MiR-103-5p deficiency suppresses lipid accumulation via upregulating PLSCR4 and its host gene PANK3 in goat mammary epithelial cells.

Lipids are intimately related to the unique flavor and nutritional values of goat milk. MicroRNAs (miRNA) participate in the regulation of various biological functions, including the synthesis and degradation of lipids. Several studies have shown that miR-103 is involved in the regulation of lipid metabolism, however, the molecular mechanism by which miR-103 regulates lipid metabolism in goat mammary gland is poorly understood. In this study, miR-103 was knocked out in goat mammary epithelial cells (GMECs) by CRISPR/Cas9, and the accumulation of lipid droplets, triglycerides, and cholesterol in the cells was suppressed subsequently. Overexpression or knockdown of miR-103-5p and miR-103-3p in GMECs revealed that it was miR-103-5p that promoted lipid accumulation but not miR-103-3p. In addition, Pantothenate Kinase 3 (PANK3), the host gene of miR-103, and Phospholipid Scramblase 4 (PLSCR4) were identified as the target genes of miR-103-5p by dual fluorescein and miRNA pulldown. Furthermore, we identified that cellular lipid levels were negatively regulated by PANK3 and PLSCR4. Lastly, in miR-103 knockout GMECs, the knockdown of PANK and PLSCR4 rescued the lipid accumulation. These findings suggest that miR-103-5p promotes lipid accumulation by targeting PLSCR4 and the host gene PANK3 in GMECs, providing new insights for the regulation of goat milk lipids via miRNAs.

Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study.

OBJECTIVE: This study aimed to establish a nomogram-based assessment for predicting the risk of hyponatremia after spinal cord injury (SCI). DESIGN: The study is a retrospective single-center study. PARTICIPANTS: SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University. SETTING: The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. METHODS: We performed a retrospective clinical study to collect SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University from 2016 to 2020. Based on their clinical scores, the SCI patients were grouped as either hyponatremic or non-hyponatremic, SCI patients in 2016-2019 were identified as the training set, and patients in 2020 were identified as the test set. A nomogram was generated, the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to validate the model. RESULTS: A total of 895 SCI patients were retrieved. After excluding patients with incomplete data, 883 patients were finally included in this study and used to construct the nomograms. The indicators used in the nomogram included sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, white blood cell (WBC), albumin and serum Ca2+. These indices were determined by the least absolute shrinkage and selection operator (LASSO) regression analysis. The C-index of the model was 0.81, the area under the curve (AUC) of the training set was 0.82(Cl:0.79-0.85), and the validation set was 0.79(Cl:0.73-0.85). CONCLUSIONS: Nomogram has good predictive ability, sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, WBC, albumin and serum Ca2+ were predictors of hyponatremia after SCI.

From π-conjugated macrocycles to heterocycloarenes based on benzo[2,1-b:3,4-b']dithiophene (BDTh): size- and geometry-dependent host-guest properties.

π-Conjugated macrocycles have been highly attractive due to their challenging synthesis, fascinating aesthetic structure and unique physical and chemical properties. Although some progress has been made in synthesis, the study of π-macrocycles with different structural characteristics and supramolecular interactions still faces major challenges. In this paper, two new single-bond linked macrocycles (MS-4T/MS-6T) were reported, and the corresponding vinyl-bridged heterocycloarenes (MF-4T/MF-6T) were synthesized by the periphery fusion strategy. Further studies have indicated that the structure of these four macrocycles is determined by both size and curvature, showing unique variations from nearly planar to bowl and then to saddle. Interestingly, the nearly planar MS-4T with a small size and the rigid saddle-shaped MF-6T show no obvious response to fullerenes C60 or C70, while the bowl-shaped MS-6T and MF-4T demonstrate a strong binding affinity towards fullerenes C60 and C70. What's more, two kinds of co-crystals with capsule-like configurations, MS-6T@C60 and MS-6T@C70, have been successfully obtained, among which the former shows a loose columnar arrangement while the latter displays a unique three-dimensional honeycomb arrangement that is extremely rare in supramolecular complexes. This work systematically studies the π-conjugated macrocycles and provides a new idea for the development of novel host-guest systems and further multifunctional applications.

Electronic Properties of CrB/Co2CO2 Superlattices by Multiple Descriptor-Based Machine Learning Combined with First-Principles.

In recent times, newly unveiled 2D materials exhibiting exceptional characteristics, such as MBenes and MXenes, have gained widespread application across diverse domains, encompassing electronic devices, catalysis, energy storage, sensors, and various others. Nonetheless, numerous technical bottlenecks persist in the development of high-performance, structurally flexible, and adjustable electronic device materials. Research investigations have demonstrated that 2D van der Waals superlattices (vdW SLs) structures comprising materials exhibit exceptional electrical, mechanical, and optical properties. In this work, the advantages of both materials are combined and compose the vdW SLs structure of MBenes and MXenes, thus obtaining materials with excellent electronic properties. Furthermore, it integrates machine learning (ML) with first-principles methods to forecast the electrical properties of MBene/MXene superlattice materials. Initially, various configurations of MBene/MXene superlattice materials are explored, revealing that distinct stacking methods exert significant influence on the electronic structure of MBene/MXene materials. Specifically, the BABA-type stacking of CrB (layer A) and Co2CO2 MXene (layer B) is most stable configureation. Subsequently, multiple descriptors of the structure are constructed to predict the density of states  of vdW SLs through the employment of ML techniques. The best model achieves a mean absolute error (MAE) as low as 0.147 eV.

Discovery of a potent and highly selective inhibitor of SIRT6 against pancreatic cancer metastasis in vivo.

Pancreatic cancer, one of the most aggressive malignancies, has no effective treatment due to the lack of targets and drugs related to tumour metastasis. SIRT6 can promote the migration of pancreatic cancer and could be a potential target for antimetastasis of pancreatic cancer. However, highly selective and potency SIRT6 inhibitor that can be used in vivo is yet to be discovered. Here, we developed a novel SIRT6 allosteric inhibitor, compound 11e, with maximal inhibitory potency and an IC50 value of 0.98 ± 0.13 µmol/L. Moreover, compound 11e exhibited significant selectivity against other histone deacetylases (HADC1‒11 and SIRT1‒3) at concentrations up to 100 µmol/L. The allosteric site and the molecular mechanism of inhibition were extensively elucidated by cocrystal complex structure and dynamic structural analyses. Importantly, we confirmed the antimetastatic function of such inhibitors in four pancreatic cancer cell lines as well as in two mouse models of pancreatic cancer liver metastasis. To our knowledge, this is the first study to reveal the in vivo effects of SIRT6 inhibitors on liver metastatic pancreatic cancer. It not only provides a promising lead compound for subsequent inhibitor development targeting SIRT6 but also provides a potential approach to address the challenge of metastasis in pancreatic cancer.

Manipulating Intramolecular Charge Transfer and Supramolecular Interaction in D-A-D Conjugated Systems by Regioisomerization.

Three new donor-acceptor-donor (D-A-D) architecture regioisomers comprising a large planar electron-withdrawing core tribenzo[a,c,i]phenazine and two identical electron-donating triphenylamines with different substitution patterns were designed and synthesized. Employing this regioisomerization strategy, the intramolecular charge-transfer interactions are effectively tuned and result in a significant bathochromic shift of photoluminescence maximum over 100 nm, which induces the corresponding emission band extending into the near-infrared region as well as giving a high solid-state quantum yield of 25%. Meanwhile, it is found that the supramolecular interactions of this series of regioisomers with planar electron-donor pyrene are greatly affected by the substitution pattern.

Joint-GWAS, Linkage Mapping, and Transcriptome Analysis to Reveal the Genetic Basis of Plant Architecture-Related Traits in Maize.

Plant architecture is one of the key factors affecting maize yield formation and can be divided into secondary traits, such as plant height (PH), ear height (EH), and leaf number (LN). It is a viable approach for exploiting genetic resources to improve plant density. In this study, one natural panel of 226 inbred lines and 150 family lines derived from the offspring of T32 crossed with Qi319 were genotyped by using the MaizeSNP50 chip and the genotyping by sequence (GBS) method and phenotyped under three different environments. Based on the results, a genome-wide association study (GWAS) and linkage mapping were analyzed by using the MLM and ICIM models, respectively. The results showed that 120 QTNs (quantitative trait nucleotides) and 32 QTL (quantitative trait loci) related to plant architecture were identified, including four QTL and 40 QTNs of PH, eight QTL and 41 QTNs of EH, and 20 QTL and 39 QTNs of LN. One dominant QTL, qLN7-2, was identified in the Zhangye environment. Six QTNs were commonly identified to be related to PH, EH, and LN in different environments. The candidate gene analysis revealed that Zm00001d021574 was involved in regulating plant architecture traits through the autophagy pathway, and Zm00001d044730 was predicted to interact with the male sterility-related gene ms26. These results provide abundant genetic resources for improving maize plant architecture traits by using approaches to biological breeding.

The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations.

Fully supervised medical image segmentation methods use pixel-level labels to achieve good results, but obtaining such large-scale, high-quality labels is cumbersome and time consuming. This study aimed to develop a weakly supervised model that only used image-level labels to achieve automatic segmentation of four types of uterine lesions and three types of normal tissues on magnetic resonance images. The MRI data of the patients were retrospectively collected from the database of our institution, and the T2-weighted sequence images were selected and only image-level annotations were made. The proposed two-stage model can be divided into four sequential parts: the pixel correlation module, the class re-activation map module, the inter-pixel relation network module, and the Deeplab v3 + module. The dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD) were employed to evaluate the performance of the model. The original dataset consisted of 85,730 images from 316 patients with four different types of lesions (i.e., endometrial cancer, uterine leiomyoma, endometrial polyps, and atypical hyperplasia of endometrium). A total number of 196, 57, and 63 patients were randomly selected for model training, validation, and testing. After being trained from scratch, the proposed model showed a good segmentation performance with an average DSC of 83.5%, HD of 29.3 mm, and ASSD of 8.83 mm, respectively. As far as the weakly supervised methods using only image-level labels are concerned, the performance of the proposed model is equivalent to the state-of-the-art weakly supervised methods.

Atmospheric CO2 and 14CO2 observations at the northern foot of the Qinling Mountains in China: Temporal characteristics and source quantification.

A two-year (March 2021 to February 2023) continuous atmospheric CO2 and a one-year regular atmospheric 14CO2 measurement records were measured at the northern foot of the Qinling Mountains in Xi'an, China, aiming to study the temporal characteristics of atmospheric CO2 and the contributions from the sources of fossil fuel CO2 (CO2ff) and biological CO2 (CO2bio) fluxes. The two-year mean CO2 mole fraction was 442.2 ± 16.3 ppm, with a yearly increase of 4.7 ppm (i.e., 1.1 %) during the two-year observations. Seasonal CO2 mole fractions were the highest in winter (452.1 ± 17.7 ppm) and the lowest in summer (433.5 ± 13.3 ppm), with the monthly CO2 levels peaking in January and troughing in June. Diurnal CO2 levels peaked at dawn (05:00-07:00) in spring, summer and autumn, and at 10:00 in winter. 14C analysis revealed that the excess CO2 (CO2ex, atmospheric CO2 minus background CO2) at this site was mainly from CO2ff emissions (67.0 ± 26.8 %), and CO2ff mole fractions were the highest in winter (20.6 ± 17.7 ppm). Local CO enhancement above the background mole fraction (ΔCO) was significantly (r = 0.74, p < 0.05) positively correlated with CO2ff in a one-year measurement, and ΔCO:CO2ff showed a ratio of 23 ± 6 ppb/ppm during summer and winter sampling days, much lower than previous measurements and suggesting an improvement in combustion efficiency over the last decade. CO2bio mole fractions also peaked in winter (14.2 ± 9.6 ppm), apparently due to biomass combustion and the lower and more stable wintertime atmospheric boundary layer. The negative CO2bio values in summer indicated that terrestrial vegetation of the Qinling Mountains had the potential to uptake atmospheric CO2 during the corresponding sampling days. This site is most sensitive to local emissions from Xi'an and to short distance transportation from the southern Qinling Mountains through the valleys.

Deciphering the comprehensive knowledgebase landscape featuring infertility with IDDB Xtra.

Infertility affects ∼15% of couples globally and half of cases are related to genetic disorders. Despite growing data and unprecedented improvements in high-throughput sequencing technologies, accumulated fertility-related issues concerning genetic diagnosis and potential treatment are urgent to be solved. However, there is a lack of comprehensive platforms that characterise various infertility-related records to provide research applications for exploring infertility in-depth and genetic counselling of infertility couple. To solve this problem, we provide IDDB Xtra by further integrating phenotypic manifestations, genomic datasets, epigenetics, modulators in collaboration with numerous interactive tools into our previous infertility database, IDDB. IDDB Xtra houses manually-curated 2369 genes of human and nine model organisms, 273 chromosomal abnormalities, 884 phenotypes, 60 genomic datasets, 464 epigenetic records, 1144 modulators relevant to infertility diagnosis and treatment. Additionally, IDDB Xtra incorporated customized graphical applications for researchers and clinicians to decipher in-depth disease mechanisms from the perspectives of developmental atlas, mutation effects, and clinical manifestations. Users can browse genes across developmental stages of human and mouse, filter candidate genes, mine potential variants and retrieve infertility biomedical network in an intuitive web interface. In summary, IDDB Xtra not only captures valuable research and data, but also provides useful applications to facilitate the genetic counselling and drug discovery of infertility. IDDB Xtra is freely available at https://mdl.shsmu.edu.cn/IDDB/and http://www.allostery.net/IDDB.

Vertical measurements of atmospheric CO2 and 14CO2 at the northern foot of the Qinling Mountains in China.

The CO2 and 14CO2 levels in air samples from the northern foot of the Qinling Mountains (Xi'an, China) were determined. In 2021, a hexacopter unmanned aerial vehicle sampled air at different heights, from near-ground to 2000 m. The objectives of this study were to determine vertical characteristics of CO2 and 14CO2, the sources of different-height CO2, and the influence of air mass transport. The CO2 concentrations mainly exhibited a slight decreasing trend with increasing height during summer observations, which was in contrast to the increasing trend that was followed by a subsequent gradual decreasing trend during early winter observations, with peak CO2 levels (443.4 ± 0.4-475.7 ± 0.5 ppm) at 100-500 m. The variation in vertical concentrations from 20 to 1000 m in early winter observations (21.6 ± 19.3 ppm) was greater than that in summer observations (14.6 ± 14.3 ppm), and the maximum vertical variation from 20 to ∼2000 m reached 61.1 ppm. Combining Δ14C and δ13C vertical measurements, the results showed that fossil fuel CO2 (CO2ff, 56.1 ± 15.2 %), which mainly come from coal combustion (81.2 ± 3.4 %), was the main contributor to CO2 levels in excess of the background level (CO2ex) during early winter observations. In contrast, biological CO2 (CO2bio) dominated CO2ex in summer observations. The vertical distributions of CO2ff in early winter observations and CO2bio in summer observations were consistent with those of CO2 during early winter and summer observations, respectively. The strong correlation between winter CO2bio and ΔCO (r = 0.81, p < 0.01) indicated that biomass burning was the main contributor to CO2bio during early winter observations. Approximately half of the air masses originated from the Guanzhong Basin during observations. The results provide insights into the vertical distribution of different-sources of atmospheric CO2 in scientific support of formulating carbon emission-reduction strategies.

Glucosylglycerol phosphorylase, a potential novel pathway of microbial glucosylglycerol catabolism.

Glucosylglycerol (GG) is a natural compatible solute that can be synthesized by many cyanobacteria and a few heterotrophic bacteria under high salinity conditions. In cyanobacteria, GG is synthesized by GG-phosphate synthase and GG-phosphate phosphatase, and a hydrolase GGHA catalyzes its degradation. In heterotrophic bacteria (such as some Marinobacter species), a fused form of GG-phosphate phosphatase and GG-phosphate synthase is present, but the cyanobacteria-like degradation pathway is not available. Instead, a phosphorylase GGP, of which the coding gene is located adjacent to the gene that encodes the GG-synthesizing enzyme, is supposed to perform the GG degradation function. In the present study, a GGP homolog from the salt-tolerant M. salinexigens ZYF650T was characterized. The recombinant GGP catalyzed GG decomposition via a two-step process of phosphorolysis and hydrolysis in vitro and exhibited high substrate specificity toward GG. The activity of GGP was enhanced by inorganic salts at low concentrations but significantly inhibited by increasing salt concentrations. While the investigation on the physiological role of GGP in M. salinexigens ZYF650T was limited due to the failed induction of GG production, the heterologous expression of ggp in the living cells of the GG-producing cyanobacterium Synechocystis sp. PCC 6803 significantly reduced the salt-induced GG accumulation. Together, these data suggested that GGP may represent a novel pathway of microbial GG catabolism. KEY POINTS: • GGP catalyzes GG degradation by a process of phosphorolysis and hydrolysis • GGP-catalyzed GG degradation is different from GGHA-based GG degradation • GGP represents a potential novel pathway of microbial GG catabolism.

ω-transaminase-catalyzed synthesis of (R)-2-(1-aminoethyl)-4-fluorophenol, a chiral intermediate of novel anti-tumor drugs.

The chiral amine (R)-2-(1-aminoethyl)-4-fluorophenol has attracted increasing attentions in recent years in the field of pharmaceuticals because of its important use as a building block in the synthesis of novel anti-tumor drugs targeting tropomyosin receptor kinases. In the present study, a ω-transaminase (ωTA) library consisting of 21 (R)-enantioselective enzymes was constructed and screened for the asymmetric biosynthesis of (R)-2-(1-aminoethyl)-4-fluorophenol from its prochiral ketone. Using (R)-α-methylbenzylamine, D-alanine, or isopropylamine as amino donor, 18 ωTAs were identified with target activity and the enzyme AbTA, which was originally identified from Arthrobacter sp. KNK168, was found to be a potent candidate. The E. coli whole cells expressing AbTA could be used as catalysts. The optimal temperature and pH for the activity were 35-40 °C and pH8.0, respectively. Simple alcohols (such as ethanol, isopropanol, and methanol) and dimethyl sulfoxide were shown to be good cosolvents. High activities were detected when using ethanol and dimethyl sulfoxide at the concentrations of 5-20%. In the scaled-up reaction of 1-liter containing 13 mM ketone substrate, about 50% conversion was achieved in 24 h. 6.4 mM (R)-2-(1-aminoethyl)-4-fluorophenol was generated. After a simple and efficient process of product isolation and purification (with 98.8% recovery), 0.986 g yellowish powder of the product (R)-2-(1-aminoethyl)-4-fluorophenol with high (R)-enantiopurity (up to 100% enantiomeric excess) was obtained. This study established an overall process for the biosynthesis of the high value pharmaceutical chiral amine (R)-2-(1-aminoethyl)-4-fluorophenol by ωTA. Its applicable potential was exemplified by gram-scale production.

ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

Culture-free identification of fast-growing cyanobacteria cells by Raman-activated gravity-driven encapsulation and sequencing.

By directly converting solar energy and carbon dioxide into biobased products, cyanobacteria are promising chassis for photosynthetic biosynthesis. To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales, exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance. To simplify and accelerate the screening for fast-growing cyanobacteria strains, a method called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS) was established. We show that the 13C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations, thus greatly accelerating the process screening for fastest-growing cells. The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing. Therefore, this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories.

Arthrospira promotes plant growth and soil properties under high salinity environments.

Salt stress detrimentally impacts plant growth, imperiling crop yield and food quality. Ameliorating plant resilience and productivity in saline environments is critical for global food security. Here, we report the positive effect of Arthrospira (Spirulina) on plant growth and salt tolerance in Arabidopsis and sweet sorghum. Arthrospira application greatly promotes seed germination and seedling growth in both species under salt stress conditions in a dosage-dependent manner. Application of 6 mg Arthrospira per plate significantly enhances K+/Na+ equilibrium and reactive oxygen species (ROS) scavenging in Arabidopsis, reducing salt-induced toxicity. The primary root length, survival rate, chlorophyll content, photosynthesis, plant height, biomass and yield were all improved in both species. Concurrently, Arthrospira demonstrated the synthesis of compatible solutes, such as trehalose (Tre) and glucosylglycerol (GG), contributing to heightened stress tolerance when co-cultivated with Arabidopsis on plates. Transcriptome analysis revealed dramatic up-/down- regulation of genes involved in phytohormone signal transduction, chlorophyll and photosynthesis metabolism, and phenylpropanoid metabolism in Arabidopsis. Furthermore, the application of Arthrospira exerted a positive influence on the rhizosphere bacteriome structure in sweet sorghum, crucial for nutrient cycling and soil health enhancement. Our findings uncovered the underlying mechanisms of algae-plants interaction in saline soil, proposing strategies to enhance crop productivity and soil quality, thereby addressing the urgent need for sustainable agriculture practices to mitigate salinity's repercussions amidst climate change challenges.

Statistical Insight into China's Indigenous Diagnosis-Related-Group System Evolution.

The use of Diagnosis-Related Groups (DRG) is a prevalent payment system employed to control hospitalization costs and improve medical efficiency. China has developed an indigenized DRG payment system including Single Disease Payment (SDP), DRGs, and Big Data Diagnosis-Intervention Packet (DIP). In this study, we took cholecystitis as an example, drawing on both primary and secondary data to verify the effectiveness of China's indigenized DRG system and to introduce the evolution of DRGs in China. Primary data were gathered from Qilu Hospital in 2019-2021. Secondary data were collected from published literature from 2004-2016. Only studies with both pre-SDP/DRG and post-SDP/DRG groups were included. Among the studies included, 92.9% (13/14) reported a significant reduction in hospitalization costs after the implementation of SDP while other studies identified length of stay (LOS) and age as the most significant influential factors in SDP. Furthermore, we elaborated the efficiency of DRGs using data from 2738 inpatients in Qilu hospital. Moreover, 60% (6/10) of the studies from the databases also showed the efficiency of DRGs in different regions. SDP is efficient in saving hospitalization costs, but its implementation is limited. DRGs have a broader scope of application, but their effectiveness remains to be validated. DIP is a brand new concept in China, and more data are needed to assess its efficiency.

Double strengthening induced by grain boundary segregation of solute elements in gradient nano Ni-Co alloys.

Gradient induced unusual strain hardening achieves the equilibrium of the strength and plasticity of alloys, and is an important strategy for the optimization of the mechanical properties of metals and alloys. The segregation of solute elements can greatly improve the grain boundary stability, inhibit grain coarsening and promote the mechanical strength of the alloy. In our efforts, the segregation structure of the solute element Co was designed and added to the gradient nano Ni-Co alloy, and the two strengthening strategies were applied simultaneously in one structure. The mechanical strength of the alloy achieved a second increase based on the unique combination of gradient induced strain hardening and high plasticity, especially the yield strength of alloy increase amplitude reach to 42%. This provides a positive direction for the alloy strengthening strategy. In the process of secondary strengthening, the micro-mechanism is divided into two stages: in the first stage, the gradient strain provides the alloy with geometrically necessary dislocations and a multi-axial stress state, and the existence of large numbers of geometrically necessary dislocations creates good conditions for the second stage strengthening. In the second stage, the solute segregation induced stable grain boundaries produce a strong pinning effect on the geometrically necessary dislocation, which realizes the coupling of grain boundary strengthening and dislocation strengthening. This provides a new strengthening strategy and positive theoretical guidance for the experimental preparation of advanced alloys with excellent properties.

In-vivoverified anatomically aware deep learning for real-time electric field simulation.

Objective.Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement.Approach.We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset.In-vivoTMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes.Significance.The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments.