Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional Winston–Lutz test using reusable polyvinyl alcohol-iodide (PVA-I) radiochromic gel dosimeter

Medical linear-accelerator-based stereotactic radiosurgery (SRS) using a stereotactic apparatus or image-guided radiotherapy system for intracranial lesions is performed widely in clinical practice. In general, Winston–Lutz (WL) tests using films or electric portal imaging devices (EPIDs) have been performed as pre-treatment and routine quality assurance (QA) for the abovementioned treatment. Two-dimensional displacements between the radiation isocentre and mechanical isocentre are analysed from the test; therefore, it is difficult to identify the three-dimensional (3D) isocentre position intuitively. In this study, we developed an innovative 3D WL test for SRS-QA using a novel radiochromic gel dosimeter based on a polyvinyl alcohol-iodide (PVA-I) complex that can be reused after annealing. A WL gel phantom that was consisted of the PVA-I gel dosimeter poured into a tall acrylic container and an embedded small tungsten sphere was used as a position detector. A flatbed scanner was used to analyse the isocentre position. The measured 3D isocentre accuracy from the gel-based WL test was within 0.1 mm compared with that obtained from the EPID-based WL test. Furthermore, excellent reusability of the WL gel phantom was observed in long-term SRS isocentre verification, in which clinical SRS cases involving repeated irradiation and annealing were analysed. These results demonstrate the high accuracy and reliable evaluation of the isocentre position using an innovative test. In addition, the clinical-based routine SRS-QA using the PVA-I gel dosimeter demonstrates a highly convenience while affording an easy and fast analysis process.
The Association of Mobility Determinants and Life Space Among Older Adults.

BACKGROUND It is important to understand the factors associated with life space mobility so that mobility disability can be prevented/treated. The purpose of this study was to identify the association between mobility determinants and life space among older adults. METHODS This study was a cross-sectional analysis of 249 community-dwelling older adults (mean age=77.4 years, 65.5% female, 88% white) who were recruited for a randomized, controlled, clinical intervention trial. Associations between cognitive, physical, psychosocial, financial, and environmental mobility determinants and the Life Space Assessment (LSA) at baseline were determined using Spearman's correlation coefficients and one-way analysis of variance. Multivariate analysis was performed using multivariable linear regression models. RESULTS The mean LSA score for the sample was 75.3 (SD=17.8). Personal factors (age, gender, education, comorbidities), cognitive (Trail Making Test A and B), physical (gait speed, lower extremity power, Six Minute Walk Test, Figure of 8 Walk Test, tandem stance, energy cost of walking, and Late Life Function and Disability Function Scale), psychosocial (Modified Gait Efficacy Scale), and financial (neighborhood socio-economic status) domains of mobility were significantly associated with LSA score. In the final regression model, age (β=-0.43), lower extremity power (β=0.03), gait efficacy (β=0.19), and energy cost of walking (β=-57.41) were associated with life space (R 2=0.238). CONCLUSIONS Younger age, greater lower extremity power, more confidence in walking, and lower energy cost of walking were associated with greater life space. Clinicians treating individuals with mobility disability should consider personal, physical, and psychosocial factors assessing barriers to life space mobility.
Physiological scaling factors and mechanistic models for prediction of renal clearance from in vitro data

The kidneys have a significant role in drug elimination through both metabolic and excretory routes. Despite a recent paradigm shift towards systems pharmacology approaches, prediction of renal drug disposition using ‘bottom-up’ and mechanistic modelling approaches remains underdeveloped. Lack of ‘gold-standard’ in vitro assays and corresponding in vitro-in vivo extrapolation (IVIVE) approaches for prediction of renal metabolic (CLR,met) and excretory (CLR) clearances contribute to this. A comprehensive literature analysis of quantitative physiological data to inform renal IVIVE scaling factors and systems parameters relevant for physiologically based pharmacokinetic (PBPK) kidney models was initially performed to identify existing knowledge gaps. Following this, microsomal protein content in dog kidney cortex (MPPGK) and liver (MPPGL) were measured in 17 samples from the same animal. Mean dog MPPGK (44.0 mg/ g kidney) and MPPGL (63.6 mg/ g liver) obtained using glucose-6-phosphatase activity as the microsomal protein marker where systematically higher than when CYP content was used as the marker (33.9 mg/ g kidney and 41.1 mg/ g liver respectively). Dog MPPGK was lower than MPPGL, with no direct correlation between the organs. In addition to dog, MPPGK and cytosolic protein per gram kidney (CPPGK) were obtained from 31 human samples, which represent the largest dataset currently available. Mean human MPPGK (25.7 mg/ g kidney) and CPPGK (52.7 mg/ g kidney), were measured using glucose-6-phosphatase and glutathione-S-transferase activities as recovery markers, respectively. Activity of prepared kidney microsomes was assessed using mycophenolic acid glucuronidation as a marker. Novel scaling factor of 25.7 mg/ g kidney was applied for IVIVE of mycophenolic acid microsomal glucuronidation data, resulting in a 2-fold increase in scaled intrinsic clearance compared with data scaled by the commonly used literature MPPGK value (12.8 mg/ g kidney). In addition to the microsomal scaling factor, several elements of a modified stereology method were developed for quantifying human proximal tubule cellularity. The methods included implementation of a systematic uniform random sampling protocol and investigation of tinctorial and immunohistochemistry based staining approaches that could be used identify and count proximal tubule cells in histology sections.A range of mechanistic models for prediction of CLR via either tubular reabsorption or active secretion were developed. A novel 5-compartment model for prediction of tubular reabsorption and CLR from Caco-2 apparent permeability data was developed. This model accounted for relevant physiological complexities of the kidney, such as regional differences in tubular filtrate flow rates and tubular surface area, including consideration of the impact of microvilli. The model predicted the CLR of 45 drugs with overall good accuracy (geometric mean fold error of 1.96), although a systematic under-prediction was noted for basic drugs. The novel 5-compartment model represents an…
Evaluating prognostic value and relevant gene signatures of tumor microenvironment characterization in esophageal carcinoma.

Background Tumor microenvironment (TME) cells are an important part of tumor tissues. There is increasing evidence that the TME plays a vital role in tumor prognosis, and is associated with patient survival in various kinds of malignances. To date, very little research has been conducted on how to effectively use TME to better evaluate the prognosis of patients with esophageal carcinoma (EC). The concept of a "TME score" was introduced to better distinguish the prognosis of patients. Methods We employed bioinformatic methods to investigate the TME infiltration patterns of 160 patients with EC from the Cancer Genome Atlas (TCGA) cohort. TME clusters were identified using k-means clustering methods with 1,000 resampling times. The significance of the survival difference among patients belonging to different TME clusters was assessed by the log-rank test and Kaplan-Meier survival curves. Correlations between immune cell types and survival were calculated by a Cox regression, and the Pearson correlation coefficient (PCC) was used to measure the relationship among different immune cell types. We classified patient into 2 subtypes based on the optimal breakpoint of TME score determined by R package maxstat. Results Two TME phenotypes were defined based on the immune cell type fractions, and patients with a high TME score phenotype had a better prognosis than those with a low TME score phenotype. Kaplan-Meier analysis for differentially expressed micro ribonucleic acids (RNAs) and messenger RNAs also showed that different TME score subtypes were significantly associated with the prognosis of EC. Just as tumor mutational burden can predict the efficacy of immunotherapy, the TME score can predict the efficacy of immune checkpoint inhibitors (ICIs). The genomic alterations of 2 TME score subtypes of EC further revealed that genomic instability is prevalent in TMEs, and patients with a low TME score subtype have a more unstable chromosome status than those with a high subtype. Conclusions Thus, TME score is an emerging prognostic biomarker for predicting the efficacy of ICIs.
Optimal population size to detect quantitative trait loci in Korean native chicken: a simulation study

Objective A genomic region associated with a particular phenotype is called quantitative trait loci (QTL). To detect the optimal F2 population size associated with QTLs in native chicken, we performed a simulation study on F2 population derived from crosses between two different breeds. Methods A total of 15 males and 150 females were randomly selected from the last generation of each F1 population which was composed of different breed to create two different F2 populations. The progenies produced from these selected individuals were simulated for six more generations. Their marker genotypes were simulated with a density of 50K at three different heritability levels for the traits such as 0.1, 0.3, and 0.5. Our study compared 100, 500, 1,000 reference population (RP) groups to each other with three different heritability levels. And a total of 35 QTLs were used, and their locations were randomly created. Results With a RP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a RP size of 500, two QTLs were detected when the heritability was 0.5. With a RP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability detected five QTLs. To sum up, RP size and heritability play a key role in detecting QTLs in a QTL study. The larger RP size and greater heritability value, the higher the probability of detection of QTLs. Conclusion Our study suggests that the use of a large RP and heritability can improve QTL detection in an F2 chicken population.
Potential mental and physical benefits of supplementation with a high-dose, B-complex multivitamin/mineral supplement: What is the evidence?

OBJECTIVE to perform a systematic literature review to examine the effects of high-dose, B-complex multivitamin/mineral supplementation on physical, mental, and energy outcomes in healthy and 'at-risk' (suboptimal nutritional status/subclinical symptoms at baseline) adult populations. METHODS PubMed was searched for relevant randomized controlled trials until January 2020. RESULTS overall, 136 publications were identified. In the seven randomised, double-blind, placebo-controlled studies considered eligible for inclusion, supplementation in healthy populations predominantly showed improvements in perceived stress, physical stamina, concentration, and general mental health, and significant reductions in anxiety and improvements in self-reported vigour. However, not all of these outcomes were significant, and statistical correction for multiple outcomes was not commonly employed. Studies investigating brain mapping following supplementation indicated increased functional activity in brain regions related to processing of attention, executive control, and working memory during cognitive tasks. CONCLUSIONS while there is certainly a need for further studies on the neurocognitive and physical benefits of micronutrient supplementation, this review provides generally supportive evidence for the benefits of a high-dose, B-complex multivitamin/mineral supplement in healthy and at-risk populations in terms of physical, mental, and energy outcomes.
Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.
Constraint and divergence in the evolution of male and female recombination rates in fishes

Recombination is a fundamental feature of sexual reproduction across eukaryotes, yet recombination rates are highly variable both within and between species. In particular, sex differences in recombination rate between males and females (heterochiasmy) are more often the rule than the exception, but despite the prevalence of heterochiasmy the ultimate causes of global patterns of heterochiasmy remain unclear. Here, we assemble a comprehensive dataset of sex‐specific recombination rate estimates for 61 fish species, and combine this with information on sex determination, fertilization mode, and sexual dimorphism to test competing theories for the causes and evolution of heterochiasmy. We find that sex differences in recombination rate are evolutionary labile, with frequent shifts in the direction and magnitude of heterochiasmy. This rapid turnover does not appear to be driven by simple neutral processes and is inconsistent with nonadaptive explanations for heterochiasmy, including biological sex differences in meiosis. Although patterns of heterochiasmy across the phylogeny indicate a potential role for adaptive processes, we are unable to directly link variation in heterochiasmy with proxies for sexual selection or sexual conflict across species, indicating that these effects—if present—are either subtle or complex. Finally, we show evidence for correlated rates of recombination rate evolution between males and females, indicating the potential for genetic constraints and sexual conflict over the recombination landscape.
Multilabel Feature Selection: A Local Causal Structure Learning Approach

Multilabel feature selection plays an essential role in high-dimensional multilabel learning tasks. Existing multilabel feature selection approaches mainly either explore the feature–label and feature–feature correlations or the label–label and feature–feature correlations. A few of them are able to deal with all three types of correlations simultaneously. To address this problem, in this article, we formulate multilabel feature selection as a local causal structure learning problem and propose a novel algorithm, M2LC. By learning the local causal structure of each class label, M2LC considers three types of feature relationships simultaneously and is scalable to high-dimensional datasets as well. To tackle false discoveries caused by the label–label correlations, M2LC consists of two novel error-correction subroutines to correct those false discoveries. Through local causal structure learning, M2LC learns the causal mechanism behind data, and thus, it can select causally informative features and visualize common features shared by class labels and specific features owned by an individual class label using the learned causal structures. Extensive experiments have been conducted to evaluate M2LC in comparison with the state-of-the-art multilabel feature selection algorithms.
Fe–Zr–O catalyzed base-free aerobic oxidation of 5-HMF to 2,5-FDCA as a bio-based polyester monomer

An environment-friendly and economical route for 5-hydroxymethylfurfural (HMF) aerobic oxidation to 2,5-furandicarboxylic acid (FDCA) in an ionic liquid (IL)-promoted base-free reaction system was reported using Fe–Zr–O as a catalyst. A series of FexZr1−xO2 catalysts were synthesized by a hydrothermal method and the catalytic performance was investigated. Among these catalysts, Fe0.6Zr0. 4O2 exhibited excellent catalytic activity in the HMF oxidation. A 60.6% FDCA yield and 99.9% HMF conversion could be obtained after 24 h under 2 MPa O2 pressure and base-free conditions. The good performance could be attributed to the large amount of acidic and basic sites on the surface of the catalyst and its high reducibility and oxygen mobility. In addition, the formation of humins and the reaction pathways in the ILs were also investigated, which revealed parallel reactions between FDCA and humin formation. A plausible reaction mechanism was proposed based on the results of a series of designed experiments. Finally, the catalyst was used five times without obvious loss of activity. To the best of our knowledge, this is the best result for a non-noble metal catalyzed base-free oxidation of HMF to FDCA using molecular oxygen as an oxidant.
A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis

The multidimensional analysis of brain MR images, including radiomic features and clinicodemographic data, is highly informative of the clinical status of patients with multiple sclerosis, representing a promising approach to bridge the gap between conventional imaging and disability. BACKGROUND AND PURPOSE: Conventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images. MATERIALS AND METHODS: In this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. T1-weighted images were processed to obtain volume, connectivity score (inferred from the T2 lesion location), and texture features for an atlas-based set of GM regions. The site 1 cohort was randomly split into training (n = 400) and test (n = 100) sets, while the site 2 cohort (n = 104) constituted the external test set. After feature selection of clinicodemographic and MR imaging–derived variables, different machine learning algorithms predicting disability as measured with the Expanded Disability Status Scale were trained and cross-validated on the training cohort and evaluated on the test sets. The effect of different algorithms on model performance was tested using the 1-way repeated-measures ANOVA. RESULTS: The selection procedure identified the 9 most informative variables, including age and secondary-progressive course and a subset of radiomic features extracted from the prefrontal cortex, subcortical GM, and cerebellum. The machine learning models predicted disability with high accuracy (r approaching 0.80) and excellent intra- and intersite generalizability (r ≥ 0.73). The machine learning algorithm had no relevant effect on the performance. CONCLUSIONS: The multidimensional analysis of brain MR images, including radiomic features and clinicodemographic data, is highly informative of the clinical status of patients with multiple sclerosis, representing a promising approach to bridge the gap between conventional imaging and disability.
The Y chromosome and its use in forensic DNA analysis

Originally relatively ignored in forensic investigations because its genetic analysis lacks inference of individual identification, the value of Y chromosome analysis has been proven in cases of sexual assault, particularly where the amount of material left by a male assailant is limited in comparison with female DNA. All routine analysis of autosomal DNA, however, targets a gene (AMELY) on the Y chromosome in order to identify the sex of the DNA source and this is discussed in the context of the genetic structure of this male-specific chromosome. Short-tandem repeat markers on the chromosome are tested in dedicated multiplexes that have developed over time and these are described alongside international guidance as to their use in a forensic setting. As a marker of lineage, the Y chromosome provides additional tools to assist in the inference of ancestry, both geographical and familial and the value of Y chromosome testing is illustrated through descriptions of cases of criminal and historical interest. A decision to analyse the Y chromosome has to be considered in the context, not only of the circumstances of the case, but also with regard to the ethical questions it might raise, and these are discussed in relation to the cases that have been described in more detail in the accompanying online supplementary material.
Decoding the Cardiac Actions of Protein Kinase D Isoforms

Protein kinase D (PKD) consists of a family of three structurally related enzymes that play key roles in a wide range of biological functions that contribute to the evolution of cardiac hypertrophy and heart failure. PKD1 (the founding member of this enzyme family) has been implicated in the phosphorylation of substrates that regulate cardiac hypertrophy, contraction, and susceptibility to ischemia/reperfusion injury, and de novo PRKD1 (protein kinase D1 gene) mutations have been identified in patients with syndromic congenital heart disease. However, cardiomyocytes coexpress all three PKDs. Although stimulus-specific activation patterns for PKD1, PKD2, and PKD3 have been identified in cardiomyocytes, progress toward identifying PKD isoform-specific functions in the heart have been hampered by significant gaps in our understanding of the molecular mechanisms that regulate PKD activity. This review incorporates recent conceptual breakthroughs in our understanding of various alternative mechanisms for PKD activation, with an emphasis on recent evidence that PKDs activate certain effector responses as dimers, to consider the role of PKD isoforms in signaling pathways that drive cardiac hypertrophy and ischemia/reperfusion injury. The focus is on whether the recently identified activation mechanisms that enhance the signaling repertoire of PKD family enzymes provide novel therapeutic strategies to target PKD enzymes and prevent or slow the evolution of cardiac injury and pathological cardiac remodeling. SIGNIFICANCE STATEMENT PKD isoforms regulate a large number of fundamental biological processes, but the understanding of the biological actions of individual PKDs (based upon studies using adenoviral overexpression or gene-silencing methods) remains incomplete. This review focuses on dimerization, a recently identified mechanism for PKD activation, and the notion that this mechanism provides a strategy to develop novel PKD-targeted pharmaceuticals that restrict proliferation, invasion, or angiogenesis in cancer and prevent or slow the evolution of cardiac injury and pathological cardiac remodeling.