Miracle-dz/UniFAR-FTUs huggingface dataset:
The module theoretical approach to quasi-hereditary algebras

Quasi-hereditary algebras were introduced by L.Scott [S] in order to deal with highest weight categories as they arise in the representation theory of semi–simple complex Lie algebras and algebraic groups. Since then, also many other algebras arising naturally, such as the Auslander algebras, have been shown to be quasi-hereditary. It seems to be rather surprising that the class of quasi-hereditary algebras, defined in purely ring–theoretical terms, has not been studied before in ring theory. The central concept of the theory of quasi-hereditary algebras are the notions of a standard and a costandard module; these modules depend in an essential way, on a (partial) ordering of the set of all simple modules. So we start with a finite dimensional algebra A, and a partial ordering of the simple A–modules, in order to define the standard modules ∆(i) and the costandard modules ∇(i); we have to impose some additional conditions on their endomorphism rings, and on the existence of some filtrations, in order to deal with quasi-hereditary algebras. This is the content of the first chapter. The second chapter collects some properties of quasi-hereditary algebras, in particular those needed in later parts of the paper. The third chapter presents the process of standardization: here, we give a characterization of the categories of ∆–filtered modules over quasi-hereditary algebras. In fact, we show that given indecomposable A–modules Θ(1), . ,Θ(n) over a finite–dimensional algebra such that rad(Θ(i),Θ(j)) = 0, and Ext(Θ(i),Θ(j)) = 0 for all i ≥ j, the category F(Θ) of all A–modules with a Θ–filtration is equivalent (as an exact category) to the category of all ∆–filtered modules over a quasi-hereditary algebra. The forth and the fifth chapter consider cases when the standard modules over a quasi-hereditary algebra have special homological properties: first, we assume that any ∆(i) has projective dimension at most 1, then we deal with the case that the dominant
Advanced ultrawide-field optical coherence tomography angiography identifies previously undetectable changes in biomechanics-related parameters in nonpathological myopic fundus

Purpose: To detect previously undetectable changes in vessel density and structural thickness, the two biomechanics-related parameters reflecting hemodynamics and tensile strength, respectively, in the peripheral and central fundi of nonpathological myopic eyes with an advanced ultrawide-field optical coherence tomography angiography (OCTA) system. Methods: A cross-sectional observational clinical study was carried out by recruiting 155 eyes from 79 college students aged 18–28 years. The eyes were stratified into normal, low-myopia, medium-myopia, and high-myopia groups according to diopter. A newly developed OCTA system with scanning dimensions of 24 mm × 20 mm, acquisition speed of 400 kHz, and imaging range of 6 mm was used to examine the vessel densities of superficial vascular complex (SVC), deep vascular complex (DVC), choriocapillary (ChC), and choroidal vessel (ChV) layers, as well as the thicknesses of the inner retina, outer retina, and choroid in the nonpathological myopic eyes. Results: The vessel densities in ChV at the temporal, inferotemporal, inferior, and inferonasal regions in the fundus periphery were significantly reduced in myopic subjects as compared to normal controls (all p < 0.05). The thicknesses of the inner retinal segments in most peripheral regions of the fundus became attenuated along with myopia severity (all p < 0.05). The thicknesses of the outer retinal segments were diminished at the superior and supranasal regions of the peripheral fundi of myopic subjects as compared to normal controls (all p < 0.05). At the central macular region, the decreased vessel densities of SVC and DVC were correlated with the attenuated thicknesses of inner retinal segments, respectively (all p < 0.05). Conclusion: As revealed for the first time by the advanced ultrawide-field OCTA system, the two biomechanics-related parameters that include the densities of the choroidal vessels and thicknesses of the inner retina segments were significantly reduced in the periphery of nonpathological myopic fundi and the reductions were associated with myopia severity. At the central macular region, the newly developed device provides consistent results with the previous findings. Therefore, it is important to use the noninvasive, ultrawide-field OCTA with high resolution for early detection of fundus changes in subjects with nonpathological high myopia. Clinical Trial Registration: clinicaltrials.gov, identifier ChiCTR2100054093.
Molecular mechanism of Epimedium in the treatment of vascular dementia based on network pharmacology and molecular docking

Backgroud: Vascular dementia is the second most common cause of dementia after Alzheimer’s disease, accounting for an estimated 15% of cases. Recently, Epimedium has attracted great attention for its potential neuroprotective benefit. However, the direct role and mechanism of Epimedium on vascular dementia still lack systematic research. To systematically explore the possible pharmacological mechanism of Epimedium for the treatment of vascular dementia, network pharmacology, molecular docking, combined with experiment validation were conducted. Methods: The bioactive compounds and targets of Epimedium were obtained from the TCMSP database. The potential targets of vascular dementia were identified from the DrugBank, OMIM, Genecards, Therapeutic Target Database, and DisGeNET databases. GO and KEGG pathway analyses were performed. Molecular docking was applied to validate the interaction between active components and hub targets. The bilateral common carotid artery occlusion (BCCAO) method was used for construction of a vascular dementia model in mice. The effects of Epimedium on learning and memory ability were examined by behavioral tests. The mechanisms of the cerebral protective effects of Epimedium were evaluated by WB, RT-PCR, and immunofluorescence. Results: A total of 23 Epimedium active ingredients, and 71 intersecting targets of Epimedium against vascular dementia were obtained. The top five hub targets AKT1, TNF, IL1β, IL6, and MMP9 were identified, and molecular docking showed good binding. GO enrichment showed a total of 602 enrichment results, with 458 (80.56%) key targets mainly focused on biological processes (BP). The response to hypoxia, positive regulation of nitric oxide biosynthetic process, aging, inflammatory response, cellular response to lipopolysaccharide, negative regulation of apoptotic process were well ranked. KEGG pathway enrichment analysis identified the TNF signaling pathway as an important pathway, with the MAPK/extracellular signal-regulated kinase (ERK) and NF-κB signaling pathways as the key pathways involved. Consistently, in vivo experiments showed that Epimedium treatment improved learning and memory functions in mice with vascular dementia. In addition, Epimedium attenuated the activation of microglia and astrocytes in the hippocampal region after BCCAO. RT-qPCR and Western blot analysis showed that Epimedium not only affected the expression of AKT, TNF, IL1β, IL6, and MMP9, but also suppressed the TNF signaling pathway. Conclusion: Epimedium may exert a protective effect against vascular dementia through the alleviation of oxidative stress, neuroinflammation, BBB dysfunction, apoptosis through TNF signaling pathway. This study explored the mechanism of Epimedium on vascular dementia systematically through network pharmacological and in vivo experiment approach, which provides insight into the treatment of vascular dementia.
Asian hate speech detection on Twitter during COVID-19

Coronavirus disease 2019 (COVID-19) started in Wuhan, China, in late 2019, and after being utterly contagious in Asian countries, it rapidly spread to other countries. This disease caused governments worldwide to declare a public health crisis with severe measures taken to reduce the speed of the spread of the disease. This pandemic affected the lives of millions of people. Many citizens that lost their loved ones and jobs experienced a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, and panic. All of the aforementioned phenomena led to the spread of racism and hate against Asians in western countries, especially in the United States. An analysis of official preliminary police data by the Center for the Study of Hate & Extremism at California State University shows that Anti-Asian hate crime in 16 of America's largest cities increased by 149% in 2020. In this study, we first chose a baseline of Americans' hate crimes against Asians on Twitter. Then we present an approach to balance the biased dataset and consequently improve the performance of tweet classification. We also have downloaded 10 million tweets through the Twitter API V-2. In this study, we have used a small portion of that, and we will use the entire dataset in the future study. In this article, three thousand tweets from our collected corpus are annotated by four annotators, including three Asian and one Asian-American. Using this data, we built predictive models of hate speech using various machine learning and deep learning methods. Our machine learning methods include Random Forest, K-nearest neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Logistic Regression, Decision Tree, and Naive Bayes. Our Deep Learning models include Basic Long-Term Short-Term Memory (LSTM), Bidirectional LSTM, Bidirectional LSTM with Drop out, Convolution, and Bidirectional Encoder Representations from Transformers (BERT). We also adjusted our dataset by filtering tweets that were ambiguous to the annotators based on low Fleiss Kappa agreement between annotators. Our final result showed that Logistic Regression achieved the best statistical machine learning performance with an F1 score of 0.72, while BERT achieved the best performance of the deep learning models, with an F1-Score of 0.85.
Ovulation induction regimens are associated with a higher rate of livebirth after frozen single-blastocyst transfer among women with polycystic ovary syndrome

Background Hormone replacement therapy (HRT) regimen was suggested to be associated with a decreased rate of livebirth and a higher risk of hypertensive disorders of pregnancy (HDP) after frozen cleavage stage embryo transfer in women with polycystic ovary syndrome (PCOS). With the dramatically increased use of elective single embryo transfer, there is great need to explore the impacts of different endometrial preparation regimens on frozen single-blastocyst transfer in women with PCOS. Methods In this study, a total of 3941 women who diagnosed with PCOS and underwent single-blastocyst transfer during their first cycles of frozen embryo transfer (FET) between March 2012 and December 2020 were included. We retrospectively compared the pregnancy and neonatal outcomes after frozen single-blastocyst transfer with endometrial preparation by HRT regimen (n = 3540), ovulation induction by human menopausal gonadotropin (hMG) regimen (n = 226), and ovulation induction by letrozole regimen (n = 175). Results After adjustment for confounders with multivariable logistic regression, the hMG regimen group [(58.4% vs. 49.6%; adjusted odds ratio (aOR): 1.43; 95% confidence interval (CI): 1.09-1.89)] and letrozole regimen group (58.9% vs. 49.6%; aOR: 1.42; 95% CI: 1.04-1.93) were associated with a higher rate of livebirth (primary outcome), compared with the group with HRT regimen. As to the secondary outcomes, the rate of pregnancy loss in the hMG regimen group (22.8% vs. 30.3%; aOR: 0.69; 95% CI: 0.48-1.00) and letrozole regimen group (16.9% vs. 30.3%; aOR: 0.48; 95% CI: 0.30-0.78) was also lower than that in the HRT regimen group. The pregnancy outcomes between the hMG regimen group and the letrozole regimen group were similar. We did not observe significant difference in the incidences of maternal and neonatal complications among these three groups. Conclusion Ovulation induction regimen with letrozole or hMG for endometrial preparation was associated with a higher livebirth rate and a lower pregnancy loss rate in frozen single-blastocyst transfer cycles among women with PCOS.
The effects of operating height and the passage of time on the end-point performance of fine manipulative tasks that require high accuracy

Sustained shoulder abduction, which results from an inappropriate worktable height or tool shape and long task hours, leads to an accumulation of muscle fatigue and subsequent work-related injuries in workers. It can be alleviated by controlling the table height or ergonomic tool design, but workers who are doing some types of work that require a discomfortable posture, such as minimally invasive surgery, cannot avoid these situations. Loads to the shoulder joint or muscles result in several problems, such as muscle fatigue, deterioration of proprioception or changing movement strategies of the central nervous system, and these are critical to work that requires a high accuracy of the upper extremities. Therefore, in this paper, we designed and conducted an experiment with human participants to discuss how an inappropriate height of the work-table affects the task performance of workers who are performing a fine manipulative task that requires high accuracy of the end point. We developed an apparatus that can control the height and has four touch screens to evaluate the end-point accuracy with two different heights. Eighteen adults (9 women and 9 men) participated in the experiments, and the electromyography of their shoulder muscles, their movement stability, and task performance were measured for the analysis. We found that inappropriate height of a table brings about muscle fatigue, and time elapsed for conducting tasks accelerated the phenomenon. Task performance deteriorated according to increased fatigue, and improved movement stability is not enough to compensate for these situations.
Assessing the suitability of long non-coding RNAs as therapeutic targets and biomarkers in SARS-CoV-2 infection

Long non-coding RNAs (lncRNAs) are RNA transcripts that are over 200 nucleotides and rarely encode proteins or peptides. They regulate gene expression and protein activities and are heavily involved in many cellular processes such as cytokine secretion in respond to viral infection. In severe COVID-19 cases, hyperactivation of the immune system may cause an abnormally sharp increase in pro-inflammatory cytokines, known as cytokine release syndrome (CRS), which leads to severe tissue damage or even organ failure, raising COVID-19 mortality rate. In this review, we assessed the correlation between lncRNAs expression and cytokine release syndrome by comparing lncRNA profiles between COVID-19 patients and health controls, as well as between severe and non-severe cases. We also discussed the role of lncRNAs in CRS contributors and showed that the lncRNA profiles display consistency with patients’ clinic symptoms, thus suggesting the potential of lncRNAs as drug targets or biomarkers in COVID-19 treatment.
Driver's turning intent recognition model based on brain activation and contextual information

Traffic situations like turning at intersections are destined for safety-critical situations and accidents. Human errors are one of the main reasons for accidents in these situations. A model that recognizes the driver's turning intent could help to reduce accidents by warning the driver or stopping the vehicle before a dangerous turning maneuver. Most models that aim at predicting the probability of a driver's turning intent use only contextual information, such as gap size or waiting time. The objective of this study is to investigate whether the combination of context information and brain activation measurements enhances the recognition of turning intent. We conducted a driving simulator study while simultaneously measuring brain activation using high-density fNIRS. A neural network model for turning intent recognition was trained on the fNIRS and contextual data. The input variables were analyzed using SHAP (SHapley Additive exPlanations) feature importance analysis to show the positive effect of the inclusion of brain activation data. Both the model's evaluation and the feature importance analysis suggest that the combination of context information and brain activation leads to an improved turning intent recognition. The fNIRS results showed increased brain activation differences during the “turn” decision-making phase before turning execution in parts of the left motor cortices, such as the primary motor cortex (PMC; putative BA 4), premotor area (PMA; putative BA 6), and supplementary motor area (SMA; putative BA 8). Furthermore, we also observed increased activation differences in the left prefrontal areas, potentially in the left middle frontal gyrus (putative BA 9), which has been associated with the control of executive functions, such as decision-making and action planning. We hypothesize that brain activation measurements could be a more direct indicator with potentially high specificity for the turning behavior and thus help to increase the recognition model's performance.
The gut microbiome molecular mimicry piece in the multiple sclerosis puzzle

The etiological complexity of multiple sclerosis, an immune-mediated, neurodegenerative disease with multifactorial etiology is still elusive because of an incomplete understanding of the complex synergy between contributing factors such as genetic susceptibility and aberrant immune response. Recently, the disease phenotypes have also been shown to be associated with dysbiosis of the gut microbiome, a dynamic reservoir of billions of microbes, their proteins and metabolites capable of mimicring the autoantigens. Microbial factors could potentially trigger the neuroinflammation and symptoms of MS. In this perspective article, we discussed how microbial molecules resulting from a leaky gut might mimic a host’s autoantigen, potentially contributing to the disease disequilibrium. It further highlights the importance of targeting the gut microbiome for alternate therapeutic options for the treatment of MS.
The Inheritance of Cancer in Mice

Investigations into the inheritance of spontaneous mammary carcinoma in mice have been extensively carried out, principally by American workers, who were the first to realize the importance of using inbred strains. Early work (16, 20) showed that a nongenetic extrachromosomal influence derived from the maternal stock was the most important factor in determining the mammary tumor incidence in the offspring, but more recently W. S. Murray (19) and Andervont (2, 3) have demonstrated that genetic (chromosomal) factors also play a definite part and control the degree of susceptibility to the extrachromosomal influence. Bittner (4) has proved that the extrachromosomal factor is contained in the maternal milk, but considers (8) that the susceptibility of the mammary gland to the factor has a genetic basis. Later investigations have been conducted to elucidate the nature of the milk influence, and an extensive literature has grown up round the subject, of which excellent summaries have appeared in publications by Andervont (1) and by Bittner (7, 9), and to these the reader is referred. Unrelated inbred strains have been shown to have different susceptibilities to the extrachromosomal influence and to differ in the concentration of that influence transmitted by the mother (19). The present communication records experiments with 3 inbred strains, Simpson (high mammary cancer incidence), Edinburgh, and Strong's CBA (both low mammary tumor strains), of which only the last has been used in the American experiments. In 1931 Simpson strain males had been mated with Edinburgh strain females to discover whether males could transmit the genetic factors for high mammary cancer susceptibility. No mammary tumors appeared in F1, but in F o there were 2 amongst 158 females and it was erroneously concluded that susceptibility was inherited as a recessive character (24). To prove this it was decided to backcross F1 males to Simpson females, but as none of the original Simpson-Edinburgh hybrids were available by that time (March,
A Comparison of Learning Subjective and Traditional Probability in Middle Grades

by Jeanne Dortch Rast The emphasis given to probability and statistics in the K-12 mathematics curriculum has brought attention to the various approaches to probability and statistics concepts, as well as how to teach these concepts. Teachers from fourth, fifth, and sixth grades from a small suburban Catholic school engaged their students (n=87) in a study to compare learning traditional probability concepts to learning traditional and subjective probability concepts. The control group (n=44) received instruction in traditional probability, while the experimental group (n=43) received instruction in traditional and subjective probability. A Multivariate Analysis of Variance and a Bayesian t-test were used to analyze pretest and posttest scores from the Making Decisions about Chance Questionnaire (MDCQ). Researcher observational notes, teacher journal entries, student activity worksheet explanations, preand post-test answers, and student interviews were coded for themes. All groups showed significant improvement on the post-MDCQ (p < .01). There was a disordinal interaction between the combined fifthand sixth-grade experimental group (n=28) and the control group (n=28), however the mean difference in performance on the pre-MDCQ and post-MDCQ was not significant (p=.096). A Bayesian t-test indicated that there is reasonable evidence to believe that the mean of the experimental group exceeded the mean of the control group. Qualitative data showed that while students have beliefs about probabilistic situations based on their past experiences and prior knowledge, and often use this information to make probability judgments, they find traditional probability problems easier than subjective probability. Further research with different grade levels, larger sample sizes or different activities would develop learning theory in this area and may provide insight about probability judgments previously labeled as misconceptions by researchers. A COMPARISON OF LEARNING SUBJECTIVE AND TRADITIONAL PROBABILITY IN MIDDLE GRADES by Jeanne Dortch Rast
The Impact of Service Quality and Brand Image toward Customer Loyalty in the Indonesian Airlines Industry

This research was conducted in order to determine and understand the impact of service quality and brand image toward customer loyalty in the Indonesian airlines industry. This research implements survey method, in which questionnaires were used as tools to collect the data needed for this research. A total of 550 respondents participated in this research, in which all questionnaires were deemed valid to be analyzed. Data were then analysed using PLS-SEM method using SmartPLS 3.2. 7 Software. Based on the results of the data analysis, it was found that both service quality and brand image had a positive and significant impact on customer loyalty, with service quality had a stronger impact compared to brand image. Based on these result, author would like to suggest to companies conducting their business in the Indonesian Airlines Industry to improve the level of quality regarding the service that they offer to the customers, while they should also be able to maintain and improve the image of the brand in order to enhance the positive image of the brand in the public’s mind. To author’s knowledge, this is one of a few researches which conduct an assessment regarding the relationship between variables in the airlines industry using PLS-SEM approach.