Plastic Evolution Characterization for 304 Stainless Steel by CQN_Chen Model under the Proportional Loading.

In this paper, the CQN_Chen function is used to characterize the plastic anisotropic evolution of 304 stainless steel (SS304). The uniaxial tensile tests along different loading directions are conducted to experimentally investigate the anisotropic hardening behavior for SS304. The experimental data indicates that the anisotropy of SS304 is weak. The convexity analysis is carried out by the geometry-inspired numerical convex analysis method for the CQN_Chen yield locus during plastic deformation. The Hill48, SY2009 and CQN functions are used as the comparison to evaluate the accuracy of the CQN_Chen function in characterizing plastic evolution. The predicted values are compared with the experimental data. The comparison demonstrates that the CQN_Chen function can accurately characterize anisotropic hardening behavior under uniaxial tension along distinct loading directions and equibiaxial tension. Simultaneously, the CQN_Chen model has the capacity to adjust the yield surface shape between uniaxial tension and equibiaxial tension. The CQN_Chen model is recommended to characterize plastic evolving behavior under uniaxial tension along different directions and equibiaxial tension.

Molecular and immune signatures, and pathological trajectories of fatal COVID-19 lungs defined by in situ spatial single-cell transcriptome analysis.

Despite intensive studies during the last 3 years, the pathology and underlying molecular mechanism of coronavirus disease 2019 (COVID-19) remain poorly defined. In this study, we investigated the spatial single-cell molecular and cellular features of postmortem COVID-19 lung tissues using in situ sequencing (ISS). We detected 10 414 863 transcripts of 221 genes in whole-slide tissues and segmented them into 1 719 459 cells that were mapped to 18 major parenchymal and immune cell types, all of which were infected by SARS-CoV-2. Compared with the non-COVID-19 control, COVID-19 lungs exhibited reduced alveolar cells (ACs) and increased innate and adaptive immune cells. We also identified 19 differentially expressed genes in both infected and uninfected cells across the tissues, which reflected the altered cellular compositions. Spatial analysis of local infection rates revealed regions with high infection rates that were correlated with high cell densities (HIHD). The HIHD regions expressed high levels of SARS-CoV-2 entry-related factors including ACE2, FURIN, TMPRSS2 and NRP1, and co-localized with organizing pneumonia (OP) and lymphocytic and immune infiltration, which exhibited increased ACs and fibroblasts but decreased vascular endothelial cells and epithelial cells, mirroring the tissue damage and wound healing processes. Sparse nonnegative matrix factorization (SNMF) analysis of niche features identified seven signatures that captured structure and immune niches in COVID-19 tissues. Trajectory inference based on immune niche signatures defined two pathological routes. Trajectory A primarily progressed with increased NK cells and granulocytes, likely reflecting the complication of microbial infections. Trajectory B was marked by increased HIHD and OP, possibly accounting for the increased immune infiltration. The OP regions were marked by high numbers of fibroblasts expressing extremely high levels of COL1A1 and COL1A2. Examination of single-cell RNA-seq data (scRNA-seq) from COVID-19 lung tissues and idiopathic pulmonary fibrosis (IPF) identified similar cell populations consisting mainly of myofibroblasts. Immunofluorescence staining revealed the activation of IL6-STAT3 and TGF-ß-SMAD2/3 pathways in these cells, likely mediating the upregulation of COL1A1 and COL1A2 and excessive fibrosis in the lung tissues. Together, this study provides a spatial single-cell atlas of cellular and molecular signatures of fatal COVID-19 lungs, which reveals the complex spatial cellular heterogeneity, organization, and interactions that characterized the COVID-19 lung pathology.

Insights into Omicron's Low Fusogenicity through In Silico Molecular Studies on Spike-Furin Interactions.

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant and its subvariants have a unique set of mutations. Two of those mutations (N679 K and P681 H) reside close to the S1 /S2 furin cleavage site (FCS; 685-686). When these mutations reside together, they exert less-efficient membrane fusion than wild type and most other variants of concern such as the Delta variant. Here, we in silico targeted these mutations to find out which of the amino acids and interactions change plays the key role in fusion. To comprehend the epistatic effect of N679 K and P681 H mutations on the spike protein, we in silico constructed three types of spike protein sequences by changing the respective amino acids on 679 and 681 positions (P681 H, N679 K, K679 N-H681 P variants). We then analyzed the binding affinity of furin and spike (Furin-Wild, Furin-Omicron, Furin-P681 H, Furin-N679 K, and Furin-K679 N/H681 P) complexes. Omicron and P681 H variants showed a similar higher binding energy trend compared to the wild type and N679 K. The variation in hydrogen, hydrophobic, and salt bridge bonds between spike protein and furin provided an explanation for the observed low fusogenicity of Omicron. The fate of the epistasis in furin binding and possible cleavage depends on the efficient interaction between FCS in spike and furin catalytic triad, and in addition, the loss of the hydrogen bond between Arg 681 (spike) and Asn 295 (furin) along with inhibitor-like ineffective higher affinity plays an important role in the enzymatic activity.

Anisotropic Hardening of TRIP780 Steel Sheet: Experiments and Analytical Modeling.

By combining experimental and theoretical models, this research investigates the anisotropic hardening behaviors of TRIP780 steel. The specimens of TRIP780 steel were subjected to uniaxial tensile and bulging tests under different loading conditions to obtain hardening data. The experimental results show that the strength and plastic deformation of TRIP780 steel vary with the loading directions, which indicates that TRIP780 steel has anisotropy characteristics. In this paper, the dichotomous method is used to ensure the convexity of the Chen-coupled quadratic and non-quadratic (CQN) function. Comparing the predictions of the hardening behavior of the TRIP780 steel sheet by the Yld2000-2d, Stoughton-Yoon'2009 and Chen-CQN functions, the results show that the Chen-CQN function exhibits the advantages of simple numerical implementation and a more realistic prediction of yield stress compared to the former two, respectively. Comparing the prediction of Chen-CQN function with the experimental hardening data, the results show that the deviation between the experimental data and the experimental response given by the function is always within 3%, and this function maintains an accurate prediction under different stress states, indicating that the Chen-CQN yield function has accuracy and flexibility for the characterization of the yield surface of TRIP780 steel.

Effect of chemical treatments on properties of injection molded Nypa fruticans fiber reinforced polypropylene composite.

The rise in environmental awareness prompted consideration of environment friendly materials. Natural fiber, on the contrary, has a structure that allows it to absorb moisture attributable to its hydrophilicity, which hinders its wide application and leads to poor interfacial bonding with the polymer matrix. Therefore, fiber surface modification is inevitable, which is usually based on using the functional group of some chemicals to replace the hydrophilic hydroxyl group to make it more moisture resistant and ameliorate the boding between fiber and polymer matrix. In this study, injection molded nypa fiber reinforced polypropylene composites were fabricated. Three different chemical modification i.e., mercerization, H2O2 treatment, maleic anhydride polypropylene (MAPP) compatibilizer, were employed. Other parameters on which the properties of the composite depend, i.e., fiber volume (30%), manufacturing process, etc. were kept the same. Field emission scanning electron microscopic (FE-SEM) images were also investigated to verify the result of experiments. Moisture resistance of the composite was also evaluated. The tensile and flexural properties of treated composite were significantly enhanced than the untreated one. The maximum strength was obtained for MAPP treated composite. The chemical treatment has a less impact on the impact strength of the composite. Better moisture resistance was observed for treated fiber composites. This study provides the insight of using chemical treatment for better adhesion between the fiber and the polymer.

Antidiabetic Potential of Commonly Available Fruit Plants in Bangladesh: Updates on Prospective Phytochemicals and Their Reported MoAs.

Diabetes mellitus is a life-threatening disorder affecting people of all ages and adversely disrupts their daily functions. Despite the availability of numerous synthetic-antidiabetic medications and insulin, the demand for the development of novel antidiabetic medications is increasing due to the adverse effects and growth of resistance to commercial drugs in the long-term usage. Hence, antidiabetic phytochemicals isolated from fruit plants can be a very nifty option to develop life-saving novel antidiabetic therapeutics, employing several pathways and MoAs (mechanism of actions). This review focuses on the antidiabetic potential of commonly available Bangladeshi fruits and other plant parts, such as seeds, fruit peals, leaves, and roots, along with isolated phytochemicals from these phytosources based on lab findings and mechanism of actions. Several fruits, such as orange, lemon, amla, tamarind, and others, can produce remarkable antidiabetic actions and can be dietary alternatives to antidiabetic therapies. Besides, isolated phytochemicals from these plants, such as swertisin, quercetin, rutin, naringenin, and other prospective phytochemicals, also demonstrated their candidacy for further exploration to be established as antidiabetic leads. Thus, it can be considered that fruits are one of the most valuable gifts of plants packed with a wide spectrum of bioactive phytochemicals and are widely consumed as dietary items and medicinal therapies in different civilizations and cultures. This review will provide a better understanding of diabetes management by consuming fruits and other plant parts as well as deliver innovative hints for the researchers to develop novel drugs from these plant parts and/or their phytochemicals.

Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions.

Deep learning has been applied in precision oncology to address a variety of gene expression-based phenotype predictions. However, gene expression data's unique characteristics challenge the computer vision-inspired design of popular Deep Learning (DL) models such as Convolutional Neural Network (CNN) and ask for the need to develop interpretable DL models tailored for transcriptomics study. To address the current challenges in developing an interpretable DL model for modeling gene expression data, we propose a novel interpretable deep learning architecture called T-GEM, or Transformer for Gene Expression Modeling. We provided the detailed T-GEM model for modeling gene-gene interactions and demonstrated its utility for gene expression-based predictions of cancer-related phenotypes, including cancer type prediction and immune cell type classification. We carefully analyzed the learning mechanism of T-GEM and showed that the first layer has broader attention while higher layers focus more on phenotype-related genes. We also showed that T-GEM's self-attention could capture important biological functions associated with the predicted phenotypes. We further devised a method to extract the regulatory network that T-GEM learns by exploiting the attributions of self-attention weights for classifications and showed that the network hub genes were likely markers for the predicted phenotypes.

Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.

Evaluation of D-loop hypervariable region I variations, haplogroups and copy number of mitochondrial DNA in Bangladeshi population with type 2 diabetes.

The profound impact of mitochondrion in cellular metabolism has been well documented. Since type 2 diabetes (T2D) is a metabolic disorder, mitochondrial dysfunction is intricately linked with the disease pathogenesis. Mitochondrial DNA (mtDNA) variants are involved with functional dysfunction of mitochondrion and play a pivotal role in the susceptibility to T2D. In this study, we opted to find the association of mtDNA variants within the D-loop hypervariable region I (HVI), haplogroups and mtDNA copy number with T2D in Bangladeshi population. A total of 300 unrelated Bangladeshi individuals (150 healthy and 150 patients with T2D) were recruited in the present study, their HVI regions were amplified and sequenced using Sanger chemistry. Haplogrep2 and Phylotree17 tools were employed to determine the haplogroups. MtDNA copy number was measured using primers of mitochondrial tRNALeu (UUR) gene and nuclear ß2-microglobulin gene. Variants G16048A (OR:0.12, p = 0.04) and G16129A (OR: 0.42, p = 0.007) were found to confer protective role against T2D according to logistic regression analysis. However along with G16129A, two new variants C16294T and T16325C demonstrated protective role against T2D when age and gender were adjusted. Haplogroups A and H showed significant association with the risk of T2D after adjustments out of total 19 major haplogroups identified. The mtDNA copy numbers were stratified into 4 groups according to the quartiles (groups with lower, medium, upper and higher mtDNA copy numbers were respectively designated as LCN, MCN, UCN and HCN). Patients with T2D had significantly lower mtDNA copy number compared to their healthy counterparts in HCN group. Moreover, six mtDNA variants were significantly associated with mtDNA copy number in the participants. Thus, our study confers that certain haplogroups and novel variants of mtDNA are significantly associated with T2D while decreased mtDNA copy number (though not significant) has been observed in patients with T2D. However, largescale studies are warranted to establish association of novel variants and haplogroup with type 2 diabetes.

Quantifying the protective capacity of mangroves from storm surges in coastal Bangladesh.

Mangroves are an important ecosystem-based protection against cyclonic storm surge. As the surge moves through the mangrove forest, the tree roots, trunks, and leaves obstruct the flow of water. Damage to adjacent coastal lands is attenuated mainly by reducing (i) surge height, which determines the area and depth of inundation and (ii) water flow velocity. But the extent of mangrove protection depends on the density of tree plantings and the diameter of trunks and roots, along with an array of other forest characteristics (e.g., floor shape, bathymetry, spectral features of waves, and tidal stage at which waves enter the forest). Making efficient use of mangroves' protective capacity has been hindered by a lack of location-specific information. This study helps to fill that gap by estimating reduction in storm surge height and water flow velocity from mangroves at selected sites in cyclone-prone, coastal Bangladesh. A hydrodynamic model for the Bay of Bengal, based on the MIKE21FM system, was run multiple times to simulate the surge of cyclone Sidr (2007) at the Barisal coast. Estimates of surge height and water flow velocity were recorded first without mangroves and then with mangroves of various forest widths and planting densities, including specific information on local topography, bathymetry, and Manning's coefficients estimated from species' root and trunk systems. The results show a significant reduction in water flow velocity (29-92%) and a modest reduction in surge height (4-16.5 cm). These findings suggest that healthy mangroves can contribute to significant savings in rehabilitation and maintenance costs by protecting embankments from breaching, toe-erosion, and other damage.

Differences in reported linguistic thermal sensation between Bangla and Japanese speakers.

BACKGROUND: Thermal sensation is a fundamental variable used to determine thermal comfort and is most frequently evaluated through the use of subjective reports in the field of environmental physiology. However, there has been little study of the relationship between the semantics of the words used to describe thermal sensation and the climatic background. The present study investigates the linguistic differences in thermal reports from native speakers of Bangla and Japanese. METHODS: A total of 1141 university students (932 in Bangladesh and 209 in Japan) responded to a questionnaire survey consisting of 20 questions. Group differences between Bangladeshi and Japanese respondents were then tested with a chi-square test in a crosstab analysis using SPSS (version 21). RESULTS: For the Bangla-speaking respondents, the closest feeling of thermal comfort was "neutral" (66.6%) followed by "slightly cool" (10.2%), "slightly cold" (6.0%), "slightly hot" (4.1%), and "cold" (3.8%). For the Japanese respondents, the closest feeling of thermal comfort was "cool" (38.3%) followed by "slightly cool" (20.4%), "neutral" (14.6%), "slightly warm" (13.1%), and "warm" (10.7%). Of the Bangladeshi respondents, 37.7% reported that they were sensitive to cold weather and 18.1% reported that they were sensitive to hot weather. Of the Japanese respondents, 20.6% reported that they were sensitive to cold weather and 29.2% reported that they were sensitive to hot weather. Of the Bangladeshi respondents, 51.4% chose "higher than 29 °C" as hot weather and 38.7% of the Japanese respondents chose "higher than 32 °C" as hot weather. In the case of cold weather, 43.1% of the Bangladeshi respondents selected "lower than 15 °C" as cold weather and 53.4% of the Japanese respondents selected "lower than 10 °C" as cold weather. CONCLUSIONS: Most of the Bangla-speaking respondents chose "neutral" as the most comfortable temperature, and most of the Japanese respondents chose "cool." Most of the Bangladeshi respondents reported that they were sensitive to "cold temperatures," but most of the Japanese respondents reported that they were sensitive to "hot temperatures."

Cold-induced vasodilation comparison between Bangladeshi and Japanese natives.

BACKGROUND: The human thermoregulation system responds to changes in environmental temperature, so humans can self-adapt to a wide range of climates. People from tropical and temperate areas have different cold tolerance. This study compared the tolerance of Bangladeshi (tropical) and Japanese (temperate) people to local cold exposure on cold-induced vasodilation (CIVD). METHODS: Eight Bangladeshi males (now residing in Japan) and 14 Japanese males (residing in Japan) participated in this study. All are sedentary, regular university students. The Bangladeshi subject's duration of stay in Japan was 2.50 ± 2.52 years. The subject's left hand middle finger was immersed in 5 °C water for 20 min to assess their CIVD response (the experiment was conducted in an artificial climate chamber controlled at 25 °C with 50% RH). RESULTS: Compared with the Bangladeshi (BD) group, the Japanese (JP) group displayed some differences. There were significant differences between the BD and JP groups in temperature before immersion (TBI), which were 33.04 ± 1.98 and 34.62 ± 0.94 °C, and time of temperature rise (TTR), which were 5.35 ± 0.82 and 3.72 ± 0.68 min, respectively. There was also a significant difference in the time of sensation rise (TSR) of 8.69 ± 6.49 and 3.26 ± 0.97 min between the BD and JP groups, respectively (P < 0.05). Moreover, the JP group showed a quick TTR after finishing immersion. CONCLUSIONS: The Japanese group (temperate) has a higher tolerance to local cold exposure than the Bangladeshi group (tropical) evaluated by the CIVD test.