Miracle-dz/UniFAR-FTUs huggingface dataset:
Context-Based Features Enhance Protein Secondary Structure Prediction Accuracy

We report a new approach of using statistical context-based scores as encoded features to train neural networks to achieve secondary structure prediction accuracy improvement. The context-based scores are pseudo-potentials derived by evaluating statistical, high-order inter-residue interactions, which estimate the favorability of a residue adopting certain secondary structure conformation within its amino acid environment. Encoding these context-based scores as important training and prediction features provides a way to address a long-standing difficulty in neural network-based secondary structure predictions of taking interdependency among secondary structures of neighboring residues into account. Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracy of secondary structure predictions. An overall 7-fold cross-validated Q3 accuracy of 82.74% and Segment Overlap Accuracy (SOV) accuracy of 86.25% are achieved on a set of more than 7987 protein chains with, at most, 25% sequence identity. The Q3 prediction accuracy on benchmarks of CB513, Manesh215, Carugo338, as well as CASP9 protein chains is higher than popularly used secondary structure prediction servers, including Psipred, Profphd, Jpred, Porter (ab initio), and Netsurf. More significant improvement is observed in the SOV accuracy, where more than 4% enhancement is observed, compared to the server with the best SOV accuracy. A Q8 accuracy of >70% (71.5%) is also found in eight-state secondary structure prediction. The majority of the Q3 accuracy improvement is contributed from correctly identifying β-sheets and α-helices. When the context-based scores are incorporated, there are 15.5% more residues predicted with >90% confidence. These high-confidence predictions usually have a rather high accuracy (averagely ~95%). The three- and eight-state prediction servers (SCORPION) implementing our methods are available online.
A shuffle frog-leaping algorithm for test scheduling of 2D/3D SoC

Test scheduling is an important issue for testing the SoC. This work proposes a modified shuffle frog-leaping algorithm for test scheduling to reduce the test application time under the peak power constraint. It is applied to the 2D as well as the 3D SoC and experimental results on benchmark circuits show that it is one of the most effective algorithms in solving the problem.
Quantization Based Watermarking Approach with Gain Attack Recovery

A new Quantization Index Modulation-based watermarking approach is proposed in this paper. With the aim to increase capacity of the watermarking channel with noise we propose Initial Data Loss during quantization for some samples in pre-defined positions. Also, the proposed approach exploits a new form of distribution of quantized samples where samples that interpret "0" and "1" have differently shaped probability density functions. This creates a distinctive feature which is expressed numerically using one out of two proposed criteria. The criteria are utilized by a procedure for recovery after possible Gain Attack. Several state of the art quantization-based watermarking methods were used for comparison on a set of natural grayscale images. The superiority of the proposed method has been confirmed for different types of popular attacks.
Parsec–scale Herbig-Haro outflows from intermediate mass stars

While there are many parsec–scale Herbig-Haro (HH) outflows known to be driven by low–mass young stars, few are associated with their intermediate mass counterparts. Here we present the discovery of five such bipolar outflows. Of these, LkH α 198, 1548C27 IRS 1, LkH α 233 and LkH α 234 were previously known to possess small-scale HH flows, while no such activity was observed before near IRAS 19395+2313. The largest of the newly discovered outflows are seen in the vicinity of LkH α 234 and 1548C27 IRS 1, and stretch (in projection) 8 pc and 7.5 pc respectively. LkH α 233 which was previously known to power a spectroscopically detected small-scale ( ≤ 10´´) jet is now seen to drive a 3 pc outflow and LkH α 198 is shown here to power a 2 pc outflow. Two HH objects in the vicinity of IRAS 19395+2313 lead us to suggest that it may also be responsible for a 5 pc outflow. In total, 27 new HH objects/complexes were discovered. Examination of these parsec–scale outflows show that they have similar lengths, morphologies, and dynamical timescales as those from low–mass sources. Many appear to have blown out of the parent cloud, suggesting that their total lengths are much greater than optically observed. The degree of collimation of these outflows is similar to those from low–mass sources suggesting that the transition to more poorly–collimated outflows must occur at higher masses than the sources observed here.
TCP performance in wireless access with adaptive modulation and coding

We study a wireless access system with adaptive modulation and coding (AMC) at the physical layer, finite-length queuing at the data link layer and a TCP protocol at the transport layer. We analyze the end-to-end TCP performance via a fixed-point procedure that effectively couples TCP with the AMC-based wireless link. Guided by the performance analysis, we present a simple cross-layer design, which optimizes the target packet error rate in AMC at the physical layer, so that the TCP throughput at the transport layer is maximized.
Bayesian Tag Estimate and Optimal Frame Length for Anti-Collision Aloha RFID System

In a dynamic framed slotted aloha RFID system, the key technique can be divided into two parts: precisely estimating tag quantity and determining an optimal frame length. For estimating tag quantity, this paper uses three risk functions to propose three Bayesian estimates, and improves the estimates to reduce computational complexity. For determining an optimal frame length, this paper derives an optimal frame length, which can make the system achieve maximum channel usage efficiency under the condition that the durations of an idle, a collision and a successful slot are not identical. In our simulations, comparison with several conventional tag estimates shows that the proposed Bayesian tag estimates have less error. In addition, the improved estimates have lower computational complexity and their estimate performance is not reduced. The simulations results also indicate that the derived optimal frame length guarantees the maximum channel usage efficiency.
Video and image copy detection demo

This paper presents a content-based copy detection (CBCD) technologies demonstrator. It is composed mainly of a graphical user interface connected to two existing CBCD systems (developed by INRIA and INA), one for still images and one for videos. The graphical interface allows the user to either browse and visualize previously computed results or to perform interactive retrieval by sending a query document to the CBCD systems (used as remote servers). The GUI has dedicated functionalities such as synchronous playing of videos, (with desynchronisation and resynchronisation options), dual sound control, multi-session mode, interactive navigation in multiple detections, etc. The interactive video retrieval is faster than real time with a 1000 hours video corpus and the interactive image retrieval works with about 5000 images. Two demo scenarios allowing the user to create its own queries will be demonstrated: one using a professional editing software for video sequences and one using printed photographs and a digital camera for still images.
The Impact of School SES on Student Achievement: Evidence From U.S. Statewide Achievement Data

After the U.S. Supreme Court restricted the use of race in assigning students to schools, there was a surge in advocacy of school integration based on student socioeconomic status (SES). Benefits of socioeconomic integration have been supported by various studies finding significant effects of school SES on achievement after controlling for individual student SES. This article investigates school SES effects using statewide longitudinal achievement data from several U.S. states. School SES effects nearly vanish after controlling for a student’s prior achievement or, alternatively, controlling for stable differences among students using fixed effects models. The article concludes that large school SES effects often found in cross-sectional studies are artifacts of aggregation and are not a sound basis for SES-based school integration policies.
Identification and characterization of small RNAs in Yersinia pestis

Yersinia pestis, the etiologic agent of plague, is closely related to Yersinia pseudotuberculosis evolutionarily but has a very different mode of infection. The RNA-binding regulatory protein, Hfq, mediates regulation by small RNAs (sRNAs) and is required for virulence of both Y. pestis and Y. pseudotuberculosis. Moreover, Hfq is required for growth of Y. pestis, but not Y. pseudotuberculosis, at 37°C. Together, these observations suggest that sRNAs play important roles in the virulence and survival of Y. pestis, and that regulation by sRNAs may account for some of the differences between Y. pestis and Y. pseudotuberculosis. We have used a deep sequencing approach to identify 31 sRNAs in Y. pestis. The majority of these sRNAs are not conserved outside the Yersiniae. Expression of the sRNAs was confirmed by Northern analysis and we developed deep sequencing approaches to map 5ʹ and 3ʹ ends of many sRNAs simultaneously. Expression of the majority of the sRNAs we identified is dependent upon Hfq. We also observed temperature-dependent effects on the expression of many sRNAs, and differences in expression patterns between Y. pestis and Y. pseudotuberculosis. Thus, our data suggest that regulation by sRNAs plays an important role in the lifestyle switch from flea to mammalian host, and that regulation by sRNAs may contribute to the phenotypic differences between Y. pestis and Y. pseudotuberculosis.
The Role of Liver in Determining Serum Colon-Derived Uremic Solutes

Evidence has shown that indoxyl sulfate (IS) and p-cresyl sulfate (PCS) may be alternative predictors of clinical outcomes in chronic kidney disease (CKD). Both toxins are derived from the gastrointestinal tract and metabolised in the liver. However, it is unclear whether the liver affects the production of IS and PCS. Here, we explore the association between IS and PCS levels in liver cirrhosis and a CKD-based cohort (N = 115). Liver and kidney function was assessed and classified by a Child-Pugh score (child A–C) and a modified version of the Modification of Diet in Renal Disease (MDRD) equation (Stages 1–4), respectively. An animal model was also used to confirm the two toxin levels in a case of liver fibrosis. In patients with early liver cirrhosis (child A), IS and PCS were significantly associated with CKD stages. In contrast, serum IS and PCS did not significantly change in advanced liver cirrhosis (child C). A stepwise multiple linear regression analysis also showed that T-PCS was significantly associated with stages of liver cirrhosis after adjusting for other confounding factors (B = -2.29, p = 0.012). Moreover, the serum and urine levels of T-PCS and T-IS were significantly lower in rats with liver failure than in those without (p<0.01, p<0.05 and p<0.01, p<0.05, respectively). These results indicated that in addition to the kidneys, the liver was an essential and independent organ in determining serum IS and PCS levels. The production rate of IS and PCS was lower in patients with advanced liver cirrhosis.
Molecular Characteristics of Pancreatic Ductal Adenocarcinoma

Pancreatic cancer is an almost universally lethal disease and despite extensive research over the last decades, this has not changed significantly. Nevertheless, much progress has been made in understanding the pathogenesis of pancreatic ductal adenocarcinoma (PDAC) suggesting that different therapeutic strategies based on these new insights are forthcoming. Increasing focus exists on designing the so-called targeted treatment strategies in which the genetic characteristics of a tumor guide therapy. In the past, the focus of research was on identifying the most frequently affected genes in PDAC, but with the complete sequencing of the pancreatic cancer genome the focus has shifted to defining the biological function that the altered genes play. In this paper we aimed to put the genetic alterations present in pancreatic cancer in the context of their role in signaling pathways. In addition, this paper provides an update of the recent advances made in the development of the targeted treatment approach in PDAC.
Detecting highly overlapping community structure based on Maximal Clique Networks

Most of overlapping community detection algorithms cannot be applied to networks with highly overlapping community such as online social networks where individuals belong to many communities. One important reason is that many algorithms detect communities based on the explicit borders where nodes have more connections inside the communities, however, when the vertices' membership number gets large, the explicit borders between communities will fade away. To overcome this disadvantage, a new algorithm named MCNLPA is proposed by expanding the traditional Label Propagation Algorithm (LPA) based on the Maximal Clique Network for highly overlapping community detection. By finding all maximal cliques in networks and defining reasonable edges between them, the maximal clique network is established. Then the updated rule of classic LPA is modified to apply to the maximal network. Experiments show that MCNLPA has a relatively good performance in highly overlapping community detection and overlapping nodes identification.
A Survey on Trust and Trustworthy E-learning System

Trust is an important topic in information security research and is oriented to increase the reliability and performance of a web-based system in open networks. A trustworthy e-learning system is a trust-based learning system which contains reliable serving peers and useful learning resources. In this paper, a survey of trust researches in trust management and computing is presented at first and then the researches of trustworthy e-learning using trust models is discussed. This survey wants to offer a succinct summary of current trust model and a panoramic view on trustworthy e-learning studies and encourages more proper trust model designed for constructing reliable e-learning system in the complex and changing network environment.
Abdominal Aortic Aneurysm Expansion: Risk Factors and Time Intervals for Surveillance

Background—Intervention to reduce abdominal aortic aneurysm (AAA) expansion and optimization of screening intervals would improve current surveillance programs. The aim of this study was to characterize AAA growth in a national cohort of patients with AAA both overall and by cardiovascular risk factors. Methods and Results—In this study, 1743 patients were monitored for changes in AAA diameter by ultrasonography over a mean follow-up of 1.9 years. Mean initial AAA diameter and growth rate were 43 mm (range 28 to 85 mm) and 2.6 mm/year (95% range, −1.0 to 6.1 mm/year), respectively. Baseline diameter was strongly associated with growth, suggesting that AAA growth accelerates as the aneurysm enlarges. AAA growth rate was lower in those with low ankle/brachial pressure index and diabetes but higher for current smokers (all P <0.001). No other factor (including lipids and blood pressure) was associated with AAA growth. Intervals of 36, 24, 12, and 3 months for aneurysms of 35, 40, 45, and 50 mm, respectively, would restrict the probability of breaching the 55-mm limit at rescreening to below 1%. Conclusions—Annual, or less frequent, surveillance intervals are safe for all AAAs ≤45 mm in diameter. Smoking increases AAA growth, but atherosclerosis plays a minor role.
Redox Signaling Is an Early Event in the Pathogenesis of Renovascular Hypertension

Activation of the renin-angiotensin-aldosterone system plays a critical role in the development of chronic renal damage in patients with renovascular hypertension. Although angiotensin II (Ang II) promotes oxidative stress, inflammation, and fibrosis, it is not known how these pathways intersect to produce chronic renal damage. We tested the hypothesis that renal parenchymal cells are subjected to oxidant stress early in the development of RVH and produce signals that promote influx of inflammatory cells, which may then propagate chronic renal injury. We established a reproducible murine model of RVH by placing a tetrafluoroethhylene cuff on the right renal artery. Three days after cuff placement, renal tissue demonstrates no histologic abnormalities despite up regulation of both pro- and anti-oxidant genes. Mild renal atrophy was observed after seven days and was associated with induction of Tnfα and influx of CD3+ T cells and F4/80+ macrophages. By 28 days, kidneys developed severe renal atrophy with interstitial inflammation and fibrosis, despite normalization of plasma renin activity. Based on these considerations, we propose that renal parenchymal cells initiate a progressive cascade of events leading to oxidative stress, interstitial inflammation, renal fibrosis, and atrophy.
Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics

Traffic flow prediction has received extensive attention recently, since it is a key step to prevent and mitigate traffic congestion in urban areas. However, most previous studies on traffic flow prediction fail to capture fine-grained traffic information (like link-level traffic) and ignore the impacts from other factors, such as route structure and weather conditions. In this paper, we propose a deep and embedding learning approach (DELA) that can help to explicitly learn from fine-grained traffic information, route structure, and weather conditions. In particular, our DELA consists of an embedding component, a convolutional neural network (CNN) component and a long short-term memory (LSTM) component. The embedding component can capture the categorical feature information and identify correlated features. Meanwhile, the CNN component can learn the 2-D traffic flow data while the LSTM component has the benefits of maintaining a long-term memory of historical data. The integration of the three models together can improve the prediction accuracy of traffic flow. We conduct extensive experiments on realistic traffic flow dataset to evaluate the performance of our DELA and make comparison with other existing models. The experimental results show that the proposed DELA outperforms the existing methods in terms of prediction accuracy.
Uplink-downlink duality via minimax duality

The sum capacity of a Gaussian vector broadcast channel is the saddle point of a minimax Gaussian mutual information expression where the maximization is over the set of transmit covariance matrices subject to a power constraint and the minimization is over the set of noise covariance matrices subject to a diagonal constraint. This sum capacity result has been proved using two different methods, one based on decision-feedback equalization and the other based on a duality between uplink and downlink channels. This paper illustrates the connection between the two approaches by establishing that uplink-downlink duality is equivalent to Lagrangian duality in minimax optimization. This minimax Lagrangian duality relation allows the optimal transmit covariance and the least-favorable-noise covariance matrices in a Gaussian vector broadcast channel to be characterized in terms of the dual variables. In particular, it reveals that the least favorable noise is not unique. Further, the new Lagrangian interpretation of uplink-downlink duality allows the duality relation to be generalized to Gaussian vector broadcast channels with arbitrary linear constraints. However, duality depends critically on the linearity of input constraints. Duality breaks down when the input constraint is an arbitrary convex constraint. This shows that the minimax representation of the broadcast channel sum capacity is more general than the uplink-downlink duality representation
The Use of Proteomics in Biomarker Discovery in Neurodegenerative Diseases

Biomarkers for neurodegenerative diseases should reflect the central pathogenic processes of the diseases. The field of clinical proteomics is especially well suited for discovery of biomarkers in cerebrospinal fluid (CSF), which reflects the proteins in the brain under healthy conditions as well as in several neurodegenerative diseases. Known proteins involved in the pathology of neurodegenerative diseases are, respectively, normal tau protein, β-amyloid (1-42), synaptic proteins, amyloid precursor protein (APP), apolipoprotein E (apoE), which previously have been studied by protein immunoassays. The objective of this paper was to summarize results from proteomic studies of differential protein patterns in neurodegenerative diseases with focus on Alzheimer's disease (AD). Today, discrimination of AD from controls and from other neurological diseases has been improved by simultaneous analysis of both β-amyloid (1-42), total-tau, and phosphorylated tau, where a combination of low levels of CSF-β-amyloid 1-42 and high levels of CSF-tau and CSF-phospho-tau is associated with an AD diagnosis. Detection of new biomarkers will further strengthen diagnosis and provide useful information in drug trials. The combination of immunoassays and proteomic methods show that the CSF proteins express differential protein patterns in AD, FTD, and PD patients, which reflect divergent underlying pathophysiological mechanisms and neuropathological changes in these diseases.
Servicescape Concept Evolution: Systematic Review of Literature 1995-2017

This document is intended to explain Servicescape concept evolution from 1995 to 2017, after was coined by Mary Jo Bitner (1992). Systematic Review of Literature (RSL) was welcomed as a methodology with scientific database Scopus, which issued 325 documents. The results present a description of: type of document, scope, variables and methodological perspectives. Future lines for concept development and management implications are presented. Main results indicate that Servicescape is a relevant concept in literature, due to an increasing number of publications in time; Likewise, areas in which it has been more reviewed are Tourism and Entertainment and Health; in the same way, most used variables in studies are quality of the experience, environmental factors and user behavior.
Arf GAPs and membrane traffic

The selective transfer of material between membrane-delimited organelles is mediated by protein-coated vesicles. In many instances, formation of membrane trafficking intermediates is regulated by the GTP-binding protein Arf. Binding and hydrolysis of GTP by Arf was originally linked to the assembly and disassembly of vesicle coats. Arf GTPase-activating proteins (GAPs), a family of proteins that induce hydrolysis of GTP bound to Arf, were therefore proposed to regulate the disassembly and dissociation of vesicle coats. Following the molecular identification of Arf GAPs, the roles for GAPs and GTP hydrolysis have been directly examined. GAPs have been found to bind cargo and known coat proteins as well as directly contribute to vesicle formation, which is consistent with the idea that GAPs function as subunits of coat proteins rather than simply Arf inactivators. In addition, GTP hydrolysis induced by GAPs occurs largely before vesicle formation and is required for sorting. These results are the primary basis for modifications to the classical model for the function of Arf in transport vesicle formation, including a recent proposal that Arf has a proofreading, rather than a structural, role.