Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient learning of statistical primary patterns via Bayesian network

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users. A large number of applications demand for primary user behavior correlation in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship of primary users by introducing a Bayesian network (BN) based framework. How to learn such a BN structure is a long standing issue, not fully understood even in the statistical learning community. To solve such an issue in CR, this paper proposes a BN structure learning scheme which incurs significantly lower computational complexity compared with previous ones. Thus, with this scheme, cognitive users could efficiently understand the statistical pattern of primary networks.
Design and Optimization of VoD Schemes With Client Caching in Wireless Multicast Networks

Due to the explosive growth in multimedia traffic, the scalability of video-on-demand (VoD) services has become increasingly important. By exploiting the potential cache ability at the client side, the performance of VoD multicast delivery can be improved through video segment precaching. In this paper, we address the performance limits of client-caching-enabled VoD schemes in wireless multicast networks with asynchronous requests. Both reactive and proactive systems are investigated. Specifically, for the reactive system where videos are transmitted on demand, we propose a joint cache allocation and multicast delivery scheme to minimize the average bandwidth consumption under the zero-delay constraint. For the proactive system where videos are periodically broadcasted, a joint design of the cache-bandwidth allocation algorithm and the delivery mechanism is developed to minimize the average waiting time under the total bandwidth constraint. In addition to the full access pattern where clients view videos in their entirety, we further consider the access patterns with random endpoints, fixed-size intervals, and downloading demand, respectively. The impacts of different access patterns on the resource-allocation algorithm and the delivery mechanism are elaborated. Simulation results validate the accuracy of the analytical results and also provide useful insights in designing VoD networks with client caching.
GeNemo: a search engine for web-based functional genomic data

A set of new data types emerged from functional genomic assays, including ChIP-seq, DNase-seq, FAIRE-seq and others. The results are typically stored as genome-wide intensities (WIG/bigWig files) or functional genomic regions (peak/BED files). These data types present new challenges to big data science. Here, we present GeNemo, a web-based search engine for functional genomic data. GeNemo searches user-input data against online functional genomic datasets, including the entire collection of ENCODE and mouse ENCODE datasets. Unlike text-based search engines, GeNemo's searches are based on pattern matching of functional genomic regions. This distinguishes GeNemo from text or DNA sequence searches. The user can input any complete or partial functional genomic dataset, for example, a binding intensity file (bigWig) or a peak file. GeNemo reports any genomic regions, ranging from hundred bases to hundred thousand bases, from any of the online ENCODE datasets that share similar functional (binding, modification, accessibility) patterns. This is enabled by a Markov Chain Monte Carlo-based maximization process, executed on up to 24 parallel computing threads. By clicking on a search result, the user can visually compare her/his data with the found datasets and navigate the identified genomic regions. GeNemo is available at www.genemo. org.
Epithelial and stromal circadian clocks are inversely regulated by their mechano-matrix environment

ABSTRACT The circadian clock is an autonomous molecular feedback loop inside almost every cell in the body. We have shown that the mammary epithelial circadian clock is regulated by the cellular microenvironment. Moreover, a stiff extracellular matrix dampens the oscillations of the epithelial molecular clock. Here, we extend this analysis to other tissues and cell types, and identify an inverse relationship between circadian clocks in epithelia and fibroblasts. Epithelial cells from mammary gland, lung and skin have significantly stronger oscillations of clock genes in soft 3D microenvironments, compared to stiff 2D environments. Fibroblasts isolated from the same tissues show the opposite response, exhibiting stronger oscillations and more prolonged rhythmicity in stiff microenvironments. RNA analysis identified that a subset of mammary epithelial clock genes, and their regulators, are upregulated in 3D microenvironments in soft compared to stiff gels. Furthermore, the same genes are inversely regulated in fibroblasts isolated from the same tissues. Thus, our data reveal for the first time an intrinsic difference in the regulation of circadian genes in epithelia and fibroblasts. Highlighted Article: Cell-matrix interactions are known to control circadian clocks. Here, we discovered that the matrix inversely regulates epithelia and fibroblasts.
Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index

Background Childhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics. Objective The aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement. Methods The Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant’s date of birth. Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models. Results The overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including “app only” users or “both” app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005). Conclusions The EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions.
Comparing three regularization methods to avoid extreme allocation probability in response-adaptive randomization

ABSTRACT We examine three variations of the regularization methods for response-adaptive randomization (RAR) and compare their operating characteristics. A power transformation (PT) is applied to refine the randomization probability. The clip method is used to bound the randomization probability within specified limits. A burn-in period of equal randomization (ER) can be added before adaptive randomization (AR). For each method, more patients are assigned to the superior arm and overall response rate increase as the scheme approximates simple AR, while statistical power increases as it approximates ER. We evaluate the performance of the three methods by varying the tuning parameter to control the extent of AR to achieve the same statistical power. When there is no early stopping rule, PT method generally performed the best in yielding higher proportion to the superior arm and higher overall response rate, but with larger variability. The burn-in method showed smallest variability compared with the clip method and the PT method. With the efficacy early stopping rule, all three methods performed more similarly. The PT and clip methods are better than the burn-in method in achieving higher proportion randomized to the superior arm and higher overall response rate but burn-in method required fewer patients in the trial. By carefully choosing the method and the tuning parameter, RAR methods can be tailored to strike a balance between achieving the desired statistical power and enhancing the overall response rate.
Clinicopathologic implications of immune classification by PD-L1 expression and CD8-positive tumor-infiltrating lymphocytes in stage II and III gastric cancer patients

We co-assessed PD-L1 expression and CD8+ tumor-infiltrating lymphocytes in gastric cancer (GC), and categorized into 4 microenvironment immune types. Immunohistochemistry (PD-L1, CD8, Foxp3, E-cadherin, and p53), PD-L1 mRNA in situ hybridization (ISH), microsatellite instability (MSI), and EBV ISH were performed in 392 stage II/III GCs treated with curative surgery and fluoropyrimidine-based adjuvant chemotherapy, and two public genome databases were analyzed for validation. PD-L1+ was found in 98/392 GCs (25.0%). The proportions of immune types are as follows: PD-L1+/CD8High, 22.7%; PD-L1−/CD8Low, 22.7%; PD-L1+/CD8Low, 2.3%; PD-L1−/CD8High, 52.3%. PD-L1+/CD8High type accounted for majority of EBV+ and MSI-high (MSI-H) GCs (92.0% and 66.7%, respectively), and genome analysis from public datasets demonstrated similar pattern. PD-L1−/CD8High showed the best overall survival (OS) and PD-L1−/CD8Low the worst (P < 0.001). PD-L1 expression alone was not associated with OS, however, PD-L1−/CD8High type compared to PD-L1+/CD8High was independent favorable prognostic factor of OS by multivariate analysis (P = 0.042). Adaptation of recent molecular classification based on EBV, MSI, E-cadherin, and p53 showed no significant survival differences. These findings support the close relationship between PD-L1/CD8 status based immune types and EBV+, MSI-H GCs, and their prognostic significance in stage II/III GCs.
Association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and breast cancer susceptibility: a meta-analysis

The association between fibroblast growth factor receptor 2 (FGFR2) polymorphism and breast cancer (BC) susceptibility remains inconclusive. The purpose of this systematic review was to evaluate the relationship between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. PubMed, Web of science and the Cochrane Library databases were searched before October 11, 2015 to identify relevant studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the strength of associations. Sensitivity and subgroup analyses were conducted. Thirty-five studies published from 2007 to 2015 were included in this meta-analysis. The pooled results showed that there was significant association between all the 3 variants and BC risk in any genetic model. Subgroup analysis was performed on rs2981582 and rs2420946 by ethnicity and Source of controls, the effects remained in Asians, Caucasians, population-based and hospital-based groups. We did not carryout subgroup analysis on rs2981578 for the variant included only 3 articles. This meta-analysis of case-control studies provides strong evidence that FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms were significantly associated with the BC risk. For rs2981582 and rs2420946, the association remained significant in Asians, Caucasians, general populations and hospital populations. However, further large scale multicenter epidemiological studies are warranted to confirm this finding and the molecular mechanism for the association need to be elucidated further.
RegA Plays a Key Role in Oxygen-Dependent Establishment of Persistence and in Isocitrate Lyase Activity, a Critical Determinant of In vivo Brucella suis Pathogenicity

For aerobic human pathogens, adaptation to hypoxia is a critical factor for the establishment of persistent infections, as oxygen availability is low inside the host. The two-component system RegB/A of Brucella suis plays a central role in the control of respiratory systems adapted to oxygen deficiency, and in persistence in vivo. Using an original “in vitro model of persistence” consisting in gradual oxygen depletion, we compared transcriptomes and proteomes of wild-type and ΔregA strains to identify the RegA-regulon potentially involved in the set-up of persistence. Consecutive to oxygen consumption resulting in growth arrest, 12% of the genes in B. suis were potentially controlled directly or indirectly by RegA, among which numerous transcriptional regulators were up-regulated. In contrast, genes or proteins involved in envelope biogenesis and in cellular division were repressed, suggesting a possible role for RegA in the set-up of a non-proliferative persistence state. Importantly, the greatest number of the RegA-repressed genes and proteins, including aceA encoding the functional IsoCitrate Lyase (ICL), were involved in energy production. A potential consequence of this RegA impact may be the slowing-down of the central metabolism as B. suis progressively enters into persistence. Moreover, ICL is an essential determinant of pathogenesis and long-term interactions with the host, as demonstrated by the strict dependence of B. suis on ICL activity for multiplication and persistence during in vivo infection. RegA regulates gene or protein expression of all functional groups, which is why RegA is a key regulator of B. suis in adaptation to oxygen depletion. This function may contribute to the constraint of bacterial growth, typical of chronic infection. Oxygen-dependent activation of two-component systems that control persistence regulons, shared by several aerobic human pathogens, has not been studied in Brucella sp. before. This work therefore contributes significantly to the unraveling of persistence mechanisms in this important zoonotic pathogen.
Four‐year follow‐up of an internet‐based brief intervention for unhealthy alcohol use in young men

AIM To estimate the long-term efficacy of an internet-based brief intervention (IBI) in decreasing alcohol use among men on (1) number of drinks/week and (2) monthly or more binge drinking prevalence. In addition, overall changes in alcohol use were assessed. DESIGN Participants in a cohort study were recruited in a two parallel-group randomized controlled trial of an IBI versus no-intervention control condition, showing a positive intervention effect at 6 months. As part of the regular cohort assessments, participants were re-assessed 47 months after the initial trial, offering an opportunity to determine long-term efficacy. SETTING Young Swiss men from the general population. PARTICIPANTS Of 737 randomized trial participants with unhealthy alcohol use (> 14 drinks/week or ≥ 6 drinks/occasion at least monthly, or Alcohol Use Disorders Identification Test (AUDIT) ≥ 8), 626 completed a cohort assessment at mean ± standard deviation (SD) = 47.4 ± 2.6) months after their randomized trial baseline assessment. INTERVENTION IBI included normative and personalized feedback on alcohol use, risk indicators, information about alcohol and health and recommendations; controls: assessment only. MEASUREMENTS Self-reported number of drinks/week and monthly or more binge drinking prevalence. FINDINGS Comparisons at follow-up were adjusted for baseline drinking. Missing values were replaced with the last observation carried forward. There was no evidence of differences between the IBI and control group on either the number of drinks/week [IBI: 10.8 (14.2); control: 10.7 (14.1), P = 0.8] or monthly or more binge drinking prevalence (IBI: 65.1%; control: 63.5%, P = 0.5). Although there was no evidence of overall change from baseline in number of drinks/week [9.8 (7.9) at baseline, 10.8 (14.1) at 47 months, P = 0.051], there was evidence that monthly or more binge drinking prevalence had decreased during the follow-up time (84.9% at baseline, 64.3% at 47 months, P < 0.001). CONCLUSIONS An internet-based brief intervention directed at unhealthy alcohol use among young men does not appear to reduce drinking over the long-term.
Professional Network Analytics Platform for Enterprise Collaboration

A fundamental challenge to effective coordination of work across various boundaries in large and geographically distributed organizations is the ability of employees to identify who knows what, who knows who, and who is doing what. In this demo, we introduce a professional network analytics platform that analyzes large and diverse set of operational records in an enterprise and constructs professional networks between employees. We describe how various challenges in constructing and analyzing networks that link various entities and time stamped events are addressed by our platform. Further, we illustrate certain collaboration support services built around them in a contextually relevant manner.
Combinatorial Penalties: Which structures are preserved by convex relaxations?

We consider the homogeneous and the non-homogeneous convex relaxations for combinatorial penalty functions defined on support sets. Our study identifies key differences in the tightness of the resulting relaxations through the notion of the lower combinatorial envelope of a set-function along with new necessary conditions for support identification. We then propose a general adaptive estimator for convex monotone regularizers, and derive new sufficient conditions for support recovery in the asymptotic setting.
Microgradient separation technique for purification and fractionation of permethylated N-glycans before mass spectrometric analyses.

Analysis of N-glycans released enzymatically from patients' sera or other clinical samples may provide diagnostically and prognostically important information on human disease. Permethylation of these biomolecules simultaneously increases their hydrophobicity and substantially improves their detection parameters in the following mass spectrometric analyses. The overall procedure, from the glycan cleavage to the final mass spectrometric determinations, includes several steps involving extraction, derivatization, and purification. During these steps, certain polymeric contaminants that may have been coincidentally introduced could hamper the final measurements. To understand and counter these interferences and further fractionate or preconcentrate these glycans, we introduce here an effective microgradient chromatographic technique that employs a small reversed-phase microcolumn connected to a gas-tight microsyringe delivering a mobile-phase gradient. After loading the glycan fraction onto the microcolumn, three elution steps are recommended: (1) remove polar contaminants; (2) recover permethylated glycans for either liquid chromatography with electrospray ionization mass spectrometry or matrix-assisted laser desorption/ionization mass spectrometry; and (3) remove larger polymeric contaminants and regenerate the precolumn. We further demonstrate that the trapped second fraction can be beneficially preconcentrated and further separated to achieve matrix-assisted laser desorption/ionization mass spectrometric detection of the derivatized N-glycans up to 6300 Da. The enhanced detection capabilities for tetra-antennary N-glycans are of increasing interest in disease biomarker discovery.
Localized Ranking in Social and Information Networks

SUMMARY In social and information network analysis, ranking has been considered to be one of the most fundamental and important tasks where the goal is to rank the nodes of a given graph according to their importance. For example, the PageRank and the HITS algorithms are well-known ranking methods. While these traditional ranking methods focus only on the structure of the entire network, we propose to incorporate a local view into node ranking by exploiting the clustering structure of real-world networks. We develop localized ranking mechanisms by partitioning the graphs into a set of tightly-knit groups and extracting each of the groups where the localized ranking is computed. Experimental results show that our localized ranking methods rank the nodes quite di ﬀ erently from the traditional global ranking methods, which indicates that our methods provide new insights and meaningful viewpoints for network analysis.
Face Tracking using Optical Flow Development of a Real-Time AdaBoost Cascade Face Tracker

In this paper a novel face tracking approach is presented where optical flow information is incorporated into the Viola-Jones face detection algorithm. In the original algorithm from Viola and Jones face detection is static as information from previous frames is not considered. In contrast to the ViolaJones face detector and also to other known dynamic enhancements, the proposed face tracker preserves information about near-positives. The algorithm builds a likelihood map from the intermediate results of the Viola-Jones algorithm which is extrapolated using optical flow. The objects get extracted from the likelihood map using image segmentation techniques. All steps can be computed very efficiently in real-time. The tracker is verified on the Boston Head Tracking Database showing that the proposed algorithm outperforms the standard Viola-Jones face detector.
Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding

Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discover various drug-drug interactions. However, these discoveries contain a huge amount of noise and provide knowledge bases far from complete and trustworthy ones to be utilized. Most existing studies focus on predicting binary drug-drug interactions between drug pairs but ignore other interactions. In this paper, we propose a novel framework, called PRD, to predict drug-drug interactions. The framework uses the graph embedding that can overcome data incompleteness and sparsity issues to achieve multiple DDI label prediction. First, a large-scale drug knowledge graph is generated from different sources. Then, the knowledge graph is embedded with comprehensive biomedical text into a common low dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world datasets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy.