Miracle-dz/UniFAR-FTUs huggingface dataset:
High-capacity in vitro assessment of anti-hepatitis B virus compound selectivity by a virion-specific polymerase chain reaction assay

An integrated assessment system specific for hepatitis B virus (HBV) Dane particle DNA was developed to examine the activity of potential anti-HBV compounds in chronic HBV-producing HepG2-derived 2.2. 15 cells. Cell culture, immunoaffinity purification, polymerase chain reaction, and hybrid-capture detection were performed in the microtiter format to facilitate increased throughput by automation. The high sensitivity afforded by the assay provided quantitative detection of less than 0.5 fg of extracellular HBV DNA from 25 microliters of cell culture supernatants, and drug-induced reductions in HBV titers greater than 100-fold were easily measured. Fluorometric determination of total cellular DNA from the same 96-well proliferating cell cultures allowed simultaneous evaluation of inhibition of cell growth, thus providing the ability to assess the overall selectivities of candidate compounds in a single experiment. The potent activities of three anti-HBV compounds, the (+) and (-) enantiomers of cis-5-fluoro-1-[2-(hydroxymethyl)-1,3-oxythiolane-5-yl]cytosine (FTC) and D-carbocyclic-2'-deoxyguanosine (CDG), were confirmed by this method. (-)-FTC was more active than its (+) enantiomer (50% inhibitory concentrations, 0.033 +/- 0.006 and 0.723 +/- 0.160 microM [standard error of the mean; SEM], respectively), while both enantiomers demonstrated a lack of cytotoxicity at 200 microM. CDG was more potent (50% inhibitory concentration, 0.0063 +/- 0.0007 microM [SEM]) but was also significantly more toxic, inhibiting cell growth by 50% at 32 +/- 6 microM (SEM). These results demonstrate the usefulness of this immunoaffinity-based, quantitative polymerase chain reaction system as a high-capacity in vitro tool for assessment of anti-HBV compound selectivity.
Prediction of high-throughput protein-protein interactions based on protein sequence information

Prediction of protein-protein interaction (PPI) is one of the most challenging problems in biology. Although great progress has been devoted to the development of methodology for predicting PPIs and PIN using machine learning methods, the problem is still far from being solved since the application of most existing methods is limited. In this study, we propose a method for PPI prediction based on amino acids differences between pairs of protein sequences. 10-fold cross-validation tests based on human PPI datasets with balanced positive-to-negative ratios indicate that it performs comparably well. Therefore, our finding suggests that amino acids differences of interacting protein pairs are relevant to the prediction of PPIs and hence provide important information on sequence-based encoding schemes.
Positive active XML

The increasing popularity of XML and Web services have given rise to a new generation of documents, called Active XML documents (AXML), where some of the data is given explicitly while other parts are given intensionally, by means of embedded calls to Web services. Web services in this context can exchange intensional information, using AXML documents as parameters and results.The goal of this paper is to provide a formal foundation for this new generation of AXML documents and services, and to study fundamental issues they raise. We focus on Web services that are (1) monotone and (2) defined declaratively as conjunctive queries over AXML documents. We study the semantics of documents and queries, the confluence of computations, termination and lazy query evaluation.
Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly

A new method of physical activity monitoring is presented, which is able to detect body postures (sifting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking. " 8.2% for sifting, and 1.3% for lying.
A Variational Bayesian EM Algorithm for Tree Similarity

In recent times, a vast amount of tree-structured data has been generated. For mining, retrieving, and integrating such data, we need a fine-grained tree similarity measure that can be adapted to objective data. To achieve this goal, this paper (1) proposes a probabilistic generative model that generates pairs of similar trees, and (2) derives a learning algorithm for estimating the parameters of the model based on the variational Bayesian expectation maximization (VBEM) method. This method can handle rooted, ordered, and labeled trees. We show that the tree similarity model obtained via the BEM technique performs better than that obtained via maximum likelihood estimation by tuning the hyper parameters.
Prostaglandin E2 release from astrocytes triggers gonadotropin-releasing hormone (GnRH) neuron firing via EP2 receptor activation

Astrocytes in the hypothalamus release prostaglandin E2 (PGE2) in response to cell–cell signaling initiated by neurons and glial cells. Upon release, PGE2 stimulates the secretion of gonadotropin-releasing hormone (GnRH), the neuropeptide that controls reproduction, from hypothalamic neuroendocrine neurons. Whether this effect on GnRH secretion is accompanied by changes in the firing behavior of these neurons is unknown. Using patch-clamp recording we demonstrate that PGE2 exerts a dose-dependent postsynaptic excitatory effect on GnRH neurons. These effects are mimicked by an EP2 receptor agonist and attenuated by protein kinase A (PKA) inhibitors. The acute blockade of prostaglandin synthesis by indomethacin (INDO) or the selective inhibition of astrocyte metabolism by fluoroacetate (FA) suppresses the spontaneous firing activity of GnRH neurons in brain slices. Similarly, GnRH neuronal activity is reduced in mice with impaired astrocytic PGE2 release due to defective erbB signaling in astrocytes. These results indicate that astrocyte-to-neuron communication in the hypothalamus is essential for the activity of GnRH neurons and suggest that PGE2 acts as a gliotransmitter within the GnRH neurosecretory system.
Data compression and database performance

Data compression is widely used in data management to save storage space and network bandwidth. The authors outline the performance improvements that can be achieved by exploiting data compression in query processing. The novel idea is to leave data in compressed state as long as possible, and to only uncompress data when absolutely necessary. They show that many query processing algorithms can manipulate compressed data just as well as decompressed data, and that processing compressed data can speed query processing by a factor much larger than the compression factor. <<ETX>>
Dewatered Sewage Biosolids Provide a Productive Larval Habitat for Stable Flies and House Flies (Diptera: Muscidae)

ABSTRACT Species diversity and seasonal abundance of muscoid flies (Diptera: Muscidae) developing in biosolid cake (dewatered biosolids) stored at a wastewater treatment facility in northeastern Kansas were evaluated. Emergence traps were deployed 19 May through 20 October 2009 (22 wk) and 27 May through 18 November 2010 (25 wk). In total, 11,349 muscoid flies were collected emerging from the biosolid cake. Stable flies (Stomoxys calcitrans (L.)) and house flies (Musca domestica (L.)), represented 80 and 18% of the muscoid flies, respectively. An estimated 550 stable flies and 220 house flies per square-meter of surface area developed in the biosolid cake annually producing 450,000 stable flies and 175,000 house flies. Stable fly emergence was seasonally bimodal with a primary peak in mid-July and a secondary peak in late August. House fly emergence peaked with the first stable fly emergence peak and then declined gradually for the remainder of the year. House flies tended to emerge from the biosolid cake sooner after its deposition than did stable flies. In addition, house fly emergence was concentrated around midsummer whereas stable fly emergence began earlier in the spring and continued later into the fall. Biosolid age and temperature were the most important parameters affecting emergence for house flies and stable flies, whereas precipitation was not important for either species. This study highlights the importance of biosolid cake as a larval developmental habitat for stable flies and house flies.
The impact of global climate change on genetic diversity within populations and species

Genetic diversity provides the basic substrate for evolution, yet few studies assess the impacts of global climate change (GCC) on intraspecific genetic variation. In this review, we highlight the importance of incorporating neutral and non‐neutral genetic diversity when assessing the impacts of GCC, for example, in studies that aim to predict the future distribution and fate of a species or ecological community. Specifically, we address the following questions: Why study the effects of GCC on intraspecific genetic diversity? How does GCC affect genetic diversity? How is the effect of GCC on genetic diversity currently studied? Where is potential for future research? For each of these questions, we provide a general background and highlight case studies across the animal, plant and microbial kingdoms. We further discuss how cryptic diversity can affect GCC assessments, how genetic diversity can be integrated into studies that aim to predict species' responses on GCC and how conservation efforts related to GCC can incorporate and profit from inclusion of genetic diversity assessments. We argue that studying the fate of intraspecifc genetic diversity is an indispensable and logical venture if we are to fully understand the consequences of GCC on biodiversity on all levels.
Mitochondrial Transporter ATP Binding Cassette Mitochondrial Erythroid Is a Novel Gene Required for Cardiac Recovery After Ischemia/Reperfusion

Background Oxidative stress and mitochondrial dysfunction are central mediators of cardiac dysfunction after ischemia/reperfusion. ATP binding cassette mitochondrial erythroid (ABC-me; ABCB10; mABC2) is a mitochondrial transporter highly induced during erythroid differentiation and predominantly expressed in bone marrow, liver, and heart. Until now, ABC-me function in heart was unknown. Several lines of evidence demonstrate that the yeast ortholog of ABC-me protects against increased oxidative stress. Therefore, ABC-me is a potential modulator of the outcome of ischemia/reperfusion in the heart. Methods and Results Mice harboring 1 functional allele of ABC-me (ABC-me+/−) were generated by replacing ABC-me exons 2 and 3 with a neomycin resistance cassette. Cardiac function was assessed with Langendorff perfusion and echocardiography. Under basal conditions, ABC-me+/− mice had normal heart structure, hemodynamic function, mitochondrial respiration, and oxidative status. However, after ischemia/reperfusion, the recovery of hemodynamic function was reduced by 50% in ABC-me+/− hearts as a result of impairments in both systolic and diastolic function. This reduction was associated with impaired mitochondrial bioenergetic function and with oxidative damage to both mitochondrial lipids and sarcoplasmic reticulum calcium ATPase after reperfusion. Treatment of ABC-me+/− hearts with the superoxide dismutase/catalase mimetic EUK-207 prevented oxidative damage to mitochondria and sarcoplasmic reticulum calcium ATPase and restored mitochondrial and cardiac function to wild-type levels after reperfusion. Conclusions Inactivation of 1 allele of ABC-me increases the susceptibility to oxidative stress induced by ischemia/reperfusion, leading to increased oxidative damage to mitochondria and sarcoplasmic reticulum calcium ATPase and to impaired functional recovery. Thus, ABC-me is a novel gene that determines the ability to tolerate cardiac ischemia/reperfusion.
Determinants of Disability in Multiple Sclerosis: An Immunological and MRI Study

Multiple sclerosis (MS) is characterized by a wide interpatient clinical variability and available biomarkers of disease severity still have suboptimal reliability. We aimed to assess immunological and MRI-derived measures of brain tissue damage in patients with different motor impairment degrees, for in vivo investigating the pathogenesis of MS-related disability. Twenty-two benign (B), 26 secondary progressive (SP), and 11 early, nondisabled relapsing-remitting (RR) MS patients and 37 healthy controls (HC) underwent conventional and diffusion tensor brain MRI and, as regards MS patients, immunophenotypic and functional analysis of stimulated peripheral blood mononuclear cells (PBMC). Corticospinal tract (CST) fractional anisotropy and grey matter volume were lower and CST diffusivity was higher in SPMS compared to RRMS and BMS patients. CD14+IL6+ and CD4+IL25+ cell percentages were higher in BMS than in SPMS patients. A multivariable model having EDSS as the dependent variable retained the following independent predictors: grey matter volume, CD14+IL6+ and CD4+IL25+ cell percentages. In patients without motor impairment after long-lasting MS, the grey matter and CST damage degree seem to remain as low as in the earlier disease stages and an immunological pattern suggestive of balanced pro- and anti-inflammatory activity is observed. MRI-derived and immunological measures might be used as complementary biomarkers of MS severity.
Delving Deep Into Coarse-to-Fine Framework for Facial Landmark Localization

In this paper we proposed a 4-stage coarse-to-fine framework to tackle the facial landmark localization problem in-the-wild. In our system, we first predict the landmark key points on a coarse level of granularity, which sets a good initialization for the whole framework. Then we group the key points into several components and refine each component with local patches cropped within them. After that we further refine them separately. Each key point is further refined with multi-scale local patches cropped according to its nearest 3-, 5-, and 7-neighbors respectively. The results are fused by an attention gate network. Since a different key-point configuration is adopted in our labeled dataset, a linear transformation is finally learned with the least square approximation to adapt our predictions to the competition's task.
Text-based language identification for the South African languages

We investigate the performance of text-based language identification systems on the 11 official languages of South Africa, when n-gram statistics are used as features for classification. In particular, we compare support vector machines (SVMs) and likelihood-based classifiers on different amounts of input text, both from a closed domain and an open domain. With as few as 15 words of input text, reliable language identification is possible. Although the SVM is generally more accurate a classifier, the additional computational complexity of training this classifier may not be justified in light of the importance of using a large value for n.
Unlocking Short Read Sequencing for Metagenomics

Background Different high-throughput nucleic acid sequencing platforms are currently available but a trade-off currently exists between the cost and number of reads that can be generated versus the read length that can be achieved. Methodology/Principal Findings We describe an experimental and computational pipeline yielding millions of reads that can exceed 200 bp with quality scores approaching that of traditional Sanger sequencing. The method combines an automatable gel-less library construction step with paired-end sequencing on a short-read instrument. With appropriately sized library inserts, mate-pair sequences can overlap, and we describe the SHERA software package that joins them to form a longer composite read. Conclusions/Significance This strategy is broadly applicable to sequencing applications that benefit from low-cost high-throughput sequencing, but require longer read lengths. We demonstrate that our approach enables metagenomic analyses using the Illumina Genome Analyzer, with low error rates, and at a fraction of the cost of pyrosequencing.
A novel transition from grounded coplanar waveguide to substrate inte grated waveguide for 60 GHz Radio-over-Fiber photonic transmitters

We present a novel transition from grounded coplanar waveguide (GCPW) to substrate integrated waveguide (SIW), designed on a ROGERS 5880 laminate for 60 GHz Radio-over-Fiber (RoF) photonic transmitters. The transition serves as connection between a 60 GHz photodiode (PD) chip and a suitable SIW antenna. In contrast to previous designs, our approach makes use of a quarter-wave coupled-lines (CL) section to transfer the signal carried by the GCPW to the SIW. This technique, creating a DC-block between the GCPW signal track and the ground layers, allows for correctly biasing the PD. In order to reduce the propagation of parasitic modes as well as the risk of interferences, the transition is fully enclosed by a fence of via holes. Simulations show that in the whole 57-64 GHz band, the return loss (RL) is higher than 17 dB, while the insertion loss (IL) is ~ 0.4 dB. To prevent the loss of RF power through the DC path, a planar RF-choke (RL > 22 dB, IL <; 0.4 dB and RF-to-DC isolation (IS) higher than 28 dB) is additionally integrated.
An effective implementation and assessment of a random forest classifier as a soil spatial predictive model

ABSTRACT Mapping the spatial distribution of soil classes is important for informing soil use and management decisions. This study aimed to effectively implement Random Forest (RF) model and to evaluate the behaviour and performance of the model for soil classification of Indian districts. Soil-forming factors, known as ‘scorpan,’ are selected as environmental covariates to tune RF model to classify 11 different soil categories. Thirty-five digital layers are prepared using different satellite data [ALOS (Advanced Land Observing Satellite) digital elevation model, Landsat-8, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product, RISAT-1 (Radar Imaging Satellite-1), Sentinel-1A] and climatic data (precipitation and temperature) to represent scorpan environmental covariates in the study area. The RF parameters corresponding to highest Cohen’s kappa coefficient (κ) value and lowest number of random split variables are considered optimum values for RF model. Model behaviour evaluation is based on mapping accuracy, sensitivity to data set size, and noise. Two other machine-learning methods, CART (Classification and Regression Tree) decision tree (CDT) and CART ensemble bagger (CEB), are used to provide the comparative study. To access behaviour of models to the false data set, noise in training set is produced by assigning a false class to the training set in 5% increment. Comparative performance of RF model is based on quality assessment measures. To evaluate the performance of models, marginal rates, F-measure, and Jaccard’s coefficient of the community, classification success index and agreement coefficients are selected under quality assessment measures. The score is calculated to rank the algorithm. RF model shows high stability against data set reduction in comparison to other methods. The results show that the abrupt change in accuracy is only observed after 60% training data reduction in RF model; however, significant decrease in accuracy can be noted after 45% and 25% data reduction in CEB and CDT, respectively. The RF model shows comparatively the greater resistance to noise. Overall, RF model has performed better than CDT and CEB to classify soil categories in the study area. The results of this research provide new insights into the performance of RF in the context of soil class mapping.
Task-Driven Comparison of Topic Models

Topic modeling, a method of statistically extracting thematic content from a large collection of texts, is used for a wide variety of tasks within text analysis. Though there are a growing number of tools and techniques for exploring single models, comparisons between models are generally reduced to a small set of numerical metrics. These metrics may or may not reflect a model's performance on the analyst's intended task, and can therefore be insufficient to diagnose what causes differences between models. In this paper, we explore task-centric topic model comparison, considering how we can both provide detail for a more nuanced understanding of differences and address the wealth of tasks for which topic models are used. We derive comparison tasks from single-model uses of topic models, which predominantly fall into the categories of understanding topics, understanding similarity, and understanding change. Finally, we provide several visualization techniques that facilitate these tasks, including buddy plots, which combine color and position encodings to allow analysts to readily view changes in document similarity.
Association of Human Leukocyte Antigen with Interstitial Lung Disease in Rheumatoid Arthritis: A Protective Role for Shared Epitope

Introduction Interstitial Lung Disease (ILD) is frequently associated with Rheumatoid Arthritis (RA) as one of extra-articular manifestations. Many studies for Human Leukocyte Antigen (HLA) allelic association with RA have been reported, but few have been validated in an RA subpopulation with ILD. In this study, we investigated the association of HLA class II alleles with ILD in RA. Methods An association study was conducted on HLA-DRB1, DQB1, and DPB1 in 450 Japanese RA patients that were or were not diagnosed with ILD, based on the findings of computed tomography images of the chest. Results Unexpectedly, HLA-DRB1*04 (corrected P [Pc] = 0.0054, odds ratio [OR] 0.57), shared epitope (SE) (P = 0.0055, OR 0.66) and DQB1*04 (Pc = 0.0036, OR 0.57) were associated with significantly decreased risk of ILD. In contrast, DRB1*16 (Pc = 0.0372, OR 15.21), DR2 serological group (DRB1*15 and *16 alleles) (P = 0.0020, OR 1.75) and DQB1*06 (Pc = 0.0333, OR 1.57, respectively) were significantly associated with risk of ILD. Conclusion HLA-DRB1 SE was associated with reduced, while DR2 serological group (DRB1*15 and *16) with increased, risk for ILD in Japanese patients with RA.