Miracle-dz/UniFAR-FTUs huggingface dataset:
Breakdown of Methods for Phasing and Imputation in the Presence of Double Genotype Sharing

In genome-wide association studies, results have been improved through imputation of a denser marker set based on reference haplotypes and phasing of the genotype data. To better handle very large sets of reference haplotypes, pre-phasing with only study individuals has been suggested. We present a possible problem which is aggravated when pre-phasing strategies are used, and suggest a modification avoiding the resulting issues with application to the MaCH tool, although the underlying problem is not specific to that tool. We evaluate the effectiveness of our remedy to a subset of Hapmap data, comparing the original version of MaCH and our modified approach. Improvements are demonstrated on the original data (phase switch error rate decreasing by 10%), but the differences are more pronounced in cases where the data is augmented to represent the presence of closely related individuals, especially when siblings are present (30% reduction in switch error rate in the presence of children, 47% reduction in the presence of siblings). The main conclusion of this investigation is that existing statistical methods for phasing and imputation of unrelated individuals might give results of sub-par quality if a subset of study individuals nonetheless are related. As the populations collected for general genome-wide association studies grow in size, including relatives might become more common. If a general GWAS framework for unrelated individuals would be employed on datasets with some related individuals, such as including familial data or material from domesticated animals, caution should also be taken regarding the quality of haplotypes. Our modification to MaCH is available on request and straightforward to implement. We hope that this mode, if found to be of use, could be integrated as an option in future standard distributions of MaCH.
Using synchronization stalls in power-aware accelerators

GPUs spend significant time on synchronization stalls. Such stalls provide ample opportunity to save leakage energy in GPU structures left idle during such periods. In this paper we focus on the register file structure of NVIDIA GPUs and introduce sync-aware low leakage solutions to reduce power. Accordingly, we show that applying the power gating technique to the register file during synchronization stalls can improve power efficiency without considerable performance loss. To this end, we equip the register file with two leakage power saving modes with different levels of power saving and wakeup latencies.
Intact single muscle fibres from SOD1G93A amyotrophic lateral sclerosis mice display preserved specific force, fatigue resistance and training‐like adaptations

How defects in muscle contractile function contribute to weakness in amyotrophic lateral sclerosis (ALS) were systematically investigated. Weakness in whole muscles from late stage SOD1G93A mice was explained by muscle atrophy as seen by reduced mass and maximal force. On the other hand, surviving single muscle fibres in late stage SOD1G93A have preserved intracellular Ca2+ handling, normal force‐generating capacity and increased fatigue resistance. These intriguing findings provide a substrate for therapeutic interventions to potentiate muscular capacity and delay the progression of the ALS phenotype.
A New Blind Identity-Based Signature Scheme

Anonymity of consumers is an essential functionality that should be supported in any digital rights management system. Privacy protection is an important aspect for wider acceptance of consumers of DRM systems. The concept of a blind signature is one possible cryptographic solution, yet it has not received much attention in the identity-based setting. In the identity-based setting, the public key of a user is derived from his identity, thus simplifying certificates management process compared to traditional public key cryptosystems. In this paper, a new blind identity-based signature scheme based on bilinear pairings on elliptic curves is presented. The use of bilinear pairings over elliptic curves enables utilizing smaller key sizes, while achieving the same level of security compared to other schemes not utilizing elliptic curves. The correctness of the proposed scheme is validated and the proof of the blindness property is provided. Performance and security related issues are addressed.
Real-Time Volume Rendering of FCC Datasets UsingBox-Splines on the Mobile Platforms

DOI: 10.9734/BJMCS/2015/19044 Editor(s): (1) Kai-Long Hsiao, Taiwan Shoufu University, Taiwan. Reviewers: (1) Anonymous, Suzhou Institute of Biomedical Engineering and Technology, China. (2) Tayfun Kucukyilmaz, University of Turkish, Turkey. (3) Anonymous, University of Zilina, Slovakia. (4) Samir Kumar Bandyopadhyay, University of Calcutta, India. Complete Peer review History: http://sciencedomain.org/review-history/10602
Cognitive Computing: A Brief Survey and Open Research Challenges

Cognitive computing is a multidisciplinary field of research aiming at devising computational models and decision making mechanisms based on the neurobiological processes of the brain, cognitive sciences, and psychology. The objective of cognitive computational models is to endow computer systems with the faculties of knowing, thinking, and feeling. The major contributions of this survey include (i) giving insights into cognitive computing by listing and describing its definitions, related fields, and terms, (ii) classifying current research on cognitive computing according to its objectives, (iii) presenting a concise review of cognitive computing approaches, and (iv) identifying the open research issues in the area of cognitive computing.
Methodology of surface electromyography in gait analysis: review of the literature

Abstract Gait analysis is a significant diagnostic procedure for the clinicians who manage musculoskeletal disorders. Surface electromyography (sEMG) combined with kinematic and kinetic data is a useful tool for decision making of the appropriate method needed to treat such patients. sEMG has been used for decades to evaluate neuromuscular responses during a range of activities and develop rehabilitation protocols. The sEMG methodology followed by researchers assessed the issues of noise control, wave frequency, cross talk, low signal reception, muscle co-contraction, electrode placement protocol and procedure as well as EMG signal timing, intensity and normalisation so as to collect accurate, adequate and meaningful data. Further research should be done to provide more information related to the muscle activity recorded by sEMG and the force produced by the corresponding muscle during gait analysis.
APPLICATIONS OF LEVIN ’ S UNIVERSAL OPTIMAL SEARCH ALGORITHM

New applications of Levin’s Universal Optimal Search are presented in this paper . Using this method one finds solutions to a few chosen problems. Such solutions are characterized by possibility of the maximal generalization. In the deterministic version of the Universal Optimal Search algorithm one can always generate the best solution. It is shown that it is possible to find a solution by this method for problems in neural networks for which back-propagation type of methods face difficulties. Another application considered here is a problem of finding an exit-way from a maze. There exists a probabilistic version of Levin’s universal search [4]. This work offers an alternative implementation of the Levin’s Universal OptimalSearch.
Performance Analysis for Molecular Dynamics Simulation on an SGI Altix Supercomputer

Fast Fourier Transforms (FFTs) are often used in Molecular Dynamics (MD) simulations to calculate force fields of interacting atoms and molecules. A performance analysis shows that the computation time and the communication time of the FFT in MD is not insignificant. Exponential speedup of FFTs, with a quantum computation platform, has been described. Until recently the ability to send classical data into, and retrieve classical data from a quantum computing device capable of performing an FFT has been problematic. Recent achievements suggest that this problem is close to solution. In this encouraging context, this paper explores the performance improvement that might be expected by use of a quantum implementation of FFT in Molecular Dynamics algorithms.
Impact of Environmental Regulation and Technical Progress on Industrial Carbon Productivity: An Approach Based on Proxy Measure

This research aims to study the main influencing factors of China’s industrial carbon productivity by incorporating environmental regulation and technical progress into an econometric model. The paper focuses on data from 35 of China’s industrial sectors and covers the period from 2006 to 2014, in order to examine the impact of environmental regulation and technical progress on carbon productivity. Methods applied include panel fixed effect model, panel random effect model and two stage least squares with instrumental variables (IV-2SLS). The effect of environmental regulation and technical progress has industrial heterogeneity. The paper subdivides industrial sectors into capital and technology intensive, resource intensive and labor intensive sectors according to factor intensiveness. The estimation results of the subgroups have uncovered that for capital and technology intensive and resource intensive sectors, environmental regulation has a more significant impact than technical progress; while for labor intensive sectors, innovation more significantly influences carbon productivity. In addition, foreign direct investment (FDI) and industrialization level facilitate improving carbon productivity for the full sample. By contrast, industrial structure inhibits the overall industrial carbon productivity. The industry-specific results indicate that for capital and technology intensive sectors, optimizing of the industrial structure can improve carbon productivity; for resource intensive sectors, FDI and energy consumption structure should be emphasized more; for labor intensive sectors, industrialization levels help enhance carbon productivity. Finally the industrial sector-specific policy suggestions are proposed.
Multigrid Methods for Policy Evaluation and Reinforcement Learning

We introduce a new class of multigrid temporal-difference learning algorithms for speeding up the estimation of the value function related to a stationary policy, within the context of discounted cost Markov decision processes with linear functional approximation. The proposed scheme builds on the multi-grid framework which is used in numerical analysis to enhance the iterative solution of linear equations. We first apply the multigrid approach to policy evaluation in the known model case. We then extend this approach to the learning case, and propose a scheme in which the basic TD(lambda) learning algorithm is applied at various resolution scales. The efficacy of the proposed algorithms is demonstrated through simulation experiments
MOLECULAR DYNAMICS OF GENOMIC DNA OF SILKWORM BREEDS FOR SCREENING UNDER HIGHER TEMPERATURE REGIMES UTILISING ISSR-PRIMERS

An experimental design was programmed to understand the level of dynamics of gDNA of silkmoth and the screening the parameters of the silkworm is relation to higher temperature regimes. The Bivoltine silkworm breeds namely, CSR 2 , CSR 4 , KA, NB 4 D 2 and JROP were chosen for the investigations and exposed to the higher temperature of 35+1 0 C, 40+1 0 C understood that these breeds are highly sensitive towards extreme temperature under natural condition, however the development of robust hardy bivoltine silkworm breeds at the age of 5 th instar imposed to 35+1 0 C, 40+1 0 C in the BOD incubator for about 4hrs daily with the interval of 1hr gap till the day of commencement of spinning for about 7 different generations in order to built up a increased range of tolerance in the system and investigated the gDNA pattern and banding profile in the selected breeds by utilizing 5 different UBC- primers as a marker in parental breeds and also different combinations of hybrids of silkworm and constructed the dendograms by using UPGMA method indicating with boot strap values. The gDNA is unique for the polymorphism of the banding pattern in the amplification of ISSR-PCR products and the phylogenetic inference was drawn on the basis of an evidence of dissimilarity index (I-F). Based on the result obtained in the present study the highest level of temperature tolerance observed in KA and CSR 2 breeds with low level of genetic distance between the breeds on the basis of gene frequency evidenced by the boot strap values in the constructed dendogram with the help of molecular markers.
Unused and Expired Medications: Are They a Threat? A Facility-Based Cross-Sectional Study

Background: A significant amount of potentially effective and lifesaving medications were wasted at the health facilities due to lack of a system for checking their effectiveness and returning them to the system. Objectives: To assess the prevalence of unused medications and related consequences in selected health facilities of Awi zone, Amhara regional state, Ethiopia. Methods: A facility-based cross-sectional study design supplemented by a qualitative approach was employed. Descriptive data were collected using checklists while interviews were conducted with respective personnel at the health facilities. Results: A total of 4 health facilities were included in the study. During the 1 month of study period, 56 types of medications were found unused at the health facilities. Anti-infective medications were the most commonly unused medications 36.4%. At the in-patient departments of the 4 hospitals, a total of 173 medications were found unused. Similarly, 605 medications were found wasted at the pharmacy stores of those hospitals in the 2009 fiscal year. Conclusion: A significant amount of unused medications were present at the health facilities. Health facilities need to monitor health professionals strictly follow national and international treatment guidelines and monitor the rational use of medications.
Classifying Stage IV Lung Cancer From Health Care Claims: A Comparison of Multiple Analytic Approaches.

PURPOSE Cancer stage is a key determinant of outcomes; however, stage is not available in claims-based data sources used for real-world evaluations. We compare multiple methods for classifying lung cancer stage from claims data. METHODS Our study used the linked SEER-Medicare data. The patient samples included fee-for-service Medicare beneficiaries diagnosed with lung cancer from 2010 to 2011 (development cohort) and 2012 to 2013 (validation cohort) who received chemotherapy. Classification algorithms considered Medicare Part A and B claims for care in the 3 months before and after chemotherapy initiation. We developed a clinical algorithm to predict stage IV (v I to III) cancer on the basis of treatment patterns (surgery, radiotherapy, chemotherapy). We also considered an ensemble of claims-based machine learning algorithms. Classification methods were trained in the development cohort, and performance was measured in both cohorts. The SEER data were the gold standard for cancer stage. RESULTS Development and validation cohorts included 14,760 and 14,620 patients with lung cancer, respectively. Validation analyses assessed clinical, random forest, and simple logistic regression algorithms. The best performing classifier within the development cohort was the random forests, but this performance was not replicated in validation analysis. Logistic regression had stable performance across cohorts. Compared with the clinical algorithm, the 14-variable logistic regression algorithm demonstrated higher accuracy in both the development (77% v 71%) and validation cohorts (77% v 73%), with improved specificity for stage IV disease. CONCLUSION Machine learning algorithms have potential to improve lung cancer stage classification but may be prone to overfitting. Use of ensembles, cross-validation, and external validation can aid generalizability. Degradation of accuracy between development and validation cohorts suggests the need for caution in implementing machine learning in research or care delivery.
An Accurate Model for EESM and its Application to Analysis of CQI Feedback Schemes and Scheduling in LTE

The Effective Exponential SNR Mapping (EESM) is an indispensable tool for analyzing and simulating next generation orthogonal frequency division multiplexing (OFDM) based wireless systems. It converts the different gains of multiple subchannels, over which a codeword is transmitted, into a single effective flat-fading gain with the same codeword error rate. It facilitates link adaptation by helping each user to compute an accurate channel quality indicator (CQI), which is fed back to the base station to enable downlink rate adaptation and scheduling. However, the highly non-linear nature of EESM makes a performance analysis of adaptation and scheduling difficult; even the probability distribution of EESM is not known in closed-form. This paper shows that EESM can be accurately modeled as a lognormal random variable when the subchannel gains are Rayleigh distributed. The model is also valid when the subchannel gains are correlated in frequency or space. With some simplifying assumptions, the paper then develops a novel analysis of the performance of LTE's two CQI feedback schemes that use EESM to generate CQI. The comprehensive model and analysis quantify the joint effect of several critical components such as scheduler, multiple antenna mode, CQI feedback scheme, and EESM-based feedback averaging on the overall system throughput.
Red blood cells donated by smokers: A pilot investigation of recipient transfusion outcomes

Current regulations do not require blood collection facilities to ask donors about cigarette smoking, and the prevalence of nicotine and its metabolites in blood products is not well established. Although smokers have higher hemoglobin (Hb) levels, smoking may adversely affect the quality of donated red blood cells through higher carboxyhemoglobin (COHb) content and premature hemolysis.
Structured P 2 P Networks in Mobile and Fixed Environments

Abstract – Peer-to-Peer (P2P) networks and their applications gain increasing importance in today’s Internet, as already today the majority of IP traffic is caused by P2P applications. Since the upcoming of Napster a lot of research has been done in this area producing interesting and promising results. Still, growing demands like less data rate consumption, faster and more reliable search responses and the development of new applications engage many researchers worldwide. In this tutorial we therefore provide an overview about the area of P2P networking, its basic methods and a classification into unstructured and structured P2P networks. However the focus of this work is put on structured P2P networks, for which we explain in detail the most important routing algorithms. Based on this overview we can provide a discussion of the major advantages and disadvantages of the different P2P approaches, focusing especially on the applicability of P2P networks in heterogeneous environments. Index terms – Structured and Unstructured Peer-to-Peer, Distributed Hash tables, signaling efficiency, cross layer communication, heterogeneous networks.
Stimulation of Microglial Metabotropic Glutamate Receptor mGlu2 Triggers Tumor Necrosis Factor α-Induced Neurotoxicity in Concert with Microglial-Derived Fas Ligand

Activated microglia may be detrimental to neuronal survival in a number of neurodegenerative diseases. Thus, strategies that reduce microglial neurotoxicity may have therapeutic benefit. Stimulation of group II metabotropic glutamate (mGlu) receptors on rat primary microglia with the specific group II agonist 2S,2′R,3′R-2-(2′,3′-dicarboxy-cyclopropyl)glycine for 24 h induced microglial activation and resulted in a neurotoxic microglial phenotype. These effects were attributable to preferential mGlu2 stimulation, because N-acetyl-l-aspartyl-l-glutamate, a specific mGlu3 agonist, did not induce microglial activation or neurotoxicity. Stimulation of microglial mGlu2 but not mGlu3 induced caspase-3 activation in cerebellar granule neurons in culture, using microglial-conditioned media as well as cocultures. Stimulation of microglial mGlu2 induced tumor necrosis factor-α (TNFα) release, which contributed to microglial neurotoxicity mediated via neuronal TNF receptor 1 and caspase-3 activation. Stimulation of microglial group I or III mGlu receptors did not induce TNFα release. TNFα was only neurotoxic in the presence of microglia or microglial-conditioned medium. The toxicity of TNFα could be prevented by coexposure of neurons to conditioned medium from microglia stimulated by the specific group III agonist l-2-amino-4-phosphono-butyric acid. The neurotoxicity of TNFα derived from mGlu2-stimulated microglia was potentiated by microglial-derived Fas ligand (FasL), the death receptor ligand. FasL was constitutively expressed in microglia and shed after mGlu2 stimulation. Our data suggest that selective and inverse modulation of microglial mGlu2 and mGlu3 may prove a therapeutic target in neuroinflammatory diseases such as Alzheimer's disease and multiple sclerosis.
State estimation for jump Markov linear systems with uncompensated biases

This paper studies the problem of state estimation for jump Markov linear systems with uncompensated biases. By describing the state and the measurement biases as additive random variables, a suboptimal filter has been developed by applying the basic interacting multiple model (IMM) approach. To derive a precise representation of the biases contributions to the state estimation, three auxiliary matrices are introduced with respect to the correlation between the state estimation errors and the biases, which helps to derive mode-conditioned estimates in the framework of the IMM. A numerical example involving tracking a maneuvering target is provided to compare the performance of the proposed filter with that of the augmented state filter.
Thieves, Thugs, and Neighborhood Poverty

This paper develops a model of crime analyzing how such behavior is associated with individual and neighborhood poverty. The model shows that even under relatively minimal assumptions, a connection between individual poverty and both property and violent crimes will arise, and moreover, "neighborhood" effects can develop, but will differ substantially in nature across crime types. A key implication is that greater economic segregation in a city should have no effect or a negative effect on property crime, but a positive effect on violent crime. Using IV methods, I show this implication to be consistent with the empirical evidence.