Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning more powerful test statistics for click-based retrieval evaluation

Interleaving experiments are an attractive methodology for evaluating retrieval functions through implicit feedback. Designed as a blind and unbiased test for eliciting a preference between two retrieval functions, an interleaved ranking of the results of two retrieval functions is presented to the users. It is then observed whether the users click more on results from one retrieval function or the other. While it was shown that such interleaving experiments reliably identify the better of the two retrieval functions, the naive approach of counting all clicks equally leads to a suboptimal test. We present new methods for learning how to score different types of clicks so that the resulting test statistic optimizes the statistical power of the experiment. This can lead to substantial savings in the amount of data required for reaching a target confidence level. Our methods are evaluated on an operational search engine over a collection of scientific articles.
Essential Role of Electrode Materials in Electrochemiluminescence Applications

The phenomenon of electrochemiluminescence (ECL) is luminescence triggered by electrochemical reactions at electrodes. Heterogeneous electron transfers are deeply dependent on electrode materials, therefore, besides the usual parameters, the right choice of the electrode is a crucial point to address properly in order to maximize the emission efficiency. Many different electrodes have been studied, from metallic -platinum or gold- through transparent indium-doped tin oxide to carbon based electrodes, and others, such as paper based and boron doped diamond. This review summarizes results of ECL at different electrode surfaces, disclosing the relative advantages and disadvantages, with a particular attention to the reference Ru(bpy)32+/TPrA coreactant system. In other words, we offer an insight to recognize and select the optimal electrode material for the ECL systems of interest with particular emphasis on the biosensing applications.
Palette-based image decomposition, harmonization, and color transfer

We present a palette-based framework for color composition for visual applications. Color composition is a critical aspect of visual applications in art, design, and visualization. The color wheel is often used to explain pleasing color combinations in geometric terms, and, in digital design, to provide a user interface to visualize and manipulate colors. We abstract relationships between palette colors as a compact set of axes describing harmonic templates over perceptually uniform color wheels. Our framework provides a basis for a variety of color-aware image operations, such as color harmonization and color transfer. To enable our approach, we introduce an extremely scalable and efficient yet simple palette-based image decomposition algorithm. Our approach is based on the geometry of images in RGBXY-space. This new geometric approach is orders of magnitude more efficient than previous work and requires no numerical optimization.
Spatial capacity of LTE-based V2V communication

This paper presents a new performance metric, called spatial capacity, in long-term evolution (LTE)-based vehicle-to-vehicle (V2V) communication. The spatial capacity is defined as the the total amount of bits that can be successfully delivered in a unit km and 1 s. Since this metric incorporates vehicle user equipment (VUE) density, message size, and reliability simultaneously, this is useful to show the comprehensive understanding of V2V network. Numerical results show the two-dimensional contour graph of traditional reliability and spatial capacity with respect to VUE density and message size. We can see the obvious difference between them that the growth of VUE density and message size always degrades the reliability; while it increases the spatial capacity to some extent.
Drug–Drug Interaction Potential of the HBV and HDV Entry Inhibitor Myrcludex B Assessed in vitro

Background Myrcludex B is a first-in-class virus entry inhibitor for patients with chronic hepatitis B or B/D infections. In patients it will be coadministered with drugs needed for the disease or comorbidities. We aimed to define the risk of drug–drug interactions by characterizing the influence of myrcludex B on relevant drug transporting and metabolizing enzymes in vitro. Methods Inhibition of P-glycoprotein (P-gp; ABCB1), breast cancer resistance protein (BCRP/ABCG2), and the organic anion transporting polypeptides 1B1 and 1B3 (OATP1B1/SLCO1B1 and OATP1B3/SLCO1B3) was measured in cells over-expressing the respective transporter using fluorogenic substrates. Inhibition of cytochrome P450 enzymes (CYPs) was assessed with commercially available kits. mRNA induction of drug transporting and metabolizing enzymes was measured in LS180 cells after 4 days of treatment by quantitative real-time PCR. Pregnane X receptor (PXR) activation was assessed using a reporter-gene assay. Results Whereas activities of P-gp and BCRP were not influenced by myrcludex B, OATP1B1 and OATP1B3 were specifically inhibited with a 50% inhibitory concentration (IC50) of 0.5 and 8.7 μM, respectively. Myrcludex B weakly inhibited all CYPs tested at concentrations ≥10 μM except CYP2D6, which was not inhibited at concentrations up to 2 μM. Myrcludex B had no influence on mRNA expression of CYP1A1, CYP3A4, UGT1A3, ABCB1, ABCC2 and ABCG2, and on PXR activity. Conclusions Our in vitro study suggests that myrcludex B is not at major risk of acting as a perpetrator drug. A potential inhibition of the uptake transporters OATP1B1 and OATP1B3 and a previous clinical finding of potential CYP3A inhibition, requires further evaluation and should be carefully addressed in future trials.
Occupancy Detection via Environmental Sensing

Sensing by proxy (SbP) is proposed in this paper as a sensing paradigm for occupancy detection, where the inference is based on “proxy” measurements such as temperature and CO2 concentrations. The effects of occupants on indoor environments are captured by constitutive models comprising a coupled partial differential equation–ordinary differential equation system that exploits the spatial and physical features. Sensor fusion of multiple environmental parameters is enabled in the proposed framework. We report on experiments conducted under simulated conditions and real-life circumstances, when the variation of occupancy follows a schedule as the ground truth. The inference of the number of occupants in the room based on CO2 concentration at the air return and air supply vents by our approach achieves an overall mean squared error of 0.6044 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Results from the projected ventilation analysis show that SbP can potentially save 55% of total ventilation compared with the traditional fixed schedule ventilation strategy, while at the same time maintain a reasonably comfort profile for the occupants.Note to Practitioners—Building indoor occupancy is essential to facilitate heating, ventilation, and air conditioning (HVAC) control, lighting adjustment, and geofencing to achieve occupancy comfort and energy efficiency. The significance of this paper is the proposal of a paradigm of sensing that results in a parsimonious and accurate occupancy inference model, which holds considerable potential for energy saving and improvement of HVAC operations. Parameters of the model are data-driven, which exhibit long-term stability and robustness across all the occupants’ experiments. The proposed framework can also be applied to other tasks, such as indoor pollutants source identification, while requiring minimal infrastructure expenses. The data set and algorithm code are available to assist the comparison study.
Down-regulated miR-448 relieves spinal cord ischemia/reperfusion injury by up-regulating SIRT1

MicroRNAs play a crucial role in the progression of spinal cord ischemia/reperfusion injury (SCII). The role of miR-448 and SIRT1 in SCII was investigated in this study, to provide further insights into prevention and improvement of this disorder. In this study, expressions of miR-448 and SIRT1 protein were determined by qRT-PCR and western blot, respectively. Flow cytometry was used to analyze cell apoptosis. The endogenous expression of genes was modulated by recombinant plasmids and cell transfection. Dual-luciferase reporter assay was performed to determine the interaction between miR-448 and SIRT1. The Basso, Beattie, and Bresnahan score was used to measure the hind-limb function of rat. The spinal cord ischemia reperfusion injury model of adult rats was developed by abdominal aorta clamping, and the nerve function evaluation was completed by motor deficit index score. In SCII tissues and cells treated with hypoxia, miR-448 was up-regulated while SIRT1 was down-regulated. Hypoxia treatment reduced the expression of SIRT1 through up-regulating miR-448 in nerve cells. Up-regulation of miR-448 induced by hypoxia promoted apoptosis of nerve cells through down-regulating SIRT1. Down-regulated miR-448 improved neurological function and hind-limb motor function of rats with SCII by up-regulating SIRT1. Down-regulated miR-448 inhibited apoptosis of nerve cells and improved neurological function by up-regulating SIRT1, which contributes to relieving SCII.
A Comparative Study of Data Mining approaches for Bag of Visual Words Based Image Classification

Image classification is one of the most significant and challenging tasks in computer vision. The goal of this task is to build a system that is capable to reveal an image label within a collection of different image categories. This paper presents and discusses the application of various data mining techniques for image classification based on Bag of Visual Words (BoVW) feature extraction algorithm. The BoVW model is constructed using grey level features: The Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) descriptors along with color features: Color correlograms and Improved Color Coherence Vector (ICCV). Five data mining techniques; Neural Networks (NN), Decision Trees (DT), Bayesian Network (BN), Discriminant Analysis (DA) and K Nearest Neighbor (KNN), are explored and evaluated on two large different datasets: Corel-1000 and COIL-100. The experimental results illustrate that BN and DA outperform the other data mining methods considered in this comparative study. For Corel-1000 dataset, BN and DA achieved an average accuracy and specificity of about 99.9% and an average sensitivity of about 99.5 and 99.4%, respectively. While for the COIL-100 dataset, BN and DA accomplished an average accuracy and sensitivity of about 100% and an average specificity of about 98.5 and 98.9, respectively.
A Fixed Point Theorem for Iterative Random Contraction Operators over Banach Spaces

Consider a contraction operator T over a Banach space X with a fixed point x. Assume that one can approximate the operator T by a random operator T̂ using N ∈ N independent and identically distributed samples of a random variable. Consider the sequence (X̂ k )k∈N, which is generated by X̂ k+1 = T̂ (X̂ k ) and is a random sequence. In this paper, we prove that under certain conditions on the random operator, (i) the distribution of X̂ k converges to a unit mass over x ⋆ as k and N goes to infinity, and (ii) the probability that X̂ k is far from x ⋆ as k goes to infinity can be made arbitrarily small by an appropriate choice of N . We also find a lower bound on the probability that X̂ k is far from x ⋆ as k → ∞. We apply the result to study probabilistic convergence of certain randomized optimization and value iteration algorithms.
The MAKE Biomarker Discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) Study: Design and Methodology

Objective Depression is associated with a major disease burden, and many individuals suffer from depressive symptoms due to an insufficient response to ostensibly adequate antidepressant treatment. Therefore, it is important to identify reliable treatment response predictors for use in developing personalized treatment strategies. Methods The MAKE Biomarker discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) study was performed to identify predictors of antidepressant response using a 2-year naturalistic prospective design. Participants in the MAKE BETTER study were consecutively recruited from patients who visited the Psychiatry Department of Chonnam National University Hospital, Gwangju, South Korea for treatment of a depressive disorder. Data on demographic and clinical characteristics, genetic markers measured by whole-exome sequencing, and blood markers were obtained. The types and doses of antidepressants were determined based on the clinical judgment of the psychiatrist, and the treatment outcomes (e.g., depressive and other psychiatric symptoms and issues related to safety) were assessed. Results We will be able to use the data collected in this study to develop a treatment-response prediction index composed of biomarkers. Conclusion The MAKE BETTER study will provide an empirical basis for a personalized medicine approach to depression by enabling the prediction of antidepressant treatment response according the characteristics of each patient. It will thereby support evidencebased decision-making that decreases the use of a trial-and-error approach to the treatment of depressive disorders.
Missing Slice Recovery for Tensors Using a Low-Rank Model in Embedded Space

Let us consider a case where all of the elements in some continuous slices are missing in tensor data. In this case, the nuclear-norm and total variation regularization methods usually fail to recover the missing elements. The key problem is capturing some delay/shift-invariant structure. In this study, we consider a low-rank model in an embedded space of a tensor. For this purpose, we extend a delay embedding for a time series to a "multi-way delay-embedding transform" for a tensor, which takes a given incomplete tensor as the input and outputs a higher-order incomplete Hankel tensor. The higher-order tensor is then recovered by Tucker-based low-rank tensor factorization. Finally, an estimated tensor can be obtained by using the inverse multiway delay embedding transform of the recovered higher-order tensor. Our experiments showed that the proposed method successfully recovered missing slices for some color images and functional magnetic resonance images.
UC Merced UC

Objectives: Health valuation studies enhance economic evaluations of treatments by estimating the value of health-related quality of life (HRQOL). The Patient-Reported Outcomes Measurement Information System (PROMIS) includes a 29-item short-form HRQOL measure, the PROMIS-29. Methods: To value PROMIS-29 responses on a qualityadjusted life-year scale, we conducted a national survey (N 1⁄4 7557) using quota sampling based on the US 2010 Census. Based on 541 paired comparisons with over 350 responses each, pair-specific probabilities were incorporated into a weighted least-squared estimator. Results: All losses in HRQOL influenced choice; however, ee front matter Copyright & 2014, International S
Amygdala–prefrontal connectivity during appraisal of symptom-related stimuli in obsessive–compulsive disorder

Abstract Background Cognitive models of obsessive–compulsive disorder (OCD) posit dysfunctional appraisal of disorder-relevant stimuli in patients, suggesting disturbances in the processes relying on amygdala–prefrontal connectivity. Recent neuroanatomical models add to the traditional view of dysfunction in corticostriatal circuits by proposing alterations in an affective circuit including amygdala–prefrontal connections. However, abnormalities in amygdala–prefrontal coupling during symptom provocation, and particularly during conditions that require stimulus appraisal, remain to be demonstrated directly. Methods Amygdala–prefrontal connectivity was examined in unmedicated OCD patients during appraisal (v. distraction) of symptom-provoking stimuli compared with an emotional control condition. Subsequent analyses tested whether hypothesized connectivity alterations could be also identified during passive viewing and the resting state in two independent samples. Results During symptom provocation, reductions in positive coupling between amygdala and orbitofrontal cortex were observed in OCD patients relative to healthy control participants during appraisal and passive viewing of OCD-relevant stimuli, whereas abnormally high amygdala–ventromedial prefrontal cortex coupling was found when appraisal was distracted by a secondary task. In contrast, there were no group differences in amygdala connectivity at rest. Conclusions Our finding of abnormal amygdala–prefrontal connectivity during appraisal of symptom-related (relative to generally aversive) stimuli is consistent with the involvement of affective circuits in the functional neuroanatomy of OCD. Aberrant connectivity can be assumed to impact stimulus appraisal and emotion regulation, but might also relate to fear extinction deficits, which have recently been described in OCD. Taken together, we propose to integrate abnormalities in amygdala–prefrontal coupling in affective models of OCD.
An Analytical Resolution Evaluation Approach for Bistatic GEOSAR Based on Local Feature of Ambiguity Function

Due to the very high orbit, the apparent features of geosynchronous synthetic aperture radar (GEOSAR) are the curved trajectory and long integration time, which can lead to severe coupling between the azimuth and the range directions and, therefore, complicates the resolution evaluation. The traditional analytical approach based on the 2-D division may produce large resolution error, and the numerical approach may suffer from huge computation burden. Therefore, an analytical resolution evaluation approach for GEOSAR based on the local feature of the ambiguity function is studied in this paper. The proposed approach is validated with simulation data to be of high efficiency and accuracy. In addition, the proposed approach is also demonstrated to be capable of evaluating the resolution for other complex platforms, and of evaluating the 3-D resolution of a SAR system.
Ancestral polymorphism and adaptive evolution in the trichothecene mycotoxin gene cluster of phytopathogenic Fusarium

Filamentous fungi within the Fusarium graminearum species complex (Fg complex) are the primary etiological agents of Fusarium head blight (scab) of wheat and barley. Scab is an economically devastating plant disease that greatly limits grain yield and quality. In addition, scabby grain is often contaminated with trichothecene mycotoxins that act as virulence factors on some hosts, and pose a serious threat to animal health and food safety. Strain-specific differences in trichothecene metabolite profiles (chemotypes) are not well correlated with the Fg complex phylogeny based on genealogical concordance at six single-copy nuclear genes. To examine the basis for this discord between species and toxin evolution, a 19-kb region of the trichothecene gene cluster was sequenced in 39 strains chosen to represent the global genetic diversity of species in the Fg complex and four related species of Fusarium. Phylogenetic analyses demonstrated that polymorphism within these virulence-associated genes is transspecific and appears to have been maintained by balancing selection acting on chemotype differences that originated in the ancestor of this important group of plant pathogens. Chemotype-specific differences in selective constraint and evidence of adaptive evolution within trichothecene genes are also reported.
Apical Localisation of Crumbs in the Boundary Cells of the Drosophila Hindgut Is Independent of Its Canonical Interaction Partner Stardust

The transmembrane protein Crumbs/Crb is a key regulator of apico-basal epithelial cell polarity, both in Drosophila and in vertebrates. In most cases studied so far, the apical localisation of Drosophila Crumbs depends on the interaction of its C-terminal amino acids with the scaffolding protein Stardust. Consequently, embryos lacking either Crumbs or Stardust develop a very similar phenotype, characterised by the loss of epithelial tissue integrity and cell polarity in many epithelia. An exception is the hindgut, which is not affected by the loss of either gene. The hindgut is a single layered epithelial tube composed of two cell populations, the boundary cells and the principal cells. Here we show that Crumbs localisation in the principal cells depends on Stardust, similarly to other embryonic epithelia. In contrast, localisation of Crumbs in the boundary cells does not require Stardust and is independent of its PDZ domain- and FERM-domain binding motifs. In line with this, the considerable upregulation of Crumbs in boundary cells is not followed by a corresponding upregulation of its canonical binding partners. Our data are the first to suggest a mechanism controlling apical Crumbs localisation, which is independent of its conserved FERM- and PDZ-domain binding motifs.
Tolerance to spikes: A comparison of sample and Bubble entropy

Bubble Entropy is a new metric aiming at the quantification of the entropy of a series, the most important property of which is the total elimination of the scale parameter (e.g., r when computing Sample Entropy) and the low dependency on the length of the runs compared (e.g., the m parameter). In this paper, we compare the tolerance of Bubble Entropy to spikes and compare it to that of Sample Entropy. We use RR series publicly available on Physionet and compute Sample and Bubble Entropy before and after artificially added spikes. We add N spikes of value a · std, where a is a parameter and std the standard deviation of the signal. We compute then the relative error (absolute error / known value). According to our experiments, Bubble Entropy exhibits a remarkable tolerance to spikes. In all our experiments, Bubble Entropy presents a low relative error. Compared to Sample Entropy, the error reported by Bubble Entropy is always smaller and sometimes remarkably (p C 0.001, Wilcoxon rank-sum test). The mean relative error for Sample Entropy ranges from 0.084 to 0.435, while the mean relative error for Bubble Entropy from 0.069 to 0.122. Sample over Bubble Entropy relative errors ratios reach up to 3.57.
3D Drone-cell deployment optimization for drone assisted radio access networks

Drone-cell can enhance both the capacity and the coverage of Radio Access Networks (RAN) through relaying data between base stations and potential users. In this paper, we investigate the 3D spatial deployment problem of drone-cell in Drone Assisted Radio Access Networks (DA-RAN), and propose a solution based on the Particle Swarm Optimization (PSO) algorithm. According to the drone-to-ground channel model, we formulate the drone-cell deployment problem with the objective to maximize coverage ratio of necessary users, while maintaining the link qualities between drone-cells and RAN. We design the per-Drone Iterated PSO (DI-PSO) algorithm to find the optimized deployments corresponding to different number of drone-cells. Simulation results show that the drone-cell deployments generated by the DI-PSO algorithm can improve RAN connectivity, and achieve higher coverage ratio when compared with the pure PSO based approach.