Browse Articles

Multi-objective optimization of the heating oil circuit inside the calendering roller for the lithium battery pole piece

Expression of interleukin-17 in oral tongue squamous cell carcinoma and its effect on biological behavior

Interaction of population density and slope will exacerbate spatiotemporal changes in land use and landscape patterns in mountain city

Smart oxygen monitoring in hospitals: a pilot study during COVID-19

Estimating self-performance when making complex decisions

Effect of artificial fish nest placement on spawning site preference and reproductive efficiency in reservoir fish with adhesive and demersal eggs

Adjusting on-scene CPR duration based on transport time interval in out-of-hospital cardiac arrest: a nationwide multicenter study

Spatial variations in urban woodland cooling between background climates

Abstract Urban woodland composition and configuration have strong associations with land surface temperatures (LST), but the evidence is contradictory due to different spatial scales, regional climate zones, woodland types and urban contexts. In this study, we analyse associations between urban woodland and LST within and between five cities in different Köppen climate zones. Our consistent methodology is framed around local climate zones and conducted at a fine spatial scale. We find that urban woodland fragmentation, connectedness, and shape complexity all influence LST, though much less than overall cover. The importance of cover holds for all climates except for hot-desert (Cairo). Otherwise, every 1% increase in woodland cover corresponds to a reduction of LST of around 0.07 °C to 0.02 °C (London-Cfb > Toronto-Dfa > Nanjing-Cfa > Shenyang-Dwa). Within cities, increasing urban woodland cover generally reduces LST more in built-up compared to vegetated zones. Nevertheless, associations between local LST and urban woodland composition and configuration are highly heterogeneous across cities, especially in cooler climates. Thus, to unravel the complexities of urban woodland cooling, systematic analysis of contemporaneous local and regional factors is required.

Identification of gas outburst precursors based on outburst percolation theory

Randomized controlled trial on the efficacy of forest walking compared to urban walking in enhancing mucosal immunity

Genetic association of lipid-lowering drug target genes with pancreatic cancer: a Mendelian randomization study

Characterizing the omics landscape based on 10,000+ datasets

Abstract The characteristics of data produced by omics technologies are pivotal, as they critically influence the feasibility and effectiveness of computational methods applied in downstream analyses, such as data harmonization and differential abundance analyses. Furthermore, variability in these data characteristics across datasets plays a crucial role, leading to diverging outcomes in benchmarking studies, which are essential for guiding the selection of appropriate analysis methods in all omics fields. Additionally, downstream analysis tools are often developed and applied within specific omics communities due to the presumed differences in data characteristics attributed to each omics technology. In this study, we investigate over ten thousand datasets to understand how proteomics, metabolomics, lipidomics, transcriptomics, and microbiome data vary in specific data characteristics. We were able to show patterns of data characteristics specific to the investigated omics types and provide a tool that enables researchers to assess how representative a given omics dataset is for its respective discipline. Moreover, we illustrate how data characteristics can impact analyses at the example of normalization in the presence of sample-dependent proportions of missing values. Given the variability of omics data characteristics, we encourage the systematic inspection of these characteristics in benchmark studies and for downstream analyses to prevent suboptimal method selection and unintended bias.

The role of Box A of HMGB1 in producing γH2AX associated DNA breaks in lung cancer

Residual nitrite and nitrate in processed meats and meat analogues in the United States

Abstract Residual nitrite (NO 2 − ) and nitrate (NO 3 − ) have been widely studied in the past few decades for their function to improve processed meat quality and their impact on human health 1–4 . In this study we examined how the residual nitrite and nitrate (NO x − ) content of major classes of processed meats products ( n  = 1132) produced locally from three regions (East Coast, Midwest and West Coast) and plant protein-based meat analogues ( n  = 53) available at retail in the United States was influenced by their composition, processing, and geographical attributes. We also conducted time-dependent depletion studies and observed different patterns of NO x − depletion and conversion during processing and storage and correlated them with product quality. Together, our results reveal a comprehensive prospective of NO x − content in processed meats and meat analogues. The NO 2 − in processed meats and meat analogues averaged (± standard error; minimum and maximum value in parentheses) 13.7 ± 0.62 (0.0-214.5) and 1.7 ± 0.34 (0.0–11.0), respectively, and the NO 3 − in processed meats and meat analogues averaged 32.6 ± 0.90 (2.0–205.9) and 7.2 ± 0.56 (4.0-25.3) ppm, respectively.

Using monitoring and simulation to analyze the failure characteristics of multizone landslides controlled by faults: a case study

Multi-objective design of multi-material truss lattices utilizing graph neural networks

Abstract The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems. In this work, we propose a novel approach that incorporates material properties as edge features within the graph representation of multi-material truss lattices, utilizing graph neural networks (GNNs) to develop a fast and efficient inverse design framework. We validate this framework by designing lattices with tunable thermal expansion and stiffness properties, showcasing its ability to explore a broad and flexible design space. We showcase the framework’s inverse design capabilities for both single and multi-objective optimization tasks and assess its limitations. Additionally, we demonstrate the superior capacity of GNNs in capturing structure-property relationships for multi-material systems. We anticipate that the continued advancement of GNN-assisted inverse design will play a key role in unlocking the full potential of multi-material truss lattices.

Atomically thin high-entropy oxides via naked metal ion self-assembly for proton exchange membrane electrolysis

Patient and public involvement and engagement to improve impact on antimicrobial resistance

Cathodic tandem alkylation/dearomatization of heterocycles enabled by Al-facilitated carbonyl deoxygenation

The α-globin super-enhancer acts in an orientation-dependent manner

Abstract Individual enhancers are defined as short genomic regulatory elements, bound by transcription factors, and able to activate cell-specific gene expression at a distance, in an orientation-independent manner. Within mammalian genomes, enhancer-like elements may be found individually or within clusters referred to as locus control regions or super-enhancers (SEs). While these behave similarly to individual enhancers with respect to cell specificity, distribution and distance, their orientation-dependence has not been formally tested. Here, using the α-globin locus as a model, we show that while an individual enhancer works in an orientation-independent manner, the direction of activity of a SE changes with its orientation. When the SE is inverted within its normal chromosomal context, expression of its normal targets, the α-globin genes, is severely reduced and the normally silent genes lying upstream of the α-globin locus are upregulated. These findings add to our understanding of enhancer-promoter specificity that precisely activate transcription.