Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user's activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%.
In this paper we discuss the design and evaluation of a mobile based tool to collect activity data on a large scale. The current approach, based on an existing activity recognition module, recognizes class transitions from a set of specific activities (for example walking and running) to the standing still activity. Once this transition is detected the system prompts the user to provide a label for their previous activity. This label, along with the raw sensor data, is then stored locally prior to being uploaded to cloud storage. The system was evaluated by ten users. Three evaluation protocols were used, including a structured, semi-structured and free living protocol. Results indicate that the mobile application could be used to allow the user to provide accurate ground truth labels for their activity data. Similarities of up to 100% where observed when comparing the user prompted labels and those from an observer during structured lab based experiments. Further work will examine data segmentation and personalization issues in order to refine the system.
Individuals in each family are genetically more homogeneous than unrelated individuals, and family-based designs are often recommended for the analysis of rare variants. However, despite the importance of family-based samples analysis, few statistical methods for rare variant association analysis are available.
Technology provides ample opportunities for the acquisition and processing of physical, mental and social health primitives. However, several challenges remain for researchers as how to define the relationship between reported physical activities, mood and social interaction to define an active lifestyle. We are conducting a project, ATHENA(activity-awareness for human-engaged wellness applications) to design and integrate the relationship between these basic health primitives to approximate the human lifestyle and real-time recommendations for wellbeing services. Our goal is to develop a system to promote an active lifestyle for individuals and to recommend to them valuable interventions by making comparisons to their past habits. The proposed system processes sensory data through our developed machine learning algorithms inside smart devices and utilizes cloud infrastructure to reduce the cost. We exploit big data infrastructure for massive sensory data storage and fast retrieval for recommendations. Our contributions include the development of a prototype system to promote an active lifestyle and a visual design capable of engaging users in the goal of increasing self-motivation. We believe that our study will impact the design of future ubiquitous wellness applications.
Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteR Interoperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.
Video-based human activity recognition (HAR) means the analysis of motions and behaviors of human from the low level sensors. Over the last decade, automatic HAR is an exigent research area and is considered a significant concern in the field of computer vision and pattern recognition. In this paper, we have presented a robust and an accurate activity recognition system called WS-HAR that consists of wavelet transform coupled with stepwise linear discriminant analysis (SWLDA) followed by hidden Markov model (HMM). Symlet wavelet has been employed in order to extract the features from the activity frames. The most prominent features were selected by proposing a robust technique called stepwise linear discriminant analysis (SWLDA) that focuses on selecting the localized features from the activity frames and discriminating their class based on regression values (i.e., partial F-test values). Finally, we applied a well-known sequential classifier called hidden Markov model (HMM) to give the appropriate labels to the activities. In order to validate the performance of the WS-HAR, we utilized two publicly available standard datasets under two different experimental settings, n??fold cross validation scheme based on subjects; and a set of experiments was performed in order to show the effectiveness of each approach. The weighted average recognition rate for the WS-HAR was 97% across the two different datasets that is a significant improvement in classication accuracy compared to the existing well-known statistical and state-of-the-art methods.
The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems' design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.
Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it dif?cult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, ?nally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER.
Clinical Decision Support Systems (CDSS) assist clinicians in making clinical decisions by using experts knowledge stored in the knowledge base. However, sharing and reusing the knowledge is a challenging task. Many systems are developed to facilitate sharing of medical knowledge and allow its reusability. These systems are compliant to standard approaches such as HL7 Arden Syntax and HL7 CDA (Clinical Document Architecture) to incorporate medical logic in standard format. The main drawback with these systems is the complicated procedure in the development of clinical knowledge by ordinary clinicians. The proposed research work is focusing on developing authoring tool that creates sharable clinical knowledge base using standards such as HL7 Arden Syntax, HL7 vMR and HL7 CDA. Moreover, the authoring tool provides user friendly GUI to facilitate clinicians in creating standard based executable clinical knowledge base. We are closely working with oncologists and clinicians of a prominent cancer hospital to deploy the tool for Head and Neck Cancer diagnosis and treatment recommendations.
Sensor nodes usually have limited energy supply and they are impractical to recharge. How to balance traffic load in sensors in order to increase network lifetime is a very challenging research issue. Many clustering algorithms have been proposed recently for wireless sensor networks (WSNs). However, sensor networks with one fixed sink node often suffer from a hot spots problem since nodes near sinks have more traffic burden to forward during a multi-hop transmission process. The use of mobile sinks has been shown to be an effective technique to enhance network performance features such as latency, energy efficiency, network lifetime, etc. In this paper, a modified Stable Election Protocol (SEP), which employs a mobile sink, has been proposed for WSNs with non-uniform node distribution. The decision of selecting cluster heads by the sink is based on the minimization of the associated additional energy and residual energy at each node. Besides, the cluster head selects the shortest path to reach the sink between the direct approach and the indirect approach with the use of the nearest cluster head. Simulation results demonstrate that our algorithm has better performance than traditional routing algorithms, such as LEACH and SEP.
Data interoperability among health information exchange (HIE) systems is a major concern for healthcare practitioners to enable provisioning of telemedicine-related services. Heterogeneity exists in these systems not only at the data level but also among different heterogeneous healthcare standards with which these are compliant. The relationship between healthcare organization data and different heterogeneous standards is necessary to achieve the goal of data level interoperability. We propose a personalized-detailed clinical model (P-DCM) approach for the generation of customized mappings that creates the necessary linkage between organization-conformed healthcare standards concepts and clinical model concepts to ensure data interoperability among HIE systems.
In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users" actions to gain knowledge about their habits and preferences.
In plants heme containing cytochrome P450 (P450) is a superfamily of monooxygenases that catalyze the addition of one oxygen atom from O2 into a substrate, with a substantial reduction of the other atom to water. The function of P450 families is attributed to chemical defense mechanism under terrestrial environmental conditions; several are involved in secondary and hormone metabolism. However, the evolutionary relationships of P450 genes in Panax ginseng remain largely unknown. In the present study, data mining methods were implemented and 116 novel putative P450 genes were identified from Expressed Sequence Tags (ESTs) of a ginseng database. These genes were classified into four clans and 22 families by sequence similarity conducted at amino acid level. The representative putative P450 sequences of P. ginseng and known P450 family from other plants were used to construct a phylogenetic tree. By comparing with other genomes, we found that most of the P450 genes from P. ginseng can be found in other dicot species. Depending on P450 family functions, seven P450 genes were selected, and for that organ specific expression, abiotic, and biotic studies were performed by quantitative reverse transcriptase-polymerase chain reaction. Different genes were found to be expressed differently in different organs. Biotic stress and abiotic stress transcript level was regulated diversely, and upregulation of P450 genes indicated the involvement of certain genes under stress conditions. The upregulation of the P450 genes under methyl jasmonate and fungal stress justifies the involvement of specific genes in secondary metabolite biosynthesis. Our results provide a foundation for further elucidating the actual function and role of P450 involved in various biochemical pathways in P. ginseng.
In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.
Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patients real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimers disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patients activity using patient profile information and customized rules.
Gynosaponins (Gypenosides) are major phyto-chemicals in Gynostemma pentaphyllum (Thunb.), with similarities to the ginsenosides present in Panax ginseng. Gynosaponins are classified as terpenoid compounds. In G. pentaphyllum, 25% of the total gynosaponins are similar to ginsenosides. In this study, we analyzed the transcriptional levels of the G. pentaphyllum genome to identify secondary metabolite genes. The complete transcriptomes for the roots and leaves were obtained using a GS-FLX pyro-sequencer. In total, we obtained 265,340 and all reads were well annotated according to biological databases. Using insilico analysis, 84% of sequence were well annotated and we obtained most of the secondary metabolite genes that represent mono-, di-, tri- and sesquiterpenoids. From our EST, most of the terpenoid genes were noted, among those few similar genes were studied in P. ginseng and these transcripts will help to characterize more triterpenoid genes in G. pentaphyllum. Also help to compare P. ginseng and G. pentaphyllum at transcriptome level.
Target-specific antibodies can be rapidly enriched and identified from an antibody library using phage display. Large, naïve antibody libraries derived from synthetic or unimmunized sources can yield antibodies against virtually any antigen, whereas libraries from immunized sources tend to be smaller and are used exclusively against the antigen of immunization. In this study, 25 scFv libraries made from the spleens of immunized rabbits, each with a size ranging from 10(8) to higher than 10(9), were combined into a single large library with > 10(10) individual clones. Panning of this combined library yielded target-specific rabbit scFv clones against many non-immunizing antigens, including proteins, peptides, and a small molecule. Notably, specific scFv clones against a rabbit self-antigen (rabbit serum albumin) and a phosphorylated protein (epidermal growth factor receptor pTyr1173) could be isolated from the library. These results suggest that the immune library contained a significant number of unimmunized clones and that a sufficiently large immune library can be utilized similarly to a naïve library, i.e., against various non-immunizing antigens to yield specific antibodies.
In this paper, we present a novel active contour (AC) model for medical image segmentation that is based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. This combination is necessary because objects in medical images, e.g., bones, are usually highly inhomogeneous while distinct organs should generate distinct image configurations. Compared with the conventional Chan-Vese AC, the proposed model yields similar performance in a set of CT images but performs better in an MRI data set, which is generally in lower contrast.
Physical-activity recognition via wearable sensors can provide valuable information regarding an individuals degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subjects chest.
Mobility is a good indicator of health status and thus objective mobility data could be used to assess the health status of elderly patients. Accelerometry has emerged as an effective means for long-term physical activity monitoring in the elderly. However, the output of an accelerometer varies at different positions on a subjects body, even for the same activity, resulting in high within-class variance. Existing accelerometer-based activity recognition systems thus require firm attachment of the sensor to a subjects body. This requirement makes them impractical for long-term activity monitoring during unsupervised free-living as it forces subjects into a fixed life pattern and impede their daily activities. Therefore, we introduce a novel single-triaxial-accelerometer-based activity recognition system that reduces the high within-class variance significantly and allows subjects to carry the sensor freely in any pocket without its firm attachment. We validated our system using seven activities: resting (lying/sitting/standing), walking, walking-upstairs, walking-downstairs, running, cycling, and vacuuming, recorded from five positions: chest pocket, front left trousers pocket, front right trousers pocket, rear trousers pocket, and inner jacket pocket. Its simplicity, ability to perform activities unimpeded, and an average recognition accuracy of 94% make our system a practical solution for continuous long-term activity monitoring in the elderly.
Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.
Automatic bone segmentation of computed tomography (CT) images is an important step in image-guided surgery that requires both high accuracy and minimal user interaction. Previous attempts include global thresholding, region growing, region competition, watershed segmentation, and parametric active contour (AC) approaches, but none claim fully satisfactory performance. Recently, geometric or level-set-based AC models have been developed and appear to have characteristics suitable for automatic bone segmentation such as initialization insensitivity and topology adaptability. In this study, we have tested the feasibility of five level-set-based AC approaches for automatic CT bone segmentation with both synthetic and real CT images: namely, the geometric AC, geodesic AC, gradient vector flow fast geometric AC, Chan-Vese (CV) AC, and our proposed density distance augmented CV AC (Aug. CV AC). Qualitative and quantitative evaluations have been made in comparison with the segmentation results from standard commercial software and a medical expert. The first three models showed their robustness to various image contrasts, but their performances decreased much when noise level increased. On the contrary, the CV ACs performance was more robust to noise, yet dependent on image contrast. On the other hand, the Aug. CV AC demonstrated its robustness to both noise and contrast levels and yielded improved performances on a set of real CT data compared with the commercial software, proving its suitability for automatic bone segmentation from CT images.
Plants have versatile detoxification systems to encounter the phytotoxicity of the wide range of natural and synthetic compounds present in the environment. Glutathione S-transferase (GST) is an enzyme that detoxifies natural and exogenous toxic compounds by conjugation with glutathione (GSH). Recently, several roles of GST giving stress tolerance in plants have demonstrated, but little is known about the role of ginseng GSTs. Therefore, this work aimed to provide further information on the GST gene present in Panax ginseng genome as well as its expression and function. A GST cDNA (PgGST) was isolated from P. ginseng cDNA library, and it showed the amino acid sequence similarity with theta type of GSTs. PgGST in ginseng plant was induced by exposure to metals, plant hormone, heavy metals, and high light irradiance. To improve the resistance against environmental stresses, full-length cDNA of PgGST was introduced into Nicotiana tabacum. Overexpression of PgGST led to twofold increase in GST-specific activity compared to the non-transgenic plants, and the GST overexpressed plant showed resistance against herbicide phosphinothricin. The results suggested that the PgGST isolated from ginseng might have a role in the protection mechanism against toxic materials such as heavy metals and herbicides.
Ginsenoside (ginseng saponin), the principal component of ginseng, is responsible for the pharmacological and biological activities of ginseng. We isolated lactic acid bacteria from Kimchi using esculin agar, to produce ?-glucosidase. We focused on the bio-transformation of ginsenoside. Phylogenetic analysis was performed by comparing the 16S rRNA sequences. We identified the strain as Lactobacillus (strain 6105). In order to determine the optimal conditions for enzyme activity, the crude enzyme was incubated with 1 mM ginsenoside Rb1 to catalyse the reaction. A carbon substrate, such as cellobiose, lactose, and sucrose, resulted in the highest yields of ?-glucosidase activity. Biotransformations of ginsenoside Rb1 were analyzed using TLC and HPLC. Our results confirmed that the microbial enzyme of strain 6105 significantly transformed ginsenoside as follows: Rb1?gypenoside XVII, Rd?F2 into compound K. Our results indicate that this is the best possible way to obtain specific ginsenosides using microbial enzymes from 6105 culture.
Interoperability is the among the key functionalities of an intelligent systems. Home Healthcare Monitoring Systems (HHMS) investigates patients activities at home, but lacks critical information exchange with Health Management Information System (HMIS). This information is vital for physicians to take necessary steps for timely and effective healthcare provisioning for patients. Physicians can only monitor and prescribe patients in time, if the data is shared with their HMIS. HMIS can be compliant to different healthcare standards. Therefore, mediation system is required to enable interoperability between HHMS and HMIS such that physicians and patients information can easily be exchanged. We propose Interoperability Mediation System (IMS) that provides interoperability services for exchange of information among HHMS and HMIS. We consider that HMIS are compliant to two heterogeneous EHR standards (HL7 CDA and openEHR). Alzheimers patient case study is described as a proof of concept. Sensory information gathered at HHMS, is communicated with HMIS compliant to EHR based healthcare standards. Sensors information in XML form is converted by interoperability service to HL7 CDA and openEHR instances and communicated to HMIS afterwards. This allows the physicians registered with HHMS to monitor the patient using their HMIS and provide timely healthcare information.
Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.
The differential transcript patterns of five antioxidant genes, four genes related to the ginsenoside pathway and five P450 genes related to defense mechanism were investigated in in vitro adventitious roots of Panax ginseng after exposure to two different concentrations of heavy metals for 7 days. PgSOD-1 and PgCAT transcription increased in a dose-dependent manner during the exposure to CuCl(2), NiCl(2), and CdCl(2), while all other tested scavenging enzymes didnt show significant increase during heavy metal exposure. Conversely, the mRNA transcripts of PgSQE, PgDDS were highly responsive to CuCl(2) compared to NiCl(2) exposure. However, the transcript profile of Pg?-AS was highly induced upon NiCl(2) treatment compared to CuCl(2) and CdCl(2) exposure. The expressions of PgCYP716A42, PgCYP71A50U, and PgCYP82C22 were regulated in similar manners, and all showed the highest transcript profile at 100 ?M of CuCl(2), CdCl(2), and NiCl(2) except PgCYP71D184, which showed the highest transcript level when subjected to 10 ?M CuCl(2) and NiCl(2). Thus it may suggest that in P. ginseng heavy metal interaction on cell membrane induced expression of various defense related genes via jasmonic acid pathway and also possesses cross talk networks with other defense related pathways.
Multifactor dimensionality reduction (MDR) method has been widely applied to detect gene-gene interactions that are well recognized as playing an important role in understanding complex traits. However, because of an exhaustive analysis of MDR, the current MDR software has some limitations to be extended to the genome-wide association studies (GWAS) with a large number of genetic markers up to approximately 1 million. To overcome this computational problem, we developed CUDA (Compute Unified Device Architecture) based genome-wide association MDR (cuGWAM) software using efficient hardware accelerators, cuGWAM has better performance than CPU-based MDR methods and other GPU-based methods.
Activity monitoring of a person for a long-term would be helpful for controlling lifestyle associated diseases. Such diseases are often linked with the way a person lives. An unhealthy and irregular standard of living influences the risk of such diseases in the later part of ones life. The symptoms and the initial signs of these diseases are common to the people with irregular lifestyle. In this paper, we propose a novel healthcare framework to manage lifestyle diseases using long-term activity monitoring. The framework recognizes the users activities with the help of the sensed data in runtime and reports the irregular and unhealthy activity patterns to a doctor and a caregiver. The proposed framework is a hierarchical structure that consists of three modules: activity recognition, activity pattern generation and lifestyle disease prediction. We show that it is possible to assess the possibility of lifestyle diseases from the sensor data. We also show the viability of the proposed framework.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.