Miracle-dz/UniFAR-FTUs huggingface dataset:
Biologically active peptides derived from the Antarctic hydrobionts

Nataliia Raksha1*, Tetiana Halenova1, Tetiana Vovk1, Olexiy Savchuk1, Viktor Tomchuk2, Tetiana Maievska3, Ludmila Ostapchenko1 1Department of Biochemistry, Taras Shevchenko National University, Kyiv, Ukraine 2Department of Biochemistry and Physiology of Animals, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine 3Ukrainian's Fishing Industry Cluster of Innovations, Kyiv, Ukraine
Multi-dimensional trading problem in multi-participant settings

The dimensionality of optimization problem arising within multi-market trading task grows exponentially with a growing number of markets. To prevent the dimensionality problem, multi-market trading is represented as a multi-participant decision making problem with ﬁnite common capital. Each local DM task is a single-market trading enriched by an ability to share a part of local capital with other local DM tasks (participants). The paper provides formulation of the problem and basic algorithmic steps. The approach is illustrated on the real market data.
Emerging Trend in Therapeutic Potential of Casein Derived Bioactive Peptides

Milk is an excellent source of protein with multiple therapeutic properties on health. It consists of two types of protein; one is a soluble protein known as whey protein that accounts for 20%, whereas another fraction consists of an insoluble protein known as casein, which constitutes 80% of total milk protein (Haug et al., 2007; Severin and Wenshui, 2005). Casein consists of four fractions, namely αs1, αs2, β, and κ-casein (Fox et al., 1998). Casein is a high biological value protein due to high indispensable amino acid and protein digested corrected amino acid score (Boye et al., 2012; Schaafsma, 2000). Bioactive peptides enchanted many activities, such as immunoregulatory, opioid, antioxidant, antihypertensive, antidiabetic, etc. Significant studies have done on the casein derived bioactive peptides. These peptides usually consist of 2-20 amino acids and become activated after release from inherent protein following enzymatic hydrolysis either in vivo or in vitro. The significant difference between drugs and bioactive peptides is that synthetic drugs are administered intentionally within the body, on the other hand, bioactive peptides may be present in humans as they may arise from the digestion of food. Examples of casein-derived bioactive peptides constitute casein phosphopeptides (CPPs) (k-casein, f(147– 153)) that helps in transportation and International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 02 (2021) Journal homepage: http://www.ijcmas. com
Research on Tai Chi Health Promotion from the Perspective of Meta Analysis

Tai Chi health promotion research is a hot topic in traditional national sports in recent years, covering many aspects of sports medicine-based research. A search of Meta analysis literature related to the health promotion of tai chi by the Cochrane website revealed that relevant journal literature had appeared since June 2011, and by December 2019, there were only 7 literatures. The research was carried out based on the literature data method and logic analysis method, and the following conclusions were drawn: (1) In recent years, the new entry of Tai Chi health promotion research Point, getting smaller and deeper, from general epidemiological research to molecular biology and cognitive neuroscience, indicating that researchers are trying to find Tai Chi for health More fundamental factors. (2) The duration of most current research trials is not enough, the design of the trials is weak, and the input intervention and output volume of the trials are not uniform, resulting in a greatly reduced scientific test. (3) Future research suggests that extracting and analyzing the effective elements of movement in Tai Chi can not only guide the crowd more clearly, but also help solve the mystery of the effective mechanism of Tai Chi, and furthermore, the extracted the elements are effectively used in different sports training.
Correlation of Depression, Anxiety and Stress with Quality of Life in COVID-19 Pandemic

Due to Covid-19 pandemic the psychological health of individuals is disturbed globally. There is a dire need of looking into details about the effects of mental health issues on quality of life (QOL). Objectives: To determine correlation between depression, anxiety, stress, and quality of life among adults in Covid-19 and evaluate the impact of demographics on quality of life. Methods: It was a cross-sectional study carried out at a tertiary care hospital. Patients presenting in the psychiatry outdoor of age 18 to 60 years, of both genders and scoring ≥21 on Depression, Anxiety, and Stress Scale (DASS) were enrolled in the study and depression, anxiety and stress severity was assessed and Quality of Life Scale (QOLS) was applied on all to assess their quality of life. All findings were then subjected to statistical analysis. Results: The mean age of the patients was 21.78±3.204, mean depression score on DASS was 8.58±4.510, mean anxiety score on DASS was 11.68±4.160 and the mean stress score on DASS was 14.84±3.192. There were 63.5% males and 36.5% females. Depression, anxiety and stress had a negative correlation with quality of life. Depression and stress were significantly correlated negatively with quality of life (p=0.000). No demographical factor was significantly associated with poor quality of life. Conclusion: Depression, anxiety and stress were negatively correlated with poorer QOL and depression and stress had significant association with poor QOL. Key words: Anxiety, Depression, Covid-19, Quality of life How to cite: Rashid A. , Mudassar U. , Tariq I. , Zaheer A. , Iftikhar M. , Mazhar N. Correlation of Depression, Anxiety and Stress with Quality of Life in COVID-19 Pandemic. Esculapio 2021;17(02):195-199.
High Quality Related Search Query Suggestions using Deep Reinforcement Learning

"High Quality Related Search Query Suggestions"task aims at recommending search queries which are real, accurate, diverse, relevant and engaging. Obtaining large amounts of query-quality human annotations is expensive. Prior work on supervised query suggestion models suffered from selection and exposure bias, and relied on sparse and noisy immediate user-feedback (e.g., clicks), leading to low quality suggestions. Reinforcement Learning techniques employed to reformulate a query using terms from search results, have limited scalability to large-scale industry applications. To recommend high quality related search queries, we train a Deep Reinforcement Learning model to predict the query a user would enter next. The reward signal is composed of long-term session-based user feedback, syntactic relatedness and estimated naturalness of generated query. Over the baseline supervised model, our proposed approach achieves a significant relative improvement in terms of recommendation diversity (3%), down-stream user-engagement (4.2%) and per-sentence word repetitions (82%).
OR/17/056 Groundwater quality studies in urban areas-Earthwise

Urbanisation processes are the cause of extensive but essentially diffuse pollution of groundwater by nitrogen and sulphur compounds, salinity as well as pathogenic bacteria, protozoa and viruses (Morris et al., 2003). Household attitudes to hazards posed by drinking water can enhance quality problems with poor water treatment, types of drinking water vessels, hand washing practices, perceptions of safe water quality using only visual parameters (normally clarity of the water), and knowledge on waste disposal practices (Kioko et al., 2012).
Addressing Methodological Issues in Simulating a Human Resources Problem across Multiple Levels of Interest

Problems of strategic human resource management, such as proposed reductions in prospective retirement benefits under an American pension plan, present complex challenges. Typically, a firm pursues strategic objectives such as Cost control through changes in focal policies and programs affecting its workforce. At such times a firm should be wary of long-term consequences among individual employees, since the firm, its programs, and its employees, comprise a three-level feedback system. Unintended consequences at the micro-organizational level may lead in turn to additional unpleasant surprises at the program or firm levels. This paper discusses the development of a simulation model combining the approaches of agent based and system dynamics disciplines in addressing a clients multi-level concerns. It articulates differences between the agent based and system dynamics modeling disciplines in the service of this task, and discusses issues of implementation and praxis that have governed our design at points of tangency between these approaches in such a hybrid model.
Simultaneous Integrated Boost Plan Comparison between Static Intensity-Modulated Radiotherapy and Volumetric-Modulated Arc Therapy for Prostate Fossa and Lymph Node Irradiation

*Correspondence: Zheng Shi, Department of Radiation Oncology, Mays Cancer Center, UT Health San Antonio MD Anderson Cancer Center, 7979 Wurzbach Rd, MC 7889, San Antonio, TX 782294427, USA, Tel: +1-2104501727; +12102372402; Fax: +1-2104505085; E-mail: shiz@uthscsa.edu Received Date: 05 Oct 2019 Accepted Date: 31 Oct 2019 Published Date: 07 Nov 2019 Citation: Kyenzeh TL, Lenards N, Gutierrez AN, Zheng Shi. Simultaneous Integrated Boost Plan Comparison between Static Intensity-Modulated Radiotherapy and Volumetric-Modulated Arc Therapy for Prostate Fossa and Lymph Node Irradiation. Clin Oncol. 2019; 4: 1667. Copyright © 2019 Zheng Shi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Research Article Published: 07 Nov, 2019
Silica Nanoparticles Induced Oxidative Stress in Different Brain Regions of Male Albino Rats

Silica nanoparticles (SiNPs) are encapsulated in an ecological platform has been planned to supply many benefits, and blood vessels used as laser-tissue-soldering within the brain, and during the course of the degradation, SiNPs given to the discharged into an encircling of the brain tissue. The goal of this modern research was to examine likely to the silicon dioxide nanoparticles are categorized in the microglial cells and uptake the cellular mechanism(s), and additionally these impacts on autophagy and inflammatory cytokines, and also it's been found in depth applications in medical specialty and biotechnological fields, for example, bio imaging, medical diagnostics, gene therapy, drug delivery, photodynamic therapy biomolecule detection and bio-imaging. It has also been added to the raising of the industrial mechanism to the exposure of silicon dioxide nanoparticles, throughout the manufacturing, transportation, storage, and consumers that are obviously increased used by human exposure and environmental burden. The purpose of this work was to examine the neurotoxicity of two different dosages (40 and 80 mg/kg body weight) of silica nanoparticles (SiNPs) in male albino rats for 14 days by intraperitoneal exposure. The neurobehavioral and neurochemical changes were analyzed. Spontaneous motor activity, catalepsy, gait and muscle incoordination were assessed. There after the brain was removed for the examination of lipid peroxide level and antioxidant enzymes (SOD and CAT). Our results indicated the SNPs could passed from the blood-brain-barrier into the brain, and essentially it increases the concentration of lipid peroxide levels and reduce the activity of SOD and CAT and also its reduce the behavioral activity were seen. The maximum changes were observed in rats, who received 80 mg/kg body weight doses. It can be concluded that neuronal toxicity of silica nanoparticles could be related to the amount of the particles and the oxidative stress could be .
Primitive Knowledge Graph Construction Base on Eventelement Driven

Knowledge graph is a popular research field in recent years. Starting from analyzing the dynamic needs and feasible ways of knowledge graph, A new idea of using matter elements as entities in the knowledge graph is proposed, and a dynamic evolution mechanism of knowledge graph driven by matter elements is established. Using the primitive theory to construct the framework structure of the primitive knowledge map.
Implicit Aspect Extraction from Online Clothing Reviews with Fine-tuning BERT Algorithm

In the era of e-commerce, tremendous product reviews can provide quick and valuable insight into the market trends. Aspect-based sentiment analysis (ABSA) on product reviews is becoming increasingly important for both companies and consumers. Implicit aspect extraction, a subtask of ABSA, aims to identify the aspect of a review sentence without explicit aspect terms. For Chinese online reviews, this paper proposes a fine-tuning BERT based method to extract implicit aspects. Fine-tuning BERT performs well on a variety of natural language processing tasks. Using labeled data, the hidden layer parameters of pre-training BERT are fine-tuned in a supervised manner to generate fine-tuning BERT. Through experiments on Chinese online reviews of clothing, it is believed that fine-tuning BERT can improve the effect of implicit aspects identification.
Features of sorption and sensory interaction of boron-nitride nanotubes with alkali metal atoms

Theoretical studies of sorption and sensory interaction of modified boronitride nanotube with functional amine group (-NH2) are presented in this article. The study is conducted on alkali metal atoms. The results presented in this paper can form the basis of a highly effective sensor for determining the presence of certain elements contained in ultra-small quantities.
A Heuristic Algorithm to Compute a Subgraph for TSP Based on Frequency Quadrilaterals

A heuristic algorithm was presented to compute certain subgraphs for symmetric traveling salesman problem using frequency quadrilaterals. If we choose a set of frequency quadrilaterals containing an edge to compute the frequency for it, the frequency of an edge contained within the optimal Hamiltonian cycle approaches an extreme value as the city number is big enough. A heuristic algorithm was designed to compute a frequency graph with randomly selected frequency quadrilaterals. A subgraph containing the optimal solution was computed according to a given frequency threshold. The experimental results illustrated that in certain instances for Euclidean TSP with scale above 1000, the number of edges in complete graph is reduced by more than 30 times. Moreover, the bigger the number of frequency quadrilaterals is used, the higher the frequency threshold is and the smaller the number of edges in the subgraphs is kept.
Combining ability analysis for yield traits in barley (Hordeum vulgare L.)

The 21 hybrids were generated by applying LxT mating design using seven lines and three testers and were subjected to study combining ability for nine yield traits of barley. ANOVA was significant for GCA and SCA effect, indicating parents and crosses differed significantly with regard to general and specific combining ability, respectively. The GCA effect recorded for most of the traits were low and non-significant. However, two lines EC-667509 and EC-667365; one tester, NDB3 recorded the high GCA effect in three traits eachviz. , productive tillers per plant, the number of grains per spike and 1000-grain weight; grain yield per plant, harvest index and 1000-grain weight; days to 75% maturity, the number of grains per spike and harvest index, respectively. Non-significant SCA effect for all the traits was observed in 12 out of 21 crosses. In respect to the SCA effect, cross EC-667498 x Azad performed better than the remaining cross combinations, as it recorded the high SCA effect in desirable direction in five out of nine traits.
A REVIEW ON BASIC PRINCIPALS OF BALANCED DIET BY AYURVEDA: AHARA VIDHI VISHESH AYATAN

Ayurveda defines health as balance of Dosha, Agni, Dhatu and Mala as well as the physical, mental, emotional and spiritual wellbeing. This definition of health is in coherence with WHO’s definition of Health is a Physical, mental, Social and spiritual wellbeing and not merely an absence of disease. Healthy life depends on proper nutrition. According to Ayurveda ahara is the most important thing for healthy life and type of ahara we eat affect our body as well as mind. Nowadays, people are adopting sedentary lifestyle. Also, the craze for junk food is increasing in rural areas also. The change in food habit, lack of knowledge about proper nutrition are causative factors for many diseases. Ayurveda has mentioned about Astavidh ahar vidhi vishesh ayatane in which Acharyas has explained in detail about properties of food, effect of combination of different food articles, quantity of food to be consumed, properties of food substances according to their cultivation area, cooking procedure etc. These eight factors are most important ways to maintain healthy diet. It gives a fair idea about what? When? Why? How? and Which? Food material should be consumed for promotion of positive health. A proper knowledge of these Ahar vidhi vishesh ayatane will help in reducing disorders caused due to improper diet and help for healthy diet.
Combining a Discrete-event Simulation Model of a Logistics Network with Deep Reinforcement Learning

This paper presents a method to combine a discrete-event simulation model of a logistics network with a deep reinforcement learning agent. The agent applies different actions to the logistics network in order to learn a strategy to improve the cost structure and the performance of the logistics network. The return for the agent is derived from the costs and the performance of the logistics network which is measured in terms of logistics costs and β service level. Possible actions comprise the relocation of inventory and the adjustment of transport relations. The authors developed a method to represent the state of the simulation model with an image generated from the simulation input data and used a well-known deep Q-learning algorithm with a convolutional neural network to train the agent.
The effect of additional protein, phosphatidylcholine, phosphatidylserine, and inulin on S100β levels of acute ischemic stroke patients at Dr. Kariadi Central Hospital, Semarang

Background: The brain releases biochemical substrates, such as S100β protein, into circulation in response to ischemic conditions as a sign of damage in nerve cells and disruption of the blood-brain barrier’s integrity. Thrombolytic therapy has led to the development of many neuroprotective therapies such as protein, phosphatidylcholine, phosphatidylserine, and inulin, which can be added to food products. Protein, phospholipids, and inulin, have a neuroprotective impact on nerve cells in the brain and blood-brain barrier.Objective: To prove the effect of protein, phosphatidylcholine, phosphatidylserine, and inulin on S100β levels and clinical outcomes in patients with acute ischemic stroke.Materials and Methods: This study was done in a single-blind RCT. Eighteen ischemic stroke patients were randomly divided into nine subjects for the intervention group and nine subjects for the control group. The Control group received 250 ml conventional formula milk (11.8 g protein) 3 times/day. The intervention group received 250 mL commercial milk 3 rimes/day which contained 15 g protein with 128 mg phosphatidylcholine, 32 mg phosphatidylserine, and 3 g inulin. All of the groups were given hospital-standard therapy for ischemic stroke. S100β levels were measured at pre and post-intervention.Results: Pre and post S100β levels in intervention and the control group did not show any statistically difference (p = 0.777 and p = 0.096), but there was a trend of decreasing levels of S100β in the intervention group (-24.6 + 252.0 pg/mL) versus control group (135.8 + 216.2 pg/mL). Conclusions: The addition of protein, phosphatidylcholine, phosphatidylserine, and inulin did not have a significant effect on S100β levels.
Management of Primary Spondylodiscitis: Clinical Outcome of a Series of Twenty-Seven Patients

Background Data: Spondylodiscitis is a major clinical condition with significant health and economic burden. There is a controversy regarding the use of conservative therapy with systemic antibiotics alone versus combined with surgery to manage primary spondylodiscitis. Study Design: Retrospective clinical case study. Purpose: To assess the clinical outcome of treatment of the patients with primary spondylodiscitis. Patients and Methods: This study was conducted on 27 patients with primary spondylodiscitis. There were 17 males and 10 females. The mean age was 49.96 ± 9.83 years. All the patients presented with local pain over the involved vertebral level. The clinical outcomes were assessed using the Visual Analogue Scale (VAS), ASIA score, and Kirkaldy-Willis functional outcome criteria. Results: Eight patients (29.6%) were managed by medical treatment alone. Nineteen patients (70.4%) were managed surgically, including seven patients who were operated on by laminectomy and 12 patients by posterior decompression and fusion, followed by subsequent treatment with antibiotics. VAS score was reduced significantly in the patients treated surgically compared with the patients treated medically at 1 and 3 months (P value < 0.001 and = 0.010, respectively) but not at 6 and 12 months of the follow-up period (P value = 0.235 and 0.886, respectively). There was no significant difference between the two groups regarding CRP and ESR reduction levels, the functional outcome, and the complications at different time intervals. Conclusion: Posterior decompression with or without fusion was more effective than medical treatment in reducing the pain in patients with primary spondylodiscitis at 1 and 3 months of the follow-up period without influencing the final clinical outcome. (2021ESJ230)
Multi Agent Based Cloud Security Model for Association Rule Mining

In the present day computer era cloud computing is emerging into a huge environment which requires security services incorporated into cloud servers or data centers or even in cloud data storages available in different locations equipped with a very high speed networks. Due to increase in number cloud users and more are showing interest to use the environment, providing security is becoming a major concern because unless the cloud environment is secure no user will show interest and will be keen to invest or migrate from distributed environment to cloud computing environment. In this paper we designed a security framework which is based on Multi Agent Based System (MABS) architecture that tends to provide security at various levels and providing the authenticated and accurate information to the cloud data seeker, our architecture mainly focuses on dual aspects such as Agent Layer and the Cloud Data Storage Layer and we have implemented the proposed framework in WEKA tool and used JDK 1.5 and generated results pertaining all the agents identified in this paper.
A Demonstration of Relic: A System for REtrospective Lineage InferenCe of Data Workflows

The ad-hoc, heterogeneous process of modern data science typically involves loading, cleaning, and mutating dataset(s) into multiple versions recorded as artifacts by various tools within a single data science workflow. Lineage information, including the source datasets, data transformation programs or scripts, or manual annotations, is rarely captured, making it difficult to infer the relationships between artifacts in a given workflow retrospectively. We demonstrate Relic, a tool to retrospectively infer the lineage of data artifacts generated as a result of typical data science workflows, with an interactive demonstration that allows users to input artifact files and visualize the inferred lineage in a web-based setting. PVLDB Reference Format: Mohammed Suhail Rehman, Silu Huang, and Aaron J. Elmore. A Demonstration of RELIC: A System for REtrospective Lineage InferenCe of Data Workflows. PVLDB, 14(12): 2795 2798, 2021. doi:10.14778/3476311.3476347