Statistical analysis of big data: an approach based on support vector machines for classification and regression problems

A new type of learning algorithms with the supervisor for estimating multidimensional functions is considered. These methods based on Support Vector Machines are widely used due to their ability to deal with high-dimensional and large datasets, and their flexibility in modeling diverse sources of data. Support vector machines and related kernel methods are extremely good at solving prediction problems in computational biology. A background about statistical learning theory and kernel feature spaces is given including practical and algorithmic considerations.     

Adaptive mining prediction model for content recommendation to coronary heart disease patients

This paper proposes the Fuzzy Rule-based Adaptive Coronary Heart Disease Prediction Support Model (FbACHD_PSM), which gives content recommendation to coronary heart disease patients. The proposed model uses a mining technique validated by medical experts to provide recommendations. FbACHD_PSM consists of three parts for heart disease risk prediction. First, a fuzzy membership function is constructed using medical guidelines and statistical methods. Then, a decision-tree rule induction technique creates mining-based rules that are subjected to validation by medical experts. As the rules may not be medically suitable, the experts add rules that have been verified and delete inappropriate rules. Thirdly, using fuzzy inference based on Mamdani’s method, the model predicts the risk of heart disease. Based on this, final recommendations are provided to patients regarding normal living, nutrition control, exercise, and drugs. To implement our proposed model and evaluate its performance, we use a dataset from a single tertiary hospital.     

A Threshold-based Dynamic Data Replication and Parallel Job Scheduling strategy to enhance Data Grid

Data Grids provide environment for huge, data-intensive applications that produce and process enormous data. Such environments are thus asked to manage data and schedule jobs at the same time. These two important operations have to be tightly coupled to achieve the best results. Replication techniques are widely used to increase the availability of data, improving performance of query latency and load balancing in Data Grid. Also effective resource scheduling is a challenging research issue. In this paper we propose a job scheduling policy, called Parallel Job Scheduling (PJS), and a dynamic data replication strategy, called Threshold-based Dynamic Data Replication (TDDR), to improve the data access efficiencies in a hierarchical Data Grid. The PJS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. The main idea of TDDR strategy is using a threshold value to determine if the requested replica needs to be copied to the node. The TDDR determines this threshold dynamically based on data request arrival rates and available storage capacities. Then, in order to overcome the problem of limited storage space in each node, we design an efficient replica replacement strategy, which is developed as a two stages process. First, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. Results from the simulation show that our proposed algorithms have better performance in comparison with other algorithms in terms of Mean Job Time, Number of Intercommunications, Number of Replications, Computing Resource Usage, and Effective Network Usage.     

Mode of Action of S-Methyl-N,N-Diethylthiocarbamate Sulfoxide (DETC-MeSO) as a Novel Therapy for Stroke in a Rat Model

One approach for protecting neurons from excitotoxic damage in stroke is to attenuate receptor activity with specific antagonists. S-Methyl-N, N-diethylthiocarbamate sulfoxide (DETC-MeSO), the active metabolite of disulfiram, has been shown to be a partial antagonist of glutamate receptors and effective in reducing seizure. First, we investigated neuroprotective effect of DETC-MeSO on primary cortical neuronal culture under hypoxia/reoxygenation condition in vitro. Then, DETC-MeSO was administered subcutaneously for 4 and 8 days with the first injection occurring 1 h before or 24 h after reperfusion in the rat middle cerebral artery occlusion stroke model. Rats were subjected to the neuroscore test, and the brain was analyzed for infarct size. Monitoring neurotransmitter release was carried out by microdialysis. Heat shock proteins, key proteins involved in apoptosis and endoplasmic reticulum (ER) stress, were analyzed by immunoblotting. DETC-MeSO greatly reduced both cell death following hypoxia/reoxygenation and brain infarct size. It improved performance on the neuroscore test and attenuated proteolysis of αII-spectrin. The level of pro-apoptotic proteins declined, and anti-apoptotic and HSP27 protein expressions were markedly increased. Levels of the ER stress protein markers p-PERK, p-eIF2α, ATF4, JNK, XBP-1, GADD34, and CHOP significantly declined after DETC-MeSO administration. Microdialysis data showed that DETC-MeSO increased high potassium-induced striatal dopamine release indicating that more neurons were protected and survived under ischemic insult in the presence of DETC-MeSO. We also showed that DETC-MeSO can prevent gliosis. DETC-MeSO elicits neuroprotection through the preservation of ER resulting in reduction of apoptosis by increase of anti-apoptotic proteins and decrease of pro-apoptotic proteins.     

Nerve Growth Factor Decreases in Sympathetic and Sensory Nerves of Rats with Chronic Heart Failure

Nerve growth factor (NGF) plays a critical role in the maintenance and survival of both sympathetic and sensory nerves. Also, NGF can regulate receptor expression and neuronal activity in the sympathetic and sensory neurons. Abnormalities in NGF regulation are observed in patients and animals with heart failure (HF). Nevertheless, the effects of chronic HF on the levels of NGF within the sympathetic and sensory nerves are not known. Thus, the ELISA method was used to assess the levels of NGF in the stellate ganglion (SG) and dorsal root ganglion (DRG) neurons of control rats and rats with chronic HF induced by myocardial infarction. Our data show for the first time that the levels of NGF were significantly decreased (P < 0.05) in the SG and DRG neurons 6–20 weeks after ligation of the coronary artery. In addition, a close relation was observed between the NGF levels and the left ventricular function. In conclusion, chronic HF impairs the expression of NGF in the sympathetic and sensory nerves. Given that sensory afferent nerves are engaged in the sympathetic nervous responses to somatic stimulation (i.e. muscle activity during exercise) via a reflex mechanism, our data indicate that NGF is likely responsible for the development of muscle reflex-mediated abnormal sympathetic responsiveness observed in chronic HF.     

Calpain cleavage prediction using multiple kernel learning

Calpain, an intracellular -dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain) modified to no longer block calpain''s proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.     

SmartJoin: a network-aware multiway join for MapReduce

MapReduce is an effective tool for processing large amounts of data in parallel using a cluster of processors or computers. One common data processing task is the join operation, which combines two or more datasets based on values common to each. In this paper, we present a network aware multi-way join for MapReduce (SmartJoin) that improves performance and considers network traffic when redistributing workload amongst reducers. SmartJoin achieves this by dynamically redistributing tuples directly between reducers with an intelligent network aware algorithm. We show that our presented technique has significant potential to minimize the time required to join multiple datasets. In our evaluation, we show that SmartJoin has up to 39 % improvement compared to the non-redistribution method, a 26.8 % improvement over random redistribution and 27.6 % improvement over worst join redistribution.     

Inhibition in the olfactory bulb of the lizard

Inhibition in neurons of the lizard olfactory bulb was investigated by intracellular recording. The hyperpolarization arising in the neurons after the spike in the response to orthodromic and antidromic activation is similar in composition and reflects the development of early and late IPSPs, differing from one another in latency, duration, and mechanism of generation. The early IPSP is evidently generated by the functioning of dendrodendritic synapses, formed by dendrites of the interglomerular cell on the membrane of the apical dendrites of the secondary neurons, whereas synapses generating the late IPSP are located on the basal dendrites and are formed by endings of the granular cells. The mechanisms of generation of the early and late IPSPs in the secondary neurons are discussed. A classification of neurons of the lizard olfactory bulb is given on the basis of analysis of their intracellular activity.     

Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): An Algorithm for Joint Optimization of Discrimination and Calibration

Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of correctness of model predictions. Using discrimination and calibration simultaneously can be helpful for many clinical decisions. We investigated tradeoffs between these goals, and developed a unified maximum-margin method to handle them jointly. Our approach called, Doubly Optimized Calibrated Support Vector Machine (DOC-SVM), concurrently optimizes two loss functions: the ridge regression loss and the hinge loss. Experiments using three breast cancer gene-expression datasets (i.e., {"type":"entrez-geo","attrs":{"text":"GSE2034","term_id":"2034"}}GSE2034, {"type":"entrez-geo","attrs":{"text":"GSE2990","term_id":"2990"}}GSE2990, and Chanrion''s datasets) showed that our model generated more calibrated outputs when compared to other state-of-the-art models like Support Vector Machine ( = 0.03,  = 0.13, and <0.001) and Logistic Regression ( = 0.006,  = 0.008, and <0.001). DOC-SVM also demonstrated better discrimination (i.e., higher AUCs) when compared to Support Vector Machine ( = 0.38,  = 0.29, and  = 0.047) and Logistic Regression ( = 0.38,  = 0.04, and <0.0001). DOC-SVM produced a model that was better calibrated without sacrificing discrimination, and hence may be helpful in clinical decision making.     

A bibliometric investigation of life cycle assessment research in the web of science databases

Purpose

Over the past few decades, life cycle assessment (LCA) methodologies have been developed extensively, and there has been a growing interest in LCA research. However, as attested by scientific literature, few systematic, synthesizing, and visualizing studies have been found on LCA research which show how this field has evolved over time. The goal of this mainly bibliometric, empirical study is to get insight into publication performance of global LCA research, characterize its intellectual structure, and trace its evolution by using the bibliometric method with visual mapping.

Methods

Based on the data from the ISI Web of Science databases Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Conference Proceedings Citation Index—Science (CPCI-S) and Conference Proceedings Citation Index —Social Science & Humanities (CPCI-SSH) in the period of 1998–2013, bibliometric methods are used to investigate general development profiles of LCA research, while knowledge domain visualization technologies are employed to conduct a further co-citation analysis.

Results and discussion

The results and discussions of this research mainly shed light on (1) basic statistics of significant publication performances, (2) research focuses and their intellectual base in LCA research, (3) how the streams of research evolved during the whole period of interest.

Conclusions

A new work on systematic and synthesizing study is conducted in this research to evaluate and map LCA research-related context. Some salient scholarly journals and institutions are identified that have shown a significant impact during the exponential growth of LCA research in the past 16 years. Biofuel, process design, solid waste management, and livestock production-related LCA researches are the main areas where interest is surging, confirmed by the active citers in each specialty. Furthermore, from the perspective of science mapping, evolution of LCA research is traced and some pivot publications are identified, which work as structural holes for the LCA-research development in the given time window.     

Pretraining and learning: Are “adaptable” monkeys “bright”?

Two groups of rhesus (Macaca mulatta),two groups of cynomolgus (M. fascicularis),and one group of stumptailed monkeys (M. arctoides)were adapted to testing in the Wisconsin General Test Apparatus using a standardized procedure that produced an objective measure of the speed of adaptation (pretraining). Four of the groups were then given standard object-discrimination learning-set training. The fifth group, one of the groups of rhesus monkeys, was given delayed response training before the learning-set training and delayed matching-to-sample learning-set training after. Only performance on the delayed response task was correlated with speed of pretraining. The results suggest that differences in pretraining performance reflect primarily emotional and/or temperamental differences, whereas differences in learning-set performance reflect primarily differences in cognitive functioning.     

Morphological approach to assess the involvement of astrocytes in prion propagation

Transmissible Spongiform Encephalopathies (TSEs) are a group of neurodegenerative disorders affecting animals and humans and for which no effective treatment is available to date. Vacuolation, neuronal/neurite degeneration, deposition of pathological prion protein (PrPsc) and gliosis are changes typically found in brains from TSE affected individuals. However, the actual role of this last feature, microgliosis and astrocytosis, has not been precisely determined. The overall objective of this work is to assess the involvement of glial cells as components of the host protective system in prion propagation; specifically, to analyze the behavior of astroglial cells in prion progression. To achieve this aim, histopathological and immunohistochemical techniques were carried out on samples from cerebella using Scrapie as the prototype of natural TSEs as this made it possible to assess different stages of the disease; specifically, ages and genotypes from Scrapie-affected animals corresponding to different sources, by using optical, confocal and electron microscopy. The results provided in the present study demonstrate the indisputable involvement of astroglia in prion progression by showing specific changes of this glial population matching up to the evolution of the disease. Moreover, cerebellar lesions mainly associated to Purkinje cells that have not previously been reported in animal prion diseases in natural transmission are described here. The close relationship between PrPsc and GFAP hiperimmunoreactivity and Purkinje cells, alongside the evident thickening of their neurites at terminal stages demonstrated in this study, suggest that these neurons are the main target of this neurodegenerative disease.     

Unit responses of the second auditory area to acoustic stimulation

Responses of 116 neurons of the second auditory area to clicks were recorded extracellularly in experiments on unanesthetized cats immobilized with D-tubocurarine. Neurons with and without (54.6%) took part in the response to clicks. The unit response to a click consisted of 1 or 2 spikes or a short volley. Different neurons responded to clicks at different times. The latent period of 25.8% of all neurons recorded was 6.5–13 msec, of 70% it was 14–25 msec, and of 4.2% it was over 25 msec. Long-latency responses to clicks (40, 50, and 100 msec) also were recorded. The responding neurons were found throughout the thickness of the cortex, but more frequently in layers III and IV. No relationship was found between the depth of the neuron and its latest period. Responses consisting of EPSP, EPSP-spike, EPSP-spike-IPSP, EPSP-IPSP, and primary IPSP were recorded intracellularly from the neurons of this area. It was concluded from the results that neurons of the second auditory area can be activated by the arrival of an afferent volley along the geniculo-cortical pathway and also by the arrival of impulses from the first auditory area.     

Performance analysis of non-PC/SC based mini-WiMAX connection manager

In order to provide WiMAX network services to specific purposed devices such as credit card systems and surveillance systems, we have proposed a new non-PC/SC based mini WiMAX Connection Manager (non-PC/SC m-WCM) that can be applied to small hardware system or architecture with Micro Controller Unit (MCU). The existing legacy WiMAX Connection Managers (WCMs) generally run on the operating systems such as Windows and Linux. This makes it hard to design and implement lightweight WCMs for the specific purposed devices that have very limited hardware capabilities. In order to solve the problem, we propose a new non-PC/SC m-WCM architecture which has almost same functionalities of the legacy WCM and implement non-PC/SC m-WCM without the performance degradations. The proposed non-PC/SC m-WCM mainly consists of three units; WiMAX module Control Unit (WCU), Data Surveillance Unit (DSU), and External device Program Unit (EPU). Besides these, it also has TCP/IP protocol stack and RTOS kernel, and can transmit date between external end user’s device and remote server system over the WiMAX networks. For the delay and throughput performance comparisons with the legacy WCMs, we have implemented the non-PC/SC m-WCM and tested it under a commercial mobile WiMAX network environment. From the experimental results, the non-PC/SC m-WCM is verified to show almost equal stability and delay performances with the legacy WCM and xDSL, but especially better than WCDMA. In the throughput tests, it has been shown that each of the modems shows almost similar throughput performance. Although the proposed WCM is designed with the very limited hardware specifications, it shows no performance degradations compared to the legacy WCMs. As a result, it has a strong possibility to be able to apply the non-PC/SC m-WCM to various types of specific purposed embedded systems, and to provide them with the same levels of WiMAX network services as those provided by the legacy WCMs.     

Characteristics of cortical inhibitory neurons

Spikes were recorded extracellularly and IPSPs intracellularly from auditory cortical neurons of cats immobilized with D-tubocurarine in response to stimulation of geniculo-cortical fibers. Fibers whose stimulation induces IPSPs in auditory cortical neurons mainly have low thresholds. When two stimuli, each of which separately evoked an IPSP of maximal amplitude, were applied to them the shortest interval at which the second stimulus evoked an effect was 2.5–3 msec. This effect consisted of an increase in the duration of the integral IPSP, the amplitude of which either remained unchanged or increased under these circumstances by only 5–10%. The interval at which a separate IPSP appeared in response to the second stimulus depended on the duration of the ascending phase of the IPSP and varied from 4 to 22 msec for different neurons. The amplitude of the second IPSP in this case depended on the interval between stimuli. Under moderately deep pentobarbital anesthesia the number of neurons responding to stimulation of the geniculo-cortical fibers by spikes fell sharply but the number of neurons responding by primary IPSPs remained almost unchanged. Under very deep pentobarbital anesthesia, when spike responses of the cortical neurons completely disappeared, the IPSPs also were completely suppressed. It is concluded that inhibitory neurons of the auditory cortex are excited by thick low-threshold fibers, they have a short refractory period, and they are resistant to the narcotic action of pentobarbital.     

Fault tolerance and QoS scheduling using CAN in mobile social cloud computing

The performance of mobile devices including smart phones and laptops is steadily rising as prices plummet sharply. So, mobile devices are changing from being a mere interface for requesting services to becoming computing resources for providing and sharing services due to immeasurably improved performance. With the increasing number of mobile device users, the utilization rate of SNS (Social Networking Service) is also soaring. Applying SNS to the existing computing environment enables members of social network to share computing services without further authentication. To use mobile device as a computing resource, temporary network disconnection caused by user mobility and various HW/SW faults causing service disruption should be considered. Also these issues must be resolved to support mobile users and to provide user requirements for services. Accordingly, we propose fault tolerance and QoS (Quality of Services) scheduling using CAN (Content Addressable Network) in Mobile Social Cloud Computing (MSCC). MSCC is a computing environment that integrates social network-based cloud computing and mobile devices. In the computing environment, a mobile user can, through mobile devices, become a member of a social network through real world relationships. Essentially, members of a social network share cloud service or data with other members without further authentication by using their mobile device. We use CAN as the underlying MSCC to logically manage the locations of mobile devices. Fault tolerance and QoS scheduling consists of four sub-scheduling algorithms: malicious-user filtering, cloud service delivery, QoS provisioning, and replication and load-balancing. Under the proposed scheduling, a mobile device is used as a resource for providing cloud services, faults caused from user mobility or other reasons are tolerated and user requirements for QoS are considered. We simulate scheduling both with and without CAN. The simulation results show that our proposed scheduling algorithm enhances cloud service execution time, finish time and reliability and reduces the cloud service error rate.     

Design of neural nets with intermittent response and certain other relevant studies

The application of the earlier results (Pavlidis, T. 1965. “A New Model for Simple Neural Nets and its Application in the Design of a Neural Oscillator.”Bull. Math. Biophysics,27, No. 2, 215–229) to the design of more complex neural nets is attempted. The following cases are considered:

1. 1.
   Chains of neurons where it is proven that the frequency of the output pulses does not depend on the value of the input as long as it is above a certain threshold.     
   
   

Monoclonal antibody reveals H-2-linked quantitative and qualitative variation in the expression of a Qa-2 region determinant

We have produced a monoclonal antibody, Y-7, that reacts with a Qa-2 region-controlled determinant. Cellular and strain distribution analyses, coupled with quantitative variation in the amount of Y-7 antigen expressed among strains, provide overwhelming evidence that Y-7 reacts with the Qa-2^a determinant. The determinant detected by Y-7 is differentially expressed in T and B lymphocytes in a strain specific manner. Y-7 reacts with the majority of T lymphocytes (> 95%) and approximately one-half of B lymphocytes in certain strains (++ strains), and with the majority of T lymphocytes (> 95%) and no B lymphocytes in other strains (+ strains). T lymphocytes in + strains express approximately three fold less of the Y-7 determinant than T lymphocytes from ++ strains. In addition, we show that the Y-7 determinant is expressed in approximately one-third to one-half of Lyb-3^?, 5^? B lymphocytes. Possible mechanisms determining quantitative and qualitative variation in the expression of the Y-7 determinant in T and B lymphocytes are discussed.