Genetic Variability of CYP4F2, CYP2D6, CYP2E1, and ACE in the Chinese Yi Population.

Genetic polymorphisms of very important pharmacogenes (VIP) are a significant factor contributing to inter-individual variability in drug therapy. The purpose of this study was to identify significantly different loci in the Yi population and to enrich their pharmacogenomic information. 54 VIP variants were selected from the Pharmacogenomics Knowledge Base (PharmGKB) and genotyped in 200 Yi individuals. Then, we compared their genotype distribution between the Yi population and the other 26 populations using the χ2 test. Compared with the other 26 populations, the genotype frequencies of 4 single nucleotide polymorphisms (SNPs), rs2108622 (CYP4F2), rs1065852 (CYP2D6), rs2070676 (CYP2E1), and rs4291 (ACE), had significant differences in the Yi population. For example, the TT genotype frequency of rs2108622 (8.1%) was higher than that of African populations, and the AA genotype frequency of rs1065852 (27.3%) was higher than that of other populations except East Asians. We also found that the Yi populations differed the least from East Asians and the most from Africans. Furthermore, the differences in these variants might be related to the effectiveness and toxicity risk of using warfarin, iloperidone, cisplatin cyclophosphamide, and other drugs in the Yi population. Our data complement the pharmacogenomic information of the Yi population and provide theoretical guidance for their personalized treatment.

Genome-wide CNV analysis reveals candidate genes associated with milk production traits in water buffalo (Bubalus bubalis).

Buffaloes are vital contributors to the global dairy industry. Understanding the genetic basis of milk production traits in buffalo populations is essential for breeding programs and improving productivity. In this study, we conducted whole-genome resequencing on 387 buffalo genomes from 29 diverse Asian breeds, including 132 river buffaloes, 129 swamp buffaloes, and 126 crossbred buffaloes. We identified 36,548 copy number variant (CNVs) spanning 133.29 Mb of the buffalo genome, resulting in 2,100 copy number variant regions (CNVRs), with 1,993 shared CNVRs being found within the studied buffalo types. Analyzing CNVRs highlighted distinct genetic differentiation between river and swamp buffalo subspecies, verified by evolutionary tree and principal component analyses. Admixture analysis grouped buffaloes into river and swamp categories, with crossbred buffaloes displaying mixed ancestry. To identify candidate genes associated with milk production traits, we employed 3 approaches. First, we used Vst-based population differentiation, revealing 11 genes within CNVRs that exhibited significant divergence between different buffalo breeds, including genes linked to milk production traits. Second, expression quantitative loci (eQTL) analysis revealed differential expression of CNVR-driven genes (DECGs) associated with milk production traits. Notably, known milk production-related genes were among these DECGs, validating their relevance. Last, a genome-wide association study (GWAS) identified 3 CNVRs significantly linked to peak milk yield. Our study provides comprehensive genomic insights into buffalo populations and identifies candidate genes associated with milk production traits. These findings facilitate genetic breeding programs aimed at increasing milk yield and improving quality in this economically important livestock species.

Multi-omics strategy reveals potential role of antimicrobial resistance and virulence factor genes responsible for Simmental diarrheic calves caused by Escherichia coli.

Escherichia coli (E. coli) is reported to be an important pathogen associated with calf diarrhea. Antibiotic resistance genes (ARGs) and virulence factor genes (VFGs) pose a considerable threat to both animal and human health. However, little is known about the characterization of ARGs and VFGs presented in the gut microbiota of diarrheic calves caused by E. coli. In this study, we used multi-omics strategy to analyze the ARG and VFG profiles of Simmental calves with diarrhea caused by E. coli K99. We found that gut bacterial composition and their microbiome metabolic functions varied greatly in diarrheic calves compared to healthy calves. In total, 175 ARGs were identified, and diarrheal calves showed a significantly higher diversity and abundance of ARGs than healthy calves. Simmental calves with diarrhea showed higher association of VFGs with pili function, curli assembly, and ferrienterobactin transport of E. coli. Co-occurrence patterns based on Pearson correlation analysis revealed that E. coli had a highly significant (P < 0.0001) correlation coefficient (>0.8) with 16 ARGs and 7 VFGs. Metabolomics analysis showed that differentially expressed metabolites in Simmental calves with diarrhea displayed a high correlation with the aforementioned ARGs and VFGs. Phylotype analysis of E. coli genomes showed that the predominant phylogroup B1 in diarrheic Simmental calves was associated with 10 ARGs and 3 VFGs. These findings provide an overview of the diversity and abundance of the gut microbiota in diarrheic calves caused by E. coli and pave the way for further studies on the mechanisms of antibiotic resistance and virulence in the calves affected with diarrhea.IMPORTANCESimmental is a well-recognized beef cattle breed worldwide. They also suffer significant economic losses due to diarrhea. In this study, fecal metagenomic analysis was applied to characterize the antibiotic resistance gene (ARG) and virulence factor gene (VFG) profiles of diarrheic Simmental calves. We identified key ARGs and VFGs correlated with Escherichia coli isolated from Simmental calves. Additionally, metabolomics analysis showed that differentially expressed metabolites in Simmental calves with diarrhea displayed a high correlation with the aforementioned ARGs and VFGs. Our findings provide an insight into the diversity and abundance of the gut microbiota in diarrheic calves caused by Escherichia coli and pave the way for further studies on the mechanisms of antibiotic resistance and virulence in the diarrheal calves from cattle hosts.

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci.

Backgrounds: Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population. Methods: A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes. Results: Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable. Conclusion: We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

Vertical migration behavior simulation and prediction of Pb and Cd in co-contaminated soil around Pb-Zn smelting slag site.

Heavy metal migration in soil poses a serious threat to the soil and groundwater. Understanding the migration pattern of heavy metals (HMs) under different factors could provide a more reasonable position for pollution evaluation and targetoriented treatment of soil heavy metal. In this study, the migration behavior of Pb and Cd in co-contaminated soil under different pH and ionic strength (NaCl concentration) was simulated using convective dispersion equation (CDE). We predicted the migration trends of Pb and Cd in soils after 5, 10, and 20 years via PHREEQC. The results showed that the migration time of Cd in the soil column experiment was about 60 days faster than that of Pb, and the migration trend was much steeper. The CDE was proved to describe the migration behavior of Pb and Cd (R2 > 0.75) in soil. The predicted results showed that Cd migrated to 15-20 cm of soil within 7 years and Pb stayed mainly in the top 0-6 cm of soil within 5 years as the duration of irrigation increased. Overall, our study is expected to provide new insight into the migration of heavy metal in soil ecosystems and guidance for reducing risk of heavy metal in the environment.

Transcriptome-Wide Association Study Reveals Potentially Candidate Genes Responsible for Milk Production Traits in Buffalo.

Identifying key causal genes is critical for unraveling the genetic basis of complex economic traits, yet it remains a formidable challenge. The advent of large-scale sequencing data and computational algorithms, such as transcriptome-wide association studies (TWASs), offers a promising avenue for identifying potential causal genes. In this study, we harnessed the power of TWAS to identify genes potentially responsible for milk production traits, including daily milk yield (MY), fat percentage (FP), and protein percentage (PP), within a cohort of 100 buffaloes. Our approach began by generating the genotype and expression profiles for these 100 buffaloes through whole-genome resequencing and RNA sequencing, respectively. Through comprehensive genome-wide association studies (GWAS), we pinpointed a total of seven and four single nucleotide polymorphisms (SNPs) significantly associated with MY and FP traits, respectively. By using TWAS, we identified 55, 71, and 101 genes as significant signals for MY, FP, and PP traits, respectively. To delve deeper, we conducted protein-protein interaction (PPI) analysis, revealing the categorization of these genes into distinct PPI networks. Interestingly, several TWAS-identified genes within the PPI network played a vital role in milk performance. These findings open new avenues for identifying potentially causal genes underlying important traits, thereby offering invaluable insights for genomics and breeding in buffalo populations.

Very important pharmacogenetic variants landscape and potential clinical relevance in the Zhuang population from Yunnan province.

The gradual evolution of pharmacogenomics has shed light on the genetic basis for inter-individual drug response variations across diverse populations. This study aimed to identify pharmacogenomic variants that differ in Zhuang population compared with other populations and investigate their potential clinical relevance in gene-drug and genotypic-phenotypic associations. A total of 48 variants from 24 genes were genotyped in 200 Zhuang subjects using the Agena MassARRAY platform. The allele frequencies and genotype distribution data of 26 populations were obtained from the 1000 Genomes Project, followed by a comparison and statistical analysis. After Bonferroni correction, significant differences in genotype frequencies were observed of CYP3A5 (rs776746), ACE (rs4291), KCNH2 (rs1805123), and CYP2D6 (rs1065852) between the Zhuang population and the other 26 populations. It was also found that the Chinese Dai in Xishuangbanna, China, Han Chinese in Beijing, China, and Southern Han Chinese, China showed least deviation from the Zhuang population. The Esan in Nigeria, Gambian in Western Division, The Gambia, and Yoruba in Ibadan, Nigeria exhibited the largest differences. This was also proved by structural analysis, Fst analysis and phylogenetic tree. Furthermore, these differential variants may be associated with the pharmacological efficacy and toxicity of Captopril, Amlodipine, Lisinopril, metoclopramide, and alpha-hydroxymetoprolol in the Zhuang population. Our study has filled the gap of pharmacogenomic information in the Zhuang population and has provided a theoretical framework for the secure administration of drugs in the Zhuang population.

Phosphorus-doped fiber for flat octave spanning supercontinuum generation.

In a fiber supercontinuum (SC) source, the Raman scattering effect plays a significant role in extending the spectrum into a longer wavelength. Here, by using a phosphorus-doped fiber with a broad Raman gain spectrum as the nonlinear medium, we demonstrate flat SC generation spanning from 850 to 2150 nm. Within the wavelength range of 1.1-2.0 µm, the spectral power density fluctuation is less than 7 dB. Compared to a similar SC source based on a germanium-doped fiber with narrower Raman gain spectrum, the wavelength span is 300 nm broader, and the spectral power density fluctuation is 5 dB lower. This work demonstrates the phosphorus-doped fiber's great advantage in spectrally flat SC generation, which is of great significance in many applications such as optical coherence tomography, absorption spectroscopy, and telecommunication.

Influence of wavelength, linewidth, and temperature on second harmonic generation of a superfluorescent fiber source.

Low-coherence tunable visible light sources have a wide range of applications in imaging, spectroscopy, medicine, and so on. Second harmonic generation (SHG) based on a superfluorescent fiber source (SFS) can produce high-brightness visible light while retaining most of the characteristics of superfluorescent sources, such as low coherence, low intensity noise and flexible tunability. However, due to the limitations in phase matching conditions, SHG based on SFS is difficult to reach an equilibrium between high efficiency and robustness of phase matching to temperature variation. In this paper, based on a spectral tunable SFS, we provide a comprehensive analysis, both experimental and theoretical, of the impact of wavelength, linewidth, and temperature on the output performance of SHG. Our findings indicate that broader linewidths adversely affect conversion efficiency, yet they enhance the capacity to withstand temperature variations and central wavelength detuning, which is an advantage that traditional SHG methods do not possess. This work may pave the way for utilizing low-coherence visible light in domains and extreme environments where robust output stability becomes imperative.

The landscape of very important pharmacogenes variants and potential clinical relevance in the Chinese Jingpo population: a comparative study with worldwide populations.

BACKGROUND: Pharmacogenomics is a facet of personalized medicine that explores how genetic variants affect drug metabolism and adverse drug reactions. Therefore, this study aims to detect distinct pharmacogenomic variations among the Jingpo population and explore their clinical correlation with drug metabolism and toxicity. METHODS: Agena MassARRAY Assay was used to genotype 57 VIP variants in 28 genes from 159 unrelated Jingpo participants. Subsequently, the chi-squared test and Bonferroni's statistical tests were utilized to conduct a comparative analysis of genotypes and allele frequencies between the Jingpo population and the other 26 populations from the 1000 Genome Project. RESULTS: We discovered that the KHV (Kinh in Ho ChiMinh City, Vietnam), CHS (Southern Han Chi-nese, China) and JPT (Japanese in Tokyo, Japan) exhibited the smallest differences from the Jingpo with only 4 variants, while ESN (Esan in Nigeria) exhibited the largest differences with 30 variants. Besides, a total of six considerably different loci (rs4291 in ACE, rs20417 in PTGS2, rs1801280 and rs1799929 in NAT2, rs2115819 in ALOX5, rs1065852 in CYP2D6, p < 3.37 × 10-5) were identified in this study. According to PharmGKB, rs20417 (PTGS2), rs4291 (ACE), rs2115819 (ALOX5) and rs1065852 (CYP2D6) were found to be associated with the metabolism efficiency of non-steroidal anti-inflammatory drugs (NSAIDs), aspirin, montelukast and tamoxifen, respectively. Meanwhile, rs1801280 and rs1799929 (NAT2) were found to be related to drug poisoning with slow acetylation. CONCLUSION: Our study unveils distinct pharmacogenomic variants in the Jingpo population and discovers their association with the metabolic efficiency of NSAIDs, montelukast, and tamoxifen.

Steel Strip Surface Defect Detection Method Based on Improved YOLOv5s.

Steel strip is an important raw material for the engineering, automotive, shipbuilding, and aerospace industries. However, during the production process, the surface of the steel strip is prone to cracks, pitting, and other defects that affect its appearance and performance. It is important to use machine vision technology to detect defects on the surface of a steel strip in order to improve its quality. To address the difficulties in classifying the fine-grained features of strip steel surface images and to improve the defect detection rate, we propose an improved YOLOv5s model called YOLOv5s-FPD (Fine Particle Detection). The SPPF-A (Spatial Pyramid Pooling Fast-Advance) module was constructed to adjust the spatial pyramid structure, and the ASFF (Adaptively Spatial Feature Fusion) and CARAFE (Content-Aware ReAssembly of FEatures) modules were introduced to improve the feature extraction and fusion capabilities of strip images. The CSBL (Convolutional Separable Bottleneck) module was also constructed, and the DCNv2 (Deformable ConvNets v2) module was introduced to improve the model's lightweight properties. The CBAM (Convolutional Block Attention Module) attention module is used to extract key and important information, further improving the model's feature extraction capability. Experimental results on the NEU_DET (NEU surface defect database) dataset show that YOLOv5s-FPD improves the mAP50 accuracy by 2.6% before data enhancement and 1.8% after SSIE (steel strip image enhancement) data enhancement, compared to the YOLOv5s prototype. It also improves the detection accuracy of all six defects in the dataset. Experimental results on the VOC2007 public dataset demonstrate that YOLOv5s-FPD improves the mAP50 accuracy by 4.6% before data enhancement, compared to the YOLOv5s prototype. Overall, these results confirm the validity and usefulness of the proposed model.

High power tunable Raman fiber laser at 1.2 µm waveband.

Development of a high power fiber laser at special waveband, which is difficult to achieve by conventional rare-earth-doped fibers, is a significant challenge. One of the most common methods for achieving lasing at special wavelength is Raman conversion. Phosphorus-doped fiber (PDF), due to the phosphorus-related large frequency shift Raman peak at 40 THz, is a great choice for large frequency shift Raman conversion. Here, by adopting 150 m large mode area triple-clad PDF as Raman gain medium, and a novel wavelength-selective feedback mechanism to suppress the silica-related Raman emission, we build a high power cladding-pumped Raman fiber laser at 1.2 µm waveband. A Raman signal with power up to 735.8 W at 1252.7 nm is obtained. To the best of our knowledge, this is the highest output power ever reported for fiber lasers at 1.2 µm waveband. Moreover, by tuning the wavelength of the pump source, a tunable Raman output of more than 450 W over a wavelength range of 1240.6-1252.7 nm is demonstrated. This work proves PDF's advantage in high power large frequency shift Raman conversion with a cladding pump scheme, thus providing a good solution for a high power laser source at special waveband.

Pan-Cancer Analysis of the Prognostic and Immunological Role of SMG5: A Biomarker for Cancers.

INTRODUCTION: SMG5 is involved in tumor cell development and viewed as a potential target for immunotherapy. The purpose of this study was to systematically analyze the expression level, function, and prognostic value of SMG5 in pan-cancers. METHODS: Differential expression of SMG5 in normal and tumor tissues was analyzed using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Database (GTEx) data. Survival analysis was performed by Kaplan-Meier method and Cox risk regression. The relationship between SMG5 expression and lymphocyte abundance, tumor cell immune infiltration level, molecular and immune subtypes as well as immune checkpoints was analyzed by tumor-immune system interactions database (TISIDB), Tumor Immune Estimation Resource (TIMER), and Sangerbox databases. The correlation between SMG5 and immune scores was studied using the Estimation of Stromal and Immune Cells in Malignant Tumours using Expression (ESTIMATE) data algorithm. Further, drug sensitivity analysis of SMG5 with low-grade glioma (LGG) was conducted using the CellMiner database. RESULTS: SMG5 was highly expressed in 23 tumors and only had a significant impact on the prognosis of patients with LGG only. In addition, in tumor microenvironment and tumor immune analysis, we found that the level of immune infiltration, tumor mutational load, microsatellite instability, and immune checkpoints of LGG were significantly correlated with SMG5 expression. Furthermore, SMG5 was significantly associated with immune scores, stromal scores, and sensitivity of some drugs in LGG. CONCLUSION: SMG5 is differentially expressed in several cancers and is significantly associated with prognosis, immune microenvironment, and immune checkpoints in LGG patients. Therefore, SMG5 could be a potential pan-cancer biomarker and an immunotherapeutic target for LGG.

Effects of CASZ1, WNT2B and PTPRG SNPs on stroke susceptibility in the Chinese Han population.

BACKGROUND: Stroke is an important cause of death and disability worldwide, ranking second in the cause of death, and it is thought to be related to genetic factors. The purpose of our study is to investigate the association between CASZ1, WNT2B and PTPRG single nucleotide polymorphisms (SNPs) and stroke risk in the Chinese population. METHODS: We recruited 1418 volunteers, comprised of 710 stroke cases and 708 controls in this study. We used MassARRAY iPLEX GOLD method to genotype the three SNPs on CASZ1, WNT2B and PTPRG. Logistic regression was used to analyse the association between these SNPs and stroke, and odds ratios (ORs) and 95% confidence intervals (CIs) were then calculated. What's more, the interactions among SNPs were predicted by multi-factor dimensionality reduction (MDR) analysis. RESULTS: This research demonstrated that CASZ1 rs880315 and PTPRG rs704341 were associated with reduced stroke susceptibility. More precisely, CASZ1 rs880315 was associated with reduced stroke susceptibility in people aged ≤64 years and women. PTPRG rs704341 was associated with reduced stroke susceptibility in people aged >64 years, women, non-smokers and non-drinkers. Conversely, WNT2B rs12037987 was related to elevated stroke susceptibility in people aged >64 years, women and non-smokers. In addition, CASZ1 rs880315, WNT2B rs12037987 and PTPRG rs704341 had a strong redundancy relationship. CONCLUSION: Our study concludes that CASZ1 rs880315, WNT2B rs12037987 and PTPRG rs704341 are associated with stroke, and the study provides a basis for assessing genetic variants associated with stroke risk in the Han Chinese population.

Polarization insensitive flexible ultra-broadband terahertz metamaterial absorber.

We propose a polarization insensitive, flexible ultra-broadband terahertz (THz) metamaterial absorber. It consists of a chromium composite resonator on the top, a polyimide (PI) dielectric layer in the middle, and a chromium substrate. The simulation results show that the absorption achieves more than 90% ultra-wideband absorption in the range of 1.92-4.34 THz. The broadband absorption is produced by the combination of electric dipole resonance and magnetic resonance, as well as impedance matching with free space. Due to the rotational symmetry of the unit structure, the absorber is insensitive to polarization of the THz wave and has a larger range of incident angles. The total thickness of the absorber is only 13.4 µm, showing highly flexible and excellent high-temperature resistance characteristics. Therefore, it has potential applications in THz wave stealth and electromagnetic shielding.

Correlation between ESR1 and APOE gene polymorphisms and risk of osteonecrosis of the femoral head: a case-control study.

BACKGROUND: Osteonecrosis of the femoral head (ONFH) is a disease with a high disability rate, and genetic factors are closely related to its pathogenesis. This study aimed to investigate the possible correlation between ESR1 and APOE gene polymorphisms and the risk of ONFH. METHODS: In this case-control study, the potential association between three genetic variants (rs2982573 C < T, rs10872678 C < T, and rs9322332 A < C) of the ESR1 gene and two genetic variants (rs7259620 A < G and rs769446 C < T) of the APOE gene with the risk of ONFH was investigated. Correlations between gene polymorphisms and ONFH risk were assessed using logistic regression analysis, with calculation of odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: The overall analysis demonstrated that rs9322332 in the ESR1 gene exhibited a correlation with a decreased risk of ONFH under the homozygous (AA vs.CC: OR = 0.69, 95% CI [0.53-0.90], p = 0.006), dominant (CA + AA vs. CC: OR = 0.70, 95% CI [0.54-0.90], p = 0.006), and additive (OR = 0.79, 95% CI [0.66-0.95], p = 0.013) models. The stratification analysis revealed that rs9322332 was linked to a lower risk of ONFH in subgroups characterized by individuals aged over 51 years and non-smokers. Nevertheless, there were no notable correlations found between ESR1 rs2982573 and rs10872678, as well as APOE rs7259620 and rs769446, with the risk of ONFH. CONCLUSION: ESR1-rs9322332 is closely linked to a decreased risk of ONFH, thereby enhancing our understanding of the relationship between gene polymorphisms and ONFH.

DLGAP5 Regulates the Proliferation, Migration, Invasion, and Cell Cycle of Breast Cancer Cells via the JAK2/STAT3 Signaling Axis.

The aim of this study was to discover new biomarkers to detect breast cancer (BC), which is an aggressive cancer with a high mortality rate. In this study, bioinformatic analyses (differential analysis, weighted gene co-expression network analysis, and machine learning) were performed to identify potential candidate genes for BC to study their molecular mechanisms. Furthermore, Quantitative Real-time PCR and immunohistochemistry assays were used to examine the protein and mRNA expression levels of a particular candidate gene (DLGAP5). And the effects of DLGAP5 on cell proliferation, migration, invasion, and cell cycle were further assessed using the Cell Counting Kit-8 assay, colony formation, Transwell, wound healing, and flow cytometry assays. Moreover, the changes in the JAK2/STAT3 signaling-pathway-related proteins were detected by Western Blot. A total of 44 overlapping genes were obtained by differential analysis and weighted gene co-expression network analysis, of which 25 genes were found in the most tightly connected cluster. Finally, NEK2, CKS2, UHRF1, DLGAP5, and FAM83D were considered as potential biomarkers of BC. Moreover, DLGAP5 was highly expressed in BC. The down-regulation of DLGAP5 may inhibit the proliferation, migration, invasion, and cell cycle of BC cells, and the opposite was true for DLGAP5 overexpression. Correspondingly, silencing or overexpression of the DLGAP5 gene inhibited or activated the JAK2/STAT3 signaling pathway, respectively. DLGAP5, as a potential biomarker of BC, may impact the cell proliferation, migration, invasion, cell cycle, and BC development by modulating the JAK2/STAT3 signaling pathway.

Engineering high-affinity dual targeting cellular nanovesicles for optimised cancer immunotherapy.

Dual targeting to immune checkpoints has achieved a better therapeutic efficacy than single targeting due to synergistic extrication of tumour immunity. However, most dual targeting strategies are usually antibody dependent which facing drawbacks of antibodies, such as poor solid tumour penetration and unsatisfied affinity. To meet the challenges, we engineered a cell membrane displaying a fusion protein composed of SIRPα and PD-1 variants, the high-affinity consensus (HAC) of wild-type molecules, and with which prepared nanovesicles (NVs). Through disabling both SIRPα/CD47 and PD-1/PD-L1 signalling, HAC NVs significantly preserved the phagocytosis and antitumour effect of macrophages and T cells, respectively. In vivo study revealed that HAC NVs had better tumour penetration than monoclonal antibodies and higher binding affinity to CD47 and PD-L1 on tumour cells compared with the NVs expressing wild-type fusion protein. Exhilaratingly, dual-blockade of CD47 and PD-L1 with HAC NVs exhibited excellent therapeutic efficacy and biosafety. This study provided a novel biomaterial against tumoural immune escape and more importantly an attractive biomimetic technology of protein delivery for multi-targeting therapies.

Improved MobileNetV2 crop disease identification model for intelligent agriculture.

Using intelligent agriculture is an important way for the industry to achieve high-quality development. To improve the accuracy of the identification of crop diseases under conditions of limited computing resources, such as in mobile and edge computing, we propose an improved lightweight MobileNetV2 crop disease identification model. In this study, MobileNetV2 is used as the backbone network for the application of an improved Bottleneck structure. First, the number of operation channels is reduced using point-by-point convolution, the number of parameters of the model is reduced, and the re-parameterized multilayer perceptron (RepMLP) module is introduced; the latter can capture long-distance dependencies between features and obtain local a priori information to enhance the global perception of the model. Second, the efficient channel-attention mechanism is added to adjust the image-feature channel weights so as to improve the recognition accuracy of the model, and the Hardswish activation function is introduced instead of the ReLU6 activation function to further improve performance. The final experimental results show that the improved MobilNetV2 model achieves 99.53% accuracy in the PlantVillage crop disease dataset, which is 0.3% higher than the original model, and the number of covariates is only 0.9M, which is 59% less than the original model. Also, the inference speed is improved by 8.5% over the original model. The crop disease identification method proposed in this article provides a reference for deployment and application on edge and mobile devices.

Phylotranscriptomics unveil a Paleoproterozoic-Mesoproterozoic origin and deep relationships of the Viridiplantae.

The Viridiplantae comprise two main clades, the Chlorophyta (including a diverse array of marine and freshwater green algae) and the Streptophyta (consisting of the freshwater charophytes and the land plants). Lineages sister to core Chlorophyta, informally refer to as prasinophytes, form a grade of mainly planktonic green algae. Recently, one of these lineages, Prasinodermophyta, which is previously grouped with prasinophytes, has been identified as the sister lineage to both Chlorophyta and Streptophyta. Resolving the deep relationships among green plants is crucial for understanding the historical impact of green algal diversity on marine ecology and geochemistry, but has been proven difficult given the ancient timing of the diversification events. Through extensive taxon and gene sampling, we conduct large-scale phylogenomic analyses to resolve deep relationships and reveal the Prasinodermophyta as the lineage sister to Chlorophyta, raising questions about the necessity of classifying the Prasinodermophyta as a distinct phylum. We unveil that incomplete lineage sorting is the main cause of discordance regarding the placement of Prasinodermophyta. Molecular dating analyses suggest that crown-group green plants and crown-group Prasinodermophyta date back to the Paleoproterozoic-Mesoproterozoic. Our study establishes a plausible link between oxygen levels in the Paleoproterozoic-Mesoproterozoic and the origin of Viridiplantae.