Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing Digital Methods of Patient-Reported Outcomes Data Collection: Prospective Cluster Randomized Trial to Test SMS Text Messaging and Mobile Surveys

Background Health care delivery continues to evolve, with an effort being made to create patient-centered care models using patient-reported outcomes (PROs) data. Collecting PROs has remained challenging and an expanding landscape of digital health offers a variety of methods to engage patients. Objective The aim of this study is to prospectively investigate two common methods of remote PRO data collection. The study sought to compare response and engagement rates for bidirectional SMS text messaging and mobile surveys following orthopedic surgery. Methods The study was a prospective, block randomized trial of adults undergoing elective orthopedic procedures over 6 weeks. The primary objective was to determine if the method of digital patient engagement would impact response and completion rates. The primary outcome was response rate and total completion of PRO questionnaires. Results A total of 127 participants were block randomized into receiving a mobile survey (n=63) delivered as a hyperlink or responding to the same questions through an automated bidirectional SMS text messaging system (n=64). Gender, age, number of comorbidities, and opioid prescriptions were similar across messaging arms. Patients receiving the mobile survey were more likely to have had a knee-related surgery (n=50, 83.3% vs n=40, 62.5%; P=.02) but less likely to have had an invasive procedure (n=26, 41.3% vs n=39, 60.9%; P=.03). Overall engagement over the immediate postoperative period was similar. Prolonged engagement for patients taking opioids past postoperative day 4 was higher in the mobile survey arm at day 7 (18/19, 94.7% vs 9/16, 56.3%). Patients with more invasive procedures showed a trend toward being responsive at day 4 as compared to not responding (n=41, 59.4% vs n=24, 41.4%; P=.05). Conclusions As mobile patient engagement becomes more common in health care, testing the various options to engage patients to gather data is crucial to inform future care and research. We found that bidirectional SMS text messaging and mobile surveys were comparable in response and engagement rates; however, mobile surveys may trend toward higher response rates over longer periods of time. Trial Registration ClinicalTrials.gov NCT03532256; https://clinicaltrials.gov/ct2/show/NCT03532256
Motivation-based approach for tailoring persuasive mental health applications

ABSTRACT The growing number of people with mental health issues and the worldwide shortage of professionals emphasise the need for tailored persuasive interventions. This paper explores the relationships between the types of motivation individuals experience and their preferences for various features that are widely used in persuasive apps for mental and emotional well-being. First, we reviewed 103 mental health apps from app stores and identified various persuasive features and then conducted focus-group studies of 32 participants. Finally, we implemented the common features in persuasive mental health app prototypes and conducted a large-scale study of 561 users to evaluate their perceived effectiveness. The results reveal that people’s motivation types significantly influence the perceived persuasiveness of different features. People high in intrinsic motivation are more motivated by apps that offer relaxation exercises while providing opportunities to track various mental health-related information. We offer design guidelines for tailoring persuasive mental health apps based on motivation types.
Analyse und Anpassung ausgewählter Optimierungsverfahren für den Digitalen Zwilling von Verdichteranlagen

Digitale Zwillinge werden in der Betriebsphase einer Maschine oder Anlage für verschiedene Fragestellungen angewendet, bspw. für die Zustandserkennung. Voraussetzung dafür ist, dass der Digitale Zwilling über Modelle des Anlagenverhaltens verfügt, die verschiedene Zustände, einschließlich von Fehlerzustände, adäquat beschreiben. Ein wesentlicher Aspekt dabei ist die Identifikation der Modellparameter. Dieser Beitrag dient der Analyse und Anpassung ausgewählter Optimierungsmethoden (gradientenbasierter Verfahren und ein Partikelschwarmoptimierer) am Beispiel von Modellen für Kompressoren und Turbinenanlagen. Dazu wird mit einem analytischen Ansatz ein Modell einer realen Kompressoranlage erstellt. Um die Qualität der Optimierungsergebnisse vergleichen zu können, wird eine Fehlermetrik eingeführt. Das Verhalten der Optimierer wird unter den folgenden realistischen Fehlerszenarien ermittelt: Fehler in den Modellparametern, Rauschen, initialer Abstand vom Optimum, fehlerhaft Messstellen (ohne und mit Detektion). Zur numerischen Bewertung der Identifizierbarkeit werden zwei Kriterien eingeführt.
Informal Coalitions and Legislative Agenda Setting in Mexico’s Multiparty Presidential System

ABSTRACT To what extent can presidents exert gatekeeping power in opposition-led legislatures? Drawing on a study of roll rates in the Mexican Chamber of Deputies, where presidents lack legislative majorities and often face a legislature controlled by the opposition, this article argues that gatekeeping power is divided among multiple actors. It finds that presidents exert weak gatekeeping power over the agenda. While presidents and their parties are rarely defeated in votes related to presidential initiatives, they generally create stable, informal coalitions with opposition parties to pass their bills. Moreover, the agenda-setting power of the president and the president’s party is weaker with bills that originate in the legislative branch, where the party is occasionally rolled on legislative initiatives and during the amendment stage if it is not also the median party.
A REVIEW OF ENDO- AND ECTO-PARASITES OF EQUIDS IN IRAQ

This article summarizes the findings of the majority of Iraqi studies and lists the most common zoonotic and non-zoonotic parasites. As a result of the proper reporting of protozoa, helminthes, hard and soft ticks, a large number of parasites have been described and distributed throughout Iraq. Toxoplasma, Theileria, Babesia, plasmodium, Anaplasma, Microfilaria, cryptosporidium, giardia, Eimeria, Balantidium, and Entamoeba were among the protozoa that were frequently recorded. Helminths of the genera Dicrocoelium, Habronema, Echinococcus, Dictyocaulus, Trichostrongylus, Parascaris, Oxyuris, Cyathostomum, Anoplocephala, Setaria , and Fasciola have been reported to affect all types of horses, whether they are on grass or in stalls. Hard and soft ticks, as well as Sarcoptes, were the only ectoparasites that were frequently observed. Parasitic fly species from the Gasterophilus genus were also discovered. In Iraq, parasitic infections in horses are common and caused by a variety of parasites, posing a health risk and causing significant economic losses. Emaciation, fever, pale mucosal membranes, jaundice, colic, and diarrhoea are all symptoms of infected equines with piroplasms, which are also accompanied by anemia, leukocytosis, and hyperbilirubinemia. Parasitic infections are linked to a number of risk factors (age, gender, activity, location, and season), and zoonotic parasites pose a greater risk to horsemen. All parasitic infections should be treated intramuscularly, with the exception of ivermectin, which should be taken orally. Not only are coprological examinations to identify distributed species and chronic infections, but also modern methods are used to control vectors and conduct further research.
Predictors of Mothers' Behavior in Diarrhea Prevention

Diarrhea causes severe dehydration and depleted nutrients due to the decreased nutritional intake, resulting in malnutrition in children. Diarrhea can also lead to death from dehydration if untreated. Globally, about 1.7 billion cases of childhood diarrhea occurred in 2017. In Indonesia, deaths caused by diarrhea in 2020 accounted for 4.55% of the 250,600 deaths of children under five years old and 9.8% of the 20,266 deaths of newborns. This study aimed to determine the predictors of mothers’ behavior in preventing diarrhea in children under five years old. The sample included mothers with children aged 1 - 5 years, who were recruited using purposive sampling. Roughly 300 mothers were interviewed using a questionnaire. Binary regression was used to measure associations between predictors and mothers’ behaviors. The results showed that the factors that significantly predicted mothers’ behaviors included perceived susceptibility to diarrhea (p < 0.01), perceived severity of diarrhea (p < 0.01), perceived barriers to diarrhea prevention behaviors (p < 0.01) and perceived self-efficacy in diarrhea prevention behaviors (p < 0.01). The model developed could predict 39.2% of the variation in mothers’ behaviors in preventing diarrhea in children. The strongest predictor of mothers’ behaviors was perceived selfefficacy in diarrhea prevention behaviors. The odds of carrying out good prevention behaviors were ten times greater for mothers who had a high level of perceived self-efficacy in preventing diarrhea in children. These results indicated that those who believe in their ability to succeed in performing an action can perform healthy behaviors. Keywords: perceived, mothers, behavior, diarrhea
Blood Circulating Non-Coding RNAs for the Clinical Management of Triple-Negative Breast Cancer

Simple Summary Nowadays, in clinics, there is a lack of reliable biomarkers that could serve as tools allowing for early cancer detection, prediction of therapy response, tumor recurrence, and TNBC course. In this review, we summarized the most recent findings on the applicability of unique blood circulating ncRNAs for management of TNBC. This review was supplemented by bioinformatics analysis for better understanding of molecular processes in which ncRNAs are involved, to promote individual TNBC phenotype and tumor action. Abstract Triple negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer, and is related to unfavorable prognosis and limited treatment strategies. Currently, there is a lack of reliable biomarkers allowing for the clinical management of TNBC. This is probably caused by a complex molecular background, leading to the development and establishment of a unique tumor phenotype. Recent studies have reported non-coding RNAs (ncRNAs) not only as the most promising class of molecular agents with a high applicability to manage human cancers, including TNBC, but also as robust and non-invasive biomarkers that are able to be monitored in blood circulation, with the application of liquid biopsy. There is a lack of papers discussing the role of blood-circulating ncRNAs as diagnostic, predictive, and prognostic biomarkers for TNBC. In this paper, we summarized the available literature reports on the utility of blood-circulating ncRNAs for TNBC management. Additionally, we supplemented this review by bioinformatics analysis, for better understanding of the role of ncRNAs’ machinery in the development of a unique TNBC phenotype.
A Paradigm Shift in Assessment of Scientific Skills in Undergraduate Medical Education

Abstract The General Medical Council’s publication ‘Outcomes for Graduates’ places emphasis on doctors being able to integrate biomedical science, research and scholarship with clinical practice. In response, a new paradigm of assessment was introduced for the intercalated Bachelor of Science program at Imperial College School of Medicine in 2019. This innovative approach involves authentic “active learning” assessments analogous to tasks encountered in a research environment and intends to test a wider range of applied scientific skills than traditional examinations. Written assessments include a “Letter to the Editor”, scientific abstract, and production of a lay summary. A clinical case study titled “Science in Context” presents a real or virtual patient, with evaluation of current and emerging evidence within that field. Another assessment emulates the academic publishing process: groups submit a literature review and engage in reciprocal peer review of another group’s work. A rebuttal letter accompanies the final submission, detailing how feedback was addressed. Scientific presentation skills are developed through tasks including a research proposal pitch, discussion of therapies or diagnostics, or review of a paper. A data management assignment develops skills in hypothesis generation, performing analysis, and drawing conclusions. Finally, students conduct an original research project which is assessed via a written report in the format of a research paper and an oral presentation involving critical analysis of their project. We aspire to train clinicians who apply scientific principles to critique the evidence base of medical practice and possess the skillset to conduct high-quality research underpinned by the principles of best clinical and academic practice. Assessment drives learning, and active learning has been demonstrated to enhance academic performance and reduce attainment gaps in science education. We therefore believe this strategy will help to successfully shape our students as future scientists and scholars as well as clinical practitioners and professionals.
Social inequality and the SARS-CoV-2 in Brazil: How prepared are Brazilâ€™s municipalities in health infrastructure to tackle the disease?

The emergence of new diseases such as SARS-CoV-2 is a reflection of the expansion of anthropic activities on natural ecosystems. The first case in Brazil was reported in February and the number of confirmed cases and the death rate is increasing at an alarming rate. In this context, this study aims to answer two main questions: is social inequality related to SARS-CoV-2 confirmed cases and death rate and how distributed is the health infrastructure in Brazil to tackle the SARS-CoV-2 disease, relating directly to the third UNâ€™s Sustainable Development Goal, Good Health and Well-being. To this purpose, the researchers applied the Moranâ€™s Bivariate Local Spatial Autocorrelation Index to assess the relationship between Gini coefficient with confirmed SARS-CoV-2 cases and the death rate of the disease and the Global Moran Index to assess the distribution of health infrastructure to tackle the disease. We focused on obtaining results that allow decision-making in the health and government sectors. The results display an imbalance between demand and supply, with precarious health infrastructure for people with severe symptoms and increased deaths. SARS-CoV-2 is spreading fast throughout Brazil, and the north and northeast region need special attention due to its lack of health infrastructure. Cartographic products are essential to decision making in epidemiological studies.
Improving MOOCs Using Information From Discussion Forums: An Opinion Summarization and Suggestion Mining Approach

Discussion forums are integral to MOOCs and a useful resource for collaborative learning. In this paper we examine if the posts made on forums by participants can also provide meaningful information to assess and improve the effectiveness of MOOCs. We present an empirical approach that uses posts in MOOC forums to summarize participants’ opinion towards aspects of a course and identify suggestions for improving a course. Specifically, our approach: (1) detects participants’ attitude towards aspects of a course (e.g. professor, lecture, assignment) at a context or sentence level, (2) extracts suggestions for course improvement, which is a novel space in MOOC learning analytics, and (3) aggregates and displays the results visually. The study used lexicon and rule-based approach and was able to identify aspect-based sentiments and suggestions related to course design elements with a good overall score (0.41 kappa score). By summarizing opinions from a vast amount of textual data on forums our approach allows instructors to improve their course and thereby student engagement and learning.
Association of Body Mass Index with Hypertension in Patients with Healthy Weight

Objective: To determine the association between body mass index (BMI) and hypertension in the healthy weight group (BMI: >18.5-<25.0) stratified by age, sex, and race. Methods: Patient age, sex, race, BMI, and a diagnosis of hypertension for 51,435 adult patients (>18 years old) seen since 2015 at either the family medicine clinic or general internal medicine clinics were obtained from the electronic health record using ICD-10 codes for hypertension. This analysis was implemented on the 14,443 of these patients whose BMI values were between 18.5 and <25. Logistic regression was used to assess the relationship between BMI and hypertension with and without adjusting for the impact of age, race, and sex. Odds ratios were calculated for both the adjusted and unadjusted models. Results: Hypertension rates for healthy weight individuals steadily increased with BMI and age, which were independently associated with hypertension. The percentage of patients with hypertension by race was 20.9%, 18.1%, 13.1%, 11.7%, and 9.5% for Black, White, Asian, Other, and Hispanic/Latino, respectively. Fewer women had hypertension compared with men (15.5% vs 20.4%). Tests of odds ratios (both adjusted and unadjusted) indicated that individuals with greater BMI measurements in the healthy weight group are at significantly greater risk for hypertension. Conclusions: Patients in the healthy weight group with higher BMI within that group are at greater risk for hypertension. Patients who are male, Black, or older are also at greater risk of hypertension relative to the rest of the patient population.
Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network

Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.
Identification of Candidate Genes for Meat Color of Chicken by Combing Selection Signature Analyses and Differentially Expressed Genes

Meat color, an important index of chicken quality, is highly related to heme pigment, glycolysis, and intramuscular fat metabolisms. The objective of this study is to obtain candidate genes associated with meat color in chickens based on the comparison of fast-growing, white-feathered chickens (Line B) and slow-growing, yellow-feathered chickens (Jingxing Yellow), which have significant differences in meat color. The differentially expressed genes (DEGs) between Line B and Jingxing Yellow were identified in beast muscle. The fixation index (FST) method was used to detect signatures of positive selection between the two breeds. Screening of 1109 genes by the FST and 1317 candidate DEGs identified by RNA-seq. After gene ontology analysis along with the Kyoto Encyclopedia of Genes and Genomes, 16 genes associated with glycolysis, fatty acid metabolism, protein metabolism, and heme content were identified as candidate genes that regulate the color of chicken breast meat, especially TBXAS1 (redness), GDPD5 (yellowness), SLC2A6 (lightness), and MMP27 (lightness). These findings should be helpful for further elucidating the molecular mechanisms and developing molecular markers to facilitate the selection of chicken meat color.
CEC: A Containerized Edge Computing Framework for Dynamic Resource Provisioning

Container has been widely used in application development and management systems. However, there are two major challenges faced in the real deployment at edge servers. The varying workload of service requests and the startup delay of containers force a flexible resource provisioning scheme in containerized edge computing. To this end, we propose <bold/><monospace><bold>CEC</bold></monospace><bold/>, a containerized edge computing framework for dynamic resource provisioning, and especially for the smart connected community where exists multiple intelligent applications. <bold/><monospace><bold>CEC</bold></monospace><bold/> integrates workload prediction and resource pre-provisioning to enable low latency of user service requests and high utilization of edge resources. First, we present an online periodic request prediction algorithm. Then, we designed a control-based resource pre-provisioning algorithm based on the predicted request distribution, which is a self-adaptive controller to tune the resource for containers. We evaluate the performance of <bold/><monospace><bold>CEC</bold></monospace><bold/> by simulation and system experiments. The simulation experiment shows that the prediction accuracy of the proposed algorithm is higher than other two prediction algorithms. The testbed experiments demonstrate that the control-based resource pre-provisioning algorithm has low service latency and high resource utilization compared with baselines.
Preparation of Bimodal Silver Nanoparticle Ink Based on Liquid Phase Reduction Method

Improving the conductivity of metal particle inks is a hot topic of scientific research. In this paper, a method for preparing metal-filled particles was proposed. By adding filled particles to the ink, the size distribution of particles could be changed to form a bimodal distribution structure in accordance with Horsfield’s stacking model. The filling particles had small volume and good fluidity, which could fill the gaps between the particles after printing and improve its electrical conductivity without significantly changing the metal solid content in the ink. Experimental results show that the silver content of the ink slightly increased from 15 wt% to 16.5 wt% after adding filled particles. However, the conductivity of the ink was significantly improved, and after sintering, the resistivity of the ink decreased from 70.2 μΩ∙cm to 31.2 μΩ∙cm. In addition, the filling particles prepared by this method is simple and has a high material utilization rate, which could be applied to the preparation of other kinds of metal particle inks.