Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term histological evaluations after achieving a sustained virologic response to direct-acting antiviral treatment for chronic hepatitis C

Background Interferon-free, direct-acting antiviral treatments can result in a sustained virologic response in nearly 100% of patients with chronic hepatitis C virus infection. Aims The purpose of this study was to evaluate histological improvement after achieving a sustained virologic response to direct-acting antiviral treatments in patients with chronic hepatitis C. Methods Among 691 patients with chronic hepatitis C who achieved a sustained virologic response to direct-acting antivirals, 51 underwent liver biopsy 41 ± 20 weeks after the end of treatment despite normal transaminase levels. In 20 patients, liver biopsy specimens obtained a median of 1.2 years before the start of treatment were available. Results Among the 51 patients who underwent post-sustained virologic response biopsies, the grade of inflammation was A0 in 18 patients, A1 in 24, A2 in eight, and A3 in one; the stage of fibrosis was F0 in three patients, F1 in 20, F2 in 15, F3 in nine, and F4 in four. Among the nine post-sustained virologic response biopsy specimens with moderate-to-severe inflammation (≥A2), four showed S1-to-S3 steatosis (>5% of hepatocytes affected). In the 20 paired biopsy specimens, the inflammation grade significantly regressed (p = 0.0043), but the fibrosis stage did not (p = 0.45). Histological improvement, defined as a ≥ 2-point decrease in the Knodell inflammatory score and no worsening of the fibrosis, was found in 11 (55%) patients. The iron accumulation had significantly regressed (p = 0.0093), but the steatosis had not (p = 0.10). Conclusions Even if transaminases become normal after obtaining a sustained virologic response, significant histological inflammation of unknown cause was found in some patients. Additionally, improvement in liver fibrosis was not evident in the short term.
Long-Term Atmospheric Visibility Trends and Their Relations to Socioeconomic Factors in Xiamen City, China

Atmospheric visibility (AV), one of the most concerning environmental issues, has shown a continuous decline in China’s urban areas, especially in Southeastern China. Existing studies have shown that AV is affected by air pollutants and climate change, which are always caused by human activities that are linked to socioeconomic factors, such as urban size, residents’ activities, industrial activities, and urban greening. However, the contribution of socioeconomic factors to AV is still not well understood, especially from a long-term perspective, which sometimes leads to ineffective policies. In this study, we used the structural equation model (SEM) in order to quantify the contribution of socioeconomic factors on AV change in Xiamen City, China, between 1987–2016. The results showed that the annual average AV of Xiamen between 1987–2016 was 12.00 km, with a change rate of −0.315 km/year. Urban size, industrial activities, and residents’ activities were found to have a negative impact on AV, while the impact of urban greening on the AV was modest. Among all of the indicators, the number of resident’s vehicles, total retail sales of consumer goods, and household electricity consumption were found to have the highest negative direct impact on the AV. The resident population, urban built-up area, and secondary industry gross domestic product (GDP) were the most important indirect impact factors. Based on our results, we evaluated the existing environmental regulations and policies of Xiamen City.
Improving Adversarial Robustness by Encouraging Discriminative Features

Deep neural networks (DNNs) have achieved state-of-the-art results in various pattern recognition tasks. However, they perform poorly on out-of-distribution adversarial examples i.e. inputs that are specifically crafted by an adversary to cause DNNs to misbehave, questioning the security and reliability of applications. In this paper, we encourage DNN classifiers to learn more discriminative features by imposing a center loss in addition to the regular softmax cross-entropy loss. Intuitively, the center loss encourages DNNs to simultaneously learns a center for the deep features of each class, and minimize the distances between the intra-class deep features and their corresponding class centers. We hypothesize that minimizing distances between intra-class features and maximizing the distances between inter-class features at the same time would improve a classifier's robustness to adversarial examples. Our results on state-of-the-art architectures on MNIST, CIFAR-10, and CIFAR-100 confirmed that intuition and highlight the importance of discriminative features.
Using Future Employers' Expectations to Inform Global Health Fellowship Curricula.

Background Fellowships in global health are increasingly popular and seek to equip physicians with the skills necessary to be effective global health practitioners. Little objective guidance exists on which skills make graduates competitive applicants from the perspective of potential global health employers. Objective We sought to provide objective evidence for the qualifications that make applicants competitive for global health positions by analyzing the listed qualifications for current job openings in large global health organizations. Methods The websites of 48 global health employers were systematically searched for current job postings between May and August 2017. Jobs were included for analysis if a medical degree was listed among the primary degrees accepted, and the job was based primarily in a low- or middle-income country. Results A total of 5849 employment opportunities were posted during the search period, and 81 (1.4%) of these met inclusion criteria. Twenty-two (27%) jobs required and 35 (43%) preferred a relevant master's degree. Few jobs requested a candidate with a PhD and none mentioned tropical medicine training as a requirement or preference. Twenty-three jobs (28%) required and 19 (23%) preferred candidates to speak another language. Most jobs (69%, 56 of 81) required more than 5 years of relevant experience. Only 11 (13%) jobs were primarily clinical positions. Conclusions For physicians pursuing a career in global health, most publicly searchable jobs require substantial previous experience and involvement in global health activities beyond clinical practice. Master's degree and language skills are frequently requested candidate qualifications.
Sequence Generation with Guider Network

Sequence generation with reinforcement learning (RL) has received significant attention recently. However, a challenge with such methods is the sparse-reward problem in the RL training process, in which a scalar guiding signal is often only available after an entire sequence has been generated. This type of sparse reward tends to ignore the global structural information of a sequence, causing generation of sequences that are semantically inconsistent. In this paper, we present a model-based RL approach to overcome this issue. Specifically, we propose a novel guider network to model the sequence-generation environment, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments show that the proposed method leads to improved performance for both unconditional and conditional sequence-generation tasks.
Replays of spatial memories suppress topological fluctuations in cognitive map

The spiking activity of the hippocampal place cells plays a key role in producing and sustaining an internalized representation of the ambient space—a cognitive map. These cells do not only exhibit location-specific spiking during navigation, but also may rapidly replay the navigated routs through endogenous dynamics of the hippocampal network. Physiologically, such reactivations are viewed as manifestations of “memory replays” that help to learn new information and to consolidate previously acquired memories by reinforcing synapses in the parahippocampal networks. Below we propose a computational model of these processes that allows assessing the effect of replays on acquiring a robust topological map of the environment and demonstrate that replays may play a key role in stabilizing the hippocampal representation of space.Author SummaryIn this manuscript, we use methods of zigzag homology theory to study the physiological role of the replays—the hippocampal networks endogenous activity that recapitulates the activity of the place cells during exploration of the environment. In particular, we demonstrate that deterioration of the hippocampal spatial memory map caused by excessive transience of synaptic connections may be mitigated by spontaneous replays. The results help to understand how transient information about local spatial connectivity may stabilize at a large scale, and shed light on the separation between faster and slower memory processing in the complementary (hippocampal and neocortical) learning systems.
Online Diverse Learning to Rank from Partial-Click Feedback

Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is unlikely to examine the entire recommended list, partial feedback arises naturally. At the same time, diverse recommendations are important because it is challenging to model all tastes of the user in practice. In this paper, we propose the first algorithm for online learning to rank diverse items from partial-click feedback. We assume that the user examines the list of recommended items until the user is attracted by an item, which is clicked, and does not examine the rest of the items. This model of user behavior is known as the cascade model. We propose an online learning algorithm, cascadelsb, for solving our problem. The algorithm actively explores the tastes of the user with the objective of learning to recommend the optimal diverse list. We analyze the algorithm and prove a gap-free upper bound on its n-step regret. We evaluate cascadelsb on both synthetic and real-world datasets, compare it to various baselines, and show that it learns even when our modeling assumptions do not hold exactly.
VizRec: A framework for secure data exploration via visual representation

Visual representations of data (visualizations) are tools of great importance and widespread use in data analytics as they provide users visual insight to patterns in the observed data in a simple and effective way. However, since visualizations tools are applied to sample data, there is a a risk of visualizing random fluctuations in the sample rather than a true pattern in the data. This problem is even more significant when visualization is used to identify interesting patterns among many possible possibilities, or to identify an interesting deviation in a pair of observations among many possible pairs, as commonly done in visual recommendation systems. We present VizRec, a framework for improving the performance of visual recommendation systems by quantifying the statistical significance of recommended visualizations. The proposed methodology allows to control the probability of misleading visual recommendations using both classical statistical testing procedures and a novel application of the Vapnik Chervonenkis (VC) dimension method which is a fundamental concept in statistical learning theory.
TRUFL: Distributed Trust Management Framework in SDN

Software Defined Networking (SDN) has emerged as a revolutionary paradigm to manage cloud infrastructure. SDN lacks scalable trust setup and verification mechanism between Data Plane-Control Plane elements, Control Plane elements, and Control Plane-Application Plane. Trust management schemes like Public Key Infrastructure (PKI) used currently in SDN are slow for trust establishment in a larger cloud environment. We propose a distributed trust mechanism — TRUFL to establish and verify trust in SDN. The distributed framework utilizes parallelism in trust management, in effect faster transfer rates and reduced latency compared to centralized trust management. The TRUFL framework scales well with the number of OpenFlow rules when compared to existing research works.
Foundations of Comparison-Based Hierarchical Clustering

We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. Instead, we assume that only a set of comparisons between objects is available, that is, statements of the form "objects $i$ and $j$ are more similar than objects $k$ and $l$. " Such a scenario is commonly encountered in crowdsourcing applications. The focus of this work is to develop comparison-based hierarchical clustering algorithms that do not rely on the principles of ordinal embedding. We show that single and complete linkage are inherently comparison-based and we develop variants of average linkage. We provide statistical guarantees for the different methods under a planted hierarchical partition model. We also empirically demonstrate the performance of the proposed approaches on several datasets.
Closed Form Variational Objectives For Bayesian Neural Networks with a Single Hidden Layer

In this note we consider setups in which variational objectives for Bayesian neural networks can be computed in closed form. In particular we focus on single-layer networks in which the activation function is piecewise polynomial (e.g. ReLU). In this case we show that for a Normal likelihood and structured Normal variational distributions one can compute a variational lower bound in closed form. In addition we compute the predictive mean and variance in closed form. Finally, we also show how to compute approximate lower bounds for other likelihoods (e.g. softmax classification). In experiments we show how the resulting variational objectives can help improve training and provide fast test time predictions.
WebHERV: A Web Server for the Computational Investigation of Gene Expression Associated With Endogenous Retrovirus-Like Sequences

More than eight percent of the human genome consists of human endogenous retroviruses (HERVs). Typically, the expression of HERVs is repressed, but varying activities of HERVs have been observed in diseases ranging from cancer to neuro-degeneration. Such activities can include the transcription of HERV-derived open reading frames, which can be translated into proteins. However, as a consequence of mutations that disrupt open reading frames, most HERV-like sequences have lost their protein-coding capacity. Nevertheless, these loci can still influence the expression of adjacent genes and, hence, mediate biological effects. Here, we present WebHERV (http://calypso.informatik.uni-halle.de/WebHERV/), a web server that enables the computational prediction of active HERV-like sequences in the human genome based on a comparison of genome coordinates of expressed sequences uploaded by the user and genome coordinates of HERV-like sequences stored in the specialized key-value store DRUMS. Using WebHERV, we predicted putative candidates of active HERV-like sequences in Hodgkin lymphoma (HL) cell lines, validated one of them by a modified SMART (switching mechanism at 5′ end of RNA template) technique, and identified a new alternative transcription start site for cytochrome P450, family 4, subfamily Z, polypeptide 1 (CYP4Z1).
In vitro Evidence That Combination Therapy With CD16-Bearing NK-92 Cells and FDA-Approved Alefacept Can Selectively Target the Latent HIV Reservoir in CD4+ CD2hi Memory T Cells

Elimination of the latent HIV reservoir remains the biggest hurdle to achieve HIV cure. In order to specifically eliminate HIV infected cells they must be distinguishable from uninfected cells. CD2 was recently identified as a potential marker enriched in the HIV-1 reservoir on CD4+ T cells, the largest, longest-lived and best-characterized constituent of the HIV reservoir. We previously proposed to repurpose FDA-approved alefacept, a humanized α-CD2 fusion protein, to reduce the HIV reservoir in CD2hi CD4+ memory T cells. Here, we show the first evidence that alefacept can specifically target and reduce CD2hi HIV infected cells in vitro. We explore a variety of natural killer (NK) cells as mediators of antibody-dependent cell-mediated cytotoxicity (ADCC) including primary NK cells, expanded NK cells as well as the CD16 transduced NK-92 cell line which is currently under study in clinical trials as a treatment for cancer. We demonstrate that CD16.NK-92 has a natural preference to kill CD2hi CD45RA– memory T cells, specifically CD45RA– CD27+ central memory/transitional memory (TCM/TM) subset in both healthy and HIV+ patient samples as well as to reduce HIV DNA from HIV+ samples from donors well controlled on antiretroviral therapy. Lastly, alefacept can combine with CD16.NK-92 to decrease HIV DNA in some patient samples and thus may yield value as part of a strategy toward sustained HIV remission.
Genetic Diversity in Echinococcus multilocularis From the Plateau Vole and Plateau Pika in Jiuzhi County, Qinghai Province, China

The Qinghai-Tibet Plateau is a highly endemic area of alveolar echinococcosis where a series of intermediate hosts, especially voles and pikas, are infected with Echinococcus multilocularis. The metacestodes of E. multilocularis are fluid-filled, asexually proliferating cysts, and they are mainly found in the host's liver in the form of tumor-like growths. In this study, we investigated the genetic variations of E. multilocularis in four mitochondrial (mt) genes, namely, NADH dehydrogenase subunit 5 (nad5), adenosine triphosphate subunit 6 (atp6), cytochrome c oxidase subunit 1 (cox1), and NADH dehydrogenase subunit 1 (nad1). The complete nad5, atp6, cox1, and nad1 genes were amplified separately from each hydatid cyst isolate using polymerase chain reaction (PCR) and then sequenced. Phylogenetic trees were then generated based on the combined mt genes using MrBayes 3.1. 2 and PAUP version 4.0b10. The results showed that thirty of 102 voles and two of 49 pikas were infected with E. multilocularis. The genetic variation distances among all E. multilocularis samples were 0.1–0.4%, 0.2–0.4%, 0.1–0.6%, and 0.1–0.4% for nad5, atp6, nad1, and cox1, respectively. Compared to previous studies of the genetic diversity of E. multilocularis based on the cox1 gene, the genetic distances within the same group were 1.3–1.7% (Mongolia strain), 0.6–0.8% (North American strain), 0.3–0.6% (European strain), and 0.1–0.4% (Asian strain). Based on concatenated sequences of the nad5, atp6, cox1, and nad1 genes all haplotypes were divided into two clusters. In conclusion, the genetic diversity of E. multilocularis based on mt genes on a small local area is at low level but between different regions with long distance and different ecological environment each other, the genetic diversity is at relatively high level; genetic variation is higher in the nad1 gene than that in the other three mt genes. The results on a local scale provide basic information for further study of the molecular epidemiology, genetic differences and control of E. multilocularis in Qinghai Province, China.
DurableFS: A File System for Persistent Memory

With the availability of hybrid DRAM-NVRAM memory on the memory bus of CPUs, a number of file systems on NVRAM have been designed and implemented. In this paper we present the design and implementation of a file system on NVRAM called DurableFS, which provides atomicity and durability of file operations to applications. Due to the byte level random accessibility of memory, it is possible to provide these guarantees without much overhead. We use standard techniques like copy on write for data, and a redo log for metadata changes to build an efficient file system which provides durability and atomicity guarantees at the time a file is closed. Benchmarks on the implementation shows that there is only a 7 %degradation in performance due to providing these guarantees.
CD40L coding oncolytic adenovirus allows long-term survival of humanized mice receiving dendritic cell therapy

ABSTRACT Dendritic cells (DCs) are crucial players in promoting immune responses. Logically, adoptive DC therapy is a promising approach in cancer immunotherapy. One of the major obstacles in cancer immunotherapy in general is the immunosuppressive tumor microenvironment, which hampers the maturation and activation of DCs. Therefore, human clinical outcomes with DC therapy alone have been disappointing. In this study, we use fully serotype 3 oncolytic adenovirus Ad3-hTERT-CMV-hCD40L, expressing human CD40L, to modulate the tumor microenvironment with subsequently improved function of DCs. We evaluated the synergistic effects of Ad3-hTERT-CMV-hCD40L and DCs in the presence of human peripheral blood mononuclear cells ex vivo and in vivo. Tumors treated with Ad3-hTERT-CMV-hCD40L and DCs featured greater antitumor effect compared with unarmed virus or either treatment alone. 100% of humanized mice survived to the end of the experiment, while mice in all other groups died by day 88. Moreover, adenovirally-delivered CD40L induced activation of DCs, leading to induction of Th1 immune responses. These results support clinical trials with Ad3-hTERT-CMV-hCD40L in patients receiving DC therapy.
Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter

Objective: To date, many brain-machine interface (BMI) studies have developed decoding algorithms for neuroprostheses that provide users with precise control of upper arm reaches with some limited grasping capabilities. However, comparatively few have focused on quantifying the performance of precise finger control. Here we expand upon this work by investigating online control of individual finger groups. Approach: We have developed a novel training manipulandum for non-human primate (NHP) studies to isolate the movements of two specific finger groups: index and middle-ring-pinkie (MRP) fingers. We use this device in combination with the ReFIT (Recalibrated Feedback Intention-Trained) Kalman filter to decode the position of each finger group during a single degree of freedom task in two rhesus macaques with Utah arrays in motor cortex. The ReFIT Kalman filter uses a two-stage training approach that improves online control of upper arm tasks with substantial reductions in orbiting time, thus making it a logical first choice for precise finger control. Results: Both animals were able to reliably acquire fingertip targets with both index and MRP fingers, which they did in blocks of finger group specific trials. Decoding from motor signals online, the ReFIT Kalman filter reliably outperformed the standard Kalman filter, measured by bit rate, across all tested finger groups and movements by 31.0 and 35.2%. These decoders were robust when the manipulandum was removed during online control. While index finger movements and middle-ring-pinkie finger movements could be differentiated from each other with 81.7% accuracy across both subjects, the linear Kalman filter was not sufficient for decoding both finger groups together due to significant unwanted movement in the stationary finger, potentially due to co-contraction. Significance: To our knowledge, this is the first systematic and biomimetic separation of digits for continuous online decoding in a NHP as well as the first demonstration of the ReFIT Kalman filter improving the performance of precise finger decoding. These results suggest that novel nonlinear approaches, apparently not necessary for center out reaches or gross hand motions, may be necessary to achieve independent and precise control of individual fingers.