Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptional networks specifying homeostatic and inflammatory programs of gene expression in human aortic endothelial cells

Endothelial cells (ECs) are critical determinants of vascular homeostasis and inflammation, but transcriptional mechanisms specifying their identities and functional states remain poorly understood. Here, we report a genome-wide assessment of regulatory landscapes of primary human aortic endothelial cells (HAECs) under basal and activated conditions, enabling inference of transcription factor networks that direct homeostatic and pro-inflammatory programs. We demonstrate that 43% of detected enhancers are EC-specific and contain SNPs associated to cardiovascular disease and hypertension. We provide evidence that AP1, ETS, and GATA transcription factors play key roles in HAEC transcription by co-binding enhancers associated with EC-specific genes. We further demonstrate that exposure of HAECs to oxidized phospholipids or pro-inflammatory cytokines results in signal-specific alterations in enhancer landscapes and associate with coordinated binding of CEBPD, IRF1, and NFκB. Collectively, these findings identify cis-regulatory elements and corresponding trans-acting factors that contribute to EC identity and their specific responses to pro-inflammatory stimuli. DOI: http://dx.doi. org/10.7554/eLife.22536. 001
Polymorphisms in Toll-Like Receptor 10 and Tuberculosis Susceptibility: Evidence from Three Independent Series

Background The toll-like receptor 2 (TLR2)-mediated immune response is critical for host defense against Mycobacterium tuberculosis. There is evidence that TLR10, a TLR2 signaling modulator, may be involved in progression of tuberculosis (TB). Methods Using a self-validating case–control design, we tested for an association between seven TLR10 polymorphisms and susceptibility to TB in three independent series with two distinct populations. Single-nucleotide polymorphism (SNP) genotypes were determined by the SNPscanTM method. Three genetic models (additive, dominant, and recessive) as well as multiple-SNP score analyses were used to evaluate the risk of TB associated with the TLR10 SNPs. Results By comparing TB patients with healthy controls, we observed two SNPs (rs11466617 and rs4129009) that were associated with decreased risk of TB in the Tibetan population, but did not in the Chinese Han population. Further analysis demonstrated that the rs11466617 Chengdu cohort genotype served as a protective factor against the progression of latent TB infection (LTBI) to active TB under the recessive model. None of the SNPs were significantly different in comparisons of TB-uninfected people with LTBI individuals. Additionally, when the underlying four TB-associated loci were considered together in a multiple-SNP score analysis, we observed an allele dose-dependent decrease in TB risk in Tibetans. Conclusion Variants of TLR10 may show an ethnic specificity on susceptibility to TB in Tibetan individuals. rs11466617 affected the susceptibility to progress from LTBI to active TB disease, but was not associated with the establishment of LTBI after M. tuberculosis exposure. More studies are needed to verify this genetic epidemiological result and unravel the role of TLR10 SNPs in the pathogenesis of TB.
Efficient Processing of Top-k Dominating Queries on Multi-Dimensional Data

The top-k dominating query returns k data objects which dominate the highest number of objects in a dataset. This query is an important tool for decision support since it provides data analysts an intuitive way for finding significant objects. In addition, it combines the advantages of top-k and skyline queries without sharing their disadvantages: (i) the output size can be controlled, (ii) no ranking functions need to be specified by users, and (iii) the result is independent of the scales at different dimensions. Despite their importance, top-k dominating queries have not received adequate attention from the research community. In this paper, we design specialized algorithms that apply on indexed multi-dimensional data and fully exploit the characteristics of the problem. Experiments on synthetic datasets demonstrate that our algorithms significantly outperform a previous skyline-based approach, while our results on real datasets show the meaningfulness of top-k dominating queries.
The value of liquid biopsy in diagnosis and monitoring of diffuse large b-cell lymphoma: recent developments and future potential

ABSTRACT Introduction: Diffuse large B-cell lymphomas (DLBCL) represent a heterogeneous subset of non-Hodgkin lymphomas (NHL) that demonstrate many molecular alterations and somatic mutations, all of which are targets for the recent development of biomarkers that use various molecular biological techniques. These non-invasive emerging biomarkers will be used in the next few years to better monitor the response to immunochemotherapeutic treatments with the aim of completely eradicating the disease in order to cure it. Areas covered: In this review, the authors conducted a literature search to identify and summarize the major advances in liquid biopsy techniques for DLBCL that are useful for diagnosis and monitoring minimal residual disease (MRD). The authors report on the major technological leaps represented by the main MRD tools (sequencing of clone-specific rearrangements of immunoglobulin genes and sequencing of somatic mutations in circulating tumor plasma DNA) and present the expected future developments and the impact of these new tools on clinical practice. Expert commentary: The monitoring of somatic mutations in tumor plasma cell-free DNA represents a promising tool for liquid biopsy, which will in the future allow non-invasive monitoring that will be used at any time to follow the response to the treatment.
Learning from Multiple Proofs: First Experiments

Mathematical textbooks typically present only one proof for most of the theorems. However, there are innitely many proofs for each theorem in rst-order logic, and mathematicians are often aware of (and even invent new) important alternative proofs and use such knowledge for (lateral) thinking about new problems. In this paper we start exploring how the explicit knowledge of multiple (human and ATP) proofs of the same theorem can be used in learning-based premise selection algorithms in large-theory mathematics. Several methods and their combinations are dened, and their eect on the ATP performance is evaluated on the MPTP2078 large-theory benchmark. Our rst ndings are that the proofs used for learning signicantly inuence the number of problems solved, and that the quality of the proofs is more important than the quantity.
An accurate saliency prediction method based on generative adversarial networks

In this paper, we propose a saliency prediction algorithm utilizing generative adversarial networks. The proposed system contains two parts: saliency network and adversarial networks. The saliency network is the basis for saliency prediction, which calculates an Euclidean cost function on the grayscale values between the predicted saliency map and the ground truth. In order to improve the accuracy of the algorithm, adversarial networks are subsequently utilized to extract the features of input data by coordinating the learning rates of the two sub-networks contained in the networks. Experimental results validate the high accuracy of the proposed approach compared with the state-of-the-art models on three public datasets, SALICON, MIT1003 and Cerf.
Self-Reported Listening-Related Effort and Fatigue in Hearing-Impaired Adults

Objective: Hearing loss may increase listening-related effort and fatigue due to the increased mental exertion required to attend to, and understand, an auditory message. Because there have been few attempts to quantify self-reported effort and fatigue in listeners with hearing loss, that was the aim of the present study. Design: Participants included three groups of hearing-impaired adults: (1) hearing aid users (HA, n = 50; 31 male, 19 female; age range = 55 to 85 years); (2) cochlear implant users (CI, n = 50; 26 male, 24 female; age range = 55 to 80 years); and (3) single sided deafness (SSD, n = 50; 30 male, 20 female; age range = 58 to 80 years). There was also a control group of adults who passed a hearing screen at 30 dB HL at the frequencies: 500, 1000, 2000, and 4000 Hz in both ears (n = 50; 22 male, 28 female; age range = 55 to 78 years). The fatigue assessment scale (FAS) was used to quantify fatigue. The FAS is a generic standardized self-report scale consisting of 10 items that are scored using a five-point Likert scale. An effort assessment scale (EAS), developed for the present study, consisted of six questions with responses provided on a visual analog scale that ranges from 0 to 10. Results: All hearing-impaired groups reported significantly increased effort and fatigue compared to the control group. The median fatigue score for the control group was 14 and around 22 for the three hearing-impaired groups. The median effort score for the control group was 20 and around 70 for the three hearing-impaired groups. There was no significant difference in mean effort or fatigue between the three groups of hearing-impaired adults. There was a weak positive correlation between fatigue and effort scores (r = 0.40, p < 0.05). The proportion of participants with extreme fatigue (scores above the 95th percentile of the control group) was 22, 10, and 22%, for the HA, CI, and SSD groups, respectively. The proportion of those with extreme effort was 46, 54, and 52%, for the HA, CI, and SSD groups, respectively. Results of factor analysis using the individual questions from both questionnaires indicated that the questions loaded into two factors: a “fatigue” factor for all of the FAS questions and an “effort” factor for all of the EAS questions. Conclusion: Hearing-impaired individuals report high levels of listening effort and fatigue in everyday life. The similarity in listening-related effort and fatigue between the different hearing-impaired groups suggests that these aspects of listening experience are not predicted by the severity of hearing impairment. Factor analysis suggests that the FAS and the EAS assess two distinct dimensions. The low correlation between FAS and EAS means that fatigue cannot be reliably predicted from self-reported effort in individual listeners.
Clustering Dycom: An Online Cross-Company Software Effort Estimation Study

Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom's predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom's CC subsets.
Esogastric pressure measurement to assist noninvasive ventilation indication and settings in infants with hypercapnic respiratory failure: A pilot study

Noninvasive ventilation (NIV) in usually set on clinical parameters. The aim of the study was to assess the value of esophageal (PES) and gastric pressure (PGAS) measurements for the indication and optimal settings of NIV in infants with hypercapnic respiratory failure in whom the efficacy of NIV was uncertain on clinical noninvasive parameters.
Development and In vitro Evaluation of Flurbiprofen Microcapsules Prepared by Modified Solvent Evaporation Technique

Purpose: To develop modified release microcapsules of flurbiprofen for sustained release and reduced gastrointestinal side effects. Method: A co-polymer containing Eudragit RS 100 and hydroxypropyl methylcellulose (HPMC) in different drug/co-polymer ratios was used for microencapsulation of flurbiprofen by modified emulsion solvent evaporation (MESE) technique. The microcapsules were evaluated by x-ray diffraction (XRD), differential scanning calorimetry (DSC), Fourier transform infra-red spectroscopy (FTIR) and scanning electron microscopy (SEM). Dissolution study was conducted in 0.1 M HCl for 2 h and phosphate buffer (pH 7.4) for 8 h, and the resulting data were analyzed by various pharmacokinetic models. Results : The data obtained from pre-formulation confirmed the purity of flurbiprofen. Particle size, flow rate and angle of repose showed good flow properties. FTIR and DSC confirmed the absence of incompatibilities among the drug and polymers. XRD of flurbiprofen showed characteristic sharp peaks confirming the crystalline nature of the drug which, however, decreased slightly in the formulation. SEM revealed that microencapsules of spherical shape and rough surface were produced at lower drug to copolymer ratio in contrast to the higher ratio which produced irregular microcapsules. Encapsulation efficiency was 65 - 85 % while regression coefficient (R2) values from kinetic analysis showed thatrelease followed Korsmeyer-Peppas model with “n” > 1 indicating release mechanism followed super case II transport. Conclusion : MESE technique using Eudragit RS 100/HPMC polymer blend is a suitable approach to development of modified release flurbiprofen microcapsules. Keywords: Microcapsules, Eudragit RS-100, Hydroxypropyl methylcellulose, Emulsion solvent evaporation, Flurbiprofen, Sustained drug release
PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach

The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.
A Broadband Class-AB Power Amplifier With Instantaneous Supply-Switching Efficiency Enhancement for Cable TV Application

A broadband power amplifier is presented, combining a Class-AB core with a novel supply-modulation technique, instantaneous supply switching. High-<inline-formula> <tex-math notation="LaTeX">$f_{{t}}$ </tex-math></inline-formula> NPN cascodes switch the amplifier signal current to high supply or low supply rails depending on instantaneous signal magnitude. Current-mode switching at gigahertz rates enhances efficiency for bandwidths well beyond existing envelope trackers. The 1.35-W peak power and efficiency of 13.6% at 14-dB peak-to-average-power ratio (PAPR) are observed. The combination of <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {{out}}}$ </tex-math></inline-formula>, large fractional bandwidth, and high-PAPR efficiency exceeds prior art.
Randomized Beamforming With Generalized Selection Transmission for Security Enhancement in MISO Wiretap Channels

Transmit beamforming (TBF) has been proposed as an effective approach to enhance the security performance of multiple-input single-output (MISO) wiretap channels. However, this secrecy enhancement comes at some expenses, such as power consumption and the amount of signal processing. In addition, in block fading channels, TBF fails to deliver secure communication against intelligent eavesdroppers equipped with advanced channel estimation techniques. Considering these issues associated with TBF, we propose and study randomized beamforming with generalized selection transmission (RBF/GST) to enhance physical layer security in MISO wiretap channels. With GST, <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> antennas out of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> antennas are selected at the transmitter to maximize the signal to noise ratio at the legitimate receiver. Moreover, RBF is responsible for delivering secure communications in the presence of advanced eavesdroppers. We characterize the physical layer secrecy of RBF/GST under passive and active eavesdropping scenarios, via our closed-form expressions for the ergodic secrecy rate and the exact and asymptotic secrecy outage probabilities. We demonstrate that RBF/GST can effectively improve communication secrecy with a reasonable amount of signal processing and power consumption.
Effect of Ball-Milling Method on the Formation of ODS Fe-14Cr-2Al-1Si-0.3Ta-1Y2O3 Powders

Mechanical alloying (MA) process has been examined to synthesize ferritic stainless steel powder dispersed with nano-sized Y2O3 particles. A pilot-scale horizontal mill was fabricated and compared with laboratory-scale ball mills and an attrition mill. Horizontal milling resulted in a much better distribution of particle size and dispersoids than other milling methods. Although horizontal milling is considered as a low-energy process requiring long times, processing time could be markedly reduced with increasing the diameter of horizontal mill with a proper control of milling parameters.
Cooperative Data Aggregation and Dynamic Resource Allocation for Massive Machine Type Communication

The accommodation of massive machine-type communication (mMTC) in cellular networks brings up serious technical challenges due to concurrent massive access of MTC devices. These challenges may further be aggravated by the presence of delay tolerant and intolerant services in an MTC network. This paper proposes a cooperative data aggregation (CDA) scheme by employing fixed data aggregator (FDA) and multiple mobile data aggregators (MDAs) to cater MTC devices having variable quality of service (QoS) requirements. In this vein, a distributed MDA selection algorithm is also proposed to designate appropriate user equipment as aggregator. The proposed CDA scheme effectively caters the massive access and provides ubiquitous availability of the aggregating devices in the MTC network. In addition, the limited channel resources impel an FDA to schedule resources besides data aggregation. Therefore, a resource allocation scheme is also proposed to dynamically allocate channels to the MTC devices subject to their QoS requirements. The proposed resource scheduling scheme ensures that transmission requests from delay intolerant MTC devices are contented on priority basis. The proposed CDA and dynamic resource scheduling schemes are analyzed and compared with the existing data aggregation and resource scheduling schemes, respectively. The numerical results corroborate that our proposed CDA scheme in conjunction with dynamic resource allocation improves the outage probability, energy efficiency, and system capacity by 30%, 25%, and 44%, respectively, as compared to the single FDA scheme.
Clinical Outcomes After Reverse Shoulder Arthroplasty With and Without Subscapularis Repair: The Importance of Considering Glenosphere Lateralization

Introduction: Recent biomechanical data suggests that repairing the subscapularis during reverse shoulder arthroplasty (RSA) can increase the force required by the posterior rotator cuff and deltoid to elevate the arm. Methods: We retrospectively studied patients who underwent primary RSA and had baseline and minimum 2-year postoperative American Shoulder and Elbow Surgeons (ASES) shoulder scores, stratified them according to subscapularis management, then subgrouped them according to lateralization of the glenosphere component. Results: Patients with subscapularis repair and a lateralized glenosphere had significantly less improvement in ASES scores than did those without lateralization (P = 0.016) and patients without subscapularis repair (P = 0.006). Individually, subscapularis management (P = 0.163) and glenosphere lateralization (P = 0.847) had no significant effect on the change in ASES score but in combination did have a significant effect on the change in ASES score (P = 0.002). Discussion: The combination of subscapularis repair and glenosphere implant lateralization in RSA translates to significantly less clinical improvement. Conclusions: Patients who underwent both subscapularis repair and glenosphere lateralization had significantly less improvement in ASES scores. Level of Evidence: Level III
Wireless Energy Harvesting Using Signals From Multiple Fading Channels

In this paper, we study the average, the probability density function, and the cumulative distribution function of the harvested power. The signals are transmitted from multiple sources. The channels are assumed to be either Rician fading or Gamma-shadowed Rician fading. The received signals are then harvested by using either a single harvester for simultaneous transmissions or multiple harvesters for transmissions at different frequencies, antennas or time slots. Both linear and nonlinear models for the energy harvester at the receiver are examined. Numerical results are presented to show that, when a large amount of harvested power is required, a single harvester or the linear range of a practical nonlinear harvester are more efficient, to avoid power outage. Further, the power transfer strategy can be optimized for fixed total power. Specifically, for Rayleigh fading, the optimal strategy is to put the total power at the source with the best channel condition and switch off all other sources, while for general Rician fading, the optimum magnitudes and phases of the transmitting waveforms depend on the channel parameters.