Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bone—Vasculature Axis: Calcium Supplementation and the Role of Vitamin K

Calcium supplements are broadly prescribed to treat osteoporosis either as monotherapy or together with vitamin D to enhance calcium absorption. It is still unclear whether calcium supplementation significantly contributes to the reduction of bone fragility and fracture risk. Data suggest that supplementing post-menopausal women with high doses of calcium has a detrimental impact on cardiovascular morbidity and mortality. Chronic kidney disease (CKD) patients are prone to vascular calcification in part due to impaired phosphate excretion. Calcium-based phosphate binders further increase risk of vascular calcification progression. In both bone and vascular tissue, vitamin K-dependent processes play an important role in calcium homeostasis and it is tempting to speculate that vitamin K supplementation might protect from the potentially untoward effects of calcium supplementation. This review provides an update on current literature on calcium supplementation among post-menopausal women and CKD patients and discusses underlying molecular mechanisms of vascular calcification. We propose therapeutic strategies with vitamin K2 treatment to prevent or hold progression of vascular calcification as a consequence of excessive calcium intake.
A Novel JT-CoMP Scheme in 5G Fractal Small Cell Networks

To satisfy the requirement of the fifth generation (5G) mobile communications that offers an ultra high data rate of 100Mbps to 1Gbps anytime and anywhere, the coordinated multipoint (CoMP) technique is proposed to mitigate intercell interference to improve the coverage of high data rate services, cell-edge throughput, and system capacity. However, the joint transmission (JT) CoMP technique is difficult to be applied in practice due to the critical time synchronization for multiple coordination links and the bottleneck of backhaul capacity and radio resource at each small cell base stations (SBSs). Moreover, since the coordination SBSs in the conditional scheme are entirely separate from each other, different time of arrivals at the user cause the severe time synchronization problem. The anisotropic propagation environment in the urban scenario makes the implementation condition even worse. To tackle these issues, we propose a novel JT-CoMP scheme with the anisotropic path loss model to minimize the network backhaul traffic subject to the constraints on the radio resource and the differences in time of arrivals. Simulation results demonstrate that the proposed distance-resource-limited CoMP scheme can obtain the maximum achievable rate with the minimum network backhaul traffic, compared with existing schemes.
First-Order Regret Analysis of Thompson Sampling

We address online combinatorial optimization when the player has a prior over the adversary's sequence of losses. In this framework, Russo and Van Roy proposed an information-theoretic analysis of Thompson Sampling based on the {\em information ratio}, resulting in optimal worst-case regret bounds. In this paper we introduce three novel ideas to this line of work. First we propose a new quantity, the scale-sensitive information ratio, which allows us to obtain more refined first-order regret bounds (i.e., bounds of the form $\sqrt{L^*}$ where $L^*$ is the loss of the best combinatorial action). Second we replace the entropy over combinatorial actions by a coordinate entropy, which allows us to obtain the first optimal worst-case bound for Thompson Sampling in the combinatorial setting. Finally, we introduce a novel link between Bayesian agents and frequentist confidence intervals. Combining these ideas we show that the classical multi-armed bandit first-order regret bound $\tilde{O}(\sqrt{d L^*})$ still holds true in the more challenging and more general semi-bandit scenario. This latter result improves the previous state of the art bound $\tilde{O}(\sqrt{(d+m^3)L^*})$ by Lykouris, Sridharan and Tardos.
Model-Based Detector for SSDs in the Presence of Inter-cell Interference

In this paper, we consider the problem of reducing the bit error rate of flash-based solid state drives (SSDs) when cells are subject to inter-cell interference (ICI). By observing that the outputs of adjacent victim cells can be correlated due to common aggressors, we propose a novel channel model to accurately represent the true flash channel. This model, equivalent to a finite-state Markov channel model, allows the use of the sum-product algorithm to calculate more accurate posterior distributions of individual cell inputs given the joint outputs of victim cells. These posteriors can be easily mapped to the log-likelihood ratios that are passed as inputs to the soft LDPC decoder. When the output is available with high precision, our simulation showed that a significant reduction in the bit-error rate can be obtained, reaching $99.99\%$ reduction compared to current methods, when the diagonal coupling is very strong. In the realistic case of low-precision output, our scheme provides less impressive improvements due to information loss in the process of quantization. To improve the performance of the new detector in the quantized case, we propose a new iterative scheme that alternates multiple times between the detector and the decoder. Our simulations showed that the iterative scheme can significantly improve the bit error rate even in the quantized case.
Crossbreeding East African Highland Bananas: Lessons Learnt Relevant to the Botany of the Crop After 21 Years of Genetic Enhancement

East African highland bananas (EAHB) were regarded as sterile. Their screening for female fertility with “Calcutta 4” as male parent revealed that 37 EAHB were fertile. This was the foundation for the establishment of the EAHB crossbreeding programs by the International Institute of Tropical Agriculture (IITA) and the National Agricultural Research Organization (NARO) in Uganda in the mid-1990s. The aim of this study was to assess the progress and efficiency of the EAHB breeding program at IITA, Sendusu in Uganda. Data on pollinations, seeds generated and germinated, plus hybrids selected between 1995 and 2015 were analyzed. Pollination success and seed germination percentages for different cross combinations were calculated. The month of pollination did not result in significantly different (P = 0.501) pollination success. Musa acuminata subsp. malaccensis accession 250 had the highest pollination success (66.8%), followed by the cultivar “Rose” (66.6%) among the diploid males. Twenty-five EAHB out of 41 studied for female fertility produced up to 305 seeds per pollinated bunch, and were therefore deemed fertile. The percentage of seed germination varied among crosses: 26% for 2x × 4x, 23% for 2x × 2x, 11% for 3x × 2x, and 7% for 4x × 2x. Twenty-seven NARITA hybrids (mostly secondary triploids ensuing from the 4x × 2x) were selected for further evaluation in the East African region. One so far –“NARITA 7”– was officially released to farmers in Uganda. Although pollination of EAHB can be conducted throughout the year, the seed set and germination is low. Thus, further research on pollination conditions and optimization of embryo culture protocols should be done to boost seed set and embryo germination, respectively. More research in floral biology and seed germination as well as other breeding strategies are required to increase the efficiency of the EAHB breeding program.
Progressive Explanation Generation for Human-robot Teaming

Generating explanation to explain its behavior is an essential capability for a robotic teammate. Explanations help human partners better understand the situation and maintain trust of their teammates. Prior work on robot generating explanations focuses on providing the reasoning behind its decision making. These approaches, however, fail to heed the cognitive requirement of understanding an explanation. In other words, while they provide the right explanations from the explainer's perspective, the explainee part of the equation is ignored. In this work, we address an important aspect along this direction that contributes to a better understanding of a given explanation, which we refer to as the progressiveness of explanations. A progressive explanation improves understanding by limiting the cognitive effort required at each step of making the explanation. As a result, such explanations are expected to be smoother and hence easier to understand. A general formulation of progressive explanation is presented. Algorithms are provided based on several alternative quantifications of cognitive effort as an explanation is being made, which are evaluated in a standard planning competition domain.
A Periplasmic Antimicrobial Peptide-Binding Protein Is Required for Stress Survival in Vibrio cholerae

Vibrio cholerae must sense and respond appropriately to stresses encountered in the aquatic environment and the human host. One stress encountered in both environments is exposure to antimicrobial peptides (AMPs), produced as a part of the innate immune response by all multicellular organisms. Previous transcriptomic analysis demonstrated that expression of Stress-inducible protein A (SipA) (VCA0732), a hypothetical protein, was highly induced by AMP exposure and was dependent on a specific uncharacterized two-component system. In order to better understand role of this protein in stress relief, we examined whether it shared any of the phenotypes reported for its homologs. SipA is required for survival in the presence of two other stressors, cadmium chloride and hydrogen peroxide, and it localizes to the bacterial periplasm, similar to its homologs. We also found that SipA physically interacts with OmpA. Importantly, we found that SipA binds AMPs in the bacterial periplasm. This suggests a model where SipA may act as a molecular chaperone, binding AMPs that enter the periplasm and delivering them to OmpA for removal from the cell. While El Tor V. cholerae strains lacking SipA do not show a survival defect in the presence of AMPs, we found that Classical sipA mutants are less able to survive in the presence of AMPs. This phenotype is likely masked in the El Tor background due to a functional lipid A modification system that increases AMP resistance in these strains. In summary, we have identified a protein that contributes to a novel mechanism of stress relief in V. cholerae.
Neuroactive Steroids Reverse Tonic Inhibitory Deficits in Fragile X Syndrome Mouse Model

Fragile X syndrome (FXS) is the most common form of inherited intellectual disability. A reduction in neuronal inhibition mediated by γ-aminobutyric acid type A receptors (GABAARs) has been implicated in the pathophysiology of FXS. Neuroactive steroids (NASs) are known allosteric modulators of GABAAR channel function, but recent studies from our laboratory have revealed that NASs also exert persistent metabotropic effects on the efficacy of tonic inhibition by increasing the protein kinase C (PKC)-mediated phosphorylation of the α4 and β3 subunits which increase the membrane expression and boosts tonic inhibition. We have assessed the GABAergic signaling in the hippocampus of fragile X mental retardation protein (FMRP) knock-out (Fmr1 KO) mouse. The GABAergic tonic current in dentate gyrus granule cells (DGGCs) from 3- to 5-week-old (p21–35) Fmr1 KO mice was significantly reduced compared to WT mice. Additionally, spontaneous inhibitory post synaptic inhibitory current (sIPSC) amplitudes were increased in DGGCs from Fmr1 KO mice. While sIPSCs decay in both genotypes was prolonged by the prototypic benzodiazepine diazepam, those in Frm1-KO mice were selectively potentiated by RO15-4513. Consistent with this altered pharmacology, modifications in the expression levels and phosphorylation of receptor GABAAR subtypes that mediate tonic inhibition were seen in Fmr1 KO mice. Significantly, exposure to NASs induced a sustained elevation in tonic current in Fmr1 KO mice which was prevented with PKC inhibition. Likewise, exposure reduced elevated membrane excitability seen in the mutant mice. Collectively, our results suggest that NAS act to reverse the deficits of tonic inhibition seen in FXS, and thereby reduce aberrant neuronal hyperexcitability seen in this disorder.
Macroscale Superlubricity Achieved With Various Liquid Molecules: A Review

Superlubricity is generally classified as solid superlubricity and liquid superlubricity according to the lubricants involved at the interfaces, and is a popular research topic in tribology, which is closely linked to energy dissipation. Significant advancements in both experimental studies and theoretical analysis have been made regarding superlubricity in the past two decades. Compared with solid superlubricity, liquid superlubricity has many advantages; e.g., it is more easily achieved at the macroscale and less sensitive to the surface smoothness and atmospheric conditions. In the present study, the advancements in liquid superlubricity at the macroscale are reviewed, and the corresponding mechanisms for various types of liquid lubricants are discussed. This investigation is important for engineering traditional mechanical lubricating systems. Finally, the issues regarding the liquid superlubricity mechanism and the future development of liquid superlubricity are addressed.
Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge

The main essence of this paper is to investigate the performance of RetinaNet based object detectors on pedestrian detection. Pedestrian detection is an important research topic as it provides a baseline for general object detection and has a great number of practical applications like autonomous car, robotics and Security camera. Though extensive research has made huge progress in pedestrian detection, there are still many issues and open for more research and improvement. Recent deep learning based methods have shown state-of-the-art performance in computer vision tasks such as image classification, object detection, and segmentation. Wider pedestrian detection challenge aims at finding improve solutions for pedestrian detection problem. In this paper, We propose a pedestrian detection system based on RetinaNet. Our solution has scored 0.4061 mAP. The code is available at this https URL.
YAFS: A Simulator for IoT Scenarios in Fog Computing

Fog computing is a paradigm that extends the cloud to intermediate network devices with computational and storage capacities. This allows the execution of applications closer to edge devices and end-users by allocating services in those intermediate devices. The placement of those services has an influence on the performance of the fog architecture. We propose a fog computing simulator for analyzing the design and deployment of applications through customized and dynamical strategies. We model the relationships among deployed applications, network connections, and infrastructure characteristics through complex network theory, enabling the integration of topological measures in dynamic and customizable strategies, such as the placement of application modules, workload location, and path routing and scheduling of services. We present a comparative analysis of the efficiency and the convergence of results of our simulator with the most referenced one, iFogSim. To highlight the YAFS functionalities, we model three scenarios that, to the best of our knowledge, cannot be implemented with current fog simulators: dynamic allocation of new application modules, dynamic failures of network nodes, and user mobility along with the topology.
The MapZ-Mediated Methylation of Chemoreceptors Contributes to Pathogenicity of Pseudomonas aeruginosa

The pathogenic bacterium Pseudomonas aeruginosa is notorious for causing acute and chronic infections in humans. The ability to infect host by P. aeruginosa is dependent on a complex cellular signaling network, which includes a large number of chemosensory signaling pathways that rely on the methyl-accepting chemotaxis proteins (MCPs). We previously found that the second messenger c-di-GMP-binding adaptor MapZ modulates the methylation of an amino acid-detecting MCP by directly interacting with a chemotaxis methyltransferase CheR1. The current study further expands our understanding of the role of MapZ in regulating chemosensory pathways by demonstrating that MapZ suppresses the methylation of multiple MCPs in P. aeruginosa PAO1. The MCPs under the control of MapZ include five MCPs (Aer, CtpH, CptM, PctA, and PctB) for detecting oxygen/energy, inorganic phosphate, malate and amino acids, and three MCPs (PA1251, PA1608, and PA2867) for detecting unknown chemoattractant or chemorepellent. Chemotaxis assays showed that overexpression of MapZ hampered the taxis of P. aeruginosa toward chemoattractants and scratch-wounded human cells. Mouse infection experiments demonstrated that a dysfunction in MapZ regulation had a profound negative impact on the dissemination of P. aeruginosa and resulted in attenuated bacterial virulence. Together, the results imply that by controlling the methylation of various MCPs via the adaptor protein MapZ, c-di-GMP exerts a profound influence on chemotactic responses and bacterial pathogenesis.
Evaluating MAP-Elites on constrained optimization problems

Constrained optimization problems are often characterized by multiple constraints that, in the practice, must be satisfied with different tolerance levels. Here, we evaluate the applicability of MAP-Elites to "illuminate" constrained search spaces by mapping them into feature spaces where each feature corresponds to a different constraint. We demonstrate the feasibility of this approach on a large set of benchmark problems, in various dimensionalities, and with different algorithmic configurations. As expected, numerical results show that a basic version of MAP-Elites cannot compete with state-of-the-art algorithms that use gradient information or constraint handling techniques. Nevertheless, it can find constraint violations vs. objectives trade-offs and thus provide new problem information. As such, it could be used in the future as an effective building-block for designing new constrained optimization algorithms.
Evaluating the Psychometric Properties of the 7-Item Persian Game Addiction Scale for Iranian Adolescents

The 7-item Gaming Addiction Scale (GAS) is a brief instrument based on DSM criteria to assess gaming addiction. Although the psychometric properties of the GAS have been tested using classical test theory, its psychometric properties have never been tested using modern test theory (e.g., Rasch analysis). The present study used a large adolescent sample in Iran to test the psychometric properties of the Persian GAS through both classical test and modern test theories. Adolescents (n = 4442; mean age = 15.3 years; 50.3% males) were recruited from Qazvin, Iran. In addition to the GAS, all of them completed the following instruments: the nine-item Internet Gaming Disorder Scale–Short Form (IGDS-SF9), Depression Anxiety Stress Scale (DASS), Pittsburgh Sleep Quality Index (PSQI), and a generic quality of life instrument. Two weeks later, all participants completed the GAS again. Confirmatory factor analysis (CFA) and Rasch analysis were used to test the unidimensionality of the GAS. Pearson correlation coefficients were used to test the test-retest reliability, and a regression model was used to test the criterion-related validity of the GAS. Both CFA and Rasch analysis supported the unidimensionality of the GAS. Pearson correlations coefficients showed satisfactory test-retest reliability of the GAS (r = 0.78 to 0.86), and the regression model demonstrated the criterion-related validity of the GAS (β = 0.31 with IGDS-SF9; 0.41 with PSQI). Based on the results, the Persian GAS is a reliable and valid instrument for healthcare providers to assess the level of gaming addiction among Persian-speaking adolescents.
End-to-end feature fusion siamese network for adaptive visual tracking

According to observations, different visual objects have different salient features in different scenarios. Even for the same object, its salient shape and appearance features may change greatly from time to time in a long-term tracking task. Motivated by them, we proposed an end-to-end feature fusion framework based on Siamese network, named FF-Siam, which can effectively fuse different features for adaptive visual tracking. The framework consists of four layers. A feature extraction layer is designed to extract the different features of the target region and search region. The extracted features are then put into a weight generation layer to obtain the channel weights, which indicate the importance of different feature channels. Both features and the channel weights are utilized in a template generation layer to generate a discriminative template. Finally, the corresponding response maps created by the convolution of the search region features and the template are applied with a fusion layer to obtain the final response map for locating the target. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the popular Temple-Color, OTB50 and UAV123 benchmarks.
“Energetic” Cancer Stem Cells (e-CSCs): A New Hyper-Metabolic and Proliferative Tumor Cell Phenotype, Driven by Mitochondrial Energy

Here, we provide the necessary evidence that mitochondrial metabolism drives the anchorage-independent proliferation of CSCs. Two human breast cancer cell lines, MCF7 [ER(+)] and MDA-MB-468 (triple-negative), were used as model systems. To directly address the issue of metabolic heterogeneity in cancer, we purified a new distinct sub-population of CSCs, based solely on their energetic profile. We propose the term “energetic” cancer stem cells (e-CSCs), to better describe this novel cellular phenotype. In a single step, we first isolated an auto-fluorescent cell sub-population, based on their high flavin-content, using flow-cytometry. Then, these cells were further subjected to a detailed phenotypic characterization. More specifically, e-CSCs were more glycolytic, with higher mitochondrial mass and showed significantly elevated oxidative metabolism. e-CSCs also demonstrated an increased capacity to undergo cell cycle progression, as well as enhanced anchorage-independent growth and ALDH-positivity. Most importantly, these e-CSCs could be effectively targeted by treatments with either (i) OXPHOS inhibitors (DPI) or (ii) a CDK4/6 inhibitor (Ribociclib). Finally, we were able to distinguish two distinct phenotypic sub-types of e-CSCs, depending on whether they were grown as 2D-monolayers or as 3D-spheroids. Remarkably, under 3D anchorage-independent growth conditions, e-CSCs were strictly dependent on oxidative mitochondrial metabolism. Unbiased proteomics analysis demonstrated the up-regulation of gene products specifically related to the anti-oxidant response, mitochondrial energy production, and mitochondrial biogenesis. Therefore, mitochondrial inhibitors should be further developed as promising anti-cancer agents, to directly target and eliminate the “fittest” e-CSCs. Our results have important implications for using e-CSCs, especially those derived from 3D-spheroids, (i) in tumor tissue bio-banking and (ii) as a new cellular platform for drug development.
Local minimax rates for closeness testing of discrete distributions

We consider the closeness testing (or two-sample testing) problem in the Poisson vector model - which is known to be asymptotically equivalent to the model of multinomial distributions. The goal is to distinguish whether two data samples are drawn from the same unspecified distribution, or whether their respective distributions are separated in $L_1$-norm. In this paper, we focus on adapting the rate to the shape of the underlying distributions, i.e. we consider a local minimax setting. We provide, to the best of our knowledge, the first local minimax rate for the separation distance up to logarithmic factors, together with a test that achieves it. In view of the rate, closeness testing turns out to be substantially harder than the related one-sample testing problem over a wide range of cases.