Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation and Characterization of Natural Nanoparticles in Naoluo Xintong Decoction and Their Brain Protection Research

Currently, researchers use modern analytical techniques in a unique perspective of physical pharmacy to analyze the phase composition of traditional Chinese medicine (TCM) and have discovered that natural nanoparticles commonly exist in decoctions. This study aims to isolate and characterize the structure and composition of nanoparticles in Naoluo Xintong (NLXT) and investigate whether the brain protection effect of NLXT is closely related to NLXT-Nanoparticles (NLXT-NPs). Firstly, the dialysis-centrifugation method was used to separate the nanoparticles and then their size distribution, potential, and morphology were characterized. In addition, infrared spectroscopy and ultra-high performance liquid chromatography-quadrupole-time of flight-mass spectrometer (UPLC-Q-TOF-MS) technology were used to analyze the composition of nanoparticles. As for the pharmacodynamic experiment, Sprague Dawley (SD) rats were randomly divided into sham, Middle cerebral artery occlusion (MCAO) model, NLXT, NLXT with nanoparticles removing (NLXT-RN), NLXT-RN+Nanoparticles (NLXT-RN+NPs), and NLXT-NPs groups. After administration, the neurological function, histopathological changes, oxidative stress, and apoptosis level were measured. Our research showed that NLXT-NPs are mainly composed of polysaccharides, proteins, and saponins, with typical characteristics of two hundred-nanometer size and negatively loaded. NLXT can improve nerve function, reduce oxidative stress, and inhibit cell apoptosis. However, removing nanoparticles can significantly reduce the brain-protective effect of NLXT, which indicates that NLXT-NPs play an essential role in the efficacy of NLXT.
Exercise with 40-Hz light flicker improves hippocampal insulin signaling in Alzheimer disease mice

We examined whether exercise is associated with hippocampus-mediated improvement in insulin signaling and cell differentiation in the triple transgenic mouse model of Alzheimer disease (3xTg AD) murine model following exposure to 40-Hz light flickering and exercise. We subjected 12-month-old 3xTg AD mice to exercise and 40-Hz light flickering for 3 months. The exercise session was proceeded for 12 consecutive weeks with gradual increase of intensity. To investigate insulin signaling proteins, western blot was conducted to detect the ratio of phosphorylated insulin receptor β (p-IRβ)/total IRβ (t-IRβ), phosphorylated insulin receptor substrate 1 (p-IRS-1)/total IRS-1 (t-IRS-1), phosphorylated phosphatidylinositide-3-kinase (p-PI3K)/total PI3K (t-PI3K), phosphorylated 3-phosphoinositide dependent protein kinase-1 (p-PDK1)/total PDK-1 (t-PDK1), phosphorylated protein kinase B (p-Akt)/total-Akt (t-Akt), and phosphorylated glycogen synthase kinase 3 beta (p-GSK3β)/total GSK3β (t-GSK3β). Doublecortin immunohistochemistry was performed for assessing cell differentiation in the hippocampus. Treatments exerted a positive effect. The combination of exercise and 40-Hz light flickering exposure was the most effective treatment enhancing insulin signaling. Increased ratio of p-IRβ/t-IRβ, p-IRS-1/t-IRS-1, p-PI3K/t-PI3K, p-PDK1/t-PDK1, p-Akt/t-Akt, and p-GSK3β/t-GSK3β and enhanced cell differentiation were observed in the 3xTg AD with exercise under 40-Hz light flickering group. Our results indicate that exercise under 40-Hz light flickering most potently improved insulin signaling, thereby promoted cell differentiation.
Expedited Online Learning With Spatial Side Information

The applicability of model-based online reinforcement learning algorithms is often limited by the amount of exploration required for learning the environment model to the desired level of accuracy. A promising approach to addressing this issue is to exploit side information, available either a priori or during the agent’s mission, for learning the unknown dynamics. Side information in our context refers to information in the form of bounds on the differences between transition probabilities at different states in the environment. We use this information as a measure of reusability of the direct experience gained by performing actions and observing the outcomes at different states. We propose a framework to integrate side information into existing model-based reinforcement learning algorithms by complementing the samples obtained directly at states with second-hand information obtained from other states with similar dynamics. Additionally, we propose an algorithm for synthesizing the optimal control strategy in unknown environments by using side information to effectively balance between exploration and exploitation. We prove that, with high probability, the proposed algorithm yields a near-optimal policy in the Bayesian sense, while also guaranteeing the safety of the agent during exploration. We obtain the near-optimal policy in time steps that are polynomial in terms of the parameters describing the model. We illustrate the utility of the proposed algorithms in a setting of a Mars rover, with data from onboard sensors and a companion aerial vehicle acting as the side information.
Analisis Faktor-Faktor Yang Mempengaruhi Keberadaan Komite Manajemen Risiko (Risk Management Committe ) (Studi Empiris Pada Perusahaan Non Perbankanyang Listing Di BEI)

Abstrak Penelitian ini bertujuan untuk memperoleh bukti empiris tentang faktor-faktor yangmempengaruhi keberadaan Risiko manajement Komite (RMS) dan Manajemen Risiko terpisahCommttee (SRMC) di non-perusahaan perbankan yang terdaftar di BEI. Faktor-faktor ini terdiri darikomisaris independen, ukuran papan, auditor eksternal, kompleksitas perusahaan, risiko pelaporankeuangan, leverage dan size.Population perusahaan penelitian ini terdiri dari non-perusahaanperbankan, yang terdaftar di BEI untuk periode 2009-2010. Menurut metode purposive sampling,terdapat 334 sampel untuk diteliti. Hipotesis penelitian diuji dengan cara Regression.Results Logistikpenelitian ini adalah sebagai berikut: ada pengaruh yang signifikan dari ukuran perusahaan padakehadiran RMC dan SRMC, ada pengaruh signifikan dari komisaris independen dan pelaporankeuangan risiko pada RMC. Dan tidak ada pengaruh yang signifikan dari ukuran papan, auditoreksternal, kompleksitas perusahaan, memanfaatkan kehadiran RMC dan SRMC di non-perbankanperusahaan yang terdaftar di BEI. Abstract This research aimed to obtain empirical evidence of factors, which affected the presence of RiskManajement Committee (RMS) and Separate Risk Management Commttee (SRMC) at non–banking firmlisted in the BEI. These factors consisted of independent commissioner, board size, external auditor,corporate complexity, financial reporting risk, leverage and firm size.Population of this researchconsisted of non-banking firm, which were listed in the BEI for the period of 2009-2010. According apurposive sampling method, there were 334 samples to be studied. Hypotheses of the study were tested bymeans of Logistic Regression.Results of the study were as the followings : there was a significant effect ofthe firm size on the presence of RMC and SRMC, there was a significant effect of the independentcommissioner and financial reporting risk on the RMC. And there were not any significant effect ofboard size, external auditor, corporate complexity, leverage on the presence of the RMC and SRMC in thenon-banking firms listed in the BEI. Keyword : Independent Commissioner, Board Size, External Auditor, Corporate Complexity, Financial Reporting Risk, Leverage, Firm Size, RMC and SRMC.
Reevaluating Green Marketing: A Strategic Approach

Today's consumers are becoming more and more conscious about the environment and are also becoming socially responsible. Therefore, more companies are responsible to consumers’ aspirations for environmentally less damaging or neutral products. It ensures sustained long-term growth along with profitability. Green marketing is a holistic approach rather than the simple “marketing hype” or tactical opportunism practiced by some. It expands on the basic transaction concept by minimising a transaction's negative impact on the natural environment. Today be it a fad or by principle green initiativeness are an inherent part of many business firms. The “Green Imperative” is impossible to ignore, and companies are scrambling to understand how to develop business models that are more sustainable. This paper provides a bird's eye view on the benefits of green marketing and how to carry out green marketing for the existence of enterprises.
The Use of miRNAs in Predicting Response to Neoadjuvant Therapy in Oesophageal Cancer

Simple Summary Chemotherapy and chemoradiotherapy are only effective in 25% to 30% of patients with oesophageal cancer. Being able to predict which patients will respond to chemotherapy and chemoradiotherapy before they receive this treatment will prevent patients undergoing unnecessary procedures that may reduce their quality of life and help source alternative treatment options faster. The scope of this review was to understand whether microRNAs, small non-coding RNA molecules that regulate gene expression, can be used as biomarkers to predict a patient’s response to chemotherapy and/or chemoradiotherapy treatment. This review showed that a number of microRNAs may have the potential to predict response to chemotherapy and chemoradiotherapy alongside other pre-treatment features already used. More research is needed to translate the use of microRNAs as biomarkers of response to the clinical setting, as well as understanding the effects different types of treatment have on predictability. Abstract Oesophageal cancer (OC) is the ninth most common cancer worldwide. Patients receive neoadjuvant therapy (NAT) as standard of care, but less than 20% of patients with oesophageal adenocarcinoma (OAC) or a third of oesophageal squamous cell carcinoma (OSCC) patients, obtain a clinically meaningful response. Developing a method of determining a patient’s response to NAT before treatment will allow rational treatment decisions to be made, thus improving patient outcome and quality of life. (1) Background: To determine the use and accuracy of microRNAs as biomarkers of response to NAT in patients with OAC or OSCC. (2) Methods: MEDLINE, EMBASE, Web of Science and the Cochrane library were searched to identify studies investigating microRNAs in treatment naïve biopsies to predict response to NAT in OC patients. (3) Results: A panel of 20 microRNAs were identified as predictors of good or poor response to NAT, from 15 studies. Specifically, miR-99b, miR-451 and miR-505 showed the strongest ability to predict response in OAC patients along with miR-193b in OSCC patients. (4) Conclusions: MicroRNAs are valuable biomarkers of response to NAT in OC. Research is needed to understand the effects different types of chemotherapy and chemoradiotherapy have on the predictive value of microRNAs; studies also require greater standardization in how response is defined.
Cysteine Donor-Based Brain-Targeting Prodrug: Opportunities and Challenges

Overcoming blood-brain barrier (BBB) to improve brain bioavailability of therapeutic drug remains an ongoing concern. Prodrug is one of the most reliable approaches for delivering agents with low-level BBB permeability into the brain. The well-known antioxidant capacities of cysteine (Cys) and its vital role in glutathione (GSH) synthesis indicate that Cys-based prodrug could potentiate therapeutic drugs against oxidative stress-related neurodegenerative disorders. Moreover, prodrug with Cys moiety could be recognized by the excitatory amino acid transporter 3 (EAAT3) that is highly expressed at the BBB and transports drug into the brain. In this review, we summarized the strategies of crossing BBB, properties of EAAT3 and its natural substrates, Cys and its donors, and Cys donor-based brain-targeting prodrugs by referring to recent investigations. Moreover, the challenges that we are faced with and future research orientations were also addressed and proposed. It is hoped that present review will provide evidence for the pursuit of novel Cys donor-based brain-targeting prodrug.
Mobility-Aware Offloading and Resource Allocation in NOMA-MEC Systems via DC

Multi-access edge computing (MEC) is a promising computing paradigm that incorporates computing capability into a radio access network (RAN) nearby user equipment (UE) to improve the quality of experience. However, MEC also encounters many challenges inherent in RANs, such as mobility management. In this letter, we develop a novel computation offloading scheme utilizing non-orthogonal multiple access (NOMA) and dual connectivity (DC) and focus on jointly optimizing task segmentation and power allocation to minimize the total energy consumption. Specifically, the joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem that is non-deterministic polynomial hard (NP-hard). To solve this problem, we propose a new heuristic algorithm to obtain a sub-optimal solution. We then propose an online learning algorithm based on a twin delayed deep deterministic policy gradient (TD3) to meet user mobility requirements. The numerical results show that the proposed scheme outperforms other schemes, and the TD3-based algorithm has comparable energy consumption performance and dramatically reduces the execution time compared with the heuristic algorithm.
Using digital differentiation to improve EFL achievement and self-regulation of tertiary learners: the Turkish context

ABSTRACT Purpose The current study examines the impact of Differentiated Instruction (DI) on students’ EFL speaking proficiency and Self-Regulated Learning (SRL) during online learning at a Turkish higher education institution’s English preparatory program. Design/methodology/approach Carried out as a sequential explanatory mixed-methods approach, this quasi-experimental study first collected quantitative data from two intact classrooms, experimental group (n = 16), control group (n = 15), through a speaking proficiency test and the Turkish translation of a Likert-type Online Self-Regulation Questionnaire (OSRLQ) as pre-/post-test; and qualitative data through a semi-structured focus group interview with six students. The qualitative data were coded and interpreted through content analysis. Findings Based on Tomlinson’s (2001) DI framework, the intervention plan of this study involved differentiating the process, product and learning environment of the online learners based on their readiness levels and interests. The results revealed that the DI-group improved its speaking significantly higher than the non-DI group whereas the overall SRL of the both groups did not differ meaningfully. The DI-group improved its help-seeking strategy use significantly. The analysis of qualitative data indicated that the students held positive views of online practices used for DI purposes such as formative assessment, differentiated speaking tasks while questioning group work arrangements. The participants also stated that they improved their use of target setting, help seeking and self-assessment. Originality/value This study contributes to the literature in providing insight into the effects of differentiation practices during online learning and suggests implications for designing innovative differentiated EFL learning experiences during remote learning.
Three-Channel Cancellation of SAR Blanketing Jamming Suppression

Aimed at the SAR blanketing jamming suppression,a method of three-channel cancellation is proposed.First,principles of the method to suppress blanketing jamming and its influences on SAR imaging are presented in theory,from which the expression of target loss period (TLP) is derived,and then the simulation experiment is carried out.The results show the superiority of the proposed method for SAR anti-jamming in comparison with the method of two-channel cancellation.In addition,this method can be extended to the general circumstance of N-channel cancellation processing,which allows the method to be applicable,effective,and theoretically complete.
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System

To tackle the challenge of accurate and timely communication regarding the COVID-19 pandemic, we present a COVID-19 Claim Radar to automatically extract supporting and refuting claims on a daily basis. We provide a comprehensive structured view of claims, including rich claim attributes (such as claimers and claimer affiliations) and associated knowledge elements as claim semantics (such as events, relations and entities), enabling users to explore equivalent, refuting, or supporting claims with structural evidence, such as shared claimers, similar centroid events and arguments. In order to consolidate claim structures at the corpus-level, we leverage Wikidata as the hub to merge coreferential knowledge elements. The system automatically provides users a comprehensive exposure to COVID-19 related claims, their importance, and their interconnections. The system is publicly available at GitHub and DockerHub, with complete documentation.
Investigation of different decoding algorithms for the automatic generation of alternative language patterns in German to English translation

A single sentence in one language can always be translated into another language in multiple ways. Translation is therefore an ambiguous task, that is difficult to realize in an automatic way. This thesis investigates different decoding algorithms for the generation of text with a neural machine translation model. The goal is to generate multiple translations with diverse language patterns. For this purpose, an artificial neural network for German to English translation is implemented and translations are generated using different decoding algorithms. The resulting translations are then analyzed with regard to their quality and diversity.
A New Direction for Machine Learning in Criminal Law

THE AMERICAN CRIMINAL LEGAL SYSTEM is rife with—and perpetuates— inequality. These discrimination problems across racial, socioeconomic, and other lines are well-documented, but studying the problem is still a resource-intensive process. Combing through court transcript hearings, manually categorizing case files, and other investigative techniques require hours of time, extensive manual labor, and research dollars that many civic and other organizations simply lack. Technology may be able to relieve some of this burden.
Analysis of Environmental Governance Expense Prediction Reform With the Background of Artificial Intelligence

In this paper, Artificial Intelligence assisted rule-based confidence metric (AI-CRBM) framework has been introduced for analyzing environmental governance expense prediction reform. A metric method is to assess a level of collective environmental governance representing general, government, and corporate aspects. The equilibrium approach is used to calculate improvements in the source of environmental management based on cost, and it is tailored to test the public sector-corporation for environmental shared governance. The overall concept of cost prediction or estimation of environmental governance is achieved by the rule-based confidence method. The framework compares the expected cost to the environment of governance to determine the efficiency of the cost prediction process.
Molecular Characterization of Interleukin-8 Polymorphism among Patients with Black Grain Mycetoma, Gezira Mycetoma Center, Sudan (2019-2020)

Interleukin 8 (IL-8) is a chemokine, mainly involved in the initiation of inflammatory reactions. It attracts and activates neutrophils that are unable to clear eumycetoma inflammatory regions. In endemic areas the majority of individuals have antibodies, and therefore, a small proportion develops the disease. The objective of the study was to identify Polymorphism in the Allele distribution for gene encoding to IL-8(CXCL8) and associated with the risk of Mycetoma infection due to black grain. A cross-sectional hospitalbased study was conducted. DNA extraction was performed for 41 blood samples. Genotyping for the IL8-251 T-A polymorphism was accomplished using the PCR ARMS method. A fragment of 349 bp of IL8 was amplified. Three specimens were detected by standard Sanger sequencing. A percent of 28 (68.3%) and 13 (31.7%) were males and females, with ages ranging (13 -36 years) consecutively. The PCR results revealed 36 (87.8%) were amplified whereas, 5(12.2%) were not. The sequencing of IL8 -251 T fi: A (66.6%) initiated an (A: allele) and, (33.3%) are excluded. In conclusion, the study abstracted that the IL8 -251 T fi A (87.8%), polymorphism prevalent in Mycetoma patients in the Gezira. Further studies are recommended for surveillance, characterization, and mapping the target pockets.
Turing’s Missing Algorithm: The Brave New World of Ian McEwan’s Android Novel Machines Like Me

In April 1980, the BBC broadcast The Imitation Game, a television play written by Ian McEwan and directed by Richard Eyre. Set in Britain during World War II, it tells the story of Cathy Raine, a fictitious member of the Auxiliary Territorial Service, who is transferred to work as a waitress at Bletchley Park where she has a short and ill-fated affair with the Turing-like mathematician John Turner. Commenting on his television play, McEwan admitted that his initial intention had been to write a play about Alan Turing, only to change his mind in favour of foregrounding what would now be called a critical feminist reading of life at Bletchley Park (“Introduction” 15). Over the years, the focus on individuals who are facing the “pressures of their time” (Head 1) has become the hallmark of McEwan’s writing. In agreement with this point, Lynn Wells notes that:
Effectiveness of Web-Based Nutrition Education for Chronic Kidney Disease Patients

Background: In the early stages of chronic kidney disease (CKD), encouraging health behaviors can help prevent the progression of kidney disease leading to eventual kidney failure. The studies of health education using computer technology have been greatly developed, especially web-based nutrition education. Objective: To determine the effectiveness of a nutrition education website for CKD patients. Method: The design of this quasi-experimental research was a group pre-test/post-test. The participants were predialysis CKD patients who were enrolled on the developed website www.banraktai. com. The participants were required to complete an eating behavior questionnaire and knowledge test. They accessed the website for eight weeks, and at week 8, they completed the eating behavior questionnaire and knowledge test again. The main outcomes were the changes in scores of nutrition knowledge and eating behavior that were compared between the baseline and after the intervention using the paired t-test. The correlation between nutrition knowledge scores and eating behavior scores was determined using Spearman’s correlation coefficient. Results: There were 44 participants that completed the study. The results showed that the participants had significant improvement in both knowledge scores and consumption behavior scores (p < 0.001 and p = 0.041, respectively). However, there was no correlation between the nutrition knowledge scores and the eating behavior scores. Conclusions: Web-based nutrition education can improve knowledge scores but is not effective enough to encourage and motivate CKD patients to make eating behavior changes.