Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of an miRNA Regulatory Network and Candidate Markers for Ischemic Stroke Related to Diabetes

Purpose Type 2 diabetes mellitus (T2DM) increases the risk of ischemic stroke and poor prognosis. This study aimed to identify molecular mechanisms that are dysregulated in T2DM-associated ischemic stroke and candidate genes that might serve as biomarkers. Methods The top 25% variance genes in the GSE21321 and GSE22255 datasets were analyzed for coexpression. The differentially expressed mRNAs (DEmRs) between patients with T2DM or ischemic stroke and controls were analyzed. Then, the union of overlapping coexpressed genes and overlapping DEmRs was analyzed. The miRNAs differentially expressed in T2DM-associated ischemic stroke were also analyzed. CIBERSORT was used to evaluate the levels of infiltration by immune cells in T2DM-associated stroke. Results Thirteen coexpression modules were identified in T2DM and 10 in ischemic stroke, and 594 module genes were shared between the two conditions. A total of 4452 mRNAs differentially expressed between T2DM patients and controls were identified, as were 2390 mRNAs differentially expressed between ischemic stroke and controls. The 771 union genes were enriched mainly in immune-related biological functions and signaling pathways. UBE2N, TGFB3, EXOSC1, and VIM were identified as candidate markers. In addition, we identified miR-576-3p as having the most regulatory roles in both T2DM and ischemic stroke. Mast cell activation was significantly down-regulated in T2DM but up-regulated in ischemic stroke. Conclusion These findings provide numerous testable hypotheses about the pathways underlying T2DM-associated ischemic stroke, which may help identify therapeutic targets.
Exploring the Impact of Technology-Facilitated Abuse and Its Relationship with Domestic Violence: A Qualitative Study on Experts’ Perceptions

Technology-facilitated abuse can be a serious form of domestic violence. Little is known about the relationship between technology-facilitated abuse and other types of domestic violence, or the impact technology-facilitated abuse has on survivors. The aim of this interpretative descriptive study is to understand domestic violence specialist service providers’ perspectives on the impact of technology-facilitated abuse, and the link between technology-facilitated abuse and other forms of domestic violence. A qualitative approach using 15 semi-structured interviews were undertaken with Australian domestic violence specialist practitioners, and three themes were identified through data coding using inductive thematic analysis. Another form of control describes technology-facilitated abuse behaviors as enacting controlling behaviors using new mediums. Amplifies level of fear characterizes the impact of technology-facilitated abuse. A powerful tool to engage others describes opportunities technology offers perpetrators to abuse through engaging others. Findings highlight technology-facilitated abuse’s complexity and integral role in domestic violence and can assist clinicians to understand the impact and harm that can result from technology-facilitated abuse.
A Deep Learning based Air Quality Prediction

1,2,3,4 Lendi Institute of Engineering & Technology, Jonnada, Vizianagaram, Andhra Pradesh ---------------------------------------------------------------------***-----------------------------------------------------------------Abstract Industries are the major means of air pollutants. Air pollution in the form of carbon dioxide and methane raises the earth’s temperature, the less gasoline we burn, the better we do to reduce air pollution and harmful effects of climate change. Especially at metropolitan cities, the change in the temperature combined with harmful chemicals may lead to dangerous signs of air pollution. Quality of air prediction techniques has a major importance in the current learning world. Many machine learning algorithms done a lot of research in identifying the air quality index. Applying deep learning models on these data can show great difference in predicting the quality of air. We proposed an LSTM based deep learning technique in evaluating hourly based encompassing air quality. The proposed results outperformed the existing model results through predicting RMSE value.
Bit Error Rate (BER) Performance of MIMO Systems in M-QAM with Nonlinear Effect

Out object in this paper it to study, the effect of nonlinearity on the bit error rate (BER) of MIMO systems in M-QAM modulation techniques. We consider Saleh’s model (power amplifier model) for the nonlinearity, and apply the nonlinear model on MIMO system with receiver diversity and transmitter diversity. For transmitter diversity, the Space-Time Block Coding (STBC) based on Alamouti scheme is used to provide transmits diversity for two transmitting antennas. The results show that, if there is a high variation in the amplitude of the MQAM symbols, there will behigh effect of nonlinearity that causes high BER especially for high amplitude symbols at high SNR.
Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Background Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression. Objective The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression. Methods Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8-86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression. Results Of the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants’ age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231 (16.81%) were depressed scores (PHQ-8 score ≥10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status–normalized entropy and depression (r=0.14, P<.001). LMM demonstrates an intraclass correlation of 0.7584 and a significant positive association between screen status–normalized entropy and depression (β=.48, P=.03). The best ML algorithms achieved the following metrics: precision, 85.55%-92.51%; recall, 92.19%-95.56%; F1, 88.73%-94.00%; area under the curve receiver operating characteristic, 94.69%-99.06%; Cohen κ, 86.61%-92.90%; and accuracy, 96.44%-98.14%. Including age group and gender as predictors improved the ML performances. Screen and internet connectivity features were the most influential in predicting depression. Conclusions Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors’ data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring.
Multidimensional Clinical Surveillance of Pseudomonas aeruginosa Reveals Complex Relationships between Isolate Source, Morphology, and Antimicrobial Resistance

P. aeruginosa is a leading cause of nosocomial infection and infection in patients with cystic fibrosis. While P. aeruginosa infection and treatment can be complicated by a variety of antimicrobial resistance and virulence mechanisms, pathogen virulence is rarely recorded in a clinical setting. ABSTRACT Antimicrobial susceptibility in Pseudomonas aeruginosa is dependent on a complex combination of host and pathogen-specific factors. Through the profiling of 971 clinical P. aeruginosa isolates from 590 patients and collection of paired patient metadata, we show that antimicrobial resistance is associated with not only patient-centric factors (e.g., cystic fibrosis and antipseudomonal prescription history) but also microbe-specific phenotypes (e.g., mucoid colony morphology). Additionally, isolates from different sources (e.g., respiratory tract, urinary tract) displayed rates of antimicrobial resistance that were correlated with source-specific antimicrobial prescription strategies. Furthermore, isolates from the same patient often displayed a high degree of heterogeneity, highlighting a key challenge facing personalized treatment of infectious diseases. Our findings support novel relationships between isolate and patient-level data sets, providing a potential guide for future antimicrobial treatment strategies. IMPORTANCE P. aeruginosa is a leading cause of nosocomial infection and infection in patients with cystic fibrosis. While P. aeruginosa infection and treatment can be complicated by a variety of antimicrobial resistance and virulence mechanisms, pathogen virulence is rarely recorded in a clinical setting. In this study, we discovered novel relationships between antimicrobial resistance, virulence-linked morphologies, and isolate source in a large and variable collection of clinical P. aeruginosa isolates. Our work motivates the clinical surveillance of virulence-linked P. aeruginosa morphologies as well as the tracking of source-specific antimicrobial prescription and resistance patterns.
Neonatal anesthesia and dysregulation of the epigenome

Abstract Each year, millions of infants and children are anesthetized for medical and surgical procedures. Yet, a substantial body of preclinical evidence suggests that anesthetics are neurotoxins that cause rapid and widespread apoptotic cell death in the brains of infant rodents and nonhuman primates. These animals have persistent impairments in cognition and behavior many weeks or months after anesthesia exposure, leading us to hypothesize that anesthetics do more than simply kill brain cells. Indeed, anesthetics cause chronic neuropathology in neurons that survive the insult, which then interferes with major aspects of brain development, synaptic plasticity, and neuronal function. Understanding the phenomenon of anesthesia-induced developmental neurotoxicity is of critical public health importance because clinical studies now report that anesthesia in human infancy is associated with cognitive and behavioral deficits. In our search for mechanistic explanations for why a young and pliable brain cannot fully recover from a relatively brief period of anesthesia, we have accumulated evidence that neonatal anesthesia can dysregulate epigenetic tags that influence gene transcription such as histone acetylation and DNA methylation. In this review, we briefly summarize the phenomenon of anesthesia-induced developmental neurotoxicity. We then discuss chronic neuropathology caused by neonatal anesthesia, including disturbances in cognition, socio-affective behavior, neuronal morphology, and synaptic plasticity. Finally, we present evidence of anesthesia-induced genetic and epigenetic dysregulation within the developing brain that may be transmitted intergenerationally to anesthesia-naïve offspring. Summary sentence Neonatal anesthesia can alter the epigenome, and these neuropathological changes can be inherited by anesthesia-naive offspring. Graphical Abstract
Optimization and Stability Testing of Four Commercially Available Dried Blood Spot Devices for Estimating Measles and Rubella IgG Antibodies

Dried blood spot (DBS) collection offers various advantages over conventional methods of blood collection, especially when collecting and transporting samples for a serosurvey. Yet use of DBS requires additional processing steps in the laboratory that can add to variability in results. ABSTRACT Blood collection using dried blood spots (DBS) provides an easier alternative to venipuncture for sample collection, transport, and storage but requires additional processing that can cause variability in results. Whole-blood samples spotted on four DBS devices and respective paired serum samples were tested for antimeasles and antirubella IgG antibody concentrations by enzyme immunoassay. Elution protocols for DBS devices were optimized for comparability relative to serum samples using 12 adult volunteers. Stability of DBS collected on HemaSpot HF was assessed under various temperature conditions (+4, 22 to 25, and 45°C) at six time points (0, 7, 15, 30, 60, and 90 days) in a controlled laboratory setting using six adult volunteers. Devices were shipped and stored for 30 days at four settings with variable temperature and humidity conditions to assess the impact on antibody concentrations. Three DBS devices demonstrated comparable antibody concentrations with paired sera following optimization. Antibodies recovered from DBS were stable for at least 90 days at 4°C and for 30 days at ambient temperature (22 to 25°C) using the HemaSpot HF device. A drastic decline in antibody concentrations was observed at 45°C, resulting in quantitative and qualitative discrepancies by day 7. HemaSpot HF devices shipped to field sites and stored at ambient temperature and humidity resulted in quantitative, but not qualitative, variability. Measurement of antimeasles and antirubella IgG antibodies with DBS devices is an accurate alternative to testing serum, provided elution protocols are optimized. Stability of HemaSpot HF devices at ambient temperature enables broader use in surveys when serum processing and cold storage are not feasible. IMPORTANCE Dried blood spot (DBS) collection offers various advantages over conventional methods of blood collection, especially when collecting and transporting samples for a serosurvey. Yet use of DBS requires additional processing steps in the laboratory that can add to variability in results. We optimized a protocol to elute IgG antibodies against measles and rubella viruses in four DBS devices, demonstrating high concordance with paired venous sera for most devices. Extensive stability studies with various temperature and storage conditions in the laboratory and in the field were conducted using HemaSpot HF DBS devices prior to its use in one of the largest community-based measles and rubella serological surveys in the world.
Influence of Cortical Layer and Surgical Techniques on the Primary Implant Stability in Low-density Bone: An In Vitro Study.

AIM This study aimed at evaluating the influence of cortical layer and surgical techniques on the primary stability of implants in low-density bone. MATERIALS AND METHODS Two solid rigid polyurethane blocks with a density equivalent to 0.32 g/cm3 simulating cancellous bone were used. A short fiber-filled epoxy resin sheet of 2 mm was layered to one block to simulate cortico cancellous bone. A total of 40 implants were used in this study (n = 40). Twenty implants each (n = 20) were inserted in cancellous (Group 1) and cortico-cancellous bone (Group 2), of which 10 implants each (n = 10) were placed using undersized preparation technique with surgical drills-A and osteotomes-B, in both the groups. Insertion torque (IT) and implant stability quotient (ISQ) for each implant placed were assessed to determine the primary stability of each implant using a digital torque meter and resonance frequency analyzer, respectively. The values were statistically analyzed using an independent t-test (p < 0.05). Pearson's correlation analysis was performed to correlate between IT and ISQ. RESULTS Technique B resulted in significantly higher IT and ISQ values in Group 1 (27.69 ± 1.2 N cm; 52.5 ± 1.05 ISQ) and Group 2 (38.8 ± 0.87 N cm; 70.1 ± 1.04 ISQ) compared to those with technique A (22.40 ± 1.62 N cm; 41.75 ± 1.20 ISQ and 33.24 ± 0.67 N cm; 63.72 ± 1.33 ISQ), respectively. Group 2 exhibited significantly higher IT and ISQ values as compared to Group 1 irrespective of the surgical technique employed (p < 0.05). CONCLUSION The presence of the cortical layer significantly influenced the primary stability and preparing low-density bone with an undersized preparation technique using osteotomes that significantly increased the IT and ISQ. CLINICAL SIGNIFICANCE Undersizing the preparation site considerably will help achieve a significant increase in primary stability in the poor quality bone as in the posterior maxilla, thereby contributing to the success of the implant.
Examining Automatic Stereotyping From a Propositional Perspective: Is Automatic Stereotyping Sensitive to Relational and Validity Information?

Research on automatic stereotyping is dominated by the idea that automatic stereotyping reflects the activation of (group–trait) associations. In two preregistered experiments (total N = 391), we tested predictions derived from an alternative perspective that suggests that automatic stereotyping is the result of the activation of propositional representations that, unlike associations, can encode relational information and have truth values. Experiment 1 found that automatic stereotyping is sensitive to the validity of information about pairs of traits and groups. Experiment 2 showed that automatic stereotyping is sensitive to the specific relations (e.g., whether a particular group is more or less friendly than a reference person) between pairs of traits and groups. Interestingly, both experiments found a weaker influence of validity/relational information on automatic stereotyping than on non-automatic stereotyping. We discuss the implications of these findings for research on automatic stereotyping.
A Locally Optimized Data-Driven Tool to Predict Sepsis-Associated Vasopressor Use in the ICU

Supplemental Digital Content is available in the text. OBJECTIVES: To train a model to predict vasopressor use in ICU patients with sepsis and optimize external performance across hospital systems using domain adaptation, a transfer learning approach. DESIGN: Observational cohort study. SETTING: Two academic medical centers from January 2014 to June 2017. PATIENTS: Data were analyzed from 14,512 patients (9,423 at the development site and 5,089 at the validation site) who were admitted to an ICU and met Center for Medicare and Medicaid Services definition of severe sepsis either before or during the ICU stay. Patients were excluded if they never developed sepsis, if the ICU length of stay was less than 8 hours or more than 20 days or if they developed shock up to the first 4 hours of ICU admission. MEASUREMENTS AND MAIN RESULTS: Forty retrospectively collected features from the electronic medical records of adult ICU patients at the development site (four hospitals) were used as inputs for a neural network Weibull-Cox survival model to derive a prediction tool for future need of vasopressors. Domain adaptation updated parameters to optimize model performance in the validation site (two hospitals), a different healthcare system over 2,000 miles away. The cohorts at both sites were randomly split into training and testing sets (80% and 20%, respectively). When applied to the test set in the development site, the model predicted vasopressor use 4–24 hours in advance with an area under the receiver operator characteristic curve, specificity, and positive predictive value ranging from 0.80 to 0.81, 56.2% to 61.8%, and 5.6% to 12.1%, respectively. Domain adaptation improved performance of the model to predict vasopressor use within 4 hours at the validation site (area under the receiver operator characteristic curve 0.81 [CI, 0.80–0.81] from 0.77 [CI, 0.76–0.77], p < 0.01; specificity 59.7% [CI, 58.9–62.5%] from 49.9% [CI, 49.5–50.7%], p < 0.01; positive predictive value 8.9% [CI, 8.5–9.4%] from 7.3 [7.1–7.4%], p < 0.01). CONCLUSIONS: Domain adaptation improved performance of a model predicting sepsis-associated vasopressor use during external validation.
Identification of a new 2‐amino acid insertion in the integrase coding region of HIV‐1 subtype G isolates

Amino acid insertions have been rarely found in the integrase (IN) coding region of Human immunodeficiency virus 1 (HIV‐1), and have been considered as natural polymorphisms. It is still unclear the potential impact of these insertion mutations on the viral replication capacity and/or susceptibility to integrase strand transfer inhibitors (INSTIs). The objective of this study was to describe a previously unreported amino acid insertion in the IN coding region of HIV‐1 isolates obtained from antiretroviral treatment‐naïve infected individuals. Nucleotide sequences of HIV‐1 isolates obtained from two infected individuals were analyzed for genotypic resistance to antiretroviral drugs. Phylogenetic inference was carried out for HIV‐1 genetic variant identification. An unreported insertion of a threonine (T) and an asparagine (N) between codon 255 and 256 (S255N_TN) was identified in the IN C‐terminal domain of HIV‐1 subtype G isolates. No resistance‐associated mutations to INSTIs were detected in the inserted sequences. Both individuals maintained undetectable HIV‐1 RNA viral load, 24 months after undergoing antiretroviral treatment with an INSTI containing regimen. The results demonstrated the possibility of transmission of this insertion mutation and suggested that the codon 255 insert by itself may not affect susceptibility to INSTIs.
Associations between physical activity motives and trends in moderate-to-Vigorous physical activity among adolescents over five years

ABSTRACT This study examined the longitudinal associations between five physical activity (PA) motives and moderate-to-vigorous PA (MVPA) across a 5-year period spanning late childhood to middle adolescence. METHODS: Data (n = 937; 55% girls; mean age = 10.33 years) were drawn from the Monitoring Activities for Teenagers to Comprehend their Habits study. PA motives and MVPA were assessed 15 times over the course of 5 years. Measurement invariance for the Motives for Physical Activity Measure-Revised (MPAM-R) questionnaire was established, and sex-stratified mixed-effects regression models were analysed. MVPA increased until a mean age of 12.18 years for girls and 12.89 years for boys before decreasing through the final assessment. From late childhood to middle adolescence, for boys, enjoyment motives were positively (β(95% CI) = 6.14(3.86–8.43)), while fitness motives were negatively (β(95% CI) = −4.80(−8.0, −1.59)) associated with MVPA. Whereas, for girls, competence motives were positively β(95% CI) = 3.44(1.59–5.28)) associated with MVPA Boys may benefit from PA interventions, if these were primarily aimed at increasing ones’ enjoyment, whereas developing a girl’s competence may provide greater contributions to a girl’s future PA behaviours. PA interventions should avoid promoting the desire to be active to improve fitness, particularly among boys.