Miracle-dz/UniFAR-FTUs huggingface dataset:
Cut-off points in the waist:height ratio for the prediction of insulin resistance, second stage of sexual maturation in Brazilian adolescents

Abstract The objective of this study was verify the best waist:height ratio (WHtR) cut-off points for the prediction of insulin resistance (IR) according to stage of sexual maturation in Brazilian adolescents. Data were analysed from 37 759 adolescents between 12 and 17 years of age, of both sexes, participating in the ERICA study on cardiovascular risk factors in adolescents (‘Estudo de Riscos Cardiovasculares em Adolescentes – ERICA’). The following variables were included sex, age and sexual maturity according to the Tanner stage, waist circumference, height, WHtR, fasting insulin and fasting blood sugar so as to obtain the homeostasis model assessment-estimated insulin resistance (HOMA-IR). We constructed an Receiver Operating characteristic (ROC) curve for each group in order to determine the best WHtR cut-off points for the sample stratified by sex and stratified by sex and sexual maturation stage. The female sex corresponded to 60% of the sample, with a mean age of 14·7 years (sd ± 0·08 years) and 13·1 % had IR. The prediction of IR through the WHtR indicator was better for boys, where all the areas under the ROC curve were superior to 0·70. When stratifying by sex alone, the best WHtR cut-off points found were equal to 0·45 for girls and 0·44 for boys. When stratifying by Tanner maturation stages, the cut-off points were reduced with advancing sexual maturation in the male group, while the opposite occurred in the female group. Among Brazilian adolescents, WHtR is a good predictor of IR evaluated by HOMA-IR, but different cut-off points should be adopted according to sex and sexual maturation stage.
Optimizing Full 3D SPARKLING Trajectories for High-Resolution Magnetic Resonance Imaging

The Spreading Projection Algorithm for Rapid K-space sampLING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D MRI using compressed sensing. It has then been extended to address 3D imaging using either stacks of 2D sampling patterns or a local 3D strategy that optimizes a single sampling trajectory at a time. 2D SPARKLING actually performs variable density sampling (VDS) along a prescribed target density while maximizing sampling efficiency and meeting the gradient-based hardware constraints. However, 3D SPARKLING has remained limited in terms of acceleration factors along the third dimension if one wants to preserve a peaky point spread function (PSF) and thus good image quality. In this paper, in order to achieve higher acceleration factors in 3D imaging while preserving image quality, we propose a new efficient algorithm that performs optimization on full 3D SPARKLING. The proposed implementation based on fast multipole methods (FMM) allows us to design sampling patterns with up to <inline-formula> <tex-math notation="LaTeX">${10}^{{7}}$ </tex-math></inline-formula> k-space samples, thus opening the door to 3D VDS. We compare multi-CPU and GPU implementations and demonstrate that the latter is optimal for 3D imaging in the high-resolution acquisition regime (<inline-formula> <tex-math notation="LaTeX">$600\mu $ </tex-math></inline-formula> m isotropic). Finally, we show that this novel optimization for full 3D SPARKLING outperforms stacking strategies or 3D twisted projection imaging through retrospective and prospective studies on NIST phantom and in vivo brain scans at 3 Tesla taking the particular case of <inline-formula> <tex-math notation="LaTeX">${T}_{{2}}$ </tex-math></inline-formula>*-w imaging. Overall the proposed method allows for 2.5-3.75x shorter scan times compared to GRAPPA-4 parallel imaging acquisition at 3 Tesla without compromising image quality.
Infusing Context Into Emotion Perception Impacts Emotion Decoding Accuracy

Abstract. The accurate decoding of facial emotion expressions lies at the center of many research traditions in psychology. Much of this research, while paying lip service to the importance of context in emotion perception, has used stimuli that were carefully created to be deprived of contextual information. The participants' task is to associate the expression shown in the face with a correct label, essentially changing a social perception task into a cognitive task. In fact, in many cases, the task can be carried out correctly without engaging emotion recognition at all. The present article argues that infusing context in emotion perception does not only add an additional source of information but changes the way that participants approach the task by rendering it a social perception task rather than a cognitive task. Importantly, distinguishing between accuracy (perceiving the intended emotions) and bias (perceiving additional emotions to those intended) leads to a more nuanced understanding of social emotion perception. Results from several studies that use the Assessment of Contextual Emotions demonstrate the significance and social functionality of simultaneously considering emotion decoding accuracy and bias for social interaction in different cultures, their key personality and societal correlates, and their function for close relationships processes.
Induction hardening of components for the aerospace industry

Induction heating has been widely used for heat treating and especially surface hardening in a broad variety of applications, ranging from the automotive to the renewable energy market. However, the lack of precise knowledge about the interrelation between all the concurrent physical phenomena occurring within the part during the heating cycle has restricted its use to mass-production items (mostly gears). The benets of this technology, which is clean, repeatable, and cost-eective, could boost its introduction into more conservative industry sectors, such as aerospace, where furnace-based treatments (e.g., carburizing) represent the golden standard. The major limitation is related to the optimization of the induction hardening process, which usually requires signicant material know-how and can thus be very long and expensive. Computer simulation could provide a general tool for understanding and improving the critical aspects of each step of the process, thus speeding up the preading of the induction technology into new markets.
Environmental Dependence of Competitive Fitness in Rifampin-Resistant rpoB Mutants of Bacillus subtilis

Microbial resistance to antibiotics is a growing threat to public health across the world. Historically, resistance to antibiotics has been associated with reduced fitness. ABSTRACT RNA polymerase (RNAP) is a highly conserved macromolecular machine that contributes to the flow of genetic information from genotype to phenotype. In Bacillus subtilis, mutations in the rpoB gene encoding the β-subunit of RNAP have been shown to alter a number of global phenotypes, including growth, utilization of unusual nutrient sources, sporulation, germination, and production of secondary metabolites. In addition, the spectrum of mutations in rpoB leading to rifampin resistance (Rifr) can change dramatically depending upon the environment to which B. subtilis cells or spores are exposed. Rifr rpoB mutations have historically been associated with slower growth and reduced fitness; however, these assessments of fitness were conducted on limited collections of mutants in rich laboratory media that poorly reflect natural environments typically inhabited by B. subtilis. Using a novel deep-sequencing approach in addition to traditional measurements of growth rate, lag time, and pairwise competitions, we demonstrated that the competitive advantages of specific rpoB alleles differ depending on the growth environment in which they are determined. IMPORTANCE Microbial resistance to antibiotics is a growing threat to public health across the world. Historically, resistance to antibiotics has been associated with reduced fitness. A growing body of evidence indicates that resistance to rifampin, a frontline antibiotic used to treat mycobacterial and biofilm-associated infections, may increase fitness given an appropriate environment even in the absence of the selective antibiotic. Here, we experimentally confirm this phenomenon by directly comparing the fitness of multiple rifampin-resistant mutants of Bacillus subtilis in rich LB medium and an asparagine minimal medium. Our research demonstrates that the fitness cost of rifampin resistance can vary greatly depending upon the environment. This has important implications for understanding how microbes develop antimicrobial resistance in the absence of antibiotic selection.
Hybrid Simulation Framework for Immediate Facility Restoration Planning after a Catastrophic Disaster

AbstractSince the effects of external conditions, such as limited resource supplies and poor working environments in damaged regions, are critical to facility restoration projects after a catastrophic disaster, a lack of understanding regarding restoration conditions may lead to unexpected delays and additional project costs. The fact that various elements of postdisaster restoration conditions interact with each other and vary with a facility’s surrounding damage and recovery situations over time makes many stakeholders’ comprehension even more challenging. To support immediate and appropriate facility restoration planning under changing project conditions, this study developed a hybrid system dynamics (SD)–discrete event simulation (DES) framework. In this hybrid restoration simulation, SD allows for a comprehensive understanding of the dynamic features of various restoration conditions, while DES enables an in-depth examination of facility restoration operations according to external condition changes.
Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study

Objectives Emergency caesarean sections (ECS) are time-sensitive procedures. Multiple factors may affect team efficiency but their relative importance remains unknown. This study aimed to identify the most important predictors contributing to quality of care during ECS in terms of the arrival-to-delivery interval. Design A retrospective cohort study. ECS were classified by urgency using emergency categories one/two and three (delivery within 30 and 60 min). In total, 92 predictor variables were included in the analysis and grouped as follows: ‘Maternal objective’, ‘Maternal psychological’, ‘Fetal factors’, ‘ECS Indication’, ‘Emergency category’, ‘Type of anaesthesia’, ‘Team member qualifications and experience’ and ‘Procedural’. Data was analysed with a linear regression model using elastic net regularisation and jackknife technique to improve generalisability. The relative influence of the predictors, percentage significant predictor weight (PSPW) was calculated for each predictor to visualise the main determinants of arrival-to-delivery interval. Setting and participants Patient records for mothers undergoing ECS between 2010 and 2017, Nordsjællands Hospital, Capital Region of Denmark. Primary outcome measures Arrival-to-delivery interval during ECS. Results Data was obtained from 2409 patient records for women undergoing ECS. The group of predictors representing ‘Team member qualifications and experience’ was the most important predictor of arrival-to-delivery interval in all ECS emergency categories (PSPW 25.9% for ECS category one/two; PSPW 35.5% for ECS category three). In ECS category one/two the ‘Indication for ECS’ was the second most important predictor group (PSPW 24.9%). In ECS category three, the second most important predictor group was ‘Maternal objective predictors’ (PSPW 24.2%). Conclusion This study provides empirical evidence for the importance of team member qualifications and experience relative to other predictors of arrival-to-delivery during ECS. Machine learning provides a promising method for expanding our current knowledge about the relative importance of different factors in predicting outcomes of complex obstetric events.
Maize Field Study Reveals Covaried Microbiota and Metabolic Changes in Roots over Plant Growth

Plant roots are inhabited by microbial communities called the root microbiota, which supports plant growth and health. We show in a maize field study that the root microbiota consists of stable and dynamic members. The dynamics of the microbial community appear to be driven by changes in the metabolic state of the roots over the life cycle of maize. ABSTRACT Plant roots are colonized by microorganisms from the surrounding soil that belong to different kingdoms and form a multikingdom microbial community called the root microbiota. Despite their importance for plant growth, the relationship between soil management, the root microbiota, and plant performance remains unknown. Here, we characterize the maize root-associated bacterial, fungal, and oomycetal communities during the vegetative and reproductive growth stages of four maize inbred lines and the pht1;6 phosphate transporter mutant. These plants were grown in two long-term experimental fields under four contrasting soil managements, including phosphate-deficient and -sufficient conditions. We showed that the maize root-associated microbiota is influenced by soil management and changes during host growth stages. We identified stable bacterial and fungal root-associated taxa that persist throughout the host life cycle. These taxa were accompanied by dynamic members that covary with changes in root metabolites. We observed an inverse stable-to-dynamic ratio between root-associated bacterial and fungal communities. We also found a host footprint on the soil biota, characterized by a convergence between soil, rhizosphere, and root bacterial communities during reproductive maize growth. Our study reveals the spatiotemporal dynamics of the maize root-associated microbiota and suggests that the fungal assemblage is less responsive to changes in root metabolites than the bacterial community. IMPORTANCE Plant roots are inhabited by microbial communities called the root microbiota, which supports plant growth and health. We show in a maize field study that the root microbiota consists of stable and dynamic members. The dynamics of the microbial community appear to be driven by changes in the metabolic state of the roots over the life cycle of maize.
Myelofibrotic and leukemic transformation in 2016 WHO-defined Philadelphia-negative myeloproliferative neoplasm

Background Information on myelofibrotic and leukemic transformations in Korean Philadelphia chromosome-negative myeloproliferative neoplasms (Ph‒ MPNs) is limited. Methods This study retrospectively analyzed transformations in patients diagnosed with essential thrombocythemia (ET), polycythemia vera (PV) prefibrotic/early primary myelofibrosis (pre-PMF), or overt primary myelofibrosis (PMF) based on the 2016 World Health Organization criteria between January 1996 and December 2020 at Chungam National University Hospital, Daejeon, Korea. Results A total of 351 patients (144 with ET, 131 with PV, 45 with pre-PMF, and 31 with PMF; 204 men and 147 women) with a median age of 64 years (range, 15‒91 years) were followed for a median of 4.6 years (range, 0.2‒24.8 years). The 10-year incidence of overt myelofibrosis was higher in pre-PMF than in ET (31.3% and 13.7%, respectively; P=0.031) and PV (12.2%; P=0.003). The 10-year incidence of leukemic transformation was significantly higher in PMF than in ET (40.0% and 7.9%, respectively; P=0.046), pre-PMF (4.7%; P=0.048), and PV (3.2%; P=0.031). The 5-year incidence of leukemic transformation was higher in patients with secondary myelofibrosis (SMF) than in those with PMF (19.0% and 11.4%, respectively; P=0.040). The 5-year overall survival of patients with SMF was significantly worse than that of patients with pre-PMF (74% and 93%, respectively; P=0.027) but did not differ from that of patients with PMF (57%; P=0.744). Conclusion The rates and clinical courses of myelofibrotic and leukemic transformations in Korean patients with Ph‒ MPN did not differ from those in Western populations.
Microbiology of bile aspirates obtained at ERCP in patients with suspected acute cholangitis

Background The cornerstone of treatment for acute cholangitis is source control with biliary drainage and early antibiotics. The primary aim of this study was to describe the microbiology of bile aspirate pathogens obtained at the time of endoscopic retrograde cholangiopancreatography (ERCP) in patients suspected of having acute cholangitis. Methods In this single-center retrospective study, patients were included if a bile aspirate was collected at ERCP for suspicion of acute cholangitis, from 1 January 2010 to 31 December 2016. Results There were 721 ERCP procedures for suspected acute cholangitis with bile culture results, with 662 positive bile cultures (91.8 %). Pathogens included: Enterococcus species (spp.) 448 (67.7 %); Klebsiella spp. 295 (44.6 %); Escherichia coli 269 (40.6 %); Pseudomonas spp. 52 (7.9 %); and anaerobes 64 (9.7 %). Susceptibility of Klebsiella pneumoniae and E.coli isolates to ciprofloxacin was 88 % and 64 %, respectively. Extended-spectrum beta-lactamases and carbapenem resistance were found in 7.9 % and 3.6 % of Enterobacteriaceae, respectively. There were 437 concurrent blood cultures, of which 174 were positive (39.8 % of cultures drawn). Prior biliary endoscopic sphincterotomy (ES) was evident in 459 ERCP cases (63.7 %), and was associated with increased frequency of Klebsiella spp. , Pseudomonas aeruginosa , Enterobacter spp. , and Enterococcus spp. Prior biliary ES significantly increased the probability of vancomycin-resistant Enterococcus (VRE). Conclusions The vast majority of bile cultures (91.8 %) were positive. The susceptibilities of E.coli and K.pneumoniae to ciprofloxacin are lower than historically noted. A notable portion of cultures contained pathogenic drug-resistant organisms. Prior biliary ES is associated with a higher frequency of certain organisms and higher frequency of VRE.
An Optimized Float for Reliable Radar Tank Level Measurement in Bypass Pipes

Tank Level Probing Radar (TLPR) systems are widely used in industrial process instrumentation for contactless level measurement of liquids in tanks. In difficult applications, like for example with agitators or other disturbing assemblies inside the tank, the radar distance measurement is sometimes performed inside a bypass, which is a liquid and gas tight communicating pipe mounted onto the side wall of the tank. In this paper, a new design of a metallic float, which is inserted into the bypass pipe as a well-defined radar target, floating on top of the liquid, is presented. The concept and the reflection properties of the proposed float have been evaluated and analyzed by means of electromagnetic field simulations. An optimized system for level measurement in a bypass pipe with an inner diameter of 38 mm has been implemented and evaluated by measurements with a Frequency Modulated Continuous Wave (FMCW) radar system working in the 5.7 to 6.7 GHz frequency range. High precision level measurements with an absolute error of smaller +/-2 mm were obtained by using the proposed float. Finally, it was found that the measurement results are largely independent of the slant and the varying eccentric position of the float inside the bypass pipe.
A new monocyte epigenetic clock reveals nonlinear effects of alcohol consumption on biological aging in three independent cohorts (N = 2242)

Abstract Background Assessing the effect of alcohol consumption on biological age is essential for understanding alcohol use‐related comorbidities and mortality. Previously developed epigenetic clocks are mainly based on DNA methylation in heterogeneous cell types, which provide limited knowledge on the impacts of alcohol consumption at the individual cellular level. Evidence shows that monocytes play an important role in both alcohol‐induced pathophysiology and the aging process. In this study, we developed a novel monocyte‐based DNA methylation clock (MonoDNAmAge) to assess the impact of alcohol consumption on monocyte age. Methods A machine learning method was applied to select a set of chronological age‐associated DNA methylation CpG sites from 1202 monocyte methylomes. Pearson correlation was tested between MonoDNAmAge and chronological age in three independent cohorts (N total = 2242). Using the MonoDNAmAge clock and four established clocks (i.e., HorvathDNAmAge, HannumDNAmAge, PhenoDNAmAge, GrimDNAmAge), we then evaluated the effect of alcohol consumption on epigenetic aging in the three cohorts [i.e., Yale Stress Center Community Study (YSCCS), Veteran Aging Cohort Study (VACS), Women's Interagency HIV Study (WIHS)] using linear and quadratic models. Results The MonoDNAmAge, comprised of 186 CpG sites, was moderately to strongly correlated with chronological age in the three cohorts (r = 0.90, p = 3.12E−181 in YSCCS; r = 0.54, p = 1.75E−96 in VACS; r = 0.66, p = 1.50E−60 in WIHS). More importantly, we found a nonlinear association between MonoDNAmAge and alcohol consumption (p model = 4.55E−08, px2 = 7.80E−08 in YSCCS; p model = 1.85E−02, px2 = 3.46E−02 in VACS). Heavy alcohol consumption increased EAAMonoDNAmAge up to 1.60 years while light alcohol consumption decreased EAAMonoDNAmAge up to 2.66 years. These results were corroborated by the four established epigenetic clocks (i.e., HorvathDNAmAge, HannumDNAmAge, PhenoDNAmAge, GrimDNAmAge). Conclusions The results suggest a nonlinear relationship between alcohol consumption and its effects on epigenetic age. Considering adverse effects of alcohol consumption on health, nonlinear effects of alcohol use should be interpreted with caution. The findings, for the first time, highlight the complex effects of alcohol consumption on biological aging.
Effect of Different Types of Diagnostic Criteria for Gestational Diabetes Mellitus on Adverse Neonatal Outcomes: A Systematic Review, Meta-Analysis, and Meta-Regression

Background Evidence supporting various diagnostic criteria for diagnose gestational diabetes mellitus (GDM) are consensus-based, needs for additional evidence related to outcomes. Therefore, the aim of this systematic-review and meta-analysis was to assess the impact of different GDM diagnostic-criteria on the risk of adverse-neonatal-outcomes. Methods Electronic databases including Scopus, PubMed, and Web of Sciences were searched to retrieve English original, population-based studies with the universal GDM screening approach, up to January-2020. GDM diagnostic criteria were classified in seven groups and International Association of the Diabetes and Pregnancy Study Groups (IADPSG) was considered as reference one. We used the Mantel–Haenszel method to calculate the pooled odds of events. The possibility of publication bias was examined by Begg’s test. Results A total of 55 population-based studies consisting of 1,604,391 pregnant women with GDM and 7,770,855 non-GDM counterparts were included. Results showed that in all diagnostic-criteria subgroups, the risk of adverse neonatal outcomes including macrosomia, hyperbilirubinemia, respiratory distress syndrome, neonatal hypoglycemia, neonatal intensive care unit admission, preterm birth, and birth-trauma were significantly higher than the non-GDM counterparts were significantly higher than non-GDM counterparts. Meta-regression analysis revealed that the magnitude of neonatal risks in all diagnostic-criteria subgroups are similar. Conclusion Our results showed that the risk of adverse-neonatal-outcome increased among women with GDM, but the magnitude of risk was not different among those women who were diagnosed through more or less intensive strategies. These findings may help health-care-providers and policy-makers to select the most cost-effective approach for the screening of GDM among pregnant women.