Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis

The goals of this study were to examine whether machine-learning algorithms outper-form multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to in-vestigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Fi-nally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response

Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis

Objective The objective was to predict insufficient response to 3 months methotrexate (MTX) in DMARD naïve rheumatoid arthritis patients. Methods A Multivariable logistic regression model of rheumatoid arthritis patients starting MTX was developed in a derivation cohort with 285 patients starting MTX in a clinical multicentre, stratified single-blinded trial, performed in seven secondary care clinics and a tertiary care clinic. The model was validated in a validation cohort with 102 patients starting MTX at a tertiary care clinic. Outcome was insufficient response (disease activity score (DAS)28 >3.2) after 3 months of MTX treatment. Clinical characteristics, lifestyle variables, genetic and metabolic biomarkers were determined at baseline in both cohorts. These variables were dichotomized and used to construct a multivariable prediction model with backward logistic regression analysis. Results The prediction model for insufficient response in the derivation cohort, included: DAS28>5.1, Health Assessment Questionnaire>0.6, current smoking, BMI>25 kg/m2, ABCB1 rs1045642 genotype, ABCC3 rs4793665 genotype, and erythrocyte-folate<750 nmol/L. In the derivation cohort, AUC of ROC curve was 0.80 (95%CI: 0.73–0.86), and 0.80 (95%CI: 0.69–0.91) in the validation cohort. Betas of the prediction model were transformed into total risk score (range 0–8). At cutoff of 4, probability for insufficient response was 44%. Sensitivity was 71%, specificity 72%, with positive and negative predictive value of 72% and 71%. Conclusions A prognostics prediction model for insufficient response to MTX in 2 prospective RA cohorts by combining genetic, metabolic, clinical and lifestyle variables was developed and validated. This model satisfactorily identified RA patients with high risk of insufficient response to MTX

Origins and genetic legacy of prehistoric dogs

Dogs were the first domestic animal, but little is known about their population history and to what extent it was linked to humans. We sequenced 27 ancient dog genomes and found that all dogs share a common ancestry distinct from present-day wolves, with limited gene flow from wolves since domestication but substantial dog-to-wolf gene flow. By 11,000 years ago, at least five major ancestry lineages had diversified, demonstrating a deep genetic history of dogs during the Paleolithic. Coanalysis with human genomes reveals aspects of dog population history that mirror humans, including Levant-related ancestry in Africa and early agricultural Europe. Other aspects differ, including the impacts of steppe pastoralist expansions in West and East Eurasia and a near-complete turnover of Neolithic European dog ancestry

Ancient chicken remains reveal the origins of virulence in Marek's disease virus

This is the author accepted manuscript. The final version is available from the American Association for the Advancement of Science via the DOI in this recordData and materials availability: All MDV sequence data generated have been deposited in GenBank under accession PRJEB64489. Code is available at GitHub (https://github.com/antonisdim/MDV) and archived at Zenodo (https://zenodo.org/records/10022436) (25).The pronounced growth in livestock populations since the 1950s has altered the epidemiological and evolutionary trajectory of their associated pathogens. For example, Marek's disease virus (MDV), which causes lymphoid tumors in chickens, has experienced a marked increase in virulence over the past century. Today, MDV infections kill >90% of unvaccinated birds, and controlling it costs more than US$1 billion annually. By sequencing MDV genomes derived from archeological chickens, we demonstrate that it has been circulating for at least 1000 years. We functionally tested the Meq oncogene, one of 49 viral genes positively selected in modern strains, demonstrating that ancient MDV was likely incapable of driving tumor formation. Our results demonstrate the power of ancient DNA approaches to trace the molecular basis of virulence in economically relevant pathogens.European Research Council (ERC)Wellcome TrustOxford Martin School Pandemic Genomics ProgrammeArts and Humanities Research Council (AHRC)European Union Horizon 2020Biotechnology and Biological Sciences Research Council (BBSRC)Research Foundation–Flanders (Fonds voor Wetenschappelijk Onderzoek

Ancient pigs reveal a near-complete genomic turnover following their introduction to Europe

Archaeological evidence indicates that pig domestication had begun by ∼10,500 y before the present (BP) in the Near East, and mitochondrial DNA (mtDNA) suggests that pigs arrived in Europe alongside farmers ∼8,500 y BP. A few thousand years after the introduction of Near Eastern pigs into Europe, however, their characteristic mtDNA signature disappeared and was replaced by haplotypes associated with European wild boars. This turnover could be accounted for by substantial gene flow from local European wild boars, although it is also possible that European wild boars were domesticated independently without any genetic contribution from the Near East. To test these hypotheses, we obtained mtDNA sequences from 2,099 modern and ancient pig samples and 63 nuclear ancient genomes from Near Eastern and European pigs. Our analyses revealed that European domestic pigs dating from 7,100 to 6,000 y BP possessed both Near Eastern and European nuclear ancestry, while later pigs possessed no more than 4% Near Eastern ancestry, indicating that gene flow from European wild boars resulted in a near-complete disappearance of Near East ancestry. In addition, we demonstrate that a variant at a locus encoding black coat color likely originated in the Near East and persisted in European pigs. Altogether, our results indicate that while pigs were not independently domesticated in Europe, the vast majority of human-mediated selection over the past 5,000 y focused on the genomic fraction derived from the European wild boars, and not on the fraction that was selected by early Neolithic farmers over the first 2,500 y of the domestication process

Ancient pigs reveal a near-complete genomic turnover following their introduction to Europe

Archaeological evidence indicates that pig domestication had begun by ~10,500 y before the present (BP) in the Near East, and mitochondrial DNA (mtDNA) suggests that pigs arrived in Europe alongside farmers ~8,500 y BP. A few thousand years after the introduction of Near Eastern pigs into Europe, however, their characteristic mtDNA signature disappeared and was replaced by haplotypes associated with European wild boars. This turnover could be accounted for by substantial gene flow from local Euro-pean wild boars, although it is also possible that European wild boars were domesticated independently without any genetic con-tribution from the Near East. To test these hypotheses, we obtained mtDNA sequences from 2,099 modern and ancient pig samples and 63 nuclear ancient genomes from Near Eastern and European pigs. Our analyses revealed that European domestic pigs dating from 7,100 to 6,000 y BP possessed both Near Eastern and European nuclear ancestry, while later pigs possessed no more than 4% Near Eastern ancestry, indicating that gene flow from European wild boars resulted in a near-complete disappearance of Near East ancestry. In addition, we demonstrate that a variant at a locus encoding black coat color likely originated in the Near East and persisted in European pigs. Altogether, our results indicate that while pigs were not independently domesticated in Europe, the vast majority of human-mediated selection over the past 5,000 y focused on the genomic fraction derived from the European wild boars, and not on the fraction that was selected by early Neolithic farmers over the first 2,500 y of the domestication process

PRIMENA METODE KRUTIH TELA ZA DISRETIZACIJU NOSEĆIH STRUKTURA PRI DINAMIČKOJ ANALIZI NA PRIMERU KONZOLNE DIZALICE

REZIME U radu se analizira dinamičko ponašanje noseće konstrukcije portalne dizalice usled dejstva kolica kao pokretnog opterećenja. Dat je prikaz klasifikacije portalnih dizalica pri čemu su izdvojene portalne dizalice za kontejnerske terminale sa svojim visokih performansama koje imaju stalnu tendenciju poboljšanja. Prvo je dat koncept primene analitičkog pristupa za modeliranje noseće konstrukcije preko sistema elastičnih tela tipa prizmatične grede i razmatranje slobodnih poprečnih oscilacija. Kao savremen i pre svega neophodan, usvojen je kombinovani pristup za istraživanje naslovnog problema, tj. konačnoelementni pristup je iskorišćen za modeliranje noseće konstrukcije portalne dizalice a principi analitičke mehanike su iskorišćeni za modeliranje kolica. Razmatraju se dva najčešća konstrukciona tipa portalne dizalice za formiranje modela strukture. Kolica su obuhvaćena kroz model pokretne mase, model pokretnog oscilatora i kroz model pokretnog oscilatora sa klatnom koji predstavlja originalan model pokretnog opterećenja. Za svaki od modela je utvrđena dinamička interakcija između ovih sistema i postavljeni su matematički modeli koji predstavljaju sistem diferencijalnih jednačina drugog reda sa promenljivim koeficijentima. Rešenja su dobijena pomoću originalnih programa, na bazi metode direktne integracijeNjumarkove metode. Identifikacija i analize odziva su izvršene za dva realna primera portalnih dizalica. Istražen je uticaj brzine, ubrzanja/usporenja i težine kolica, kao i uticaj klaćenja tereta i elastične opruge u sistemu kolica. Dobijeni rezultati se mogu iskoristiti u početnim fazama konstruisanja portalnih dizalica koje imaju tendenciju da ostvare veoma visoke performanse, u smislu ostvarivanja boljeg uvida u dinamičko ponašanje

Obaveštajna priprema bojišta i modelovanje upotrebe snaga za operacije u urbanim sredinama

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Learning Strategies and Student Achievement – Experience from Implementation of an Educational Programme

This article presents research on the results of one educational programme aimed at fostering self-regulation in learning among students. The programme is designed as enabling students to use Bloom’s taxonomy in their learning, which is accompanied by demystifying the assessment process, students’ new roles and activities, and changes in interpersonal relations in the teaching process. The programme was implemented in one student group in a vocational school from Belgrade. The research aimed to examine the programme’s contributions to students’ learning strategies and their achievement. Data on students’ learning strategies were gathered using the MSLQ questionnaire, while the results of knowledge tests were used as a measure of students’ achievement. For data analysis, we used descriptive statistics, t-test and Pearson correlation coefficient. The results show that the programme has contributed both to students’ learning strategies and achievement. However, the statistically significant relationship between students’ use of different learning strategies and their achievement has not been found. From socioconstructivistic viewpoints on teaching and learning, this could be interpreted by a complex interrelation between learning strategies, achievement and the context in which learning is occurring. Thus, it is inadequate to research them as separate variables. The pedagogical implication of the research is that for a change in the quality of education it is not sufficient to change individual segments of teaching/learning, but it is important to change the complete context