Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and safety of enzalutamide in patients 75 years or older with chemotherapy-naive metastatic castration-resistant prostate cancer: results from PREVAIL.

BACKGROUND Prostate cancer disproportionately affects older men. Because age affects treatment decisions, it is important to understand the efficacy and tolerability of therapies for advanced prostate cancer in elderly men. This analysis describes efficacy and safety outcomes in men aged ≥75 years who received enzalutamide, an androgen receptor inhibitor, in the phase III PREVAIL trial. PATIENTS AND METHODS PREVAIL was a randomised, double-blind, multinational study of oral enzalutamide 160 mg/day (N = 872) versus placebo (N = 845) in chemotherapy-naive men with metastatic castration-resistant prostate cancer. Overall survival (OS) and radiographic progression-free survival (rPFS) were coprimary end points. Subgroup analysis of men aged ≥75 years (elderly) and men aged <75 years was pre-specified for the coprimary end points and adverse events (AEs). RESULTS Among 609 elderly patients (35%) who participated in PREVAIL, median treatment duration was 16.6 and 5.0 months in the enzalutamide and placebo arms, respectively. In the elderly subgroup, OS was greater with enzalutamide than with placebo [32.4 months (95% confidence interval (CI) 27.7-not yet reached] versus 25.1 months (95% CI 22.6-28.0); hazard ratio (HR) = 0.61 (95% CI 0.47-0.79); P = 0.0001], as was rPFS [not yet reached (95% CI 12.3-not yet reached) versus 3.7 months (95% CI 3.6-5.3); HR = 0.17 (95% CI 0.12-0.24); P < 0.0001]. Irrespective of treatment assignment, incidence of AEs was similar between the two age groups, except for an overall higher incidence of falls among elderly patients than younger patients [84/609 (13.8%) versus 62/1106 (5.6%)] and among elderly patients receiving enzalutamide than those receiving placebo [61/317 (19.2%) versus 23/292 (7.9%)]. CONCLUSIONS Elderly men benefited from treatment with enzalutamide in terms of OS and rPFS. Enzalutamide was well tolerated in the elderly subgroup and those aged <75 years. Age and enzalutamide treatment were associated with a higher incidence of falls. CLINICAL TRIAL IDENTIFIER NCT01212991, ClinicalTrials.gov.
Probabilistic topic model based approach for detecting bursty events from social media data

To detect bursty events from the huge amount of real-time data generated from various social networks has attracted more and more research efforts. Most of existing algorithms detect the bursty events either by discovering the co-occurrent bursty words or the emerging topics, ignoring the association between bursty and topics. Meanwhile, these algorithms are not able to cope with short text data like Weibo and Twitter. This paper proposes two novel probabilistic generative models (TBE/TBEP). TBE model can detect bursty events on long articles which can simultaneously consider the co-occurrent relationships among bursty words as well as the co-occurrent relationships among occurrent words and the underlying topics which generate the bursty events. TBEP model captures the assumption: one post are always have the one topic, which can handle the bursty events on Weibo and Twitter. The Gibbs sampling technique is adopted to estimate the model parameters. Extensive experiments are performed on three real data sets and the promising results, compared with the state-of-the-art Hot-Bursty-Event detection algorithms, have demonstrated that the proposed approach can: (1) achieve better model performance with respect to the evaluation criteria; (2) achieve more accurate bursty evnets on long/short text data.
Improved dynamic performance of shunt active power filter using particle swarm optimization

In this paper, a novel particle swarm optimization (PSO) technique is proposed to tune the proportional-integral (PI) controller gain parameters for enhancing the dynamic performance of the shunt active power filter (APF). The shunt APFs are well established filter to compensate current harmonics, reactive power to maintain the power factor unity. The compensation is highly influenced by the DC-link voltage regulation. The calculated PI controller gain parameters conventionally, are giving satisfactory results under steady state condition of the load. However, tuning of the PI controller parameters under fast changing loads are very difficult. To improve the dynamic performance of the system and optimize the gain parameters of the PI controller, a PSO technique is proposed. The modified p-q theory uses a composite observer filter to extract fundamental component of voltage from the distorted supply voltage for the further process of calculating reference current. A complete comparison of conventional and PSO based PI controller gain tuning have been simulated using MATLAB® Simulink software under different supply voltage and load condition of the system. The results show that the dynamic response is improved with PSO based PI tuning compared to conventional PI tuning.
Likelihood-Based Inference of B Cell Clonal Families

The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called “rearrangement” forming progenitor B cells, then a Darwinian process of lineage diversification and selection called “affinity maturation. ” The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem “clonal family inference. ” In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets.
The benefits of low operating voltage devices to the energy efficiency of parallel systems

Programmable circuits such as general-purpose processors or FPGAs have their end-user energy efficiency strongly dependent on the program that they execute. Ultimately, it is the programmer's ability to code and, in the case of general purpose processors, the compiler's ability to translate source code into a sequence of native instructions that make the circuit deliver the expected performance to the end user. This way, the benefits of energy-efficient circuits build upon energy-efficient devices could be obfuscated by poorly written software. Clearly, having well-written software running on conventional circuits is no better in terms of energy efficiency than having poorly written software running on energy-efficient circuits. Therefore, to get the most out of the energy-saving capabilities of programmable circuits that support low voltage operating modes, it is necessary to address software issues that might work against the benefits of operating in such modes.
METHODOLOGY AND DATASET 3 . 1 Overview of Hola

Headers are a critical part of HTTP, and it has been shown that they are increasingly subject to middlebox manipulation. Although this is well known, little is understood about the general regional and network trends that underpin these manipulations. In this paper, we collect data on thousands of networks to understand how they intercept HTTP headers in-the-wild. Our analysis reveals that 25% of measured ASes modify HTTP headers. Beyond this, we witness distinct trends among different regions and AS types; e.g., we observe high numbers of cache headers in poorly connected regions. Finally, we perform an in-depth analysis of the types of manipulations and how they differ across regions.
Self-organised criticality in the evolution of a thermodynamic model of rodent thermoregulatory huddling

A thermodynamic model of thermoregulatory huddling interactions between endotherms is developed. The model is presented as a Monte Carlo algorithm in which animals are iteratively exchanged between groups, with a probability of exchanging groups defined in terms of the temperature of the environment and the body temperatures of the animals. The temperature-dependent exchange of animals between groups is shown to reproduce a second-order critical phase transition, i.e., a smooth switch to huddling when the environment gets colder, as measured in recent experiments. A peak in the rate at which group sizes change, referred to as pup flow, is predicted at the critical temperature of the phase transition, consistent with a thermodynamic description of huddling, and with a description of the huddle as a self-organising system. The model was subjected to a simple evolutionary procedure, by iteratively substituting the physiologies of individuals that fail to balance the costs of thermoregulation (by huddling in groups) with the costs of thermogenesis (by contributing heat). The resulting tension between cooperative and competitive interactions was found to generate a phenomenon called self-organised criticality, as evidenced by the emergence of avalanches in fitness that propagate across many generations. The emergence of avalanches reveals how huddling can introduce correlations in fitness between individuals and thereby constrain evolutionary dynamics. Finally, a full agent-based model of huddling interactions is also shown to generate criticality when subjected to the same evolutionary pressures. The agent-based model is related to the Monte Carlo model in the way that a Vicsek model is related to an Ising model in statistical physics. Huddling therefore presents an opportunity to use thermodynamic theory to study an emergent adaptive animal behaviour. In more general terms, huddling is proposed as an ideal system for investigating the interaction between self-organisation and natural selection empirically.
Th1/Th2 Balance and Th17/Treg-Mediated Immunity in relation to Murine Resistance to Dextran Sulfate-Induced Colitis

Background The role of the Th17/Treg balance in the development of experimental colitis remains poorly understood. Methods We exploited the differential response of BALB/c mice and C57BL/6 mice towards drinking water mediated by dextran sulfate sodium (DSS) challenge. Results DSS-resistant BALB/c mice were characterized by low levels of IFN-γ and TNF-α but high levels of IL-4, IL-6, IL-10, IL-17A, IL-17F, and colon lamina propria and mesenteric lymph node (MLN) CD4+CD25+FoxP3+ T cells when compared to C57BL/6 mice. Collectively, these data indicate the propensity of BALB/c mice towards a Th2/Th17/Treg-polarized immunity protecting these animals against DSS challenge, whereas Th1-polarization of C57BL/6 mice confers sensitivity to DSS-induced colitis. Conclusions The intrinsic congenital capacity of mouse strains with respect to T cell proliferation determines sensitivity to experimental colitis.
Challenges and Opportunities in Adapting Clinical Trial Design for Immunotherapies

Immunotherapy adds an exciting new dimension to the treatment of cancer, joining other approaches as a key pillar in the oncotherapeutics armamentarium. Immuno-oncology agents harbor unique mechanisms of antitumor activity by leveraging the host immune system, which may result in response patterns, resistance kinetics, and toxicity profiles that differ from other systemic therapies. These features have led to many discussions on ways to optimally integrate immunotherapy into cancer clinical trials. This overview provides an introduction to the four CCR Focus articles that ensue, with special thoughts paid to clinical trial endpoints, biomarker development and validation, combination strategies, and limitations that arise with increasing use of these agents. In addition, this overview examines design concepts that may be applied to invigorate clinical trials and to maximize their impact in the immuno-oncology era. Clin Cancer Res; 23(17); 4950–8. ©2017 AACR. See all articles in this CCR Focus section, “Clinical Trial Design Considerations in the Immuno-oncology Era. ”
Public Debt, Child Allowances and Pension Benefits with Endogenous Fertility

Abstract The public debt stock in some economically developed countries continues to increase because of a lack of tax revenues and the concomitant burdens of social security. Many of those countries suffer from lower birth rates and consequently, have fewer children. Child allowances might be an effective way to increase fertility, leading to higher future tax revenues through an increase in the number of younger people. In this paper, the authors examine whether or not child allowances reduce the public debt stock as a share of GDP in an economy with a pension system. As long as a non-negative debt policy is adopted in the long run, child allowances financed by bonds always increase the public debt stock as a share of GDP.
Equational Approach to Formal Analysis of TLS

TLS has been formally analyzed with the OTS/CafeOBJ method. In the method, distributed systems are modeled as transition systems, which are written in terms of equations, and it is verified that the models have properties by means of equational reasoning. TLS is the latest version, or the successor of SSL, which is probably the most widely deployed security protocol. Among the results of the analysis are that pre-master secrets cannot be leaked, when a client has negotiated a cipher suite and security parameters with a server, the server has really agreed on them, and client cannot be identified if they do not send their certificates to servers
Data-Filtering System to Avoid Total Data Distortion in IoT Networking

In the Internet of Things (IoT) networking, numerous objects are connected to a network. They sense events and deliver the sensed information to the cloud. A lot of data is generated in the IoT network, and servers in the cloud gather the sensed data from the objects. Then, the servers analyze the collected data and provide proper intelligent services to users through the results of the analysis. When the server analyzes the collected data, if there exists malfunctioning data, distortional results of the analysis will be generated. The distortional results lead to misdirection of the intelligent services, leading to poor user experience. In the analysis for intelligent services in IoT, malfunctioning data should be avoided because integrity of the collected data is crucial. Therefore, this paper proposes a data-filtering system for the server in the cloud. The proposed data-filtering system is placed in front of the server and firstly receives the sensed data from the objects. It employs the naive Bayesian classifier and, by learning, classifies the malfunctioning data from among the collected data. Data with integrity is delivered to the server for analysis. Because the proposed system filters the malfunctioning data, the server can obtain accurate analysis results and reduce computing load. The performance of the proposed data-filtering system is evaluated through computer simulation. Through the simulation results, the efficiency of the proposed data-filtering system is shown.
GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications

Currently, non-rigid image registration algorithms are too computationally intensive to use in time-critical applications. Existing implementations that focus on speed typically address this by either parallelization on GPU-hardware, or by introducing methodically novel techniques into CPU-oriented algorithms. Stochastic gradient descent (SGD) optimization and variations thereof have proven to drastically reduce the computational burden for CPU-based image registration, but have not been successfully applied in GPU hardware due to its stochastic nature. This paper proposes 1) NiftyRegSGD, a SGD optimization for the GPU-based image registration tool NiftyReg, 2) random chunk sampler, a new random sampling strategy that better utilizes the memory bandwidth of GPU hardware. Experiments have been performed on 3D lung CT data of 19 patients, which compared NiftyRegSGD (with and without random chunk sampler) with CPU-based elastix Fast Adaptive SGD (FASGD) and NiftyReg. The registration runtime was 21.5s, 4.4s and 2.8s for elastix-FASGD, NiftyRegSGD without, and NiftyRegSGD with random chunk sampling, respectively, while similar accuracy was obtained. Our method is publicly available at https://github.com/SuperElastix/NiftyRegSGD.
NOx-Sensing Characteristics of Sensor with ZnFe2O4 Sensing Electrode

Electrochemical sensors based on tubular yttria-stabilized zerconia (YSZ) with the spinel-type oxide as a sensing-electrode (SE), which is prepared by sol-gel method, were fabricated and examined for NO2 detection in the temperature range 450~500°C. The results show that ZnFe2O4, shows extreme sensitivity to NO2. The EMF varies linearly as a function of the concentration of NO2 (0 ~ 463 ppm) at 500 °C.
37.1: Invited Paper: 1‐mm Diameter, Full‐color Scanning Fiber Pico Projector

Media playback capabilities of mobile devices demand a large display in a small package. We have developed a 1‐mm x 9‐mm scan engine, projecting full‐color images through a vibrating optical fiber tip and miniature lens system. Connected by optical fiber, the projection head and RGB light sources can be distributed across the mobile device.
Persistence of Innate Immune Pathways in Late Stage Human Bacterial and Fungal Keratitis: Results from a Comparative Transcriptome Analysis

Microbial keratitis (MK) is a major cause of blindness worldwide. Despite adequate antimicrobial treatment, tissue damage can ensue. We compared the human corneal transcriptional profile in late stage MK to normal corneal tissue to identify pathways involved in pathogenesis. Total RNA from MK tissue and normal cadaver corneas was used to determine transcriptome profiles with Illumina HumanHT-12 v4 beadchips. We performed differential expression and network analysis of genes in bacterial keratitis (BK) and fungal keratitis (FK) compared with control (C) samples. Results were validated by RTqPCR for 45 genes in an independent series of 183 MK patients. For the microarray transcriptome analysis, 27 samples were used: 12 controls, 7 BK culture positive for Streptococcus pneumoniae (n = 6), Pseudomonas aeruginosa (n = 1), and 8 FK, culture positive for Fusarium sp. (n = 5), Aspergillus sp. (n = 2), or Lasiodiplodia sp. (n = 1). There were 185 unique differentially expressed genes in BK, 50 in FK, and 339 common to both [i.e., genes with fold-change (FC) < −4 or ≥4 and false discovery rate (FDR) adjusted P < 0.05]. MMP9 had the highest FC in BK (91 FC, adj p = 3.64 E-12) and FK (FC 64, adj. p = 6.10 E-11), along with other MMPs (MMP1, MMP7, MMP10, MMP12), pro-inflammatory cytokines (IL1B, TNF), and PRRs (TLR2, TLR4). HIF1A and its induced genes were upregulated uniquely in BK. Immune/defense response and extracellular matrix terms were the most enriched Gene Ontology terms in both BK and FK. In the network analysis, chemokines were prominent for FK, and actin filament reorganization for BK. Microarray and RTqPCR results were highly correlated for the same samples tested with both assays, and with the larger RTqPCR series. In conclusion, we found a great deal of overlap in the gene expression profile of late stage BK and FK, however genes unique to fungal infection highlighted a corneal epithelial wound healing response and for bacterial infection the prominence of HIF1A-induced genes. These sets of genes may provide new targets for future research into therapeutic agents.
A stack cross-layer analytical model for CSMA/CA IEEE 802.15.4 networks

Because of the specifications in low-cost, low-power IEEE 802.15. 4 wireless sensor networks, comprehensive analytical model is important for evaluating performance under varying wireless channel constraints. The systematic properties of single physical layer and medium access control (MAC) layer protocol have been studied through the techniques based on mathematical models or experiment-based approaches. However, It is insufficient to evaluate network performance on the basis of existing single layer model or cross-layer model with stationary parameters, especially for the multi-variable parameters-based wireless network environment. In this paper, we propose an enhanced stack cross-layer analytical model based on the comprehensive combination and interaction between PHY layer propagation model and MAC layer Markov chain model. Dynamic interaction between sublayer models achieve adaptive performance estimation with hyperparameters sets. Cross-layer performance degradation is analyzed under the varying inputs of multi-parameters vectors, several Quality of Service (QoS) metrics and effective energy consumption metric are proposed and evaluated, respectively. From the simulation results compared with benchmark models, stack cross-layer model offers more comprehensive performance analysis with different cross-layer parameters sets which include distance, transmit power, noise power, and information loads, etc.
The Effects of Haptic Feedback and Visual Distraction on Pointing Task Performance

Previous research has not fully examined the effect of additional sensory feedback, particularly delivered through the haptic modality, in pointing task performance with visual distractions. This study examined the effect of haptic feedback and visual distraction on pointing task performance in a 3D virtual environment. Results indicate a strong positive effect of haptic feedback on performance in terms of task time and root mean square error of motion. Level of similarity between distractor objects and the target object significantly reduced performance, and subjective ratings indicated a sense of increased task difficulty as similarity increased. Participants produced the best performance in trials where distractor objects had a different color but the same shape as the target object and constant haptic assistive feedback was provided. Overall, this study provides insight toward the effect of object features and similarity and the effect of haptic feedback on pointing task performance.
Copy detection for digital documents

Plagiarism is becoming a commonly concerned problem in recent years. With the advance of Internet, it is easier and easier to access the writings of other people. When someone uses the content of a writing inappropriately, it may cause the problem of plagiarism. Plagiarism may infringe the intellectual property rights, so it is a serious problem nowadays. In this paper, we propose a new method for detecting plagiarism. We use Word2vec to obtain the vector representations of the sentences involved in the documents. We also apply principal component analysis (PCA) to reduce the size of dimensionality. By using sentence-level representations, plagiarisms can be effectively detected. We will present experimental results and compare with other methods to show the plagiarism detection capability of our proposed method.