Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging the BAME Attainment Gap: Student and Staff Perspectives on Tackling Academic Bias

The Higher Education attainment gap between BAME students and their White peers is well documented. The cause of this gap is multifactorial, and there is a need to understand contributing factors to support the design of meaningful interventions. This study aimed to probe student and staff understanding of what contributes to the attainment gap and to collect feedback on how to reduce it. Qualitative data were collected from 110 STEM students (95% BAME and 75% female) and 20 staff (70% BAME and 80% female) from Universities in the West Midlands and London via one-to-one interviews and focus groups. Questions were developed across themes of support, inclusivity, and development. Transcripts were subjected to inductive and deductive thematic analysis. Key findings included: the need for cultural awareness and representation within student support services and pastoral care provision and tailored support for students who were the first in their families to attend university. Students also felt the academic staff disproportionally represent their backgrounds, leading to a sense of not belonging amongst BAME students. BAME staff felt like tokens for diversity and reported having higher workloads than White staff, the social drinking culture felt isolating, participants felt that all staff should engage with cultural/religious training and diversity of academic staff should be improved through inclusive recruitment practices and mentoring. The results highlight the need for access to academic and pastoral support that is culturally sensitive to all backgrounds.
Bionic Prostheses: The Emerging Alternative to Vascularised Composite Allotransplantation of the Limb

Twenty years have surpassed since the first vascularised composite allotransplantation (VCA) of the upper limb. This is an opportunity to reflect on the position of VCA as the gold standard in limb reconstruction. The paucity of recipients, tentative clinical outcomes, and insufficient scientific progress question whether VCA will remain a viable treatment option for the growing numbers of amputees. Bionic technology is advancing at a rapid pace. The prospect of widely available, affordable, safely applied prostheses with long-standing functional benefit is appealing. Progress in the field stems from the contributions made by engineering, electronic, computing and material science research groups. This review will address the ongoing reservations surrounding VCA whilst acknowledging the future impact of bionic technology as a realistic alternative for limb reconstruction.
Machine Learning for Prediction of Outcomes in Cardiogenic Shock

Objective The management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly patients with CS. This study aims to build a predictive model, which could better predict the 30-day mortality of elderly patients with CS. Methods We extracted data from the Medical Information Mart for Intensive Care III version 1.4 (MIMIC-III) as the training set and the data of validation sets were collected from the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. Three models, including the cox regression model, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and the CoxBoost model, were established using the training set. Through the comparison of area under the receiver operating characteristic (ROC) curve (AUC), C index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and median improvement in risk score, the best model was selected. Then for external validation, compared the best model with the simplified acute physiology score II (SAPSII) and the CardShock risk score. Results A total of 919 patients were included in the study, of which 804 patients were in the training set and 115 patients were in the verification set. Using the training set, we built three models: the cox regression model including 6 predictors, the LASSO regression model including 4 predictors, and the CoxBoost model including 16 predictors. Among them, the CoxBoost model had good discrimination [AUC: 0.730; C index: 0.6958 (0.6657, 0.7259)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of other models were all<0. In the validation set, the CoxBoost model was also well-discriminated [AUC: 0.770; C index: 0.7713 (0.6751, 0.8675)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of SAPS II and the CardShock risk score were all < 0. And we constructed a dynamic nomogram to visually display the model. Conclusion In conclusion, this study showed that in predicting the 30-day mortality of elderly CS patients, the CoxBoost model was superior to the Cox regression model, LASSO regression model, SAPS II, and the CardShock risk score.
What Is the Current State of Awareness, Knowledge, and Attitudes Toward Breast Cancer? A Cross-Sectional Survey Among Health and Non-health College Students

Objective To document breast cancer (BC) knowledge, awareness, and attitudes among female undergraduate students studying at health and non-health colleges. Methods A 3-month cross-sectional study was conducted among female undergraduate students studying at health and non-health subject colleges affiliated to a public university. Convenience sampling was employed, and a previously validated questionnaire available in English and Arabic languages was used. Multiple linear regression was used to report the predictors of BC knowledge. A two-tailed p-value of < 0.05 was considered significant. The study was approved by an ethics committee. Results A total of 506 responses were analyzed. The mean knowledge score was 13.98 ± 4.1. The findings of the surveyed students suggested that more than 55% of the students had an acceptable level of knowledge. By education sector, approximately 70% and 40% of health and non-health college students, respectively, had an acceptable level of knowledge. The mean difference in knowledge scores between students of health and non-health colleges was significant (p < 0.001) as students at health colleges had a higher score. Age, college type and the presence of the disease in family/relatives were significant predictors of students' BC knowledge (p < 0.05). Conclusion By comparing it with previous evidence, the knowledge of BC has improved. The role of awareness campaigns as an information medium for students from non-health backgrounds is greatly appreciated. Moreover, the internet and electronic media have emerged as new sources of information for non-health college students, and therefore, more efforts are needed to utilize this medium in empowering this student population in understanding of this disease.
Examining the Constructs of Burnout, Compassion Fatigue, Secondary Traumatic Stress in Physicians Using Factor Analyses

Background Adverse affective experiences have been well-documented in healthcare providers. Research describes them under a variety of terms, including burnout, secondary traumatic stress (STS), and compassion fatigue (CF). The present study evaluates conflicting models of STS, CF, and burnout constructs in physicians. Methods Surveys were mailed to all allopathic physicians with active Rhode Island medical licenses. Three hundred and seventy-five complete responses were received. The survey included common measures of STS, CF, and burnout. Confirmatory Factor Analysis (CFA) was used to evaluate discriminant validity of the three constructs and test 5 a priori (1-, 2-, and 3-factor) theoretical models, and Exploratory Factor Analysis (EFA) was planned assess underlying factor structure in the case that CFA did not provide evidence supporting any existing model. Results By CFA, all five a priori models of burnout, CF, and STS fail to demonstrate adequate model fit (Standardized Root Mean Square Residual >0.10, Tucker-Lewis Index <0.90). EFA with parallel analysis extracts four factors underlying the three burnout, STS, and CF measures. The four factors describe 54.3% of variance and can be described as (1) depressive mood; (2) primary traumatic stress-like symptoms; (3) responses to patients' trauma; and (4) sleep disturbances. Conclusion In spite of abundant discussion surrounding burnout, CF, and STS in physicians, measures of these constructs did not uphold their theoretical factor structures in the present study. Future research might explore other constructs and measures that may describe adverse affective physician experiences.
Proteome Profiling Identifies Serum Biomarkers in Rheumatoid Arthritis

Rheumatoid arthritis (RA) causes serious disability and productivity loss, and there is an urgent need for appropriate biomarkers for diagnosis, treatment assessment, and prognosis evaluation. To identify serum markers of RA, we performed mass spectrometry (MS)-based proteomics, and we obtained 24 important markers in normal and RA patient samples using a random forest machine learning model and 11 protein–protein interaction (PPI) network topological analysis methods. Markers were reanalyzed using additional proteomics datasets, immune infiltration status, tissue specificity, subcellular localization, correlation analysis with disease activity-based diagnostic indications, and diagnostic receiver-operating characteristic analysis. We discovered that ORM1 in serum is significantly differentially expressed in normal and RA patient samples, which is positively correlated with disease activity, and is closely related to CD56dim natural killer cell, effector memory CD8+T cell, and natural killer cell in the pathological mechanism, which can be better utilized for future research on RA. This study supplies a comprehensive strategy for discovering potential serum biomarkers of RA and provides a different perspective for comprehending the pathological mechanism of RA, identifying potential therapeutic targets, and disease management.
Gradient Boosting Machine Learning Model for Defective Endometrial Receptivity Prediction by Macrophage-Endometrium Interaction Modules

Background A receptive endometrium is a prerequisite for successful embryo implantation. Mounting evidence shows that nearly one-third of infertility and implantation failures are caused by defective endometrial receptivity. This study pooled 218 subjects from multiple datasets to investigate the association of the immune infiltration level with reproductive outcome. Additionally, macrophage-endometrium interaction modules were constructed to explore an accurate and cost-effective approach to endometrial receptivity assessment. Methods Immune-infiltration levels in 4 GEO datasets (n=218) were analyzed and validated through meta-analysis. Macrophage-endometrium interaction modules were selected based on the weighted gene co-expression network in GSE58144 and differentially expressed genes dominated by GSE19834 dataset. Xgboost, random forests, and regression algorithms were applied to predictive models. Subsequently, the efficacy of the models was compared and validated in the GSE165004 dataset. Forty clinical samples (RT-PCR and western blot) were performed for expression and model validation, and the results were compared to those of endometrial thickness in clinical pregnancy assessment. Results Altered levels of Mϕs infiltration were shown to critically influence embryo implantation. The three selected modules, manifested as macrophage-endometrium interactions, were enrichment in the immunoreactivity, decidualization, and signaling functions and pathways. Moreover, hub genes within the modules exerted significant reproductive prognostic effects. The xgboost algorithm showed the best performance among the machine learning models, with AUCs of 0.998 (95% CI 0.994-1) and 0.993 (95% CI 0.979-1) in GSE58144 and GSE165004 datasets, respectively. These results were significantly superior to those of the other two models (random forest and regression). Similarly, the model was significantly superior to ultrasonography (endometrial thickness) with a better cost-benefit ratio in the population. Conclusion Successful embryo implantation is associated with infiltration levels of Mϕs, manifested in genetic modules involved in macrophage-endometrium interactions. Therefore, utilizing the hub genes in these modules can provide a platform for establishing excellent machine learning models to predict reproductive outcomes in patients with defective endometrial receptivity.
Presenting a New Framework to Improve Engagement in Physical Activity Programs for Children and Adolescents With Social, Emotional, and Behavioral Disabilities

Children and adolescents with psychiatric and neurodevelopmental diagnoses such as anxiety, depression, autism, and attention-deficit/hyperactivity disorder (ADHD) face enormous health disparities, and the prevalence of these disorders is increasing. Social, emotional, and behavioral disabilities (SEBD) often co-occur with each other and are associated with unique barriers to engaging in free-living physical activity (PA), community-based exercise and sports programming, and school-based physical education. Some examples of these barriers include the significantly depleted parental reserve capacity associated with SEBD in children, child dysregulation, and previous negative experiences with PA programming and/or exclusion. Importantly, most SEBD are “invisible,” so these parents and children may face more stigma, have less support, and fewer inclusive programming opportunities than are typically available for children with physical or intellectual disabilities. Children’s challenging behavioral characteristics are not visibly attributable to a medical or physical condition, and thus are not often viewed empathetically, and cannot easily be managed in the context of programming. Existing research into PA engagement barriers and facilitators shows significant gaps in existing health behavior change (HBC) theories and implementation frameworks that result in a failure to address unique needs of youth with SEBD and their parents. Addressing these gaps necessitates the creation of a simple but comprehensive framework that can better guide the development and implementation of engaging, effective, and scalable PA programming for these youth and their families. Therefore, the aim of this article is to: (1) summarize existing research into SEBD-related child and parent-level barriers and facilitators of PA evidence-based program engagement; (2) review the application of the most commonly used HBC and disability health theories used in the development of evidence-based PA programs, and implementation science frameworks used in adaptation and dissemination efforts; (3) review the SEBD-related gaps that may negatively affect engagement; and (4) describe the new Pediatric Physical Activity Engagement for Invisible Social, Emotional, and Behavioral Disabilities (PAID) Framework, a comprehensive adapted PA intervention development and implementation adaptation framework that we created specifically for youth with SEBD and their parents.
The impact of CAD on the design of low power digital circuits

The power consumption and dissipation of electronic systems is linked to many elements such as display devices, disk drives and even fans; however, the focus of this paper is on the electronic sub-system and specifically the power dissipation of integrated circuits. This paper will first briefly motivate the topical concerns about power consumption and dissipation in integrated circuits and will then outline the automated techniques that are currently being developed to reduce power consumption.
Design of SMS (Single mode-Multi mode coreless-Single mode) optical fiber as corrosion sensor

Conventional corrosion sensors such as ultrasonic guided waves, eddy current and thermography have been widely applied, however, these sensors have disadvantage of suffering from additional damage, as these sensors are placed in corrosive environment, often hidden and could not be directly observed, therefore corrosion inspection using conventional sensor is much more expensive and time consuming. Usage of optical fiber as corrosion sensor, based on changes in light intensity due to corrosion effect, is an alternative to overcome the disadvantage of conventional sensors. We proposed to use Single mode – Multimode Coreless – Single mode structured optical fiber as sensor. Multimode section of the optical fiber is coated with aluminium as the corrosion object. Experiment result shows that the corrosion process have an effect of decreasing the output sensor power as the corrosion rate increasing. Experimental test is conducted on the sensor, in which time interval of corrosion treatment is 9 hours. It is obtained from the test result that the highest value of output power is 13.19 dBm. Furthermore when the weight loss range of aluminium layer is 0 – 140 mg after corrosion treatment and the length of multimode fiber is 10 mm, the highest sensitivity of the sensor is 0.094 dBm/mg.
ASSESSMENT OF THE EFFECTIVENESS OF ERP SYSTEMS BY A FUZZY LOGIC APPROACH

In today’s intensely competitive business world, there is an increasing demand for Enterprise Resource Planning (ERP) systems. This is because there is an essential need for efficient ways of continuous assessment, identifying shortcomings and improving system performance. On one hand, the quality of ERP systems is related to the user satisfaction. On the other hand, measuring human’s satisfaction is intermingled by uncertainty and vagueness. That is why ordinary statistical analysis is not necessarily efficient in this context. This motivated us to use fuzzy logic methods in assessing the effectiveness of ERP. We conducted this research in an Iranian company producing automobile parts. We also used Ifinedo & Nahar’s model for the assessment of ERP effectiveness. We had interviews with the employees, managers and experts. Then we analyzed the collected data using MATLAB©. Our analysis showed that ERP effectiveness in the company is fairly good.
Impacts of science-based research and innovation program: The case of farmers’ transition to organic production in Camargue

This paper attempts to assess the impacts of science-based research and innovation program (ISRIP) of farmer’s transition to organic rice in Camargue. We applied a methods-mix revolving around the Impact Pathway Analysis (IPA) with the goal of shedding light on complex mechanisms that occur along innovation processes and ex-post reconstructing the impact pathway of the “basket of innovations” which was identified as the main innovation in the theory of change of the program. The analysis allows identification of the specific role of the research. We demonstrate that the French National Institute of Research Agriculture (INRA) contributed to change in supporting growers through experimental trials, discussions and leaflets even though the rice producers underlined that their trials played a more important role to find relevant techniques. Farmers also reported other driving factors like the selling price of organic rice or CAP subsidies for conversion and maintenance of land for organic rice. Finally, the analysis emphasises that the research system is not the central node in the innovation pathway, although it does play a substantial role.
Chromosome-level Genomes Reveal the Genetic Basis of Descending Dysploidy and Sex Determination in Morus Plants

Multiple plant lineages have independently evolved sex chromosomes and variable karyotypes to maintain their sessile lifestyles through constant biological innovation. Morus notabilis, a dioecious mulberry species, has the fewest chromosomes among Morus spp. , but the genetic basis of sex determination and karyotype evolution in this species have not been identified. Three high-quality genome assemblies generated of Morus spp. (including those of dioecious M. notabilis and Morus yunnanensis) were within the range 301-329 Mb in size which were grouped into six pseudochromosomes. Using a combination of genomic approaches, we showed that the putative ancestral karyotype of Morus was close to 14 protochromosomes, and that several chromosome fusion events resulted in descending dysploidy (2n = 2x = 12). We also characterized a ∼6.2-Mb sex-determining region on chromosome 3. The four potential male-specific genes, including a partially duplicated DNA helicase gene orthologue (named MSDH) and three Ty3_Gypsy long terminal repeat retrotransposons (named MSTG), were solely identified in the Y-linked area and considered to be strong candidate genes for sex determination or differentiation. Population genomic analysis showed that Guangdong accessions in China were genetically similar to Japanese accessions of mulberry. In addition, genomic areas containing selective sweeps that distinguish domesticated mulberry trees from wild populations in terms of flowering and disease resistance were identified. Our findings provide an important genetic resource for sex identification and molecular breeding in mulberry.
Incisional Negative Pressure Wound Therapy Is Protective Against Postoperative Cardiothoracic Wound Infection

Background Sternal wound infections are a rare but life-threatening complication of cardiothoracic surgery. Prior literature has supported the use of negative pressure wound therapy to decrease sternal wound infections and promote healing. This study sought to determine whether closed incision negative pressure therapy reduced wound infection and improved outcomes in cardiothoracic surgery. Methods A retrospective cohort study was performed including all adult patients who underwent nontraumatic cardiothoracic surgery at a single institution between 2016 and 2018 (n = 1199). Patient characteristics, clinical variables, and surgical outcomes were compared between those who did and did not receive incisional negative pressure wound therapy intraoperatively. Multivariable logistic regression analysis determined factors predictive or protective of the development of complications. Results Incisional negative pressure wound therapy was used in 58.9% of patients. Patients who received this therapy were older with statistically higher rates of hyperlipidemia, statin, and antihypertensive use. The use of negative pressure wound therapy was found to significantly reduce rates of both wound infection (3.0% vs 6.3%, P = 0.01) and readmission for wound infection (0.7% vs 2.6%, P = 0.01). After controlling for confounding variables, negative pressure wound therapy was found to be a protective factor of surgical wound infection (odds ratio, 0.497; 95% confidence interval, 0.262–0.945). Conclusions In the largest population studied to date, this study supported the expanded use of negative pressure therapy on sternal wound incisions to decrease infection rates.
Approaching independent living with robots

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