Miracle-dz/UniFAR-FTUs huggingface dataset:
Insulin-like growth factor 1 induces oxidative damage, but does not affect survival in a songbird

Lifespan evolves as a compromise between antagonistic selection forces. Insulin-like growth factor 1 (IGF-1) is a pleiotropic hormone that regulates several life-history traits. High levels of IGF-1 have been linked to increased mortality, partly by causing oxidative stress. However, these effects have no experimental evidence in wild animals. We implanted microspheres loaded with exogenous IGF-1 into bearded reedlings, a common short-lived Eurasian songbird. The treatment elevated plasma IGF-1 levels for at least 24 h. Oxidative damage to lipids significantly increased the day after the manipulation in treated birds, but returned to baseline levels four days post-treatment. The treatment had no effect on survival over 16 months; however, birds with higher pre-treatment (baseline) IGF-1 levels had better survival prospects. These results suggest that, although high IGF-1 levels may induce oxidative damage, natural variation in this hormone’s level may reflect the outcome of individual optimization.
Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic Islands

Metagenomic methods are an important tool in the life sciences, as they enable simultaneous characterisation of all microbes in a community without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods have emerged as a promising approach to recover individual genomes from metagenomic data. However, MAG binning has not been well assessed for its ability to recover mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), that have very high clinical/agricultural/environmental importance. Certain antimicrobial resistance (AMR) genes and virulence factor (VF) genes are noted to be disproportionately associated with MGEs, making studying their transmission a public health priority. However, the variable copy number and sequence composition of MGEs relative to the majority of the host genome makes them potentially problematic for MAG binning methods. To systematically investigate this, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated for recovery of MGE-associated AMR/VF genes. Here we show that while 82-94% of chromosomes could be correctly recovered and binned, only 38-44% of GIs were recovered and, even more notably, only 1-29% of plasmid sequences were found. Most strikingly, no plasmid-borne VF or AMR genes were recovered and within GIs, only between 0-45% of AMR or VF genes were identified. We conclude that short-read MAGs are largely ineffective for the analysis of mobile genes, including those of public-health importance like AMR and VF genes. We propose that microbiome researchers should instead primarily utilise unassembled short reads and/or long-read approaches to more accurately analyse metagenomic data
Coordinate genomic association of transcription factors controlled by an imported quorum sensing peptide in Cryptococcus neoformans

Qsp1 is a secreted quorum sensing peptide required for virulence of the fungal meningitis pathogen Cryptococcus neoformans. Qsp1 functions to control cell wall integrity in vegetatively growing cells and also functions in mating. Rather than acting on a cell surface receptor, Qsp1 is imported to act intracellularly via the predicted oligopeptide transporter Opt1. Here, we identify a transcription factor network as a target of Qsp1. Using whole-genome chromatin immunoprecipitation, we find Qsp1 controls the genomic associations of three transcription factors to genes whose outputs are regulated by Qsp1. One of these transcription factors, Cqs2, is also required for the action of Qsp1 during mating, indicating that it might be a shared proximal target of Qsp1. Consistent with this hypothesis, deletion of CQS2 impacts the binding of other network transcription factors specifically to Qsp1-regulated genes. These genetic and genomic studies illuminate mechanisms by which an imported peptide acts to modulate eukaryotic gene expression. AUTHOR SUMMARY For many fungal pathogens, the ability to adapt to changing and diverse environments forms the basis for their ability to infect and survive inside macrophages and other niches in the human body, and these changes are accomplished by transcription factors. Many pathogenic microbes coordinate their gene expression as a function of cell density in a process known as quorum sensing. Here, in the human fungal meningitis pathogen Cryptococcus neoformans, we find that an imported eukaryotic quorum sensing peptide that is important for virulence, Qsp1, controls the binding of three different transcription factors to promoters, thereby modulating the expression of Qsp1-regulated genes. This discovery reveals the mechanism for how an imported peptide affects gene expression.
Progress and issues in second-order analysis of hippocampal replay

Patterns of neural activity that occur spontaneously during sharp-wave ripple (SWR) events in the hippocampus are thought to play an important role in memory formation, consolidation and retrieval. Typical studies examining the content of SWRs seek to determine whether the identity and/or temporal order of cell firing is different from chance. Such ‘first-order’ analyses are focused on a single time point and template (map), and have been used to show, for instance, the existence of preplay. The major methodological challenge in first-order analyses is the construction and interpretation of different chance distributions. By contrast, ‘second-order’ analyses involve a comparison of SWR content between different time points, and/or between different templates. Typical second-order questions include tests of experience-dependence (replay) that compare SWR content before and after experience, and comparisons or replay between different arms of a maze. Such questions entail additional methodological challenges that can lead to biases in results and associated interpretations. We provide an inventory of analysis challenges for second-order questions about SWR content, and suggest ways of preventing, identifying and addressing possible analysis biases. Given evolving interest in understanding SWR content in more complex experimental scenarios and across different time scales, we expect these issues to become increasingly pervasive. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.
Cross Refinement Techniques for Markerless Human Motion Capture

This article presents a global 3D human pose estimation method for markerless motion capture. Given two calibrated images of a person, it first obtains the 2D joint locations in the images using a pre-trained 2D Pose CNN, then constructs the 3D pose based on stereo triangulation. To improve the accuracy and the stability of the system, we propose two efficient optimization techniques for the joints. The first one, called cross-view refinement, optimizes the joints based on epipolar geometry. The second one, called cross-joint refinement, optimizes the joints using bone-length constraints. Our method automatically detects and corrects the unreliable joint, and consequently is robust against heavy occlusion, symmetry ambiguity, motion blur, and highly distorted poses. We evaluate our method on a number of benchmark datasets covering indoors and outdoors, which showed that our method is better than or on par with the state-of-the-art methods. As an application, we create a 3D human pose dataset using the proposed motion capture system, which contains about 480K images of both indoor and outdoor scenes, and demonstrate the usefulness of the dataset for human pose estimation.
RCE-HIL

Entailment recognition is an important paradigm of reasoning that judges if a hypothesis can be inferred from given premises. However, previous efforts mainly concentrate on text-based reasoning as recognizing textual entailment (RTE), where the hypotheses and premises are both textual. In fact, humans’ reasoning process has the characteristic of cross-media reasoning. It is naturally based on the joint inference with different sensory organs, which represent complementary reasoning cues from unique perspectives as language, vision, and audition. How to realize cross-media reasoning has been a significant challenge to achieve the breakthrough for width and depth of entailment recognition. Therefore, this article extends RTE to a novel reasoning paradigm: recognizing cross-media entailment (RCE), and proposes heterogeneous interactive learning (HIL) approach. Specifically, HIL recognizes entailment relationships via cross-media joint inference, from image-text premises to text hypotheses. It is an end-to-end architecture with two parts: (1) Cross-media hybrid embedding is proposed to perform cross embedding of premises and hypotheses for generating their fine-grained representations. It aims to achieve the alignment of cross-media inference cues via image-text and text-text interactive attention. (2) Heterogeneous joint inference is proposed to construct a heterogeneous interaction tensor space and extract semantic features for entailment recognition. It aims to simultaneously capture the interaction between cross-media premises and hypotheses and distinguish their entailment relationships. Experimental results on widely used Stanford natural language inference (SNLI) dataset with image premises from Flickr30K dataset verify the effectiveness of HIL and the intrinsic inter-media complementarity in reasoning.
Load-balancing Sparse Matrix Vector Product Kernels on GPUs

Efficient processing of Irregular Matrices on Single Instruction, Multiple Data (SIMD)-type architectures is a persistent challenge. Resolving it requires innovations in the development of data formats, computational techniques, and implementations that strike a balance between thread divergence, which is inherent for Irregular Matrices, and padding, which alleviates the performance-detrimental thread divergence but introduces artificial overheads. To this end, in this article, we address the challenge of designing high performance sparse matrix-vector product (SpMV) kernels designed for Nvidia Graphics Processing Units (GPUs). We present a compressed sparse row (CSR) format suitable for unbalanced matrices. We also provide a load-balancing kernel for the coordinate (COO) matrix format and extend it to a hybrid algorithm that stores part of the matrix in SIMD-friendly Ellpack format (ELL) format. The ratio between the ELL- and the COO-part is determined using a theoretical analysis of the nonzeros-per-row distribution. For the over 2,800 test matrices available in the Suite Sparse matrix collection, we compare the performance against SpMV kernels provided by NVIDIA’s cuSPARSE library and a heavily-tuned sliced ELL (SELL-P) kernel that prevents unnecessary padding by considering the irregular matrices as a combination of matrix blocks stored in ELL format.
The Impact of Social Media Use and Knowledge Management Capability on Supply Chain Agility

In order to achieve a superior performance, enterprises must align with suppliers to coordinate operations and together to achieve a level of agility beyond that of competitors. At the same time, how social media integrate with comprehensive processes to increase their knowledge management capability (KMC) and contribute to stronger supply chain agility is an important issue. Despite the increased usage of social media, operations research has yet to build on the rich body of supply chain agility literature to explain how social media use and KMC influence on supply chain agility. Therefore, this study applies the questionnaire method to explore the relationships among social media use, KMC, and supply chain agility. The results revealed that the level of social media use can enhance KMC and in turn enhance supply chain agility. Managers thus should increase the level of social media use, then enhance firms' KMC to manage their suppliers. Lastly, this study revealed constructive suggestions that allow enterprises to strengthen the usage of social media and KMC, as well as enhance their supply chain agility.
Staffing Optimization of One-Period Multi-Skill Call Center

Multi-skill call centers are complex systems which need to optimize the trade-off between the cost for the personnel and the service level provided to the calls. The goal of multi-skill call centers is to find an optimal amount of agents for minimizing the operating costs while satisfying the service level constraints. In this paper, an approach that integrates ordinal optimization (OO) with elephant herding optimization (EHO) is developed to determine a superb solution in a reasonable computation time. The proposed approach has two modules. The first module used the EHO assisted by a rough estimation to construct a candidate subset containing excellent solutions. The second module utilized the optimal computing budget allocation (OCBA) procedure to identify a superb solution among the candidate subset. The developed approach has been applied to a one-period multi-skill call center consisting of 5 call types and 12 agent groups. Experimental results reveal that the superiority of the developed approach in the computing efficiency and solution quality.
How Users' Mental Imagery and Mental Simulation Influence Their Urge to Buy Impulsively: The Context of Facebook Video Ads

As more and more advertisers begin using video ads on social networking sites (SNS) as a leading advertising strategy, understanding the effectiveness of video ads on SNS is becoming a crucial research issue. This study aimed to empirically explore how the vividness of Facebook video ads could evoke viewers' urge to buy impulsively based on mental imagery processing and mental simulation perspectives. Mental simulation can be divided into two types, namely process-focused and outcome-focused. This study also investigated whether these two types of simulation have different effects on viewers' urge to buy impulsively. The results revealed that viewers' perception of the vividness of Facebook video ads could entail their mental imagery, their simulation of the product using process, and their simulation of product using outcomes. The results also showed that mental imagery is positively related to both types of simulation. Interesting, only mental imagery and process-focused simulation could lead to urge to buy impulsively; while outcome-focused simulation cannot. Finally, the results unveiled that mental imagery and process-focused simulation played as two vital mediators in the relationship between vividness of Facebook video ads and urge to buy impulsively. Some suggestions were provided for advertisers to enhance the effectiveness of video ads on SNS.
Exploring Deep Learning for View-Based 3D Model Retrieval

In recent years, view-based 3D model retrieval has become one of the research focuses in the field of computer vision and machine learning. In fact, the 3D model retrieval algorithm consists of feature extraction and similarity measurement, and the robust features play a decisive role in the similarity measurement. Although deep learning has achieved comprehensive success in the field of computer vision, deep learning features are used for 3D model retrieval only in a small number of works. To the best of our knowledge, there is no benchmark to evaluate these deep learning features. To tackle this problem, in this work we systematically evaluate the performance of deep learning features in view-based 3D model retrieval on four popular datasets (ETH, NTU60, PSB, and MVRED) by different kinds of similarity measure methods. In detail, the performance of hand-crafted features and deep learning features are compared, and then the robustness of deep learning features is assessed. Finally, the difference between single-view deep learning features and multi-view deep learning features is also evaluated. By quantitatively analyzing the performances on different datasets, it is clear that these deep learning features can consistently outperform all of the hand-crafted features, and they are also more robust than the hand-crafted features when different degrees of noise are added into the image. The exploration of latent relationships among different views in multi-view deep learning network architectures shows that the performance of multi-view deep learning outperforms that of single-view deep learning features with low computational complexity.
Mean-value theorems and ergodicity of certain geodesic random walks

We give some geometric conditions which guarantee that all the invariant functions of the spherical mean operator with certain radius on a Riemannian manifold are necessarily constant. A geometric model of a Markov process, so-called geodesic random walks, whose transition operator is the spherical mean, plays a fundamental role in our argument.
Predictive Biomarkers May allow Precision Therapy of Endometriosis

Introduction Endometriosis is a chronic inflammatory gynecological disorder that causes pelvic pain. Due to the heterogeneity of the disease, response to any treatment in an individual is variable. We aimed to develop in vitro testing that could be adapted for use in precision therapy for endometriosis. We piloted a personalized medicine approach by identifying predictive biomarkers while determining the effect of bazedoxifene (BZA) and medroxyprogesterone acetate (MPA) on the gene expression of a progesterone receptor (PR), an estrogen receptor (ER), and an aromatase (CYP19A1) enzyme in cells cultured from biopsies of endometriosis patients. The differential expression of the most common molecular targets in endometriosis therapy correlated with cellular response. Methods Primary eutopic endometrial stromal cells were cultured from endometrial biopsies obtained in secretory phase from women between 24 and 42 years old with moderate-to-severe endometriosis (stages III and IV). Exclusion criteria included use of hormonal treatments and intrauterine contraception in the 6 months prior to surgery. Cells were treated either with BZA, MPA, or vehicle control. Total RNA was extracted from the treated and untreated cells. Differential expression of genes that are involved in the pathogenesis of endometriosis was determined by quantitative reverse transcriptase-polymerase chain reaction analysis. Results After determining the baseline expression levels of PRA/B, ERα and CYP19A1, response to treatment was monitored using Ki-67 as a marker of cell proliferation. MPA was effective in blocking proliferation in the group expressing high levels of PRA/B. Endometrium expressing high levels of CYP19A1 preferentially responded to BZA, a selective estrogen receptor modulator known to block estrogen action in endometrium. Conclusions PR expression may predict progestin resistance in endometriosis while CYP19A1 expression may indicate the need to block estrogen signaling.
Cytochrome P450 expression patterns in human osteoblasts during osteogenic differentiation with or without TNFα treatment

Recently the expression patterns of several cytochrome P450 (CYP) genes in different human osteoblast models were reported. However, the various expression patterns of CYPs in human osteoblasts during different stages of osteogenic differentiation have not been investigated and the effect of inflammatory cytokines on CYPs expression in osteoblasts is unknown.
Support Vector Machine with K-fold Cross Validation Model for Software Fault Prediction

: The most important goal of the software industry is to build high quality software products. Defective software modules lead to software system failure. The aim of reliable software is to minimize the number of failures that occur when software program runs. Software fault prediction is an important area to develop quality software. By using essential prediction metrics and historical fault data one can predict the software faults. An efficient software fault prediction model makes test effort easier, and at the same time improves quality of software and its consistency. In the literature various techniques have been proposed for fault prediction methods based on various metrics. But unfortunately none of the models satisfy cost reduction and software reliability. This paper proposes a methodology for software fault prediction using support vector machine along with K-fold cross validation procedure. The proposed methodology is suitable for all software metrics for fault prediction with empirical validations used for standard data set in terms of cost reduction and software reliability. A short and pregnant description of the content and intent of the article. Please try to avoid mathematical symbols and special characters as much as possible. Make sure the heading of the article, the names of the authors and their affiliations are formatted as shown above. State the names of the city and country in the affiliations.
Endogenous Entry and Financial Contagion Across Debt Markets

I develop a short-term debt model with two sectors where the secondary asset markets are partially segmented. In this model, every potential buyer must pay a fixed cost to buy an asset in default in the other sector. I show that policies that reduce the entry cost may decrease welfare. Specifically, when the entry cost is lowered, the gap in the liquidation prices in the two markets shrinks to eliminate cross-market arbitrage opportunities. Then, every firm in a more liquid market that faces a price drop will default earlier to avoid increased rollover losses, triggering more asset liquidation. If this contagion effect dominates positive effects of expedited capital flows, welfare will be decreased.
ARE EUROPEAN BUSINESS CYCLES CLOSE ENOUGH TO BE JUST ONE? ARE EUROPEAN BUSINESS CYCLES CLOSE ENOUGH TO BE JUST ONE?(*)

We propose a comprehensive methodology to characterize the business cycle comovements across European economies and some industrialized countries, always trying to “leave the data speak”. Out of this framework, we propose a novel method to show that there is no an “Euro economy” that acts as an attractor to the other economies of the area. We show that the relative comovements across EU economies are prior to the establishment of the Monetary Union. We are able to explain an important proportion of the distances across their business cycles using macrovariables related to the structure of the economy, to the directions of trade, and to the size of the public sector. Finally, we show that the distances across countries that belong to the European Union are smaller than the distances across newcomers.
Evaluation of Indonesian Traditional Herbal Medicine Ontology Quality

Research and explorations about the benefit of Traditional Medicine (TM) Knowledge are still remained recently. Due to its benefits and widespread ancient practices, the information contained within the knowledge of traditional medicine has potential to be taken in studies that support modern healing methods. This research is focused on the evaluation of Traditional Medicine knowledge ontology which is developed for the purpose to distinctively solve encountered problems in traditional way which are still in use by native people such as healing wound, curing diseases, and so on. A quantitative approach is conducted by using reliable measurement and evaluation methods in order to acquire a valid evaluation result of the ontology. The result of this research indicates the reliability of the developed ontology in storing provided knowledge as a knowledge base according to the measurement evaluation and validation.