Miracle-dz/UniFAR-FTUs huggingface dataset:
High concordance of genotypic coreceptor prediction in plasma-viral RNA and proviral DNA of HIV-1 subtype C: implications for use of whole blood DNA in resource-limited settings.

OBJECTIVES Genotypic tropism testing (GTT) of HIV is increasingly used prior to the initiation of CCR5 antagonist therapy in HIV-infected individuals. Normally performed on plasma-derived virus, the test is challenging when performed in patients with suppressed viraemia. We aimed to evaluate the performance of cell-associated proviral DNA against plasma-derived viral RNA as the genetic material for GTT in an Indian clinical setting. METHODS From 52 HIV-1-infected individuals, the env V3 region was successfully amplified and sequenced from both proviral DNA and plasma RNA paired samples having a viral load >2500 copies/mL (n = 42) and from proviral DNA only in 10 antiretroviral therapy (ART)-experienced patients with a viral load <500 copies/mL. GTT was performed using the Geno2Pheno algorithm with the interpretative false positive rate (FPR) cut-off of 10%. RESULTS Among paired samples, 40 of 42 patients harboured subtype C strains. Plasma RNA tropism prediction revealed X4 tropism in 4 of 42 (9.5%). A high concordance of 97.6% in tropism prediction was noted in simultaneous RNA/DNA samples (38 R5 and 3 X4). Discordance was observed in one sample showing R5 tropism in proviral DNA and X4 tropism in plasma RNA. Comparison of Geno2Pheno FPRs in both the plasma and proviral compartments showed good correlation (overall, r = 0.87; ART-naive patients, r = 0.79; ART-failing patients, r = 0.97). GTT was successfully performed in all 10 whole blood DNA samples having a viral load <500 copies/mL, all showing R5 tropism. CONCLUSIONS High concordance in tropism prediction from proviral DNA and plasma-viral RNA suggests that prediction of viral tropism using proviral DNA is accurate and feasible in resource-limited clinical settings, particularly in patients with low or suppressed viraemia.
SuperSelect: An Interactive Superpixel-Based Segmentation Method for Touch Displays

We present the concept of an interactive image segmentation method, which allows a fast and precise extraction of foreground objects from natural images. The method is especially suited for mobile, touch-based devices. The approach combines automatic image segmentation with interactive refinement. In a first step, the user extracts an object using the first stage of the GrabCut algorithm. Any errors in the resulting segmentation can then be corrected via a novel correction step in which the underlying image is over-segmented into superpixels using the SLIC algorithm. The user can select individual superpixels in order to adjust the image segmentation. This image segmentation method is implemented as a full working demo on a Microsoft Surface Pro 3.
Charge-Controlled Readout and BIST Circuit for MEMS Sensors

In this paper, we present a new readout circuit with an integrated built-in self-test (BIST) structure for capacitive microelectromechanical system (MEMS). In the proposed solution, instead of commonly used voltage control signals to test the device, charge-controlled stimuli are employed to cover a wider range of structural defects. The proposed test solution eliminates the risk of structural collapse in the test phase for gap-varying parallel-plate MEMS devices. Measurement results using a prototype fabricated in TSMC 65-nm CMOS technology indicate that the proposed BIST scheme can successfully detect minor structural defects altering MEMS nominal capacitance.
Active Decisions and Prosocial Behaviour: A Field Experiment on Blood Donation

In this paper, we propose a decision framework where people are individually asked to either actively consent or dissent to some pro-social behavior. We hypothesize that confronting individuals with the choice of engaging in a specific pro-social behavior contributes to the formation of issue-specific altruistic preferences while simultaneously involving a commitment. The hypothesis is tested in a large-scale field experiment on blood donation. We find that this "active-decision" intervention substantially increases the stated willingness to donate blood, as well as the actual donation behavior of people who have not fully formed preferences beforehand.
An Invited Review

From Pain Management Center of Paducah, Paducah, Kentucky, and Pain Diagnostic Associates, Niagara, Wisconsin. Address Correspondence: Laxmaiah Manchikanti, MD, 2831 Lone Oak Road, Paducah, Kentucky 42003 E-mail: drm@apex.net Funding: There was no external funding in preparation of this manuscript. Conflict of Interest: None Since the first medical description of post-amputation phenomena reported by Ambrose Paré, persistent phantom pain syndromes have been well recognized. However, they continue to be difficult to manage. The three most commonly utilized terms include phantom sensation, phantom pain, and stump pain. Phantom limb sensation is an almost universal occurrence at some time during the first month following surgery. However, most phantom sensations generally resolve after two to three years without treatment, except in the cases where phantom pain develops. The incidence of phantom limb pain has been reported to vary from 0% to 88%. The incidence of phantom limb pain increases with more proximal amputations. Even though phantom pain may diminish with time and eventually fade away, it has been shown that even two years after amputation, the incidence is almost the same as at onset. Consequently, almost 60% of patients continue to have phantom limb pain after one year. In addition, phantom limb pain may also be associated with multiple pain problems in other areas of the body. The third symptom, stump pain, is located in the stump itself. The etiology and pathophysiological mechanisms of phantom pain are not clearly defined. However, both peripheral and central neural mechanisms have been described, along with superimposed psychological mechanisms. Literature describing the management of phantom limb pain or stump pain is in its infancy. While numerous treatments have been described, there is little clinical evidence supporting drug therapy, psychological therapy, interventional techniques or surgery. This review will describe epidemiology, etiology and pathophysiological mechanisms, risk factors, and treatment modalities. The review also examines the effectiveness of various described modalities for prevention, as well as management of established phantom pain syndromes.
Using lepton charge asymmetry to investigate the spin of supersymmetric particles at the LHC

If signals suggesting supersymmetry (SUSY) are discovered at the LHC then it will be vital to measure the spin of the new particles to demonstrate that they are indeed the predicted super-partners. A method is discussed by which the spins of some of the SUSY particles can be investigated. Angular distributions in sparticle decays lead to charge asymmetry in lepton-jet invariant mass distributions. The size of the asymmetry is proportional to the primary production asymmetry between squarks and anti-squarks. Monte Carlo simulations are performed for a particular mSUGRA model point at the LHC, demonstrating that the shape of the resultant asymmetry distributions are consistent with a spin-0 slepton and a spin2 χ̃2, but are not consistent with the pure phase-space decays one would obtain from both sparticles being scalars.
WePS 2 Evaluation Campaign: Overview of the Web People Search Clustering Task

The second WePS (Web People Search) Evaluation campaign took place in 2008-2009 with the participation of 19 research groups from Europe, Asia and North America. Given the output of a Web Search Engine for a (usually ambiguous) person name as query, two tasks were addressed: a clustering task, which consists of grouping together web pages referring to the same person, and an extraction task, which consists of extracting salient attributes for each of the persons sharing the same name. This paper presents the definition, resources, methodology and evaluation metrics, participation and comparative results for the clustering task.
Optimization of Cellulase Production from Bacteria Isolated from Soil

Cellulase-producing bacteria were isolated from soil and identified as Pseudomonas fluorescens, Bacillus subtilIs, E. coli, and Serratia marcescens. Optimization of the fermentation medium for maximum cellulase production was carried out. The culture conditions like pH, temperature, carbon sources, and nitrogen sources were optimized. The optimum conditions found for cellulase production were 40°C at pH 10 with glucose as carbon source and ammonium sulphate as nitrogen source, and coconut cake stimulates the production of cellulase. Among bacteria, Pseudomonas fluorescens is the best cellulase producer among the four followed by Bacillus subtilis, E. coli, and Serratia marscens.
Measuring and reporting intellectual capital

Purpose – Despite the plethora of frameworks proposed, there is little evidence of organizations measuring and reporting their IC. This paper aims to shed light on the factors that can hinder or promote the utilization of IC accounting for managerial purposes, thus contributing to understanding why IC frameworks are not so widespread in practice.Design/methodology/approach – To investigate the issue at hand, a multiple case study of three Italian companies, which have been measuring their IC for several years, will be used, adopting a critical perspective. The actual research project was conducted using an interventionist approach.Findings – The paper shows how IC model utilization and IC mobilization are influenced by several factors such as: the project's connection to a specific strategic objective, the system's interactive design, its complexity, the predominant use of non‐financial indicators (which can be ambiguous and provocative), the backward‐ or forward‐looking nature of the resultant measuremen.
Individual nodeʼs contribution to the mesoscale of complex networks

The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.
High-Dimensional Crowdsourced Data Distribution Estimation with Local Privacy

High-dimensional crowdsourced data collected from a large number of users may produc3 rich knowledge for our society but also bring unprecedented privacy threats to participants. Recently differential privacy has been proposed as an effective means to mitigate privacy concerns. However, existing work on differential privacy suffers from the "curse of high-dimensionality" (data with multiple attributes) and high scalability (data with large scale records). Moreover, traditional methods of differential privacy were achieved via aggregation results, which cannot guarantee local privacy for distributed users in crowdsourced systems. To deal with these issues, in this paper we propose a novel scheme that can efficiently estimate multivariate joint distribution for high-dimensional data with local privacy. On the client side, we employ randomized response techniques to locally transform data from distributed users into privacy-preserving bit strings, which can prevent potential inside privacy attacks in crowdsourced systems. On the server side, the crowdsourced bit strings are aggregated for multivariate distribution estimation. Specifically, we first propose a multivariate version of the expectation maximization (EM) based algorithm to estimate the joint distribution of high dimensional data. To speed up the performance, unlike the EM-based method that needs to scan each user's bit string, we propose to use Lasso regression to obtain the distribution estimation from the aggregation information only once, which can significantly reduce the computation time for multivariate distribution estimation. Extensive experiments on real-world datasets demonstrate the efficiency of our multivariate distribution estimation scheme over existing estimation schemes.
Connectionist Modeling of Part-Whole Analogy Learning

Analogical reasoning, along with inductive, deductive or abductive reasoning, belongs to the fundamental human mechanisms for the environment exploration, learning, or problem solving. Modeling this ability using computer simulations is important, as it might offer mechanistic explanation of these phenomena. In this work, we focus on the part–whole analogies in a separation task where the analogical objects between two scenes show a mutual resemblance. In simulations, using a simple recurrent network, we deal with the problem of geometrical analogies, inspired by the Analogator model (Blank, 1997). The original model had learning limitations hindering its full potential, combined with increased time and memory complexity. We propose model modifications for removing these limitations which leads to superior learning performance both in terms of speed and accuracy.
Design and Implementation of a Version Management System for Reference Modeling

The central idea in reference modeling is the reutilization of the business knowledge contained in a reference model. The user’s task in reference model-based construction is the adaptation of the reference model. The derivation of specific models from reference models corresponds with the creation of reference model variants. Research on the design of such variant constructions generally assumes an unchangeable stock of reference models. The potential inherent in the management of these variant constructions, which reflect the changes in designed reference models through time and in doing so, their evolutionary development, has not yet been tapped into. The article at hand analyzes this problem and presents a concept for the version management of reference models as a solution. The task to be mastered with the proposed approach will be concretized using data structures and a system architecture and then prototypically implemented.
A Novel Cluster Parallel File System

We propose a novel cluster parallel file system: high-throughput & scalable parallel file system (HTSPFS). There are some new features in it such as automatic adaptive file striping policy based on file size, request arrival rate, concurrent of accessing files, storage servers' networking and disk performance; prefetching read-request and writing behind write-request to enable a higher throughput in HTSPFS. We give a detail discuss of Markov spatial predicting method for read request, experiment results show that Markov predicting technology has high prediction veracity. Overall, HTSPFS has some autonomies characteristics
The Term Spread International Evidence of Non-Linear Adjustment

This study tests whether changes in the short-term interest rate can best be modelled in a nonlinear fashion. We argue that there are good theoretical and empirical reasons for adopting this strategy. Using monthly data from several industrialized countries, namely Canada, Germany, Sweden, Switzerland, UK, and US, we show that the short-term interest rate movements are better explained, usually via the exponential smooth transition autoregression (ESTR). Unlike the existing literature on non-linear estimation, we consider a number of candidates for the transition variable. These include: an error correction term, estimated from an underlying cointegrating relationship predicted by the expectations hypothesis, the US spread, the domestic spread, inflation and output growth forecasts, and deviations from an inflation target in the case of Canada, the UK and Sweden. The sample spans the period from 1960-1998. We cannot reject non-linearity in the behavior of interest rate changes most often when the (lagged) domestic spread serves as the transition variable. In the case of the inflation targeting countries in our sample, the most appropriate transition variable can be the deviation from the publicly announced inflation target. We supplement estimates with extensive diagnostic testing to ensure that we can reject the linear alternative with reasonable confidence. We believe that changes in central bank policies and in the reaction of market participants over time to such changes argue in favor of the non-linear estimation approach. We would also argue that any model of the term spread over a fairly long span of time necessitates resort to non-linear estimation methods.
Valproic acid inhibits tumor angiogenesis in mice transplanted with Kasumi-1 leukemia cells

Histone deacetylase (HDAC) inhibitors have been reported to inhibit tumor angiogenesis via the downregulation of angiogenic factors. Our previous in vitro studies demonstrated that valproic acid (VPA) exerted antitumor effects on Kasumi-1 cells, which are human acute myeloid leukemia cells with an 8;21 chromosome translocation. In the present study, the effects of VPA on tumor angiogenesis were investigated in mice transplanted with Kasumi-1 cells. Semi-quantitative reverse transcription-polymerase chain reaction, western blotting and immunohistochemistry were used to detect the expression of vascular endothelial growth factor (VEGF), VEGF receptor (VEGFR2) and basic fibroblast growth factor (bFGF). The tumor microvessel density was measured following staining with an anti-CD34 antibody. Chromatin immunoprecipitation was used to study the effect of VPA-induced histone hyperacetylation on VEGF transcription. An intraperitoneal injection of VPA inhibited tumor growth and angiogenesis in mice transplanted with Kasumi-1 cells. The mRNA and protein expression of VEGF, VEGFR2 and bFGF were inhibited by VPA treatment. In addition, VPA downregulated HDAC, increased histone H3 acetylation and enhanced the accumulation of hyperacetylated histone H3 on the VEGF promoters. The findings of the present study indicate that VPA, an HDAC inhibitor, exerts an antileukemic effect through an anti-angiogenesis mechanism. In conclusion, the mechanism underlying VPA-induced anti-angiogenesis is associated with the suppression of angiogenic factors and their receptors. VPA may increase the accumulation of acetylated histones on the VEGF promoters, which possibly contributes to the regulation of angiogenic factors.
SELF-LEARNING SIMULATION FOR MODELING OF BEAM-COLUMN CONNECTIONS IN STEEL FRAMES

The inelastic response of beam-column connections significantly affects the seismic performances of steel frames. Accurate models of beam-column connections are important both in understanding the seismic behavior and in design. An effective approach towards developing accurate models for connections is self-learning simulation, which is presented in this paper. The latter simulation method has the unique advantage in that it can extract material models from global structural response. Selflearning simulation is based on auto-progressive algorithms that employ the principles of equilibrium and compatibility, and the self-organizing capability of artificial neural network material models. Steel beam-column connections are represented by point plastic hinges that are modeled by a new neural network representation for modeling of cyclic behavior of connections. The neural network based connection model has significant advantages over conventional models in that it can handle complex behavior due to local buckling and tearing of components. Moreover, its implementation is more efficient than conventional connection models since it does not need interaction equation and plastic potential. In this paper, the performance of the neural network based connection model is verified with experimental data and the proposed self-learning simulation methodology is demonstrated with a simulated structural testing of a two-story one bay frame with semi-rigid connections.
High energy-efficient clustering algorithm for WSNs

This paper proposes a high energy-efficient clustering algorithm for WSNs (HEEC). In HEEC, an improved threshold of cluster head election based on optimal probability is presented. The threshold doesn't only consider the remaining energy of nodes, but also take the cluster heads number into account even when nodes have little energy. When the ordinary nodes join the clusters, the ordinary nodes adopt CDMA other than CSMA to reduce the interference with the nearby clusters. Simulation results show that our algorithm performs better in energy consumption and effectively improves the lifetime of WSNs.
Defence spending and unemployment rates: an empirical analysis disaggregated by race

This analysis brings U.S. time series evidence to bear on the relationship of defense spending and unemployment rates. Results indicate that defense increases during the 1970s were associated with improvements in the overall unemployment rate. However, during the 1980s such increases were associated with a worsening of the unemployment rate. Furthermore, upon disaggregation into black and white unemployment rates, it was found that, in general, whites were helped by defense spending increases while blacks were hurt. This evidence offers support for the claim that an increasing complex military production process will lead to diminished employment gains, particularly for minorities who are not proportionately represented in the high-tech industries that serve the military. Copyright 1990 by Oxford University Press.
Avert E-mail Hacking System with Face Recognition

Now-a-days E-mail system acts as a major role in industry, business, educational institutes etc. Even though many encryption software's and methods trying to prevent the hacking, but still there are possibilities of hacking the user information. This paper tells or ex- plores the new security measures to mail system with the help of Biometric concept called Face Recognition. Face recognition is one of the largely elastic systems. It works effective than others even when the subject or person is unaware of being scanned. It also illustrate assure as a way to search through a lot of people who spent only seconds in front of a scanner. DSCP Algorithm (Depth Surface Closest Point) with e-mail system solves the previous problem and provides effective accuracy. Avert e-mail hacking system shields the user information from hackers, unauthorized users and other hacking techniques so user information will be safe.
Stable channel allocation in hybrid overlay/underlay cognitive radio networks

Fast and high utility channel allocation is one of the key issues of cognitive radio. In this paper, the channel allocation is formulated as a one-to-one matching game, and a distributed user-channel stable matching algorithm with low complexity is proposed to optimize the utility. We apply the algorithm to a hybrid underlay/overlay cognitive network in which secondary users utilize different manners of spectrum sharing due to their different geographic locations. The simulation results demonstrate that the convergence time of the proposed algorithm is shorter than G-S algorithm and the utility is close to the upper bound obtained by the Hungarian method.