Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor ferroptosis status demonstrated vulnerability to chemotherapy and reflected immune-activation in colorectal cancer

Background: Existing studies for ferroptosis and prognosis in colorectal cancer (CRC) were limited. In this study, we aim to investigate the prognostic role of ferroptosis markers in patients with CRC and exploration of its micro-environmental distributions. Methods: A total of 911 patients from 2008 to 2013 with CRC were enrolled. Immunohistochemical staining was performed for CRC patients’ tissue microarray. Selection and prognostic validation of markers were based on mRNA data from the cancer genome atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed to indicate relative immune landmarks and hallmarks. Ferroptosis and immune contexture were examined by CIBERSORT. Survival outcomes were analyzed by Kaplan-meier analysis and cox analysis.Results: A panel of 42 genes was selected. Through mRNA expression difference and prognosis analysis, GPX4, NOX1 and ACSL4 were selected as candidate markers. By IHC, increased GPX4, decreased NOX1 and decreased FACL4 indicate poor prognosis and worse clinical characteristics. Ferroptosis score based on GPX4, NOX1 and ACSL4 was constructed and validated with high C-index. Low ferroptosis score can also demonstrate the better progression free survival and better adjuvant chemotherapy (ACT) responsiveness. Moreover, tumor with low ferroptosis score tend to be infiltrated with more CD4+ T cells, CD8+ T cells and less M1 macrophage. Finally, we found that IFN-γ was potentially the central molecule at the crossroad between ferroptosis and onco-immune response. Conclusion: Ferroptosis plays important role on CRC tumor progression, ACT response and prognosis. Ferroptosis contributes to immune-supportive responses and IFN-γ was the central molecule for this process.
Social Network Analysis Using Twitter Data

- Social media is a well-known platform for user to create, share and check the new information which is being updated on a daily basis. The world has become a global village because of the utilization of the internet and social media like Facebook, Twitter and various other social media sites. These platforms have become a wide source of growth and spreading of daily opinions related to feelings, thoughts, and moods, and also what is going on in today's world. Sentiment-Analysis is a combination of two words Sentiment and Analysis. Any opinion of an individual through which the feelings, attitude, and thoughts can be expressed is known as sentiment. The process of recognizing and classifying general public opinions, about any topic expressed in any piece of text, or in social media especially in order to determine whether the writer’s attitude towards a particular topic, product, is negative, positive or neutral is known as analysis. Twitter, being the most popular micro-blogging social site is used to collect data to perform analysis. As Twitter’s special and unique infrastructure and almost, total availability of its data, ensured its high popularity among researchers. Twitter is the most popular platform, when it comes to social media research both in academics and in industry; no other social media website has attracted as much attention from academics or industry. Twitter has an open infrastructure which allows any user to follow another user, and it provides almost 100% of its data through its API (Application Program Interface). This survey paper briefs about studies and approaches which are being used in on-going project of developing fact extraction technology on social media analysis of tweeter data. This survey paper brief about recent studies or approaches which have been used in social media analysis using the twitter data.
Examining Physical Exertion as a Potential Cause of Choking 1 2 3

15 Choking in sport is precipitated by a broad range of documented antecedents. One potential 16 antecedent that may hinder performance under pressure is physical exertion. In the current 17 experiment, a within-subjects design was implemented with 50 student-athletes who 18 completed 40 basketball free-throws in four manipulated conditions: higher pressure-running, 19 higher pressure-no running, lower pressure-running, and lower pressure-no running. A 20 repeated measures analysis of variance revealed that participants scored significantly lower in 21 the higher-pressure conditions than the lower-pressure conditions. Furthermore, participants 22 scored significantly higher in the no-running conditions compared to the running conditions. 23 The current results are in keeping with the conventional wisdom that physical effort can 24 undermine performance in pressure circumstances. The applied implications of these results 25 are discussed and tentative conclusions drawn for sport psychologists, coaches, and athletes. 26
GameWiki: Aspect Extraction for Video Games

In this project we aim to 1) Predict if a game review is helpful or not 2) Extract aspects from helpful game reviews. This is helpful for gamers in identifying the most interesting aspects as well as the most disliked aspects of a game before purchasing it. The text classification task of identifying helpful reviews is implemented using a ULMFit model[2]. Past works use topic models to extract aspects, but these extracted aspects are not highly coherent. This project aims to use an unsupervised neural attention model for aspect extraction described in [1] and apply gaming domain knowledge like genre to improve the aspect extraction. The neural attention model improves coherence since it uses neural word embeddings which consider the distribution of word co-occurrences. Further, the interpretability of the aspects have been improved by splitting the dataset by genre. It was able to predict more granular aspects particular to each game genre as opposed to aspects generated from the entire gaming dataset.
Generation Environment, Execution and Test Management Software Embedded

This paper presents a proposed architecture for creating and managing test embedded system to be used in Software testing lab which is being deployed at INPE. It will be considered as a technique for generating tests, tests based on models, where test cases are generated automatically based on a finite state machine. The architecture also includes the management of test plans, results of their execution, and the management of defects detected. This architecture can contribute to the process of creating and managing tests in the INPE´s testing lab.
Monte Carlo Simulation on Living Radical Polymerization with RAFT Process

Monte Carlo simulation is applied, with a high effectiveness and versatility, to the studies on the kinetics and chain length distribution in radical polymerization with RAFT process. The results show that the molecular weight the resulting polymers increase linearly with monomer conversion, and the polydispersity is lower than 1.1, a typical feature of living polymerization. The simulation results are in consistent with the experimental results in literature. Additionally, that the molecular weight is proportional to the initial concentration of dithioester, [DSE]_0, and the polymerization rate is [DSE]_0-independent, could be of great help for controlling molecular weight in experiment.
Antimicrobial Effect of Alpha Lipoic Acid Against Oral Microorganisms.

Aim: This study was aimed to assess the antibacterial effect of alpha lipoic acids against various oral microorganisms. Background: Oral microorganisms are responsible for oral related diseases.Oral microorganisms implicated in the etiology of dental caries, stomatitis, gingivitis and periodontitis. Gradual increase in the rate of resistance to antibiotics leads to increase in oral pathologies. Materials and method: 100 mg of alpha lipoic acid was measured and dissolved in 2 ml of ethanol. 250 ml of Muller Hinton Agar (MHA) was prepared, sterilised, poured onto the petri plates. After solidification each MHA plates were swabbed by overnight culture of enteric organisms such as Staphylococcus aureus, Streptococcus mutans, Pseudomonas species, Enterococcus faecalis, Escherichia coli, Candida albicans. Using a well cutter 3 wells were formed on each MHA plate. In each well, alpha lipoic acid was added at the concentration of 30, 60 and 90 ul and the petri plates were kept for incubation at 37 °C for 24 h. The zone of inhibition was recorded in each plate Result: Alpha lipoic acid prominently inhibited the growth of all the organisms invested. It possesses inhibitory antimicrobial effects even at low concentrations and it has shown maximum inhibitory antimicrobial activity at higher concentrations. Candida albicans was found as highly susceptible to alpha lipoic acid. Conclusion: The potential use of alpha lipoic acids provided in a local delivery vehicle to infected sites in the oral cavity, could be considered as an additional therapeutic approach to improve oral health
Multiple Correspondence Analysis, newspaper discourse and subregister

Abstract This article introduces a new method for grouping keywords and examines the extent to which it also allows analysts to explore the interaction of discourse and subregister. It uses the multivariate statistical technique, Multiple Correspondence Analysis, to reveal dimensions of keywords which co-occur across the texts of a corpus. These dimensions are then interpreted in terms of the discourses to which they contribute within the data, thus forming the basis of a corpus-assisted discourse analysis. The approach is demonstrated through analysis of the discourses that are used to represent Muslims and Islam in a corpus of UK national newspaper articles published on these topics spanning 2010–2019. The approach reveals an interaction between discourse and subregister, hence this article argues for the need for (corpus-assisted) discourse analysts to account for subregister as a level of meaningful variation when analysing press discourse.
Emotions and Perceived Productivity of Software Developers at the Workplace

Emotions are known to impact cognitive skills, thus influencing job performance. This is also true for software development, which requires creativity and problem-solving abilities. In this paper, we report the results of a field study involving professional developers from five different companies. We provide empirical evidence that a link exists between emotions and perceived productivity at the workplace. Furthermore, we present a taxonomy of triggers for developers’ positive and negative emotions, based on the qualitative analysis of participants’ self-reported answers collected through daily experience sampling. Finally, we experiment with a minimal set of non-invasive biometric sensors that we use as input for emotion detection. We found that positive emotional valence, neutral arousal, and high dominance are prevalent. We also found a positive correlation between emotional valence and perceived productivity, with a stronger correlation in the afternoon. Both social and individual breaks emerge as useful for restoring a positive mood. Furthermore, we found that a minimum set of non-invasive biometric sensors can be used as a predictor for emotions, provided that training is performed on an individual basis. While promising, our classifier performance is not yet robust enough for practical usage. Further data collection is required to strengthen the classifier, by also implementing individual fine-tuning of emotion models.
A Classification Model to Generate Prognosis of Satellite States

The prospect of multiple launches by INPE's satellite program, has motivated the development of an application using techniques based on Artificial Intelligence (AI) concepts for automatic generation of flight operation plans to control satellite activities. However, making a critical analysis of these plans before real world implementation is not possible. We propose a decision support tool AI-based data mining technique to generate prognosis of satellite states for assisting experts in evaluating the performance of the plan. To build the tool, a comparative study of performance between classic data mining classifiers is accomplished to determine the classification model that provides greater accuracy to predict satellite future states.
Reliability Analysis of Compressed CNNs

The use of artiﬁcial intelligence, Machine Learning and in particular Deep Learning (DL), have recently become a effective and standard de-facto solution for complex problems like image classiﬁcation, sentiment analysis or natural language processing. In order to address the growing demand of performance of ML applications, research has focused on techniques for compressing the large amount of the parameters required by the Deep Neural Networks (DNN) used in DL. Some of these techniques include parameter pruning, weight-sharing, i.e. clustering of the weights, and parameter quantization. However, reducing the amount of parameters can lower the fault tolerance of DNNs, already sensitive to software and hardware faults caused by, among others, high particles strikes, row hammer or gradient descent attacks, et cetera . In this work we analyze the sensitivity to faults of widely used DNNs, in particular Convolutional Neural Networks (CNN), that have been compressed with the use of pruning, weight clustering and quantization. Our analysis shows that in DNNs that employ all such compression mechanisms, i.e. with their memory footprint reduced up to 86.3 × , random single bit faults can result in accuracy drops up to 13.56%.
LEVEL OF KNOWLEDGE, BELIEF AND ALTRUISM TOWARDS ORGAN DONATION AMONG FINAL YEAR STUDENTS IN THE FACULTY OF MEDICINE AT UNIVERSITY KEBANGSAAN MALAYSIA, KUALA LUMPUR, MALAYSIA

Higher medical technological development and better awareness has affected the public’s perception on organ donation positively. One of the strategic plans by the Ministry of Health is to target the younger generation to achieve long-term changes in this issue through a variety of appropriate channels. This study aimed to assess the level of knowledge, belief and altruism towards organ donation among final year students from the Faculty of Medicine in University Kebangsaan Malaysia, Kuala Lumpur. Questionnaires were distributed to 190 respondents from two programs involving 173 (91.1%) medical students and 17 (8.9%) nursing students via systematic random sampling. The Cronbach’s alpha coefficient value for belief was 0.626 and 0.853 for altruism. This study showed that level knowledge of students with mean score of 60.92 (S.P ± 8.79) was at a moderate level for 109 (57.4%), 80 students (42.1%) at a high level and one for (0.5%) at low level. The overall students level of belief with the mean score of 27.00 (± 4.93 S.P) was positive with 110 (57.9%) while 80 students (42.1%) with negative belief. The altruistic level with mean score 35.46 (S.P ± 10.76) of 146 students (76.8%) at a moderate level, 41 students (21.6%) at high level and only 3 (1.6%) at low level. In correlation analysis, there are relationship between level of knowledge towards altruistic and knowledge towards beliefs (p <0.05). However, there is no significant relationship between knowledge with beliefs because of the value of p> 0.05. In conclusion, the level of knowledge should be further enhanced among medical and nursing students with the hope that increased knowledge will positively influence the belief system and the altruistic behaviour of respondents regarding the concept of organ donation.
Multiple power-law viscoelastic relaxation in time and frequency domains with atomic force microscopy

Complex viscoelastic materials exhibit power law (PL) relaxations, as opposed to simple materials described by exponential decays. Other interesting materials, like living cells, hold a universal double PL behavior whose exponents depend on the health and type of the cells. Usually, only dynamic assays are considered capable to study such viscoelastic relaxation mechanisms. In this work, we propose analytical responses with single or multiple power-law relaxation behavior by generalizing classical viscoelastic models in terms of fractional derivatives of arbitrary order α (0 ⩽α⩽ 1). In addition, we demonstrate that simple atomic force microscopy force curves are powerful methods to directly observe the viscoelastic relaxation of such complex materials. In order to validate our findings, we compare the viscoelastic relaxation exponents measured directly from simple force curves (SFCs) with those measured with dynamic techniques in both living cells and polyacrylamide gels. We believe the fractional models unveiled here describe a variety of complex materials and may be used (with SFCs) to explore sophisticated viscoelastic phenomena.
Simultaneous Determination of Nonanthocyanin Phenolic Compounds in Red Wines by HPLC-DAD/ESI-MS

The nonanthocyanin phenolic compounds of wines from Vitis vinifera L. cv. Tempranillo, Graciano, Cabernet Sauvignon, and Merlot (vintage 2000, Navarra, Spain), vinified under the same conditions, were extracted with ethyl acetate and diethyl ether and analyzed by high-performance liquid chromatography–diode array detection/electrospray ionization–mass spectrometry (HPLC–DAD/ESI–MS) (negative mode). A total of 47 phenolic compounds were identified in the different wines, including nonflavonoids (hydroxybenzoic and hydroxycinnamic acids and their derivatives, stilbenes, and phenolic alcohols and other related compounds) and flavonoids (flavanols, flavonols, and dihydroflavonols). Novel phenolic acid derivatives, such as the methyl and ethyl esters of gallic acid and some hexose esters of vanillic and p -coumaric acids, were also detected. The concentration of nonflavonoid compounds was higher for Cabernet Sauvignon (62.23 mg/L) and Graciano (57.82 mg/L) wines than for Merlot (47.52 mg/L) and Tempranillo (43.70 mg/L). The concentration of flavonoid compounds was highest for Graciano wine (119.41 mg/L), followed by Cabernet Sauvignon (104.58 mg/L), Merlot (77.54 mg/L), and Tempranillo (50.56 mg/L) wines. Differences between wines were found in the quantified proportion of hydroxybenzoic acids (16.3 to 29.5%), stilbenes (0.3 to 2.9%), phenolic alcohols and other related compounds (9.4 to 17.0%), flavanols (42.9 to 56.1%), and flavonols (10.1 to 15.3%). ESI–MS was confirmed as a valuable tool for obtaining potentially important information on specific phenolic compounds in wine.
The Effect Of Supply Chain Risk Management On Supply Chain Performance Through Supply Chain Integration And Information Quality In Paper Manufacturer

Globalization affects the changes in business in an area. China's regulation in forbidding the import of recycled paper affects many paper manufacturers almost in the whole of South-East Asia. Paper manufacturer overgrew in East Java in 2017. As the demand grew fast, the Indonesian government revised an old regulation that limits the usage of woods in the paper industry. Because of the disruption, the government take policy the risk that the paper manufacturer's owner must face just got bigger. This research aimed to identify how big supply chain risk management can affect supply chain performance through supply chain integration and information quality. The data was gathered from the paper manufacturing industry using a questionnaire. Thirty questionnaires were received from 40 questionnaires that were sent (response rate of 75 %.). Only a few samples were used because there were only 46 paper manufacturers categorized as the middle and large industry with a total worker of more than 100 people. Partial Least Square analyzed the hypothesis. The result indicates that supply chain risk management affects supply chain integration. Information quality doesn’t directly affect supply chain performance; supply chain risk management affects information quality; supply chain integration affects supply chain performance. The next research should use another type of industry and study the suppliercustomer relationship.
Biosensing by Direct Observation of Leaky Waveguide Modes

The resonance angles of leaky waveguides (LWs) can be observed directly as exponentially decaying interference fringes in waveguides with refractive index that is 0.001–0.01 higher than cover, and when illuminated with wedge-shaped light beam. This work for the first time shows that such LWs can be realised using hydrogels of acrylamide copolymerised with N-(3-aminopropyl)methacrylamide hydrochloride (APMA). LWs made of 4.5% (w:v) acrylamide/APMA crosslinked with poly(ethylene glycol) diacrylate (PEGDA, Mn: 700Da) were ∼59% and ∼56% porous to 100kDa and 300kDa species, which are typical molecular weights of biomolecules, and hence were selected for biosensing of immunoglobulin G (IgG) with protein A–biotin (PAB) as biomolecular recognition element. The limit of detection of the acrylamide/APMA LW for IgG was 3.69±0.37 nM, which is at least 1/100th of the dissociation constant of the PAB-IgG binding partners used in this work. Future work will investigate the application of acrylamide/APMA LW for biosensing of clinically relevant analytes.
Discovery of a Highly Neutronized Ejecta Clump in the Type Ia Supernova Remnant 3C 397

The supernova remnant (SNR) 3C 397 is thought to originate from a Type Ia supernova (SN Ia) explosion of a near-Chandrasekhar-mass (M Ch) progenitor, based on the enhanced abundances of Mn and Ni revealed by previous X-ray study with Suzaku. Here we report follow-up XMM-Newton observations of this SNR, conducted with the aim of investigating the detailed spatial distribution of the Fe-peak elements. We have discovered an ejecta clump with extremely high abundances of Ti and Cr, in addition to Mn, Fe, and Ni, in the southern part of the SNR. The Fe mass of this ejecta clump is estimated to be ∼0.06 M ⊙, under the assumption of a typical Fe yield for SNe Ia (i.e., ∼0.8 M ⊙). The observed mass ratios among the Fe-peak elements and Ti require substantial neutronization that is achieved only in the innermost regions of a near-M Ch SN Ia with a central density of ρ c ∼ 5 × 109 g cm−3, significantly higher than typically assumed for standard near-M Ch SNe Ia (ρ c ∼ 2 × 109 g cm−3). The overproduction of the neutron-rich isotopes (e.g., 50Ti and 54Cr) is significant in such high-ρ c SNe Ia, with respect to the solar composition. Therefore, if 3C 397 is a typical high-ρ c near-M Ch SN Ia remnant, the solar abundances of these isotopes could be reproduced by the mixture of the high- and low-ρ c near-M Ch and sub-M Ch Type Ia events, with ≲20% being high-ρ c near-M Ch.
GaN-based micro-LEDs and detectors defined by current spreading layer: size-dependent characteristics and their multifunctional applications

An integrated device of GaN-based micro-LED and detector was developed for multifunctional applications in visible light communication (VLC). The size of the current spreading layer was used to define the micro-LED size for high-bandwidth transmitter. The device can also be used as high-sensitivity photodetector (PD). In this scenario, the light receiving area is not restricted by the size of the current spreading layer. The size-dependent characteristics of the PD have been investigated in detail. Such integrated devices can be used as both micro-LED and PD for duplex high-speed VLC. Besides, we demonstrated the functions of solid-state lighting and VLC when the device was integrated with broad-area LED, as well as the potential application in micro-LED display combining with VLC.