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Why don’t new memories overwrite old ones? Sleep science holds clues
Association between triglyceride/high density lipoprotein ratio and incidence risk of Parkinson’s disease: a population-based cohort study
‘Precocious’ early-career scientists with high citation counts proliferate
Response of negative ion beamlet width and axis deflection to RF field in beam extraction region
Abstract Beam-divergence characteristics of single negative ion beamlet have been experimentally investigated with a superimposition of a controlled perturbation of a radio frequency wave (RF) field in a filament-arc discharge negative ion source. Oscillations of a negative-ion beamlet width and axis responding to the RF perturbation were observed, which may be a cause of the larger beam divergence angle of the RF negative ion source for ITER. It is pointed out that the oscillation of the beamlet width depends on the perveance and on an RF frequency such that the oscillation is suppressed at perveance-matched conditions and at low RF frequency.
Highly efficient and rapid removal of Congo red dye from textile wastewater using facile synthesized Mg/Ni/Al layered double hydroxide
Abstract Layered double hydroxides (LDH) are compounds with unique structures of hydroxide functional groups on their surfaces, and they have the proper arrangement of divalent and trivalent cations to adjust their unique catalytic actions. LDH was synthesized utilizing the co-precipitation technique and was thermally treated at 300 °C. The prepared compounds were chemically and structurally elucidated using FT-IR, XRD, SEM, BET, TG-DTA, and XPS characterization. We found that the thermal treatment of the prepared magnesium/nickel-LDH resulted in dehydration and dehydroxylation in its chemical structure. The crystallinity, the surface area, and the pore volume of the formed meso- and micropores were improved considerably after the thermal treatment. The efficiency of the uptake process was increased from 84 to 97% after the thermal treatment process, and the adsorption process tracked the Freundlich adsorption isotherm and pseudo-second-order kinetic model. The kinetics indicated the occurrence of three stages, and the diffusion of dye molecules into the pores was the rate-determining step. Different real water sample treatments showed the applicability of the thermally treated Mg/Ni/Al-LDH in the treatment process under optimized conditions. The presented mechanism of the uptake process using the prepared compounds comprises several interactions between the dye molecules and the thermally treated Mg/Ni/Al-LDH. The study presented the new application for Mg/Ni/Al-LDH in the as-prepared and thermally treated forms to uptake Congo-red (CR) dye from textile effluents.
Damage due to ice crystallization
Abstract The freezing of water is one of the major causes of mechanical damage in materials during wintertime; surprisingly this happens even in situations where water only partially saturates the material so that the ice has room to grow. Here we perform freezing experiments in cylindrical glass vials of various sizes and wettability properties, using a dye that exclusively colors the liquid phase; this allows precise observation of the freezing front. The visualization reveals that damage occurs in partially water-saturated media when a closed liquid inclusion forms within the ice due to the freezing of the air/water meniscus. When this water inclusion subsequently freezes, the volume expansion leads to very high pressures leading to the fracture of both the surrounding ice and the glass vial. The pressure can be understood quantitatively based on thermodynamics which correctly predicts that the crystallization pressure on the inclusion boundary is independent of the volume of the liquid pocket. Finally, our results also reveal that by changing the wetting properties of the confining walls, the formation of the liquid pockets that cause the mechanical damage can be avoided.
Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification
Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns
Abstract Concrete-filled double-skin steel tubular (CFDST) columns have become widely utilized in building construction and bridges, thanks to their exceptional structural capabilities. Therefore, this study investigates the axial compressive behavior of square CFDST columns. The study aims to explore the influence of external and internal plate shapes (flat or corrugated plates) and different widths of internal steel tubes on the axial compressive behavior. The effects of varying internal widths of the internal steel tube (60 mm, 116 mm, and 160 mm) on the performance of CFDST columns were examined. Additionally, the study compared the performance of concrete-filled steel tubular (CFST) and CFDST columns with external flat or corrugated plates. The findings indicated that incorporating internal corrugated plates notably improved both the load-carrying capacity and ductility of the specimens. Notably, CFDST columns featuring corrugated internal plates (116 mm width) exhibited strength enhancements of 25.3% and 7.4% compared to internal widths of 160 mm and 60 mm, respectively. Furthermore, the study proposed two machine-learning models, namely Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to estimate the ultimate compressive strength of square CFDST columns. The findings indicated that the GPR model outperformed the ANN model in predicting the bearing capacity of square CFDST columns. Additionally, the Shapley Additive Explanation technique was employed for feature analysis. The outcomes of this analysis revealed that parameters such as section width and concrete strength positively influence the compressive strength index.
Versatile application of fast green FCF as a visible cholangiogram in adult mice to medium-sized mammals
Diversity and biogeography of the bacterial microbiome in glacier-fed streams
Urinary prostaglandin metabolites evaluation for activity monitoring in inflammatory bowel disease
Opposite outcomes of triglyceride-glucose index and associated cardiovascular mortality risk in type 2 diabetes mellitus participants by different obesity criteria
Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients
Abstract South Africa was the most affected country in Africa by the coronavirus disease 2019 (COVID-19) pandemic, where over 4 million confirmed cases of COVID-19 and over 102,000 deaths have been recorded since 2019. Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. However, limited application of AI for COVID-19 in Africa has been reported in the literature. This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. For this purpose, a dataset with 154 features from 490 COVID-19 patients admitted into the intensive care unit (ICU) of Tygerberg Hospital in Cape Town, South Africa, during the first wave of COVID-19 in 2020 was retrospectively analysed. Our results show that Deep MLP had the best overall performance (F1 = 0.92; area under the curve (AUC) = 0.94) when CV and the synthetic minority oversampling technique (SMOTE) were applied without PCA. By using the Shapley Additive exPlanations (SHAP) model to interpret the mortality risk predictions, we identified the Length of stay (LOS) in the hospital, LOS in the ICU, Time to ICU from admission, days discharged alive or death, D-dimer (blood clotting factor), and blood pH as the six most critical variables for mortality risk prediction. Also, Age at admission, Pf ratio (PaO2/FiO2 ratio), troponin T (TropT), ferritin, ventilation, C-reactive protein (CRP), and symptoms of acute respiratory distress syndrome (ARDS) were associated with the severity and fatality of COVID-19 cases. The study reveals how ML could assist medical practitioners in making informed decisions on handling critically ill COVID-19 patients with comorbidities. It also offers insight into the combined effect of CV, PCA, and SMOTE on the performance of ML models for COVID-19 mortality risk prediction, which has been little explored.
Publisher Correction: A generic self-learning emotional framework for machines
Striking a Balance — Advancing Physician Collective-Bargaining Rights and Patient Protections
EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis
Learning the fitness dynamics of pathogens from phylogenies
Abstract The dynamics of the genetic diversity of pathogens, including the emergence of lineages with increased fitness, is a foundational concept of disease ecology with key public-health implications. However, the identification of such lineages and estimation of associated fitness remain challenging, and is rarely done outside densely sampled systems 1,2 . Here we present phylowave, a scalable approach that summarizes changes in population composition in phylogenetic trees, enabling the automatic detection of lineages based on shared fitness and evolutionary relationships. We use our approach on a broad set of viruses and bacteria (SARS-CoV-2, influenza A subtype H3N2, Bordetella pertussis and Mycobacterium tuberculosis ), which include both well-studied and understudied threats to human health. We show that phylowave recovers the main known circulating lineages for each pathogen and that it can detect specific amino acid changes linked to fitness changes. Furthermore, phylowave identifies previously undetected lineages with increased fitness, including three co-circulating B. pertussis lineages. Inference using phylowave is robust to uneven and limited observations. This widely applicable approach provides an avenue to monitor evolution in real time to support public-health action and explore fundamental drivers of pathogen fitness.