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Structural and functional changes in the oral microbiome of patients with craniofacial microsomia
Polytype switching by super-lubricant van der Waals cavity arrays
Nepenthes pitcher fluid for the green synthesis of silver nanoparticles with biofilm inhibition, anticancer and antioxidant properties
Impact of subcutaneous tunnel length on infection risk in tunneled PICCs: a study in cancer patient
A comprehensive and quantitative SEM–EDS analytical process applied to lithium-ion battery electrodes
Abstract The combination of scanning electron microscopy (SEM) images and energy-dispersive X-ray spectroscopy (EDS) maps (SEM–EDS analysis) enables the analysis of the relationship between the microstructures and elemental compositions of the surfaces of materials. However, conventional SEM–EDS analyses lack comprehensiveness and quantitativeness, resulting in potential inaccuracies in reflecting the properties of the entire sample and variations in the results depending on the analyst. Therefore, herein, we propose an objective SEM–EDS analytical process that addresses the aforementioned issues. Comprehensiveness was addressed by acquiring large volumes of SEM images through automated capturing, whereas quantitativeness was addressed through microstructural analysis of the SEM images based on image features, model-based dimension reduction and clustering methods, and similarity analysis of the elemental distribution in EDS maps based on statistical distances. The proposed method was used to analyze the degradation of lithium-ion battery electrodes, affording objective results that align with subjective insights into the changes in the morphology and composition of solid electrolyte interphase (SEI) films accompanying degradation.
Hyperspectral imaging suggests potential for rapid quantification of fission products in spent nuclear fuel
Abstract An analysis of sintered uranium dioxide has been conducted using a hyperspectral camera sensitive to short-wave infrared wavelengths in the range 949–2472 nm. Three groups of sintered UO2 nuclear fuel pellets were prepared and analysed, with stable sub-group surrogates introduced at the preparation stage to emulate the presence of fission product elements. Results show a clear, consistent, and reproducible spectral response across the pellet groups for pure UO2. Furthermore, the addition of fission product elements is observed to affect the shortwave infrared response, causing an overall flattening of the spectra. We have shown that this spectral change is correlated significantly with the presence of lanthanides in the fuel matrix. This result could have important potential in post-irradiation examination for quantifying nuclear fuel burn-up and radiotoxicity at discharge, as the hyperspectral imaging setup allows multiple (> 20) samples to be analysed in a single image, captured in under 30 s.
Author Correction: Paternal addiction to cigarettes, alcohol, and opium and the psychological distress of their youths in southeast Iran
Two-dimensional polyaniline crystal with metallic out-of-plane conductivity
Coordinating multiple mental faculties during learning
Abstract Complex behavior is supported by the coordination of multiple brain regions. How do brain regions coordinate absent a homunculus? We propose coordination is achieved by a controller-peripheral architecture in which peripherals (e.g., the ventral visual stream) aim to supply needed inputs to their controllers (e.g., the hippocampus and prefrontal cortex) while expending minimal resources. We developed a formal model within this framework to address how multiple brain regions coordinate to support rapid learning from a few example images. The model captured how higher-level activity in the controller shaped lower-level visual representations, affecting their precision and sparsity in a manner that paralleled brain measures. In particular, the peripheral encoded visual information to the extent needed to support the smooth operation of the controller. Alternative models optimized by gradient descent irrespective of architectural constraints could not account for human behavior or brain responses, and, typical of standard deep learning approaches, were unstable trial-by-trial learners. While previous work offered accounts of specific faculties, such as perception, attention, and learning, the controller-peripheral approach is a step toward addressing next generation questions concerning how multiple faculties coordinate.
Cationic peptides cause memory loss through endophilin-mediated endocytosis
Validity and reliability of the arabic national nutrition plans checklist
Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning
Abstract Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. Aiming to systematically characterize candidate genes for a role in accumulation of lipids in adipocytes and other cardiometabolic traits, we developed a pipeline using CRISPR/Cas9, non-invasive, semi-automated fluorescence imaging and deep learning-based image analysis in live zebrafish larvae. Results from a dietary intervention show that 5 days of overfeeding is sufficient to increase the odds of lipid accumulation in adipocytes by 10 days post-fertilization (dpf, n = 275). However, subsequent experiments show that across 12 to 16 established obesity genes, 10 dpf is too early to detect an effect of CRISPR/Cas9-induced mutations on lipid accumulation in adipocytes (n = 1014), and effects on food intake at 8 dpf (n = 1127) are inconsistent with earlier results from mammals. Despite this, we observe effects of CRISPR/Cas9-induced mutations on ectopic accumulation of lipids in the vasculature (sh2b1 and sim1b) and liver (bdnf); as well as on body size (pcsk1, pomca, irs1); whole-body LDLc and/or total cholesterol content (irs2b and sh2b1); and pancreatic beta cell traits and/or glucose content (pcsk1, pomca, and sim1a). Taken together, our results illustrate that CRISPR/Cas9- and image-based experiments in zebrafish larvae can highlight direct effects of obesity genes on cardiometabolic traits, unconfounded by their – not yet apparent – effect on excess adiposity.
GZMK-expressing CD8+ T cells promote recurrent airway inflammatory diseases
Simulation of energy management system using model predictive control in AC/DC microgrid
Carbon risk and labor investment efficiency
Abstract Labor capital plays a critical role in shaping enterprise competitiveness and in the allocation of macroeconomic resources, while the prominent of carbon risk significantly influences corporate labor allocation patterns. In this paper, utilizing data sampled from China’s A-share listed companies between 2012 and 2021, the impact and mechanism of corporate carbon risk on labor investment efficiency have been examined. The results reveal that carbon risk significantly inhibits labor investment efficiency. Moreover, environmental uncertainty, agency problems, and managerial ability are the primary channels through which carbon risk can affect corporate labor investment efficiency. Additional tests reveal that the impact of carbon risk on labor investment efficiency is more pronounced in firms with lower competitive positions and more severe financing constraints. The detrimental effect of carbon risk on labor investment efficiency is particularly evident in firms characterized by labor underinvestment, high labor intensity, high pollution, and non-high-tech industries. The findings of this study can provide valuable insights for policymakers to improve carbon risk management, enhance labor investment efficiency, and optimize the institutional mechanisms for factor market allocation.
City-scale residential energy consumption prediction with a multimodal approach
Abstract The key role of buildings in tackling climate change has gained global recognition. To avoid unnecessary costs and time wasted, it is important to understand the conditions and energy usage for existing housing stock to identify the most important features affecting energy consumption and to guide the relevant retrofit measures. This paper investigated how the spatial, morphological and thermal characteristics of residential houses contribute to housing energy consumption. Additionally, it presents a rapid assessment tool using minimum data input to answer two main questions: 1) What type of properties may need retrofit? 2) What building elements/features may be prioritised to be retrofitted? A case study was performed with around 143,000 residential properties in Sheffield. An automated machine approach was applied which successfully estimated the energy consumption of target buildings with an $$R^2$$ score of 0.828. Permutation feature importance and partial dependence of the features were examined against energy consumption. The results indicate that housing sizes and conditions of the external walls are found to be the most important features when estimating the energy consumption of residential buildings in Sheffield. Relatively larger and older detached houses in neighbourhoods with higher build density may benefit the most from home upgrading projects for energy consumption reduction.