Predicting Surface-Energy Anisotropy of Metals with Geometric Properties of Surfaces and Atoms.

Surface-energy anisotropy of metals is crucial for the stability and structure, however, its determining factors and structure-property relationship are still elusive. Herein, we identify three key factors for predicting surface-energy anisotropy of pure metals and alloys: the surface-atom density, coordination numbers and atomic radius. We find that the coupling rules of surface geometric determinants, which determining surface-energy anisotropy of face-centred-cubic (FCC), hexagonal-close-packed (HCP) and body-centred-cubic (BCC) metals, are essentially controlled by the crystal structures instead of chemical bonds, alloying or electronic structures. Furthermore, BCC metals exhibit material-dependent surface-energy anisotropy depending on the atomic radius, unlike FCC and HCP metals. The underlying mechanism can be understood from the bonding properties in the framework of the tight-binding model. Our scheme provides not only a new physical picture of surface stability but also a useful tool for material design.

Exploring Bacillus species xylanases for industrial applications: screening via thermostability and reaction modelling.

CONTEXT: Xylanases derived from Bacillus species hold significant importance in various large-scale production sectors, with increasing demand driven by biofuel production. However, despite their potential, the extreme environmental conditions often encountered in production settings have led to their underutilisation. To address this issue and enhance their efficacy under adverse conditions, we conducted a theoretical investigation on a group of five Bacillus species xylanases belonging to the glycoside hydrolase GH11 family. Bacillus sp. NCL 87-6-10 (sp_NCL 87-6-10) emerged as a potent candidate among the selected biocatalysts; this Bacillus strain exhibited high thermal stability and achieved a transition state with minimal energy requirements, thereby accelerating the biocatalytic reaction process. Our approach aims to provide support for experimentalists in the industrial sector, encouraging them to employ structural-based reaction modelling scrutinisation to predict the ability of targeted xylanases. METHODS: Utilising crystal structure data available in the Carbohydrate-Active enzymes database, we aimed to analyse their structural capabilities in terms of thermal-stability and activity. Our investigation into identifying the most prominent Bacillus species xylanases unfolds with the help of the semi-empirical quantum mechanics MOPAC method integrated with the DRIVER program is used in calculations of reaction pathways to understand the activation energy. Additionally, we scrutinised the selected xylanases using various analyses, including constrained network analyses, intermolecular interactions of the enzyme-substrate complex and molecular orbital assessments calculated using the AM1 method with the MO-G model (MO-G AM1) to validate their reactivity.

The effects of family, culture and sex on linguistic development across 20 languages.

Languages vary in their complexity; caregivers vary in the way they structure their communicative interactions with children; and boys and girls can differ in their language skills. Using a multilevel modelling approach, we explored how these factors influence the path of language acquisition for young children growing up around the world (mean age 2-years 9-months; 56 girls). Across 43 different sites, we analysed 103 mother-child pairs who spoke 3,170,633 utterances, 16,209,659 morphemes, divided across 20 different languages: Afrikaans, Catalan, Cantonese, Danish, Dutch, English, Farsi, French, German, Hebrew, Icelandic, Irish, Italian, Japanese, Mandarin, Norwegian, Portuguese, Spanish, Swedish and Turkish. Using mean length of utterance (MLU) as a measure of language complexity and developmental skill, we found that variation in children's MLU was significantly explained by (a) between-language differences; namely the rate of child MLU growth was attuned to the complexity of their mother tongue, and (b) between-mother differences; namely mothers who used higher MLUs tended to have children with higher MLUs, regardless of which language they were learning and especially in the very young (<2.5 years-old). Controlling for family and language environment, we found no evidence of MLU sex differences in child speech nor in the speech addressed to boys and girls. By modelling language as a multilevel structure with cross-cultural variation, we were able to disentangle those factors that make children's pathway to language different and those that make it alike. RESEARCH HIGHLIGHTS: The speech of 103 mother-child pairs from 20 different languages showed large variation in the path of early language development. Language, family, but not the sex of the child, accounted for a significant proportion of individual differences in child speech, especially in the very young. The rate at which children learned language was attuned to the complexity of their mother tongue, with steeper trajectories for more complex language. Results demonstrate the relative influence of culture, family, and sex in shaping the path of language acquisition for different children.

Expression, Purification and Biophysical Characterisation of Klebsiella Pneumoniae Protein Adenylyltransferase: A Systematic Integration of Empirical and Computational Modelling Approaches.

Infections that are acquired due to a prolonged hospital stay and manifest 2 days following the admission of a patient to a health-care institution can be classified as hospital-acquired infections. Klebsiella pneumoniae (K. pneumoniae) has become a critical pathogen, posing serious concern globally due to the rising incidences of hypervirulent and carbapenem-resistant strains. Glutaredoxin is a redox protein that protects cells from oxidative stress as it associates with glutathione to reduce mixed disulfides. Protein adenylyltransferase (PrAT) is a pseudokinase with a proposed mechanism of transferring an AMP group from ATP to glutaredoxin. Inducing oxidative stress to the bacterium by inhibiting the activity of PrAT is a promising approach to combating its contribution to hospital-acquired infections. Thus, this study aims to overexpress, purify, and analyse the effects of ATP and Mg2+ binding to Klebsiella pneumoniae PrAT (KpPrAT). The pET expression system and nickel affinity chromatography were effective in expressing and purifying KpPrAT. Far-UV CD spectroscopy demonstrates that the protein is predominantly α-helical, even in the presence of Mg2+. Extrinsic fluorescence spectroscopy with ANS indicates the presence of a hydrophobic pocket in the presence of ATP and Mg2+, while mant-ATP studies allude to the potential nucleotide binding ability of KpPrAT. The presence of Mg2+ increases the thermostability of the protein. Isothermal titration calorimetry provides insight into the binding affinity and thermodynamic parameters associated with the binding of ATP to KpPrAT, with or without Mg2+. Conclusively, the presence of Mg2+ induces a conformation in KpPrAT that favours nucleotide binding.

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

Next-generation of BBQ analogues that selectively target breast cancer.

We previously reported on the interaction of 10-chloro-7H-benzo[de]benzo[4,5]imidazo[2,1-a]isoquinolin-7-one (10-Cl-BBQ) with the Aryl hydrocarbon Receptor (AhR) and selective growth inhibition in breast cancer cell lines. We now report on a library of BBQ analogues with substituents on the phenyl and naphthyl rings for biological screening. Herein, we show that absence of the phenyl Cl of 10-Cl-BBQ to produce the simple BBQ molecule substantially enhanced the growth inhibitory effect with GI50 values of 0.001-2.1 µM in select breast cancer cell lines MCF-7, T47D, ZR-75-1, SKBR3, MDA-MB-468, BT20, BT474 cells, while having modest effects of 2.1-7 µM in other cell lines including HT29, U87, SJ-G2, A2780, DU145, BE2-C, MIA, MDA-MB-231 or normal breast cells, MCF10A (3.2 µM). The most potent growth inhibitory effect of BBQ was observed in the triple negative cell line, MDA-MB-468 with a GI50 value of 0.001 µM, presenting a 3,200-fold greater response than in the normal MCF10A breast cells. Additions of Cl, CH3, CN to the phenyl ring and ring expansion from benzoimidazole to dihydroquinazoline hindered the growth inhibitory potency of the BBQ analogues by blocking potential sites of CYP1 oxidative metabolism, while addition of Cl or NO2 to the naphthyl rings restored potency. In a cell-based reporter assay all analogues induced 1.2 to 10-fold AhR transcription activation. Gene expression analysis confirmed the induction of CYP1 oxygenases by BBQ. The CYP1 inhibitor α-naphthoflavone, and the SULT1A1 inhibitor quercetin significantly reduced the growth inhibitory effect of BBQ, confirming the importance of both phase I and II metabolic activation for growth inhibition. Conventional molecular modelling/docking revealed no significant differences between the binding poses of the most and least active analogues. More detailed DFT analysis at the DSD-PBEP86/Def-TZVPP level of theory could not identify significant geometric or electronic changes which would account for this varied AhR activation. Generation of Fukui functions at the same level of theory showed that CYP1 metabolism will primarily occur at the phenyl head group of the analogues, and substituents within this ring lead to lower cytotoxicity.

Causal prior-embedded physics-informed neural networks and a case study on metformin transport in porous media.

This study introduces a novel approach to transport modelling by integrating experimentally derived causal priors into neural networks. We illustrate this paradigm using a case study of metformin, a ubiquitous pharmaceutical emerging pollutant, and its transport behaviour in sandy media. Specifically, data from metformin's sandy column transport experiment was used to estimate unobservable parameters through a physics-based model Hydrus-1D, followed by a data augmentation to produce a more comprehensive dataset. A causal graph incorporating key variables was constructed, aiding in identifying impactful variables and estimating their causal dynamics or "causal prior." The causal priors extracted from the augmented dataset included underexplored system parameters such as the type-1 sorption fraction F, first-order reaction rate coefficient α, and transport system scale. Their moderate impact on the transport process has been quantitatively evaluated (normalized causal effect 0.0423, -0.1447 and -0.0351, respectively) with adequate confounders considered for the first time. The prior was later embedded into multilayer neural networks via two methods: causal weight initialization and causal prior regularization. Based on the results from AutoML hyperparameter tuning experiments, using two embedding methods simultaneously emerged as a more advantageous practice since our proposed causal weight initialization technique can enhance model stability, particularly when used in conjunction with causal prior regularization. amongst those experiments utilizing both techniques, the R-squared values peaked at 0.881. This study demonstrates a balanced approach between expert knowledge and data-driven methods, providing enhanced interpretability in black-box models such as neural networks for environmental modelling.

Calibration using R-programming and parallel processing at the HUC12 subbasin scale in the Mid-Atlantic region: Development of national SWAT hydrologic calibration.

The first phase of a national scale Soil and Water Assessment Tool (SWAT) model calibration effort at the HUC12 (Hydrologic Unit Code 12) watershed scale was demonstrated over the Mid-Atlantic Region (R02), consisting of 3036 HUC12 subbasins. An R-programming based tool was developed for streamflow calibration including parallel processing for SWAT-CUP (SWAT- Calibration and Uncertainty Programs) to streamline the computational burden of calibration. Successful calibration of streamflow for 415 gages (KGE ≥0.5, Kling-Gupta efficiency; PBIAS ≤15%, Percent Bias) out of 553 selected monitoring gages was achieved in this study, yielding calibration parameter values for 2106 HUC12 subbasins. Additionally, 67 more gages were calibrated with relaxed PBIAS criteria of 25%, yielding calibration parameter values for an additional 150 HUC12 subbasins. This first phase of calibration across R02 increases the reliability, uniformity, and replicability of SWAT-related hydrological studies. Moreover, the study presents a comprehensive approach for efficiently optimizing large-scale multi-site calibration.

Evaluation of temperature- and ethanol-related developmental degree variations by a new scoring system (FETAX-score) applicable to Frog Embryo Teratogenicity Assay: Xenopus.

The aim of the present work is to propose a new quantitative assessment method (FETAX-score) for determining the degree of Xenopus laevis embryo development intended for use in embryotoxicity studies. Inspired by a similar scoring system used to evaluate developmental delays (young-for-age phenotypes) in rat embryos cultured in vitro, the FETAX-score was established by considering seven morphological features (head, naris, mouth, lower jaw, tentacles, intestine, anus) that are easily evaluable in tadpoles during the late stages of development at the conclusion of the test. Given that X. laevis development is temperature-dependent and that temperatures below 14°C and above 26°C are teratogenic, the FETAX-score was tested in embryos maintained at 17, 20, 23 and 26°C. No abnormalities were observed in any group, while the total score was temperature-related, suggesting that the FETAX-score is sensitive to moderate distress that does not influence general morphology. Intestine and anus were the least sensitive structures to temperature variations. To assess the applicability of the FETAX-score in developmental toxicological studies, we evaluated FETAX-score in tadpoles exposed during the morphogenetic period to Ethanol (Eth) at concentrations of 0, 0.25, 0.5, 1, 1.5, and 2 % v/v. Gross malformations were observed only in tadpoles from the Eth 2 % group. By contrast, data analysis of the other Eth groups showed dose-related reductions in the FETAX-score. Tentacles were the most sensitive structures to Eth-related delays. These results support the use of the FETAX-score to quantitatively assess developmental deviations in FETAX embryotoxicity studies.

Optimization of Manning's roughness coefficient using 1-dimensional hydrodynamic modelling in the perennial river system: A case of lower Narmada Basin, India.

This research bears significant implications for river management, flood forecasting, and ecosystem preservation in the Lower Narmada Basin. A more precise estimation of Manning's Roughness Coefficeint (n) will enhance the accuracy of hydraulic models and facilitate informed decision-making regarding flood risk management, water resource allocation, and environmental conservation efforts. Ultimately, this study aspires to contribute to the sustainable management of perennial river systems in India and beyond by offering a robust methodology for optimizing Manning's n tailored to the complex hydrological dynamics of the Lower Narmada Basin. Through a synthesis of empirical evidence and computational modelling, it seeks to empower stakeholders with actionable insights toward preserving and enhancing these invaluable natural resources. Using the new HEC-RAS v 6.0, a one-dimensional hydrodynamic model was developed to predict overbank discharge at different points along the basin. The study analyzes water levels, stream discharges, and river stage, optimizing Manning's n and required flood risk management. The model predicted a strong output agreement with R2, NSE, and RMSE for the 2020 event as 0.83, 0.81, and 0.36, respectively, with an optimum Manning's n of 0.03. The lower Narmada Basin part near the coastal zone (validation point) appears inundated frequently. The paper aims to provide insights into optimizing Manning's coefficient, which can ultimately lead to better water flow predictions and more efficient water management in the region.

Multiscale Modeling of CO2 Electrochemical Reduction on Copper Electrocatalysts: A Review of Advancements, Challenges, and Future Directions.

Although CO2 contributes to global warming, it also offers potential as a raw material for the production of hydrocarbons (CH4, C2H4 and CH3OH). Electrochemical CO2 reduction reaction (eCO2RR) is an emerging technology that utilizes renewable energy to convert CO2 into valuable fuels, solving environmental and energy problems simultaneously. Insights gained at any individual scale can only provide a limited view of that specific scale. Multiscale modeling, which involves coupling atomistic-level insights (DFT) and (MD), with mesoscale (KMC and MK) and macroscale (CFD) simulations, has received significant attention recently. While multiscale modeling of eCO2RR on electrocatalysts across all scales is limited due to its complexity, this review offers an overview of recent works on single scales and the coupling of two and three scales, such as "DFT+MD", "DFT+KMC", "DFT+MK", "KMC/MK+CFD" and "DFT+MK/KMC+CFD", focusing particularly on Cu-based electrocatalysts. This sets it apart from other reviews that solely focus exclusively on a single scale or only on a combination of DFT and MK/KMC scales. Furthermore, this review offers a concise overview of machine learning (ML) applications for eCO2RR, an emerging approach that has not yet been reviewed. Finally, this review highlights the key challenges, research gaps and perspectives of multiscale modeling for eCO2RR.

The impact of the COVID-19 pandemic on TB notifications in Ukraine in 2020.

BACKGROUND: We assessed the impact of the COVID-19 pandemic on TB notifications in Ukraine, stratified by multiple subgroups. DESIGN/METHODS: We analyzed data from Ukraine's National TB Program from January 2015 to December 2020 using interrupted time series models. We compared observed cases to counterfactual estimated cases had the pandemic not occurred and estimated trends through December 2020 nationally and by various demographics. We compared the proportions of individuals who underwent drug susceptibility testing (DST) in February 2020 and April 2020 to assess the pandemic impact on drug resistance testing. RESULTS: In April 2020, there were 39% (95% CI 36-42) fewer TB notifications than the estimated counterfactual (3,060 estimated; 95% CI 2,918-3,202; 1,872 observed). We observed a greater decrease in notifications among refugees/migrants compared with non-refugees/migrants (64%, 95% CI 60-67 vs. 39%, 95% CI 36-42), and individuals aged <15 years compared with those aged ≥15 years (60%, 95% CI 57-64 vs. 38%, 95% CI 36-41). We also observed a decrease in the proportion of individuals receiving DST for several drugs. CONCLUSIONS: These findings underscore the challenges to TB prevention and care during disruption and may be generalizable to the current wartime situation, especially considering the substantial increase in refugees within and leaving Ukraine.

CONTEXTE: Nous avons évalué l'impact de la pandémie de COVID-19 sur les notifications de TB en Ukraine, stratifiées en plusieurs sous-groupes. CONCEPTION/MÉTHODES: Nous avons analysé les données du Programme national de lutte contre la TB de l'Ukraine de janvier 2015 à décembre 2020 à l'aide de modèles de séries chronologiques interrompues. Nous avons comparé les cas observés aux cas contrefactuels estimés si la pandémie n'avait pas eu lieu et les tendances estimées jusqu'en décembre 2020 à l'échelle nationale et selon divers groupes démographiques. Nous avons comparé les proportions de personnes ayant subi un test de sensibilité aux médicaments (DST) en février 2020 et avril 2020 pour évaluer l'impact de la pandémie sur les tests de résistance aux médicaments. RÉSULTATS: En avril 2020, il y avait 39% (IC à 95% 36­42) de notifications de TB de moins que le contrefactuel estimé (3 060 estimés ; IC à 95% 2 918­3 202 ; 1 872 observés). Nous avons observé une plus grande diminution des notifications chez les réfugiés/migrants par rapport aux non-réfugiés/migrants (64%, IC à 95% 60­67 contre 39%, IC à 95% 36­42), et les personnes âgées de <15 ans par rapport à celles âgées de ≥15 ans (60% ; IC à 95% 57­64 contre 38% ; IC à 95% 36­41). Nous avons également observé une diminution de la proportion de personnes recevant le DST pour plusieurs médicaments. CONCLUSIONS: Ces résultats soulignent les défis de la prévention et des soins de la TB pendant les perturbations et peuvent être généralisés à la situation actuelle en temps de guerre, en particulier compte tenu de l'augmentation substantielle du nombre de réfugiés à l'intérieur et à l'extérieur de l'Ukraine.

What the fish? Tracing the geographical origin of fish using NIR spectroscopy.

Food authentication is a growing concern with rising complexities of the food supply network, with fish being an easy target of food fraud. In this regard, NIR spectroscopy has been used as an efficient tool for food authentication. This article reviews the latest research advances on NIR based fish authentication. The process from sampling/sample preparation to data analysis has been covered. Special attention was given to NIR spectra pre-processing and its unsupervised and supervised analysis. Sampling is an important aspect of traceability study and samples chosen ought to be a true representative of the population. NIR spectra acquired is often laden with overlapping bands, scattering and highly multicollinear. It needs adequate pre-processing to remove all undesirable features. The pre-processing technique can make or break a model and thus need a trial-and-error approach to find the best fit. As for spectral analysis and modelling, multicollinear nature of NIR spectra demands unsupervised analysis (PCA) to compact the features before application of supervised multivariate techniques such as LDA, PLS-DA, QDA etc. Machine learning approach of modelling has shown promising result in food authentication modelling and negates the need for unsupervised analysis before modelling.

An agent-based modelling framework to study growth mechanisms in EGFR-L858R mutant cell alveolar type II cells.

Mutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), particularly in never-smoker patients. However, these mutations are not always carcinogenic, and have recently been reported in histologically normal lung tissue from patients with and without lung cancer. To investigate the outcome of EGFR mutation in healthy lung stem cells, we grow murine alveolar type II organoids monoclonally in a three-dimensional Matrigel. Our experiments show that the EGFR-L858R mutation induces a change in organoid structure: mutated organoids display more 'budding', in comparison with non-mutant controls, which are nearly spherical. We perform on-lattice computational simulations, which suggest that this can be explained by the concentration of division among a small number of cells on the surface of the mutated organoids. We are currently unable to distinguish the cell-based mechanisms that lead to this spatial heterogeneity in growth, but suggest a number of future experiments which could be used to do so. We suggest that the likelihood of L858R-fuelled tumorigenesis is affected by whether the mutation arises in a spatial environment that allows the development of these surface protrusions. These data may have implications for cancer prevention strategies and for understanding NSCLC progression.

Mapping TB incidence across districts in Uganda to inform health program activities.

BACKGROUND: Identifying spatial variation in TB burden can help national TB programs effectively allocate resources to reach and treat all people with TB. However, data limitations pose challenges for subnational TB burden estimation. METHODS: We developed a small-area modeling approach using geo-positioned prevalence survey data, case notifications, and geospatial covariates to simultaneously estimate spatial variation in TB incidence and case notification completeness across districts in Uganda from 2016-2019. TB incidence was estimated using 1) cluster-level data from the national 2014-2015 TB prevalence survey transformed to incidence, and 2) case notifications adjusted for geospatial covariates of health system access. The case notification completeness surface was fit jointly using observed case notifications and estimated incidence. RESULTS: Estimated pulmonary TB incidence among adults varied >10-fold across Ugandan districts in 2019. Case detection increased nationwide from 2016 to 2019, and the number of districts with case detection rates >70% quadrupled. District-level estimates of TB incidence were five times more precise than a model using TB prevalence survey data alone. CONCLUSION: A joint spatial modeling approach provides useful insights for TB program operation, outlining areas where TB incidence estimates are highest and health programs should concentrate their efforts. This approach can be applied in many countries with high TB burden.

CONTEXTE: L'identification des variations spatiales de la charge de morbidité de la TB peut aider les programmes nationaux de lutte contre la TB à allouer efficacement les ressources pour atteindre et traiter toutes les personnes atteintes de TB. Cependant, les limites des données posent des problèmes pour l'estimation de la charge de morbidité infranationale. MÉTHODES: Nous avons développé une approche de modélisation à petite échelle en utilisant des données d'enquête de prévalence géolocalisées, des notifications de cas et des covariables géospatiales pour estimer simultanément la variation spatiale de l'incidence de la TB et l'exhaustivité de la notification des cas dans les districts de l'Ouganda de 2016 à 2019. L'incidence de la TB a été estimée à l'aide 1) des données au niveau des grappes de l'enquête nationale sur la prévalence de la TB de 2014­2015, transformées en incidence, et 2) des notifications de cas ajustées pour tenir compte des covariables géospatiales de l'accès au système de santé. La surface de complétude des notifications de cas a été ajustée conjointement à l'aide des notifications de cas observés et de l'incidence estimée. RÉSULTATS: L'incidence estimée de la TB pulmonaire chez les adultes a été multipliée par >10 dans les districts ougandais en 2019. La détection des cas a augmenté à l'échelle nationale entre 2016 et 2019, et le nombre de districts avec des taux de détection des cas >70% a quadruplé. Les estimations de l'incidence de la TB au niveau des districts étaient cinq fois plus précises qu'un modèle utilisant uniquement les données de l'enquête sur la prévalence de la TB. CONCLUSION: Une approche conjointe de modélisation spatiale fournit des informations utiles pour le fonctionnement des programmes de lutte contre la TB, en décrivant les domaines où les estimations de l'incidence de la TB sont les plus élevées et où les programmes de santé devraient concentrer leurs efforts. Cette approche peut être appliquée dans de nombreux pays où la charge de morbidité de la TB est élevée.

PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.

Unlike animals, variability in transcription factors (TFs) and their binding regions (TFBRs) across the plants species is a major problem that most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitation has resulted into underdevelopment of plant regulatory research and rampant use of Arabidopsis-like model species, generating misleading results. Here, we report a revolutionary transformers-based deep-learning approach, PTFSpot, which learns from TF structures and their binding regions' co-variability to bring a universal TF-DNA interaction model to detect TFBR with complete freedom from TF and species-specific models' limitations. During a series of extensive benchmarking studies over multiple experimentally validated data, it not only outperformed the existing software by >30% lead but also delivered consistently >90% accuracy even for those species and TF families that were never encountered during the model-building process. PTFSpot makes it possible now to accurately annotate TFBRs across any plant genome even in the total lack of any TF information, completely free from the bottlenecks of species and TF-specific models.

An articulated shape model to predict paediatric lower limb bone geometry using sparse landmarks.

Creating musculoskeletal models in a paediatric population currently involves either creating an image-based model from medical imaging data or a generic model using linear scaling. Image-based models provide a high level of accuracy but are time-consuming and costly to implement, on the other hand, linear scaling of an adult template musculoskeletal model is faster and common practice, but the output errors are significantly higher. An articulated shape model incorporates pose and shape to predict geometry for use in musculoskeletal models based on existing information from a population to provide both a fast and accurate method. From a population of 333 children aged 4-18 years old, we have developed an articulated shape model of paediatric lower limb bones to predict bone geometry from eight bone landmarks commonly used for motion capture. Bone surface root mean squared errors were found to be 2.63 ± 0.90 mm, 1.97 ± 0.61 mm, and 1.72 ± 0.51 mm for the pelvis, femur, and tibia/fibula, respectively. Linear scaling produced bone surface errors of 4.79 ± 1.39 mm, 4.38 ± 0.72 mm, and 4.39 ± 0.86 mm for the pelvis, femur, and tibia/fibula, respectively. Clinical bone measurement errors were low across all bones predicted using the articulated shape model, which outperformed linear scaling for all measurements. However, the model failed to accurately capture torsional measures (femoral anteversion and tibial torsion). Overall, the articulated shape model was shown to be a fast and accurate method to predict lower limb bone geometry in a paediatric population, superior to linear scaling.

Vaccinating parent flocks against colibacillosis reduces broiler mortality - A retrospective observational study from 2016 to 2019 in Finland.

Colibacillosis is one of the most important infectious diseases in modern poultry production. The complex nature of colibacillosis has made it challenging to produce an effective vaccine. As a control measure for colibacillosis outbreak in Finland, a vaccination program with a commercial colibacillosis vaccine and later also an autogenous vaccine was started for parent flocks in 2017. In this retrospective observational study from years 2016-2019, we evaluated first week and total mortality of broiler flocks (n= 6969) originating from parents with different colibacillosis vaccination status. Broiler flocks were divided into three groups according to vaccination status of their parent flocks. First group were flocks from parents with no colibacillosis vaccines; second group was flocks from parents vaccinated with commercial vaccine only; and third group was flocks from parents with both commercial and autogenous vaccine. Bayesian modelling was used to predict posterior distributions of first week mortality and total mortality of the broiler flocks. Results of the modelling revealed that broiler flocks from unvaccinated parents had the highest mortality rates (mean first week mortality 1.40 % and mean total mortality 4.33 %, respectively) whereas flocks from parents with a combination of commercial and autogenous vaccinations had the lowest mortality rates (mean first week mortality 0,91 % and mean total mortality 3,14 %). The mortalities from broilers flocks from parents with only commercial vaccine fell in between these groups. Also, standard deviations of mortality rates were lower in broilers from parents with commercial or both vaccines. This demonstrates that in addition to lowering the mean mortality rates, the vaccinations made high mortality broiler flocks less common. Best performance was obtained when autogenous vaccine was combined to the commercial vaccine. The autogenous vaccine consists of the same type of Escherichia coli strain that was causing most colibacillosis cases during the study period in Finland. This study adds to the evidence of benefits of colibacillosis vaccines during outbreaks. It also demonstrates the importance of the knowledge of the types of APEC strains causing outbreaks to produce effective autogenous vaccines.

Evaluation of adherence to abiraterone therapy in prostate cancer patients based on a population pharmacokinetic model.

AIMS: Abiraterone treatment requires regular drug intake under fasting conditions due to pronounced food effect, which may impact patient adherence. The aim of this prospective study was to evaluate adherence to abiraterone treatment in patients with prostate cancer. To achieve this aim, an abiraterone population pharmacokinetic model was developed and patients' adherence has been estimated by comparison of measured levels of abiraterone with population model-based simulations. METHODS: A total of 1469 abiraterone plasma levels from 83 healthy volunteers collected in a bioequivalence study were analysed using a nonlinear mixed-effects model. Monte Carlo simulation was used to describe the theoretical distribution of abiraterone pharmacokinetic profiles at a dose of 1000 mg once daily. Adherence of 36 prostate cancer patients treated with abiraterone was then evaluated by comparing the real abiraterone concentration measured in each patient during follow-up visit with the theoretical distribution of profiles based on simulations. Patients whose abiraterone levels were ˂5th or ˃95th percentile of the distribution of simulated profiles were considered to be non-adherent. RESULTS: Based on this evaluation, 13 patients (36%) have been classified as non-adherent. We observed significant association (P = .0361) between richness of the breakfast and rate of non-adherence. Adherent patients reported significantly better overall condition in self-assessments (P = .0384). A trend towards a higher occurrence of adverse effects in non-adherent patients was observed. CONCLUSIONS: We developed an abiraterone population pharmacokinetic model and proposed an advanced approach to medical adherence evaluation. Due to the need for administration under fasting conditions, abiraterone therapy is associated with a relatively high rate of non-adherence.

Feasibility of aligning creatine kinase MB activity and mass data in multicenter trials using generalized additive modeling.

OBJECTIVES: Elevated serum creatine kinase isoenzyme MB (CK-MB) levels indicate myocardial ischaemia and periprocedural myocardial injury during treatment of heart diseases. We established a method to predict CK-MB mass from activity data based on a prospective pilot study in order to simplify multicentre trials. METHODS: 38 elective cardiac surgery patients without acute myocardial ischaemia and terminal renal failure were recruited. CK-MB mass and activity were determined in venous blood samples drawn preoperatively, postoperatively, 6 h post-op, and 12 h post-op. Linear regression and generalized additive models (GAMs) were applied to describe the relationship of mass and activity. Influences of demographic and perioperative factors on the fit of GAMs was evaluated. The agreement of predicted and measured CK-MB masses was assessed by Bland-Altman analyses. RESULTS: Linear regression provided an acceptable overall fit (r2=0.834) but showed deviances at low CK-MB levels. GAMs did not benefit from the inclusion of age, body mass index, and surgical times. The minimal adequate model predicted CK-MB masses from activities, sex, and sampling time with an r2 of 0.981. Bland-Altman analyses confirmed narrow limits of agreement (spread: 8.87 µg/L) and the absence of fixed (p = 0.41) and proportional (p = 0.21) biases. CONCLUSIONS: GAM-based modelling of CK-MB data in a representative patient cohort allowed to predict CK-MB masses from activities, sex, and sampling time. This approach simplifies the integration of study centers with incompatible CK-MB data into multicentre trials in order to facilitate inclusion of CK-MB levels in statistical models.