Miracle-dz/UniFAR-FTUs huggingface dataset:
Samarangenin B from Limonium sinense Suppresses Herpes Simplex Virus Type 1 Replication in Vero Cells by Regulation of Viral Macromolecular Synthesis

ABSTRACT Inhibitory effects of ethanolic extracts from 10 Chinese herbs on herpes simplex virus type 1 (HSV-1) replication were investigated. By a bioassay-guided fractionation procedure, samarangenin B (Sam B) was isolated from Limonium sinense; Sam B significantly suppressed HSV-1 multiplication in Vero cells without apparent cytotoxicity. Time-of-addition experiments suggested that the inhibitory action of Sam B on HSV-1 replication was not due to the blocking of virus adsorption. In an attempt to further localize the point in the HSV-1 replication cycle where arrest occurred, a set of key regulatory events leading to viral multiplication was examined, including viral immediate-early (α), early (β), and late (γ) gene expression and DNA replication. Results indicated that levels of glycoprotein B (gB), gC, gD, gG, and infected-cell protein 5 (ICP5) expression and gB mRNA expression in Vero cells were impeded by Sam B. Data from PCR showed that replication of HSV-1 DNA in Vero cells was arrested by Sam B. Furthermore, Sam B decreased DNA polymerase, ICP0, and ICP4 gene expression in Vero cells. Results of an electrophoretic mobility shift assay demonstrated that Sam B interrupted the formation of an α-trans-induction factor/C1/Oct-1/GARAT multiprotein complex. The mechanisms of antiviral action of Sam B seem to be mediated, at least in part, by inhibiting HSV-1 α gene expression, including expression of the ICP0 and ICP4 genes, by blocking β transcripts such as DNA polymerase mRNA, and by arresting HSV-1 DNA synthesis and structural protein expression in Vero cells. These results show that Sam B is an antiviral agent against HSV-1 replication.
The complexity of searching a graph

T. Parsons proposed and partially analyzed the following pursuit-evasion problem on graphs: A team of searchers traverse the edges of a graph G in pursuit of a fugitive, who moves along the edges of the graph with complete knowledge of the locations of the pursuers. What is the smallest number s(G) of searchers that will suffice for guaranteeing capture of the fugitive? We show that determining whether s(G) ≤ K, for a given integer K, is NP-hard for general graphs but can be solved in linear time for trees. We also provide a structural characterization of those graphs with s(G) ≤ K for K = 1,2,3.
Online Entropy-Based Model of Lexical Category Acquisition

Children learn a robust representation of lexical categories at a young age. We propose an incremental model of this process which efficiently groups words into lexical categories based on their local context using an information-theoretic criterion. We train our model on a corpus of child-directed speech from CHILDES and show that the model learns a fine-grained set of intuitive word categories. Furthermore, we propose a novel evaluation approach by comparing the efficiency of our induced categories against other category sets (including traditional part of speech tags) in a variety of language tasks. We show the categories induced by our model typically outperform the other category sets.
Exon Organization and Novel Alternative Splicing of Ank3 in Mouse Heart

Ankyrin-G is an adaptor protein that links membrane proteins to the underlying cytoskeletal network. Alternative splicing of the Ank3 gene gives rise to multiple ankyrin-G isoforms in numerous tissues. To date, only one ankyrin-G isoform has been characterized in heart and transcriptional regulation of the Ank3 gene is completely unknown. In this study, we describe the first comprehensive analysis of Ank3 expression in heart. Using a PCR-based screen of cardiac mRNA transcripts, we identify two new exons and 28 alternative splice variants of the Ank3 gene. We measure the relative expression of each splice variant using quantitative real-time PCR and exon-exon boundary spanning primers that specifically amplify individual Ank3 variants. Six variants are rarely expressed (<1%), while the remaining variants display similar expression patterns in three hearts. Of the five first exons in the Ank3 gene, exon 1d is only expressed in heart and skeletal muscle as it was not detected in brain, kidney, cerebellum, and lung. Immunoblot analysis reveals multiple ankyrin-G isoforms in heart, and two ankyrin-G subpopulations are detected in adult cardiomyocytes by immunofluorescence. One population co-localizes with the voltage-gated sodium channel NaV1.5 at the intercalated disc, while the other population expresses at the Z-line. Two of the rare splice variants excise a portion of the ZU5 motif, which encodes the minimal spectrin-binding domain, and these variants lack β-spectrin binding. Together, these data demonstrate that Ank3 is subject to complex splicing regulation resulting in a diverse population of ankyrin-G isoforms in heart.
Compositional Sequence Labeling Models for Error Detection in Learner Writing

In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing. Finally, the model is integrated with a publicly deployed self-assessment system, leading to performance comparable to human annotators.
Adaptive protection scheme for MVC-encoded stereoscopic video streaming in IP-based networks

We present an adaptive unequal error protection (UEP) strategy built on the 1-D interleaved parity Application Layer Forward Error Correction (AL-FEC) code for protecting the transmission of stereoscopic 3D video content encoded with Multiview Video Coding (MVC) through IP-based networks. Our scheme targets the minimization of quality degradation produced by packet losses during video transmission in time-sensitive application scenarios. To that end, based on a novel packet-level distortion model, it selects in real time the most suitable packets within each Group of Pictures (GOP) to be protected and the most convenient FEC technique parameters, i.e., the size of the FEC generator matrix. In order to make these decisions, it considers the relevance of the packet, the behavior of the channel, and the available bitrate for protection purposes. Simulation results validate both the distortion model introduced to estimate the importance of packets and the optimization of the FEC technique parameter values.
Characterization of multiple myeloma vesicles by label‐free relative quantitation

Multiple myeloma (MM) is a hematological malignancy caused by a microenviromentally aided persistence of plasma cells in the bone marrow. The role that extracellular vesicles (EVs), microvesicles and exosomes, released by MM cells have in cell‐to‐cell communication and signaling in the bone marrow is currently unknown. This paper describes the proteomic content of EVs derived from MM.1S and U266 MM cell lines. First, we compared the protein identifications between the vesicles and cellular lysates of each cell line finding a large overlap in protein identifications. Next, we applied label‐free spectral count quantitation to determine proteins with differential abundance between the groups. Finally, we used bioinformatics to categorize proteins with significantly different abundances into functional groups. The results illustrate the first use of label‐free spectral counting applied to determine relative protein abundances in EVs.
Indoor air quality monitor based on solidly mounted resonators for the detection of VOCs

This work describes the development of an indoor air quality monitor based on high frequency solidly mounted resonators (SMR) for the detection of volatile organic compounds (V OC). The sensor system operates in a differential mode with two SMR devices driven by Pierce oscillators. The active resonator was spray-coated with a selective polymer compound, while the second one was used as a reference. The absorption of vapor molecules into the polymer layer causes a frequency shift in the resonator. A study of suitable polymer coatings and finite element simulations on the sensor response were performed. Experimental results demonstrated that the VOC monitor is capable of detecting toluene concentrations down to 5 ppm with a high sensitivity of 60 Hz/ppm for a 180 nm PDMS polymer coated SMR resonating at 900 MHz. We believe that the system could be used as a robust, low-cost and low power microsensor for the real-time and ubiquitous monitoring of VOCs, particularly in a personal air quality monitor.
Semantic Interaction: Coupling Cognition and Computation through Usable Interactive Analytics

The success of visual analytics is predicated on the ability of users to interactively explore information. Humans think about their data through interactive visual exploration, including testing hypotheses, exploring anomalies, and other cognitive processes of building understanding from data. The claim that these insights are generated as a result of the interaction led the attendees at the Pacific Northwest National Laboratory (PNNL) workshop on "Semantic Interaction: Coupling Cognition and Computation through Usable Interactive Analytics" to posit that user interaction must play a more central role in visual analytics systems, serving as the method for coupling cognition and computation. The claims and design principles discussed in this workshop report present research directions to advance visual analytics via a user interaction approach called semantic interaction.
Star Formation in the Galaxy : An Observational Overview(Origins : From Early Universe to Extrasolar Planets)

The problems of star and planet formation are among the most important challenges facing modern astrophysical research. Stars and their accompanying planetary systems are continuously being formed in the Galaxy enabling direct observation and investigation of the star forming process. However, stars form invisibly deep within cold and dark molecular clouds. Observations of these stellar birth sites at infrared and millimeter wavelengths from space and the ground have resulted in considerable progress toward a physical understanding of stellar origins. In this contribution I will review the empirical basis for our current understanding of the process of star formation with an emphasis on the origin of low mass (sunlike) stars.
Association between coffee consumption and the risk of oral cancer: a meta-analysis of observational studies.

OBJECTIVE Quantification of the association between the coffee consumption and risk of oral cancer is still conflicting. Thus, we conducted a meta-analysis to summarize the evidence from epidemiological studies of coffee consumption with the risk of oral cancer. METHODS Pertinent studies were identified by a search of PubMed and Web of Knowledge to March 2015. The random effect model was used. Sensitivity analysis and publication bias were conducted. RESULTS Data from 12 studies including 4,037 oral cancer cases and 1,872,231 participants were used in this meta-analysis. Pooled results suggested that highest coffee consumption amount versus lowest amount was significantly associated with the risk of oral cancer [summary relative risk (RR) = 0.694, 95% CI = 0.543-0.886, I(2) = 65.0%]. The association was also significant in Europe [summary RR = 0.571, 95% CI = 0.378-0.863], but not in America. No publication bias was found. CONCLUSIONS Our analysis suggested that the higher coffee consumption might reduce the risk of oral cancer, especially in Europe.
Assembling A Multi-Feature EEG Classifier for Left-Right Motor Imagery Data Using Wavelet-Based Fuzzy Approximate Entropy for Improved Accuracy

An EEG classifier is proposed for application in the analysis of motor imagery (MI) EEG data from a brain-computer interface (BCI) competition in this study. Applying subject-action-related brainwave data acquired from the sensorimotor cortices, the system primarily consists of artifact and background removal, feature extraction, feature selection and classification. In addition to background noise, the electrooculographic (EOG) artifacts are also automatically removed to further improve the analysis of EEG signals. Several potential features, including amplitude modulation, spectral power and asymmetry ratio, adaptive autoregressive model, and wavelet fuzzy approximate entropy (wfApEn) that can measure and quantify the complexity or irregularity of EEG signals, are then extracted for subsequent classification. Finally, the significant sub-features are selected from feature combination by quantum-behaved particle swarm optimization and then classified by support vector machine (SVM). Compared with feature extraction without wfApEn on MI data from two data sets for nine subjects, the results indicate that the proposed system including wfApEn obtains better performance in average classification accuracy of 88.2% and average number of commands per minute of 12.1, which is promising in the BCI work applications.
Detection of Atrial Fibrillation in ECGs

Automatic detection and classification of arrhythmia in electrocardiograms (ECG) provides a framework for efficient diagnosis and broader outreach to patients at risk for cardiac diseases. While prevalent types of arrhythmia include premature ventricular contractions (PVC) and atrial fibrillation, the majority of existing literature focuses on automatic detection and classification of the former type. In this report, we discuss our heuristic and implementation for detection of atrial fibrillation, using a dataset provided by the MIT/BIH Arrhythmia Database. Using features from Fourier analysis, wavelet transformation, and R-R interval analysis, linear discriminant analysis (LDA) on individual segments performed well with classification error of approximately 10%. Our detector, which built on top of our classifier, successfully identified regions of atrial fibrillation with less than 2% error.
A Constructive Meta-Level Feature Selection Method based on Method Repositories

Feature selection is one of key issues related with data pre-processing of classification task in a data mining process. Although many efforts have been done to improve typical feature selection algorithms (FSAs), such as filter methods and wrapper methods, it is hard for just one FSA to manage its performances to various datasets. To above problems, we propose another way to support feature selection procedure, constructing proper FSAs to each given dataset. Here is discussed constructive metalevel feature selection that re-constructs proper FSAs with a method repository every given datasets, de-composing representative FSAs into methods. After implementing the constructive meta-level feature selection system, we show how constructive meta-level feature selection goes well with 34 UCI common data sets, comparing with typical FSAs on their accuracies. As the result, our system shows the high performance on accuracies with lower computational costs to construct a proper FSA to each given data set automatically.
Constraints as Features

In this paper, we introduce a new approach to constrained clustering which treats the constraints as features. Our method augments the original feature space with additional dimensions, each of which derived from a given Cannot-link constraints. The specified Cannot-link pair gets extreme coordinates values, and the rest of the points get coordinate values that express their spatial influence from the specified constrained pair. After augmenting all the new features, a standard unconstrained clustering algorithm can be performed, like k-means or spectral clustering. We demonstrate the efficacy of our method for active semi-supervised learning applied to image segmentation and compare it to alternative methods. We also evaluate the performance of our method on the four most commonly evaluated datasets from the UCI machine learning repository.
IVTURS: A Linguistic Fuzzy Rule-Based Classification System Based On a New Interval-Valued Fuzzy Reasoning Method With Tuning and Rule Selection

Interval-valued fuzzy sets have been shown to be a useful tool to deal with the ignorance related to the definition of the linguistic labels. Specifically, they have been successfully applied to solve classification problems, performing simple modifications on the fuzzy reasoning method to work with this representation and making the classification based on a single number. In this paper, we present IVTURS, which is a new linguistic fuzzy rule-based classification method based on a new completely interval-valued fuzzy reasoning method. This inference process uses interval-valued restricted equivalence functions to increase the relevance of the rules in which the equivalence of the interval membership degrees of the patterns and the ideal membership degrees is greater, which is a desirable behavior. Furthermore, their parametrized construction allows the computation of the optimal function for each variable to be performed, which could involve a potential improvement in the system's behavior. Additionally, we combine this tuning of the equivalence with rule selection in order to decrease the complexity of the system. In this paper, we name our method IVTURS-FARC, since we use the FARC-HD method to accomplish the fuzzy rule learning process. The experimental study is developed in three steps in order to ascertain the quality of our new proposal. First, we determine both the essential role that interval-valued fuzzy sets play in the method and the need for the rule selection process. Next, we show the improvements achieved by IVTURS-FARC with respect to the tuning of the degree of ignorance when it is applied in both an isolated way and when combined with the tuning of the equivalence. Finally, the significance of IVTURS-FARC is further depicted by means of a comparison by which it is proved to outperform the results of FARC-HD and FURIA, which are two high performing fuzzy classification algorithms.
Lazy binary-splitting: a run-time adaptive work-stealing scheduler

We present Lazy Binary Splitting (LBS), a user-level scheduler of nested parallelism for shared-memory multiprocessors that builds on existing Eager Binary Splitting work-stealing (EBS) implemented in Intel's Threading Building Blocks (TBB), but improves performance and ease-of-programming. In its simplest form (SP), EBS requires manual tuning by repeatedly running the application under carefully controlled conditions to determine a stop-splitting-threshold (sst)for every do-all loop in the code. This threshold limits the parallelism and prevents excessive overheads for fine-grain parallelism. Besides being tedious, this tuning also over-fits the code to some particular dataset, platform and calling context of the do-all loop, resulting in poor performance portability for the code. LBS overcomes both the performance portability and ease-of-programming pitfalls of a manually fixed threshold by adapting dynamically to run-time conditions without requiring tuning. We compare LBS to Auto-Partitioner (AP), the latest default scheduler of TBB, which does not require manual tuning either but lacks context portability, and outperform it by 38.9% using TBB's default AP configuration, and by 16.2% after we tuned AP to our experimental platform. We also compare LBS to SP by manually finding SP's sst using a training dataset and then running both on a different execution dataset. LBS outperforms SP by 19.5% on average. while allowing for improved performance portability without requiring tedious manual tuning. LBS also outperforms SP with sst=1, its default value when undefined, by 56.7%, and serializing work-stealing (SWS), another work-stealer by 54.7%. Finally, compared to serializing inner parallelism (SI) which has been used by OpenMP, LBS is 54.2% faster.
A Pt/TiO2/Ti Schottky-type selection diode for alleviating the sneak current in resistance switching memory arrays

This study examined the properties of Schottky-type diodes composed of Pt/TiO2/Ti, where the Pt/TiO2 and TiO2/Ti junctions correspond to the blocking and ohmic contacts, respectively, as the selection device for a resistive switching cross-bar array. An extremely high forward-to-reverse current ratio of ∼ 109 was achieved at 1 V when the TiO2 film thickness was 19 nm. TiO2 film was grown by atomic layer deposition at a substrate temperature of 250 °C. Conductive atomic force microscopy revealed that the forward current flew locally, which limits the maximum forward current density to < 10 A cm − 2 for a large electrode (an area of ∼ 60 000 µm2). However, the local current measurement showed a local forward current density as high as ∼ 105 A cm − 2. Therefore, it is expected that this type of Schottky diode effectively suppresses the sneak current without adverse interference effects in a nano-scale resistive switching cross-bar array with high block density.
A Strategic Model of Social and Economic Networks

We study the stability and efficiency of social and economic networks, when self-interested individuals have the discretion to form or sever links. First, in the context of two stylized models, we characterize the sets of stable networkds (immune to incentives to form or sever links) and the sets of efficient networks (those which maximize total production or utility). The sets of stable networks and efficients networks do not always intersect. Next, we show that this tension is not unique to these models, but persists generally. In order to assure that there is always at least one efficient graph which is stable, one is forced to allocate resources to nodes (players) who are not responsible for any of the production. We characterize one such allocations rule: the equal split rule. We characterize another rule which fails to assure that efficient graphs are stable, but arises naturally if the allocations result from the bargaining of players.
Prognosis of octogenarians with severe aortic valve stenosis at high risk for cardiovascular surgery

Background Calcified aortic valve stenosis (AVS) is a disease found in the elderly which is often complicated by severe co-morbidities. Aims To assess the survival of conservatively treated patients >75 years with severe AVS compared to patients with non-severe AVS but who have a similar clinical risk profile; and to identify risk factors affecting prognosis. Methods and results From 2002 to 2006, 161 patients (mean age 86±7.7 years) were studied: 79 with aortic valve area (AVA) <1 cm2 (group A), 82 with AVA ≥1 cm2 (group B). Cumulative mortality rates were 77.5% in group A, and 44.4% in group B. Survival rates at 6, 12, 18 and 24 months in groups A and B were: 70.9% versus 98.8%, 65.8% versus 84.1%, 49.4% versus 69.5% and 41.8% versus 59.8%, respectively. Independent predictors for death in group A were pulmonary artery pressure (PAP), the STS-PROM score, serum creatinine and diabetes. PAP >30 mm Hg identified patients at high mortality risk. In group B the predictive variables were ejection fraction, PAP, serum creatinine, and treatment with β-blockers or ACE inhibitors. Conclusion Severe aortic valve stenosis is a medical condition with limited short-term survival in patients over the age of 75 years at high surgical risk. Clinical variables rather than symptomatic status were able to predict the patients' outcome. Patients with the highest surgical risk have the worst prognosis if AVS is not treated.