Miracle-dz/UniFAR-FTUs huggingface dataset:
Gastric H+, K+-ATPase Inhibition, and Antioxidant Properties of Selected Commonly Consumed Vegetable Sources

Oxidative stress and upregulation of gastric H+, K+-ATPase enzyme activity have been known to cause ulcer pathogenicity for which safer drugs are yet to be identified. Aqueous extracts of seven commonly consumed vegetable sources were screened for inhibition of H+, K+-ATPase and antioxidant activities. Results indicated that Z. officinale (Ginger) followed by M. arvensis (Pudina) are potent gastroprotective sources with inhibition of H+, K+-ATPase of IC50 of 18.3 ± 0.7 and 25.2 ± 0.9 μg gallic acid equivalents/ml respectively, which is almost equivalent or better than the known inhibition of H+, K+-ATPase—Omeprazole (IC50 ˜27 μg/ml). Further, all these vegetable extracts showed multi-potent antioxidant activity, such as free radical scavenging, reducing power ability, and inhibition of lipid peroxidation, which are required to inhibit complex steps of ulcerations. On the basis of the absolute amounts and potency of inhibition of H+, K+-ATPase as well as antioxidant activity of individual phenolic acids, the relative percentage contribution of phenolic acids from different vegetable extracts to both inhibition of H+, K+-ATPase and antioxidant activity was calculated and data revealed that gentisic and protocatechuic acid contributes significantly to both inhibition of H+, K+-ATPase and antioxidant activity.
COL4A family: potential prognostic biomarkers and therapeutic targets for gastric cancer

Background The type IV collagen alpha chain (COL4A) family is a major component of the basement membrane (BM) that has recently been found to be involved in tumor angiogenesis and progression. However, the expression levels and the exact roles of distinct COL4A family members in gastric cancer (GC) have not been completely understood. Methods Here, the expression levels of COL4As in GC and normal gastric tissues were calculated by using TCGA datasets and the predicted prognostic values by the GEPIA tool. Furthermore, the cBioPortal and Metascape tools were integrated to analyze the genetic alterations, correlations and potential functions of COL4As, and their frequently altered neighboring genes in GC. Results Notably, the expression levels of COL4A1/2/4 in GC were higher to those in normal gastric tissues, while the expression levels of COL4A3/5/6 were lower in GC than normal. Survival analysis revealed that lower expression levels of COL4A1/5 led to higher overall survival (OS) rate. Multivariate analysis using the Cox proportional-hazards model indicated that age, gender, pathological grade, metastasis and COL4A5 expression, are independent prognostic factors for OS. However, TNM stage, lymph node metastasis, Lauren’s classification, COL4A1-4 and COL4A6 were associated with poor OS but not independent prognostic factors. Function-enriched analysis of COL4As and their frequently altered neighboring genes was involved in tumor proliferation and metastasis in GC. Conclusions These results implied that COL4A1/2 were potential therapeutic targets for GC. COL4A3/4/6 might have an impact on gastric carcinogenesis and subsequent progression, whereas COL4A5 was an independent prognostic marker for GC.
Workgroup Diversity and Employees Work Performance: Insights into Lebanon

Purpose The aim of the current study is to examine the relationship between workgroup diversity and employee work performance in Lebanon. Design-Methodology-Approach – The study used a sample of 187 employees from 18 service organizations in Lebanon that employ diverse workgroups, and a convenience sampling technique was employed as well. Nevertheless, the respondents were requested to designate their level of agreement or disagreement with a number of statements using 5 points Likert scale, which was divided into two sections, comprised of 57 questions. Findings The results have shown that there was a significant and positive relationship between workgroup diversity and employees’ performance in terms of task performance, organizational citizenship behaviour, and creative performance. Specific details and practical implications are intoduced. Research limitations are recommendation are also presented.
Correlation study between body anthropometric measurements with body fat in adult

Introduction: The purpose of this study is to assess the validity of Bioelectrical Impedance Analysis (BIA) to characterize the body fat percentage and its correlation with anthropometric indices. Materials and Methods: The following indices were carried out: weight, height, body mass index (BMI), waist-hip ratio (WHR) taken random samples of 100 subject. The body fat percentages were assessed by bioelectrical impedance analysis. Results: Positive Correlation between body anthropometric indices with body fat. Conclusion: That study concluded that body fat was strong positive correlated with BMI and fair positive correlated with WHR means body fat increased that leads to waist hip ratio and body mass index.
A Volunteer-Supported Fog Computing Environment for Delay-Sensitive IoT Applications

Fog computing (FC) has emerged as a complementary solution to the centralized cloud infrastructure. An FC node is available in closer proximity to users and extends cloud services to the edge of the network in a highly distributed manner. However, with an increase in streaming and delay-sensitive Internet-of-Things (IoT) applications, FC also needs to address the issue of higher latency while forwarding compute-intensive jobs to remote cloud data centers. Hence, there is a need to investigate the use of computational resources at the edge of the network. Volunteer computing (VC) offers a reduction in the cost of maintaining high-performance computing by making use of user-owned underutilized or idle resources, e.g., laptops and desktop computers closer to fog devices. We propose volunteer-supported FC (VSFC), as a computing paradigm, that explores the interplay of these two distributed computing domains to help minimize inherent communication delays of cloud computing, energy consumption, and network usage. To this effect, we have extended the iFogSim toolkit to support VSFC. Extensive simulations show that VSFC outperforms traditional FC-cloud computing by reducing delay by 47.5%, energy by 93%, and network usage by 92% under normal to heavy load conditions.
Spontaneously retrievable knowledge of German general practitioners depending on time since graduation, measured with the progress test medicine

Background: General practitioners and general internists occupy a key position in German and Austrian healthcare systems. They provide primary care and act as gatekeepers between medical disciplines and sectors of care. Their explicit medical knowledge levels, however, can be quite disparate. Objective: This study analyses whether general practitioners’ performances on a standardised knowledge test changes with four relevant socio-demographic variables. Design: The survey was based on the Progress Test Medicine (PTM), a standardised 200 item knowledge test on graduate level. After formal blueprinting and item analysis, 60 items of PTM were selected (“PTM-GP”) for our study. Participants: PTM-GP was presented ad hoc to general practitioners and internists from Germany and Austria at a number of professional meetings in 2011. 161 volunteers completed the survey. Main measures: For evaluation, correlation analysis (Spearman), Kruskal Wallis-tests for non-parametric data and an analysis of covariance (ANCOVA) were calculated. Results: Overall, four indicators turned out to be slightly significant for the performance on the PTM-GP, namely: time passed since graduation, the grade received in the licensing examination, the type of institution for postgraduate training, and the medical specialisation. Conclusions: Recent graduates performed better in the PTM-GP; a doctor’s licensing examination grade as well as training at a university hospital correlated positively with PTM-GP performance. A general doctor’s knowledge level is moderately influenced by exam grades, time since graduation, the institutional affiliation of postgraduate training and medical specialisation. Individual changes in knowledge over time have to be deliberately considered in lifelong learning. In consequence, the on-going teaching of medical knowledge should be integrated mandatory and verifiable into general doctors’ everyday practices, e.g. through repetitive knowledge tests with individual feedback and recommendations for further continuing medical education (CME).
The Relationship Between Human Values and Acceptability of Corruption in Russia and Greece

Background. In both Russia and Greece, corruption is a serious problem. In Greece, the level of corruption is one of the highest in the EU, and in Russia it is one of the highest in the world. Objective . Th ree questions were addressed: (1) Are basic human values related to the acceptability of corruption for individuals in both countries? (2) Are these relationships the same in Russia and Greece? (3) Are levels of acceptance of corruption the same in Russia and Greece? Design . Following S.H. Schwartz’s model, four higher-order values were assessed: Conservation versus Openness to Change, and Self-Transcendence versus Self-Enhancement. Th e studies were conducted in Russia ( N = 256) and Greece ( N = 469). To analyze the associations of individual values with the acceptability of corruption, we constructed a multigroup regression model using structural equation modelling so ft ware. Results . Identical relationships were found in the two countries. Conservation values and Self-Transcendence were negatively related to the acceptability of corruption, whereas Self-Enhancement was positively related to the acceptability of corruption. Russians scored higher on acceptance of corruption. Implications are discussed. Conclusion . Th e acceptability of corruption seems to be interrelated with basic human values across di ff erent cultural conditions. Our study shows that the relationships between higher-order values on the one hand, measured in the framework of Schwartz’s values model, and the acceptability of corruption on the other, are identical in Russia and Greece, suggesting that the acceptability of corruption is related to personal values.
Infection virale associée à la maladie des branchies de l'huître portugaise Crassostrea angulata LmK

Des particules et des lesions virales liees a une forte hypertrophie cellulaire ont ete observees au niveau de necroses branchiales d'huitres portugaises atteintes de la maladie des branchies . La structure et la formation des virions ainsi que les alterations cellulaires sont etudiees en microscopie electronique.
Data Mules on Cycler Orbits for High-Latency, Planetary-Scale Data Transfers

In this work, we explore the concept of a secondary “data mule” consisting of a small satellite used to ferry data from a Mars mission to Earth for downlink. The concept exploits the fact that two nearby optical communicators can achieve extremely high data rates, and that a class of trajectories called “cyclers” can carry a satellite between Mars and Earth regularly. By exploiting cycler orbits, the courier needs minimal onboard propulsion. However, cycler orbits have long periodicity, with years for the satellite, Mars, and Earth to repeat their relative geometry. Therefore, we propose the use of a network of such cycler “couriers” on phase-shifted trajectories to achieve a regular cadence of downlink trips. We design a series of search and optimization steps that can output a set of trajectories that at first approximation have low onboard propulsion requirements and can be used for any logistics network to and from Mars, then derive the link budget for proximity optical communications to show that this network can ferry large amounts of data.
UvA-DARE (Digital Academic Repository) Physics-based Shading Reconstruction for Intrinsic Image Decomposition

We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading). We propose albedo and shading gradient descriptors which are derived from physics-based models. Using the descriptors, albedo transitions are masked out and an initial sparse shading map is calculated directly from the corresponding 𝑅𝐺𝐵 image gradients in a learning-free unsupervised manner. Then, an optimization method is proposed to reconstruct the full dense shading map. Finally, we integrate the generated shading map into a novel deep learning framework to refine it and also to predict corresponding albedo image to achieve intrinsic image decomposition. By doing so, we are the first to directly address the texture and intensity ambiguity problems of the shading estimations. Large scale experiments show that our approach steered by physics-based invariant descriptors achieve superior results on MIT Intrinsics, NIR-RGB Intrinsics, Multi-Illuminant Intrinsic Images, Spectral Intrinsic Images, As Realistic As Possible, and competitive results on Intrinsic Images in the Wild datasets while achieving state-of-the-art shading estimations.
orgFAQ: A New Dataset and Analysis on Organizational FAQs and User Questions

Frequently Asked Questions (FAQ) webpages are created by organizations for their users. FAQs are used in several scenarios, e.g., to answer user questions. On the other hand, the content of FAQs is affected by user questions by definition. In order to promote research in this field, several FAQ datasets exist. However, we claim that being collected from community websites, they do not correctly represent challenges associated with FAQs in an organizational context. Thus, we release orgFAQ, a new dataset composed of $6988$ user questions and $1579$ corresponding FAQs that were extracted from organizations' FAQ webpages in the Jobs domain. In this paper, we provide an analysis of the properties of such FAQs, and demonstrate the usefulness of our new dataset by utilizing it in a relevant task from the Jobs domain. We also show the value of the orgFAQ dataset in a task of a different domain - the COVID-19 pandemic.
Synchronization of a Kuramoto-like model for power grids with frustration

We discuss the complete synchronization for a Kuramoto-like model for power grids with frustration. For identical oscillators without frustration, it will converge to complete phase and frequency synchronization exponentially fast if the initial phases are distributed in a half circle. For nonidentical oscillators with frustration, we present a framework leading to complete frequency synchronization where the initial phase configurations are located inside the half of a circle. Our estimates are based on the monotonicity arguments of extremal phase and frequency.
A Mixed Copula-Based Vector Autoregressive Model for Econometric Analysis

In many practical applications, the dynamics of different quantities is reasonably well described by linear equations. In economics, such linear dynamical models are known as vector autoregressive (VAR) models. These linear models are, however, only approximate. The deviations of the actual value of each quantity from the predictions of the linear model are usually well described by normal or Student-t distributions. To complete the description of the joint distribution of all these deviations, we need to supplement these marginal distributions with the information about the corresponding copula. To describe this dependence, in the past, researchers followed the usual idea of trying copulas from several standard families: Gaussian, Student, Clayton, Frank, Gumbel, and Joe families. To get a better description, we propose to also use convex combinations of copulas from different families; such convex combinations are known as mixed copulas. On the example of the dynamics of US macroeconomic data, including GDP, unemployment, consumer price index, and the real effective exchange rate, we show that mixed copulas indeed lead to a better description of the actual data. Specifically, it turns out that the best description is obtained if we use a convex combination of Student and Frank copulas.
Voice-based interface for accessible soundscape composition: composing soundscapes by vocally querying online sounds repositories

This paper presents an Internet of Audio Things ecosystem devised to support soundscape composition via vocal interactions. The ecosystem involves a commercial voice-based interface and the cloud-based repository of audio content Freesound.org. The user-system interactions are exclusively based on vocal input/outputs, and differ from the conventional methods for retrieval and sound editing which involve a browser and programs running on a desktop PC. The developed ecosystem targets sound designers interested in soundscape composition and in particular the visually-impaired ones, with the aim of making the soundscape composition practice more accessible. We report the results of a user study conducted with twelve participants. Overall, results show that the interface was found usable and was deemed easy to use and to learn. Participants reported to have enjoyed using the system and generally felt that it was effective in supporting their creativity during the process of composing a soundscape.
Extending Market Basket Analysis Using Multidimensional Customer Based Profile In Graph Mining

Market basket analysis is a mathematical modelling techniques based upon the theory that if you buy a certain group of items, you are likely to buy another group of items. It is used to analyze the customer purchasing behaviour and helps in increasing the sales and maintain inventory by focusing on point of sale transactional data. The purpose of market basket analysis is to get a customer to spend more money based on two different principles: the first one is Up-Selling, which consists in buying a large quantity of the same product
Emotional speech production: From data to computational models and applications

This paper provides the overview of a part of my doctoral thesis work on emotional speech production. The goal is two-folds: (i) to discover the rules in phonological-level and surface-level emotional speech production processes from the direct measurements of articulatory movements, and (ii) to develop computational models for the production processes and their applications. The achievements towards this goal comprise resource creation (data collection and tool development), scientiﬁc investigation and computational modeling. Also, this paper discusses on-going research on rich inversion modeling.
In a short but interesting passage in his ground-breaking book The Practice of Everyday Life

Both have the task of mediating between society and a body of knowledge, the first insofar as he introduces his specialty into a wider and more complex arena of socio-political decision, the second insofar as he reestablishes the relevance of general questions to a particular technique (mathematics, logic, psychiatry, history, etc.). In the Expert, competence is transmuted into social authority; in the Philosopher, ordinary questions become a skeptical principle in a technical field (pp. 6-7).
Understanding User Experience of COVID-19 Maps through Remote Elicitation Interviews

During the coronavirus pandemic, visualizations gained a new level of popularity and meaning for a wider audience. People were bombarded with a wide set of public health visualizations ranging from simple graphs to complex interactive dashboards. In a pandemic setting, where large amounts of the world population are socially distancing themselves, it becomes an urgent need to refine existing user experience evaluation methods for remote settings to understand how people make sense out of COVID-19 related visualizations. When evaluating visualizations aimed towards the general public with vastly different socio-demographic backgrounds and varying levels of technical savviness and data literacy, it is important to understand user feedback beyond aspects such as speed, task accuracy, or usability problems. As a part of this wider evaluation perspective, micro-phenomenology has been used to evaluate static and narrative visualizations to reveal the lived experience in a detailed way. Building upon these studies, we conducted a user study to understand how to employ Elicitation (aka Micro-phenomenological) interviews in remote settings. In a case study, we investigated what experiences the participants had with map-based interactive visualizations. Our findings reveal positive and negative aspects of conducting Elicitation interviews remotely. Our results can inform the process of planning and executing remote Elicitation interviews to evaluate interactive visualizations. In addition, we share recommendations regarding visualization techniques and interaction design about public health data.
Dexterous Robotic Grasping with Object-Centric Visual Affordances

Dexterous robotic hands are appealing for their agility and human-like morphology, yet their high degree of freedom makes learning to manipulate challenging. We introduce an approach for learning dexterous grasping. Our key idea is to embed an object-centric visual affordance model within a deep reinforcement learning loop to learn grasping policies that favor the same object regions favored by people. Unlike traditional approaches that learn from human demonstration trajectories (e.g., hand joint sequences captured with a glove), the proposed prior is object-centric and image-based, allowing the agent to anticipate useful affordance regions for objects unseen during policy learning. We demonstrate our idea with a 30-DoF five-fingered robotic hand simulator on 40 objects from two datasets, where it successfully and efficiently learns policies for stable grasps. Our affordance-guided policies are significantly more effective, generalize better to novel objects, and train 3 X faster than the baselines. Our work offers a step towards manipulation agents that learn by watching how people use objects, without requiring state and action information about the human body. Project website: this http URL
Computing the matrix geometric mean: Riemannian versus Euclidean conditioning, implementation techniques, and a Riemannian BFGS method

This paper addresses the problem of computing the Riemannian center of mass of a collection of symmetric positive definite matrices. We show in detail that the condition number of the Riemannian Hessian of the underlying optimization problem is never very ill conditioned in practice, which explains why the Riemannian steepest descent approach has been observed to perform well. We also show theoretically and empirically that this property is not shared by the Euclidean Hessian. We then present a limited‐memory Riemannian BFGS method to handle this computational task. We also provide methods to produce efficient numerical representations of geometric objects that are required for Riemannian optimization methods on the manifold of symmetric positive definite matrices. Through empirical results and a computational complexity analysis, we demonstrate the robust behavior of the limited‐memory Riemannian BFGS method and the efficiency of our implementation when compared to state‐of‐the‐art algorithms.