Wright-Fisher diffusion bridges

The trajectory of the frequency of an allele which begins at $x$ at time $0$ and is known to have frequency $z$ at time $T$ can be modelled by the bridge process of the Wright-Fisher diffusion. Bridges when $x=z=0$ are particularly interesting because they model the trajectory of the frequency of an allele which appears at a time, then is lost by random drift or mutation after a time $T$. The coalescent genealogy back in time of a population in a neutral Wright-Fisher diffusion process is well understood. In this paper we obtain a new interpretation of the coalescent genealogy of the population in a bridge from a time $t\in (0,T)$. In a bridge with allele frequencies of 0 at times 0 and $T$ the coalescence structure is that the population coalesces in two directions from $t$ to $0$ and $t$ to $T$ such that there is just one lineage of the allele under consideration at times $0$ and $T$. The genealogy in Wright-Fisher diffusion bridges with selection is more complex than in the neutral model, but still with the property of the population branching and coalescing in two directions from time $t\in (0,T)$. The density of the frequency of an allele at time $t$ is expressed in a way that shows coalescence in the two directions. A new algorithm for exact simulation of a neutral Wright-Fisher bridge is derived. This follows from knowing the density of the frequency in a bridge and exact simulation from the Wright-Fisher diffusion. The genealogy of the neutral Wright-Fisher bridge is also modelled by branching P\'olya urns, extending a representation in a Wright-Fisher diffusion. This is a new very interesting representation that relates Wright-Fisher bridges to classical urn models in a Bayesian setting. This paper is dedicated to the memory of Paul Joyce

Supported Ultrafine NiCo Bimetallic Alloy Nanoparticles Derived from Bimetal–Organic Frameworks: A Highly Active Catalyst for Furfuryl Alcohol Hydrogenation

Highly dispersed NiCo bimetallic alloy nanoparticles have been successfully immobilized on the SiO₂ frameworks by using heteronuclear metal–organic frameworks (MOFs) as metal alloy precursors. Catalyst characterizations revealed that the average size of NiCo alloy particles was less than 1 nm, with a total metal loading of about 20 wt %. As compared to individual Ni or Co MOF-derived catalysts and the catalysts prepared by the conventional impregnation method, the ultrafine NiCo/SiO₂-MOF catalyst showed a much better catalytic performance in the catalytic hydrogenation of furfuryl alcohol (FA) to tetrahydrofurfuryl alcohol (THFA) under mild conditions, giving 99.8% conversion of FA and 99.1% selectivity to THFA. It was found that a significant synergistic effect existed between Co and Ni within the subnanometer NiCo/SiO₂-MOF catalyst, which was 2 and 20 times more active than Ni/SiO₂-MOF and Co/SiO₂-MOF, respectively

Data_Sheet_1_Transcriptomic and metabolomic analysis reveals the influence of carbohydrates on lignin degradation mediated by Bacillus amyloliquefaciens.zip

IntroductionLigninolytic bacteria can secrete extracellular enzymes to depolymerize lignin into small-molecular aromatics that are subsequently metabolized and funneled into the TCA cycle. Carbohydrates, which are the preferred carbon sources of bacteria, influence the metabolism of lignin-derived aromatics through bacteria.MethodsIn this study, untargeted metabolomics and transcriptomics analyses were performed to investigate the effect of carbohydrates on lignin degradation mediated by Bacillus amyloliquefaciens MN-13, a strain with lignin-degrading activity that was isolated in our previous work.ResultsThe results demonstrated that the cell growth of the MN-13 strain and lignin removal were promoted when carbohydrates such as glucose and sodium carboxymethyl cellulose were added to an alkaline lignin-minimal salt medium (AL-MSM) culture. Metabolomics analysis showed that lignin depolymerization took place outside the cells, and the addition of glucose regulated the uptake and metabolism of lignin-derived monomers and activated the downstream metabolism process in cells. In the transcriptomics analysis, 299 DEGs were screened after 24 h of inoculation in AL-MSM with free glucose and 2 g/L glucose, respectively, accounting for 8.3% of the total amount of annotated genes. These DEGs were primarily assigned to 30 subcategories, including flagellar assembly, the PTS system, RNA degradation, glycolysis/gluconeogenesis, the TCA cycle, pyruvate metabolism, and tryptophan metabolism. These subcategories were closely associated with the cell structure, generation of cellular energy, and precursors for biosynthetic pathways, based on a − log 10 (P adjust) value in the KEGG pathway analysis.ConclusionIn summary, the addition of glucose increased lignin degradation mediated by the MN-13 strain through regulating glycolysis, TCA cycle, and central carbon metabolism.</p

Additional file 1 of The effectiveness of online team-based learning in introduction to medical ethics education for medical students at a medical college of Nepal: a pilot study

Supplementary Material

Carrier Step-by-Step Transport Initiated by Precise Defect Distribution Engineering for Efficient Photocatalytic Hydrogen Generation

Semiconductor photocatalysts have been widely used for solar-to-hydrogen conversion; however, efficient photocatalytic hydrogen generation still remains a challenge. To improve the photocatalytic activity, the critical step is the transport of photogenerated carriers from bulk to surface. Here, we report the carrier step-by-step transport (CST) for semiconductor photocatalysts through precise defect engineering. In CST, carriers can fast transport from bulk to shallow traps in the defective subsurface first, and then transfer to the surface active acceptors. The key challenge of initiating CST lies in fine controlling defect distribution in semiconductor photocatalysts to introduce the special band matching between the crystalline bulk and defect-controllable surface, moderate bridgelike shallow traps induced by subsurface defects, and abundant surface active sites induced by surface defects. In our proof-of-concept demonstration, the CST was introduced into typical semiconductor TiO₂ assisted by the fluorine-assisted kinetic hydrolysis method, and the designed TiO₂ can exhibit the state-of-the-art photocatalytic hydrogen generation rate among anatase TiO₂ up to 13.21 mmol h^–1 g^–1, which is 120 times enhanced compared with crystalline anatase TiO₂ under sunlight. The CST initiated by precise defect distribution engineering provides a new sight on greatly improving photocatalytic hydrogen generation performance of semiconductor catalysts

Improvement strategies study for outdoor wind environment in a University in Beijing based on CFD simulation

© 2020 Xiaodan Li et al. The outdoor wind environment is one of the most important factors influencing the biological environment and human comfort. The campus of China University of Mining & Technology (Beijing) (CUMTB) is the case study for this research. PHOENICS simulation software was used to carry out a numerical simulation study of the outdoor wind environment. The pedestrian-level wind (PLW) environments for both winter and summer were investigated. Based on the numerical simulation results, the overall evaluation and subregional analysis of the campus' current outdoor wind environment were carried out. Then, according to the results and problems of wind environment assessment, improvement measures and strategies of outdoor wind environment in universities are proposed from the aspects of campus planning, single building form, and greening configuration. The main goal is to further improve the outdoor space and environment of universities in Beijing and provide general guidelines to improve the quality of the universities' wind environment effectively. The strategies can provide a reference for the same type of campus on wind environment renovation and improvement. This study can also provide data support and reference significance for outdoor thermal environment/pedestrian environment research

STROBE statement—checklist of items that should be included in reports of observational studies.

STROBE statement—checklist of items that should be included in reports of observational studies.</p

<i>PLOS ONE</i> clinical studies checklist.

BackgroundMachine learning algorithms used to classify cystic renal masses (CRMs) nave not been applied to unenhanced CT images, and their diagnostic accuracy had not been compared against radiologists.MethodThis retrospective study aimed to develop radiomics models that discriminate between benign and malignant CRMs in a triple phase computed tomography (CT) protocol and compare the diagnostic accuracy of the radiomics approach with experienced radiologists. Predictive models were established using a training set and validation set of unenhanced and enhanced (arterial phase [AP] and venous phase [VP]) CT images of benign and malignant CRMs. The diagnostic capabilities of the models and experienced radiologists were compared using Receiver Operating Characteristic (ROC) curves.ResultsOn unenhanced, AP and VP CT images in the validation set, the AUC, specificity, sensitivity and accuracy for discriminating between benign and malignant CRMs were 90.0 (95%CI: 81–98%), 90.0%, 90.5% and 90.2%; 93.0% (95%CI: 86–99%), 86.7%, 95.2% and 88.3%; and 95.0% (95%CI: 90%-100%), 93.3%, 90.5% and 92.1%, respectively, for the radiomics models. Diagnostic accuracy of the radiomics models differed significantly on unenhanced images in the training set vs. each radiologist (p = 0.001 and 0.003) but not in the validation set (p = 0.230 and 0.590); differed significantly on AP images in the validation set vs. each radiologist (p = 0.007 and 0.007) but not in the training set (p = 0.663 and 0.663); and there were no differences on VP images in the training or validation sets vs. each radiologist (training set: p = 0.453 and 0.051, validation set: p = 0.236 and 0.786).ConclusionsRadiomics models may have clinical utility for discriminating between benign and malignant CRMs on unenhanced and enhanced CT images. The performance of the radiomics model on unenhanced CT images was similar to experienced radiologists, implying it has potential as a screening and diagnostic tool for CRMs.</div

Workflow for extracting radiomics features.

BackgroundMachine learning algorithms used to classify cystic renal masses (CRMs) nave not been applied to unenhanced CT images, and their diagnostic accuracy had not been compared against radiologists.MethodThis retrospective study aimed to develop radiomics models that discriminate between benign and malignant CRMs in a triple phase computed tomography (CT) protocol and compare the diagnostic accuracy of the radiomics approach with experienced radiologists. Predictive models were established using a training set and validation set of unenhanced and enhanced (arterial phase [AP] and venous phase [VP]) CT images of benign and malignant CRMs. The diagnostic capabilities of the models and experienced radiologists were compared using Receiver Operating Characteristic (ROC) curves.ResultsOn unenhanced, AP and VP CT images in the validation set, the AUC, specificity, sensitivity and accuracy for discriminating between benign and malignant CRMs were 90.0 (95%CI: 81–98%), 90.0%, 90.5% and 90.2%; 93.0% (95%CI: 86–99%), 86.7%, 95.2% and 88.3%; and 95.0% (95%CI: 90%-100%), 93.3%, 90.5% and 92.1%, respectively, for the radiomics models. Diagnostic accuracy of the radiomics models differed significantly on unenhanced images in the training set vs. each radiologist (p = 0.001 and 0.003) but not in the validation set (p = 0.230 and 0.590); differed significantly on AP images in the validation set vs. each radiologist (p = 0.007 and 0.007) but not in the training set (p = 0.663 and 0.663); and there were no differences on VP images in the training or validation sets vs. each radiologist (training set: p = 0.453 and 0.051, validation set: p = 0.236 and 0.786).ConclusionsRadiomics models may have clinical utility for discriminating between benign and malignant CRMs on unenhanced and enhanced CT images. The performance of the radiomics model on unenhanced CT images was similar to experienced radiologists, implying it has potential as a screening and diagnostic tool for CRMs.</div

S1 Data -

BackgroundMachine learning algorithms used to classify cystic renal masses (CRMs) nave not been applied to unenhanced CT images, and their diagnostic accuracy had not been compared against radiologists.MethodThis retrospective study aimed to develop radiomics models that discriminate between benign and malignant CRMs in a triple phase computed tomography (CT) protocol and compare the diagnostic accuracy of the radiomics approach with experienced radiologists. Predictive models were established using a training set and validation set of unenhanced and enhanced (arterial phase [AP] and venous phase [VP]) CT images of benign and malignant CRMs. The diagnostic capabilities of the models and experienced radiologists were compared using Receiver Operating Characteristic (ROC) curves.ResultsOn unenhanced, AP and VP CT images in the validation set, the AUC, specificity, sensitivity and accuracy for discriminating between benign and malignant CRMs were 90.0 (95%CI: 81–98%), 90.0%, 90.5% and 90.2%; 93.0% (95%CI: 86–99%), 86.7%, 95.2% and 88.3%; and 95.0% (95%CI: 90%-100%), 93.3%, 90.5% and 92.1%, respectively, for the radiomics models. Diagnostic accuracy of the radiomics models differed significantly on unenhanced images in the training set vs. each radiologist (p = 0.001 and 0.003) but not in the validation set (p = 0.230 and 0.590); differed significantly on AP images in the validation set vs. each radiologist (p = 0.007 and 0.007) but not in the training set (p = 0.663 and 0.663); and there were no differences on VP images in the training or validation sets vs. each radiologist (training set: p = 0.453 and 0.051, validation set: p = 0.236 and 0.786).ConclusionsRadiomics models may have clinical utility for discriminating between benign and malignant CRMs on unenhanced and enhanced CT images. The performance of the radiomics model on unenhanced CT images was similar to experienced radiologists, implying it has potential as a screening and diagnostic tool for CRMs.</div