Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions.

Hydrogen is considered a clean and efficient energy carrier crucial for shaping the net-zero future. Large-scale production, transportation, storage, and use of green hydrogen are expected to be undertaken in the coming decades. As the smallest element in the universe, however, hydrogen can adsorb on, diffuse into, and interact with many metallic materials, degrading their mechanical properties. This multifaceted phenomenon is generically categorized as hydrogen embrittlement (HE). HE is one of the most complex material problems that arises as an outcome of the intricate interplay across specific spatial and temporal scales between the mechanical driving force and the material resistance fingerprinted by the microstructures and subsequently weakened by the presence of hydrogen. Based on recent developments in the field as well as our collective understanding, this Review is devoted to treating HE as a whole and providing a constructive and systematic discussion on hydrogen entry, diffusion, trapping, hydrogen-microstructure interaction mechanisms, and consequences of HE in steels, nickel alloys, and aluminum alloys used for energy transport and storage. HE in emerging material systems, such as high entropy alloys and additively manufactured materials, is also discussed. Priority has been particularly given to these less understood aspects. Combining perspectives of materials chemistry, materials science, mechanics, and artificial intelligence, this Review aspires to present a comprehensive and impartial viewpoint on the existing knowledge and conclude with our forecasts of various paths forward meant to fuel the exploration of future research regarding hydrogen-induced material challenges.

Evolution and diversity of biomineralized columnar architecture in early Cambrian phosphatic-shelled brachiopods.

Biologically-controlled mineralization producing organic-inorganic composites (hard skeletons) by metazoan biomineralizers has been an evolutionary innovation since the earliest Cambrian. Among them, linguliform brachiopods are one of the key invertebrates that secrete calcium phosphate minerals to build their shells. One of the most distinct shell structures is the organo-phosphatic cylindrical column exclusive to phosphatic-shelled brachiopods, including both crown and stem groups. However, the complexity, diversity, and biomineralization processes of these microscopic columns are far from clear in brachiopod ancestors. Here, exquisitely well-preserved columnar shell ultrastructures are reported for the first time in the earliest eoobolids Latusobolus xiaoyangbaensis gen. et sp. nov. and Eoobolus acutulus sp. nov. from the Cambrian Series 2 Shuijingtuo Formation of South China. The hierarchical shell architectures, epithelial cell moulds, and the shape and size of cylindrical columns are scrutinised in these new species. Their calcium phosphate-based biomineralized shells are mainly composed of stacked sandwich columnar units. The secretion and construction of the stacked sandwich model of columnar architecture, which played a significant role in the evolution of linguliforms, is highly biologically controlled and organic-matrix mediated. Furthermore, a continuous transformation of anatomic features resulting from the growth of diverse columnar shells is revealed between Eoobolidae, Lingulellotretidae, and Acrotretida, shedding new light on the evolutionary growth and adaptive innovation of biomineralized columnar architecture among early phosphatic-shelled brachiopods during the Cambrian explosion.

Unraveling the correlations between microbial communities and metabolic profiles of strong-flavor Jinhui Daqu with different storage periods.

Daqu is a saccharification agent required for fermenting Baijiu, a popular Chinese liquor. Our objective was to investigate the relationships between physicochemical indices, microbial community diversity, and metabolite profiles of strong-flavor Jinhui Daqu during different storage periods. During different storage periods of Jinhui Daqu, we combined Illumina MiSeq sequencing and non-target sequencing techniques to analyze dynamic changes of the microbial community and metabolite composition, established a symbiotic network and explored the correlation between dominant microorganisms and differential metabolites in Daqu. Fungal community diversity in 8d_Daqu was higher than that in 45d_Daqu and 90d_Daqu, whereas bacterial community diversity was higher in 90d_Daqu. Twelve bacterial and four fungal genera were dominant during storage of Daqu. Bacillus, Leuconostoc, Kroppenstedtia, Lactococcus, Thermomyces and Wickerhamomyces decreased as the storage period increased. Differences of microbiota structure led to various metabolic pathways, and 993 differential metabolites were found in all Daqu samples. Differential microorganisms were significantly related to key metabolites. Major metabolic pathways involved in the formation of amino acids and lipids, such as l-arogenate and hydroxyproline, were identified. Interactions between moisture, acidity, and microbes may drive the succession of the microbial community, which further affects the formation of metabolites.

Transferability of Temperature Evolution of Dissimilar Wire-Arc Additively Manufactured Components by Machine Learning.

Wire-arc additive manufacturing (WAAM) is a promising industrial production technique. Without optimization, inherent temperature gradients can cause powerful residual stresses and microstructural defects. There is therefore a need for data-driven methods allowing real-time process optimization for WAAM. This study focuses on machine learning (ML)-based prediction of temperature history for WAAM-produced aluminum bars with different geometries and process parameters, including bar length, number of deposition layers, and heat source movement speed. Finite element (FE) simulations are used to provide training and prediction data. The ML models are based on a simple multilayer perceptron (MLP) and performed well during baseline training and testing, giving a testing mean absolute percentage error (MAPE) of less than 0.7% with an 80/20 train-test split, with low variation in model performance. When using the trained models to predict results from FE simulations with greater length or number of layers, the MAPE increased to an average of 3.22% or less, with greater variability. In the cases of greatest difference, some models still returned a MAPE of less than 1%. For different scanning speeds, the performance was worse, with some outlier models giving a MAPE of up to 14.91%. This study demonstrates the transferability of temperature history for WAAM with a simple MLP approach.

Bimetallic sulfide/N-doped carbon composite derived from Prussian blue analogues/cellulose nanofibers film toward enhanced oxygen evolution reaction.

Exploiting effective, stable, and cost-efficient electrocatalysts for the water oxidation reaction is highly desirable for renewable energy conversion techniques. Constructional design and compositional manipulation are widely used approaches to efficaciously boost the electrocatalytic performance. Herein, we designed a NiFe-bimetallic sulfide/N-doped carbon composite via a two-step thermal treatment of Prussian blue analogues/cellulose nanofibers (PBA/CNFs) film. The NiFe-bimetallic sulfide/N-doped carbon composite displayed enhanced OER performance in an alkaline environment, with an overpotential of 282 mV at 10 mA cm-2, a Tafel slope of 59.71 mV dec-1, and good stability, making the composite a candidate electrocatalyst for OER-related energy equipment. The introduction of CNFs in the precursor prevented the aggregation of PBA nanoparticles (NPs), exposed more active sites, and the resulting carbon substrate enhanced the electroconductivity of the composite. Moreover, the synergistic effect of Ni and Fe in the bimetallic sulfide could modulate the configuration of electrons, enrich the catalytically active sites, and augment the electric conductivity, thus ameliorating the OER performance. This study broadens the application of MOF-CNF composites to construct hierarchical structures of metal compounds and provides some thoughts for the development of cost-effective precious-metal-free catalysts for electrocatalysis.

Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning.

Background: Cerebral small vessel disease lacks specific clinical manifestations, and extraction of valuable features from multimodal images is expected to improve its diagnostic accuracy. In this study, we used deep learning techniques to segment cerebral small vessel disease imaging markers in multimodal magnetic resonance images and analyze them with clinical risk factors. Methods and results: We recruited 211 lacunar stroke patients and 83 control patients. The patients' cerebral small vessel disease markers were automatically segmented using a V-shaped bottleneck network, and the number and volume were calculated after manual correction. The segmentation results of the V-shaped bottleneck network for white matter hyperintensity and recent small subcortical infarction were in high agreement with the ground truth (DSC>0.90). In small lesion segmentation, cerebral microbleed (average recall=0.778; average precision=0.758) and perivascular spaces (average recall=0.953; average precision=0.923) were superior to lacunar infarct (average recall=0.339; average precision=0.432) in recall and precision. Binary logistic regression analysis showed that age, systolic blood pressure, and total cerebral small vessel disease load score were independent risk factors for lacunar stroke (P<0.05). Ordered logistic regression analysis showed age was positively correlated with cerebral small vessel disease load score and total cholesterol was negatively correlated with cerebral small vessel disease score (P<0.05). Conclusion: Lacunar stroke patients exhibited higher cerebral small vessel disease imaging markers, and age, systolic blood pressure, and total cerebral small vessel disease score were independent risk factors for lacunar stroke patients. V-shaped bottleneck network segmentation network based on multimodal deep learning can segment and quantify various cerebral small vessel disease lesions to some extent.

Sweat as a source of non-invasive biomarkers for clinical diagnosis: An overview.

Sweat has excellent potential as one of the sources of non-invasive biomarkers for clinical diagnosis. It is relatively easy to collect and process and may contain different disease-specific markers and drug metabolites, making it ideal for various clinical applications. This article discusses the anatomy of sweat glands and their role in sweat production, as well as the history and development of multiple sweat sample collection and analysis techniques. Another primary focus of this article is the application of sweat detection in clinical disease diagnosis and other life scenarios. Finally, the limitations and prospects of sweat analysis are discussed.

Endothelial Response to Type I Interferon Contributes to Vasculopathy and Fibrosis and Predicts Disease Progression of Systemic Sclerosis.

OBJECTIVE: Interferon (IFN)-1 signatures are a hallmark of patients with systemic sclerosis (SSc). However, its significance in clinical stratification and contribution to deterioration still need to be better understood. METHODS: For hypothesis generation, we performed single-cell RNA sequencing (scRNA-seq) on skin biopsies (four patients with SSc and two controls) using the BD Rhapsody platform. Two publicly available data sets of skin scRNA-seq were used for validation (GSE138669: 12 patients with diffuse cutaneous SSc [dcSSc] and 10 controls; GSE195452: 52 patients with dcSSc and 41 patients with limited cutaneous SSc [lcSSc] and 54 controls). The IFN-1 signature was mapped, functionally investigated in a bleomycin plus IFNα-2 adenovirus-associated virus (AAV)-induced model and verified in an SSc cohort (n = 61). RESULTS: The discovery and validation data sets showed similar findings. Endothelial cells (ECs) had the most prominent IFN-1 signature among dermal nonimmune cells. The EC IFN-1 signature was increased both in patients with SSc versus controls and in patients with dcSSc versus those with lcSSc. Among EC subclusters, the IFN-1 signature was statistically higher in the capillary ECs of patients with dcSSc, which was higher than those in patients with lcSSc, which in turn was higher than those in healthy controls (HCs). Endothelial-to-mesenchymal transition (EndoMT) scores increased in parallel. Deteriorated bleomycin-induced dermal fibrosis, EndoMT, and perivascular fibrosis and caused blood vessel loss with EC apoptosis. Vascular myxovirus resistance (MX) 1, an IFN-1 response protein, was significantly increased both in total SSc versus HC skin and in dcSSc versus lcSSc skin. Baseline vascular MX1 performed similarly to skin score in predicting disease progression over 6 to 34 months in total SSc and was superior in the dcSSc subpopulation. CONCLUSION: The EC IFN-1 signature distinguished SSc skin subtypes and disease progression and may contribute to vasculopathy and fibrosis.

Adaptive rotation attention network for accurate defect detection on magnetic tile surface.

Defect detection on magnetic tile surfaces is of great significance for the production monitoring of permanent magnet motors. However, it is challenging to detect the surface defects from the magnetic tile due to these issues: 1) Defects appear randomly on the surface of the magnetic tile; 2) the defects are tiny and often overwhelmed by the background. To address such problems, an Adaptive Rotation Attention Network (ARA-Net) is proposed for defect detection on the magnetic tile surface, where the Adaptive Rotation Convolution (ARC) module is devised to capture the random defects on the magnetic tile surface by learning multi-view feature maps, and then the Rotation Region Attention (RAA) module is designed to locate the small defects from the complicated background by focusing more attention on the defect features. Experiments conducted on the MTSD3C6K dataset demonstrate the proposed ARA-Net outperforms the state-of-the-art methods, further providing assistance for permanent magnet motor monitoring.

Role of the RNA-binding protein family in gynecologic cancers.

Gynecological cancers pose a threat to women's health. Although early-stage gynecological cancers show good outcomes after standardized treatment, the prognosis of patients with advanced, met-astatic, and recurrent cancers is poor. RNA-binding proteins (RBPs) are important cellular proteins that interact with RNA through RNA-binding domains and participate extensively in post-transcriptional regulatory processes, such as mRNA alternative splicing, polyadenylation, intracellular localization and stability, and translation. Abnormal RBP expression affects the normal function of oncogenes and tumor suppressor genes in many malignancies, thus leading to the occurrence or progression of cancers. Similarly, RBPs play crucial roles in gynecological carcinogenesis. We summarize the role of RBPs in gynecological malignancies and explore their potential in the diagnosis and treatment of cancers. The findings summarized in this review may provide a guide for future research on the functions of RBPs.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

TfOH-Catalyzed Cascade Reaction: Metal-Free Access to 3,3-Disubstituted Phthalides from o-Alkynylbenzoic Acids.

A facile new method for the synthesis of 3,3-disubstituted phthalides is reported. A successive reaction process begins with the TfOH-catalyzed cyclization of o-alkynylbenzoic acids followed by an ortho-regioselective electrophilic alkylation of various electron-rich aromatic compounds or alkenes, which has been successfully developed. The corresponding regioselective products of 3-substituted phthalide were obtained in good to high yields.

A high-resolution genotype-phenotype map identifies the TaSPL17 controlling grain number and size in wheat.

BACKGROUND: Large-scale genotype-phenotype association studies of crop germplasm are important for identifying alleles associated with favorable traits. The limited number of single-nucleotide polymorphisms (SNPs) in most wheat genome-wide association studies (GWASs) restricts their power to detect marker-trait associations. Additionally, only a few genes regulating grain number per spikelet have been reported due to sensitivity of this trait to variable environments. RESULTS: We perform a large-scale GWAS using approximately 40 million filtered SNPs for 27 spike morphology traits. We detect 132,086 significant marker-trait associations and the associated SNP markers are located within 590 associated peaks. We detect additional and stronger peaks by dividing spike morphology into sub-traits relative to GWAS results of spike morphology traits. We propose that the genetic dissection of spike morphology is a powerful strategy to detect signals for grain yield traits in wheat. The GWAS results reveal that TaSPL17 positively controls grain size and number by regulating spikelet and floret meristem development, which in turn leads to enhanced grain yield per plant. The haplotypes at TaSPL17 indicate geographical differentiation, domestication effects, and breeding selection. CONCLUSION: Our study provides valuable resources for genetic improvement of spike morphology and a fast-forward genetic solution for candidate gene detection and cloning in wheat.

Iguratimod attenuated fibrosis in systemic sclerosis via targeting early growth response 1 expression.

BACKGROUND: The early growth response 1 (EGR1) is a central transcription factor involved in systemic sclerosis (SSc) pathogenesis. Iguratimod is a synthesized anti-rheumatic disease-modifying drug, which shows drastic inhibition to EGR1 expression in B cells. This study is aiming to investigate the anti-fibrotic effect of iguratimod in SSc. METHODS: EGR1 was detected by immunofluorescence staining real-time PCR or western blot. Iguratimod was applied in EGR1 overexpressed or knockdown human dermal fibroblast, bleomycin pre-treated mice, tight skin 1 mice, and SSc skin xenografts. RNA sequencing was performed in cultured fibroblast and xenografts to identify the iguratimod regulated genes. RESULTS: EGR1 overexpressed predominantly in non-immune cells of SSc patients. Iguratimod reduced EGR1 expression in fibroblasts and neutralized changes of EGR1 response genes regulated by TGFß. The extracellular matrix (ECM) production and activation of fibroblasts were attenuated by iguratimod while EGR1 overexpression reversed this effect of iguratimod. Iguratimod ameliorated the skin fibrosis induced by bleomycin and hypodermal fibrosis in TSK-1 mice. Decreasing in the collagen content as well as the density of EGR1 or TGFß positive fibroblasts of skin xenografts from naïve SSc patients was observed after local treatment of iguratimod. CONCLUSION: Targeting EGR1 expression is a probable underlying mechanism for the anti-fibrotic effect of iguratimod.

Genome-wide mapping of DNase I hypersensitive sites in pineapple leaves.

Pineapple [Ananas comosus (L.) Merr.] is the most economically important crop possessing crassulacean acid metabolism (CAM) photosynthesis which has a higher water use efficiency by control of nocturnal opening and diurnal closure of stomata. To provide novel insights into the diel regulatory landscape in pineapple leaves, we performed genome-wide mapping of DNase I hypersensitive sites (DHSs) in pineapple leaves at day (2a.m.) and night (10a.m.) using a simplified DNase-seq method. As a result, totally 33340 and 28753 DHSs were found in green-tip tissue, and 29597 and 40068 were identified in white-base tissue at 2a.m. and 10a.m., respectively. We observed that majority of the pineapple genes occupied less than two DHSs with length shorter than 1 kb, and the promotor DHSs showed a proximal trend to the transcription start site (>77% promotor DHSs within 1 kb). In addition, more intergenic DHSs were identified around transcription factors or transcription co-regulators (TFs/TCs) than other functional genes, indicating complex regulatory contexts around TFs/TCs. Through combined analysis of tissue preferential DHSs and genes, we respectively found 839 and 888 coordinately changed genes in green-tip at 2a.m. and 10a.m. (AcG2 and AcG10). Furthermore, AcG2-specific, AcG10-specific and common accessible DHSs were dissected from the total photosynthetic preferential DHSs, and the regulatory networks indicated dynamic regulations with multiple cis-regulatory elements occurred to genes preferentially expressed in photosynthetic tissues. Interestingly, binding motifs of several cycling TFs were identified in the DHSs of key CAM genes, revealing a circadian regulation to CAM coordinately diurnal expression. Our results provide a chromatin regulatory landscape in pineapple leaves during the day and night. This will provide important information to assist with deciphering the circadian regulation of CAM photosynthesis.

Real-Time Recognition and Detection of Bactrocera minax (Diptera: Trypetidae) Grooming Behavior Using Body Region Localization and Improved C3D Network.

Pest management has long been a critical aspect of crop protection. Insect behavior is of great research value as an important indicator for assessing insect characteristics. Currently, insect behavior research is increasingly based on the quantification of behavior. Traditional manual observation and analysis methods can no longer meet the requirements of data volume and observation time. In this paper, we propose a method based on region localization combined with an improved 3D convolutional neural network for six grooming behaviors of Bactrocera minax: head grooming, foreleg grooming, fore-mid leg grooming, mid-hind leg grooming, hind leg grooming, and wing grooming. The overall recognition accuracy reached 93.46%. We compared the results obtained from the detection model with manual observations; the average difference was about 12%. This shows that the model reached a level close to manual observation. Additionally, recognition time using this method is only one-third of that required for manual observation, making it suitable for real-time detection needs. Experimental data demonstrate that this method effectively eliminates the interference caused by the walking behavior of Bactrocera minax, enabling efficient and automated detection of grooming behavior. Consequently, it offers a convenient means of studying pest characteristics in the field of crop protection.

Overexpression of cannabinoid receptor 2 is associated with human breast cancer proliferation, apoptosis, chemosensitivity and prognosis via the PI3K/Akt/mTOR signaling pathway.

INTRODUCTION: The cannabinoid receptor 2 (CB2) is mainly involved in the immune system. However, although CB2 has been reported to play an anti-tumor function in breast cancer (BC), its specific mechanism in BC remains unclear. METHODS: We examined the expression and prognostic significance of CB2 in BC tissues by qPCR, second-generation sequencing, western blot, and immunohistochemistry. We assessed the impacts of overexpression and a specific agonist of CB2 on the growth, proliferation, apoptosis, and drug resistance of BC cells in vitro and in vivo using CCK-8, flow cytometry, TUNEL staining, immunofluorescence, tumor xenografts, western blot, and colony formation assays. RESULTS: CB2 expression was significantly lower in BC compared with paracancerous tissues. It was also highly expressed in benign tumors and ductal carcinoma in situ, and its expression was correlated with prognosis in BC patients. CB2 overexpression and treatment of BC cells with a CB2 agonist inhibited proliferation and promoted apoptosis, and these actions were achieved by suppressing the PI3K/Akt/mTOR signaling pathway. Moreover, CB2 expression was increased in MDA-MB-231 cell treated with cisplatin, doxorubicin, and docetaxel, and sensitivity to these anti-tumor drugs was increased in BC cells overexpressing CB2. CONCLUSIONS: These findings reveal that CB2 mediates BC via the PI3K/Akt/mTOR signaling pathway. CB2 could be a novel target for the diagnosis and treatment of BC.

Population genomics unravels the Holocene history of bread wheat and its relatives.

Deep knowledge of crop biodiversity is essential to improving global food security. Despite bread wheat serving as a keystone crop worldwide, the population history of bread wheat and its relatives, both cultivated and wild, remains elusive. By analysing whole-genome sequences of 795 wheat accessions, we found that bread wheat originated from the southwest coast of the Caspian Sea and underwent a slow speciation process, lasting ~3,300 yr owing to persistent gene flow from its relatives. Soon after, bread wheat spread across Eurasia and reached Europe, South Asia and East Asia ~7,000 to ~5,000 yr ago, shaping a diversified but occasionally convergent adaptive landscape in novel environments. By contrast, the cultivated relatives of bread wheat experienced a population decline by ~82% over the past ~2,000 yr due to the food choice shift of humans. Further biogeographical modelling predicted a continued population shrinking of many bread wheat relatives in the coming decades because of their vulnerability to the changing climate. These findings will guide future efforts in protecting and utilizing wheat biodiversity to enhance global wheat production.

Self-Seeding Synthesis of Hierarchically Branched Rutile TiO2 for High-Efficiency Dye-Sensitized Solar Cells.

This study presents a unique and straightforward room temperature-based wet-chemical technique for the self-seeding preparation of three-dimensional (3D) hierarchically branched rutile TiO2, abbreviated HTs, employing titanate nanotubes as the precursor. In the course of the synthesis, spindle-like rutile TiO2 and the intermediate anatase phase were first obtained through a dissolution/precipitation/recrystallization process, with the former serving as the substrates and the latter as the nucleation precursor to growing the branches, which finally gave birth to the production of 3D HTs nanostructures. When the specifically created hierarchical TiO2 was used as the photoanode in dye-sensitized solar cells (DSCs), a significantly improved power conversion efficiency (PCE) of 8.32% was achieved, outperforming a typical TiO2 (P25) nanoparticle-based reference cell (η = 5.97%) under the same film thickness. The effective combination of robust light scattering, substantial dye loading, and fast electron transport for the HTs nanostructures is responsible for the remarkable performance.