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This study examines the concentrations and population-normalized mass loads (PNML) of five phthalate esters (PAEs) and four metabolites (mPAEs) in 390 sewage sludge samples collected from two municipal wastewater treatment plants in Beijing between July 2020 and June 2023, amidst the COVID-19 pandemic. Through GC/MS analysis, the compounds were simultaneously quantified, with peak concentrations in 2020. Bis(2-ethylhexyl) phthalate (DEHP) and mono(2-ethyl-5-oxohexyl) phthalate emerged as predominant PAE and mPAE congeners with concentrations of 78.7 µg/g dw and 259 µg/g dw, respectively. DEHP and monobenzyl phthalate had the highest median PNML among PAEs and mPAEs, respectively, at 128 µg/inhabitant/day and 798 µg/inhabitant/day. Linear regression models revealed a positive association between PNML of PAEs and five public health and social measures aimed at mitigating the COVID-19 pandemic. This research contributes to the expanding body of literature by emphasizing the role of wastewater-based epidemiology as a vital tool for monitoring community-level exposure to environmental contaminants.

Helicobacter pylori (H. pylori) is an infectious bacterium that colonizes the stomach of approximately half of the global population. It has been classified as a Group I carcinogen by the World Health Organization due to its strong association with an increased incidence of gastric cancer and exacerbation of stomach diseases. The primary treatment for H. pylori infection currently involves triple or quadruple therapy, primarily consisting of antibiotics and proton pump inhibitors. However, the increasing prevalence of antibiotic resistance poses significant challenges to this approach, underscoring the urgent need for an effective vaccine. In this study, a novel multi-epitope H. pylori vaccine was designed using immunoinformatics. The vaccine contains epitopes derived from nine essential proteins. Software tools and online servers were utilized to predict, evaluate, and analyze the physiochemical properties, secondary and tertiary structures, and immunogenicity of the candidate vaccine. These comprehensive assessments ultimately led to the formulation of an optimal design scheme for the vaccine. Through constructing a novel multi-epitope vaccine based on immunoinformatics, this study offers promising prospects and great potential for the prevention of H. pylori infection. This study also provides a reference strategy to develop multi-epitope vaccines for other pathogens.

Long-term continuous cropping affects the soil microecological community and leads to nutrient imbalances, which reduces crop yields, and crop rotation can increase soil productivity. To study the effects of the cultivation of tomato (Solanum lycopersicum) and corn (Zea mays) on the microbial community, physical and chemical factors and the structure of aggregates in cotton (Gossypium hirsutum) long-term continuous cropping soils were examined. Four cropping patterns were established, including one continuous cropping pattern and three crop rotation patterns, and the diversity of the soil microecological community was measured using high-throughput sequencing. The physical and chemical properties of different models of soil were measured, and the soil aggregate structure was determined by dry and wet sieving. Planting of aftercrop tomato and corn altered the bacterial community of the cotton continuous soil to a lesser extent and the fungal community to a greater extent. In addition, continuous cropping reduced the diversity and richness of the soil fungal community. Different aftercrop planting patterns showed that there were very high contents of soil organic carbon and organic matter in the cotton-maize rotation model, while the soil aggregate structure was the most stable in the corn-cotton rotation model. Planting tomato in continuous cropping cotton fields has a greater effect on the soil microbial community than planting maize. Therefore, according to the characteristics of different succeeding crop planting patterns, the damage of continuous cropping of cotton to the soil microenvironment can be alleviated directionally, which will enable the sustainable development of cotton production.

Abstract Hepatocellular carcinoma (HCC) is significantly associated with adverse prognostic outcomes. The development and progression of different types of human tumors are significantly influenced by APOB. Nevertheless, the significance and pathomechanisms of APOB in HCC have not been conclusively determined. We assessed APOB expression levels in HCC using three publicly available databases of TIMER2.0, UALCAN and Human Protein Atlas. To identify the biological function of APOB, we conducted enrichment analysis via LinkedOmics. Moreover, UALCAN was employed to assess the relationship between APOB expression and clinicopathological features among HCC patients. Additionally, the Kaplan–Meier plotter was utilized to investigate the prognostic relevance of APOB in HCC. To explore potential regulatory ncRNAs that could bind to APOB, we utilized StarBase and GEPIA. Furthermore, the correlation between APOB expression and immune cell infiltration, as well as immune checkpoint genes, was investigated using Spearman's correlation analysis in TISIDB, GEPIA, and TIMER2.0. The findings of our investigation showed a notable decrease in the expression levels of APOB among individuals diagnosed with HCC. Moreover, a noteworthy correlation was observed between the expression of APOB and immune checkpoint genes, alongside the occurrence of immune cell infiltration. The levels of APOB expression in HCC tissues also showed correlations with various clinicopathological features. According to Cox regression analysis, decreased APOB expression emerged as a potential autonomous predictor for OS, RFS, DSS, and PFS among HCC patients. Furthermore, we identified six potential pathways associated with non-coding RNA (ncRNA) as the most promising pathway for APOB in HCC. Our results illuminate the possible involvement of APOB in HCC and offer understanding into its governing mechanisms and medical importance.

Abstract Background Evidence from the Istanbul consensus workshop suggests correlations between morphological parameters and embryo developments. 8-cell embryos are the best blastomere stage on day 3. No good quality evidence exists to support high-quality embryonic selection following blastulation and clinical outcomes. This study aimed to investigate the factors that affect blastocyst formation, blastocyst quality, and clinical outcomes of high-quality cleavage-stage embryos in fresh cycles. Methods This study was a retrospective analysis of 9608 high-quality cleavage-stage embryos from 2987 couples between January 2017 to June 2021, namely 1520 embryos categorized as “812” (8-cell, grade 2, mild fragmentation), 2961 as “821” (8-cell, grade 2, mild asymmetry), 896 as “711” (7-cell, grade 1), and 517 as “911” (9-cell, grade 1) compared with 3714 embryos categorized as “811” (8-cell, grade 1). The primary outcomes were clinical pregnancy rate (CPR) and live birth rate (LBR). Blastulation rate (BR), available late blastocyst rate (ABR) and high-quality late blastocyst rate (HBR) were secondary outcome measures. Results BR, ABR, and HBR had significant differences among the five groups (P

This study investigated the influence of green manure planting on the spectroscopic properties of dissolved organic matter (DOM) in saline–alkali soil under freshwater leaching conditions at different soil depths. The UV254, UV253/UV203, α300, α355, SUVA254, SUVA260, and SR ultraviolet parameters indicated reductions in the content of large molecular substances, benzene ring substitution degree, colored dissolved organic matter, aromaticity, and hydrophobic components in the soil leachate DOM with an increasing soil depth. Compared with the non-green manure treatment control, green manure planting mitigated the leaching of dissolved organic matter in soil during saline irrigation, with rape green manure demonstrating superior effectiveness. Utilizing three-dimensional fluorescence combined with parallel factor analysis, this study analyzed three fluorescent components of soil leachate DOM: C1 (visible-light fulvic acid), C2 (humic acid), and C3 (tyrosine-like protein). The combined contribution of the two humic substance components (C1 + C2) was approximately 70%, indicating the dominance of humic substances in leachate DOM. The fluorescence parameters of soil leachate DOM included an average of the fluorescence index (FI) values between 1.4 and 1.9, low humification index (HIX) values consistently below 4, and biological index (BIX) values ranging from 0.8 to 1.0, suggesting a mixed source, low humification degree, poor stability, and moderate self-source characteristics. Compared with the non-green manure treatment control, both the green manure treatments exhibited a relatively higher proportion of biogenic sources and humification degree in soil leachate DOM. This suggests that planting green manure can reduce the relative DOM content under freshwater leaching conditions, increase the proportion of biogenic sources in soil leachate DOM, and enhance soil humification. Planting rapeseed green manure can diminish the leaching of DOM from land sources and augment soil humification.

BackgroundThis study focuses on the risk of early miscarriage in patients undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). These patients commonly experience heightened stress levels and may discontinue treatment due to emotional burdens associated with repeated failures. Despite the identification of numerous potential factors contributing to early miscarriage, there exists a research gap in integrating these factors into predictive models specifically for IVF/ICSI patients. The objective of this study is to develop a user-friendly nomogram that incorporates relevant risk factors to predict early miscarriage in IVF/ICSI patients. Through internal and external validation, the nomogram facilitates early identification of high-risk patients, supporting clinicians in making informed decisions.MethodsA retrospective analysis was conducted on 20,322 first cycles out of 31,307 for IVF/ICSI treatment at Sun Yat-sen Memorial Hospital between January 2011 and December 2020. After excluding ineligible cycles, 6,724 first fresh cycles were included and randomly divided into a training dataset (n = 4,516) and an internal validation dataset (n = 2,208). An external dataset (n = 1,179) from another hospital was used for validation. Logistic and LASSO regression models identified risk factors, and a multivariable logistic regression constructed the nomogram. Model performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA).ResultsSignificant risk factors for early miscarriage were identified, including female age, BMI, number of spontaneous abortions, number of induced abortions and medical abortions, basal FSH levels, endometrial thickness on hCG day, and number of good quality embryos. The predictive nomogram demonstrated good fit and discriminatory power, with AUC values of 0.660, 0.640, and 0.615 for the training, internal validation, and external validation datasets, respectively. Calibration curves showed good consistency with actual outcomes, and DCA confirmed the clinical usefulness. Subgroup analysis revealed variations; for the elder subgroup (age ≥35 years), female age, basal FSH levels, and number of available embryos were significant risk factors, while for the younger subgroup (age

Nano-silver (Ag) with ultra-small particle sizes have strong antibacterial activity because it can pass through the bacterial cell wall and enter the internal environment, causing irreversible damage to bacteria, but the disadvantages such as easy agglomeration and high toxicity alone limit its application. Therefore, In this study, by loading ∼ 3 nm ultrasmall particle sizes Ag (QDs) on the Fe3O4/Cu/CuO (FC) surface, core–shell type Fe3O4/Cu/CuO@Ag nanocomposite (FAN) was created. We used commercially available Bordeaux liquid (BM) as a control and targeted Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus) to investigate the antibacterial activity of the material and its antibacterial mechanism. To examine the biocompatibility of materials with human cells and their effect on plant growth, the model plant employs mung bean as its objectives. The results showed that FAN can effectively inhibit E. coli and S. aureus for up to 99.99 % within 20 min, which is ten times greater than BM. It had a delaying effect on the adaptation period and logarithmic phase of bacterial growth, and could effectively destroy bacterial cell walls and respiratory enzymes. According to biocompatibility studies, FAN has little effect on lactation cells and can promote mung bean germination, root growth, and chlorophyll content with twice the efficiency of BM. This research opens the door to inhibition agents for bacterial control in applications.

Abstract T-cell exhaustion (Tex) is considered to be a reason for immunotherapy resistance and poor prognosis in lung adenocarcinoma. Therefore, we used weighted correlation network analysis to identify Tex-related genes in the cancer genome atlas (TCGA). Unsupervised clustering approach based on Tex-related genes divided patients into cluster 1 and cluster 2. Then, we utilized random forest and the least absolute shrinkage and selection operator to identify nine key genes to construct a riskscore. Patients were classified as low or high-risk groups. The multivariate cox analysis showed the riskscore was an independent prognostic factor in TCGA and GSE72094 cohorts. Moreover, patients in cluster 2 with high riskscore had the worst prognosis. The immune response prediction analysis showed the low-risk group had higher immune, stromal, estimate scores, higher immunophenscore (IPS), and lower tumor immune dysfunction and exclusion score which suggested a better response to immune checkpoint inhibitors (ICIs) therapy in the low-risk group. In the meantime, we included two independent immunotherapy cohorts that also confirmed a better response to ICIs treatment in the low-risk group. Besides, we discovered differences in chemotherapy and targeted drug sensitivity between two groups. Finally, a nomogram was built to facilitate clinical decision making.

Abstract Background Obesity has been confirmed to be associated with infertility. However, the association between metabolically healthy obesity (MHO), a subset of obesity with no metabolic abnormalities, and female infertility has not yet been investigated. This study aimed to examine the association between MHO and the risk of female infertility among United States. Methods This study utilized a cross-sectional design and included 3542 women aged 20–45 years who were selected from the National Health and Nutrition Examination Survey (NHANES) 2013–2020 database. The association between MHO and the risk of infertility was evaluated using risk factor–adjusted logistic regression models. Results Higher BMI and WC were associated with increased infertility risk after adjusting for potential confounding factors (OR (95% CI): 1.04(1.02, 1.06), P = 0.001; OR (95% CI): 1.02 (1.01, 1.03), P

Deep learning has increasingly been used to identify minerals. However, deep learning can only be used to identify minerals within the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categorized erroneously within a predetermined class from the training set. To solve this problem, this study introduces the approach that combines a One-Class Support Vector Machine (OCSVM) with the ResNet architecture for out-of-distribution mineral detection. Initially, ResNet undergoes training using a training set comprising well-defined minerals. Subsequently, the first two layers obtained from the trained ResNet are employed to extract the discriminative features of the mineral under consideration. These extracted mineral features then become the input for OCSVM. When OCSVM discerns the mineral in the training set’s distribution, it triggers the subsequent layers within the trained ResNet, facilitating the accurate classification of the mineral into one of the predefined categories encompassing the known minerals. In the event that OCSVM identifies a mineral outside of the training set’s distribution, it is categorized as an unclassified or ‘unknown’ mineral. Empirical results substantiate the method’s capability to identify out-of-distribution minerals while concurrently maintaining a commendably high accuracy rate for the classification of the 36 in-distribution minerals.

Abstract Background Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. Patients and methods This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. Results In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. Conclusions PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center.

AbstractBackground To establish a risk stratification score to facilitate individualized treatment for patients with advanced epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC).Methods We enrolled 160 advanced EGFR-mutated NSCLC who received first-generation EGFR-tyrosine kinase inhibitor (EGFR-TKI) with or without bevacizumab. Kaplan–Meier curves were used for survival analysis. Univariate and multivariate analyses were used to identify independent prognostic factors associated with progression-free survival (PFS) and overall survival (OS).Results There were 107 patients in EGFR-TKI monotherapy (T group) and 53 patients in EGFR-TKI with bevacizumab (A + T group). The median PFS in the A + T group was significantly longer than that in the T group (p = 0.002). No difference in the median OS between the A + T and T groups (p = 0.721). The multivariate analyses showed that Eastern Cooperative Oncology Group performance status (ECOG PS) and the pre-treatment lactate dehydrogenase-albumin ratio (LAR) were independent prognostic factors for PFS and OS. The LAR-ECOG PS (LAPS) score was constructed by combining the pre-treatment LAR and ECOG PS. We defined ECOG PS 2 and high pre-treatment LAR as a score of 1. Then, patients with a total LAPS score of 0 were categorized as low-risk and those with 1–2 scores were classified as high-risk. For patients in low-risk group, there was no significant difference in PFS, OS, objective response rate (ORR), and disease control rate (DCR) among those who received EGFR-TKI with or without bevacizumab. However, patients in high-risk group had a significant benefit in PFS and DCR when treated with EGFR-TKI plus bevacizumab compared to those who received EGFR-TKI alone.Conclusions Novel LAPS score may help to facilitate individualized treatment of advanced EGFR-mutated NSCLC receiving EGFR-TKI with or without bevacizumab.

AbstractBackground This study aimed to compare the efficacy and safety of different treatment modalities for previously untreated advanced EGFR-mutated non-squamous non-small-cell lung cancer (NSCLC).Methods This retrospective study included 196 advanced EGFR-mutated non-squamous NSCLC. 107 received EGFR-tyrosine kinase inhibitor (EGFR-TKI) monotherapy (T), 53 received EGFR-TKI + bevacizumab (T + A), and 36 received EGFR-TKI + bevacizumab + chemotherapy (T + A + C). The endpoints included progression-free survival (PFS), overall survival (OS), objective response rate (ORR) and adverse events (AEs).Results The median PFS was 27 months in the T + A + C group, 17 months in the T + A group, and 10 months in the T group. The multivariate analysis showed lower disease progression in the T + A + C group (HR, 0.377; 95% CI, 0.224–0.634; p

BackgroundThis study aimed to develop and validate a novel nomogram to predict survival in advanced non-small cell lung cancer (NSCLC) receiving programmed cell death 1 (PD-1) inhibitor plus chemotherapy with or without antiangiogenic therapy.MethodsA total of 271 patients with advanced NSCLC who received anti-PD-1 plus chemotherapy with or without antiangiogenic therapy were enrolled in our center and randomized into the training cohort (n = 133) and the internal validation cohort (n = 138). Forty-five patients from another center were included as an independent external validation cohort. The nomogram was created based on the multivariate Cox regression analysis to predict overall survival (OS) and progression-free survival (PFS). The performance of the nomogram was assessed using the concordance index (C-index), the time-dependent area under the receiver operating (ROC) curves (AUCs), calibration curves, and decision curve analysis (DCA).ResultsFour factors significantly associated with OS were utilized to create a nomogram to predict OS: Eastern Cooperative Oncology Group performance status (ECOG PS), programmed cell death-ligand 1 (PD-L1) expression, chemotherapy cycle, and pretreatment lactate dehydrogenase–albumin ratio (LAR). Six variables significantly associated with PFS were incorporated into the development of a nomogram for predicting PFS: ECOG PS, histology, PD-L1 expression, chemotherapy cycle, pretreatment platelet to lymphocyte (PLR), and pretreatment LAR. The C-indexes of the nomogram for predicting OS and PFS were 0.750 and 0.747, respectively. The AUCs for predicting the 6-month, 12-month, and 18-month OS and PFS were 0.847, 0.791, and 0.776 and 0.810, 0.787, and 0.861, respectively. The calibration curves demonstrated a good agreement between predictions and actual observations. The DCA curves indicated that the nomograms had good net benefits. Furthermore, the nomogram model was well-validated in the internal and external cohorts.ConclusionThe novel nomogram for predicting the prognosis of advanced NSCLC receiving anti-PD-1 plus chemotherapy with or without antiangiogenic therapy may help guide clinical treatment decisions.

How to efficiently and quickly train large-scale AI models has become a hot topic in recent deep learning. Mixed-precision training is an effective technique to speed up training and reduce memory usage. At present, the automatic mixed-precision training method mainly uses half-precision (FP16) for the matrix operations of forward and backward propagation of the entire model and accumulates the FP32 weight copies to avoid rounding errors. However, this method is not optimized for each layer individually, leading to poor convergence in large-scale model training because different layers have different data patterns. Therefore, this paper proposes a layered mixed-precision training method, which can flexibly adjust training precisions according to the contribution of each layer to the training effect. Applying the layered mixed-precision method, the ResNet model achieves a 1.9× speedup compared to the baseline and a lower percentage of accuracy loss. In addition, this paper combines the layered mixed-precision method with distributed training strategies. Combining data parallel training, the model achieves a 3.74× speedup by using four Tesla V100 GPUs. The applicability of the layered mixed-precision method in model parallel training has been verified. Combining optimized pipeline parallel training, the model achieves a 3.26× speedup by using three Tesla V100 GPUs.

As the most popular power source to energy storage equipment Lithium-ion battery (LIB), it has the advantages of high-energy density, high power, long cycle life, as well as low pollution output. However, owing to increased battery impedance under low-temperature conditions, the lithium-ion diffusion in the battery is reduced, and the polarization of the electrode materials is accelerated, resulting in poor electrochemical activity and a drop in capacity during cycling. This issue is greatly hindering the further advancement of LIBs. In this review, we comprehensively review the factors affecting the performance of LIBs at low temperatures and introduce key methods and related mechanisms for improving the capacity of various electrode materials. Our overview aims to fully elucidate the basic principles of the low-temperature property of LIBs from a material point of view and to facilitate the advancement of high-performance LIB electrode materials that can be used in the wide working temperature range.

Abstract Despite advancements made in the therapeutic strategies on hepatocellular carcinoma (HCC), the survival rate of HCC patient is not satisfactory enough. Therefore, there is an urgent need for the valuable prognostic biomarkers in HCC therapy. In this study, we aimed to screen hub genes correlated with prognosis of HCC via multiple databases. 117 HCC-related genes were obtained from the intersection of the four databases. We subsequently identify 10 hub genes (JUN, IL10, CD34, MTOR, PTGS2, PTPRC, SELE, CSF1, APOB, MUC1) from PPI network by Cytoscape software analysis. Significant differential expression of hub genes between HCC tissues and adjacent tissues were observed in UALCAN, HCCDB and HPA databases. These hub genes were significantly associated with immune cell infiltrations and immune checkpoints. The hub genes were correlated with clinical parameters and survival probability of HCC patients. 147 potential targeted therapeutic drugs for HCC were identified through the DGIdb database. These hub genes could be used as novel prognostic biomarkers for HCC therapy.

Adenolymphoma is the second most common benign parotid tumor after pleomorphic adenoma. Although microwave ablation (MWA) has been proved to be effective for benign breast and thyroid nodules, there are few studies on ablation of adenolymphoma of parotid gland. An 82-year-old male with adenolymphoma of bilateral parotid gland treated with ultrasound-guided MWA. After 12 months of follow-up, the volume of bilateral parotid nodules was significantly reduced without recurrence and obvious complications. In conclusion, ultrasound-guided MWA is a safe and effective method for patients with parotid adenolymphoma who cannot tolerate surgery.

The formation of atherosclerotic plaques is one of the main sources of cardiovascular disease. In addition to known risk factors such as dyslipidemia, diabetes, obesity, and hypertension, endothelial dysfunction has been shown to play a key role in the formation and progression of atherosclerosis. Peroxisome proliferator-activated receptor-gamma (PPARγ), a transcription factor belonging to the steroid superfamily, is expressed in the aorta and plays a critical role in protecting endothelial function. It thereby serves as a target for treating both diabetes and atherosclerosis. Although many studies have examined endothelial cell disorders in atherosclerosis, the role of PPARγ in endothelial dysfunction is still not well understood. In this review, we summarize the possible mechanisms of action behind PPARγ regulatory compounds and post-translational modifications (PTMs) of PPARγ in the control of endothelial function. We also explore the potential use of endothelial PPARγ-targeted agents in the prevention and treatment of atherosclerosis.

For the purpose of regulating the visible-light-driven photocatalytic properties of photocatalysts, we selected BiOBr as the research target and various routes were used. Herein, via the use of a hydrothermal method with various solvents, BiOBr particles with controllable morphology and photocatalytic activities are obtained. In particular, through changing the volume ratio of ethylene glycol (EG) to ethanol (EtOH), BiOBr compounds possess microspheres, in which samples synthesized by using EG:EtOH = 1:2 have the highest photocatalytic activity, and can completely decompose RhB under visible light irradiation within 14 min. Furthermore, we also used different volume ratios of EG and H2O reaction solvents to prepare BiOBr particles so as to further improve its pollutant removal ability. When the volume ratio of EG to H2O is 1:1, the synthesized BiOBr particles have the best photocatalytic activity, and RhB can be degraded in only 10 min upon visible light irradiation. Aside from the reaction solvent, the impact of sintering temperature on the photocatalytic properties of BiOBr particles is also explored, where its pollutant removal capacities are restrained due to the reduced specific surface area. Additionally, the visible-light-triggered photocatalytic mechanism of BiOBr particles is determined by h+, ·OH and ·O2− active species.

Zygotes without a pronuclear (0PN) or with one pronuclear (1PN) were defined as abnormal fertilization in conventional in vitro fertilization (IVF). The removal of 0PN and 1PN zygotes from conventional IVF cycles has always been controversial. This study aimed to investigate the chromosomal aneuploidy rates of 0PN- and 1PN-derived blastocysts in conventional IVF cycles and to assess the concordance rate between TE-biopsy PGT-A and miPGT-A. TE biopsies and culture media with blastocoel fluid (CM-BF) samples were whole-genome amplified by multiple annealing and looping-based amplification cycle-based single-cell ChromInst method. Next generation sequencing was performed for comprehensive chromosomal screening on a NextSeq550 sequencer using the NextSeq 500/550 High Output kit v2. The aneuploidy rates of 0PN-derived blastocysts were 19.7% for TE-biopsy PGT-A, and 36.1% for miPGT-A; the concordance rate for ploidy was 77.0%; and the sensitivity and specificity were 83.3% and 75.5%, respectively. The aneuploidy rates of 1PN-derived blastocysts were 37.5% and 37.5% by TE-biopsy PGT-A and miPGT-A, respectively; the concordance rate between TE biopsies and CM-BF samples was 83.3%; and the sensitivity and specificity were 77.8% and 86.7%, respectively. Regarding TE-biopsy PGT-A, there were no significant differences in aneuploidy rates among 0PN-, 1PN- and 2PN-derived blastocysts (PGT-M cycles) (19.7% vs. 37.5% vs. 24.3%, P = 0.226), but the aneuploidy rate of 1PN-derived blastocysts was slightly higher than the other two groups. An increase in aneuploidy rates was observed for 0PN/1PN-derived day 6 blastocysts compared to 0PN/1PN-derived day 5 blastocysts (TE-biopsy PGT-A: 35.7% vs. 19.3%, P = 0.099; miPGT-A: 39.3% vs. 35.1%, P = 0.705). The present study is the first that contributes to understanding the chromosomal aneuploidies in 0PN- and 1PN-derived blastocysts in conventional IVF cycles using TE-biopsy PGT-A and miPGT-A. The clinical application value of 0PN- and 1PN-derived blastocysts in conventional IVF should be assessed using TE-biopsy PGT-A or miPGT-A due to the existence of chromosomal aneuploidies.. In terms of consistency, the miPGT-A using blastocoel fluid enriched culture medium is promising as an alternative to TE-biopsy PGT-A for aneuploidy testing of 0PN- or 1PN-derived blastocysts in conventional IVF.

BackgroundUnexplained recurrent spontaneous abortion is a serious reproductive problem of unknown etiology. Thyroid peroxidase antibodies (TPO-Ab) may be associated with pregnancy outcomes in unexplained recurrent spontaneous abortion with normal thyroid function.ObjectiveThis study aimed to investigate the relationship between TPO-Ab and the first trimester miscarriage rate/live birth rate in women of unexplained recurrent spontaneous abortion with normal thyroid function.MethodsWe retrospectively analyzed the clinical data of 297 women who met our strict inclusion criteria, comparing the first trimester miscarriage rate/live birth rate between the TPO-Ab positive and TPO-Ab negative groups. For the same purpose, we also performed subgroup analysis.ResultsOf the included women, 76 (25.6%) were TPO-Ab positive, and 221 (74.4%) were negative. First trimester miscarriage rate differed between the two groups (36.8% vs 24.0%, RR = 1.54, 95% CI: 1.05–2.24, P = 0.030). In the younger subgroup (

1,3-bis(cyclohexylmethyl)cyclopentane, a renewable high-density fuel, was first produced in a high overall carbon yield (79.5%) with vanillin and cyclopentanone, which can be derived from biomass. The synthetic route used in this work contains two steps. In the first step, 2,5-bis(4-hydroxy-3-methoxybenzylidene)cyclopentanone was synthesized by aldol condensation of vanillin and cyclopentanone under the catalysis of sulphuric acid. Over the optimized condensation, a high carbon yield (82.6%) of 2,5-bis(4-hydroxy-3-methoxybenzylidene) cyclopentanone was achieved at 80 ºC. In the second step, 2,5-bis(4-hydroxy-3-methoxybenzylidene) cyclopentanone was hydrodeoxygenated over the Pd/HY catalyst in cyclohexane as solvent. High carbon yields of 1,3-bis(cyclohexylmethyl)cyclopentane (96.2%) was obtained. The polycycloalkane mixture as obtained has a density of 0.943 g mL−1 and a freezing point of −35 °C. It can be blended into conventional high-density fuels (e.g., JP-10) for rockets and missile propulsion as a potential application.

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10; the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2.

Plusieurs target classification based on remotely sensed imagery is a hot and difficult-ridden topic in recent years. Specifically, the time and space complexity of the classical pattern recognition model in the classification task based on remote sensing image is usually high, and the remotely sensed imagery is usually not normally captured. For the former, we introduce the low delay and low storage hash method into the plusieurs target classification of remotely sensed imagery. Aiming at the latter, in order to improve the effectiveness of the proposed model in monitoring perspective transformation data, a dissociation perspective invariant model is constructed. By fusing these two solutions, a perspective invariant dissociaton hash model for remotely sensed imagery plusieurs target classification is obtained. By adding perspective invariant constraint to the supervised dissociaton hash method, our method forces the same type of target to share the same binary code, increases the similarity of the hash code of the same type of target, and thus improves the performance. In order to verify the validity and universality of our method, two different data sets were used for experiments, in which different hash methods and different classification methods are compared. The experimental results show that, compared with the comparison methods, the proposed method improves the accuracy of plusieurs target classes with a small number of samples, thus achieving a higher overall classification accuracy. In addition, the advantages of hash method in low storage make the speed of proposed method improved compared with the classical pattern recognition method.

Atherosclerosis is the hallmark of cardiovascular disease (CVD) which is a leading cause of death in type 2 diabetes patients, and glycemic control is not beneficial in reducing the potential risk of CVD. Clinically, it was shown that Thiazolidinediones (TZDs), a class of peroxisome proliferator-activated receptor gamma (PPARγ) agonists, are insulin sensitizers with reducing risk of CVD, while the potential adverse effects, such as weight gain, fluid retention, bone loss, and cardiovascular risk, restricts its use in diabetic treatment. PPARγ, a ligand-activated nuclear receptor, has shown to play a crucial role in anti-atherosclerosis by promoting cholesterol efflux, repressing monocytes infiltrating into the vascular intima under endothelial layer, their transformation into macrophages, and inhibiting vascular smooth muscle cells proliferation as well as migration. The selective activation of subsets of PPARγ targets, such as through PPARγ post-translational modification, is thought to improve the safety profile of PPARγ agonists. Here, this review focuses on the significance of PPARγ activity regulation (selective activation and post-translational modification) in the occurrence, development and treatment of atherosclerosis, and further clarifies the value of PPARγ as a safe therapeutic target for anti-atherosclerosis especially in diabetic treatment.

Abstract Purpose To determine the utility of short gamete coincubation in in vitro fertilization (IVF‐S) combined with early rescue intracytoplasmic sperm injection (R‐ICSI) and split IVF‐ICSI in preventing low fertilization based on a retrospective cohort study. Methods Couples with a high risk of low IVF fertilization during the first ART cycle underwent IVF‐S with R‐ICSI or split IVF‐ICSI. Fertilization rate, embryo quality, and clinical outcomes were measured. Results After propensity score matching, we included 188 couples in the IVF‐S with R‐ICSI group as Group 1 and 720 in the split IVF‐ICSI group as Group 2. Normal fertilization rates were similar; however, Group 1 had a higher multiple pronuclei rate (10.42% vs. 4.50%, p

As common noble metal, Ag nanoparticles (Ag NPs) have unique physical and chemical properties and it is widely used in daily life, scientific research, industrial production, environment, medical and healthcare systems. Core/shell of Ag nanocomposite have attracted attention due to the significant properties and wide applications by combination with the core and shell. As a mini-review, it is emphasized the approaches for fabrication Ag shell coating on core nanoparticles as core@Ag nanocomposite, including situ growth method, phenol formaldehyde resin method, silane coupling agent method, ultrasound method, PEI method, dopamine method and other methods. The procedures and mechanism for construction of core@Ag nanocomposite are highlighted. The comparison of those approaches, including their advantages and disadvantages are described in detail. At the same time, the properties and applications of Ag NPs are briefly introduced. The current challenges and perspectives in the future for synthesis of core/shell nanocomposite is proposed. The review is very useful for quickly gaining knowledge and experience for synthesis of core@Ag nanocomposite.

Background: Emerging research suggests that long non-coding RNAs (lncRNAs) play an important role in a variety of developmental or physiological processes of hepatocellular carcinoma (HCC). Various differentially expressed lncRNAs have been identified in HCC. Thus, a deeper analysis of recent research concerning lncRNA and HCC development could provide scientists with a valuable reference for future studies.Methods: Related publications were retrieved from the Web of Science Core Collection database. CiteSpace version 5.6.R4 was employed to conduct bibliometric analysis. Several network maps were constructed to evaluate the collaborations between different countries, institutions, authors, journals, and keywords.Results: A total of 2,667 records were initially found from the year of 2010–2020. The annual related publications output had increased dramatically during these years. Although China was the most prolific country in terms of research publication, the United States played a leading role in collaborative network. The Nanjing Medical University was the most productive institute in the field of lncRNAs in HCC development. Gang Chen was the most prolific researcher, while Yang F was the most frequently co-cited author. Oncotarget, Cell, and Oncogene were the most highly co-cited journals. The most recent burst keywords were interaction, database, and pathway.Conclusion: This study provides a comprehensive overview for the field of lncRNAs in HCC development based on bibliometric and visualized methods. The results would provide a reference for scholars focusing on this field.

The number of patients with type 2 diabetes mellitus (T2DM), which is mainly characterized by insulin resistance and insulin secretion deficiency, has been soaring in recent years. Accompanied by many other metabolic syndromes, such as cardiovascular diseases, T2DM represents a big challenge to public health and economic development. Peroxisome proliferator-activated receptor γ (PPARγ), a ligand-activated nuclear receptor that is critical in regulating glucose and lipid metabolism, has been developed as a powerful drug target for T2DM, such as thiazolidinediones (TZDs). Despite thiazolidinediones (TZDs), a class of PPARγ agonists, having been proven to be potent insulin sensitizers, their use is restricted in the treatment of diabetes for their adverse effects. Post-translational modifications (PTMs) have shed light on the selective activation of PPARγ, which shows great potential to circumvent TZDs’ side effects while maintaining insulin sensitization. In this review, we will focus on the potential effects of PTMs of PPARγ on treating T2DM in terms of phosphorylation, acetylation, ubiquitination, SUMOylation, O-GlcNAcylation, and S-nitrosylation. A better understanding of PTMs of PPARγ will help to design a new generation of safer compounds targeting PPARγ to treat type 2 diabetes.

Obesity is a growing global epidemic linked to many diseases, including diabetes, cardiovascular diseases, and musculoskeletal disorders. Exercise can improve bone density and decrease excess bone marrow adipose tissue (BMAT) in obese individuals. However, the mechanism of exercise regulating bone marrow microenvironment remains unclear. This study examines how exercise induces bone marrow remodeling in diet-induced obesity. We employed unbiased RNA-Seq to investigate the effect of exercise on the bone marrow of diet-induced obese male mice. Bone mesenchymal stem cells (BMSCs) were isolated to explore the regulatory effects of exercise in vitro. Our data demonstrated that exercise could slow down the progression of obesity and improve trabecular bone density. RNA-seq data revealed that exercise inhibited secreted phosphoprotein 1 (Spp1), which was shown to mediate bone resorption through mechanosensing mechanisms. Interactome analysis of Spp1 using the HINT database showed that Spp1 interacted with the adipokine adipsin. Moreover, exercise decreased BMAT, which induced osteoclast differentiation and promoted bone loss. Our study reveals that exercise improves the bone marrow microenvironment by at least partially inhibiting the adipsin–Spp1 signaling pathway so as to inhibit the alternative complement system from activating osteoclasts in diet-induced obese mice.

The identification of minerals is indispensable in geological analysis. Traditional mineral identification methods are highly dependent on professional knowledge and specialized equipment which often consume a lot of labor. To solve this problem, some researchers use machine learning algorithms to quickly identify a single mineral in images. However, in the natural environment, minerals often exist in an associated form, which makes the identification impossible with traditional machine learning algorithms. For the identification of associated minerals, this paper proposes a deep learning model based on the transformer and multi-label image classification. The model uses transformer architecture to model mineral images and outputs the probability of the existence of various minerals in an image. The experiments on 36 common minerals show that the model can achieve a mean average precision of 85.26%. The visualization of the class activation mapping indicates that our model can roughly locate the identified minerals.

Air pollution places a considerable disease burden on the public. Compared with the widely applied air quality index (AQI), the air quality health index (AQHI) provides a more comprehensive measure of multiple pollutants. In this study, AQHI was constructed using environmental data, meteorological data, and the daily mortality data of Beijing residents from 2018 to 2020. Factors increasing the susceptibility of the population to the health effects of air pollution were identified to aid the construction of a specific AQHI (S-AQHI) for susceptible populations. The findings indicated that older adults, women, and people with respiratory disease are more susceptible to the short-term health effects of air pollution. The relative deviation in the AQHI and S-AQHI for changes in daily mortality percentage of various specific populations ranged from only 1.4% to 10.3%, indicating the universality of the AQHI in its capacity to predict health risks. The Spearman coefficient of correlation between the AQHI and AQI was 0.78 (p < 0.01). Each increase in the interquartile range of the AQHI and AQI results in an increase of 1.894% and 1.029% in the total daily mortality, respectively, demonstrating the stronger capacity of the AQHI to predict daily mortality compared to the AQI.

In recent years, remote sensing images have played an important role in environmental monitoring, weather forecasting, and agricultural planning. However, remote sensing images often contain a large number of cloud layers. These clouds can cover a large amount of surface information. Therefore, an increasing number of cloud recognition methods have been proposed to reduce the impact of cloud cover. There are many difficulties in traditional cloud recognition methods. For example, the threshold method based on spectral features improves the accuracy of cloud detection, but it often leads to omission or misjudgment in cloud detection and depends on prior knowledge. To improve the accuracy and efficiency of cloud recognition, we use deep learning to address cloud recognition problems in remote sensing imagery. We propose a series of methods from the acquisition and production of training datasets to neural network training and cloud recognition applications. This paper describes a realization of cloud recognition of remote sensing imagery based on SegNet architecture. We have proposed two architectures named P_SegNet and NP_SegNet, which are modified from SegNet. Some parallel structures were also employed into the SegNet architecture to improve the accuracy of cloud recognition. Due to these changes, this paper also discusses the impact of the symmetry network structure on the final accuracy. Our proposed method was compared with the well-known fully convolutional neural network (FCNN) approach. The results have demonstrated the feasibility and practicality of using deep learning approach for cloud recognition in remote sensing images.

Abstract Background Long non‐coding RNA (lncRNA) plays an essential role in hepatitis B virus‐related hepatocellular carcinoma (HBV‐related HCC) occurrence and development. Single nucleotide polymorphism (SNP) may affect HBV‐related HCC susceptibility by altering the function of lncRNA. However, the relationship between lncRNA SNPs and HBV‐related HCC occurrence and development is still unclear. Methods In the present study, based on HBV‐related HCC genome‐wide association studies, eight potentially functional SNPs from two lncRNAs were predicted using a set of bioinformatics strategies. In 643 HBV‐related HCC patients, 549 CHB carriers, and 553 HBV natural clearance subjects from Southern Chinese, we evaluated associations between SNPs and HBV‐related HCC occurrence or development with odds ratio (OR) and 95% confidence interval (CI) under credible genetic models. Results In HBV‐related HCC patients, rs9908998 was found to significantly increase the risk of lymphatic metastasis under recessive model (Adjusted OR = 1.95, 95% CI = 1.20–3.17). Lnc‐RP11‐150O12.3 rs2275959, rs1008547, and rs11776545 with cancer family history may show significant multiplicative and additive interactions on HBV‐related HCC susceptibility (all pAdjusted < .05). The associations of rs2275959, rs1008547, and rs11776545 with distant metastasis of HBV‐related HCC patients were observed in additive model (Adjusted OR = 1.45, 95% CI = 1.06–1.97 for rs2275959; Adjusted OR = 1.45, 95% CI = 1.06–1.98 for rs1008547; Adjusted OR = 1.40, 95% CI = 1.03–1.91 for rs11776545). Conclusion Taken together, lnc‐ACACA‐1 rs9908998, lnc‐RP11‐150O12.3 rs2275959, rs1008547, and rs11776545 might be predictors for HBV‐related HCC risk or prognosis.

This work presents a cost-effective experimental method for measuring multiple mechanical properties for carbon fiber reinforced modified epoxy matrix composites using the digital image correlation method at elevated temperatures. The method is based on combining a calculated stress distribution and a surface full-field strain measurement in a custom short-beam shear specimen. As the in-plane shear strain exceeds 0.01, all unidirectional SBS specimens exhibited highly nonlinear shear stress-strain behavior. Shear delamination failure occurred at elevated temperatures up to 150oC. Axial elastic modulus and in-plane shear modulus were determined from 2-mm-wide gage regions mid-way between the loading nose and lower support in a single experiment using the closed-form approximation and finite-element-model-updating method. The average axial moduli determined using two different methods are highly consistent. The variation of multiple mechanical properties for the composite material with the temperature rising to 150oC has been obtained simultaneously. The experimental results demonstrate that the axial elastic modulus decreased by only 5.2% at 100oC and 7.8% at 150oC, respectively. However, the in-plane shear modulus decreased by 20.8% at 150oC. It indicates that the degradation of the in-plane shear behavior is sensitive to temperature rising since it is dominated by the matrix, which is sensitive to the glass transition temperature Tg of epoxy resin.

Formic acid (FA) is one of the most prospective hydrogen carriers for renewable energy transformation. In this context, the addition of extra-amine is always required for promoting the reactivity of FA, which is still a key challenge. Herein, we report a simple but effective strategy to synthesize Pd nanoparticles, supported on NH2-functionalized, phosphorous-doped glucose-based porous carbon (NH2-P-GC). The introduction of NH2- groups on the support acts as an immobilized amine-additive for FA dehydrogenation, while phosphorus not only serves as an electronic promoter to keep Pd in the electronic deficient state for FA dehydrogenation, but also as an enlarger of the aperture size of the carbon. As a result, the Pd/NH2-P-GC has exceptional catalytic activity, 100% H2 selectivity, CO generation that is undetectable, and good reusability for hydrogen production from FA. In the additive-free dehydrogenation of aqueous FA solution, the initial turnover frequency (TOF) can reach 5126 h−1 at room temperature, which is substantially higher than the best heterogeneous catalyst so far recorded. Overall, the system’s high activity, selectivity, stability, and simplicity in producing CO-free H2/CO2 gas from FA, without the need for any additive, makes it attractive for practical deployment.

Discriminative detection of GSH is achieved by employing a highly sensitive and selective fluorescent probe (KL-DN) that bears β-carboline alkaloid as a potential fluorophore and an azide group as the recognition unit. A rapid fluorescence off-on change is caused by special redox reaction; KL-DN has the capability of monitoring endogenous GSH in wheat germ tissues, indicating that this probe holds great potential for biological applications in plant tissues.

Background/Aims: Y+LAT1 protein, encoded by the SLC7A7 gene (a member of the SLC7 family), forms the cationic amino acid transport system y+L (system y+L). This system transports cationic amino acids such as arginine and lysine out of the cell. Arginine, in particular, is critical for T-cell activation and function in the immune response. Methods: We analyzed the role of the SLC7A7 gene in the cellular activities of Jurkat cells, specifically the cell cycle and cell proliferation, apoptosis, migration, and invasion. Cell proliferation was assessed using the Cell Counting Kit-8. Apoptosis and the cell cycle were determined with a FACSCalibur flow cytometer. A Transwell chamber was used to measure cell invasion and migration. Results: The proliferative ability of Jurkat cells was not significantly altered by transfection with SLC7A7 overexpression vectors. However, SLC7A7 overexpression significantly decreased the percentage of apoptotic Jurkat cells (P = 0.007) but significantly increased the proportion of G1 phase cells (P = 0.029) and cell migration (P < 0.001) and invasion (P < 0.001). Knockdown of SLC7A7 increased the cell apoptosis rate (P = 0.006) but decreased the G1 phase ratio (P = 0.002) and cell migration (P < 0.001) and invasion (P < 0.001). Conclusions: SLC7A7 plays a significant role in the pathogenesis of T-cell acute lymphoblastic leukemia.

Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experience of the appraisers or the various measuring instruments, and the methods are either easily influenced by appraisers’ experience or require too much work. To solve the above problems, there are studies using image recognition and intelligent algorithms to identify minerals. However, current studies cannot identify many minerals, and the accuracy is low. To increase the number of identified minerals and accuracy, we propose a method that uses both mineral photo images and the Mohs hardness in deep neural networks to identify the minerals. The experimental results showed that the method can reach 90.6% top-1 accuracy and 99.6% top-5 accuracy for 36 common minerals. An app based on the model was implemented on smartphones with no need for accessing the internet and communication signals. Tested on 73 real mineral samples, the app achieved top-1 accuracy of 89% when the mineral image and hardness are both used and 71.2% when only the mineral image is used.

Abstract The establishment of a collaborative network of transcription factors (TFs) followed by decomposition and then construction of subnetworks is an effective way to obtain sets of collaborative TFs; each set controls a biological process or a complex trait. We previously developed eight gene association methods for genome-wide coexpression analysis between each TF and all other genomic genes and then constructing collaborative networks of TFs but only one algorithm, called Triple-Link Algorithm, for building collaborative subnetworks. In this study, we developed two more algorithms, Single Seed-Growing Algorithm (SSGA) and Multi-Seed Growing Algorithm (MSGA), for building collaborative subnetworks of TFs by identifying the fully-linked triple-node seeds from a decomposed collaborative network and then growing them into subnetworks with two different strategies. The subnetworks built from the three algorithms described above were comparatively appraised in terms of both functional cohesion and intra-subnetwork association strengths versus inter-subnetwork association strengths. We concluded that SSGA and MSGA, which performed more systemic comparisons and analyses of edge weights and network connectivity during subnetwork construction processes, yielded more functional and cohesive subnetworks than Triple-Link Algorithm. Together, these three algorithms provide alternate approaches for acquiring subnetworks of collaborative TFs. We also presented a framework to outline how to use these three algorithms to obtain collaborative TF sets governing biological processes or complex traits.

We investigated the mechanisms of mushroom toxin resistance in the Drosophila Genetic Reference Panel (DGRP) fly lines, using genome-wide association studies (GWAS). While Drosophila melanogaster avoids mushrooms in nature, some lines are surprisingly resistant to α-amanitin-a toxin found solely in mushrooms. This resistance may represent a pre-adaptation, which might enable this species to invade the mushroom niche in the future. Although our previous microarray study had strongly suggested that pesticide-metabolizing detoxification genes confer α-amanitin resistance in a Taiwanese D. melanogaster line Ama-KTT, none of the traditional detoxification genes were among the top candidate genes resulting from the GWAS in the current study. Instead, we identified Megalin, Tequila, and widerborst as candidate genes underlying the α-amanitin resistance phenotype in the North American DGRP lines, all three of which are connected to the Target of Rapamycin (TOR) pathway. Both widerborst and Tequila are upstream regulators of TOR, and TOR is a key regulator of autophagy and Megalin-mediated endocytosis. We suggest that endocytosis and autophagy of α-amanitin, followed by lysosomal degradation of the toxin, is one of the mechanisms that confer α-amanitin resistance in the DGRP lines.

Climatic simulations rely heavily on high-performance computing. As one of the atmospheric radiative transfer models, the rapid radiative transfer model for general circulation models (RRTMG) is used to calculate the radiative transfer of electromagnetic radiation through a planetary atmosphere. Radiation physics is one of the most time-consuming physical processes, so the RRTMG presents large-scale and long-term simulation challenges to the development of efficient parallel algorithms that fit well into multicore clusters. This paper presents a method for improving the calculative efficiency of radiation physics, an RRTMG long-wave radiation scheme (RRTMG_LW) that is accelerated on a graphics processing unit (GPU). First, a GPU-based acceleration algorithm with one-dimensional domain decomposition is proposed. Then, a second acceleration algorithm with two-dimensional domain decomposition is presented. After the two algorithms were implemented in Compute Unified Device Architecture (CUDA) Fortran, a GPU version of the RRTMG_LW, namely G-RRTMG_LW, was developed. Results demonstrated that the proposed acceleration algorithms were effective and that the G-RRTMG_LW achieved a significant speedup. In the case without I/O transfer, the 2-D G-RRTMG_LW on one K40 GPU obtained a speed increase of 18.52× over the baseline performance on a single Intel Xeon E5-2680 CPU core.

Sucrose synthase (SuSy) is considered the first key enzyme for secondary growth because it is a highly regulated cytosolic enzyme that catalyzes the reversible conversion of sucrose and UDP into UDP-glucose and fructose. Although SuSy enzymes preferentially functions in the direction of sucrose cleavage at most cellular condition, they also catalyze the synthetic reaction. We isolated a gene that encodes a SuSy from Populus simonii×Populus nigra and named it PsnSuSy2 because it shares high similarity to SuSy2 in Populus trichocarpa. RT-PCR revealed that PsnSuSy2 was highly expressed in xylem, but lowly expressed in young leaves. To characterize its functions in secondary growth, multiple tobacco overexpression transgenic lines of PnsSuSy2 were generated via Agrobacterium-mediated transformation. The PsnSuSy2 expression levels and altered wood properties in stem segments from the different transgenic lines were carefully characterized. The results demonstrated that the levels of PsnSuSy2 enzyme activity, chlorophyll content, total soluble sugars, fructose and glucose increased significantly, while the sucrose level decreased significantly. Consequently, the cellulose content and fiber length increased, whereas the lignin content decreased, suggesting that PsnSuSy2 plays a significant role in cleaving sucrose into UDP-glucose and fructose to facilitate cellulose biosynthesis and that promotion of cellulose biosynthesis suppresses lignin biosynthesis. Additionally, the noticeable increase in the lodging resistance in transgenic tobacco stem suggested that the cell wall characteristics were altered by PsnSuSy2 overexpression. Scanning electron microscopy was performed to study the cell wall morphology of stem, and surprisingly, we found that the secondary cell wall was significantly thicker in transgenic tobacco. However, the thickened secondary cell wall did not negatively affect the height of the plants because the PsnSuSy2- overexpressing lines grew taller than the wildtype plants. This systematic analysis demonstrated that PsnSuSy2 plays an important role in cleaving sucrose coupled with cellulose biosynthesis in wood tissue.

Insertion of T4 lysozyme (T4L) into the GPCR successfully enhanced GPCR protein stability and solubilization. However, the biological functions of the recombinant GPCR protein have not been analyzed.We engineered the CCR5-T4L mutant and expressed and purified the soluble recombinant protein using an E.coli expression system. The antiviral effects of this recombinant protein in THP-1 cell lines, primary human macrophages, and PBMCs from different donors were investigated. We also explored the possible mechanisms underlying the observed antiviral effects.Our data showed the biphasic inhibitory and promotion effects of different concentrations of soluble recombinant CCR5-T4L protein on R5 tropic human immunodeficiency virus-1 (HIV-1) infection in THP-1 cell lines, human macrophages, and PBMCs from clinical isolates. We demonstrated that soluble recombinant CCR5-T4L acts as a HIV-1 co-receptor, interacts with wild type CCR5, down-regulates the surface CCR5 expression in human macrophages, and interacts with CCL5 to inhibit macrophage migration. Using binding assays, we further determined that recombinant CCR5-T4L and [125I]-CCL5 compete for the same binding site on wild type CCR5.Our results suggest that recombinant CCR5-T4L protein marginally promotes HIV-1 infection at low concentrations and markedly inhibits infection at higher concentrations. This recombinant protein may be helpful in the future development of anti-HIV-1 therapeutic agents.