Miracle-dz/UniFAR-FTUs huggingface dataset:
ICICD: An Efficient Content Distribution Architecture in Mobile Cellular Network

With the recent proliferation of powerful mobile devices, such as smart phones and tablets, data traffic on mobile networks is growing explosively. To cope with the challenges brought by the explosive growth of mobile traffic, mobile network operators tend to deploy and operate their own content distribution system within their existing network infrastructure. In this paper, we present a novel content distribution architecture called ISP-Controlled In-network Content Distribution (ICICD) to improve the content delivery performance in mobile network, which benefits from content request aggregation, hybrid cache decision mechanism, distributed load balancing, and seamless mobility support. Content request aggregation is used to decrease duplicate transmission. Hybrid cache decision mechanism enables the ICICD to cache content more efficiently. Distributed load balancing is able to ensure the overall performance and reliability of the content distribution architecture. Seamless mobility support function enables service continuity when mobile users move from one eNodeB to another. In this paper, we first give a detailed description about the key components of the ICICD architecture and then discuss a number of challenges in deploying the proposed ICICD architecture. Finally, simulation results are presented to show the performance and benefits of the proposed mobile content distribution architecture.
An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments

Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user’s queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user’s queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios.
On the inhibition of muscle membrane chloride conductance by aromatic carboxylic acids

25 aromatic carboxylic acids which are analogs of benzoic acid were tested in the rat diaphragm preparation for effects on chloride conductance (G(Cl)). Of the 25, 19 were shown to reduce membrane G(Cl) with little effect on other membrane parameters, although their apparent K(i) varied widely. This inhibition was reversible if exposure times were not prolonged. The most effective analog studied was anthracene-9-COOH (9-AC; K(i) = 1.1 x 10(-5) M). Active analogs produced concentration-dependent inhibition of a type consistent with interaction at a single site or group of sites having similar binding affinities, although a correlation could also be shown between lipophilicity and K(i). Structure-activity analysis indicated that hydrophobic ring substitution usually increased inhibitory activity while para polar substitutions reduced effectiveness. These compounds do not appear to inhibit G(Cl) by altering membrane surface charge and the inhibition produced is not voltage dependent. Qualitative characteristics of the I-V relationship for Cl(-) current are not altered. Conductance to all anions is not uniformly altered by these acids as would be expected from steric occlusion of a common channel. Concentrations of 9-AC reducing G(Cl) by more than 90 percent resulted in slight augmentation of G(I). The complete conductance sequence obtained at high levels of 9-AC was the reverse of that obtained under control conditions. Permeability sequences underwent progressive changes with increasing 9-AC concentration and ultimately inverted at high levels of the analog. Aromatic carboxylic acids appear to inhibit G(Cl) by binding to a specific intramembrane site and altering the selectivity sequence of the membrane anion channel.
Efficient inference for sparse latent variable models of transcriptional regulation

Abstract Motivation Regulation of gene expression in prokaryotes involves complex co-regulatory mechanisms involving large numbers of transcriptional regulatory proteins and their target genes. Uncovering these genome-scale interactions constitutes a major bottleneck in systems biology. Sparse latent factor models, assuming activity of transcription factors (TFs) as unobserved, provide a biologically interpretable modelling framework, integrating gene expression and genome-wide binding data, but at the same time pose a hard computational inference problem. Existing probabilistic inference methods for such models rely on subjective filtering and suffer from scalability issues, thus are not well-suited for realistic genome-scale applications. Results We present a fast Bayesian sparse factor model, which takes input gene expression and binding sites data, either from ChIP-seq experiments or motif predictions, and outputs active TF-gene links as well as latent TF activities. Our method employs an efficient variational Bayes scheme for model inference enabling its application to large datasets which was not feasible with existing MCMC-based inference methods for such models. We validate our method on synthetic data against a similar model in the literature, employing MCMC for inference, and obtain comparable results with a small fraction of the computational time. We also apply our method to large-scale data from Mycobacterium tuberculosis involving ChIP-seq data on 113 TFs and matched gene expression data for 3863 putative target genes. We evaluate our predictions using an independent transcriptomics experiment involving over-expression of TFs. Availability and implementation An easy-to-use Jupyter notebook demo of our method with data is available at https://github.com/zhenwendai/SITAR. Supplementary information Supplementary data are available at Bioinformatics online.
POTENTIAL USE OF NEW METHODS FOR IDENTIFICATION OF HOLLOW POLYESTER FIBRES KANALLI POLİESTER LİFLERİNİN TANINMASINDA YENİ YÖNTEMLERİN KULLANIM OLANAKLARI

Today, manufactured textile fibres have many types of chemical structures and finishes and the majority of production processes are new. Therefore, it has been very difficult to identify the different types of fibres in recent times. Even though these fibres may come from a known group of fibres, they do not have the same thermal behaviour, melting point or solubility as classical fibres. So their analysis methods must be more specific than classical fibre analysis methods. The purpose of this study is to investigate various properties of some hollow polyester fibres by new identification methods. For this aim, chemical composition analysis, microscopic analysis, FT-IR and DSC analyses were done. Among these methods, microscopic analyses were found to be most useful in the identification of the hollow PET fibres.
People Opinion Topic Model: Opinion based User Clustering in Social Networks

Mining various hot discussed topics and corresponding opinions from different groups of people in social media (e.g., Twitter) is very useful. For example, a decision maker in a company wants to know how different groups of people (customers, staff, competitors, etc.) think about their services, facilities, and things happened around. In this paper, we are focusing on the problem of finding opinion variations based on different groups of people and introducing the concept of opinion based community detection. Further, we also introduce a generative graphic model, namely People Opinion Topic (POT) model, which detects social communities, associated hot discussed topics, and perform sentiment analysis simultaneously by modelling user's social connections, common interests, and opinions in a unified way. This paper is the first attempt to study community and opinion mining together. Compared with traditional social communities detection, the detected communities by POT model are more interpretable and meaningful. In addition, we further analyse how diverse opinions distributed and propagated among various social communities. Experiments on real twitter dataset indicate our model is effective.
Frequent silencing of the candidate tumor suppressor TRIM58 by promoter methylation in early-stage lung adenocarcinoma

In this study, we aimed to identify novel drivers that would be epigenetically altered through aberrant methylation in early-stage lung adenocarcinoma (LADC), regardless of the presence or absence of tobacco smoking-induced epigenetic field defects. Through genome-wide screening for aberrantly methylated CpG islands (CGIs) in 12 clinically uniform, stage-I LADC cases affecting six non-smokers and six smokers, we identified candidate tumor-suppressor genes (TSGs) inactivated by hypermethylation. Through systematic expression analyses of those candidates in panels of additional tumor samples and cell lines treated or not treated with 5-aza-deoxycitidine followed by validation analyses of cancer-specific silencing by CGI hypermethylation using a public database, we identified TRIM58 as the most prominent candidate for TSG. TRIM58 was robustly silenced by hypermethylation even in early-stage primary LADC, and the restoration of TRIM58 expression in LADC cell lines inhibited cell growth in vitro and in vivo in anchorage-dependent and -independent manners. Our findings suggest that aberrant inactivation of TRIM58 consequent to CGI hypermethylation might stimulate the early carcinogenesis of LADC regardless of smoking status; furthermore, TRIM58 methylation might be a possible early diagnostic and epigenetic therapeutic target in LADC.
A comparative evaluation of migration sedimentation method for sperm preparation

ABSTRACT The effectiveness of semen preparation using the migration-sedimentation (MS) method was evaluated, and compared to density gradient centrifuge and swim-up combination (DGC+SU). Sperm selection using MS is based on motility, thus, deleterious effects for which centrifugation has been blamed, are believed to be avoided. Normozoospermic male patients who had more than 10% forward progressive motile sperm in their ejaculate were included in the study. Spermatozoa selected by two different methods were investigated and compared according to sperm motility, concentration, morphology, vitality, DNA fragmentation, and presence of persistent histones. The concentration and motility of sperm in the MS group was improved when compared to the DGC+SU group, but the difference between groups was not significant. The proportion of sperm with normal morphology was found to be 12.19 ± 6.45% vs. 10.67 ± 5.44%, vitality rate was 74.09 ± 16.65% vs. 70.45 ± 16.78%, DNA fragmentation rate was 3.91 ± 3.96%, vs. 2.95 ± 3.33%, presence of persistent histone proportion was 10.59 ± 13.40%, vs. 8.86 ± 7.89% in DGC+SU and MS groups respectively, without significance. The simple technique avoids centrifuge-based damage.
A Clinical Update and Global Economic Burden of Rheumatoid Arthritis.

BACKGROUND AND OBJECTIVE Rheumatoid arthritis (RA) is a predominant inflammatory autoimmune disorder. The incidence and prevalence of RA is increasing with considerable morbidity and mortality worldwide. The pathophysiology of RA has become clearer due to many significant research outputs during the last two decades. Many inflammatory cytokines involved in RA pathophysiology and the presence of autoantibodies are being used as potential biomarkers via the use of effective diagnostic techniques for the early diagnosis of RA. Currently, several disease-modifying anti-rheumatic drugs are being prescribed targeting RA pathophysiology, which have shown significant contributions in improving the disease outcomes. DISCUSSION Even though innovations in treatment strategies and monitoring are helping the patients to achieve early and sustained clinical and radiographic remission, the high cost of drugs and limited health care budgets are restricting the easy access of RA treatment. Both direct and indirect high cost of treatment are creating economic burden for the patients and affecting their quality of life. CONCLUSION The aim of this review is to describe the updated concept of RA pathophysiology and highlight current diagnostic tools used for the early detection as well as prognosis - targeting several biomarkers of RA. Additionally, we explored the updated treatment options with side effects besides discussing the global economic burden.
Strength Assessment of Fly Ash Concrete with Water Containing Sodium Hydroxide

This paper presents the effect of sodium hydroxide (NaOH) present in the curing water on the strength of fly ash cement concrete. The concrete is produced by mixing of % replacement of cement by fly ash and curing water containing NaOH of 5% concentration with constant dosages. This research work describes the feasibility of using the fly ash (Class-F) in concrete production as partial replacement of cement by weight. The cement has been replaced by fly ash accordingly in the range of 0% (without fly ash), 10%, 20%, 30% and 40% by weight of cement for M-40 mix. The compressive strengths were evaluated for 56 days of normal curing and 28 days normal + 28 days 5% NaOH contain water curing. The results show that, the compressive strength of fly ash cement concrete has come down with an increase in the % replacement of cement by fly ash with constant dosages of 5% concentrated NaOH solution at 56 days. Compressive strengths of fly ash Cement Concrete have decreased in the range of 8.22 to 36.11%, when compared with the control specimens. By using Regression Models we can predict the compressive strength value and the ratio with predicted values.
PAPOLB/TPAP regulates spermiogenesis independently of chromatoid body-associated factors

Mutant mice lacking a testis-specific cytoplasmic poly(A) polymerase, PAPOLB/TPAP, exhibit spermiogenesis arrest and male infertility. However, the mechanism by which PAPOLB regulates spermiogenesis remains unclear. In this study, we examined the relationships between PAPOLB and other spermiogenesis regulators present in the chromatoid body (CB). The loss of PAPOLB had no impact either on the abundance of CB components such as PIWIL1, TDRD6, YBX2, and piRNAs, or on retrotransposon expression. In addition, localization of CB proteins and CB architecture were both normal in PAPOLB-null mice. No interactions were observed between PAPOLB and PIWIL1 or YBX2. While PIWIL1 and YBX2 were associated with translationally inactive messenger ribonucleoproteins and translating polyribosomes, PAPOLB was present almost exclusively in the mRNA-free fractions of sucrose gradients. These results suggest that PAPOLB may regulate spermiogenesis through a pathway distinct from that mediated by CB-associated factors.
Intravital Imaging of Neuroimmune Interactions Through a Thinned Skull

Innate and adaptive immune interactions within the central nervous system (CNS) and surrounding meninges contribute significantly to neural homeostasis as well as a variety of different neurological disorders. Two‐photon laser scanning microscopy is a deep tissue imaging technique that provides a means to image immune cell dynamics and interactions in the living CNS with high spatial and temporal resolution. Optical access to the brain and meninges can be achieved through the creation of thinned skull windows, which can be made without inducing damage and inflammation in the underlying tissue. This protocol provides guidance on how to create a thinned skull window without causing CNS injury. We also describe a highly reproducible method to induce a mild traumatic brain injury using the thinned skull approach. © 2018 by John Wiley & Sons, Inc.
Automated Agatston score computation in non-ECG gated CT scans using deep learning

Introduction: The Agatston score is a well-established metric of cardiovascular disease related to clinical outcomes. It is computed from CT scans by a) measuring the volume and intensity of the atherosclerotic plaques and b) aggregating such information in an index. Objective: To generate a convolutional neural network that inputs a non-contrast chest CT scan and outputs the Agatston score associated with it directly, without a prior segmentation of Coronary Artery Calcifications (CAC). Materials and methods: We use a database of 5973 non-contrast non-ECG gated chest CT scans where the Agatston score has been manually computed. The heart of each scan is cropped automatically using an object detector. The database is split in 4973 cases for training and 1000 for testing. We train a 3D deep convolutional neural network to regress the Agatston score directly from the extracted hearts. Results: The proposed method yields a Pearson correlation coefficient of r = 0.93; p ≤ 0.0001 against manual reference standard in the 1000 test cases. It further stratifies correctly 72.6% of the cases with respect to standard risk groups. This compares to more complex state-of-the-art methods based on prior segmentations of the CACs, which achieve r = 0.94 in ECG-gated pulmonary CT. Conclusions: A convolutional neural network can regress the Agatston score from the image of the heart directly, without a prior segmentation of the CACs. This is a new and simpler paradigm in the Agatston score computation that yields similar results to the state-of-the-art literature.
Track Facial Points in Unconstrained Videos

Tracking Facial Points in unconstrained videos is challenging due to the non-rigid deformation that changes over time. In this paper, we propose to exploit incremental learning for person-specific alignment in wild conditions. Our approach takes advantage of part-based representation and cascade regression for robust and efficient alignment on each frame. Unlike existing methods that usually rely on models trained offline, we incrementally update the representation subspace and the cascade of regressors in a unified framework to achieve personalized modeling on the fly. To alleviate the drifting issue, the fitting results are evaluated using a deep neural network, where well-aligned faces are picked out to incrementally update the representation and fitting models. Both image and video datasets are employed to valid the proposed method. The results demonstrate the superior performance of our approach compared with existing approaches in terms of fitting accuracy and efficiency.
Hyperspectral segmentation with active learning

This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation, with two main steps: (a) learning, for each class label, the posterior probability distributions, based on a multinomial logistic regression model; (b) segmenting the hyperspectral image, based on the posterior probability distribution learnt in step (a) and on a multi-level logistic prior encoding the spatial information. The multinomial logistic regressors are learnt by using the recently introduced LORSAL (logistic regression via splitting and augmented Lagrangian) algorithm. The maximum a posterior segmentation is efficiently computed by the �-Expansion min-cut based integer optimization algorithm. Aiming at reducing the costs of acquiring large training sets, active learning is performed using a mutual information based criterion. State-of-the-art performance of the proposed approach is illustrated with simulated and real hyperspectral data sets in a number of experimental comparisons with recently introduced hyperspectral classification methods.
PknG senses amino acid availability to control metabolism and virulence of Mycobacterium tuberculosis

Sensing and response to changes in nutrient availability are essential for the lifestyle of environmental and pathogenic bacteria. Serine/threonine protein kinase G (PknG) is required for virulence of the human pathogen Mycobacterium tuberculosis, and its putative substrate GarA regulates the tricarboxylic acid cycle in M. tuberculosis and other Actinobacteria by protein-protein binding. We sought to understand the stimuli that lead to phosphorylation of GarA, and the roles of this regulatory system in pathogenic and non-pathogenic bacteria. We discovered that M. tuberculosis lacking garA was severely attenuated in mice and macrophages and furthermore that GarA lacking phosphorylation sites failed to restore the growth of garA deficient M. tuberculosis in macrophages. Additionally we examined the impact of genetic disruption of pknG or garA upon protein phosphorylation, nutrient utilization and the intracellular metabolome. We found that phosphorylation of GarA requires PknG and depends on nutrient availability, with glutamate and aspartate being the main stimuli. Disruption of pknG or garA caused opposing effects on metabolism: a defect in glutamate catabolism or depletion of intracellular glutamate, respectively. Strikingly, disruption of the phosphorylation sites of GarA was sufficient to recapitulate defects caused by pknG deletion. The results suggest that GarA is a cellular target of PknG and the metabolomics data demonstrate that the function of this signaling system is in metabolic regulation. This function in amino acid homeostasis is conserved amongst the Actinobacteria and provides an example of the close relationship between metabolism and virulence.
Productivity and influence in bioinformatics: A bibliometric analysis using PubMed central

Bioinformatics is a fast‐growing field based on the optimal use of “big data” gathered in genomic, proteomics, and functional genomics research. In this paper, we conduct a comprehensive and in‐depth bibliometric analysis of the field of bioinformatics by extracting citation data from PubMed Central full‐text. Citation data for the period 2000 to 2011, comprising 20,869 papers with 546,245 citations, was used to evaluate the productivity and influence of this emerging field. Four measures were used to identify productivity; most productive authors, most productive countries, most productive organizations, and most popular subject terms. Research impact was analyzed based on the measures of most cited papers, most cited authors, emerging stars, and leading organizations. Results show the overall trends between the periods 2000 to 2003 and 2004 to 2007 were dissimilar, while trends between the periods 2004 to 2007 and 2008 to 2011 were similar. In addition, the field of bioinformatics has undergone a significant shift, co‐evolving with other biomedical disciplines.