idre.ucla.edu/ contains example open source skeleton codes (mini-apps) illustrating each of these three programming models, individually and in combination. Fortran2003 now supports abstract data types, and design patterns can be used to support a variety of implementations within the same code base. Fortran2003 also supports interoperability with C so that implementations in C languages are also easy to use. Finally, main codes can be translated into dynamic environments such as Python, while still taking advantage of high performing compiled languages. Parallel languages are still evolving with interesting developments in co-Array Fortran, UPC, and OpenACC, among others, and these can also be supported within the same software architecture. Work supported by NSF and DOE Grants.

Investigating DNA-, RNA-, and protein-based features as a means to discriminate pathogenic synonymous variants.

PubMed

Livingstone, Mark; Folkman, Lukas; Yang, Yuedong; Zhang, Ping; Mort, Matthew; Cooper, David N; Liu, Yunlong; Stantic, Bela; Zhou, Yaoqi

2017-10-01

Synonymous single-nucleotide variants (SNVs), although they do not alter the encoded protein sequences, have been implicated in many genetic diseases. Experimental studies indicate that synonymous SNVs can lead to changes in the secondary and tertiary structures of DNA and RNA, thereby affecting translational efficiency, cotranslational protein folding as well as the binding of DNA-/RNA-binding proteins. However, the importance of these various features in disease phenotypes is not clearly understood. Here, we have built a support vector machine (SVM) model (termed DDIG-SN) as a means to discriminate disease-causing synonymous variants. The model was trained and evaluated on nearly 900 disease-causing variants. The method achieves robust performance with the area under the receiver operating characteristic curve of 0.84 and 0.85 for protein-stratified 10-fold cross-validation and independent testing, respectively. We were able to show that the disease-causing effects in the immediate proximity to exon-intron junctions (1-3 bp) are driven by the loss of splicing motif strength, whereas the gain of splicing motif strength is the primary cause in regions further away from the splice site (4-69 bp). The method is available as a part of the DDIG server at http://sparks-lab.org/ddig. © 2017 Wiley Periodicals, Inc.

Phylogenetic convolutional neural networks in metagenomics.

PubMed

Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare

2018-03-08

Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

A neural network detection model of spilled oil based on the texture analysis of SAR image

NASA Astrophysics Data System (ADS)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

A shape-based inter-layer contours correspondence method for ICT-based reverse engineering

PubMed Central

Duan, Liming; Yang, Shangpeng; Zhang, Gui; Feng, Fei; Gu, Minghui

2017-01-01

The correspondence of a stack of planar contours in ICT (industrial computed tomography)-based reverse engineering, a key step in surface reconstruction, is difficult when the contours or topology of the object are complex. Given the regularity of industrial parts and similarity of the inter-layer contours, a specialized shape-based inter-layer contours correspondence method for ICT-based reverse engineering was presented to solve the above problem based on the vectorized contours. In this paper, the vectorized contours extracted from the slices consist of three graphical primitives: circles, arcs and segments. First, the correspondence of the inter-layer primitives is conducted based on the characteristics of the primitives. Second, based on the corresponded primitives, the inter-layer contours correspond with each other using the proximity rules and exhaustive search. The proposed method can make full use of the shape information to handle industrial parts with complex structures. The feasibility and superiority of this method have been demonstrated via the related experiments. This method can play an instructive role in practice and provide a reference for the related research. PMID:28489867

A shape-based inter-layer contours correspondence method for ICT-based reverse engineering.

PubMed

Duan, Liming; Yang, Shangpeng; Zhang, Gui; Feng, Fei; Gu, Minghui

2017-01-01

The correspondence of a stack of planar contours in ICT (industrial computed tomography)-based reverse engineering, a key step in surface reconstruction, is difficult when the contours or topology of the object are complex. Given the regularity of industrial parts and similarity of the inter-layer contours, a specialized shape-based inter-layer contours correspondence method for ICT-based reverse engineering was presented to solve the above problem based on the vectorized contours. In this paper, the vectorized contours extracted from the slices consist of three graphical primitives: circles, arcs and segments. First, the correspondence of the inter-layer primitives is conducted based on the characteristics of the primitives. Second, based on the corresponded primitives, the inter-layer contours correspond with each other using the proximity rules and exhaustive search. The proposed method can make full use of the shape information to handle industrial parts with complex structures. The feasibility and superiority of this method have been demonstrated via the related experiments. This method can play an instructive role in practice and provide a reference for the related research.

Could the Chagas disease elimination programme in Venezuela be compromised by reinvasion of houses by sylvatic Rhodnius prolixus bug populations?

PubMed

Sanchez-Martin, Maria J; Feliciangeli, M Dora; Campbell-Lendrum, Diarmid; Davies, Clive R

2006-10-01

The Andean Pact Initiative (1997) committed Andean countries to eliminate vectorial transmission of Chagas disease by 2010 via widespread residual insecticide spraying. In Venezuela, this aim could be compromised by reinvasion of houses by palm tree populations of the major vector Rhodnius prolixus. To test this hypothesis, a multivariate logistic regression was undertaken of risk factors for triatomine infestation and colonization in 552 houses and 1068 peri-domestic outbuildings in Barinas State. After adjusting for other risk factors, including palm roofs, R. prolixus infestation and colonization of outbuildings (and, to some extent, houses) was significantly associated with proximity to high densities of Attalea butyracea palm trees. House infestation and/or colonization was also positively associated with bug density in peri-domestic outbuildings, the presence of pigsties and nests. Hence, R. prolixus populations in ineffectively sprayed outbuildings could also provide an important source of house re-infestations. The secondary vector Triatoma maculata was mainly found associated with the presence of hens nesting both indoors and outdoors.

Changes in species richness and spatial distribution of mosquitoes (Diptera: Culicidae) inferred from museum specimen records and a recent inventory: a case study from Belgium suggests recent expanded distribution of arbovirus and malaria vectors.

PubMed

Dekoninck, W; Hendrickx, F; Versteirt, V; Coosemans, M; De Clercq, E M; Hendrickx, G; Hance, T; Grootaert, P

2013-03-01

Mosquito (Diptera: Culicidae) distribution data from a recent inventory of native and invading mosquito species in Belgium were compared with historical data from the period 1900-1960 that were retrieved from a revision of the Belgian Culicidae collection at the Royal Belgian Institute of Natural Sciences. Both data sets were used to investigate trends in mosquito species richness in several regions in Belgium. The relative change in distribution area of mosquito species was particularly important for species that use waste waters and used tires as larval habitats and species that recently shifted their larval habitat to artificial larval habitats. More importantly, several of these species are known as vectors of arboviruses and Plasmodium sp. and the apparent habitat shift of some of them brought these species in proximity to humans. Similar studies comparing current mosquito richness with former distribution data retrieved from voucher specimens from collections is therefore encouraged because they can generate important information concerning health risk assessment at both regional and national scale.

Lightweight Filter Architecture for Energy Efficient Mobile Vehicle Localization Based on a Distributed Acoustic Sensor Network

PubMed Central

Kim, Keonwook

2013-01-01

The generic properties of an acoustic signal provide numerous benefits for localization by applying energy-based methods over a deployed wireless sensor network (WSN). However, the signal generated by a stationary target utilizes a significant amount of bandwidth and power in the system without providing further position information. For vehicle localization, this paper proposes a novel proximity velocity vector estimator (PVVE) node architecture in order to capture the energy from a moving vehicle and reject the signal from motionless automobiles around the WSN node. A cascade structure between analog envelope detector and digital exponential smoothing filter presents the velocity vector-sensitive output with low analog circuit and digital computation complexity. The optimal parameters in the exponential smoothing filter are obtained by analytical and mathematical methods for maximum variation over the vehicle speed. For stationary targets, the derived simulation based on the acoustic field parameters demonstrates that the system significantly reduces the communication requirements with low complexity and can be expected to extend the operation time considerably. PMID:23979482

The DTW-based representation space for seismic pattern classification

NASA Astrophysics Data System (ADS)

Orozco-Alzate, Mauricio; Castro-Cabrera, Paola Alexandra; Bicego, Manuele; Londoño-Bonilla, John Makario

2015-12-01

Distinguishing among the different seismic volcanic patterns is still one of the most important and labor-intensive tasks for volcano monitoring. This task could be lightened and made free from subjective bias by using automatic classification techniques. In this context, a core but often overlooked issue is the choice of an appropriate representation of the data to be classified. Recently, it has been suggested that using a relative representation (i.e. proximities, namely dissimilarities on pairs of objects) instead of an absolute one (i.e. features, namely measurements on single objects) is advantageous to exploit the relational information contained in the dissimilarities to derive highly discriminant vector spaces, where any classifier can be used. According to that motivation, this paper investigates the suitability of a dynamic time warping (DTW) dissimilarity-based vector representation for the classification of seismic patterns. Results show the usefulness of such a representation in the seismic pattern classification scenario, including analyses of potential benefits from recent advances in the dissimilarity-based paradigm such as the proper selection of representation sets and the combination of different dissimilarity representations that might be available for the same data.

Tenofovir Nephrotoxicity: 2011 Update

PubMed Central

Fernandez-Fernandez, Beatriz; Montoya-Ferrer, Ana; Sanz, Ana B.; Sanchez-Niño, Maria D.; Izquierdo, Maria C.; Poveda, Jonay; Sainz-Prestel, Valeria; Ortiz-Martin, Natalia; Parra-Rodriguez, Alejandro; Selgas, Rafael; Ruiz-Ortega, Marta; Egido, Jesus; Ortiz, Alberto

2011-01-01

Tenofovir is an acyclic nucleotide analogue reverse-transcriptase inhibitor structurally similar to the nephrotoxic drugs adefovir and cidofovir. Tenofovir is widely used to treat HIV infection and approved for treatment of hepatitis B virus. Despite initial cell culture and clinical trials results supporting the renal safety of tenofovir, its clinical use is associated with a low, albeit significant, risk of kidney injury. Proximal tubular cell secretion of tenofovir explains the accumulation of the drug in these mitochondria-rich cells. Tenofovir nephrotoxicity is characterized by proximal tubular cell dysfunction that may be associated with acute kidney injury or chronic kidney disease. Withdrawal of the drug leads to improvement of analytical parameters that may be partial. Understanding the risk factors for nephrotoxicity and regular monitoring of proximal tubular dysfunction and serum creatinine in high-risk patients is required to minimize nephrotoxicity. Newer, structurally similar molecular derivatives that do not accumulate in proximal tubules are under study. PMID:21716719

The Rendezvous Monitoring Display Capabilities of the Rendezvous and Proximity Operations Program

NASA Technical Reports Server (NTRS)

Brazzel, Jack; Spehar, Pete; Clark, Fred; Foster, Chris; Eldridge, Erin

2013-01-01

The Rendezvous and Proximity Operations Program (RPOP) is a laptop computer- based relative navigation tool and piloting aid that was developed during the Space Shuttle program. RPOP displays a graphical representation of the relative motion between the target and chaser vehicles in a rendezvous, proximity operations and capture scenario. After being used in over 60 Shuttle rendezvous missions, some of the RPOP display concepts have become recognized as a minimum standard for cockpit displays for monitoring the rendezvous task. To support International Space Station (ISS) based crews in monitoring incoming visiting vehicles, RPOP has been modified to allow crews to compare the Cygnus visiting vehicle s onboard navigated state to processed range measurements from an ISS-based, crew-operated Hand Held Lidar sensor. This paper will discuss the display concepts of RPOP that have proven useful in performing and monitoring rendezvous and proximity operations.

First experience of vectorizing electromagnetic physics models for detector simulation

NASA Astrophysics Data System (ADS)

Amadio, G.; Apostolakis, J.; Bandieramonte, M.; Bianchini, C.; Bitzes, G.; Brun, R.; Canal, P.; Carminati, F.; de Fine Licht, J.; Duhem, L.; Elvira, D.; Gheata, A.; Jun, S. Y.; Lima, G.; Novak, M.; Presbyterian, M.; Shadura, O.; Seghal, R.; Wenzel, S.

2015-12-01

The recent emergence of hardware architectures characterized by many-core or accelerated processors has opened new opportunities for concurrent programming models taking advantage of both SIMD and SIMT architectures. The GeantV vector prototype for detector simulations has been designed to exploit both the vector capability of mainstream CPUs and multi-threading capabilities of coprocessors including NVidia GPUs and Intel Xeon Phi. The characteristics of these architectures are very different in terms of the vectorization depth, parallelization needed to achieve optimal performance or memory access latency and speed. An additional challenge is to avoid the code duplication often inherent to supporting heterogeneous platforms. In this paper we present the first experience of vectorizing electromagnetic physics models developed for the GeantV project.

Vector-Based Data Services for NASA Earth Science

NASA Astrophysics Data System (ADS)

Rodriguez, J.; Roberts, J. T.; Ruvane, K.; Cechini, M. F.; Thompson, C. K.; Boller, R. A.; Baynes, K.

2016-12-01

Vector data sources offer opportunities for mapping and visualizing science data in a way that allows for more customizable rendering and deeper data analysis than traditional raster images, and popular formats like GeoJSON and Mapbox Vector Tiles allow diverse types of geospatial data to be served in a high-performance and easily consumed-package. Vector data is especially suited to highly dynamic mapping applications and visualization of complex datasets, while growing levels of support for vector formats and features in open-source mapping clients has made utilizing them easier and more powerful than ever. NASA's Global Imagery Browse Services (GIBS) is working to make NASA data more easily and conveniently accessible than ever by serving vector datasets via GeoJSON, Mapbox Vector Tiles, and raster images. This presentation will review these output formats, the services, including WFS, WMS, and WMTS, that can be used to access the data, and some ways in which vector sources can be utilized in popular open-source mapping clients like OpenLayers. Lessons learned from GIBS' recent move towards serving vector will be discussed, as well as how to use GIBS open source software to create, configure, and serve vector data sources using Mapserver and the GIBS OnEarth Apache module.

Evidence that explains absence of a latent period for Xylella fastidiosa in its sharpshooter vectors

USDA-ARS?s Scientific Manuscript database

The glassy-winged sharpshooter (GWSS), Homalodisca vitripennis (Germar), and other sharpshooter (Cicadelline) leafhoppers transmit Xylella fastidiosa (Xf), the causative agent of Pierce’s disease of grapevine and other scorch diseases. Past research has supported that vectors have virtually no late...

Statistical learning algorithms for identifying contrasting tillage practices with landsat thematic mapper data

USDA-ARS?s Scientific Manuscript database

Tillage management practices have direct impact on water holding capacity, evaporation, carbon sequestration, and water quality. This study examines the feasibility of two statistical learning algorithms, such as Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for cla...

Application of Classification Models to Pharyngeal High-Resolution Manometry

ERIC Educational Resources Information Center

Mielens, Jason D.; Hoffman, Matthew R.; Ciucci, Michelle R.; McCulloch, Timothy M.; Jiang, Jack J.

2012-01-01

Purpose: The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Method: Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ), as well as support vector machines (SVM), were…

A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners

DTIC Science & Technology

2013-08-01

best-suited for regression. Our baseline uses z-normalized shallow length features and TF -LOG weighted vectors on bag-of-words for Arabic, Dari...length features and TF -LOG weighted vectors on bag-of-words for Arabic, Dari, English and Pashto. We compare Support Vector Machines and the Margin...football, whereas they are much less common in documents about opera). We used TF -LOG weighted word frequencies on bag-of-words for each document

Proximal Corporal Perforation During Penile Prosthesis Surgery: Prevention, Recognition, and Review of Historical and Novel Management Strategies.

PubMed

Pearlman, Amy M; Terlecki, Ryan P

2018-05-02

Proximal corporal perforation at time of dilation, although rare, may occur due to factors related to patient anatomy, presence of intra-cavernosal fibrosis, and/or surgical technique. To describe tools and techniques designed to prevent and identify proximal corporal perforation, and maneuvers to minimize the risk of subsequent cylinder migration once proximal perforation has been recognized, such that the operation may proceed and result in an acceptable outcome. We discuss tips for prevention, recognition, and management of proximal corporal perforation by presenting a review of the literature as well as our preferences based on a high-volume experience with penile prosthesis surgery. Described techniques aim to minimize risk of cylinder migration in the absence of true proximal repair. Although proximal perforation may be obvious at times, particularly with a sudden loss of resistance during dilation, discrepant corporal measurements and/or dissimilar proximal deflection of the dilator should also increase the index of suspicion. Numerous techniques have been employed to theoretically reduce the risk of cylinder migration in the setting of proximal corporal perforation. These include formal corporal repair (historical), windsock repairs with non-absorbable grafts, absorbable plugs, and suture fixation of the rear tip extender or shod material covering implant tubing. Intra-operative recognition of proximal corporal perforation, coupled with understanding of surgical strategies to minimize the risk of future device migration, may allow completion of an operation that still results in an optimal outcome. Techniques described to prevent proximal migration are not strongly evidence-based, but rooted in logic and supported by high-volume implanters. Intra-operative perforation of the proximal corpora, although rare, can threaten the success of penile implant surgery, though the techniques described herein have been developed to mitigate the potential for subsequent device migration, allowing surgery to proceed and to achieve the desired clinical result. Pearlman AM, Terlecki RP. Proximal Corporal Perforation During Penile Prosthesis Surgery: Prevention, Recognition, and Review of Historical and Novel Management Strategies. J Sex Med 2018;XX:XXX-XXX. Copyright © 2018 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

Additional electrodes on the Quartet™ LV lead provide more programmable pacing options than bipolar and tripolar equivalents.

PubMed

O'Donnell, David; Sperzel, Johannes; Thibault, Bernard; Rinaldi, Christopher A; Pappone, Carlo; Gutleben, Klaus-Jürgen; Leclercq, Christopher; Razavi, Hedi; Ryu, Kyungmoo; Mcspadden, Luke C; Fischer, Avi; Tomassoni, Gery

2017-04-01

The aim of this study was to evaluate any benefits to the number of viable pacing vectors and maximal spatial coverage with quadripolar left ventricular (LV) leads when compared with tripolar and bipolar equivalents in patients receiving cardiac resynchronization therapy (CRT). A meta-analysis of five previously published clinical trials involving the Quartet™ LV lead (St Jude Medical, St Paul, MN, USA) was performed to evaluate the number of viable pacing vectors defined as capture thresholds ≤2.5 V and no phrenic nerve stimulation and maximal spatial coverage of viable vectors in CRT patients at pre-discharge (n = 370) and first follow-up (n = 355). Bipolar and tripolar lead configurations were modelled by systematic elimination of two and one electrode(s), respectively, from the Quartet lead. The Quartet lead with its four pacing electrodes exhibited the greatest number of pacing vectors per patient when compared with the best bipolar and the best tripolar modelled equivalents. Similarly, the Quartet lead provided the highest spatial coverage in terms of the distance between two furthest viable pacing cathodes when compared with the best bipolar and the best tripolar configurations (P < 0.05). Among the three modelled bipolar configurations, the lead configuration with the two most distal electrodes resulted in the highest number of viable pacing vectors. Among the four modelled tripolar configurations, elimination of the second proximal electrode (M3) resulted in the highest number of viable pacing options per patient. There were no significant differences observed between pre-discharge and first follow-up analyses. The Quartet lead with its four electrodes and the capability to pace from four anatomical locations provided the highest number of viable pacing vectors at pre-discharge and first follow-up visits, providing more flexibility in device programming and enabling continuation of CRT in more patients when compared with bipolar and tripolar equivalents. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.

Instrumentation Strategies to Reduce the Risks of Proximal Junctional Kyphosis in Adult Scoliosis: A Detailed Biomechanical Analysis.

PubMed

Aubin, Carl-Eric; Cammarata, Marco; Wang, Xiaoyu; Mac-Thiong, Jean-Marc

2015-05-01

Biomechanical analysis of proximal junctional kyphosis (PJK) through numerical simulations. Assessment of the effect of sagittal alignment, the upper instrumented vertebral level (UIV), and 4 other surgical variables on biomechanical indices related to the PJK risks. Despite retrospective clinical studies, biomechanical analysis of individual parameters associated with PJK is lacking to support instrumentation strategies to reduce the PJK risks. Instrumentations of 6 adult scoliosis cases with different operative strategies were simulated (1,152 simulations). Proximal junctional (PJ) angle and flexion loads were evaluated against the sagittal alignment and the proximal instrumentation level. Instrumenting 1 more proximal vertebra allowed the PJ angle, proximal moment, and force to be reduced by 18%, 25%, and 16%, respectively. Shifting sagittal alignment by 20 mm posteriorly increased the PJ angle and proximal moment by 16% and 22%, and increased the equivalent posterior extensor force by 37%. Bilateral complete facetectomy, posterior ligaments resection, and the combination of the 2 resulted in an increase of the PJ angle (by 10%, 28%, and 53%, respectively), flexion forces (by 4%, 12%, and 22%, respectively), and proximal moments (by 16%, 44%, and 83%, respectively). Transverse process hooks at UIV compared with pedicle screws allowed 26% lower PJ angle and flexion loads. The use of proximal transition rods with proximal diameter reduced from 5.5 to 4 mm slightly reduced PJ angle, flexion force, and moment (less than 8%). The increase in sagittal rod curvature from 10° to 40° increased the PJ angle (from 6% to 19%), flexion force (from 3% to 10%), and moment (from 9% to 27%). Simulated posteriorly shifted sagittal alignment was associated with higher PJK risks, whereas extending instrumentation proximally allowed a lower mechanical risk of PJK. Preserving PJ intervertebral elements and using a more flexible anchorage at UIV help reduce the biomechanical risks of PJK. Copyright © 2015 Scoliosis Research Society. Published by Elsevier Inc. All rights reserved.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Extraction of inland Nypa fruticans (Nipa Palm) using Support Vector Machine

NASA Astrophysics Data System (ADS)

Alberto, R. T.; Serrano, S. C.; Damian, G. B.; Camaso, E. E.; Biagtan, A. R.; Panuyas, N. Z.; Quibuyen, J. S.

2017-09-01

Mangroves are considered as one of the major habitats in coastal ecosystem, providing a lot of economic and ecological services in human society. Nypa fruticans (Nipa palm) is one of the important species of mangroves because of its versatility and uniqueness as halophytic palm. However, nipas are not only adaptable in saline areas, they can also managed to thrive away from the coastline depending on the favorable soil types available in the area. Because of this, mapping of this species are not limited alone in the near shore areas, but in areas where this species are present as well. The extraction process of Nypa fruticans were carried out using the available LiDAR data. Support Vector Machine (SVM) classification process was used to extract nipas in inland areas. The SVM classification process in mapping Nypa fruticans produced high accuracy of 95+%. The Support Vector Machine classification process to extract inland nipas was proven to be effective by utilizing different terrain derivatives from LiDAR data.

Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

NASA Astrophysics Data System (ADS)

Fei, Cheng-Wei; Bai, Guang-Chen

2014-12-01

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

Support vector machine firefly algorithm based optimization of lens system.

PubMed

Shamshirband, Shahaboddin; Petković, Dalibor; Pavlović, Nenad T; Ch, Sudheer; Altameem, Torki A; Gani, Abdullah

2015-01-01

Lens system design is an important factor in image quality. The main aspect of the lens system design methodology is the optimization procedure. Since optimization is a complex, nonlinear task, soft computing optimization algorithms can be used. There are many tools that can be employed to measure optical performance, but the spot diagram is the most useful. The spot diagram gives an indication of the image of a point object. In this paper, the spot size radius is considered an optimization criterion. Intelligent soft computing scheme support vector machines (SVMs) coupled with the firefly algorithm (FFA) are implemented. The performance of the proposed estimators is confirmed with the simulation results. The result of the proposed SVM-FFA model has been compared with support vector regression (SVR), artificial neural networks, and generic programming methods. The results show that the SVM-FFA model performs more accurately than the other methodologies. Therefore, SVM-FFA can be used as an efficient soft computing technique in the optimization of lens system designs.

Using Support Vector Machines to Automatically Extract Open Water Signatures from POLDER Multi-Angle Data Over Boreal Regions

NASA Technical Reports Server (NTRS)

Pierce, J.; Diaz-Barrios, M.; Pinzon, J.; Ustin, S. L.; Shih, P.; Tournois, S.; Zarco-Tejada, P. J.; Vanderbilt, V. C.; Perry, G. L.; Brass, James A. (Technical Monitor)

2002-01-01

This study used Support Vector Machines to classify multiangle POLDER data. Boreal wetland ecosystems cover an estimated 90 x 10(exp 6) ha, about 36% of global wetlands, and are a major source of trace gases emissions to the atmosphere. Four to 20 percent of the global emission of methane to the atmosphere comes from wetlands north of 4 degrees N latitude. Large uncertainties in emissions exist because of large spatial and temporal variation in the production and consumption of methane. Accurate knowledge of the areal extent of open water and inundated vegetation is critical to estimating magnitudes of trace gas emissions. Improvements in land cover mapping have been sought using physical-modeling approaches, neural networks, and active microwave, examples that demonstrate the difficulties of separating open water, inundated vegetation and dry upland vegetation. Here we examine the feasibility of using a support vector machine to classify POLDER data representing open water, inundated vegetation and dry upland vegetation.

Color image segmentation with support vector machines: applications to road signs detection.

PubMed

Cyganek, Bogusław

2008-08-01

In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.

Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.

PubMed

Wang, Hsin-Wei; Lin, Ya-Chi; Pai, Tun-Wen; Chang, Hao-Teng

2011-01-01

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).

Sparse Solutions for Single Class SVMs: A Bi-Criterion Approach

NASA Technical Reports Server (NTRS)

Das, Santanu; Oza, Nikunj C.

2011-01-01

In this paper we propose an innovative learning algorithm - a variation of One-class nu Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent. We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class nu SVMs while reducing both training time and test time by several factors.

Testing the relationship between personality characteristics, contextual factors and entrepreneurial intentions in a developing country.

PubMed

Karimi, Saeid; Biemans, Harm J A; Naderi Mahdei, Karim; Lans, Thomas; Chizari, Mohammad; Mulder, Martin

2017-06-01

Drawing upon the theory of planned behaviour (TPB), we developed and tested a conceptual model which integrates both internal personality factors and external contextual factors to determine their associations with motivational factors and entrepreneurial intentions (EIs). We then investigated if the model of EI applies in a developing country, namely Iran. We also set out to identify the most relevant factors for EI within this developing country context. Do distal predictors of EI including personality factors (i.e. need for achievement, risk taking and locus of control) and contextual factors (i.e. perceived barriers and support) significantly relate to EI via proximal predictors including motivational factors (i.e. attitudes towards entrepreneurship and perceived behavioural control [PBC])? Data were collected on 331 students from 7 public universities. The findings support the TPB for EI in Iran. All three motivational factors related to EI, but PBC showed the strongest association, which is different than in developed country contexts. Possible explanations for these differences are discussed. All three personality characteristics indirectly related to EI via the proximal attitudes towards entrepreneurship and PBC. Perceived contextual support and barriers indirectly related to EI via proximal PBC while perceived barriers also directly related to EI. © 2015 International Union of Psychological Science.

Food Impaction and Periodontal/Peri-Implant Tissue Conditions in Relation to the Embrasure Dimensions Between Implant-Supported Fixed Dental Prostheses and Adjacent Teeth: A Cross-Sectional Study.

PubMed

Jeong, Jin-Seok; Chang, Moontaek

2015-12-01

Food impaction and periodontal/peri-implant tissue conditions were evaluated in relation to the embrasure dimensions between implant-supported fixed dental prostheses (FDPs) and adjacent teeth. A total of 215 embrasures of 150 FDPs in 100 patients (55 males and 45 females, aged 27 to 83 years; mean age: 56 years) were included in the study. Clinical assessments of the periodontal/peri-implant mucosal conditions, radiographic assessments of embrasure dimensions, and overall patient satisfaction were used as explanatory variables for the food impaction and periodontal/peri-implant tissue conditions adjacent to implant-supported FDPs in the generalized estimating equation (GEE) analysis. Food impaction was reported in 96 (44.7%) of 215 embrasures between implant-supported FDPs and adjacent teeth. Food impaction was reported more frequently in the embrasures with proximal contact loss than in those with tight contact (P = 0.009). Overall patient satisfaction was influenced negatively by food impaction in the proximal embrasures (P = 0.01). Among embrasure dimensions, only the embrasure surface area (ESA) significantly influenced food impaction (P = 0.03). Significant influences of various embrasure dimensions on the periodontal/peri-implant mucosal conditions and bone level at the implant were found in the univariate and multivariate GEE analyses. Food impaction between implant-supported FDPs and adjacent teeth occurred more frequently when proximal contact was lost and ESA increased. Food impaction negatively affected overall patient satisfaction. Embrasure dimensions influenced the periodontal/peri-implant mucosal conditions and bone level at the implant.

Towards Deep Learning from Twitter for Improved Tsunami Alerts and Advisories

NASA Astrophysics Data System (ADS)

Lumb, L. I.; Freemantle, J. R.

2017-12-01

Data from social-networking services increasingly complements that from traditional sources in scenarios that seek to 'cultivate' situational awareness. As false-positive alerts and retracted advisories appear to suggest, establishing a causal connection between earthquakes and tsunamis remains an extant challenge that could prove life-critical. Because posts regarding such natural disasters typically 'trend' in real time via social media, we extract tweets in an effort to elucidate this cause-effect relationship from a very different perspective. To extract content of potential geophysical value from a multiplicity of 140-character tweets streamed in real time, we apply Natural Language Processing (NLP) to the unstructured data and metadata available via Twitter. In Deep Learning from Twitter, words such as "earthquake" are represented as vectors embedded in a corpora of tweets, whose proximity to words such as "tsunami" can be subsequently quantified. Furthermore, when use is made of pre-trained word vectors available for various reference corpora, geophysically credible tweets are rendered distinguishable by quantifying similarities through use of a word-vector dot product. Finally, word-vector analogies are shown to be promising in terms of deconstructing the earthquake-tsunami relationship in terms of the cumulative effect of multiple, contributing factors (see figure). Because diction is anticipated to differ in tweets that follow a tsunami-producing earthquake, our emphasis here is on the re-analysis of actual event data extracted from Twitter that quantifies word sense relative to earthquake-only events. If proven viable, our approach could complement those measures already in place to deliver real-time alerts and advisories following tsunami-causing earthquakes. With climate change accelerating the frequency of glacial calving, and in so doing providing an alternate, potential source for tsunamis, our approach is anticipated to be of value in broader contexts.

Dengue Vector Dynamics (Aedes aegypti) Influenced by Climate and Social Factors in Ecuador: Implications for Targeted Control

PubMed Central

Stewart Ibarra, Anna M.; Ryan, Sadie J.; Beltrán, Efrain; Mejía, Raúl; Silva, Mercy; Muñoz, Ángel

2013-01-01

Background Dengue fever, a mosquito-borne viral disease, is now the fastest spreading tropical disease globally. Previous studies indicate that climate and human behavior interact to influence dengue virus and vector (Aedes aegypti) population dynamics; however, the relative effects of these variables depends on local ecology and social context. We investigated the roles of climate and socio-ecological factors on Ae. aegypti population dynamics in Machala, a city in southern coastal Ecuador where dengue is hyper-endemic. Methods/Principal findings We studied two proximate urban localities where we monitored weekly Ae. aegypti oviposition activity (Nov. 2010-June 2011), conducted seasonal pupal surveys, and surveyed household to identify dengue risk factors. The results of this study provide evidence that Ae. aegypti population dynamics are influenced by social risk factors that vary by season and lagged climate variables that vary by locality. Best-fit models to predict the presence of Ae. aegypti pupae included parameters for household water storage practices, access to piped water, the number of households per property, condition of the house and patio, and knowledge and perceptions of dengue. Rainfall and minimum temperature were significant predictors of oviposition activity, although the effect of rainfall varied by locality due to differences in types of water storage containers. Conclusions These results indicate the potential to reduce the burden of dengue in this region by conducting focused vector control interventions that target high-risk households and containers in each season and by developing predictive models using climate and non-climate information. These findings provide the region's public health sector with key information for conducting time and location-specific vector control campaigns, and highlight the importance of local socio-ecological studies to understand dengue dynamics. See Text S1 for an executive summary in Spanish. PMID:24324542

3. VIEW OF WEST AND SOUTH FACES OF VEHICLE SUPPORT ...

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

3. VIEW OF WEST AND SOUTH FACES OF VEHICLE SUPPORT BUILDING. NOTE BLAST-PROTECTION WALL SURROUNDING DOOR TO ROOM 106 (PYROTECHNIC STORAGE ROOM) NEAR SOUTHWEST CORNER. East face not accesible for photography due to proximity of security fence. - Vandenberg Air Force Base, Space Launch Complex 3, Vehicle Support Building, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

Improvements on ν-Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Saigal, Pooja; Chandra, Suresh

2016-07-01

In this paper, we propose two novel binary classifiers termed as "Improvements on ν-Twin Support Vector Machine: Iν-TWSVM and Iν-TWSVM (Fast)" that are motivated by ν-Twin Support Vector Machine (ν-TWSVM). Similar to ν-TWSVM, Iν-TWSVM determines two nonparallel hyperplanes such that they are closer to their respective classes and are at least ρ distance away from the other class. The significant advantage of Iν-TWSVM over ν-TWSVM is that Iν-TWSVM solves one smaller-sized Quadratic Programming Problem (QPP) and one Unconstrained Minimization Problem (UMP); as compared to solving two related QPPs in ν-TWSVM. Further, Iν-TWSVM (Fast) avoids solving a smaller sized QPP and transforms it as a unimodal function, which can be solved using line search methods and similar to Iν-TWSVM, the other problem is solved as a UMP. Due to their novel formulation, the proposed classifiers are faster than ν-TWSVM and have comparable generalization ability. Iν-TWSVM also implements structural risk minimization (SRM) principle by introducing a regularization term, along with minimizing the empirical risk. The other properties of Iν-TWSVM, related to support vectors (SVs), are similar to that of ν-TWSVM. To test the efficacy of the proposed method, experiments have been conducted on a wide range of UCI and a skewed variation of NDC datasets. We have also given the application of Iν-TWSVM as a binary classifier for pixel classification of color images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.

PubMed

Hsieh, Chung-Ho; Lu, Ruey-Hwa; Lee, Nai-Hsin; Chiu, Wen-Ta; Hsu, Min-Huei; Li, Yu-Chuan Jack

2011-01-01

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. Copyright © 2011 Mosby, Inc. All rights reserved.

Small-scale land-use variability affects Anopheles spp. distribution and concomitant Plasmodium infection in humans and mosquito vectors in southeastern Madagascar.

PubMed

Zohdy, Sarah; Derfus, Kristin; Headrick, Emily G; Andrianjafy, Mbolatiana Tovo; Wright, Patricia C; Gillespie, Thomas R

2016-02-24

Deforestation and land-use change have the potential to alter human exposure to malaria. A large percentage of Madagascar's original forest cover has been lost to slash-and-burn agriculture, and malaria is one of the top causes of mortality on the island. In this study, the influence of land-use on the distribution of Plasmodium vectors and concomitant Plasmodium infection in humans and mosquito vectors was examined in the southeastern rainforests of Madagascar. From June to August 2013, health assessments were conducted on individuals living in sixty randomly selected households in six villages bordering Ranomafana National Park. Humans were screened for malaria using species-specific rapid diagnostic tests (RDTs), and surveyed about insecticide-treated bed net (ITN) usage. Concurrently, mosquitoes were captured in villages and associated forest and agricultural sites. All captured female Anopheline mosquitoes were screened for Plasmodium spp. using a circumsporozoite enzyme-linked immunosorbent assay (csELISA). Anopheles spp. dominated the mosquito communities of agricultural and village land-use sites, accounting for 41.4 and 31.4 % of mosquitoes captured respectively, whereas Anopheles spp. accounted for only 1.6 % of mosquitoes captured from forest sites. Interestingly, most Anopheles spp. (67.7 %) were captured in agricultural sites in close proximity to animal pens, and 90.8 % of Anopheles mosquitoes captured in agricultural sites were known vectors of malaria. Three Anopheline mosquitoes (0.7 %) were positive for malaria (Plasmodium vivax-210) and all positive mosquitoes were collected from agricultural or village land-use sites. Ten humans (3.7 %) tested were positive for P. falciparum, and 23.3 % of those surveyed reported never sleeping under ITNs. This study presents the first report of malaria surveillance in humans and the environment in southeastern Madagascar. These findings suggest that even during the winter, malaria species are present in both humans and mosquitoes; with P. falciparum found in humans, and evidence of P. vivax-210 in mosquito vectors. The presence of P. vivax in resident vectors, but not humans may relate to the high incidence of humans lacking the Duffy protein. The majority of mosquito vectors were found in agricultural land-use sites, in particular near livestock pens. These findings have the potential to inform and improve targeted malaria control and prevention strategies in the region.

Clinical Trials Using Anti-CD19/CD28/CD3zeta CAR Gammaretroviral Vector-transduced Autologous T Lymphocytes KTE-C19

Cancer.gov

NCI supports clinical trials that test new and more effective ways to treat cancer. Find clinical trials studying anti-cd19/cd28/cd3zeta car gammaretroviral vector-transduced autologous t lymphocytes kte-c19.

Elucidating the Potential of Plant Rhabdoviruses as Vector Expressions Systems

USDA-ARS?s Scientific Manuscript database

Maize fine streak virus (MFSV) is a member of the genus Nucleorhabdovirus that is transmitted by the leafhopper Graminella nigrifons. The virus replicates in both its maize host and its insect vector. To determine whether Drosophila S2 cells support the production of full-length MFSV proteins, we ...

Impact of a preventive intervention for perinatal depression on mood regulation, social support, and coping.

PubMed

Mendelson, Tamar; Leis, Julie A; Perry, Deborah F; Stuart, Elizabeth A; Tandon, S Darius

2013-06-01

Perinatal depression prevention trials have rarely examined proximal outcomes that may be relevant for understanding long-term risk for depression. The Mothers and Babies (MB) Course is a cognitive-behavioral depression prevention intervention, which has been shown to prevent depressive symptoms among at-risk perinatal women of color. This study examined intervention impact on three proximal outcomes that are theoretically linked with the intervention's model of change and have been empirically linked with risk for depression: mood regulation expectancies, perceived social support, and coping. The study used data from a randomized intervention trial of the MB Course with 78 low-income, predominantly African-American perinatal women enrolled at one of four home visitation programs in Baltimore City. Mood regulation expectancies, perceived social support, and coping were assessed with self-report instruments at baseline, post-intervention, and 3- and 6-month follow-ups. The intervention group experienced 16 % greater growth in mood regulation from baseline to 6-month follow-up compared to the usual care group, suggesting a prevention effect. The pattern of findings was similar, although not statistically significant, for social support. Contrary to prediction, the control group experienced less growth in avoidant coping than the intervention group. Findings indicate the MB Course enhances mood regulation, which may facilitate prevention of depression over time. Assessment of intervention effects on proximal outcomes is beneficial for understanding how interventions may enhance protective factors relevant to successful long-term outcomes.

Galvanic Tongue Stimulation Inhibits Five Basic Tastes Induced by Aqueous Electrolyte Solutions.

PubMed

Aoyama, Kazuma; Sakurai, Kenta; Sakurai, Satoru; Mizukami, Makoto; Maeda, Taro; Ando, Hideyuki

2017-01-01

Galvanic tongue stimulation (GTS) modulates taste sensation. However, the effect of GTS is contingent on the electrode polarity in the proximity of the tongue. If an anodal electrode is attached in the proximity of the tongue, an electrical or metallic taste is elicited. On the other hand, if only cathodal electrode is attached in the proximity of the tongue, the salty taste, which is induced by electrolyte materials, is inhibited. The mechanism of this taste inhibition is not adequately understood. In this study, we aim to demonstrate that the inhibition is cause by ions, which elicit taste and which migrate from the taste sensors on the tongue by GTS. We verified the inhibitory effect of GTS on all five basic tastes induced by electrolyte materials. This technology is effective for virtual reality systems and interfaces to support dietary restrictions. Our findings demonstrate that cathodal-GTS inhibits all the five basic tastes. The results also support our hypothesis that the effects of cathodal-GTS are caused by migrating tasting ions in the mouth.

Tibiotalocalcaneal arthrodesis with a retrograde intramedullary nail: a biomechanical analysis of the effect of nail length.

PubMed

Noonan, Timothy; Pinzur, Michael; Paxinos, Odysseas; Havey, Robert; Patwardhin, Avinash

2005-04-01

Fatigue fractures of the tibia have been observed at the level of the proximal end of the nail after successful tibiocalcaneal arthrodesis with a retrograde intramedullary device. To study the effect of nail length, five matched pairs of cadaver tibiae were instrumented with strain gauges and potted in methylmethacrylate from a level 3 cm proximal to the distal medial malleolus to simulate a successful tibiocalcaneal arthrodesis. A standard length (15 cm) ankle arthrodesis nail and an identical longer device terminating in the proximal tibial metaphysis were inserted in each paired tibia using appropriate technique. The strain of the posterior cortex of the tibia was recorded under bending moments of up to 50 Nm for each intact specimen after nail insertion and after proximal locking of the nail. The nails were then exchanged between the specimens of the same pairs and the experiment was repeated to insure uniformity. The standard length locked nail increased the principal strain of the posterior cortex of the tibia at the level of the proximal screw holes 5.3 times more than the locked long nail (353 and 67 microstrains), respectively. This stress concentration was not observed when the proximal extent of the nail terminated within the proximal tibial metaphysis. A successful tibiocalcaneal arthrodesis with a standard length locked intramedullary nail creates stress concentration around the proximal screw holes that may be responsible for the fractures observed clinically. This study supports the use of a "long" retrograde locked intramedullary nail for tibiocalcaneal arthrodesis in patients with systemic or localized osteopenia.

Vectorization and parallelization of the finite strip method for dynamic Mindlin plate problems

NASA Technical Reports Server (NTRS)

Chen, Hsin-Chu; He, Ai-Fang

1993-01-01

The finite strip method is a semi-analytical finite element process which allows for a discrete analysis of certain types of physical problems by discretizing the domain of the problem into finite strips. This method decomposes a single large problem into m smaller independent subproblems when m harmonic functions are employed, thus yielding natural parallelism at a very high level. In this paper we address vectorization and parallelization strategies for the dynamic analysis of simply-supported Mindlin plate bending problems and show how to prevent potential conflicts in memory access during the assemblage process. The vector and parallel implementations of this method and the performance results of a test problem under scalar, vector, and vector-concurrent execution modes on the Alliant FX/80 are also presented.

Multiaxis Thrust-Vectoring Characteristics of a Model Representative of the F-18 High-Alpha Research Vehicle at Angles of Attack From 0 deg to 70 deg

NASA Technical Reports Server (NTRS)

Asbury, Scott C.; Capone, Francis J.

1995-01-01

An investigation was conducted in the Langley 16-Foot Transonic Tunnel to determine the multiaxis thrust-vectoring characteristics of the F-18 High-Alpha Research Vehicle (HARV). A wingtip supported, partially metric, 0.10-scale jet-effects model of an F-18 prototype aircraft was modified with hardware to simulate the thrust-vectoring control system of the HARV. Testing was conducted at free-stream Mach numbers ranging from 0.30 to 0.70, at angles of attack from O' to 70', and at nozzle pressure ratios from 1.0 to approximately 5.0. Results indicate that the thrust-vectoring control system of the HARV can successfully generate multiaxis thrust-vectoring forces and moments. During vectoring, resultant thrust vector angles were always less than the corresponding geometric vane deflection angle and were accompanied by large thrust losses. Significant external flow effects that were dependent on Mach number and angle of attack were noted during vectoring operation. Comparisons of the aerodynamic and propulsive control capabilities of the HARV configuration indicate that substantial gains in controllability are provided by the multiaxis thrust-vectoring control system.

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Foamy Virus Vector Carries a Strong Insulator in Its Long Terminal Repeat Which Reduces Its Genotoxic Potential

PubMed Central

2017-01-01

ABSTRACT Strong viral enhancers in gammaretrovirus vectors have caused cellular proto-oncogene activation and leukemia, necessitating the use of cellular promoters in “enhancerless” self-inactivating integrating vectors. However, cellular promoters result in relatively low transgene expression, often leading to inadequate disease phenotype correction. Vectors derived from foamy virus, a nonpathogenic retrovirus, show higher preference for nongenic integrations than gammaretroviruses/lentiviruses and preferential integration near transcriptional start sites, like gammaretroviruses. We found that strong viral enhancers/promoters placed in foamy viral vectors caused extremely low immortalization of primary mouse hematopoietic stem/progenitor cells compared to analogous gammaretrovirus/lentivirus vectors carrying the same enhancers/promoters, an effect not explained solely by foamy virus' modest insertional site preference for nongenic regions compared to gammaretrovirus/lentivirus vectors. Using CRISPR/Cas9-mediated targeted insertion of analogous proviral sequences into the LMO2 gene and then measuring LMO2 expression, we demonstrate a sequence-specific effect of foamy virus, independent of insertional bias, contributing to reduced genotoxicity. We show that this effect is mediated by a 36-bp insulator located in the foamy virus long terminal repeat (LTR) that has high-affinity binding to the CCCTC-binding factor. Using our LMO2 activation assay, LMO2 expression was significantly increased when this insulator was removed from foamy virus and significantly reduced when the insulator was inserted into the lentiviral LTR. Our results elucidate a mechanism underlying the low genotoxicity of foamy virus, identify a novel insulator, and support the use of foamy virus as a vector for gene therapy, especially when strong enhancers/promoters are required. IMPORTANCE Understanding the genotoxic potential of viral vectors is important in designing safe and efficacious vectors for gene therapy. Self-inactivating vectors devoid of viral long-terminal-repeat enhancers have proven safe; however, transgene expression from cellular promoters is often insufficient for full phenotypic correction. Foamy virus is an attractive vector for gene therapy. We found foamy virus vectors to be remarkably less genotoxic, well below what was expected from their integration site preferences. We demonstrate that the foamy virus long terminal repeats contain an insulator element that binds CCCTC-binding factor and reduces its insertional genotoxicity. Our study elucidates a mechanism behind the low genotoxic potential of foamy virus, identifies a unique insulator, and supports the use of foamy virus as a vector for gene therapy. PMID:29046446

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

2013-01-01

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

Predicting healthcare associated infections using patients' experiences

NASA Astrophysics Data System (ADS)

Pratt, Michael A.; Chu, Henry

2016-05-01

Healthcare associated infections (HAI) are a major threat to patient safety and are costly to health systems. Our goal is to predict the HAI performance of a hospital using the patients' experience responses as input. We use four classifiers, viz. random forest, naive Bayes, artificial feedforward neural networks, and the support vector machine, to perform the prediction of six types of HAI. The six types include blood stream, urinary tract, surgical site, and intestinal infections. Experiments show that the random forest and support vector machine perform well across the six types of HAI.

Spray drying egg using either maltodextrin or nopal mucilage as stabilizer agents.

PubMed

Medina-Torres, L; Calderas, F; Nuñez Ramírez, D M; Herrera-Valencia, E E; Bernad Bernad, M J; Manero, O

2017-12-01

In this work, a comparative study between spray drying (SD) of fresh egg by either maltodextrin (MD) or nopal-mucilage (MN) as stabilizing vectors was made. The powders obtained were characterized for drying performance, moisture content, chemical proximate analysis, thermal analysis (TGA), chemical composition (FTIR), microscopy (SEM) and rheology (viscoelasticity and steady state simple shear viscosity). Infrared analysis showed that MN has the effect of a thickening agent rather than an encapsulating one. Results indicated that SD egg with MN produced a high thermal and mechanical stable product and rendered the highest drying performance, producing a more uniform and defined sphere-shaped morphology in comparison to egg SD either alone and with MD.

The geo-control system for station keeping and colocation of geostationary satellites

NASA Technical Reports Server (NTRS)

Montenbruck, O.; Eckstein, M. C.; Gonner, J.

1993-01-01

GeoControl is a compact but powerful and accurate software system for station keeping of single and colocated satellites, which has been developed at the German Space Operations Center. It includes four core modules for orbit determination (including maneuver estimation), maneuver planning, monitoring of proximities between colocated satellites, and interference and event prediction. A simple database containing state vector and maneuver information at selected epochs is maintained as a central interface between the modules. A menu driven shell utilizing form screens for data input serves as the central user interface. The software is written in Ada and FORTRAN and may be used on VAX workstations or mainframes under the VMS operating system.

Location of fractures and the characteristics of patients with atypical femoral fractures: analyses of 38 Japanese cases.

PubMed

Hyodo, Kojiro; Nishino, Tomofumi; Kamada, Hiroshi; Nozawa, Daisuke; Mishima, Hajime; Yamazaki, Masashi

2017-03-01

The purpose of this study was to determine fracture location and the characteristics of patients with atypical femoral fractures (AFFs). We studied 38 AFFs in 34 patients admitted to our institution between November 2007 and July 2013. The diagnostic criteria for the AFFs were based on 2014 American Society of Bone and Mineral Research guidelines. We classified the fracture location as proximal, middle, or distal to trisect the femoral diaphysis from just distal to the lesser trochanter to just proximal to the supracondylar flare. Bowing was defined as a line through the inside of the tip of the great trochanter and a condylar center that was outside the medullary cavity. We investigated the fracture's location, existence of coronal bowing, and bisphosphonates (BPs), glucocorticoids (GCs), and proton pump inhibitors therapy. We analyzed associations between fracture location and demographic and clinical factors. Twelve fractures were proximal, 25 were middle, and one was distal. Nineteen limbs showed femoral bowing. Thirty-one patients received BP treatment-20 patients received alendronic acid, eight risedronic acid, and three minodronic acid. Fourteen patients received a GC, and 16 received a proton pump inhibitor. There was a significant association between coronal bowing and middle fracture locations, GC therapy and proximal fracture locations, and older age and middle fracture locations. Tall height and heavy weight had an association with proximal fracture location, and short height and light weight had an association with middle fracture location. In conclusion, we provide evidence supporting a causal relationship between BP-related severely suppressed bone turnover and AFFs. We also provide evidence supporting additional influences from altered distribution of mechanical stress with femoral bowing and various factors, such as GC therapy, age, body weight, and height, which might negatively affect bone intensity and quality and result in fracture.

Optical devices for proximity operations study and test report. [intensifying images for visual observation during space transportation system activities

NASA Technical Reports Server (NTRS)

Smith, R. A.

1979-01-01

Operational and physical requirements were investigated for a low-light-level viewing device to be used as a window-mounted optical sight for crew use in the pointing, navigating, stationkeeping, and docking of space vehicles to support space station operations and the assembly of large structures in space. A suitable prototype, obtained from a commercial vendor, was subjected to limited tests to determine the potential effectiveness of a proximity optical device in spacecraft operations. The constructional features of the device are discussed as well as concepts for its use. Tests results show that a proximity optical device is capable of performing low-light-level viewing services and will enhance manned spacecraft operations.

Vector control activities: Fiscal Year, 1986

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

Not Available

1987-04-01

The program is divided into two major components - operations and support studies. The support studies are designed to improve the operational effectiveness and efficiency of the control program and to identify other vector control problems requiring TVA attention and study. Nonchemical methods of control are emphasized and are supplemented with chemical measures as needed. TVA also cooperates with various concerned municipalities in identifying blood-sucking arthropod pest problems and demonstrating control techniques useful in establishing abatement programs, and provides technical assistance to other TVA programs and organizations. The program also helps Land Between The Lakes (LBL) plan and conduct vectormore » control operations and tick control research. Specific program control activities and support studies are discussed.« less

Objective quantification of the food proximity effect on grapes, chocolate and cracker consumption in a Swedish high school. A temporal analysis.

PubMed

Langlet, Billy; Fagerberg, Petter; Glossner, Andrew; Ioakimidis, Ioannis

2017-01-01

Close food proximity leads to increased short-term energy intake, potentially contributing to the long-term development of obesity. However, its precise effects on eating behaviour are still unclear, especially with food available for extended periods of time. This study involved two similar high school student groups (15-17 years old), which had ad libitum access to grapes, chocolates and crackers during an hour-long experimental session. In the distal condition the foods were placed 6 meters away from the students (n = 24), in contrast to the proximal condition (n = 17) were the food was placed near the students. The identification of the type and the quantification of the amount of each food selected, for each individual serving, was facilitated through use of food scales and video recording. In the proximal condition individuals served themselves grapes and crackers more often and consumed more chocolate than in the distal condition. In total, participants in the proximal condition ingested significantly more energy (726 kcal vs. 504 kcal; p = 0.029), without reporting higher fullness. Food proximity also affected the temporal distribution of servings, with the first five minutes of the sessions corresponding to 53.1% and 45.6% of the total energy intake for the distal and proximal conditions, respectively. After the first five minutes, the servings in the distal condition were strongly clustered in time, with many students getting food together. In the proximal condition however, students displayed an unstructured pattern of servings over time. In conclusion, this study strengthens past evidence regarding the important role of food proximity on individual energy intake and, for the first time, it associates continuous food proximity to the emergence of unstructured eating over time. These conclusions, expanded upon by future studies, could support the creation of meaningful intervention strategies based on spatially and temporally controlled food availability.

Objective quantification of the food proximity effect on grapes, chocolate and cracker consumption in a Swedish high school. A temporal analysis

PubMed Central

Fagerberg, Petter; Glossner, Andrew; Ioakimidis, Ioannis

2017-01-01

Close food proximity leads to increased short-term energy intake, potentially contributing to the long-term development of obesity. However, its precise effects on eating behaviour are still unclear, especially with food available for extended periods of time. This study involved two similar high school student groups (15–17 years old), which had ad libitum access to grapes, chocolates and crackers during an hour-long experimental session. In the distal condition the foods were placed 6 meters away from the students (n = 24), in contrast to the proximal condition (n = 17) were the food was placed near the students. The identification of the type and the quantification of the amount of each food selected, for each individual serving, was facilitated through use of food scales and video recording. In the proximal condition individuals served themselves grapes and crackers more often and consumed more chocolate than in the distal condition. In total, participants in the proximal condition ingested significantly more energy (726 kcal vs. 504 kcal; p = 0.029), without reporting higher fullness. Food proximity also affected the temporal distribution of servings, with the first five minutes of the sessions corresponding to 53.1% and 45.6% of the total energy intake for the distal and proximal conditions, respectively. After the first five minutes, the servings in the distal condition were strongly clustered in time, with many students getting food together. In the proximal condition however, students displayed an unstructured pattern of servings over time. In conclusion, this study strengthens past evidence regarding the important role of food proximity on individual energy intake and, for the first time, it associates continuous food proximity to the emergence of unstructured eating over time. These conclusions, expanded upon by future studies, could support the creation of meaningful intervention strategies based on spatially and temporally controlled food availability. PMID:28797048

Operator-assisted planning and execution of proximity operations subject to operational constraints

NASA Technical Reports Server (NTRS)

Grunwald, Arthur J.; Ellis, Stephen R.

1991-01-01

Future multi-vehicle operations will involve multiple scenarios that will require a planning tool for the rapid, interactive creation of fuel-efficient trajectories. The planning process must deal with higher-order, non-linear processes involving dynamics that are often counter-intuitive. The optimization of resulting trajectories can be difficult to envision. An interaction proximity operations planning system is being developed to provide the operator with easily interpreted visual feedback of trajectories and constraints. This system is hosted on an IRIS 4D graphics platform and utilizes the Clohessy-Wiltshire equations. An inverse dynamics algorithm is used to remove non-linearities while the trajectory maneuvers are decoupled and separated in a geometric spreadsheet. The operator has direct control of the position and time of trajectory waypoints to achieve the desired end conditions. Graphics provide the operator with visualization of satisfying operational constraints such as structural clearance, plume impingement, approach velocity limits, and arrival or departure corridors. Primer vector theory is combined with graphical presentation to improve operator understanding of suggested automated system solutions and to allow the operator to review, edit, or provide corrective action to the trajectory plan.

Interactive orbital proximity operations planning system instruction and training guide

NASA Technical Reports Server (NTRS)

Grunwald, Arthur J.; Ellis, Stephen R.

1994-01-01

This guide instructs users in the operation of a Proximity Operations Planning System. This system uses an interactive graphical method for planning fuel-efficient rendezvous trajectories in the multi-spacecraft environment of the space station and allows the operator to compose a multi-burn transfer trajectory between orbit initial chaser and target trajectories. The available task time (window) of the mission is predetermined and the maneuver is subject to various operational constraints, such as departure, arrival, spatial, plume impingement, and en route passage constraints. The maneuvers are described in terms of the relative motion experienced in a space station centered coordinate system. Both in-orbital plane as well as out-of-orbital plane maneuvering is considered. A number of visual optimization aids are used for assisting the operator in reaching fuel-efficient solutions. These optimization aids are based on the Primer Vector theory. The visual feedback of trajectory shapes, operational constraints, and optimization functions, provided by user-transparent and continuously active background computations, allows the operator to make fast, iterative design changes that rapidly converge to fuel-efficient solutions. The planning tool is an example of operator-assisted optimization of nonlinear cost functions.

Detection of distorted frames in retinal video-sequences via machine learning

NASA Astrophysics Data System (ADS)

Kolar, Radim; Liberdova, Ivana; Odstrcilik, Jan; Hracho, Michal; Tornow, Ralf P.

2017-07-01

This paper describes detection of distorted frames in retinal sequences based on set of global features extracted from each frame. The feature vector is consequently used in classification step, in which three types of classifiers are tested. The best classification accuracy 96% has been achieved with support vector machine approach.

Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.

PubMed

An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Fang, Yu-Hong; Zhao, Yu-Jun; Zhang, Ming

2016-01-01

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Study on vibration characteristics and fault diagnosis method of oil-immersed flat wave reactor in Arctic area converter station

NASA Astrophysics Data System (ADS)

Lai, Wenqing; Wang, Yuandong; Li, Wenpeng; Sun, Guang; Qu, Guomin; Cui, Shigang; Li, Mengke; Wang, Yongqiang

2017-10-01

Based on long term vibration monitoring of the No.2 oil-immersed fat wave reactor in the ±500kV converter station in East Mongolia, the vibration signals in normal state and in core loose fault state were saved. Through the time-frequency analysis of the signals, the vibration characteristics of the core loose fault were obtained, and a fault diagnosis method based on the dual tree complex wavelet (DT-CWT) and support vector machine (SVM) was proposed. The vibration signals were analyzed by DT-CWT, and the energy entropy of the vibration signals were taken as the feature vector; the support vector machine was used to train and test the feature vector, and the accurate identification of the core loose fault of the flat wave reactor was realized. Through the identification of many groups of normal and core loose fault state vibration signals, the diagnostic accuracy of the result reached 97.36%. The effectiveness and accuracy of the method in the fault diagnosis of the flat wave reactor core is verified.

Orthogonal vector algorithm to obtain the solar vector using the single-scattering Rayleigh model.

PubMed

Wang, Yinlong; Chu, Jinkui; Zhang, Ran; Shi, Chao

2018-02-01

Information obtained from a polarization pattern in the sky provides many animals like insects and birds with vital long-distance navigation cues. The solar vector can be derived from the polarization pattern using the single-scattering Rayleigh model. In this paper, an orthogonal vector algorithm, which utilizes the redundancy of the single-scattering Rayleigh model, is proposed. We use the intersection angles between the polarization vectors as the main criteria in our algorithm. The assumption that all polarization vectors can be considered coplanar is used to simplify the three-dimensional (3D) problem with respect to the polarization vectors in our simulation. The surface-normal vector of the plane, which is determined by the polarization vectors after translation, represents the solar vector. Unfortunately, the two-directionality of the polarization vectors makes the resulting solar vector ambiguous. One important result of this study is, however, that this apparent disadvantage has no effect on the complexity of the algorithm. Furthermore, two other universal least-squares algorithms were investigated and compared. A device was then constructed, which consists of five polarized-light sensors as well as a 3D attitude sensor. Both the simulation and experimental data indicate that the orthogonal vector algorithms, if used with a suitable threshold, perform equally well or better than the other two algorithms. Our experimental data reveal that if the intersection angles between the polarization vectors are close to 90°, the solar-vector angle deviations are small. The data also support the assumption of coplanarity. During the 51 min experiment, the mean of the measured solar-vector angle deviations was about 0.242°, as predicted by our theoretical model.

Impact of Academic and Nonacademic Support Structures on Third Grade Reading Achievement

ERIC Educational Resources Information Center

Peugeot, Megan A.

2017-01-01

Through a Whole Child lens a cross-sectional quantitative research design evaluated the impact of academic and nonacademic support structures on student reading achievement per the third grade Ohio Achievement Assessment (OAA). Two demographically similar public school districts within geographical proximity in Ohio were involved utilizing…

Learning and Language: Supporting Group Work so Group Work Supports Learning

ERIC Educational Resources Information Center

Mylett, Terri; Gluck, Russell

2005-01-01

This paper reports on developments in teaching and learning for first year employment relations students at the University of Wollongong based on creating conditions of learning informed by Vygotsky's "zone of proximal development" theory. Essentially, this meant emphasising collaborative learning (group work) in the lecture theatre and…

Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires

PubMed Central

Cinelli, Mattia; Sun, , Yuxin; Best, Katharine; Heather, James M.; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-01-01

Abstract Motivation: Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund’s adjuvant (CFA) or CFA alone. Results: The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases. Summary: The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund’s Adjuvant. Availability and implementation: The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893. The Decombinator package is available at github.com/innate2adaptive/Decombinator. The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html. Contact: b.chain@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28073756

A lentiviral vector with a short troponin-I promoter for tracking cardiomyocyte differentiation of human embryonic stem cells.

PubMed

Gallo, P; Grimaldi, S; Latronico, M V G; Bonci, D; Pagliuca, A; Gallo, P; Ausoni, S; Peschle, C; Condorelli, G

2008-02-01

Human embryonic stem cells (hESCs) may become important for cardiac repair due to their potentially unlimited ability to generate cardiomyocytes (CMCs). Moreover, genetic manipulation of hESC-derived CMCs would be a very promising technique for curing myocardial disorders. At the present time, however, inducing the differentiation of hESCs into CMCs is extremely difficult and, therefore, an easy and standardizable technique is needed to evaluate differentiation strategies. Vectors driving cardiac-specific expression may represent an important tool not only for monitoring new cardiac-differentiation strategies, but also for the manipulation of cardiac differentiation of ESCs. To this aim, we generated cardiac-specific lentiviral vectors (LVVs) in which expression is driven by a short fragment of the cardiac troponin-I proximal promoter (TNNI3) with a human cardiac alpha-actin enhancer, and tested its suitability in inducing tissue-specific gene expression and ability to track the CMC lineage during differentiation of ESCs. We determined that (1) TNNI3-LVVs efficiently drive cardiac-specific gene expression and mark the cardiomyogenic lineage in human and mouse ESC differentiation systems (2) the cardiac alpha-actin enhancer confers a further increase in gene-expression specificity of TNNI3-LVVs in hESCs. Although this technique may not be useful in tracking small numbers of cells, data suggested that TNNI3-based LVVs are a powerful tool for manipulating human ESCs and modifying hESC-derived CMCs.

Enhanced expression of EGFP gene in CHSE-214 cells by an ARS element from mud loach (Misgurnus mizolepis).

PubMed

Kim, Moo-Sang; Lim, Hak-Seob; Ahn, Sang Jung; Jeong, Yong-Kee; Kim, Chul Geun; Lee, Hyung Ho

2007-11-01

The origins of replication are associated with nuclear matrices or are found in close proximity to matrix attachment regions (MARs). In this report, fish MARs were cloned into an autonomously replicating sequence (ARS) cloning vector and were screened for ARS elements in Saccharomyces cerevisiae. Sixteen clones were isolated that were able to grow on the selective plates. In particular, an ARS905 that shows high efficiency among them was selected for this study. Southern hybridization indicated the autonomous replication of the transformation vector containing the ARS905 element. DNA sequences analysis showed that the ARS905 contained two ARS consensus sequences as well as MAR motifs, such as AT tracts, ORI patterns, and ATC tracts. In vitro matrix binding analysis, major matrix binding activity and ARS function coincided in a subfragment of the ARS905. To analyze the effects of ARS905 on expression of a reporter gene, an ARS905(E1158) with ARS activity was inserted into pBaEGFP(+) containing mud loach beta-actin promoter, EGFP as a reporter gene, and SV40 poly(A) signal. The pBaEGFP(+)-ARS905(E1158) was transfected into a fish cell line, CHSE-214. The intensity of EGFP transfected cells was a 7-fold of the control at 11days post-transfection. These results indicate that ARS905 enhances the expression of the EGFP gene and that it should be as a component of expression vectors in further fish biotechnological studies.

Self-regulation of 70-kilodalton heat shock proteins in Saccharomyces cerevisiae.

PubMed Central

Stone, D E; Craig, E A

1990-01-01

To determine whether the 70-kilodalton heat shock proteins of Saccharomyces cerevisiae play a role in regulating their own synthesis, we studied the effect of overexpressing the SSA1 protein on the activity of the SSA1 5'-regulatory region. The constitutive level of Ssa1p was increased by fusing the SSA1 structural gene to the GAL1 promoter. A reporter vector consisting of an SSA1-lacZ translational fusion was used to assess SSA1 promoter activity. In a strain producing approximately 10-fold the normal heat shock level of Ssa1p, induction of beta-galactosidase activity by heat shock was almost entirely blocked. Expression of a transcriptional fusion vector in which the CYC1 upstream activating sequence of a CYC1-lacZ chimera was replaced by a sequence containing a heat shock upstream activating sequence (heat shock element 2) from the 5'-regulatory region of SSA1 was inhibited by excess Ssa1p. The repression of an SSA1 upstream activating sequence by the SSA1 protein indicates that SSA1 self-regulation is at least partially mediated at the transcriptional level. The expression of another transcriptional fusion vector, containing heat shock element 2 and a lesser amount of flanking sequence, is not inhibited when Ssa1p is overexpressed. This suggests the existence of an element, proximal to or overlapping heat shock element 2, that confers sensitivity to the SSA1 protein. Images PMID:2181281

Frontotemporal lobar degeneration related proteins induce only subtle memory-related deficits when bilaterally overexpressed in the dorsal hippocampus

PubMed Central

Dayton, Robert D.; Wang, David B.; Cain, Cooper D.; Schrott, Lisa M.; Ramirez, Julio J.; King, Michael A.; Klein, Ronald L.

2011-01-01

Frontotemporal lobar degeneration (FTLD) is a neurodegenerative disease that involves cognitive decline and dementia. To model the hippocampal neurodegeneration and memory-related behavioral impairment that occurs in FTLD and other tau and TDP-43 proteinopathy diseases, we used an adeno-associated virus serotype 9 (AAV9) vector to induce bilateral expression of either microtubule-associated protein tau or transactive response DNA binding protein 43 kDa (TDP-43) in adult rat dorsal hippocampus. Human wild-type forms of tau or TDP-43 were expressed. The vectors/doses were designed for moderate expression levels within neurons. Rats were evaluated for acquisition and retention in the Morris water task over 12 weeks after gene transfer. Neither vector altered acquisition performance compared to controls. In measurements of retention, there was impairment in the TDP-43 group. Histological examination revealed specific loss of dentate gyrus granule cells and concomitant gliosis proximal to the injection site in the TDP-43 group, with shrinkage of the dorsal hippocampus. Despite specific tau pathology, the tau gene transfer surprisingly did not cause obvious neuronal loss or behavioral impairment. The data demonstrate that TDP-43 produced mild behavioral impairment and hippocampal neurodegeneration in rats, whereas tau did not. The models could be of value for studying mechanisms of FTLD and other diseases with tau and TDP-43 pathology in the hippocampus including Alzheimer's disease, with relevance to early stage mild impairment. PMID:22177996

A gene cassette for adapting Escherichia coli strains as hosts for att-Int-mediated rearrangement and pL expression vectors.

PubMed

Balakrishnan, R; Bolten, B; Backman, K C

1994-01-28

A cassette of genes from bacteriophage lambda, when carried on a derivative of bacteriophage Mu, renders strains of Escherichia coli (and in principle other Mu-sensitive bacteria) capable of supporting lambda-based expression vectors, such as rearrangement vectors and pL vectors. The gene cassette contains a temperature-sensitive allele of the repressor gene, cIts857, and a shortened leftward operon comprising, oLpL, N, xis and int. Transfection and lysogenization of this cassette into various host bacteria is mediated by phage Mu functions. Examples of regulated expression of the gene encoding T4 DNA ligase are presented.

Approaches to utilize mesenchymal progenitor cells as cellular vehicles.

PubMed

Pereboeva, L; Komarova, S; Mikheeva, G; Krasnykh, V; Curiel, D T

2003-01-01

Mammalian cells represent a novel vector approach for gene delivery that overcomes major drawbacks of viral and nonviral vectors and couples cell therapy with gene delivery. A variety of cell types have been tested in this regard, confirming that the ideal cellular vector system for ex vivo gene therapy has to comply with stringent criteria and is yet to be found. Several properties of mesenchymal progenitor cells (MPCs), such as easy access and simple isolation and propagation procedures, make these cells attractive candidates as cellular vehicles. In the current work, we evaluated the potential utility of MPCs as cellular vectors with the intent to use them in the cancer therapy context. When conventional adenoviral (Ad) vectors were used for MPC transduction, the highest transduction efficiency of MPCs was 40%. We demonstrated that Ad primary-binding receptors were poorly expressed on MPCs, while the secondary Ad receptors and integrins presented in sufficient amounts. By employing Ad vectors with incorporated integrin-binding motifs (Ad5lucRGD), MPC transduction was augmented tenfold, achieving efficient genetic loading of MPCs with reporter and anticancer genes. MPCs expressing thymidine kinase were able to exert a bystander killing effect on the cancer cell line SKOV3ip1 in vitro. In addition, we found that MPCs were able to support Ad replication, and thus can be used as cell vectors to deliver oncolytic viruses. Our results show that MPCs can foster expression of suicide genes or support replication of adenoviruses as potential anticancer therapeutic payloads. These findings are consistent with the concept that MPCs possess key properties that ensure their employment as cellular vehicles and can be used to deliver either therapeutic genes or viruses to tumor sites.

Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI3g data

NASA Astrophysics Data System (ADS)

Gerber, Florian; Mösinger, Kaspar; Furrer, Reinhard

2017-07-01

Software packages for spatial data often implement a hybrid approach of interpreted and compiled programming languages. The compiled parts are usually written in C, C++, or Fortran, and are efficient in terms of computational speed and memory usage. Conversely, the interpreted part serves as a convenient user-interface and calls the compiled code for computationally demanding operations. The price paid for the user friendliness of the interpreted component is-besides performance-the limited access to low level and optimized code. An example of such a restriction is the 64-bit vector support of the widely used statistical language R. On the R side, users do not need to change existing code and may not even notice the extension. On the other hand, interfacing 64-bit compiled code efficiently is challenging. Since many R packages for spatial data could benefit from 64-bit vectors, we investigate strategies to efficiently pass 64-bit vectors to compiled languages. More precisely, we show how to simply extend existing R packages using the foreign function interface to seamlessly support 64-bit vectors. This extension is shown with the sparse matrix algebra R package spam. The new capabilities are illustrated with an example of GIMMS NDVI3g data featuring a parametric modeling approach for a non-stationary covariance matrix.

Experimental Stage Separation Tool Development in NASA Langley's Aerothermodynamics Laboratory

NASA Technical Reports Server (NTRS)

Murphy, Kelly J.; Scallion, William I.

2005-01-01

As part of the research effort at NASA in support of the stage separation and ascent aerothermodynamics research program, proximity testing of a generic bimese wing-body configuration was conducted in NASA Langley's Aerothermodynamics Laboratory in the 20-Inch Mach 6 Air Tunnel. The objective of this work is the development of experimental tools and testing methodologies to apply to hypersonic stage separation problems for future multi-stage launch vehicle systems. Aerodynamic force and moment proximity data were generated at a nominal Mach number of 6 over a small range of angles of attack. The generic bimese configuration was tested in a belly-to-belly and back-to-belly orientation at 86 relative proximity locations. Over 800 aerodynamic proximity data points were taken to serve as a database for code validation. Longitudinal aerodynamic data generated in this test program show very good agreement with viscous computational predictions. Thus a framework has been established to study separation problems in the hypersonic regime using coordinated experimental and computational tools.

Scoping the proximal and distal dimensions of climate change on health and wellbeing.

PubMed

Morris, George Paterson; Reis, Stefan; Beck, Sheila Anne; Fleming, Lora Elderkin; Adger, William Neil; Benton, Timothy Guy; Depledge, Michael Harold

2017-12-05

The impacts of climate on health and wellbeing occur in time and space and through a range of indirect, complicated mechanisms. This diversity of pathways has major implications for national public health planning and influence on interventions that might help to mitigate and adapt to rapidly changing environmental conditions, nationally and internationally. This paper draws upon evidence from public health and adverse impact studies across climate science, hydrology, agriculture, public health, and the social sciences. It presents a conceptual model to support decision-making by recognizing both the proximal and distal pathways from climate-induced environmental change to national health and wellbeing. The proximal and distal pathways associated with food security, migration and mobility illustrate the diverse climate change influences in different geographic locations over different timescales. We argue that greater realization and articulation of proximal and distal pathways should radically alter how climate change is addressed as a national and international public health challenge.

Tracking planets and moons: mechanisms of object tracking revealed with a new paradigm

PubMed Central

Tombu, Michael

2014-01-01

People can attend to and track multiple moving objects over time. Cognitive theories of this ability emphasize location information and differ on the importance of motion information. Results from several experiments have shown that increasing object speed impairs performance, although speed was confounded with other properties such as proximity of objects to one another. Here, we introduce a new paradigm to study multiple object tracking in which object speed and object proximity were manipulated independently. Like the motion of a planet and moon, each target–distractor pair rotated about both a common local point as well as the center of the screen. Tracking performance was strongly affected by object speed even when proximity was controlled. Additional results suggest that two different mechanisms are used in object tracking—one sensitive to speed and proximity and the other sensitive to the number of distractors. These observations support models of object tracking that include information about object motion and reject models that use location alone. PMID:21264704

Tracking planets and moons: mechanisms of object tracking revealed with a new paradigm.

PubMed

Tombu, Michael; Seiffert, Adriane E

2011-04-01

People can attend to and track multiple moving objects over time. Cognitive theories of this ability emphasize location information and differ on the importance of motion information. Results from several experiments have shown that increasing object speed impairs performance, although speed was confounded with other properties such as proximity of objects to one another. Here, we introduce a new paradigm to study multiple object tracking in which object speed and object proximity were manipulated independently. Like the motion of a planet and moon, each target-distractor pair rotated about both a common local point as well as the center of the screen. Tracking performance was strongly affected by object speed even when proximity was controlled. Additional results suggest that two different mechanisms are used in object tracking--one sensitive to speed and proximity and the other sensitive to the number of distractors. These observations support models of object tracking that include information about object motion and reject models that use location alone.

Hepatic Expression of Adenovirus 36 E4ORF1 Improves Glycemic Control and Promotes Glucose Metabolism Through AKT Activation

PubMed Central

McMurphy, Travis B.; Huang, Wei; Xiao, Run; Liu, Xianglan; Dhurandhar, Nikhil V.

2017-01-01

Considering that impaired proximal insulin signaling is linked with diabetes, approaches that enhance glucose disposal independent of insulin signaling are attractive. In vitro data indicate that the E4ORF1 peptide derived from human adenovirus 36 (Ad36) interacts with cells from adipose tissue, skeletal muscle, and liver to enhance glucose disposal, independent of proximal insulin signaling. Adipocyte-specific expression of Ad36E4ORF1 improves hyperglycemia in mice. To determine the hepatic interaction of Ad36E4ORF1 in enhancing glycemic control, we expressed E4ORF1 of Ad36 or Ad5 or fluorescent tag alone by using recombinant adeno-associated viral vector in the liver of three mouse models. In db/db or diet-induced obesity (DIO) mice, hepatic expression of Ad36E4ORF1 but not Ad5E4ORF1 robustly improved glycemic control. In normoglycemic wild-type mice, hepatic expression of Ad36E4ORF1 lowered nonfasting blood glucose at a high dose of expression. Of note, Ad36E4ORF1 significantly reduced insulin levels in db/db and DIO mice. The improvement in glycemic control was observed without stimulation of the proximal insulin signaling pathway. Collectively, these data indicate that Ad36E4ORF1 is not a typical sensitizer, mimetic, or secretagogue of insulin. Instead, it may have insulin-sparing action, which seems to reduce the need for insulin and, hence, to reduce insulin levels. PMID:27903748

Hepatic Expression of Adenovirus 36 E4ORF1 Improves Glycemic Control and Promotes Glucose Metabolism Through AKT Activation.

PubMed

McMurphy, Travis B; Huang, Wei; Xiao, Run; Liu, Xianglan; Dhurandhar, Nikhil V; Cao, Lei

2017-02-01

Considering that impaired proximal insulin signaling is linked with diabetes, approaches that enhance glucose disposal independent of insulin signaling are attractive. In vitro data indicate that the E4ORF1 peptide derived from human adenovirus 36 (Ad36) interacts with cells from adipose tissue, skeletal muscle, and liver to enhance glucose disposal, independent of proximal insulin signaling. Adipocyte-specific expression of Ad36E4ORF1 improves hyperglycemia in mice. To determine the hepatic interaction of Ad36E4ORF1 in enhancing glycemic control, we expressed E4ORF1 of Ad36 or Ad5 or fluorescent tag alone by using recombinant adeno-associated viral vector in the liver of three mouse models. In db/db or diet-induced obesity (DIO) mice, hepatic expression of Ad36E4ORF1 but not Ad5E4ORF1 robustly improved glycemic control. In normoglycemic wild-type mice, hepatic expression of Ad36E4ORF1 lowered nonfasting blood glucose at a high dose of expression. Of note, Ad36E4ORF1 significantly reduced insulin levels in db/db and DIO mice. The improvement in glycemic control was observed without stimulation of the proximal insulin signaling pathway. Collectively, these data indicate that Ad36E4ORF1 is not a typical sensitizer, mimetic, or secretagogue of insulin. Instead, it may have insulin-sparing action, which seems to reduce the need for insulin and, hence, to reduce insulin levels. © 2017 by the American Diabetes Association.

Proximity to coast is linked to climate change belief.

PubMed

Milfont, Taciano L; Evans, Laurel; Sibley, Chris G; Ries, Jan; Cunningham, Andrew

2014-01-01

Psychologists have examined the many psychological barriers to both climate change belief and concern. One barrier is the belief that climate change is too uncertain, and likely to happen in distant places and times, to people unlike oneself. Related to this perceived psychological distance of climate change, studies have shown that direct experience of the effects of climate change increases climate change concern. The present study examined the relationship between physical proximity to the coastline and climate change belief, as proximity may be related to experiencing or anticipating the effects of climate change such as sea-level rise. We show, in a national probability sample of 5,815 New Zealanders, that people living in closer proximity to the shoreline expressed greater belief that climate change is real and greater support for government regulation of carbon emissions. This proximity effect held when adjusting for height above sea level and regional poverty. The model also included individual differences in respondents' sex, age, education, political orientation, and wealth. The results indicate that physical place plays a role in the psychological acceptance of climate change, perhaps because the effects of climate change become more concrete and local.

Deep Venous Thrombosis: An Interventionalist's Approach

PubMed Central

Jenkins, J. Stephen; Michael, Paul

2014-01-01

Background Deep venous thrombosis (DVT) of the lower extremity has traditionally been anatomically categorized into proximal DVT (thrombosis involving the popliteal vein and above) and distal DVT (isolated calf vein thrombosis). Proximal DVT involving the common femoral and/or iliac veins, referred to as iliofemoral DVT (IFDVT), represents a disease process with a worse prognosis and higher risk for poor clinical outcomes compared to proximal DVT not involving the common femoral or iliac draining veins. Methods This review discusses therapeutic options for treatment of lower extremity IFDVT, including adjuvant anticoagulation and catheter-based invasive therapies; literature supporting current acute interventional techniques; and the recommendations from the recently published American Heart Association guidelines. Results Patients with IFDVT represent an opportune subset of patients for acute interventional management with currently available techniques. This subset of patients with proximal DVT has a worse prognosis, is less well studied, and benefits more from acute intervention compared to patients with proximal DVT or distal DVT. Conclusion Invasive catheter-based therapies that remove thrombus and correct venous outflow obstructions improve outcomes and morbidity in patients with IFDVT. Future trials that address IFDVT specifically will improve our understanding and the proper management of this higher-risk subset of patients with DVT. PMID:25598728

Polynomial interpretation of multipole vectors

NASA Astrophysics Data System (ADS)

Katz, Gabriel; Weeks, Jeff

2004-09-01

Copi, Huterer, Starkman, and Schwarz introduced multipole vectors in a tensor context and used them to demonstrate that the first-year Wilkinson microwave anisotropy probe (WMAP) quadrupole and octopole planes align at roughly the 99.9% confidence level. In the present article, the language of polynomials provides a new and independent derivation of the multipole vector concept. Bézout’s theorem supports an elementary proof that the multipole vectors exist and are unique (up to rescaling). The constructive nature of the proof leads to a fast, practical algorithm for computing multipole vectors. We illustrate the algorithm by finding exact solutions for some simple toy examples and numerical solutions for the first-year WMAP quadrupole and octopole. We then apply our algorithm to Monte Carlo skies to independently reconfirm the estimate that the WMAP quadrupole and octopole planes align at the 99.9% level.

LANDMARK-BASED SPEECH RECOGNITION: REPORT OF THE 2004 JOHNS HOPKINS SUMMER WORKSHOP.

PubMed

Hasegawa-Johnson, Mark; Baker, James; Borys, Sarah; Chen, Ken; Coogan, Emily; Greenberg, Steven; Juneja, Amit; Kirchhoff, Katrin; Livescu, Karen; Mohan, Srividya; Muller, Jennifer; Sonmez, Kemal; Wang, Tianyu

2005-01-01

Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines, dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an automatic speech recognizer, current theories of human speech perception and phonology (specifically landmark-based speech perception, nonlinear phonology, and articulatory phonology). All three systems begin with a high-dimensional multiframe acoustic-to-distinctive feature transformation, implemented using support vector machines trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the support vector machines are then integrated using one of three pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification. Log probability scores computed by these models are then combined, using log-linear combination, with other word scores available in the lattice output of a first-pass recognizer, and the resulting combination score is used to compute a second-pass speech recognition output.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

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

Miltiadis Alamaniotis; Vivek Agarwal

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are thenmore » inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.« less

Incremental classification learning for anomaly detection in medical images

NASA Astrophysics Data System (ADS)

Giritharan, Balathasan; Yuan, Xiaohui; Liu, Jianguo

2009-02-01

Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

Agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)

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

Archibald, Richard K; Filippi, Anthony M; Bhaduri, Budhendra L

Extracting endmembers from remotely sensed images of vegetated areas can present difficulties. In this research, we applied a recently developed endmember-extraction algorithm based on Support Vector Machines (SVMs) to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically and effectively estimate endmembers; synthetic data and a geologicmore » scene were previously analyzed. Here we compared the efficacies of the SVM-BEE and N-FINDR algorithms in extracting endmembers from a predominantly agricultural scene. SVM-BEE was able to estimate vegetation and other endmembers for all classes in the image, which N-FINDR failed to do. Classifications based on SVM-BEE endmembers were markedly more accurate compared with those based on N-FINDR endmembers.« less

Exploring the capabilities of support vector machines in detecting silent data corruptions

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

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based onmore » different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.« less

Exploring the capabilities of support vector machines in detecting silent data corruptions

DOE PAGES

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo; ...

2018-02-01

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based onmore » different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.« less

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

Analysis of programming properties and the row-column generation method for 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, WeiDa

2013-12-01

This paper deals with fast methods for training a 1-norm support vector machine (SVM). First, we define a specific class of linear programming with many sparse constraints, i.e., row-column sparse constraint linear programming (RCSC-LP). In nature, the 1-norm SVM is a sort of RCSC-LP. In order to construct subproblems for RCSC-LP and solve them, a family of row-column generation (RCG) methods is introduced. RCG methods belong to a category of decomposition techniques, and perform row and column generations in a parallel fashion. Specially, for the 1-norm SVM, the maximum size of subproblems of RCG is identical with the number of Support Vectors (SVs). We also introduce a semi-deleting rule for RCG methods and prove the convergence of RCG methods when using the semi-deleting rule. Experimental results on toy data and real-world datasets illustrate that it is efficient to use RCG to train the 1-norm SVM, especially in the case of small SVs. Copyright © 2013 Elsevier Ltd. All rights reserved.

Application of support vector machines for copper potential mapping in Kerman region, Iran

NASA Astrophysics Data System (ADS)

Shabankareh, Mahdi; Hezarkhani, Ardeshir

2017-04-01

The first step in systematic exploration studies is mineral potential mapping, which involves classification of the study area to favorable and unfavorable parts. Support vector machines (SVM) are designed for supervised classification based on statistical learning theory. This method named support vector classification (SVC). This paper describes SVC model, which combine exploration data in the regional-scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps were in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration and geochemistry. The SVC modeling result selected 2220 pixels as favorable zones, approximately 25 percent of the study area. Besides, 66 out of 86 copper indices, approximately 78.6% of all, were located in favorable zones. Other main goal of this study was to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data to its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer had a certain pattern. These patterns of SVC results could be considered as regional copper exploration characteristics.

Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain).

PubMed

García Nieto, P J; Alonso Fernández, J R; de Cos Juez, F J; Sánchez Lasheras, F; Díaz Muñiz, C

2013-04-01

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. Copyright © 2013 Elsevier Inc. All rights reserved.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

NASA Astrophysics Data System (ADS)

Valizadeh, Maryam; Sohrabi, Mahmoud Reza

2018-03-01

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

[Effect of the vacuum-formed retainer on preventing the proximal contact loss between implant supported crown and adjacent natural teeth].

PubMed

Zeng, B J; Guo, Y; Yu, R Y

2018-06-18

To evaluate the effect of the vacuum-formed retainer on preventing the proximal contact loss between the implant supported crown and its adjacent natural teeth. Forty-six posterior implant crowns in the mandible including 92 interproximal contacts in 46 patients (19 men, 27 women) aged from 25 to 66 years were included. The participants in experimental group (22 cases) were vacuum-formed retainers at night, while participants in control group (24 cases) only received routine examination. The two groups were not different in age, gender, the time interval of the tooth loss and tooth position at baseline. Mesial and distal proximal contact tightness was measured using the orthodontic dynamometer and metallic articulating film immediately after crown delivery, and 1-month, 3-month, 6-month, and 1-year follow-up respectively. The articulating film was inserted interdentally from the occlusal direction, and then it was slowly removed in the buccallingual direction by the dynamometer. Increasing the number of films (N) piece by piece until the frictional force (F) was great than 0, and the number of films (N) was recorded. At each follow-up, proximal contact between implant crown and its adjacent teeth was considered to be loss if the number of films (N) used at immediate crown delivery passed without frictional force (F=0). Besides, the periodontal conditions [scored according to the probing depth (PD), bleeding index (BI), mobility (M)] and complaint of food impaction were recorded. The mesial and distal proximal contact loss rates were compared between the two groups at different times. Chi-square test or Fisher's exact test was used for statistical analysis. The proximal contact loss rate on the mesial surface of the implant supported crown continuously increased over the follow-up periods. At the end of the 1-, 3-, and 6-month follow ups, 18.2%, 22.7% and 27.3% were identified for the contact loss rates on the mesial surface of the implant supported crown in the experimental group, respectively. Meanwhile in control group, the rates were 20.8%, 37.5% and 45.8%. No significant differences were observed at the end of the 1-, 3-, and 6-month follow ups(1-month: χ 2 =0.000, P=1.000; 3-month: χ 2 =1.183, P=0.277; 6-month: χ 2 =1.697, P=0.193). The proximal contact loss rate on the mesial surface in control group (62.5%) was significantly higher than that in the experimental group (31.8%, χ 2 =4.330, P=0.037) at the end of the 1-year follow-up. However, no statistical difference was found on the distal surfaces between the two groups during the whole follow-up periods. The first open contact was noted 1 month after crown insertion. By wearing vacuum-formed retainer for one year, the incidence of open contacts between the posterior implant prostheses and mesial adjacent teeth in the mandible has been reduced.

Evidence for involvement of nonesterified fatty acid-induced protonophoric uncoupling during mitochondrial dysfunction caused by hypoxia and reoxygenation

PubMed Central

Feldkamp, Thorsten; Weinberg, Joel M.; Hörbelt, Markus; Von Kropff, Christina; Witzke, Oliver; Nürnberger, Jens; Kribben, Andreas

2009-01-01

Background. Proximal tubules subjected to hypoxia in vitro under conditions relevant to ischaemia in vivo develop an energetic deficit that is not corrected even after full reoxygenation. We have provided evidence that accumulation of nonesterified fatty acids (NEFA) is the primary reason for this energetic deficit. In this study, we have further investigated the mechanism for the NEFA-induced energetic deficit. Methods. Mitochondrial membrane potential (Δψ) was measured in digitonin-permeabilized, freshly isolated proximal tubules by safranin O uptake. Addition of the potassium/proton exchanger nigericin enables the determination of the mitochondrial proton motive force (Δp) and the proton gradient (ΔpH). ATP was measured luminometrically and NEFA colorimetrically. Results. Tubule ATP content was depleted after hypoxia and recovered incompletely, even after full reoxygenation. Mitochondrial safranin O uptake was decreased in proximal tubules after hypoxia and reoxygenation (H/R). This decrease was attenuated by delipidated bovine serum albumin (dBSA) or citrate. Addition of nigericin increased safranin O uptake of mitochondria in normoxic proximal tubules, but not in proximal tubules after H/R. Addition of dBSA restored the effect of nigericin to increase mitochondrial safranin O uptake. Addition of the NEFA oleate had the same impact on mitochondrial safranin O uptake as subjecting proximal tubules to H/R. Conclusion. The mechanism of the NEFA-induced energetic deficit in freshly isolated rat proximal tubules induced by H/R is characterized by impaired ATP production after full reoxygenation, impaired recovery of Δψ and Δp, abrogation of ΔpH and sensitivity to citrate, consistent with involvement of the tricarboxylate carrier. The data support the concept that protonophoric uncoupling by NEFA movement on anion carriers plays a critical role in proximal tubule mitochochondrial dysfunction after H/R. PMID:18678559

Emotional personality/proximity versus emotional authenticity in patient-physician communication in healthy study participants, and in patients with benign breast disease, and breast cancer: a prospective case-control study in Finland.

PubMed

Eskelinen, Matti; Korhonen, Riika; Selander, Tuomas; Ollonen, Paula

2015-03-01

The associations between emotional personality, proximity and authenticity in patient-physician communication during breast cancer (BC) consultations are rarely considered together in a prospective study. We, therefore, investigated emotional personality/proximity versus authenticity in patient-physician communication in healthy study subjects (HSS) and in patients with benign breast disease (BBD) and breast cancer (BC). In the Kuopio Breast Cancer Study, 115 women with breast symptoms were evaluated regarding emotional personality, proximity and authenticity in their a patient-physician communication before any diagnostic procedures were carried-out. The emotional personality and the emotional proximity in patient-physician communication was highly significantly positively correlated in the BBD group. The kappa-values for emotional personality versus emotional proximity in the HSS, BBD and BC groups were statistically significant. There was also a highly significant positive correlation between emotional personality and emotional authenticity in the HSS, BBD and BC groups and the kappa values in the HSS, BBD and BC groups were statistically significant. There was a highly significant positive correlation between emotional proximity and emotional authenticity in the BBD group, and the weighted kappa-values in the BBD group were statistically significant. The results of the present study support a powerful link between emotional personality/proximity and emotional authenticity, and provides new information in patient-physician communication in the HSS, BBD and BC groups. This finding is of clinical importance, since during breast disease consultation, barriers to patient-physician communication may be associated with difficulties in early BC diagnosis in the breast cancer diagnostic unit. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

The visual properties of proximal and remote distractors differentially influence reaching planning times: evidence from pro- and antipointing tasks.

PubMed

Heath, Matthew; DeSimone, Jesse C

2016-11-01

The saccade literature has consistently reported that the presentation of a distractor remote to a target increases reaction time (i.e., the remote distractor effect: RDE). As well, some studies have shown that a proximal distractor facilitates saccade reaction time. The lateral inhibition hypothesis attributes the aforementioned findings to the inhibition/facilitation of target selection mechanisms operating in the intermediate layers of the superior colliculus (SC). Although the impact of remote and proximal distractors has been extensively examined in the saccade literature, a paucity of work has examined whether such findings generalize to reaching responses, and to our knowledge, no work has directly contrasted reaching RTs for remote and proximal distractors. To that end, the present investigation had participants complete reaches in target only trials (i.e., TO) and when distractors were presented at "remote" (i.e., the opposite visual field) and "proximal" (i.e., the same visual field) locations along the same horizontal meridian as the target. As well, participants reached to the target's veridical (i.e., propointing) and mirror-symmetrical (i.e., antipointing) location. The basis for contrasting pro- and antipointing was to determine whether the distractor's visual- or motor-related activity influence reaching RTs. Results demonstrated that remote and proximal distractors, respectively, increased and decreased reaching RTs and the effect was consistent for pro- and antipointing. Accordingly, results evince that the RDE and the facilitatory effects of a proximal distractor are effector independent and provide behavioral support for the contention that the SC serves as a general target selection mechanism. As well, the comparable distractor-related effects for pro- and antipointing trials indicate that the visual properties of remote and proximal distractors respectively inhibit and facilitate target selection.

Bundles over nearly-Kahler homogeneous spaces in heterotic string theory

NASA Astrophysics Data System (ADS)

Klaput, Michael; Lukas, Andre; Matti, Cyril

2011-09-01

We construct heterotic vacua based on six-dimensional nearly-Kahler homogeneous manifolds and non-trivial vector bundles thereon. Our examples are based on three specific group coset spaces. It is shown how to construct line bundles over these spaces, compute their properties and build up vector bundles consistent with supersymmetry and anomaly cancelation. It turns out that the most interesting coset is SU(3)/U(1)2. This space supports a large number of vector bundles which lead to consistent heterotic vacua, some of them with three chiral families.

Inter-epidemic abundance and distribution of potential mosquito vectors for Rift Valley fever virus in Ngorongoro district, Tanzania.

PubMed

Mweya, Clement N; Kimera, Sharadhuli I; Mellau, Lesakit S B; Mboera, Leonard E G

2015-01-01

Rift Valley fever (RVF) is a mosquito-borne viral zoonosis that primarily affects ruminants but also has the capacity to infect humans. To determine the abundance and distribution of mosquito vectors in relation to their potential role in the virus transmission and maintenance in disease epidemic areas of Ngorongoro district in northern Tanzania. A cross-sectional entomological investigation was carried out before the suspected RVF outbreak in October 2012. Mosquitoes were sampled both outdoors and indoors using the Centre for Disease Control (CDC) light traps and Mosquito Magnets baited with attractants. Outdoor traps were placed in proximity with breeding sites and under canopy in banana plantations close to the sleeping places of animals. A total of 1,823 mosquitoes were collected, of which 87% (N=1,588) were Culex pipiens complex, 12% (N=226) Aedes aegypti, and 0.5% (N=9) Anopheles species. About two-thirds (67%; N=1,095) of C. pipiens complex and nearly 100% (N=225) of A. aegypti were trapped outdoors using Mosquito Magnets. All Anopheles species were trapped indoors using CDC light traps. There were variations in abundance of C. pipiens complex and A. aegypti among different ecological and vegetation habitats. Over three quarters (78%) of C. pipiens complex and most (85%) of the A. aegypti were trapped in banana and maize farms. Both C. pipiens complex and A. aegypti were more abundant in proximity with cattle and in semi-arid thorn bushes and lower Afro-montane. The highest number of mosquitoes was recorded in villages that were most affected during the RVF epidemic of 2007. Of the tested 150 pools of C. pipiens complex and 45 pools of A. aegypti, none was infected with RVF virus. These results provide insights into unique habitat characterisation relating to mosquito abundances and distribution in RVF epidemic-prone areas of Ngorongoro district in northern Tanzania.

Effects of Human Metapneumovirus and Respiratory Syncytial Virus Antigen Insertion in Two 3′ Proximal Genome Positions of Bovine/Human Parainfluenza Virus Type 3 on Virus Replication and Immunogenicity

PubMed Central

Tang, Roderick S.; Schickli, Jeanne H.; MacPhail, Mia; Fernandes, Fiona; Bicha, Leenas; Spaete, Joshua; Fouchier, Ron A. M.; Osterhaus, Albert D. M. E.; Spaete, Richard; Haller, Aurelia A.

2003-01-01

A live attenuated bovine parainfluenza virus type 3 (PIV3), harboring the fusion (F) and hemagglutinin-neuraminidase (HN) genes of human PIV3, was used as a virus vector to express surface glycoproteins derived from two human pathogens, human metapneumovirus (hMPV) and respiratory syncytial virus (RSV). RSV and hMPV are both paramyxoviruses that cause respiratory disease in young children, the elderly, and immunocompromised individuals. RSV has been known for decades to cause acute lower respiratory tract infections in young children, which often result in hospitalization, while hMPV has only been recently identified as a novel human respiratory pathogen. In this study, the ability of bovine/human PIV3 to express three different foreign transmembrane surface glycoproteins and to induce a protective immune response was evaluated. The RNA-dependent RNA polymerase of paramyxoviruses binds to a single site at the 3′ end of the viral RNA genome to initiate transcription of viral genes. The genome position of the viral gene determines its level of gene expression. The promoter-proximal gene is transcribed with the highest frequency, and each downstream gene is transcribed less often due to attenuation of transcription at each gene junction. This feature of paramyxoviruses was exploited using the PIV3 vector by inserting the foreign viral genes at the 3′ terminus, at position 1 or 2, of the viral RNA genome. These locations were expected to yield high levels of foreign viral protein expression stimulating a protective immune response. The immunogenicity and protection results obtained with a hamster model showed that bovine/human PIV3 can be employed to generate bivalent PIV3/RSV or PIV3/hMPV vaccine candidates that will be further evaluated for safety and efficacy in primates. PMID:14512532

Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

PubMed Central

2011-01-01

The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular. PMID:22206355

Tubular Obstruction Leads to Progressive Proximal Tubular Injury and Atubular Glomeruli in Polycystic Kidney Disease

PubMed Central

Galarreta, Carolina I.; Grantham, Jared J.; Forbes, Michael S.; Maser, Robin L.; Wallace, Darren P.; Chevalier, Robert L.

2015-01-01

In polycystic kidney disease (PKD), renal parenchyma is destroyed by cysts, hypothesized to obstruct nephrons. A signature of unilateral ureteral obstruction, proximal tubular atrophy leads to formation of atubular glomeruli. To determine whether this process occurs in PKD, kidneys from pcy mice (moderately progressive PKD), kidneys from cpk mice (rapidly progressive PKD), and human autosomal dominant PKD were examined in early and late stages. Integrity of the glomerulotubular junction and proximal tubular mass were determined in sections stained with Lotus tetragonolobus lectin. Development of proximal tubular atrophy and atubular glomeruli was determined in serial sections of individual glomeruli. In pcy mice, most glomerulotubular junctions were normal at 20 weeks, but by 30 weeks, 56% were atrophic and 25% of glomeruli were atubular; glomerulotubular junction integrity decreased with increasing cyst area (r = 0.83, P < 0.05). In cpk mice, all glomerulotubular junctions were normal at 10 days, but by 19 days, 26% had become abnormal. In early-stage autosomal dominant PKD kidneys, 50% of glomeruli were atubular or attached to atrophic tubules; in advanced disease, 100% were abnormal. Thus, proximal tubular injury in cystic kidneys closely parallels that observed with ureteral obstruction. These findings support the hypothesis that, in renal cystic disorders, cyst-dependent obstruction of medullary and cortical tubules initiates a process culminating in widespread destruction of proximal convoluted tubules at the glomerulotubular junction. PMID:24815352

A Mathematical and Sociological Analysis of Google Search Algorithm

DTIC Science & Technology

2013-01-16

through the collective intelligence of the web to determine a page’s importance. Let v be a vector of RN with N ≥ 8 billion. Any unit vector in RN is...scrolled up by some artifical hits. Aknowledgment: The authors would like to thank Dr. John Lavery for his encouragement and support which enable them to

Automated Creation of Labeled Pointcloud Datasets in Support of Machine-Learning Based Perception

DTIC Science & Technology

2017-12-01

computationally intensive 3D vector math and took more than ten seconds to segment a single LIDAR frame from the HDL-32e with the Dell XPS15 9650’s Intel...Core i7 CPU. Depth Clustering avoids the computationally intensive 3D vector math of Euclidean Clustering-based DON segmentation and, instead

EMMA: An Extensible Mammalian Modular Assembly Toolkit for the Rapid Design and Production of Diverse Expression Vectors.

PubMed

Martella, Andrea; Matjusaitis, Mantas; Auxillos, Jamie; Pollard, Steven M; Cai, Yizhi

2017-07-21

Mammalian plasmid expression vectors are critical reagents underpinning many facets of research across biology, biomedical research, and the biotechnology industry. Traditional cloning methods often require laborious manual design and assembly of plasmids using tailored sequential cloning steps. This process can be protracted, complicated, expensive, and error-prone. New tools and strategies that facilitate the efficient design and production of bespoke vectors would help relieve a current bottleneck for researchers. To address this, we have developed an extensible mammalian modular assembly kit (EMMA). This enables rapid and efficient modular assembly of mammalian expression vectors in a one-tube, one-step golden-gate cloning reaction, using a standardized library of compatible genetic parts. The high modularity, flexibility, and extensibility of EMMA provide a simple method for the production of functionally diverse mammalian expression vectors. We demonstrate the value of this toolkit by constructing and validating a range of representative vectors, such as transient and stable expression vectors (transposon based vectors), targeting vectors, inducible systems, polycistronic expression cassettes, fusion proteins, and fluorescent reporters. The method also supports simple assembly combinatorial libraries and hierarchical assembly for production of larger multigenetic cargos. In summary, EMMA is compatible with automated production, and novel genetic parts can be easily incorporated, providing new opportunities for mammalian synthetic biology.

Effects of Cucumber mosaic virus infection on vector and non-vector herbivores of squash.

PubMed

Mauck, Kerry E; De Moraes, Consuelo M; Mescher, Mark C

2010-11-01

Plant chemicals mediating interactions with insect herbivores seem a likely target for manipulation by insectvectored plant pathogens. Yet, little is currently known about the chemical ecology of insect-vectored diseases or their effects on the ecology of vector and nonvector insects. We recently reported that a widespread plant pathogen, Cucumber mosaic virus (CMV), greatly reduces the quality of host-plants (squash) for aphid vectors, but that aphids are nevertheless attracted to the odors of infected plants-which exhibit elevated emissions of a volatile blend otherwise similar to the odor of healthy plants. This finding suggests that exaggerating existing host-location cues can be a viable vector attraction strategy for pathogens that otherwise reduce host quality for vectors. Here we report additional data regarding the effects of CMV infection on plant interactions with a common nonvector herbivore, the squash bug, Anasa tristis, which is a pest in this system. We found that adult A. tristis females preferred to oviposit on healthy plants in the field, and that healthy plants supported higher populations of nymphs. Collectively, our recent findings suggest that CMV-induced changes in host plant chemistry influence the behavior of both vector and non-vector herbivores, with significant implications both for disease spread and for broader community-level interactions.

Scorebox extraction from mobile sports videos using Support Vector Machines

NASA Astrophysics Data System (ADS)

Kim, Wonjun; Park, Jimin; Kim, Changick

2008-08-01

Scorebox plays an important role in understanding contents of sports videos. However, the tiny scorebox may give the small-display-viewers uncomfortable experience in grasping the game situation. In this paper, we propose a novel framework to extract the scorebox from sports video frames. We first extract candidates by using accumulated intensity and edge information after short learning period. Since there are various types of scoreboxes inserted in sports videos, multiple attributes need to be used for efficient extraction. Based on those attributes, the optimal information gain is computed and top three ranked attributes in terms of information gain are selected as a three-dimensional feature vector for Support Vector Machines (SVM) to distinguish the scorebox from other candidates, such as logos and advertisement boards. The proposed method is tested on various videos of sports games and experimental results show the efficiency and robustness of our proposed method.

Discontinuity Detection in the Shield Metal Arc Welding Process

PubMed Central

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-01-01

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries. PMID:28489045

Discontinuity Detection in the Shield Metal Arc Welding Process.

PubMed

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-05-10

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors-a microphone and piezoelectric-that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system's high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.

A Review of the Positive Influence of Crown Contours on Soft-Tissue Esthetics.

PubMed

Kinsel, Richard P; Pope, Bryan I; Capoferri, Daniele

2015-05-01

Successful crown restorations duplicate the natural tooth in hue, chroma, value, maverick colors, and surface texture. Equally important is the visual harmony of the facial and proximal soft-tissue contours, which requires the collaborative skills of the restorative dentist, periodontist, and dental technician. The treatment team must understand the biologic structures adjacent to natural dentition and dental implants. This report describes the potential for specifically designed restorative contours to dictate the optimal gingival profile for tooth-supported and implant-supported crowns. Showing several cases, the article explains how esthetic soft-tissue contours enhance the definitive crown restoration, highlights the importance of clinical evaluation of adjacent biologic structures, and discusses keys to predicting when the proximal papilla has the potential to return to a favorable height and shape.

Peer interactions and academic engagement of youth with developmental disabilities in inclusive middle and high school classrooms.

PubMed

Carter, Erik W; Sisco, Lynn G; Brown, Lissa; Brickham, Dana; Al-Khabbaz, Zainab A

2008-11-01

We examined the peer interactions and academic engagement of 23 middle and high school students with developmental disabilities within inclusive academic and elective classrooms. The extent to which students with and without disabilities interacted socially was highly variable and influenced by instructional format, the proximity of general and special educators, and curricular area. Peer interactions occurred more often within small group instructional formats, when students were not receiving direct support from a paraprofessional or special educator, and in elective courses. Academic engagement also varied, with higher levels evidenced during one-to-one or small group instruction and when in proximity of general or special educators. Implications for designing effective support strategies for students with autism and/or intellectual disability within general education classrooms are discussed.

Information Sharing Modalities for Mobile Ad-Hoc Networks

NASA Astrophysics Data System (ADS)

de Spindler, Alexandre; Grossniklaus, Michael; Lins, Christoph; Norrie, Moira C.

Current mobile phone technologies have fostered the emergence of a new generation of mobile applications. Such applications allow users to interact and share information opportunistically when their mobile devices are in physical proximity or close to fixed installations. It has been shown how mobile applications such as collaborative filtering and location-based services can take advantage of ad-hoc connectivity to use physical proximity as a filter mechanism inherent to the application logic. We discuss the different modes of information sharing that arise in such settings based on the models of persistence and synchronisation. We present a platform that supports the development of applications that can exploit these modes of ad-hoc information sharing and, by means of an example, show how such an application can be realised based on the supported event model.

Analysis of Particle Content of Recombinant Adeno-Associated Virus Serotype 8 Vectors by Ion-Exchange Chromatography

PubMed Central

Lock, Martin; Alvira, Mauricio R.

2012-01-01

Abstract Advances in adeno-associated virus (AAV)-mediated gene therapy have brought the possibility of commercial manufacturing of AAV vectors one step closer. To realize this prospect, a parallel effort with the goal of ever-increasing sophistication for AAV vector production technology and supporting assays will be required. Among the important release assays for a clinical gene therapy product, those monitoring potentially hazardous contaminants are most critical for patient safety. A prominent contaminant in many AAV vector preparations is vector particles lacking a genome, which can substantially increase the dose of AAV capsid proteins and lead to possible unwanted immunological consequences. Current methods to determine empty particle content suffer from inconsistency, are adversely affected by contaminants, or are not applicable to all serotypes. Here we describe the development of an ion-exchange chromatography-based assay that permits the rapid separation and relative quantification of AAV8 empty and full vector particles through the application of shallow gradients and a strong anion-exchange monolith chromatography medium. PMID:22428980

Vector-borne diseases in Haiti: a review.

PubMed

Ben-Chetrit, Eli; Schwartz, Eli

2015-01-01

Haiti lies on the western third of the island of Hispaniola in the Caribbean, and is one of the poorest nations in the Western hemisphere. Haiti attracts a lot of medical attention and support due to severe natural disasters followed by disastrous health consequences. Vector-borne infections are still prevalent there with some unique aspects comparing it to Latin American countries and other Caribbean islands. Although vector-borne viral diseases such as dengue and recently chikungunya can be found in many of the Caribbean islands, including Haiti, there is an apparent distinction of the vector-borne parasitic diseases. Contrary to neighboring Carribbean islands, Haiti is highly endemic for malaria, lymphatic filariasis and mansonellosis. Affected by repeat natural disasters, poverty and lack of adequate infrastructure, control of transmission within Haiti and prevention of dissemination of vector-borne pathogens to other regions is challenging. In this review we summarize some aspects concerning diseases caused by vector-borne pathogens in Haiti. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine

NASA Astrophysics Data System (ADS)

Cheng, Yixuan; Fan, Wenqing; Huang, Wei; An, Jing

2017-09-01

Dynamic monitoring the behavior of a program is widely used to discriminate between benign program and malware. It is usually based on the dynamic characteristics of a program, such as API call sequence or API call frequency to judge. The key innovation of this paper is to consider the full Native API sequence and use the support vector machine to detect the shellcode. We also use the Markov chain to extract and digitize Native API sequence features. Our experimental results show that the method proposed in this paper has high accuracy and low detection rate.

Localization of U(1) gauge vector field on flat branes with five-dimension (asymptotic) AdS5 spacetime

NASA Astrophysics Data System (ADS)

Zhao, Zhen-Hua; Xie, Qun-Ying

2018-05-01

In order to localize U(1) gauge vector field on Randall-Sundrum-like braneworld model with infinite extra dimension, we propose a new kind of non-minimal coupling between the U(1) gauge field and the gravity. We propose three kinds of coupling methods and they all support the localization of zero mode. In addition, one of them can support the localization of massive modes. Moreover, the massive tachyonic modes can be excluded. And our method can be used not only in the thin braneword models but also in the thick ones.

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

PubMed

Chen, S; Samingan, A K; Hanzo, L

2001-01-01

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

NASA Astrophysics Data System (ADS)

Shastri, Niket; Pathak, Kamlesh

2018-05-01

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

Implementation of support vector machine for classification of speech marked hijaiyah letters based on Mel frequency cepstrum coefficient feature extraction

NASA Astrophysics Data System (ADS)

Adhi Pradana, Wisnu; Adiwijaya; Novia Wisesty, Untari

2018-03-01

Support Vector Machine or commonly called SVM is one method that can be used to process the classification of a data. SVM classifies data from 2 different classes with hyperplane. In this study, the system was built using SVM to develop Arabic Speech Recognition. In the development of the system, there are 2 kinds of speakers that have been tested that is dependent speakers and independent speakers. The results from this system is an accuracy of 85.32% for speaker dependent and 61.16% for independent speakers.

Early Support Development of Children with Disorders of the Biopsychosocial Functioning in Poland

ERIC Educational Resources Information Center

Czyz, Anna

2017-01-01

This article presents the results of a research study on the system of early child development support with developmental disabilities and their families in Poland. The analysis covered areas such as proximity and accessibility of services, infrastructural conditions, preparation of personnel, and occurrence of systemic barriers. The article…

Multi-Tiered System of Support: Best Differentiation Practices for English Language Learners in Tier 1

ERIC Educational Resources Information Center

Izaguirre, Cecilia

2017-01-01

Purpose: This qualitative case study explored the best practices of differentiation of Tier 1 instruction within a multi-tiered system of support for English Language Learners who were predominately Spanish speaking. Theoretical Framework: The zone of proximal development theory, cognitive theory, and the affective filter hypothesis guided this…

78 FR 29120 - Notice of Intent (NOI) To Prepare an Environmental Impact Statement (EIS) for Proposed...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-17

...) for Proposed Establishment and Expansion of Military Airspace in Support of the Oregon Air National Guard (ORANG), Portland International Airport, Portland, and Kingsley Field, Klamath Falls, OR AGENCY... proximity to ORANG flying units to support advanced 21st-century air-to-air tactical fighter technologies...

A Proposed Systems Model for Socializing the Graduate Writer

ERIC Educational Resources Information Center

Jones, David R.

2018-01-01

Although researchers chorus the need to support graduate students toward higher levels of writing proficiency, their findings lack a holistic model for doing so. A model emerges upon scrutiny of the factors that have been implicated in supporting writing proficiency. In the proposed model, a socialization theory fits as a proximal process into the…

The stability of a hip fracture determines the fatigue of an intramedullary nail.

PubMed

Eberle, S; Bauer, C; Gerber, C; von Oldenburg, G; Augat, P

2010-01-01

The purpose of this study was to address the question of how the stability of a proximal hip fracture determines the fatigue and failure mechanism of an intramedullary implant. To answer this question, mechanical experiments and finite element simulations with two different loading scenarios were conducted. The two load scenarios differed in the mechanical support of the fracture by an artificial bone sleeve, representing the femoral head and neck. The experiments confirmed that an intramedullary nail fails at a lower load in an unstable fracture situation in the proximal femur than in a stable fracture. The nails with an unstable support failed at a load 28 per cent lower than the nails with a stable support by the femoral neck. Hence, the mechanical support of a fracture is crucial to the fatigue failure of an implant. The simulation showed why the fatigue fracture of the nail starts at the aperture of the lag screw. It is the location of the highest von Mises stress, which is the failure criterion for ductile materials.

Optimizing structure of complex technical system by heterogeneous vector criterion in interval form

NASA Astrophysics Data System (ADS)

Lysenko, A. V.; Kochegarov, I. I.; Yurkov, N. K.; Grishko, A. K.

2018-05-01

The article examines the methods of development and multi-criteria choice of the preferred structural variant of the complex technical system at the early stages of its life cycle in the absence of sufficient knowledge of parameters and variables for optimizing this structure. The suggested methods takes into consideration the various fuzzy input data connected with the heterogeneous quality criteria of the designed system and the parameters set by their variation range. The suggested approach is based on the complex use of methods of interval analysis, fuzzy sets theory, and the decision-making theory. As a result, the method for normalizing heterogeneous quality criteria has been developed on the basis of establishing preference relations in the interval form. The method of building preferential relations in the interval form on the basis of the vector of heterogeneous quality criteria suggest the use of membership functions instead of the coefficients considering the criteria value. The former show the degree of proximity of the realization of the designed system to the efficient or Pareto optimal variants. The study analyzes the example of choosing the optimal variant for the complex system using heterogeneous quality criteria.

Higgs-like boson at 750 GeV and genesis of baryons

DOE PAGES

Davoudiasl, Hooman; Giardino, Pier Paolo; Zhang, Cen

2016-07-06

Here, we propose that the diphoton excess at 750 GeV reported by ATLAS and CMS is due to the decay of an exo-Higgs scalar η associated with the breaking of a new SU(2) e symmetry, dubbed exo-spin. New fermions, exo-quarks and exo-leptons, get TeV-scale masses through Yukawa couplings with η and generate its couplings to gluons and photons at one loop. Furthermore, the matter content of our model yields a B-L anomaly under SU(2) e, whose breaking we assume entails a first-order phase transition. A nontrivial B-L asymmetry may therefore be generated in the early Universe, potentially providing a baryogenesismore » mechanism through the Standard Model (SM) sphaleron processes. The spontaneous breaking of SU(2) e can, in principle, directly lead to electroweak symmetry breaking, thereby accounting for the proximity of the mass scales of the SM Higgs and the exo-Higgs. This model can be distinguished from those comprising a singlet scalar and vector fermions by the discovery of TeV scale exo-vector bosons, corresponding to the broken SU(2) e generators, at the LHC.« less

Higgs-like boson at 750 GeV and genesis of baryons

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

Davoudiasl, Hooman; Giardino, Pier Paolo; Zhang, Cen

Here, we propose that the diphoton excess at 750 GeV reported by ATLAS and CMS is due to the decay of an exo-Higgs scalar η associated with the breaking of a new SU(2) e symmetry, dubbed exo-spin. New fermions, exo-quarks and exo-leptons, get TeV-scale masses through Yukawa couplings with η and generate its couplings to gluons and photons at one loop. Furthermore, the matter content of our model yields a B-L anomaly under SU(2) e, whose breaking we assume entails a first-order phase transition. A nontrivial B-L asymmetry may therefore be generated in the early Universe, potentially providing a baryogenesismore » mechanism through the Standard Model (SM) sphaleron processes. The spontaneous breaking of SU(2) e can, in principle, directly lead to electroweak symmetry breaking, thereby accounting for the proximity of the mass scales of the SM Higgs and the exo-Higgs. This model can be distinguished from those comprising a singlet scalar and vector fermions by the discovery of TeV scale exo-vector bosons, corresponding to the broken SU(2) e generators, at the LHC.« less

Energy-exchange collisions of dark-bright-bright vector solitons.

PubMed

Radhakrishnan, R; Manikandan, N; Aravinthan, K

2015-12-01

We find a dark component guiding the practically interesting bright-bright vector one-soliton to two different parametric domains giving rise to different physical situations by constructing a more general form of three-component dark-bright-bright mixed vector one-soliton solution of the generalized Manakov model with nine free real parameters. Moreover our main investigation of the collision dynamics of such mixed vector solitons by constructing the multisoliton solution of the generalized Manakov model with the help of Hirota technique reveals that the dark-bright-bright vector two-soliton supports energy-exchange collision dynamics. In particular the dark component preserves its initial form and the energy-exchange collision property of the bright-bright vector two-soliton solution of the Manakov model during collision. In addition the interactions between bound state dark-bright-bright vector solitons reveal oscillations in their amplitudes. A similar kind of breathing effect was also experimentally observed in the Bose-Einstein condensates. Some possible ways are theoretically suggested not only to control this breathing effect but also to manage the beating, bouncing, jumping, and attraction effects in the collision dynamics of dark-bright-bright vector solitons. The role of multiple free parameters in our solution is examined to define polarization vector, envelope speed, envelope width, envelope amplitude, grayness, and complex modulation of our solution. It is interesting to note that the polarization vector of our mixed vector one-soliton evolves in sphere or hyperboloid depending upon the initial parametric choices.

Using an object-based grid system to evaluate a newly developed EP approach to formulate SVMs as applied to the classification of organophosphate nerve agents

NASA Astrophysics Data System (ADS)

Land, Walker H., Jr.; Lewis, Michael; Sadik, Omowunmi; Wong, Lut; Wanekaya, Adam; Gonzalez, Richard J.; Balan, Arun

2004-04-01

This paper extends the classification approaches described in reference [1] in the following way: (1.) developing and evaluating a new method for evolving organophosphate nerve agent Support Vector Machine (SVM) classifiers using Evolutionary Programming, (2.) conducting research experiments using a larger database of organophosphate nerve agents, and (3.) upgrading the architecture to an object-based grid system for evaluating the classification of EP derived SVMs. Due to the increased threats of chemical and biological weapons of mass destruction (WMD) by international terrorist organizations, a significant effort is underway to develop tools that can be used to detect and effectively combat biochemical warfare. This paper reports the integration of multi-array sensors with Support Vector Machines (SVMs) for the detection of organophosphates nerve agents using a grid computing system called Legion. Grid computing is the use of large collections of heterogeneous, distributed resources (including machines, databases, devices, and users) to support large-scale computations and wide-area data access. Finally, preliminary results using EP derived support vector machines designed to operate on distributed systems have provided accurate classification results. In addition, distributed training time architectures are 50 times faster when compared to standard iterative training time methods.

Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.

PubMed

Jaya, T; Dheeba, J; Singh, N Albert

2015-12-01

Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.

Repeat Transduction in the Mouse Lung by Using Adeno-Associated Virus Vectors with Different Serotypes

PubMed Central

Halbert, Christine L.; Rutledge, Elizabeth A.; Allen, James M.; Russell, David W.; Miller, A. Dusty

2000-01-01

Vectors derived from adeno-associated virus type 2 (AAV2) promote gene transfer and expression in the lung; however, we have found that while gene expression can persist for at least 8 months in mice, it was reduced dramatically in rabbits over a period of 2 months. The efficiency and persistence of AAV2-mediated gene expression in the human lung have yet to be determined, but it seems likely that readministration will be necessary over the lifetime of an individual. Unfortunately, we have found that transduction by a second administration of an AAV2 vector is blocked, presumably due to neutralizing antibodies generated in response to the primary vector exposure. Here, we have explored the use of AAV2 vectors pseudotyped with capsid proteins from AAV serotypes 2, 3, and 6 for readministration in the mouse lung. We found that an AAV6 vector transduced airway epithelial and alveolar cells in the lung at rates that were at least as high as those of AAV2 pseudotype vectors, while transduction rates mediated by AAV3 were much lower. AAV6 pseudotype vector transduction was unaffected by prior administration of an AAV2 or AAV3 vector, and transduction by an AAV2 pseudotype vector was unaffected by prior AAV6 vector administration, showing that cross-reactive neutralizing antibodies against AAV2 and AAV6 are not generated in mice. Interestingly, while prior administration of an AAV2 vector completely blocked transduction by a second AAV2 pseudotype vector, prior administration of an AAV6 vector only partially inhibited transduction by a second administration of an AAV6 pseudotype vector. Analysis of sera obtained from mice and humans showed that AAV6 is less immunogenic than AAV2, which helps explain this finding. These results support the development of AAV6 vectors for lung gene therapy both alone and in combination with AAV2 vectors. PMID:10627564

Ontology for Vector Surveillance and Management

PubMed Central

LOZANO-FUENTES, SAUL; BANDYOPADHYAY, ARITRA; COWELL, LINDSAY G.; GOLDFAIN, ALBERT; EISEN, LARS

2013-01-01

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an “umbrella” for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a “term tree” to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has_vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO *.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use. PMID:23427646

Ontology for vector surveillance and management.

PubMed

Lozano-Fuentes, Saul; Bandyopadhyay, Aritra; Cowell, Lindsay G; Goldfain, Albert; Eisen, Lars

2013-01-01

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an "umbrella" for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a "term tree" to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO*.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use.

Paleomagnetic evidence for high-temperature emplacement of the 1883 subaqueous pyroclastic flows from Krakatau Volcano, Indonesia

NASA Astrophysics Data System (ADS)

Mandeville, Charles W.; Carey, Steven; Sigurdsson, Haraldur; King, John

1994-05-01

The paroxysmal 1883 eruption of Krakatau volcano in Indonesia discharge at least 6.5 cu km (dense rock equivalent) of pyroclastic material into the shallow waters of the Sunda Straits within a 15-km radius of the volcano. Progressive thermal demagnetization studies of individually oriented pumice clasts from a core sample of the submarine pyroclastic deposits show that 41 out of 47 clasts exhibit single-component remanence with mean inclination of -24 deg. The partial thermoremanent magnetization components of both pumice and lithic clasts are well grouped in orientation, indicating that substantial cooling of clasts must have occurred following deposition. Estimated subaqueous emplacement temperature for such clasts is greater than 500 C. Rare two-component lithic fragments exhibit inflection points on vector endpoint diagrams that mark the temperature below which the fragments acquired magnetization of similar orientation. These inflection points range from 350 to 550 C, indicating a minimum subaqueous emplacement temperature of 350 C. Paleomagnetic evidence for high-emplacement temperature supports the hypothesis that proximal 1883 submarine pyroclastic deposits resulted from entrance of hot, subaerially generated pyroclastic flows into the sea. Similar deposits have been interpreted from the geologic record, but this is the first documented example of submarine pyroclastic flows from a historic eruption. The Kratatau deposits thus serve as an important modern analog for the study of pyroclastic flow/seawater interactions.

How can video supported reflection enhance teachers' professional development?

NASA Astrophysics Data System (ADS)

McCullagh, John F.

2012-03-01

This paper responds to Eva Lundqvist, Jonas Almqvist and Leif Ostman's account of how the manner of teaching can strongly influence pupil learning by recommending video supported reflection as a means by which teachers can transform the nature of their practice. Given the complex nature of the many conditions which influence and control teachers' actions the reframing of routine practice through reflection-in-action can prove challenging. This response paper describes how video can empower teachers to take greater control of their progress and allows for a more social constructivist approach to professional development. Along with a consideration of the difficulties associated with the notion of `reflection' and a short case study, the paper uses Lev Semenovich Vygotsky's zone of proximal development and the notion of scaffolding to propose that video offers a Video Supported Zone of Proximal Development which can ease the process of teacher development. In capturing permanent and exchangeable representations of practice video encourages a collaborative approach to reflection and is consistent with the original ideas of John Dewey.

Structural Analysis and Optimization of the Support Device Used for a Proximal Fracture of the Femur

DTIC Science & Technology

2008-12-01

support system and bone , the models were considered under two different load conditions. From these results, a recommendation can be made to the...support system and bone , the models were considered under two different load conditions. From these results, a recommendation can be made to the...nail with coordinate reference ............................................................30  Figure 27.  Model with bone and Nail

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

PubMed

Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

2016-01-01

Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

Snack food as a modulator of human resting-state functional connectivity.

PubMed

Mendez-Torrijos, Andrea; Kreitz, Silke; Ivan, Claudiu; Konerth, Laura; Rösch, Julie; Pischetsrieder, Monika; Moll, Gunther; Kratz, Oliver; Dörfler, Arnd; Horndasch, Stefanie; Hess, Andreas

2018-04-04

To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation. Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques. Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus. The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

A new artefacts resistant method for automatic lineament extraction using Multi-Hillshade Hierarchic Clustering (MHHC)

NASA Astrophysics Data System (ADS)

Šilhavý, Jakub; Minár, Jozef; Mentlík, Pavel; Sládek, Ján

2016-07-01

This paper presents a new method of automatic lineament extraction which includes the removal of the 'artefacts effect' which is associated with the process of raster based analysis. The core of the proposed Multi-Hillshade Hierarchic Clustering (MHHC) method incorporates a set of variously illuminated and rotated hillshades in combination with hierarchic clustering of derived 'protolineaments'. The algorithm also includes classification into positive and negative lineaments. MHHC was tested in two different territories in Bohemian Forest and Central Western Carpathians. The original vector-based algorithm was developed for comparison of the individual lineaments proximity. Its use confirms the compatibility of manual and automatic extraction and their similar relationships to structural data in the study areas.

Ground reaction vector re-adjustment-the secret of success in treatment of medial compartment knee osteoarthritis by novel high fibular osteotomy.

PubMed

Xie, Wenjun; Zhang, Yu; Qin, Xiaodong; Song, Lijun; Chen, Qun

2018-03-01

High fibular osteotomy has been preliminarily proved to be an effective treatment of knee osteoarthritis by excising a segment of bone at the proximal part of fibula. This imaginative procedure is clinical validated by its instant and explicit knee pain resorption and eventually deformity correction. The rationale of this treatment is named non-uniform settlement of the tibial plateau and used to elucidate the cause of knee joint degeneration, but cannot illuminate the reason of prompt postoperative pain resorption faithfully. To assist in better understanding of this therapeutic method and raising alert to possible unexpected complications, we proposed a new theory to elucidate the pain relief mechanism.

Extraction and classification of 3D objects from volumetric CT data

NASA Astrophysics Data System (ADS)

Song, Samuel M.; Kwon, Junghyun; Ely, Austin; Enyeart, John; Johnson, Chad; Lee, Jongkyu; Kim, Namho; Boyd, Douglas P.

2016-05-01

We propose an Automatic Threat Detection (ATD) algorithm for Explosive Detection System (EDS) using our multistage Segmentation Carving (SC) followed by Support Vector Machine (SVM) classifier. The multi-stage Segmentation and Carving (SC) step extracts all suspect 3-D objects. The feature vector is then constructed for all extracted objects and the feature vector is classified by the Support Vector Machine (SVM) previously learned using a set of ground truth threat and benign objects. The learned SVM classifier has shown to be effective in classification of different types of threat materials. The proposed ATD algorithm robustly deals with CT data that are prone to artifacts due to scatter, beam hardening as well as other systematic idiosyncrasies of the CT data. Furthermore, the proposed ATD algorithm is amenable for including newly emerging threat materials as well as for accommodating data from newly developing sensor technologies. Efficacy of the proposed ATD algorithm with the SVM classifier is demonstrated by the Receiver Operating Characteristics (ROC) curve that relates Probability of Detection (PD) as a function of Probability of False Alarm (PFA). The tests performed using CT data of passenger bags shows excellent performance characteristics.

Endovascular Perfusion Augmentation for Critical Care (EPACC) as a Resuscitative Adjunct in a Swine (Sus scrofa) Polytrauma Model of Ischemia Reperfusion Injury

DTIC Science & Technology

2017-11-29

debated topic. We sought to compare the proximal hemodynamic support provided by Zone 1versus Zone 3 REBOA placement, and the degree ofhemodynamic...minutes of hemorrhage. During the intervention period, average proximal MAP was significantly greater in Zone 1 animals when compared to Zone 3 animals...127.9=1=1.3 mmHg versus 53.4±1.1 mmHg), and greater in Zone 3 animals when compared to control animals (42.9±0.9 mmHg). Lactate concentrationswere

Making an implant-level impression using solid plastic, press-fit, closed-tray impression copings: a clinical report.

PubMed

Selecman, Audrey M; Wicks, Russell A

2009-03-01

An implant-level impression is often desired for designing and fabricating an implant-supported fixed restoration, especially when 2 or more implants have been placed. However, convergent implants placed too close in proximity pose several problems, starting with the impression. In situations of extreme convergence or close proximity, modification of conventional metal copings may be impossible. This clinical report describes the use and associated advantages and disadvantages of solid plastic, press-fit, closed-tray impression copings as a mechanism suitable to create an implant-level cast.

STM/STS on proximity-coupled superconducting graphene

NASA Astrophysics Data System (ADS)

Ovadia, Maoz; Ji, Yu; Lee, Gil-Ho; Fang, Wenjing; Hoffman, Jennifer; Jarillo-Herrero, Pablo; Kong, Jing; Kim, Philip

Graphene in good electrical contact with a superconductor has been observed to have an enhanced proximity effect. Application of a magnetic field is expected to generate an Abrikosov lattice of superconducting vortices, each containing Andreev bound states in its core. With our versatile, homebuilt, low temperature scanning tunneling force microscope (STM/SFM), we investigate the electronic properties of graphene on superconducting NbSe2 in a magnetic field and search for signatures of these vortex core states. This work was supported by the STC Center for Integrated Quantum Materials, NSF Grant No. DMR-1231319.

STM/STS on proximity-coupled superconducting graphene

NASA Astrophysics Data System (ADS)

Ovadia, Maoz; Ji, Yu; Hoffman, Jennifer; Wang, Joel I.-Jan; Jarillo-Herrero, Pablo

2015-03-01

Graphene in good electrical contact with a superconductor has been observed to have an enhanced proximity effect. Application of a magnetic field is expected to generate an Abrikosov lattice of superconducting vortices, each containing Andreev bound states in its core. With our versatile, homebuilt, low temperature scanning tunneling force microscope (STM/SFM), we investigate the electronic properties of graphene on superconducting NbSe2 in a magnetic field and search for signatures of these vortex core states. This work was supported by the STC Center for Integrated Quantum Materials, NSF Grant No. DMR-1231319.

Abundance and distribution of sylvatic dengue virus vectors in three different land cover types in Sarawak, Malaysian Borneo.

PubMed

Young, Katherine I; Mundis, Stephanie; Widen, Steven G; Wood, Thomas G; Tesh, Robert B; Cardosa, Jane; Vasilakis, Nikos; Perera, David; Hanley, Kathryn A

2017-08-31

Mosquito-borne dengue virus (DENV) is maintained in a sylvatic, enzootic cycle of transmission between canopy-dwelling non-human primates and Aedes mosquitoes in Borneo. Sylvatic DENV can spill over into humans living in proximity to forest foci of transmission, in some cases resulting in severe dengue disease. The most likely vectors of such spillover (bridge vectors) in Borneo are Ae. albopictus and Ae. niveus. Borneo is currently experiencing extensive forest clearance. To gauge the effect of this change in forest cover on the likelihood of sylvatic DENV spillover, it is first necessary to characterize the distribution of bridge vectors in different land cover types. In the current study, we hypothesized that Ae. niveus and Ae. albopictus would show significantly different distributions in different land cover types; specifically, we predicted that Ae. niveus would be most abundant in forests whereas Ae. albopictus would have a more even distribution in the landscape. Mosquitoes were collected from a total of 15 sites using gravid traps and a backpack aspirator around Kampong Puruh Karu, Sarawak, Malaysian Borneo, where sylvatic DENV spillover has been documented. A total of 2447 mosquitoes comprising 10 genera and 4 species of Aedes, were collected over the three years, 2013, 2014 and 2016, in the three major land cover types in the area, homestead, agriculture and forest. Mosquitoes were identified morphologically, pooled by species and gender, homogenized, and subject to DNA barcoding of each Aedes species and to arbovirus screening. As predicted, Ae. niveus was found almost exclusively in forests whereas Ae. albopictus was collected in all land cover types. Aedes albopictus was significantly (P = 0.04) more abundant in agricultural fields than forests. Sylvatic DENV was not detected in any Aedes mosquito pools, however genomes of 14 viruses were detected using next generation sequencing. Land cover type affects the abundance and distribution of the most likely bridge vectors of sylvatic DENV in Malaysia Borneo. Conversion of forests to agriculture will likely decrease the range and abundance of Ae. niveus but enhance the abundance of Ae. albopictus.

Teacher Academy Induction Learning Community: Guiding Teachers through Their Zone of Proximal Development

ERIC Educational Resources Information Center

Flores, Belinda Bustos; Hernandez, Arcelia; Garcia, Claudia Trevino; Claeys, Lorena

2011-01-01

This is a preliminary analysis of The Academy for Teacher Excellence (ATE) induction support provided through the Teacher Academy Induction Learning Community (TAILC). In response to current US teacher attrition rates, ATE-TAILC's primary objective is to retain teachers in the classroom and provide support to ensure they are fully prepared to meet…

Assembly and Functional Analysis of an S/MAR Based Episome with the Cystic Fibrosis Transmembrane Conductance Regulator Gene.

PubMed

De Rocco, Davide; Pompili, Barbara; Castellani, Stefano; Morini, Elena; Cavinato, Luca; Cimino, Giuseppe; Mariggiò, Maria A; Guarnieri, Simone; Conese, Massimo; Del Porto, Paola; Ascenzioni, Fiorentina

2018-04-17

Improving the efficacy of gene therapy vectors is still an important goal toward the development of safe and efficient gene therapy treatments. S/MAR (scaffold/matrix attached region)-based vectors are maintained extra-chromosomally in numerous cell types, which is similar to viral-based vectors. Additionally, when established as an episome, they show a very high mitotic stability. In the present study we tested the idea that addition of an S/MAR element to a CFTR (cystic fibrosis transmembrane conductance regulator) expression vector, may allow the establishment of a CFTR episome in bronchial epithelial cells. Starting from the observation that the S/MAR vector pEPI-EGFP (enhanced green fluorescence protein) is maintained as an episome in human bronchial epithelial cells, we assembled the CFTR vector pBQ-S/MAR. This vector, transfected in bronchial epithelial cells with mutated CFTR , supported long term wt CFTR expression and activity, which in turn positively impacted on the assembly of tight junctions in polarized epithelial cells. Additionally, the recovery of intact pBQ-S/MAR, but not the parental vector lacking the S/MAR element, from transfected cells after extensive proliferation, strongly suggested that pBQ-S/MAR was established as an episome. These results add a new element, the S/MAR, that can be considered to improve the persistence and safety of gene therapy vectors for cystic fibrosis pulmonary disease.

Aedes hensilli as a Potential Vector of Chikungunya and Zika Viruses

PubMed Central

Ledermann, Jeremy P.; Guillaumot, Laurent; Yug, Lawrence; Saweyog, Steven C.; Tided, Mary; Machieng, Paul; Pretrick, Moses; Marfel, Maria; Griggs, Anne; Bel, Martin; Duffy, Mark R.; Hancock, W. Thane; Ho-Chen, Tai; Powers, Ann M.

2014-01-01

An epidemic of Zika virus (ZIKV) illness that occurred in July 2007 on Yap Island in the Federated States of Micronesia prompted entomological studies to identify both the primary vector(s) involved in transmission and the ecological parameters contributing to the outbreak. Larval and pupal surveys were performed to identify the major containers serving as oviposition habitat for the likely vector(s). Adult mosquitoes were also collected by backpack aspiration, light trap, and gravid traps at select sites around the capital city. The predominant species found on the island was Aedes (Stegomyia) hensilli. No virus isolates were obtained from the adult field material collected, nor did any of the immature mosquitoes that were allowed to emerge to adulthood contain viable virus or nucleic acid. Therefore, laboratory studies of the probable vector, Ae. hensilli, were undertaken to determine the likelihood of this species serving as a vector for Zika virus and other arboviruses. Infection rates of up to 86%, 62%, and 20% and dissemination rates of 23%, 80%, and 17% for Zika, chikungunya, and dengue-2 viruses respectively, were found supporting the possibility that this species served as a vector during the Zika outbreak and that it could play a role in transmitting other medically important arboviruses. PMID:25299181

Metal accumulation and nephron heterogeneity in mercuric chloride-induced acute renal failure.

PubMed

Wilks, M F; Gregg, N J; Bach, P H

1994-01-01

The present study was designed to assess the effects of mercury on glomerular integrity during the early phase of acute renal failure. The silver amplification method showed distribution of mercury in midcortical and juxtamedullary glomeruli and on the brush border of the S2 segment of the proximal tubule 15 min after treatment. At 30 min, there was a decrease in glomerular staining and increased mercury in the proximal tubule. After 3 hr, mercury was no longer detectable in glomeruli but was widespread in the lumen of the proximal tubule. By 24 hr, mercury was prominent in all proximal tubular segments throughout the cortex. The presence of mercury in glomeruli was not related to hemodynamic changes, as there was no evidence for blood redistribution toward juxtamedullary glomeruli as assessed by the filling of the microvascular system with Monastral Blue B. The reduced activity of horseradish peroxidase (administered i.v. 90 sec and 10 min before sacrifice) in juxtamedullary glomeruli 30 min after mercury administration suggests a decreased uptake of horseradish peroxidase or an increased glomerular protein filtration. These data support glomerular filtration as the predominant excretory route for mercury, highlight the marked nephron heterogeneity in the distribution of this metal, and show that impairment of glomerular integrity occurs before necrosis of the proximal tubules and acute renal failure.

Proximity under Threat: The Role of Physical Distance in Intergroup Relations

PubMed Central

Wohl, Michael J. A.; Van Bavel, Jay J.

2016-01-01

Throughout human history, social groups have invested immense amounts of wealth and time to keep threatening out-groups at a distance. In the current research, we explored the relationship between intergroup threat, physical distance, and discrimination. Specifically, we examined how intergroup threat alters estimates of physical distance to out-groups and how physical proximity affects intergroup relations. Previous research has found that people judge threatening out-groups as physically close. In Studies 1 and 2, we examined ways to attenuate this bias. In Study 1 a secure (vs. permeable) US-Mexico border reduced the estimated proximity to Mexico City among Americans who felt threatened by Mexican immigration. In Study 2, intergroup apologies reduced estimates of physical proximity to a threatening cross-town rival university, but only among participants with cross-group friendships. In Study 3, New York Yankees fans who received an experimental induction of physical proximity to a threatening out-group (Boston Red Sox) had a stronger relationship between their collective identification with the New York Yankees and support for discriminatory policies toward members of the out-group (Red Sox fans) as well as how far they chose to sit from out-group members (Red Sox fans). Together, these studies suggest that intergroup threat alters judgment of physical properties, which has important implications for intergroup relations. PMID:27467267

Genetics Home Reference: Dent disease

MedlinePlus

... 20. Review. Citation on PubMed Wrong OM, Norden AG, Feest TG. Dent's disease; a familial proximal renal ... qualified healthcare professional . About Selection Criteria for Links Data Files & API Site Map Subscribe Customer Support USA. ...

A micropuncture study of the effect of parathyroid hormone on renal bicarbonate reabsorption.

PubMed Central

Bank, N; Aynediian, H S

1976-01-01

Renal micropuncture and clearance experiments were carried out in rats to study the effect of parathyroid hormone (PTH) on renal tubular HCO-/3 reabsorption. The rats were studied during an initial period of parathyroid deficiency (acute thyroidparathyroidectomy, TPTX) and during infusion of large amounts of bovine PTH. Under normal acid-base conditions, PTH administration to TPTX rats caused a significant rise in proximal tubular fluid HCO-/3 concentration (TFHCO-/3), a decrease in fluid reabsorption, and a fall in proximal HCO-/3 reabsorption from 94.0 to 88.2% (P less than 0.01). In control experiments with mannitol infusion, a comparable reduction in proximal fluid reabsorption occurred without any significant effect on intraluminal HCO-/3 concentration. During acute intravenous HCO-/3 loading, PTH inhibited proximal HCO-/3 reabsorption. However, no change in whole kidney HCO-/3 reabsorption was observed in these experiments or in the animals studied under normal acid-base conditions. The findings are consistent with the view that PTH inhibits proximal tubular HCO-/3 reabsorption with normal or high filtered loads of HCO-/3, but distal segments of the nephron are able to reabsorb the excess delivered from the proximal tubule. Measurements of urinary ammonium and titratable acid indicate that net acid excretion (NH+/4 + TA -- HCO-/3) increases significantly after PTH administration. These results do not provide support for the view that PTH excess causes metabolic acidosis by reducing renal acid excretion. PMID:956369

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

The maximum vector-angular margin classifier and its fast training on large datasets using a core vector machine.

PubMed

Hu, Wenjun; Chung, Fu-Lai; Wang, Shitong

2012-03-01

Although pattern classification has been extensively studied in the past decades, how to effectively solve the corresponding training on large datasets is a problem that still requires particular attention. Many kernelized classification methods, such as SVM and SVDD, can be formulated as the corresponding quadratic programming (QP) problems, but computing the associated kernel matrices requires O(n2)(or even up to O(n3)) computational complexity, where n is the size of the training patterns, which heavily limits the applicability of these methods for large datasets. In this paper, a new classification method called the maximum vector-angular margin classifier (MAMC) is first proposed based on the vector-angular margin to find an optimal vector c in the pattern feature space, and all the testing patterns can be classified in terms of the maximum vector-angular margin ρ, between the vector c and all the training data points. Accordingly, it is proved that the kernelized MAMC can be equivalently formulated as the kernelized Minimum Enclosing Ball (MEB), which leads to a distinctive merit of MAMC, i.e., it has the flexibility of controlling the sum of support vectors like v-SVC and may be extended to a maximum vector-angular margin core vector machine (MAMCVM) by connecting the core vector machine (CVM) method with MAMC such that the corresponding fast training on large datasets can be effectively achieved. Experimental results on artificial and real datasets are provided to validate the power of the proposed methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

What is the risk for exposure to vector-borne pathogens in United States national parks?

PubMed

Eisen, Lars; Wong, David; Shelus, Victoria; Eisen, Rebecca J

2013-03-01

United States national parks attract > 275 million visitors annually and collectively present risk of exposure for staff and visitors to a wide range of arthropod vector species (most notably fleas, mosquitoes, and ticks) and their associated bacterial, protozoan, or viral pathogens. We assessed the current state of knowledge for risk of exposure to vector-borne pathogens in national parks through a review of relevant literature, including internal National Park Service documents and organismal databases. We conclude that, because of lack of systematic surveillance for vector-borne pathogens in national parks, the risk of pathogen exposure for staff and visitors is unclear. Existing data for vectors within national parks were not based on systematic collections and rarely include evaluation for pathogen infection. Extrapolation of human-based surveillance data from neighboring communities likely provides inaccurate estimates for national parks because landscape differences impact transmission of vector-borne pathogens and human-vector contact rates likely differ inside versus outside the parks because of differences in activities or behaviors. Vector-based pathogen surveillance holds promise to define when and where within national parks the risk of exposure to infected vectors is elevated. A pilot effort, including 5-10 strategic national parks, would greatly improve our understanding of the scope and magnitude of vector-borne pathogen transmission in these high-use public settings. Such efforts also will support messaging to promote personal protection measures and inform park visitors and staff of their responsibility for personal protection, which the National Park Service preservation mission dictates as the core strategy to reduce exposure to vector-borne pathogens in national parks.

Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM

NASA Astrophysics Data System (ADS)

Sheng, Hanlin; Zhang, Tianhong

2017-08-01

In view of the necessity of highly precise and reliable thrust estimator to achieve direct thrust control of aircraft engine, based on support vector regression (SVR), as well as least square support vector machine (LSSVM) and a new optimization algorithm - gravitational search algorithm (GSA), by performing integrated modelling and parameter optimization, a GSA-LSSVM-based thrust estimator design solution is proposed. The results show that compared to particle swarm optimization (PSO) algorithm, GSA can find unknown optimization parameter better and enables the model developed with better prediction and generalization ability. The model can better predict aircraft engine thrust and thus fulfills the need of direct thrust control of aircraft engine.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

PubMed

Nuryani, Nuryani; Ling, Steve S H; Nguyen, H T

2012-04-01

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

NASA Astrophysics Data System (ADS)

Zhang, Qigui; Deng, Kai

2016-12-01

As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

DOA Finding with Support Vector Regression Based Forward-Backward Linear Prediction.

PubMed

Pan, Jingjing; Wang, Yide; Le Bastard, Cédric; Wang, Tianzhen

2017-05-27

Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward-backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This paper proposes the combination of the advantages of FBLP and SVR in the estimation of DOAs of coherent incoming signals with low snapshots. The performance of the proposed method is validated with numerical simulations in coherent scenarios, in terms of different angle separations, numbers of snapshots, and signal-to-noise ratios (SNRs). Simulation results show the effectiveness of the proposed method.

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Support vector machine based classification of fast Fourier transform spectroscopy of proteins

NASA Astrophysics Data System (ADS)

Lazarevic, Aleksandar; Pokrajac, Dragoljub; Marcano, Aristides; Melikechi, Noureddine

2009-02-01

Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

PubMed Central

Song, Kai; Wang, Qi; Liu, Qi; Zhang, Hongquan; Cheng, Yingguo

2011-01-01

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR)estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process. PMID:22346587

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

PubMed

Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

2017-04-01

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2016-01-01

Sediment transport without deposition is an essential consideration in the optimum design of sewer pipes. In this study, a novel method based on a combination of support vector regression (SVR) and the firefly algorithm (FFA) is proposed to predict the minimum velocity required to avoid sediment settling in pipe channels, which is expressed as the densimetric Froude number (Fr). The efficiency of support vector machine (SVM) models depends on the suitable selection of SVM parameters. In this particular study, FFA is used by determining these SVM parameters. The actual effective parameters on Fr calculation are generally identified by employing dimensional analysis. The different dimensionless variables along with the models are introduced. The best performance is attributed to the model that employs the sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)) and overall sediment friction factor (λ(s)) parameters to estimate Fr. The performance of the SVR-FFA model is compared with genetic programming, artificial neural network and existing regression-based equations. The results indicate the superior performance of SVR-FFA (mean absolute percentage error = 2.123%; root mean square error =0.116) compared with other methods.

Rainfall-induced Landslide Susceptibility assessment at the Longnan county

NASA Astrophysics Data System (ADS)

Hong, Haoyuan; Zhang, Ying

2017-04-01

Landslides are a serious disaster in Longnan county, China. Therefore landslide susceptibility assessment is useful tool for government or decision making. The main objective of this study is to investigate and compare the frequency ratio, support vector machines, and logistic regression. The Longnan county (Jiangxi province, China) was selected as the case study. First, the landslide inventory map with 354 landslide locations was constructed. Then landslide locations were then randomly divided into a ratio of 70/30 for the training and validating the models. Second, fourteen landslide conditioning factors were prepared such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Using the frequency ratio, support vector machines, and logistic regression, a total of three landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using the Receiver operating characteristic (ROC) curve technique. The result showed that the support vector machines model is the best model in the study area. The success rate is 88.39 %; and prediction rate is 84.06 %.

Human action recognition with group lasso regularized-support vector machine

NASA Astrophysics Data System (ADS)

Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei

2016-05-01

The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.

PubMed

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

2016-03-04

Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.

Recursive feature selection with significant variables of support vectors.

PubMed

Tsai, Chen-An; Huang, Chien-Hsun; Chang, Ching-Wei; Chen, Chun-Houh

2012-01-01

The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t) based on the use of t-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM). The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.

T-wave end detection using neural networks and Support Vector Machines.

PubMed

Suárez-León, Alexander Alexeis; Varon, Carolina; Willems, Rik; Van Huffel, Sabine; Vázquez-Seisdedos, Carlos Román

2018-05-01

In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines. Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included. The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques. FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM.

PubMed

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

2016-10-14

A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine. The chaotic ions motion algorithm is introduced to find the best hyper-parameters of the Least Squares Support Vector Machine. The temperature data from a calibration experiment is conducted to validate the proposed method. With attention on algorithm robustness and engineering applications, the compensation result shows the proposed scheme outperforms other compared methods on several performance measures as maximum absolute relative error, minimum absolute relative error mean and variance of the averaged value on fifty runs. Furthermore, the proposed temperature compensation approach lays a foundation for more extensive research.

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

PubMed Central

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

2016-01-01

Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications. PMID:26959020

Parenting and proximity to social services: Lessons from Los Angeles County in the community context of child neglect.

PubMed

Maguire-Jack, Kathryn; Klein, Sacha

2015-07-01

Using a sample of 438 parents in Los Angeles County, CA, this study examines the role of proximity to social services in child neglect. In an extension of social disorganization theory, it seeks to understand the potential sources of support in neighborhoods for families. It uses ordinary least squares regression to examine driving distance from parents' residences to four types of services (child care, domestic violence, mental health/substance abuse, and poverty). The results show an association between proximity to mental health and substance abuse services and parents' self-reported neglectful behaviors. Additionally, higher levels of socioeconomic disadvantage (poverty, unemployment, and low education), having older children, respondents being male, and respondents being older parents are associated with higher levels of child neglect, while being white is associated with lower levels. Overall, the findings suggest a potentially protective role of geographic access to mental health and substance abuse services in child maltreatment. Additional research on the pathways through which proximity to services influences child neglect is needed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Superconducting proximity effect in MBE grown Nb-InAs junctions

NASA Astrophysics Data System (ADS)

Kan, Carolyn; Xue, Chi; Law, Stephanie; Eckstein, James

2013-03-01

Several proposals for the realization of Majorana fermions rely on excellent quality proximity coupling between a superconductor and a high-mobility semiconductor. We examine the long-range proximity coupling between MBE-grown InAs and in situ grown superconducting overlayers by fabricating transport devices, and investigate the effect of substrate choice and growth conditions on the quality of the MBE InAs. GaAs is commonly available as a high quality insulating substrate. Overcoming its lattice mismatch with InAs using GaSb and AlSb layers results in locally smooth terraced surfaces, but global spiral dislocation structures also appear and have a negative impact on the InAs mobility. Growing InAs on homoepitaxial GaSb results in improved morphology and increases the mean free path. We compare the proximity effect in devices made both ways. This material is based upon work supported by the U.S. Department of Energy, Division of Materials Sciences under Award No. DE-FG02 07ER46453, through the Frederick Seitz Materials Research Laboratory at the University of Illinois at Urbana-Champaign.

Parenting and Proximity to Social Services: Lessons from Los Angeles County in the Community Context of Child Neglect

PubMed Central

Maguire-Jack, Kathryn; Klein, Sacha

2015-01-01

Using a sample of 438 parents in Los Angeles County, CA, this study examines the role of proximity to social services in child neglect. In an extension of social disorganization theory, it seeks to understand the potential sources of support in neighborhoods for families. It uses ordinary least squares regression to examine driving distance from parents’ residences to four types of services (child care, domestic violence, mental health/substance abuse, and poverty). The results show an association between proximity to mental health and substance abuse services and parents’ self-reported neglectful behaviors. Additionally, higher levels of socioeconomic disadvantage (poverty, unemployment, and low education), having older children, respondents being male, and respondents being older parents are associated with higher levels of child neglect, while being white is associated with lower levels. Overall, the findings suggest a potentially protective role of geographic access to mental health and substance abuse services in child maltreatment. Additional research on the pathways through which proximity to services influences child neglect is needed. PMID:26026359

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

PubMed

Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

Decision support system for diabetic retinopathy using discrete wavelet transform.

PubMed

Noronha, K; Acharya, U R; Nayak, K P; Kamath, S; Bhandary, S V

2013-03-01

Prolonged duration of the diabetes may affect the tiny blood vessels of the retina causing diabetic retinopathy. Routine eye screening of patients with diabetes helps to detect diabetic retinopathy at the early stage. It is very laborious and time-consuming for the doctors to go through many fundus images continuously. Therefore, decision support system for diabetic retinopathy detection can reduce the burden of the ophthalmologists. In this work, we have used discrete wavelet transform and support vector machine classifier for automated detection of normal and diabetic retinopathy classes. The wavelet-based decomposition was performed up to the second level, and eight energy features were extracted. Two energy features from the approximation coefficients of two levels and six energy values from the details in three orientations (horizontal, vertical and diagonal) were evaluated. These features were fed to the support vector machine classifier with various kernel functions (linear, radial basis function, polynomial of orders 2 and 3) to evaluate the highest classification accuracy. We obtained the highest average classification accuracy, sensitivity and specificity of more than 99% with support vector machine classifier (polynomial kernel of order 3) using three discrete wavelet transform features. We have also proposed an integrated index called Diabetic Retinopathy Risk Index using clinically significant wavelet energy features to identify normal and diabetic retinopathy classes using just one number. We believe that this (Diabetic Retinopathy Risk Index) can be used as an adjunct tool by the doctors during the eye screening to cross-check their diagnosis.

Extrapolation methods for vector sequences

NASA Technical Reports Server (NTRS)

Smith, David A.; Ford, William F.; Sidi, Avram

1987-01-01

This paper derives, describes, and compares five extrapolation methods for accelerating convergence of vector sequences or transforming divergent vector sequences to convergent ones. These methods are the scalar epsilon algorithm (SEA), vector epsilon algorithm (VEA), topological epsilon algorithm (TEA), minimal polynomial extrapolation (MPE), and reduced rank extrapolation (RRE). MPE and RRE are first derived and proven to give the exact solution for the right 'essential degree' k. Then, Brezinski's (1975) generalization of the Shanks-Schmidt transform is presented; the generalized form leads from systems of equations to TEA. The necessary connections are then made with SEA and VEA. The algorithms are extended to the nonlinear case by cycling, the error analysis for MPE and VEA is sketched, and the theoretical support for quadratic convergence is discussed. Strategies for practical implementation of the methods are considered.

Proviruses with Long-Term Stable Expression Accumulate in Transcriptionally Active Chromatin Close to the Gene Regulatory Elements: Comparison of ASLV-, HIV- and MLV-Derived Vectors

PubMed Central

Miklík, Dalibor; Šenigl, Filip; Hejnar, Jiří

2018-01-01

Individual groups of retroviruses and retroviral vectors differ in their integration site preference and interaction with the host genome. Hence, immediately after infection genome-wide distribution of integrated proviruses is non-random. During long-term in vitro or persistent in vivo infection, the genomic position and chromatin environment of the provirus affects its transcriptional activity. Thus, a selection of long-term stably expressed proviruses and elimination of proviruses, which have been gradually silenced by epigenetic mechanisms, helps in the identification of genomic compartments permissive for proviral transcription. We compare here the extent and time course of provirus silencing in single cell clones of the K562 human myeloid lymphoblastoma cell line that have been infected with retroviral reporter vectors derived from avian sarcoma/leukosis virus (ASLV), human immunodeficiency virus type 1 (HIV) and murine leukaemia virus (MLV). While MLV proviruses remain transcriptionally active, ASLV proviruses are prone to rapid silencing. The HIV provirus displays gradual silencing only after an extended time period in culture. The analysis of integration sites of long-term stably expressed proviruses shows a strong bias for some genomic features—especially integration close to the transcription start sites of active transcription units. Furthermore, complex analysis of histone modifications enriched at the site of integration points to the accumulation of proviruses of all three groups in gene regulatory segments, particularly close to the enhancer loci. We conclude that the proximity to active regulatory chromatin segments correlates with stable provirus expression in various retroviral species. PMID:29517993

Liner mounting assembly

NASA Technical Reports Server (NTRS)

Halila, Ely E. (Inventor)

1994-01-01

A mounting assembly includes an annular supporting flange disposed coaxially about a centerline axis which has a plurality of circumferentially spaced apart supporting holes therethrough. An annular liner is disposed coaxially with the supporting flange and includes a plurality of circumferentially spaced apart mounting holes aligned with respective ones of the supporting holes. Each of a plurality of mounting pins includes a proximal end fixedly joined to the supporting flange through a respective one of the supporting holes, and a distal end disposed through a respective one of the liner mounting holes for supporting the liner to the supporting flange while unrestrained differential thermal movement of the liner relative to the supporting flange.

Perceived and Objective Measures of Neighborhood Environment for Physical Activity Among Mexican Adults, 2011

PubMed Central

Salvo, Deborah; Lamadrid-Figueroa, Héctor; Hernández, Bernardo; Rivera-Dommarco, Juan A.; Pratt, Michael

2016-01-01

Introduction Environmental supports for physical activity may help residents to be physically active. However, such supports might not help if residents’ perceptions of the built environment do not correspond with objective measures. We assessed the associations between objective and perceived measures of the built environment among adults in Cuernavaca, Mexico, and examined whether certain variables modified this relationship. Methods We conducted a population-based (n = 645) study in 2011 that used objective (based on geographic information systems) and perceived (by questionnaire) measures of the following features of the built environment: residential density, mixed-land use, intersection density, and proximity to parks and transit stops. We used linear regression to assess the adjusted associations between these measures and to identify variables modifying these relationships. Results Adjusted associations were significant for all features (P < .05) except intersection density and proximity to transit stops. Significantly stronger associations between perceived and objective measures were observed among participants with low socioeconomic status, participants who did not own a motor vehicle or did not meet physical activity recommendations, and participants perceiving parks as safe. Conclusion Perceived measures of residential density, mixed-land use, and proximity to parks are associated with objective environmental measures related to physical activity. However, in Mexico, it should not be assumed that perceived measures of intersection density and proximity to transit stops are the same as objective measures. Our results are consistent with those from high-income countries in that associations between perceived and objective measures are modified by individual sociodemographic and psychosocial factors. PMID:27281391

Socio-environmental correlates of physical activity in patients with chronic obstructive pulmonary disease (COPD)

PubMed Central

Arbillaga-Etxarri, Ane; Gimeno-Santos, Elena; Barberan-Garcia, Anael; Benet, Marta; Borrell, Eulàlia; Dadvand, Payam; Foraster, Maria; Marín, Alicia; Monteagudo, Mònica; Rodriguez-Roisin, Robert; Vall-Casas, Pere; Vilaró, Jordi; Garcia-Aymerich, Judith

2017-01-01

Background Study of the causes of the reduced levels of physical activity in patients with COPD has been scarce and limited to biological factors. Aim To assess the relationship between novel socio-environmental factors, namely dog walking, grandparenting, neighbourhood deprivation, residential surrounding greenness and residential proximity to green or blue spaces, and amount and intensity of physical activity in COPD patients. Methods This cross-sectional study recruited 410 COPD patients from five Catalan municipalities. Dog walking and grandparenting were assessed by questionnaire. Neighbourhood deprivation was assessed using the census Urban Vulnerability Index, residential surrounding greenness by the satellite-derived Normalized Difference Vegetation Index, and residential proximity to green or blue spaces as living within 300 m of such a space. Physical activity was measured during 1 week by accelerometer to assess time spent on moderate-to-vigorous physical activity (MVPA) and vector magnitude units (VMU) per minute. Findings Patients were 85% male, had a mean (SD) age of 69 (9) years, and post-bronchodilator FEV1 of 56 (17) %pred. After adjusting for age, sex, socio-economic status, dyspnoea, exercise capacity and anxiety in a linear regression model, both dog walking and grandparenting were significantly associated with an increase both in time in MVPA (18 min/day (p<0.01) and 9 min/day (p<0.05), respectively) and in physical activity intensity (76 VMU/min (p=0.05) and 59 VMUs/min (p<0.05), respectively). Neighbourhood deprivation, surrounding greenness and proximity to green or blue spaces were not associated with physical activity. Conclusions Dog walking and grandparenting are associated with a higher amount and intensity of physical activity in COPD patients. Trial registration number Pre-results, NCT01897298. PMID:28250201

Novel Hg2+-Induced Nephropathy in Rats and Mice Lacking Mrp2: Evidence of Axial Heterogeneity in the Handling of Hg2+ Along the Proximal Tubule

PubMed Central

Zalups, Rudolfs K.; Joshee, Lucy; Bridges, Christy C.

2014-01-01

The role of the multi-resistance protein 2 (Mrp2) in the nephropathy induced by inorganic mercuric mercury (Hg2+) was studied in rats (TR−) and mice (Mrp2−/−), which lack functional Mrp2, and control animals. Animals were exposed to nephrotoxic doses of HgCl2. Forty-eight or 24 hours after exposure, tissues were harvested and analyzed for Hg content and markers of injury. Histological analyses revealed that the proximal tubular segments affected pathologically by Hg2+ were significantly different between Mrp2-deficient animals and controls. In the absence of Mrp2, cellular injury localized almost exclusively in proximal tubular segments in the subcapsular (S1) to midcortical regions (early S2) of the kidney. In control animals, cellular death occurred mainly in the proximal tubular segments in the inner cortex (late S2) and outer stripe of the outer medulla (S3). These differences in renal pathology indicate that axial heterogeneity exists along the proximal tubule with respect to how mercuric ions are handled. Total renal and hepatic accumulation of mercury was also greater in animals lacking Mrp2 than in controls, indicating that Mrp2 normally plays a significant role in eliminating mercuric ions from within proximal tubular cells and hepatocytes. Analyses of plasma creatinine, BUN, and renal expression of Kim-1 and Ngal tend to support the severity of the nephropathies detected histologically. Collectively, our findings indicate that a fraction of mercuric ions is normally secreted by Mrp2 in early portions of proximal tubules into the lumen and then is absorbed downstream in straight portions, where mercuric species typically induce toxic effects. PMID:25145654

Short frontal plane fractures involving the dorsoproximal articular surface of the proximal phalanx: Description of the injury and a technique for repair.

PubMed

Wright, I M; Minshall, G J

2018-01-01

Chip fractures of the dorsoproximal articular margin of the proximal phalanx are common injuries in racehorses. Large fractures can extend distal to the joint capsule insertion and have been described as dorsal frontal fractures. To report the location and morphology of short frontal plane fractures involving the dorsoproximal articular surface of the proximal phalanx and describe a technique for repair under arthroscopic and radiographic guidance. Single centre retrospective case study. Case records of horses with frontal plane fractures restricted to the dorsoproximal epiphysis and metaphysis of the proximal phalanx referred to Newmarket Equine Hospital were retrieved, images reviewed and lesion morphology described. A technique for repair and the results obtained are reported. A total of 22 fractures in 21 horses commencing at the proximal articular surface exited the dorsal cortex of the proximal phalanx distal to the metacarpophalangeal/metatarsophalangeal joint capsule in 17 hind- and five forelimbs. All were in Thoroughbred racehorses. In 16 cases these were acute racing or training injuries; 20 fractures were medial, one lateral and one was midline. All were repaired with a single lag screw using arthroscopic and radiographically determined landmarks. A total of 16 horses raced after surgery with performance data similar to their preinjury levels. The study demonstrates substantial morphological similarities between individual lesions supporting a common pathophysiology, but does not identify precise causation. There are no cases managed differently that might permit assessment of the comparative efficacy of the treatment described. Short frontal plane fractures involving the dorsoproximal margin of the proximal phalanx that exit the bone distal to the metacarpophalangeal/metatarsophalangeal joint capsule have substantial morphological similarities, are amenable to minimally invasive repair and carry a good prognosis for return to training and racing. © 2017 EVJ Ltd.

3D Proximal Tubule Tissues Recapitulate Key Aspects of Renal Physiology to Enable Nephrotoxicity Testing

PubMed Central

King, Shelby M.; Higgins, J. William; Nino, Celina R.; Smith, Timothy R.; Paffenroth, Elizabeth H.; Fairbairn, Casey E.; Docuyanan, Abigail; Shah, Vishal D.; Chen, Alice E.; Presnell, Sharon C.; Nguyen, Deborah G.

2017-01-01

Due to its exposure to high concentrations of xenobiotics, the kidney proximal tubule is a primary site of nephrotoxicity and resulting attrition in the drug development pipeline. Current pre-clinical methods using 2D cell cultures and animal models are unable to fully recapitulate clinical drug responses due to limited in vitro functional lifespan, or species-specific differences. Using Organovo's proprietary 3D bioprinting platform, we have developed a fully cellular human in vitro model of the proximal tubule interstitial interface comprising renal fibroblasts, endothelial cells, and primary human renal proximal tubule epithelial cells to enable more accurate prediction of tissue-level clinical outcomes. Histological characterization demonstrated formation of extensive microvascular networks supported by endogenous extracellular matrix deposition. The epithelial cells of the 3D proximal tubule tissues demonstrated tight junction formation and expression of renal uptake and efflux transporters; the polarized localization and function of P-gp and SGLT2 were confirmed. Treatment of 3D proximal tubule tissues with the nephrotoxin cisplatin induced loss of tissue viability and epithelial cells in a dose-dependent fashion, and cimetidine rescued these effects, confirming the role of the OCT2 transporter in cisplatin-induced nephrotoxicity. The tissues also demonstrated a fibrotic response to TGFβ as assessed by an increase in gene expression associated with human fibrosis and histological verification of excess extracellular matrix deposition. Together, these results suggest that the bioprinted 3D proximal tubule model can serve as a test bed for the mechanistic assessment of human nephrotoxicity and the development of pathogenic states involving epithelial-interstitial interactions, making them an important adjunct to animal studies. PMID:28337147

Vector Fluxgate Magnetometer (VMAG) Development for DSX

DTIC Science & Technology

2008-05-19

AFRL-RV-HA-TR-2008-1108 Vector Fluxgate Magnetometer (VMAG) Development for DSX Mark B. Moldwin Q. O O O I- UCLA Q Institute of...for Public Release; Distribution Unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT UCLA is building a three-axis fluxgate magnetometer for the Air... fluxgate magnetometer provides the necessary data to support both the Space Weather (SWx) specification and mapping requirements and the WPIx

Single inverted terminal repeats of the Junonia coenia Densovirus promotes somatic chromosomal integration of vector plasmids in insect cells and supports high efficiency expression

USDA-ARS?s Scientific Manuscript database

Plasmids that contain a disrupted genome of the Junonia coenia densovirus (JcDNV) integrate into the chromosomes of the somatic cells of insects. When subcloned individually, both the P9 inverted terminal repeat (P9-ITR) and the P93-ITR promote the chromosomal integration of vector plasmids in insec...

Ability of herpes simplex virus vectors to boost immune responses to DNA vectors and to protect against challenge by simian immunodeficiency virus

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

Kaur, Amitinder; Sanford, Hannah B.; Garry, Deirdre

2007-01-20

The immunogenicity and protective capacity of replication-defective herpes simplex virus (HSV) vector-based vaccines were examined in rhesus macaques. Three macaques were inoculated with recombinant HSV vectors expressing Gag, Env, and a Tat-Rev-Nef fusion protein of simian immunodeficiency virus (SIV). Three other macaques were primed with recombinant DNA vectors expressing Gag, Env, and a Pol-Tat-Nef-Vif fusion protein prior to boosting with the HSV vectors. Robust anti-Gag and anti-Env cellular responses were detected in all six macaques. Following intravenous challenge with wild-type, cloned SIV239, peak and 12-week plasma viremia levels were significantly lower in vaccinated compared to control macaques. Plasma SIV RNAmore » in vaccinated macaques was inversely correlated with anti-Rev ELISPOT responses on the day of challenge (P value < 0.05), anti-Tat ELISPOT responses at 2 weeks post challenge (P value < 0.05) and peak neutralizing antibody titers pre-challenge (P value 0.06). These findings support continued study of recombinant herpesviruses as a vaccine approach for AIDS.« less

Amino acid "little Big Bang": representing amino acid substitution matrices as dot products of Euclidian vectors.

PubMed

Zimmermann, Karel; Gibrat, Jean-François

2010-01-04

Sequence comparisons make use of a one-letter representation for amino acids, the necessary quantitative information being supplied by the substitution matrices. This paper deals with the problem of finding a representation that provides a comprehensive description of amino acid intrinsic properties consistent with the substitution matrices. We present a Euclidian vector representation of the amino acids, obtained by the singular value decomposition of the substitution matrices. The substitution matrix entries correspond to the dot product of amino acid vectors. We apply this vector encoding to the study of the relative importance of various amino acid physicochemical properties upon the substitution matrices. We also characterize and compare the PAM and BLOSUM series substitution matrices. This vector encoding introduces a Euclidian metric in the amino acid space, consistent with substitution matrices. Such a numerical description of the amino acid is useful when intrinsic properties of amino acids are necessary, for instance, building sequence profiles or finding consensus sequences, using machine learning algorithms such as Support Vector Machine and Neural Networks algorithms.

Structural Analysis of Biodiversity

PubMed Central

Sirovich, Lawrence; Stoeckle, Mark Y.; Zhang, Yu

2010-01-01

Large, recently-available genomic databases cover a wide range of life forms, suggesting opportunity for insights into genetic structure of biodiversity. In this study we refine our recently-described technique using indicator vectors to analyze and visualize nucleotide sequences. The indicator vector approach generates correlation matrices, dubbed Klee diagrams, which represent a novel way of assembling and viewing large genomic datasets. To explore its potential utility, here we apply the improved algorithm to a collection of almost 17000 DNA barcode sequences covering 12 widely-separated animal taxa, demonstrating that indicator vectors for classification gave correct assignment in all 11000 test cases. Indicator vector analysis revealed discontinuities corresponding to species- and higher-level taxonomic divisions, suggesting an efficient approach to classification of organisms from poorly-studied groups. As compared to standard distance metrics, indicator vectors preserve diagnostic character probabilities, enable automated classification of test sequences, and generate high-information density single-page displays. These results support application of indicator vectors for comparative analysis of large nucleotide data sets and raise prospect of gaining insight into broad-scale patterns in the genetic structure of biodiversity. PMID:20195371

Development of procedures for programmable proximity aperture lithography

NASA Astrophysics Data System (ADS)

Whitlow, H. J.; Gorelick, S.; Puttaraksa, N.; Napari, M.; Hokkanen, M. J.; Norarat, R.

2013-07-01

Programmable proximity aperture lithography (PPAL) with MeV ions has been used in Jyväskylä and Chiang Mai universities for a number of years. Here we describe a number of innovations and procedures that have been incorporated into the LabView-based software. The basic operation involves the coordination of the beam blanker and five motor-actuated translators with high accuracy, close to the minimum step size with proper anti-collision algorithms. By using special approaches, such writing calibration patterns, linearisation of position and careful backlash correction the absolute accuracy of the aperture size and position, can be improved beyond the standard afforded by the repeatability of the translator end-point switches. Another area of consideration has been the fluence control procedures. These involve control of the uniformity of the beam where different approaches for fluence measurement such as simultaneous aperture current and the ion current passing through the aperture using a Faraday cup are used. Microfluidic patterns may contain many elements that make-up mixing sections, reaction chambers, separation columns and fluid reservoirs. To facilitate conception and planning we have implemented a .svg file interpreter, that allows the use of scalable vector graphics files produced by standard drawing software for generation of patterns made up of rectangular elements.

Effect of irrigated rice agriculture on Japanese encephalitis, including challenges and opportunities for integrated vector management.

PubMed

Keiser, Jennifer; Maltese, Michael F; Erlanger, Tobias E; Bos, Robert; Tanner, Marcel; Singer, Burton H; Utzinger, Jürg

2005-07-01

Japanese encephalitis (JE) is a disease caused by an arbovirus that is spread by marsh birds, amplified by pigs, and mainly transmitted by the bite of infected Culex tritaeniorhynchus mosquitoes. The estimated annual incidence and mortality rates are 30,000--50,000 and 10,000, respectively, and the estimated global burden of JE in 2002 was 709,000 disability-adjusted life years lost. Here, we discuss the contextual determinants of JE, and systematically examine studies assessing the relationship between irrigated rice agriculture and clinical parameters of JE. Estimates of the sizes of the rural population and population in irrigated areas are presented, and trends of the rural population, the rice-irrigated area, and the rice production are analyzed from 1963 to 2003. We find that approximately 1.9 billion people currently live in rural JE-prone areas of the world. Among them 220 million people live in proximity to rice-irrigation schemes. In 2003, the total rice harvested area of all JE-endemic countries (excluding the Russian Federation and Australia) was 1,345,000 km(2). This is an increase of 22% over the past 40 years. Meanwhile, the total rice production in these countries has risen from 226 millions of tonnes to 529 millions of tonnes (+134%). Finally, we evaluate the effect of different vector control interventions in rice fields, including environmental measures (i.e. alternate wet and dry irrigation (AWDI)), and biological control approaches (i.e. bacteria, nematodes, invertebrate predators, larvivorous fish, fungi and other natural products). We conclude that in JE-endemic rural settings, where vaccination rates are often low, an integrated vector management approach with AWDI and the use of larvivorous fish as its main components can reduce vector populations, and hence has the potential to reduce the transmission level and the burden of JE.

Genetics Home Reference: proximal 18q deletion syndrome

MedlinePlus

... Feenstra I, Vissers LE, Orsel M, van Kessel AG, Brunner HG, Veltman JA, van Ravenswaaij-Arts CM. ... qualified healthcare professional . About Selection Criteria for Links Data Files & API Site Map Subscribe Customer Support USA. ...

The cation transporters rOCT1 and rOCT2 interact with bicarbonate but play only a minor role for amantadine uptake into rat renal proximal tubules.

PubMed

Goralski, Kerry B; Lou, Ganlu; Prowse, Matthew T; Gorboulev, Valentin; Volk, Christopher; Koepsell, Hermann; Sitar, Daniel S

2002-12-01

In renal proximal tubules, the organic cation transporters rOCT1 and rOCT2 are supposed to mediate the first step in organic cation secretion. We investigated whether previously described differences in amantadine and tetraethylammonium (TEA) uptake into isolated renal proximal tubules could be explained by differences in their transport by rOCT1 and rOCT2. By expressing rOCT1 and rOCT2 in Xenopus oocytes and HEK 293 cells, we demonstrated that both transporters translocated amantadine. In Xenopus oocytes, the inhibitory potency of several rOCT1/2 inhibitors was similar for amantadine compared to TEA uptake and supports amantadine transport by rOCT1 and rOCT2. In proximal tubules, procainamide, quinine, cyanine(863), choline, and guanidine in concentrations that inhibit rOCT1/2-mediated TEA or amantadine uptake in Xenopus oocytes exhibited no effect on amantadine uptake. At variance, these inhibitors blocked TEA uptake into proximal tubules. Amantadine and TEA transport were sensitive to modulation by 25 mM bicarbonate. The effect of bicarbonate on organic cation transport was dependent on substrate (amantadine or TEA), cell system (oocytes, HEK 293 cells, or proximal tubules), and transporter (rOCT1 or rOCT2). In proximal tubules, only amantadine uptake was stimulated by bicarbonate. The data suggested that rat renal proximal tubules contain an organic cation transporter in addition to rOCT1 and rOCT2 that mediates amantadine uptake and requires bicarbonate for optimal function. TEA uptake by the basolateral membrane may be mediated mainly by rOCT1 and rOCT2, but these transporters may be in a different functional or regulatory state when expressed in cells or oocytes compared with expression in vivo.

Efficient boundary hunting via vector quantization

NASA Astrophysics Data System (ADS)

Diamantini, Claudia; Panti, Maurizio

2001-03-01

A great amount of information about a classification problem is contained in those instances falling near the decision boundary. This intuition dates back to the earliest studies in pattern recognition, and in the more recent adaptive approaches to the so called boundary hunting, such as the work of Aha et alii on Instance Based Learning and the work of Vapnik et alii on Support Vector Machines. The last work is of particular interest, since theoretical and experimental results ensure the accuracy of boundary reconstruction. However, its optimization approach has heavy computational and memory requirements, which limits its application on huge amounts of data. In the paper we describe an alternative approach to boundary hunting based on adaptive labeled quantization architectures. The adaptation is performed by a stochastic gradient algorithm for the minimization of the error probability. Error probability minimization guarantees the accurate approximation of the optimal decision boundary, while the use of a stochastic gradient algorithm defines an efficient method to reach such approximation. In the paper comparisons to Support Vector Machines are considered.

Automatic sleep staging using multi-dimensional feature extraction and multi-kernel fuzzy support vector machine.

PubMed

Zhang, Yanjun; Zhang, Xiangmin; Liu, Wenhui; Luo, Yuxi; Yu, Enjia; Zou, Keju; Liu, Xiaoliang

2014-01-01

This paper employed the clinical Polysomnographic (PSG) data, mainly including all-night Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG) signals of subjects, and adopted the American Academy of Sleep Medicine (AASM) clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM) were learned and the multi-kernel FSVM (MK-FSVM) was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.

Environmental and socio-economic change in Thailand: quantifying spatio-temporal risk factors of dengue to inform decision making

NASA Astrophysics Data System (ADS)

Rodo, X.; Lowe, R.; Karczewska-Gibert, A.; Cazelles, B.

2013-12-01

Dengue is a peri-urban mosquito-transmitted disease, ubiquitous in the tropics and the subtropics. The geographic distribution of dengue and its more severe form, dengue haemorrhagic fever (DHF), have expanded dramatically in the last decades and dengue is now considered to be the world's most important arboviral disease. Recent demographic changes have greatly contributed to the acceleration and spread of the disease along with uncontrolled urbanization, population growth and increased air travel, which acts as a mechanism for transporting and exchanging dengue viruses between endemic and epidemic populations. The dengue vector and virus are extremely sensitive to environmental conditions such as temperature, humidity and precipitation that influence mosquito biology, abundance and habitat and the virus replication speed. In order to control the spread of dengue and impede epidemics, decision support systems are required that take into account the multi-faceted array of factors that contribute to increased dengue risk. Due to availability of seasonal climate forecasts, that predict the average climate conditions for forthcoming months/seasons in both time and space, there is an opportunity to incorporate precursory climate information in a dengue decision support system to aid epidemic planning months in advance. Furthermore, oceanic indicators from teleconnected areas in the Pacific and Indian Ocean, that can provide some indication of the likely prevailing climate conditions in certain regions, could potentially extend predictive lead time in a dengue early warning system. In this paper we adopt a spatio-temporal Bayesian modelling framework for dengue in Thailand to support public health decision making. Monthly cases of dengue in the 76 provinces of Thailand for the period 1982-2012 are modelled using a multi-layered approach. Explanatory variables at various spatial and temporal resolutions are incorporated into a hierarchical model in order to make spatio-temporal probabilistic predictions of dengue. Potential risk factors considered include altitude, land cover, proximity to road/rail networks and water bodies, temperature and precipitation, oceanic indicators, intervention activities, air traffic volume, population movement, urbanisation and sanitation indicators. In order to quantify unknown or unmeasured dengue risk factors, we use spatio-temporal random effects in the model framework. This helps identify those available indicators which could significantly contribute to a dengue early warning system. We use this model to quantify the extent to which climate indicators can explain variations in dengue risk. This allows us to assess the potential utility of forecast climate information in a dengue decision support system for Thailand. Taking advantage of lead times of several months provided by climate forecasts, public health officials may be able to more efficiently allocate intervention measures, such as targeted vector control activities and provision of medication to deal with more deadly forms of the disease, well ahead of an imminent dengue epidemic.

Soil Organic Carbon Variability in High-Andean Ecosystems: Bringing Together Machine Learning and Proximal Soil Sensing

NASA Astrophysics Data System (ADS)

Gavilan, C.; Grunwald, S.; Quiroz, R.

2017-12-01

The Andes represent the largest and highest mountain range in the tropics and is considered an important reserve of biodiversity, water provision and soil organic carbon (SOC) stocks. Nevertheless, limited attention has been given to estimate these stocks due to the lack of recent soil data, the poor accessibility and the wide range of coexistent ecosystems. In addition, conventional methods to determine SOC are usually time consuming and expensive to use in large-scale studies, hindering the possibility to have an accurate SOC assessment in the region. Proximal soil sensing techniques, such as visible near infrared (VNIR) and mid infrared (MIR) spectroscopy, have proven to be useful as an alternative to conventional methods for characterizing SOC but have not been tested in Andean soils. The aim of this study was to evaluate the potential of using VNIR and MIR spectroscopy to predict SOC content in the Central Andean region, using multivariate methods. Three study areas were selected across the Peruvian Central Andes. A total of 400 topsoil samples (0-30 cm) were collected and analyzed for SOC. The VNIR and MIR reflectance of the soil samples was measured in the laboratory. Three modeling approaches: Partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to predict SOC from VNIR and MIR spectra in the study areas. The data was preprocessed in order to minimize the noise and optimize the accuracy of predictions. The models, for each study area, were assessed using 10-fold cross validation. Independent validation was implemented in the whole dataset (400 observations) by splitting it into calibration (70 %) and validation (30%) sets. Overall, the results indicate potential for both VNIR and MIR spectra to predict SOC content in the Andean soils. SOC content predictions from MIR spectra outperformed those from VNIR spectra. The evaluation of model performance shows that RF and SVM provide more accurate SOC predictions compared to PLSR. These findings suggest that integrating VNIR and MIR spectroscopy with machine learning algorithms constitutes a promising approach for assessing SOC content in high-Andean ecosystems.