Miracle-dz/UniFAR-FTUs huggingface dataset:
A Guide to Updating the US Government’s Social Cost of Carbon

Since its release in 2010, the US government’s social cost of carbon (SCC) has played a central role in climate policy both domestically and internationally. However, rapid progress in climate science and economics over the past decade means that the original SCC estimate is no longer based on the frontier of scientific knowledge. Specifically, extensive new research about the climate, the economy, and their relationship has altered our understanding of the magnitudes of the projected physical and economic impacts of climate change, as well as their heterogeneity across space and time. This article, which was written as the Biden presidential administration was actively rebuilding the US SCC, provides concrete recommendations on how to base the SCC on the most recent research advances and thus return it to the scientific frontier.
HMGB1 Upregulates RAGE to Trigger the Expression of Inflammatory Factors in the Lung Tissue in a Hypoxic Pulmonary Hypertension Rat Model

Hypoxic pulmonary hypertension (HPH), a form of pulmonary hypertension (PH) caused by hypoxia, could cause serious complications and has a high mortality rate, and no clinically effective treatments are currently available. In this study, we established an HPH preclinical model in rats by simulating clinicopathological indicators of the disease. Our results showed that high mobility group box-1 protein (HMGB1) aggravated the clinical symptoms of HPH. We aimed at establishing protocols and ideas for the clinical treatment of HPH by identifying downstream HMGB1 binding receptors. Our investigation showed that continuous hypoxia could cause significant lung injury in rats. ELISA and western blotting experiments revealed that HPH induces inflammation in the lung tissue and increases the expression of a hypoxia-inducible factor. Testing of lung tissue proteins in vivo and in human pulmonary artery endothelial cells in vitro revealed that the HMGB1-mediated increase in the receptor for advanced glycation end products (RAGE) expression promoted inflammation. In summary, we successfully established an HPH rat model providing a new model for preclinical HPH research and elucidated the role of HMGB1 in HPH. Furthermore, we describe that HMGB1 induced inflammation in the HPH model via RAGE, causing severe lung dysfunction. This study could potentially provide novel ideas and methods for the clinical treatment of HPH.
The Confluence of Networks, Games, and Learning a Game-Theoretic Framework for Multiagent Decision Making Over Networks

Multiagent decision making over networks has recently attracted an exponentially growing number of researchers from the systems and control community. The area has gained increasing momentum in engineering, social sciences, economics, urban science, and artificial intelligence as it serves as a prevalent framework for studying large and complex systems and has been widely applied to many problems, such as social networks analysis <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>, smart grid management <xref ref-type="bibr" rid="ref3">[3]</xref>, <xref ref-type="bibr" rid="ref4">[4]</xref>, wireless and communication networks <xref ref-type="bibr" rid="ref5">[5]</xref>–<xref ref-type="bibr" rid="ref6"/><xref ref-type="bibr" rid="ref7">[7]</xref>, cybersecurity <xref ref-type="bibr" rid="ref8">[8]</xref>–<xref ref-type="bibr" rid="ref9"/><xref ref-type="bibr" rid="ref10">[10]</xref>, critical infrastructures <xref ref-type="bibr" rid="ref11">[11]</xref>–<xref ref-type="bibr" rid="ref12"/><xref ref-type="bibr" rid="ref13">[13]</xref>, and cyberphysical systems <xref ref-type="bibr" rid="ref14">[14]</xref>–<xref ref-type="bibr" rid="ref15"/><xref ref-type="bibr" rid="ref16">[16]</xref>.
Changing the Volume of Online Lectures: Does Audio Level Affect Learning Quality?

Due to the onset of the Covid-19 pandemic, students have increasingly begun to learn virtually, often watching online lectures. Some students might believe that turning up the volume on those lectures can enhance their learning efficiency, and this assumption is supported by a 2009 study (Rhodes & Castel, 2009) where students were shown to have higher judgments of learning (JOLs) for louder words compared to quiet words. I designed a within-participants experiment under two conditions to study the real effect of louder audio on learning by making the participants watch videos at different volumes, answer several questions regarding their JOLs, and complete one quiz for each video. The final data suggest that louder audio had significant influence neither on students’ JOLs nor on their assessment performance. Although the online participants’ assessment performance presented a similar result as that in the Rhodes and Castel study, the finding of no influence of volume on JOLs contradicts Rhodes and Castel.
A High-Sugar Diet Consumption, Metabolism and Health Impacts with a Focus on the Development of Substance Use Disorder: A Narrative Review

Carbohydrates are important macronutrients in human and rodent diet patterns that play a key role in crucial metabolic pathways and provide the necessary energy for proper body functioning. Sugar homeostasis and intake require complex hormonal and nervous control to proper body energy balance. Added sugar in processed food results in metabolic, cardiovascular, and nervous disorders. Epidemiological reports have shown enhanced consumption of sweet products in children and adults, especially in reproductive age and in pregnant women, which can lead to the susceptibility of offspring’s health to diseases in early life or in adulthood and proneness to mental disorders. In this review, we discuss the impacts of high-sugar diet (HSD) or sugar intake during the perinatal and/or postnatal periods on neural and behavioural disturbances as well as on the development of substance use disorder (SUD). Since several emotional behavioural disturbances are recognized as predictors of SUD, we also present how HSD enhances impulsive behaviour, stress, anxiety and depression. Apart from the influence of HSD on these mood disturbances, added sugar can render food addiction. Both food and addictive substances change the sensitivity of the brain rewarding neurotransmission signalling. The results of the collected studies could be important in assessing sugar intake, especially via maternal dietary patterns, from the clinical perspective of SUD prevention or pre-existing emotional disorders. Methodology: This narrative review focuses on the roles of a high-sugar diet (HSD) and added sugar in foods and on the impacts of glucose and fructose on the development of substance use disorder (SUD) and on the behavioural predictors of drugs abuse. The literature was reviewed by two authors independently according to the topic of the review. We searched the PubMed and Scopus databases and Multidisciplinary Digital Publishing Institute open access scientific journals using the following keyword search strategy depending on the theme of the chapter: “high-sugar diet” OR “high-carbohydrate diet” OR “sugar” OR “glucose” OR “fructose” OR “added sugar” AND keywords. We excluded inaccessible or pay-walled articles, abstracts, conference papers, editorials, letters, commentary, and short notes. Reviews, experimental studies, and epidemiological data, published since 1990s, were searched and collected depending on the chapter structure. After the search, all duplicates are thrown out and full texts were read, and findings were rescreened. After the selection process, appropriate papers were included to present in this review.
Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

Simple Summary Early detection of oral cancer is important to increase the survival rate and reduce morbidity. For the past few years, the early detection of oral cancer using artificial intelligence (AI) technology based on autofluorescence imaging, photographic imaging, and optical coherence tomography imaging has been an important research area. In this study, diagnostic values including sensitivity and specificity data were comprehensively confirmed in various studies that performed AI analysis of images. The diagnostic sensitivity of AI-assisted screening was 0.92. In subgroup analysis, there was no statistically significant difference in the diagnostic rate according to each image tool. AI shows good diagnostic performance with high sensitivity for oral cancer. Image analysis using AI is expected to be used as a clinical tool for early detection and evaluation of treatment efficacy for oral cancer. Abstract The accuracy of artificial intelligence (AI)-assisted discrimination of oral cancerous lesions from normal mucosa based on mucosal images was evaluated. Two authors independently reviewed the database until June 2022. Oral mucosal disorder, as recorded by photographic images, autofluorescence, and optical coherence tomography (OCT), was compared with the reference results by histology findings. True-positive, true-negative, false-positive, and false-negative data were extracted. Seven studies were included for discriminating oral cancerous lesions from normal mucosa. The diagnostic odds ratio (DOR) of AI-assisted screening was 121.66 (95% confidence interval [CI], 29.60; 500.05). Twelve studies were included for discriminating all oral precancerous lesions from normal mucosa. The DOR of screening was 63.02 (95% CI, 40.32; 98.49). Subgroup analysis showed that OCT was more diagnostically accurate (324.33 vs. 66.81 and 27.63) and more negatively predictive (0.94 vs. 0.93 and 0.84) than photographic images and autofluorescence on the screening for all oral precancerous lesions from normal mucosa. Automated detection of oral cancerous lesions by AI would be a rapid, non-invasive diagnostic tool that could provide immediate results on the diagnostic work-up of oral cancer. This method has the potential to be used as a clinical tool for the early diagnosis of pathological lesions.
Exosomes and exosomal non-coding RNAs throughout human gestation (Review)

In recent years, research on exosomes and their content has been intensive, which has revealed their important role in cell-to-cell communication, and has implicated exosomal biomolecules in a broad spectrum of physiological processes, as well as in the pathogenesis of various diseases. Pregnancy and its normal progression rely highly on the efficient communication between the mother and the fetus, mainly mediated by the placenta. Recent studies have established the placenta as an important source of circulating exosomes and have demonstrated that exosome release into the maternal circulation gradually increases during pregnancy, starting from six weeks of gestation. This orchestrates maternal-fetal crosstalk, including maternal immune tolerance and pregnancy-associated metabolic adaptations. Furthermore, an increased number of secreted exosomes, along with altered patterns of exosomal non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs (lncRNAs), have been observed in a number of pregnancy complications, such as gestational diabetes mellitus and preeclampsia. The early detection of exosomes and specific exosomal ncRNAs in various biological fluids during pregnancy highlights them as promising candidate biomarkers for the diagnosis, prognosis and treatment of numerous pregnancy disorders in adolescents and adults. The present review aimed to provide insight into the current knowledge regarding the potential, only partially elucidated, role of exosomes and exosomal cargo in the regulation and progression of normal pregnancy, as well as their potential dysregulation and contribution to pathological pregnancy situations.
Élaboration et processus de validation de l’Échelle d’évaluation des difficultés aux études de cycles supérieurs (EDECS) : étude préliminaire

Many graduate students encounter challenges in their academic formation for which they consult university support services. The first aim of this study is to conduct a literature review of the main difficulties encountered. A classification of the latter, by content analysis, reveals nine categories. From this preliminary classification, a scale screening these difficulties, the EDECS, was constructed for graduate students support services. The exploratory factor analysis of the EDECS with 500 students grouped the nine initial categories of difficulties into four subscales, thus modifying the preliminary classification. The use of the test-retest method among 188 participants revealed good temporal stability. Various proofs of validity (content, internal structure and correlation between variables) were obtained. The conclusion offers suggestions for using the EDECS by student support services and discusses clinical considerations. Many graduate students encounter challenges in their academic formation for which they consult university support services. The first aim of this study is to conduct a literature review of the main difficulties encountered. A classification of the latter, by content analysis, reveals nine categories. From this preliminary classification, a scale screening these difficulties, the EDECS, was constructed for graduate students support services. The exploratory factor analysis of the EDECS with 500 students grouped the nine initial categories of difficulties into four subscales, thus modifying the preliminary classification. The use of the test-retest method among 188 participants revealed good temporal stability. Various proofs of validity (content, internal structure and correlation between variables) were obtained. The conclusion offers suggestions for using the EDECS by student support services and discusses clinical considerations.
Time perception changes in stroke patients: A systematic literature review

Introduction Time perception comprises the subjective experience of passing of time and of the duration of an event. Although already described in some neurological and psychiatric conditions, there is a paucity of details regarding this neurocognitive change in stroke patients. We aimed to describe time perception dysfunction in stroke patient. Methods We performed a systematic review of the literature in Pubmed, PsycInfo and EMBASE including manuscripts from their inception until December 2020. We collected data regarding the type of time perception that was detected, type of stroke, most common location of lesions, evaluation tests that were used and time of evaluation after stroke onset. Results A total of 27 manuscripts were selected, concerning a total of 418 patients (n = 253 male; 60.5%). Most manuscripts (n = 21) evaluated patients with ischaemic lesions (n = 407; 97.4%). The majority referred to evaluations between 2 months and seven years after stroke. Underestimation in temporal evaluation in sub- and supra-second was the most common dysfunction (n = 165; 41.7%). Overestimation of time (n = 116; 27.8%) and impaired time interval comparison (n = 88; 22.2%) were also found. Most patients had right hemisphere lesions (n = 219 patients; 52.4%). Common reported lesion locations included the thalamus, insula, basal ganglia, dorsolateral prefrontal cortex, parietal cortex including supramarginal, angular gyrus and right inferior parietal cortex and cerebellum. Conclusion There are multiple stroke locations associated with time perception dysfunction, which highlights the complex system involved in time perception. There is still scarce knowledge about specific time perception deficits after stroke. Most studies rely in psychometric analysis without clear clinical and functional translation, namely regarding impact on daily activities.
Perinatal Depression as a Growing Mental Health Concern: A Systematic Review

Mental health today, is a rising concern the world over. In order to focus prevention strategies and treatment options, it is important to study the types, and attributes, of illnesses under mental health. Among all types, depression is found to be the major contributor to the global burden of this disease and it is found to affect more women than men. Among women, perinatal depression is a serious and highly prevalent form of depression that affects women of childbearing age. Perinatal depression comprises of antenatal depression (AD) and postnatal depression (PD). AD is a precursor of PD and while it negatively impacts the health of the woman herself, it also has potential adverse effects on the health and socio-psychological development of the new born. Research from low- and middle-income countries (LMICs) finds prevalence of AD at 25.8%, and of PD at 19.7%. In spite of this, while studies on PD are available, studies and understanding of AD are limited and have not received the desired attention of the scientific community. Various global and local organisations have done policy advocacy on mental health, and to some extent within this framework, on perinatal depression, however, focus is still required in the LMICs for strengthening health systems for prevention and treatment.
The Prevalence of Hepatitis B Virus Infection among Patients Attending Defence Headquarters Medical Centre, Asokoro, Abuja, Nigeria

Hepatitis B infection is a global public health problem and is endemic in sub Saharan Africa. This disease has different modes of transmission and the infection is usually common in developing countries, including Nigeria. This study was carried out to determine the prevalence of HBsAg among patients attending Defence Headquarters Medical Centre, Asokoro, Abuja. The work was a random cross-sectional study that included 260 patients during the period of May to July 2020. A rapid diagnostic test kit was used for the screening for HBsAg. Out of the 260 patients tested, 10 were positive, giving a prevalence of 3.8%. The highest prevalence based on age was found among those aged 27 to 36 years with 2.28% seropositivity. The infection was highest among the low class of socioeconomic status with 5.21%. Patients who were not knowledgeable about the disease had a higher prevalence rate of 13.60% and lastly females had a higher prevalence rate of 4.55% compared to males (3.33). The prevalence of Hepatitis B infection is quite high in the studied population, which could have some public health implications. Recommendations have, therefore, been advanced to control and possibly eradicate the disease in no distant time.
Relationship between Yes-Associated Protein 1 and Prognosis of Digestive System Neoplasm: Quantitative Analysis and Bioinformatics Analysis Based on 4023 Patients

Yes-associated protein 1 (YAP1) is involved in the development of a variety of malignancies. However, the prognosis of malignant digestive tumors with YAP1 expression is still controversial. This study searched 31 articles with 36 data sets of 4023 patients to explore the role of YAP1 expression on the prognosis of digestive malignant tumors by searching the PubMed, Embase, Web of Science, Google Scholar, and Cochrane Library databases. Specifically, relevant cancer expression matrix data were downloaded from The Cancer Genome Atlas (TCGA) database. In this meta-analysis, quantitative analysis showed that the overexpression of YAP1 was not conducive to OS (1.62, 95% CI (1.38, 1.90), P=0.001) and DFS (1.59, 95% CI (1.31, 1.93), P=0.001) in patients with digestive malignant tumors. In addition, TCGA database analysis showed that YAP1 was overexpressed in gastric cancer, cholangiocarcinoma, and colorectal cancer. Survival analysis showed that the patients with high expression of YAP1 in pancreatic cancer have a poor OS (MST: 394 vs. 691 days, P < 0.0001) and DFS (MST: 371 vs. 542 days, P=0.026) prognosis. YAP1 may be a molecular marker that effectively predicts the survival of malignant digestive tumors, especially pancreatic cancer, and is a potential therapeutic target for malignant digestive tumors.
Radar HRRP Target Recognition Method Based on Multi-Input Convolutional Gated Recurrent Unit With Cascaded Feature Fusion

Over the past decades, radar high-resolution range profile (HRRP) has been one of the research highlights in the field of radar automatic target recognition (RATR) due to its advantages of easy acquisition, small amount of data, and rich target structure information. However, most of the existing methods only consider its amplitude (time domain) characteristics, thereby neglecting the temporal dependence and multi-domain features inside the HRRP sequence. To this end, we propose an end-to-end multi-input convolutional gated recurrent unit neural network, called MIConvGRU, for RATR by both exploiting the multi-domain and temporal information to improve the recognition performance of HRRP target. Initially, the data-preprocessing module is employed to extract the multi-domain features of the target, including time domain, frequency domain, and time–frequency domain features, in order to further enhance the target representation. In addition, a cascaded multi-input GRU structure is designed to acquire the multi-domain temporal dependence feature of HRRP sequence from low to high level. Finally, these temporal features are adaptively fused by a parameter learnable strategy. The experimental results show that the proposed MIConvGRU can effectively learn the multi-domain temporal dependence correlation features in HRRP sequences, improving the target recognition performance.
NOVEL DISTANCE-LEARNING METHODS TO OVERCOME CHALLENGES CAUSED BY COVID-19 IN UNDERGRADUATE PROGRAMMING COURSES

With the rise of COVID-19 cases, all Sharif University of Technology undergraduate courses shifted toward a completely online paradigm, which caused new challenges in the education and assessment of students. Two of the most important courses for Computer Engineering students with lots of challenges due to the pandemic were Fundamentals of Programming and Advanced Object-Oriented Programming. Various distance-learning techniques were used to enhance the quality of education to overcome these difficulties. These techniques include restructuring the teaching assistant selection process, automating the evaluation and grading of homework and projects, holding interactive online workshop sessions, assigning guides to each student group, and using modern code plagiarism detection systems. Some of them existed before the COVID-19 era, while others are new. This article presents an in-depth analysis of each technique and its effect on improving students' ability to efficiently design and develop programming projects. We also cover special considerations needed to enforce these methods competently. We will back the methods' effectiveness with quantitative data, including a statistical comparison between groups that implemented the mentioned tactics and those that did not.