Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Subclinical Hypothyroidism in Chronic Kidney Disease in a Population-based Study: Tehran Thyroid Study

Background Chronic kidney disease (CKD) is a rising public health concern that has detrimental effects on cardiovascular health and overall survival. Subclinical hypothyroidism (SCH) has been associated with poor outcomes in the general population. It is thought to be more prevalent in CKD subjects, and their coexistence may contribute to poor outcomes in these patients. We aimed to determine the prevalence of SCH in CKD. Methods Using data from the Tehran thyroid study, which is a prospective population-based cohort study, adult subjects with an estimated Glomerular Filtration Rate (eGFR) of 60 mL/min/1.73 m2 or less were selected for studying the prevalence of thyroid abnormalities, as well as other known cardiovascular risk factors. Results Of 5,626 subjects recruited, 823 (14.6%) individuals had CKD. Individuals with CKD were older, heavier, had a higher prevalence of diabetes, higher serum thyrotropin, and thyroid peroxidase anti-body levels, but lower free thyroxine levels. The prevalence of SCH was 7.3% and 5.2% (P < 0.001) in kidney disease and non-kidney disease subjects, respectively. However, there was no difference in the risk of SCH between CKD and non-CKD subjects after adjustment for age, sex, BMI, smoking, and TPOAb (OR: 1.28; 95%CI, 0.89 - 1.83). None of the metabolic markers compared between the CKD subgroups of those with and without SCH remained statistically significantly different after adjusting for age and gender. Conclusions The prevalence of SCH was not higher in CKD after controlling for confounding factors. Besides, CKD subjects with and without SCH had no different metabolic parameters.
A novel method for credit scoring based on feature transformation and ensemble model

Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.
The first complete chloroplast genome of Haymondia wallichii (Fabaceae) and its phylogenetic analysis

Abstract Haymondia wallichii DC. was a scandent shrub. In this study, we sequenced the complete chloroplast (cp) genome of H. wallichii to investigate its phylogenetic relationship in Fabaceae. The total length of the cp genome was 153,668 bp, consisted of a large single copy (LSC) region of 84,310 bp, a small single copy (SSC) region of 17,918 bp, and a pair of inverted repeat regions (IRs) of 25,720 bp. The genome contained 132 genes, namely 37 tRNA genes, 87 protein-coding genes, and 8 rRNA genes. The overall GC content was 35.4%. The phylogenetic analysis indicated H. wallichii was closely related to Pueraria montana var. thomsonii and Pueraria montana var. lobata.
The complete plastid genome sequence of Vaccinium japonicum (Ericales: Ericaceae), a deciduous broad-leaved shrub endemic to East Asia

Abstract We here sequenced the complete plastid genome (plastome) of Vaccinium japonicum (Ericaceae), a deciduous broad-leaved shrub endemic to East Asia. This species has considerable practical economic value. The plastome of V. japonicum is assembled as a single contig (187,213 bp). A large single copy (104,637 bp) and a small single copy (3,000 bp) of the genome are separated by a pair of inverted repeats (39,788 bp). The genome consists of 135 genes, which include 88 protein coding, eight ribosomal RNA, and 39 transfer RNA genes. The plastome of V. japonicum is similar to that of Vaccinium macrocarpon in gene content and order. Our phylogenetic analysis revealed the phylogenetic position of V. japonicum in a highly supported clade of the genus Vaccinium together with other four congeners, V. bracteatum, V. vitis-idaea, V. uliginosum and V. macrocarpon.
A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.
Association of Multimorbidity With Frailty in Older Adults for Elective Non-Cardiac Surgery

Introduction Frailty is associated with adverse surgical outcomes. While existing studies describe the prevalence of multimorbidity and frailty in the community, the surgical population may have more severe disease and significant surgical stress. This study aims to describe the distribution of frailty and multimorbidity in the older surgical population and examine if specific comorbidities are more strongly associated with frailty. Methods This is a single-centre retrospective cohort study using an electronic database in the preoperative evaluation clinic, conducted in Singapore General Hospital, Singapore. All patients above 70 years old going for elective non-cardiac surgery were included. Demographics and comorbidities were analysed for their association with frailty according to the Edmonton Frail Scale. Results A total of 1396 out of 1398 patients were analyzed. The overall incidence of frailty was 27.8% and multimorbidity was 63.4%. Factors independently associated with frailty were age (adjusted Odds Ratio [aOR] = 1.07), female gender (aOR = 1.67), type 2 diabetes mellitus (aOR = 1.69), chronic kidney disease (aOR = 1.47), end-stage renal failure (aOR = 3.58), history of cerebrovascular accident or transient ischemic attack (aOR = 1.87), moderate anaemia (aOR = 2.11), dementia (aOR = 6.38), depression (aOR = 3.82), and peptic ulcer disease (aOR = 1.98). The presence of multi-morbidity was significantly associated with frailty, with overall increasing strength of association. Conclusion As the number of comorbidities increases, the odds of frailty increase. Only a small proportion of those with multimorbidity accumulate enough biological deficits to develop frailty, putting them at higher risk than with solely multimorbidity or frailty. Dementia and depression are comorbidities with strong associations that have yet to see coordinated interventional efforts in the preoperative setting.
Aerothermodynamics of Pre-Flight and In-Flight Testing Methodologies for Atmospheric Entry Probes

Spacecraft, returning back to Earth, experience a very harsh environment during the encounter with the particles of the atmosphere. One of the major issues of the atmospheric entry is the extreme aerodynamic heating and the exothermic chemical reactions due to the gas-surface interaction at hypersonic free stream velocities. There is a constant effort by the space agencies to increase the understanding of the re-entry flight dynamics to optimize the spacecraft and especially its thermal protection system design. During the design process, ground tests and numerical tools are extensively used for their low cost and controlled environment abilities. However, real flight tests are indispensable for ground test and numerical tools validation. Due to high costs, such missions are rarely launched and thus there is an increasing interest in small affordable entry probes. Such platforms, once matured enough, may serve as an easily accessible tool to produce experimental data. It is the aim of this dissertation to propose tools to improve ground test capabilities and on the other hand to present the design, and using the developed tools, the testing of aerothermodynamic experimental payloads to collect flight data with a small entry probe. QARMAN (QubeSat for Aerothermodynamic Research and Measurements on AblatioN) is a triple unit CubeSat with ablative and ceramic thermal protection systems. It will perform an atmospheric entry with 7.7 km/s and a peak heat flux of 1.7 MW/m. The aim of the in-flight experiments is to retrieve real flight data on ablator efficiency (temperature, pressure, recession) and temperature-pressure measurements for transition on the side panels. The peculiar squared geometry of QARMAN led to the development of a Flight-to-Ground Duplication methodology accounting for spacecraft geometries. It allows duplicating fully the stagnation region of a spacecraft with an arbitrary geometry in subsonic plasma wind tunnels. As a requirement of this methodology, free stream characterization techniques, specifically enthalpy measurement techniques are introduced. Experimental and numerical databases are built. A thorough ablation characterization campaign in VKI Plasmatron is conducted to provide input for building material response models. The cork P50 ablator is studied in terms of surface and sub-surface temperatures, emissivity, mass loss, char-pyrolysis layers, outgassing species and recession and swelling profiles. Similar in-flight experiments are proposed for QARMAN flight for in-depth temperature and pressure. Methods to build models for advanced data treatment are proposed. A full picture of post-flight analysis strategy is described for each study to relate the ground tools and flight data.
Examining contextual influences on the service needs of homeless and unstably housed domestic violence survivors

Abstract Domestic violence (DV) is a leading cause of homelessness for women, yet many DV agencies are just beginning to focus on helping clients stabilize their housing situations. The purpose of this study was to better understand the contexts and service needs of unstably housed and homeless DV survivors, to promote more efficient and successful service matching from DV agencies. We examined whether DV survivors could be grouped by particular features, histories, and contextual factors, and how these group differences impacted what they needed from DV agencies. The sample included 406 homeless and unstably housed DV survivors who had recently sought DV services. Latent class analysis supported the identification of four distinct classes: (1) highest disadvantages service seeker, (2) moderate disadvantages—criminal legal system service seeker, (3) moderate disadvantages service seeker, and (4) lower disadvantages service seeker. Additionally, we were able to profile each class, and test the types of services survivors in each class needed from agencies.
AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry–mass spectrometry

Abstract Motivation Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion’s collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. Results We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. Availability and implementation https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. Supplementary information Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).
Antimicrobial Resistance Trends in Urine Escherichia coli Isolates From Adult and Adolescent Females in the United States From 2011 to 2019: Rising ESBL Strains and Impact on Patient Management

Abstract Background Uncomplicated urinary tract infection (uUTI) is predominantly caused by Escherichia coli, which has increasing antimicrobial resistance (AMR) at the United States (US)-community level. As uUTI is often treated empirically, assessing AMR is challenging, and there are limited contemporary data characterizing period prevalence in the US. Methods This was a retrospective study of AMR using Becton, Dickinson and Company Insights Research Database (Franklin Lakes, New Jersey, US) data collected 2011–2019. Thirty-day, nonduplicate Escherichia coli urine isolates from US female outpatients (aged ≥12 years) were included. Isolates were evaluated for nonsusceptibility (intermediate/resistant) to trimethoprim-sulfamethoxazole, fluoroquinolones, or nitrofurantoin, and assessed for extended-spectrum β-lactamase production (ESBL+) and for ≥2 or ≥3 drug-resistance phenotypes. Generalized estimating equations were used to model AMR trends over time and by US census region. Results Among 1 513 882 E. coli isolates, the overall prevalence of isolates nonsusceptible to trimethoprim-sulfamethoxazole, fluoroquinolones, and nitrofurantoin was 25.4%, 21.1%, and 3.8%, respectively. Among the isolates, 6.4% were ESBL+, 14.4% had ≥2 drug-resistance phenotypes, and 3.8% had ≥3. Modeling demonstrated a relative average yearly increase of 7.7% (95% confidence interval [CI], 7.2–8.2%) for ESBL+ isolates and 2.7% (95% CI, 2.2–3.2%) for ≥3 drug-phenotypes (both P < .0001). Modeling also demonstrated significant variation in AMR prevalence between US census regions (P < .001). Conclusions Period prevalence of AMR among US outpatient urine-isolated E. coli was high, and for multidrug-resistance phenotypes increased during the study period with significant variation between census regions. Knowledge of regional AMR rates helps inform empiric treatment of community-onset uUTI and highlights the AMR burden to physicians.
Effect of myopia and optic disc area on ganglion cell-inner plexiform layer and retinal nerve fiber layer thickness

Purpose: The aim of this work was to study the impact of myopia and different optic disc areas on ganglion cell-inner plexiform layer (GCIPL) and retinal nerve fiber layer (RNFL) thickness profiles in myopic patients by spectral-domain optical coherence tomography (SD-OCT). Methods: This was a cross-sectional study of 100 eyes of 50 myopic individuals. All patients underwent complete ophthalmic evaluation and SD-OCT examination. According to spherical equivalent (SE), patients were divided into M1, M2, and M3 (low, moderate, and high myopia group). According to optic disc area values, patients were divided into D1, D2 and D3 (small, medium and large disc groups). Average GCIPL and RNFL thickness recorded globally and separately for all quadrants and also according to 12 clock hours and analyzed with respect to different myopic groups, optic disc area groups, and axial length. Results: Quadrantic RNFL thickness profiles and their average RNFL thickness were significantly thinner in high myopic group compared to low myopic group, except for the temporal quadrant (P < 0.05). Average RNFL and RNFL thickness of all quadrants were significantly thicker in the large disc group than in the small disc group (P < 0.05). Average GCIPL and GCIPL thicknesses of all sectors were significantly thinner in high myopic group compared to low myopic group (P < 0.05). No significant correlation was observed between GCIPL and disc area changes. Average RNFL thickness correlated significantly with SE (3.667 μm/diopter), axial length (–5.3805 μm/mm) and optic disc area (9.4617 μm/mm2). Also, average GCIPL thickness correlated statistically significantly with SE (1.6807 μm/diopter) and axial length (–2.626 μm/mm). Conclusion: Myopia and axial length significantly reduce RNFL and GCIPL thickness profiles but the optic disc area significantly increases RNFL thickness, but not GCIPL thickness.
Spatio-temporal modelling of the first Chikungunya epidemic in an intra-urban setting: The role of socioeconomic status, environment and temperature

Three key elements are the drivers of Aedes-borne disease: mosquito infestation, virus circulating, and susceptible human population. However, information on these aspects is not easily available in low- and middle-income countries. We analysed data on factors that influence one or more of those elements to study the first chikungunya epidemic in Rio de Janeiro city in 2016. Using spatio-temporal models, under the Bayesian framework, we estimated the association of those factors with chikungunya reported cases by neighbourhood and week. To estimate the minimum temperature effect in a non-linear fashion, we used a transfer function considering an instantaneous effect and propagation of a proportion of such effect to future times. The sociodevelopment index and the proportion of green areas (areas with agriculture, swamps and shoals, tree and shrub cover, and woody-grass cover) were included in the model with time-varying coefficients, allowing us to explore how their associations with the number of cases change throughout the epidemic. There were 13627 chikungunya cases in the study period. The sociodevelopment index presented the strongest association, inversely related to the risk of cases. Such association was more pronounced in the first weeks, indicating that socioeconomically vulnerable neighbourhoods were affected first and hardest by the epidemic. The proportion of green areas effect was null for most weeks. The temperature was directly associated with the risk of chikungunya for most neighbourhoods, with different decaying patterns. The temperature effect persisted longer where the epidemic was concentrated. In such locations, interventions should be designed to be continuous and to work in the long term. We observed that the role of the covariates changes over time. Therefore, time-varying coefficients should be widely incorporated when modelling Aedes-borne diseases. Our model contributed to the understanding of the spatio-temporal dynamics of an urban Aedes-borne disease introduction in a tropical metropolitan city.
Applicability of Adduct Detection in Liquid Chromatography–Tandem Mass Spectrometry

To investigate the process of analyte adduct formation with the components of the mobile phase in the negative electrospray ionization mode and to answer the question if the strategy of adduct identification/fragmentation is applicable for a bigger number of drugs, which meet the appropriate ionization conditions and compare the detection of appropriate analyte adducts to deprotonated analytes with tandem mass spectrometry, ten drugs containing carboxyl groups capable of negative electrospray ionization were chosen for appropriate experiments. The continuous infusion of captopril, fenbufen, gabapentin, ibuprofen, ketoprofen, naproxen, ramipril, salicylic acid, tiaprofenic acid, and 11-nor-9-carboxy-delta-9-tetrahydrocannabinol made the adduct identification/fragmentation experiments possible. The results achieved show that the adduct formation/fragmentation process can be used for LC–MS/MS analysis of drugs that meet the appropriate ionization conditions like the presence of a carboxyl group. It was also demonstrated that signal intensities achieved by the fragmentation of deprotonated drugs are in most cases higher than the signals connected with the fragmentation of appropriate adducts.
Conditional inference trees in the assessment of tree mortality rates in the transitional mixed forests of Atlantic Canada

Long-term predictions of forest dynamics, including forecasts of tree growth and mortality, are central to sustainable forest-management planning. Although often difficult to evaluate, tree mortality rates under different abiotic and biotic conditions are vital in defining the long-term dynamics of forest ecosystems. In this study, we have modeled tree mortality rates using conditional inference trees (CTREE) and multi-year permanent sample plot data sourced from an inventory with coverage of New Brunswick (NB), Canada. The final CTREE mortality model was based on four tree- and three stand-level terms together with two climatic terms. The correlation coefficient (R2) between observed and predicted mortality rates was 0.67. High cumulative annual growing degree-days (GDD) was found to lead to increased mortality in 18 tree species, including Betula papyrifera, Picea mariana, Acer saccharum, and Larix laricina. In another ten species, including Abies balsamea, Tsuga canadensis, Fraxinus americana, and Fagus grandifolia, mortality rates tended to be higher in areas with high incident solar radiation. High amounts of precipitation in NB’s humid maritime climate were also found to contribute to heightened tree mortality. The relationship between high GDD, solar radiation, and high mortality rates was particularly strong when precipitation was also low. This would suggest that although excessive soil water can contribute to heightened tree mortality by reducing the supply of air to the roots, occasional drought in NB can also contribute to increased mortality events. These results would have significant implications when considered alongside regional climate projections which generally entail both components of warming and increased precipitation.
Prediction of spontaneous coal combustion tendency using multinomial logistic regression

Spontaneous combustion of coal is a complex underground mining disaster, which mainly threats mine safety and efficiency. Several factors usually cause spontaneous combustion of coal, such as gas concentration, ventilation and coal properties. In this study, spontaneous combustion tendencies of coal mines were predicted considering the effective parameters for an underground coal mine in Turkey. Multinomial logistic regression, a multivariate statistical technique, was applied. Gas concentrations (CH4, CO, O2) and air velocity were defined as factors affecting spontaneous coal combustion. Fire hazard levels of the coal mines were determined as ‘normal situation’ and ‘potential combustion’. It was observed that CH4 and CO variables and CH4 × CO interaction were effective in the formation of clusters. The results indicate that Mine I is more liable to spontaneous combustion than Mine II and Mine III. At the same time, the effects of variations in factors are examined in the study.