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Background: Acute myeloid leukemia (AML) is a hematological malignancy with a heterogeneous prognosis. Novel markers are required to accurately assess the prognosis and formulate treatment plans. Methods: The association of ARHGAP family genes with prognostic value in acute myeloid leukemia (AML) was assessed using public databases (CCLE, GEPIA, TCGA, and GEO). Results: Elevated expression of ARHGAP43 (SH3BP1) was associated with poor prognosis in patients with acute myeloid leukemia. ARHGAP43 (SH3BP1) expression was higher in the poor/adverse prognosis (P

Abstract The COVID-19 pandemic has imposed significant challenges on global health, emphasizing the persistent threat of large-scale infectious diseases in the future. This study addresses the need to enhance pooled testing efficiency for large populations. The common approach in pooled testing involves consolidating multiple test samples into a single tube to efficiently detect positivity at a lower cost. However, what is the optimal number of samples to be grouped together in order to minimize costs? i.e. allocating ten individuals per group may not be the most cost-effective strategy. In response, this paper introduces the hierarchical quotient space, an extension of fuzzy equivalence relations, as a method to optimize group allocations. In this study, we propose a cost-sensitive multi-granularity intelligent decision model to further minimize testing costs. This model considers both testing and collection costs, aiming to achieve the lowest total cost through optimal grouping at a single layer. Building upon this foundation, two multi-granularity models are proposed, exploring hierarchical group optimization. The experimental simulations were conducted using MATLAB R2022a on a desktop with Intel i5-10500 CPU and 8G RAM, considering scenarios with a fixed number of individuals and fixed positive probability. The main findings from our simulations demonstrate that the proposed models significantly enhance the efficiency and reduce the overall costs associated with pooled testing. For example, testing costs were reduced by nearly half when the optimal grouping strategy was applied, compared to the traditional method of grouping ten individuals. Additionally, the multi-granularity approach further optimized the hierarchical groupings, leading to substantial cost savings and improved testing efficiency.

Abstract Background Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human beings. In this case, deep neural networks have been increasingly employed to mine cell type specific patterns and have outperformed statistic approaches in cell clustering. Results Using cross-correlation to capture gene–gene interactions, this study proposes the scCompressSA method to integrate topological patterns from scRNA-seq data, with support of self-attention (SA) based coefficient compression (CC) block. This SA-based CC block is able to extract and employ static gene–gene interactions from scRNA-seq data. This proposed scCompressSA method has enhanced clustering accuracy in multiple benchmark scRNA-seq datasets by integrating topological and temporal features. Conclusion Static gene–gene interactions have been extracted as temporal features to boost clustering performance in single-cell clustering For the scCompressSA method, dual-channel SA based CC block is able to integrate topological features and has exhibited extraordinary detection accuracy compared with previous clustering approaches that only employ temporal patterns.

Breast cancer, a leading cause of cancer-related mortality in women, is characterized by its propensity for metastasis. Heat shock protein 110 (Hsp110), a molecular chaperone encoded by the HSPH1 gene, has been implicated in cancer progression, including breast cancer, where it is upregulated and associated with worse outcomes. However, the role of Hsp110 in breast cancer pathogenesis and its potential as a therapeutic target have not been thoroughly investigated. This study utilized the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database to analyze HSPH1 gene expression in breast cancer and its correlation with tumor progression and survival. Furthermore, a comprehensive screen of the Specs database led to the identification of AN-329, a novel inhibitor that binds directly to the nucleotide-binding domain of Hsp110, neutralizing its chaperone activity and inhibiting breast cancer cell growth. AN-329 was validated in vitro for its antitumor efficacy and was found to regulate the cell cycle through the p-STAT3/c-Myc axis. This work suggests that AN-329 could be a promising lead for developing innovative therapeutic agents against breast cancer, warranting further research and potential clinical translation.

PurposeApproximately one-third of hepatocellular carcinoma (HCC) cases are characterized by alpha-fetoprotein (AFP) negativity (AFP-NHCC. Among these patients, around 60% exhibit des-gamma-carboxyprothrombin (DCP) positivity, and DCP-positive patients have a poorer prognosis. As a curative treatment, recurrence after liver resection poses significant challenges to the prognosis of HCC patients. Therefore, it is necessary to determine the relevant risk factors of these patients and provide timely treatment options.MethodsThis study included 540 patients who underwent resection at Beijing You’an Hospital. 292 patients from 2014 to 2018 constituted the training cohort, while 248 patients from 2018 to 2020 constituted the validation cohort. All patients underwent routine follow-ups until December 2023. Variables were identified through Cox regression, and a nomogram was developed. The nomogram was evaluated using time-dependent receiver operating characteristic curves (ROC), calibration curves, Decision curve analysis (DCA), and Kaplan-Meier (KM) curve analysisResultsWe found that age, tumor number, tumor size, γ-glutamyl transpeptidase (γ-GT), and prothrombin time (PT) are independent risk factors for HCC recurrence, and a nomogram was developed and validated based on this result to predict recurrence-free survival (RFS) at 1, 2, and 3 years. The performance of the nomogram was further confirmed by the ROC curve, calibration curve, and DCA, all of which showed favorable results. The KM curve analysis clearly distinguishes between two groups of people with different risks in terms of prognosis in both the training and validation sets.ConclusionIn summary, we established and validated a novel nomogram by multivariate Cox regression analysis to predict recurrence in DCP-positive patients with AFP-NHCC after resection. The nomogram, including age, tumor number, tumor size, γ-GT, and PT, demonstrates better predictive ability for AFP-NHCC patients with DCP positive.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high lethality. This study aimed to determine whether prolonged activated partial thromboplastin time (APTT) predicted SFTS mortality. Methods: SFTS patients were enrolled from 6 hospitals in the north China. Subjects were divided into training cohort and 5 externally validation cohorts. The least absolute shrinkage and selection operator Cox regression model was performed to screen potential prognostic factors. Risk factors were analyzed using multivariable regression models. Prognostic models were established by Cox regression and random survival forest (RSF) methods, and evaluated regarding discrimination, validity and clinical benefit. Time-dependent receiver operating characteristic (ROC) curve was used to evaluate the predictive effectiveness of variables. Results: 1332 SFTS cases were included, in which 211 patients died. Six potential prognostic factors were screened, and pulse, breath, APTT and aspartic transaminase (AST) were independently associated with mortality in both training cohort (Yantai, N = 791) and external validation cohort (N = 541). APTT was steadily correlated with the fatality (HR: 1.039–1.144; all P

Abstract Background To identify incidence and underlying risk factors for unsuspected placenta accreta spectrum (PAS) and compare the maternal outcomes between suspected and unsuspected cases in three large academic referral centers. Methods A retrospective cohort study was conducted in three university-based tertiary referral centers from Jan 1st, 2013, to Dec 31st, 2022. All cases of PAS confirmed by pathology were included in the study. Unsuspected PAS cases were diagnosed at the time of delivery, while suspected cases served as the control group. Potential risk factors were compared between the two groups. Multivariable regression model was also performed to identify risk factors. Maternal outcomes were also evaluated. Results A total of 339 pathology-confirmed PAS cases were included in the study out of 415,470 deliveries, of which 35.4% (n = 120) were unsuspected cases. Unsuspected PAS cases were 7.9 times more likely to have a history of intrauterine adhesions (adjusted odds ratio [aOR] 7.93; 95% confidence interval [CI] 2.35–26.81), 7.0 times more likely to have a history of clinically confirmed PAS (aOR, 6.99; 95% CI 2.85–17.18), 6.3 times more likely to have a posterior placenta (aOR, 6.30; 95% CI 3.48–11.40), and 3.4 times more likely to have a history of placenta previa (aOR, 3.41; 95% CI 1.18–9.82). On the other hand, cases with gravidity > 3, placenta previa, and/or a history of previous cesarean delivery were more likely to be diagnosed antenatally (aOR 0.40, 0.19, 0.36; 95% CI 0.22–0.74, 0.09–0.40, 0.19–0.70). Although the suspected PAS group had a higher proportion of invasive cases and abdominal and pelvic organ injuries (74.4% vs. 25.8%, p 3, a history of previous cesarean delivery, and placenta previa increase the likelihood of antenatal diagnosis.

Abstract Expanded carrier screening (ECS) based on next-generation sequencing has been the subject of few studies to estimate the effectiveness of ECS in the Chinese population. A total of 3737 individuals from Southwest China or the general Chinese population, including 1048 pairs and 1641 individuals, were analysed by ECS for 155 monogenetic diseases. An ECS panel was used to detect 147 genes and 10,449 variants in 145 autosomal recessive and 10 X-linked recessive disorders. A total of 43.27% (1617/3737) were found to be carriers of at least one of the 155 monogenetic diseases. The average number of carriers of these recessive mutations was 0.54 and ranged from 0 to 4. Of the 1048 couples, 74.81% (n = 784) were found to have at least one partner carrying more than one disease. In addition, 5.34% of the couples at risk (n = 56) were heterozygous for the same autosomal recessive disease, and 0.37% of the women (9/2440) were carriers of X-linked diseases. Our study demonstrated the clinical significance of ECS in Chinese populations and the need for a programme of familial screening for the prevention of severe recessive monogenetic diseases.

Lymantria xylina Swinhoe (Lepidoptera: Erebidae) is a potentially invasive pest, similar to Lymantria dispar asiatica Vnukovskij and Lymantria dispar japonica Motschulsky (Lepidoptera: Erebidae). To evaluate its potential for spread and flight distance related to egg deposition on vessels at ports, we employed a flight mill to assess the flight capabilities of its adults under varying conditions. Our findings revealed that females primarily flew short distances and ceased flying after 3:00 AM, whereas males covered much longer distances throughout the day. Sex, age, and flight duration significantly influenced flight ability. Females exhibited weaker flight capability than males, and their ability declined with increasing age or flight duration. Notably, 1-day-old moths displayed the strongest flight ability, with average flight distances of up to 3.975 km for females and 8.441 km for males. By the fifth day, females no longer flew, and males experienced reduced flight ability. After continuous hanging for 16 h, females lost most of their flight capacity, while males remained capable of flight even after 32 h. Additionally, female flight ability decreased significantly after mating, possibly due to factors such as egg-carrying capacity, weight, and load ratio. This study provides a foundation for assessing the risk of long-distance dispersal of L. xylina via ocean-going freighters, considering female moths’ phototactic flight and oviposition.

Emotion recognition from physiological signals (ERPS) has drawn tremendous attention and can be potentially applied to numerous fields. Since physiological signals are nonstationary time series with high sampling frequency, it is challenging to directly extract features from them. Additionally, there are 2 major challenges in ERPS: (a) how to adequately capture the correlations between physiological signals at different times and between different types of physiological signals and (b) how to effectively minimize the negative effect caused by temporal covariate shift (TCS). To tackle these problems, we propose a domain generalization and residual network-based approach for emotion recognition from physiological signals (DGR-ERPS). We first pre-extract time- and frequency-domain features from the original time series to compose a new time series. Then, in order to fully extract the correlation information of different physiological signals, these time series are converted into 3D image data to serve as input for a residual-based feature encoder (RBFE). In addition, we introduce a domain generalization-based technique to mitigate the issue posed by TCS. We have conducted extensive experiments on 2 real-world datasets, and the results indicate that our DGR-ERPS achieves superior performance under both TCS and non-TCS scenarios.

The nitrogen cycling has an important ecological role in aquaculture ponds, and microbes mainly carry out the related processes. To better reveal the microbial community and nitrogen cycling processes in shrimp and clam polyculture ponds, we analyzed the microbial communities of water and sediment of four ponds containing Litopenaeus vannamei with different stocking densities of Mercenaria mercenaria (LM, 0 ind/m2; LM-L, 17.4 ind/m2; LM-M, 34.8 ind/m2; LM-H, 52.2 ind/m2) from May to September. Additionally, the possible reaction rates and gene abundances involved in nitrogen cycling were determined in sediments of LM and LM-M in July. The results showed that the dominant phyla in the water included Proteobacteria, Cyanobacteria, and Bacteroidetes and in sediment were Proteobacteria, Desulfobacterota, and Chloroflexi. According to principal coordinate analysis, microbial communities of water and sediment significantly differed with and without M. mercenaria. The LEfSe analysis indicated that nitrogen cycling processes in the water of L. vannamei ponds were denitrification and dissimilatory nitrate reduction to ammonium (DNRA), while nitrification and denitrification were observed in the water of L. vannamei-M. mercenaria polyculture ponds. In the sediments of L. vannamei ponds, DNRA was dominant process, whereas in the sediments of L. vannamei-M. mercenaria polyculture ponds, DNRA, incomplete nitrification and denitrification were identified. Compared with LM, LM-M increased the nitrification rate and enhanced nitrate reduction with denitrification and anaerobic ammonium oxidation (anammox) instead of DNRA in sediments. The abundance of genes involved in nitrification and denitrification was significantly enriched in the sediments of LM-M. These results suggest that L. vannamei-M. mercenaria polyculture ponds may enhance nitrifying bacteria and denitrifiers, promoting nitrification and denitrification in sediment. This study helps understand shrimp polyculture ponds’ microbial community composition and nitrogen cycling.

Abstract Background Myeloid neoplasms (MN) tend to relapse and deteriorate. Exploring the genomic mutation landscape of MN using next‐generation sequencing (NGS) is a great measure to clarify the mechanism of oncogenesis and progression of MN. Methods This multicenter retrospective study investigated 303 patients with MN using NGS from 2019 to 2021. The characteristics of the mutation landscape in the MN subgroups and the clinical value of gene variants were analyzed. Results At least one mutation was detected in 88.11% of the patients (267/303). TET2 was the most common mutation in the cohort, followed by GATA2, ASXL1, FLT3, DNMT3A, and TP53. Among patients with myeloid leukemia (ML), multivariate analysis showed that patients aged ≥60 years had lower overall survival (OS, p = 0.004). Further analysis showed TET2, NPM1, SRSF2, and IDH1 gene mutations, and epigenetic genes (p

Abstract Background The discrepancy between the results of cytogenetics and the results of chromosome microarray analysis (CMA) has often led to confusion over genetic counselling for prenatal diagnosis. Case presentation The prenatal ultrasound results of a congenital heart defect (CHD) foetus displayed an apartial endocardial pad defect and permanently dilated coronary sinus and left superior vena cava at 21 weeks of gestation. Cytogenetic analysis, CMA, fluorescent in situ hybridization (FISH) and multiplex ligation-dependent probe amplification (MLPA) with foetal cord blood samples were used to detect the genetic aetiology. Routine G-binding cytogenetic analysis showed normal karyotypes in both the foetus’ and parents’ blood samples. CMA results demonstrated that there were 53.973-Mb recurrent CNVs at Xp22.33-p11.22, as confirmed by MLPA assay. Conclusions Herein, we described the CNV of six duplications at Xp22.33-p11.22 and the 53.973 Mb duplication CNV that was not found in foetal cord blood samples by conventional cytogenetic methods, and it was confirmed by CMA and MLPA. Our novel findings will provide helpful information for prenatal diagnosis and genetic counselling for foetal CHDs.

Psammosilene tunicoides is an important ethnic medicine in Southwest China and the main raw material for Chinese patent medicines like Yunnan Baiyao. The purpose of this study was to isolate and identify the pathogenic fungus of root rot of Psammosilene tunicoides in Yunnan. In this study, the disease-health junction of the root tissue of diseased plant was isolated and purified by pure culture method to obtain the main pathogenic microorganisms of P. tunicoides. Pathogenicity of isolated pathogenic microorganisms was analyzed the according to Koch’s Postulates, and then were identified by morphological observation, rDNA-ITS and TEF-1α sequence analysis and phylogenetic tree construction. The results were as follows: (1) A total of 85 strains of fungi were obtained, among which PSD-1, PSD-2 and PSD-3 of Fusarium fungi caused root rot of Psammosilene tunicoides. (2) These three strains were re-inoculated into the P. tunicoides, which produced similar disease symptoms occurred with field plants, and the disease incidences after inoculation with pathogenic fungi were 60%, 61.7% and 71.7%, respectively. (3) Combined with morphological observation and gene sequence analysis, the three strains were identified as Fusarium oxysporum. This study firstly found that the F. oxysporum was one of the main pathogenic fungi causing root rot of Psammosilene tunicoides. The results of this study lay a foundation for the in-depth exploration of the endophytic flora with biological control of root rot and its mechanism, and serve as a theoretical basis for the follow-up prevention and control of the disease.

The self-training methods have been praised by extensive research in semi-supervised classification. Mislabeling is the main challenge in self-training methods. Multiple variations of self-training methods are recently proposed against mislabeling from the following one of two aspects: a) using heuristic rules to find high-confidence unlabeled samples that can easily be predicted correctly in each iteration; b) enhancing prediction performance by employing ensemble classifiers composed of multiple weak classifiers. Yet, they still suffer from the following issues: a); most strategies for finding high-confidence unlabeled samples heavily rely on parameters; b) almost all employed ensemble classifiers originally designed for supervised classifiers and may not be suitable for semi-supervised classification due to the limited number and unrepresented distribution of the initial labeled data; c) few can overcome mislabeling from the above two aspects at the same time. To advance the state of the art, a new self-training method based on density peaks clustering and improved Adaboost is presented and named as STDPboost. In the iterative self-taught process, a new density peaks clustering-based strategy is proposed to find high-confidence unlabeled samples and a new ensemble classifier named AdaboostSEMI and more suitable for semi-supervised classification is proposed to predict high-confidence unlabeled samples, which overcomes mislabeling and the mentioned shortcomings of existing self-training methods. Intensive experiments on benchmark data sets have proven that STDPboost outperforms 7 state-of-the-art self-training methods in average classification accuracy of KNN classifier and CART classifier with the percentages of the initial labeled data from 10% to 50% due to further alleviating mislabeling.

Abstract Background Antitumor T cell immunotherapy as a novel cancer therapeutic strategy has shown enormous promise. However, the tumor microenvironment (TME) is characterized by the low immunogenicity, hypoxia, and immunosuppressive condition that dramatically limit effective T cell immunotherapy. Thus, an ideal immunotherapy strategy that is capable of reversing the immunosuppressive TME is highly imperative. Results In this article, we reported that Fe-doped and doxorubicin (DOX) loaded HA@Cu2−XS-PEG (PHCN) nanomaterials were rationally designed as targeted Fe-PHCN@DOX nano-nuclear-reactors, which evoked persistent T cell immune response together with anti-PD-L1 nanobodies. It was confirmed that nano-nuclear-reactors displayed strong nanocatalytic effect for effective antitumor effects. Consequently, they maximized the immunogenic cell death (ICD) effect for antigen presentation and then stimulated T cell activation. In addition, Fe-PHCN@DOX could reprogram M2-phenotype tumor-associated macrophages (TAMs) into M1-phenotype TAMs by relieving tumor hypoxia. Meanwhile, blockade of the anti-PD-L1 nanobody promoted T cell activation through targeting the PD-1/PD-L1 immunosuppressive pathway. Notably, in vivo tumor therapy verified that this nano-nuclear-reactor could be used as an excellent immunotherapy nanoplatform for tumor eradication and metastasis prevention with nanobody. Conclusions Our findings demonstrated that nano-nuclear-reactors in combination with nanobody could evoke persistent T cell immune activation, suggesting them potential as a promising immunotherapy option for reversing immunosuppressive immune-cold tumors. Graphical Abstract

Abstract Gastric neuroendocrine carcinoma (GNEC) is a common type of neuroendocrine carcinoma (NEC) with a poor prognosis and limited therapeutic options. The underlying mechanisms of chemoresistance in patients with GNEC and those with NEC are largely unknown, and thus, reliable biomarkers and therapeutic targets that could improve treatment outcomes in patients with NECs are lacking. The aim of this study was to identify specific targets and investigate their roles in GNEC progression and treatment resistance. Differentially expressed genes (DEGs) were identified in GNEC specimens and were further analysed by focusing on their roles in chemoresistance. Gene Ontology (GO) and pathway enrichment analyses of GNEC DEGs revealed that synapse-related function was the most prominent cellular function perturbed in GNEC. SNAP25 was identified as the target gene involved in most of the enriched pathways. In vitro and in vivo experiments showed that SNAP25 plays a role in proliferation and chemoresistance in GNEC cell lines. AKT has been identified as a downstream target, and SNAP25 binds to AKT protein and promotes AKT protein half-life. Further analysis of other types of NEC as well as small cell lung cancer, which resembles NEC on a molecular level, has identified RUNDC3A as an upstream molecule that regulates SNAP25 expression and the associated phenotypes that could enhance chemoresistance in NECs. Our results show that SNAP25 expression in GNEC is mediated by RUNDC3A and promotes GNEC progression and chemoresistance via posttranslational modification of AKT. Thus, our results suggest that the RUNDC3A/SNAP25/Akt axis could be a potential therapeutic target in GNEC.

Background: Despite the increasing reports of re-positive SARS-CoV-2 cases after recovery and discharge from hospitals, our knowledge remains very limited regarding the contributing factors of re-positivity and its roles in the transmission and epidemiology of the Omicron variant. Methods: In this retrospective study, re-positivity is defined as the positive nucleic acid result (Ct

Abstract Background Rare chromosomal structural abnormalities, including ring chromosomes, often pose challenges to clinical genetic counselling. Results Here, we report a newborn with congenital heart disease and developmental delay who inherited ring chromosome 6 [46,XY,r(6)(p25q27)mat] from a phenotypically normal mother. Genotypes and phenotypes were analysed by molecular cytogenetic analysis, whole-exome sequencing and literature review. Conclusions Our study showed that the pathogenicity of the ring chromosome abnormality [r(6)(p25q27)] was mainly affected by chromosome imbalance, deletions of genes with haploinsufficiency, duplications of genes with triple sensitivity, parental inheritance of the imbalance and the imprinting status of the affected genes.

SMOTE is a classical oversampling method and aims to improve imbalanced classification by creating synthetic minority class samples. Overgeneralization is a great challenge in SMOTE and its improvements. Multiple variations of SMOTE are proposed against imbalances between classes and overgeneralization. However, they still have the following issues: 1) most methods depend on too many parameters; 2) most methods fail to detect suspicious noise effectively and modify them; 3) interpolation of almost all methods is susceptible to abnormal samples. To overcome the above issues, a new synthetic minority oversampling technique based on adaptive local mean vectors and improved differential evolution (SMOTE-LMVDE) is proposed. First, a new noise detection technique based on the defined adaptive local mean vectors (NDALMV) is proposed to find suspicious noise. Second, an improved differential evolution is proposed to modify and improve detected suspicious noise. Finally, a new interpolation based on the defined adaptive local mean vectors is proposed to create synthetic minority class samples. Experiments prove that the proposed method superior to 7 popular oversampling approaches on extensive data sets in the training nearest neighbor classifier and the decision tree classifier.

Abstract Background Fetal growth velocity standards have yet to be established for the Chinese population. This study aimed to establish such standards suitable for the Chinese population. Methods We performed a multicenter, population–based longitudinal cohort study including 9075 low–risk singleton pregnant women. Data were collected from the clinical records of 24 hospitals in 18 provinces of China. Demographic characteristics, reproductive history, fetal ultrasound measurements, and perinatal outcome data were collected. The fetal ultrasound measurements included biparietal diameter (BPD), abdominal circumference (AC), head circumference (HC), and femur diaphysis length (FDL). We used linear mixed models with cubic splines to model the trajectory of four ultrasound parameters and estimate fetal weight. Fetal growth velocity was determined by calculating the first derivative of fetal size curves. We also used logistic regression to estimate the association between fetal growth velocities in the bottom 10th percentile and adverse perinatal outcomes. Results Fetal growth velocity was not consistent over time or among individuals. The estimated fetal weight (EFW) steadily increased beginning at 12 gestational weeks and peaked at 35 gestational weeks. The maximum velocity was 211.71 g/week, and there was a steady decrease in velocity from 35 to 40 gestational weeks. The four ultrasound measurements increased in the early second trimester; BPD and HC peaked at 13 gestational weeks, AC at 14 gestational weeks, and FDL at 15 gestational weeks. BPD and HC also increased from 19 to 24 and 19 to 21 gestational weeks, respectively. EFW velocity in the bottom 10th percentile indicated higher risks of neonatal complications (odds ratio [OR] = 2.23, 95% confidence interval [CI]: 1.79–2.78) and preterm birth

Abstract. Congenital diaphragmatic hernia is a congenital fetal disease, which mainly causes pulmonary hypoplasia and pulmonary hypertension. Effective early prenatal diagnosis can detect and predict the prognosis of congenital diaphragmatic hernia in infants, thus provide a reference for prenatal counseling, early intervention, and potential choices for the child's family. Ultrasound and magnetic resonance imaging are the most commonly used methods for non-invasive examination of the fetus. This paper discusses evaluation parameters based on these two imaging modalities.

Objective To explore the predictive capacity of umbilical artery (UA) velocities at 37 weeks of gestation in identifying fetal growth restriction (FGR).Design Cross-sectional study.Setting and participants We retrospectively recruited 569 fetuses in the study. Thirty-nine FGR infants and 57 small-for-gestational-age (SGA) infants with normal UA Doppler at 37 weeks, as the study groups and 473 adequate-for-gestational-age (AGA) infants as a control group in a tertiary referral centre.Methods All the parameters of UA velocities, including the UA end-diastolic velocity (UA-EDV), UA peak systolic velocity (UA-PSV), UA mean diastolic velocity (UA-MDV) and UA time-averaged maximum velocity (UA-TAMXV), and UA Doppler were measured at approximately 37 weeks of gestation.Results Among the FGR, SGA and AGA groups, the UA-MDV, UA-TAMXV, UA-PSV and UA-EDV decreased with the loss of fetal weight. Multivariate logistic regression analyses showed that the UA-TAMXV was an independent predicting factor of FGR. It had a moderate predictive value for FGR. The area under the receiver operating characteristic curve was 0.82 (95% CI: 0.79 to 0.85).Conclusions The UA velocities decreased with the loss of fetal weight among the FGR, SGA and AGA groups and the UA-TAMXV was independently predictive of FGR. The results suggest that the UA-TAMXV might be a new parameter to predict FGR prior to delivery.

The heat transfer mechanism and water-cooling effect during oxygen lance blowing changes by slag sticking at the nozzle. In order to effectively overcome this problem, in this study, the equivalent heat transfer method was applied to modify the heat transfer coefficient of the oxygen lance nozzle under different slag thicknesses and steel slag thermal conductivities. In addition, the gas–liquid two-phase heat transfer model was established by computational fluid dynamics numerical simulation, and the cooling effects for different types of oxygen lance nozzles were calculated by the improved model. The results show that the error rate between the calculated value of the modified model and the field measured value was reduced to 3.3%–4.6%. The nozzle with double angle and double flow design was found to be conducive to the improvement of the cooling system. In this study, the best cooling effect was obtained when the flow ratio of large nozzles and small nozzles is 60%/40% and the angle is 12°/17°.

Objectives: To investigate metabolomic perturbations caused by twin-twin transfusion syndrome, metabolic changes associated with fetoscopic laser coagulation in both placental tissue and cord plasma, and to investigate differential metabolites pertinent to varying fetal outcomes, including hemodynamic status, birth weight, and cardiac function, of live-born babies.Methods: Placental tissue and cord plasma samples from normal term or uncomplicated preterm-born monochorionic twins and those complicated by twin-twin transfusion syndrome treated with or without fetoscopic laser coagulation were analyzed by high-performance liquid chromatography metabolomic profiling. Sixteen comparisons of different co-twin groups were performed. Partial least squares–discriminant analysis, metabolic pathway analysis, biomarker analysis, and Spearman’s correlation analysis were conducted based on differential metabolites used to determine potential biomarkers in different comparisons and metabolites that are pertinent to neonatal birth weight and left ventricular ejection fraction.Results: These metabolomic investigations showed that the cord plasma metabolome has a better performance in discriminating fetuses among different hemodynamic groups than placental tissue. The metabolic alteration of twin-twin transfusion syndrome in these two types of samples centers on fatty acid and lipid metabolism. The fetoscopic laser coagulation procedure improves the metabolomic change brought by this syndrome, making the metabolomes of the treated group less distinguishable from those of the control and preterm birth groups. Certain compounds, especially lipids and lipid-like molecules, are noted to be potential biomarkers of this morbid disease and pertinent to neonatal birth weight and ejection fraction.Conclusions: Fetoscopic laser coagulation can ameliorate the metabolomic alteration caused by twin-twin transfusion syndrome in placental tissue and cord plasma, which are involved mainly in fatty acid and lipid-like molecule metabolism. Certain lipids and lipid-like molecules are helpful in differentiating co-twins of different hemodynamic statuses and are significantly correlated with neonatal birth weight or ejection fraction.

The instance reduction is one of the data preprocessing methods and aims to remove noises and (or) redundant instances from the training set. In the instance reduction, one of the most representative techniques is the edition method which can remove harmful instances from the training set to improve the prediction accuracy of k nearest neighbor (KNN). Nevertheless, most of existing edition methods still have some drawbacks, such as the parameter dependency, high computational time and relatively low accuracy. To solve these problems, we present a new fast parameter-free edition method based on local sets with natural neighbors (ELS). In ELS, we define a new concept of local sets by introducing natural neighbors. ELS can use the local sets to keep more reasonable class boundaries and effectively filtering out noisy instances (including global outliers). The main advantages of ELS are that (a) it is parameter-free; (b) it can remove global outliers and noisy instances; (c) it is relatively fast. Experiments clearly verify that (a) ELS outperforms existing representative edition methods in improving the prediction accuracy of KNN; (b) ELS can improve the performance of the condensation method and hybrid method in terms of both accuracy and reduction; (c) ELS consumes relatively low running time.

Packet classification is widely used in Software-Defined Networking (SDN). At present, packet classification is mainly implemented by software, such as OpenvSwitch, which has the disadvantage of low performance. On the Field Programmable Gate Array (FPGA) platform, FPGA has the advantages of reconfigurability and high processing performance. The current work proposes the FPGA-based Bit-Vector algorithms in packet classification, which has the advantages of determining latency and high throughput. In the Bit-Vector-based algorithms, stringent memory resources in FPGA are wasted to store relatively useless wildcards because there are plenty of wildcards in the rules. A bit-vector-based memory compression scheme named Memory-shared Bit-Vector (MsBV) is proposed. MsBV adopts a memory-shared homogeneous two-dimensional pipeline architecture, and it can reduce memory consumption and ensure the correctness of packet classification. In MsBV, we reduce memory consumption better by rearranging the bit matrix. The experimental results show that MsBV saves about 43.69% memory resources, 57.53% the number of ALUTs, and 37.59% the number of Registers compared to StrideBV of 100K OpenFlow rules.

Individuals voluntarily internalize gender stereotypes and present personality characteristics and behaviors that conform to gender role requirements. The aim of the current study was to explore the reasons people internalize gender stereotypes. We conducted surveys with 317 college students in China to examine the relationship between gender self-stereotyping and life satisfaction. We also analyzed the mediating roles of relational self-esteem (RSE) and personal self-esteem (PSE) and the moderation role of gender. The results of path analysis showed that gender self-stereotyping directly affected life satisfaction and indirectly affected life satisfaction through RSE and PSE in a serial pattern; however, the serial mediation model was only significant in the male sample. Higher gender self-stereotyping was associated with male participants’ higher level of RSE and PSE and further correlated with higher life satisfaction. This study addressed the questions: “What are the benefits of gender self-stereotyping?” and “What are the major barriers to counter-stereotyping?” The results enrich our understanding of these issues, especially relative to the collectivist culture in China, and may be used to create more effective interventions to help people break through the stereotypes.

Summary: With an increasing global emphasis on renewable energy, electrosynthetic technologies stand to play a substantial role in generating the fuels and chemicals that power today’s society. While directions such as water electrolysis and CO2 directions have been heavily researched in the last decade, the scope of electrosynthesis can be greatly expanded to cover the full range of chemical targets that serve as building blocks for materials, pharmaceuticals, fertilizers, and more. To this end, the main challenges lie in the discovery of novel reaction routes and innovative catalytic systems that circumvent conventional limitations of electrocatalysis. Against this backdrop, this perspective will focus on the use of emerging methodologies to pioneer new electrosynthetic reaction systems. In this work, strategies of environmental control, phase change materials, reactant-selective membranes, and mediated approaches are discussed, before touching on the innovative spectroscopic approaches used to probe these systems and wrapping up with a forward-thinking outlook.

Abstract. The management of preterm prelabor rupture of membranes (PPROM) before 34 weeks of gestation is intractable, due to pulmonary immaturity, many complications, poor pregnancy outcomes. In particular, the pre-viable PPROM (

Objective: To analyze the discrepancy between self-rating and professional evaluation of mental health status in coronavirus disease 2019 (COVID-19) cluster cases.Method: A total of 65 COVID-19 cluster cases admitted to Beijing Ditan Hospital Capital Medical University from June 14, 2020 to June 16, 2020 were included in the study. Mental health assessment was completed by self-rating and professional evaluation. The gaps between self-rating and professional evaluation in different demographic characteristics were compared.Results: The results of self-rating were inconsistent with those of professional evaluation. The gap was statistically different among certain demographic subgroups. As for anxiety, the gaps had remarkable statistics differences in subgroups of sex, monthly income, infection way, and anxiety/depression medical history. Similarly, in the terms of depression, the gaps had significant statistic differences in the subgroups of the medical history of anxiety/depression, history of physical disease, employment status and the insurance type, marriage, education (year), residing in Beijing (year), and the monthly income.Conclusion: Compared to the professional evaluation, patients had a higher self-rating, which may be related to some demographic characteristics. It suggests that screening can be conducted in patients with COVID-19 by self-rating first, and then professional evaluation should be carried out in the patients with suspicious or positive results.

Patients with hepatitis C virus (HCV) genotype 3 (GT3) infection are resistant to direct-acting antiviral (DAA) treatments. This study aimed to analyze the effectiveness of sofosbuvir (SOF) + daclatasvir (DCV) ± ribavirin (RBV); SOF + velpatasvir (VEL) ± RBV; SOF + VEL + voxilaprevir (VOX); and glecaprevir (GLE) + pibrentasvir (PIB) in the treatment of HCV GT3-infected patients in real-world studies.Articles were identified by searching the PubMed, EMBASE, and Cochrane Library databases from January 1, 2016 to September 10, 2019. The meta-analysis was conducted to determine the sustained virologic response (SVR) rate, using R 3.6.2 software.Thirty-four studies, conducted on a total of 7328 patients from 22 countries, met the inclusion criteria. The pooled SVR rate after 12/24 weeks of treatment was 92.07% (95% CI: 90.39–93.61%) for the evaluated regimens. Also, the SVR rate was 91.17% (95% CI: 89.23–92.94%) in patients treated with SOF + DCV ± RBV; 95.08% (95% CI: 90.88–98.13%) in patients treated with SOF + VEL ± RBV; 84.97% (95% CI: 73.32–93.91%) in patients treated with SOF + VEL + VOX; and 98.54% (95% CI: 96.40–99.82%) in patients treated with GLE + PIB. The pooled SVR rate of the four regimens was 95.24% (95% CI: 93.50–96.75%) in non-cirrhotic patients and 89.39% (95% CI: 86.07–92.33%) in cirrhotic patients. The pooled SVR rate was 94.41% (95% CI: 92.02–96.42%) in treatment-naive patients and 87.98% (95% CI: 84.31–91.25%) in treatment-experienced patients.The SVR rate of GLE + PIB was higher than other regimens. SOF + VEL + VOX can be used as a treatment regimen following DAA treatment failure.

People typically reject being negatively stereotyped but overlook the ways in which they are positively stereotyped. The current study focused on the attitude of Chinese women toward being evaluated based on the traditional positive stereotype that women are virtuous; family/work centrality as a boundary condition of these attitudes; and three perceptions that may mediate the link between this type of evaluation and attitudes of women. In experiment 1, female college students were identified as work-oriented or family-oriented based on their responses to a questionnaire regarding their focus on these two domains. They then read a vignette in which a man evaluated a female target under random assignment to one of three conditions, namely: group positive stereotype evaluation, individual positive stereotype evaluation, or unstereotypical positive evaluation. The participants rated how much they liked the female target, as an indicator of their attitude toward evaluations based on the stereotype that women are virtuous. In experiment 2, female college students were classified as work- oriented or family-oriented, and then read a vignette in which a man (the target) evaluated them. They were randomly assigned to the group positive stereotype evaluation, individual positive stereotype evaluation, or unstereotypical positive evaluation. Participants rated how much they liked the male target, as an indicator of their attitude toward evaluations based on the positive stereotype that women are virtuous. Across both studies, ANOVA showed that work-oriented women liked evaluations based on both group and individual stereotypes less than the family-oriented women. Regression-based analyses showed evidence of a mediation process in which work-oriented women viewed the virtuous positive stereotype as implying a prescriptive social demand that women should engage in family roles, resulting in a negative reaction to this type of evaluation.

Semi-supervised self-training method can train an effective classifier by exploiting labeled and unlabeled samples. Recently, a self-training method based on density peaks of data (STDP) is proposed. However, it still suffers from some shortcomings to be addressed. For example, STDP is affected by cut-off distance dc. As a result, it is tricky for STDP to select an optimal parameter dc on each data set. Furthermore, STDP has a poor performance on data sets with some variations in density because of cut-off distance dc. In order to solve these problems, we present a new self-training method which connects unlabeled and labeled samples as vertexes of an optimum path forest to discover the underlying structure of feature space. Furthermore, the underlying structure of the feature space is used to guide the self-training method to train a classifier. Compared with STDP, our algorithm is free of parameters and can work better on data sets with some variations in density. Moreover, we are surprised to find that our algorithm also has some advantages in dealing with overlapping data sets. The experimental results on real data sets clearly demonstrate that our algorithm has better performance than some previous works in improving the performance of base classifiers of k-nearest neighbor, support vector machine and cart.

Nonintrusive industrial load identification can accurately acquire the operation data of each load in the plant, which is the benefit of intelligent power management. The identification method of the industrial load is complicated and difficult to be realized due to the difficulty in collecting transient data for modeling, and high-precision measuring equipment is required. Aiming at this situation, the article proposes a nonintrusive industrial load identification method using a random forest algorithm and steady-state waveform. Firstly, by monitoring the change of the industrial load power state, when the load changes and becomes stable, the steady-state waveform is extracted. Due to different electrical characteristics of industrial loads, the current waveform of loads is different to some extent. We can construct characteristic data for each industrial load to construct its own current steady-state waveform. Then, using the high-dimensional data of the steady-state waveform as the sample data, the bootstrap sampling method and the CART algorithm in the random forest algorithm are used to generate multiple decision trees. Finally, the industrial load types are identified by voting multiple decision trees. The actual operating load data of a factory are used as the sample data in the simulation, and the effectiveness and rapidity of the proposed identification algorithm are verified by the combined load method simulation comparison. The simulation results show that the accuracy of the proposed identification algorithm is more than 99%, the identification time is 3.36 s, which is much higher than that of other methods, and the operation time is less than that of other methods. Therefore, the proposed identification algorithm can effectively realize the nonintrusive industrial load identification.

Anthocyanins with important physiological functions mainly accumulate in grape berry, but teinturier grape cultivars can accumulate anthocyanins in both reproductive and vegetative tissues. The molecular regulatory mechanisms of anthocyanin biosynthesis in grapevine reproductive and vegetative tissues are different. Therefore, teinturier grapevine cultivar provides opportunities to investigate transcriptional regulation of vegetative anthocyanins, and to compare with mechanisms that regulate grape berry anthocyanins. Yan73 is a teinturier Vitis vinifera variety with vegetative tissues able to accumulate anthocyanins, but the anthocyanin pattern and the molecular mechanism regulating anthocyanin biosynthesis in these tissues remain uncharacterized. We analyzed the anthocyanin metabolic and transcriptome profiles of the vegetative tissues of Yan73 and its male parent with HPLC-ESI-MS/MS and RNA-sequencing technologies. Yan73 vegetative tissues had relatively high 3′-OH, acylated, and methoxylated anthocyanins. Furthermore, peonidin-3-O-(trans-6-coumaryl)-glucoside is the most abundant anthocyanin in Yan73 grapevine vegetative tissues. A total of 30,17 and 10 anthocyanin biosynthesis genes showed up-regulated expression in Yan73 leaf, stem and tendril, respectively, indicating anthocyanin biosynthesis in Yan73 vegetative tissues is regulated by transcription factors. The up-regulated expression of VvMYBA1 on chromosome 2 and VvMYBA5, VvMYBA6, and VvMYBA7 on chromosome 14 are responsible for the anthocyanin patterns of Yan73 vegetative tissues. The expression of a set of R2R3-MYB C2 repressor genes is activated and may negatively regulate anthocyanin biosynthesis in Yan73 vegetative tissues. These findings enhance our understanding of anthocyanin biosynthesis in grapevine.

The sugar content of grape berries is affected by many factors. To explore the hexose content in different cultivars, the photosynthesis, vegetative, and reproductive biomass, as well as the enzyme activities and expression levels of genes related to sugar metabolism and sugar contents were measured. Samples were collected 70–110 days after anthesis (DAA), from Riesling (RI), Petit Manseng (PM), and Cabernet Sauvignon (CS) berries cultivated in the field. The results indicated that high expression levels of VvSWEET15 and VvSS3 and a high activity of sucrose synthase (SS) are associated with a higher hexose content in the berries of PM than in the berries of the other two cultivars. These genes promoted hexose accumulation in the berries by regulating sugar hydrolysis and transport. The results of this study indicate that active sugar hydrolysis and transport increase the hexose content of PM berries, which provides insights for grape berry quality improvement and breeding projects in wine production.Main Conclusion: The active VvSS3, sucrose synthase (SS), and VvSWEET15 increases the hexose content in Petit Manseng berries, which are associated with sugar hydrolysis and transport.

The microbial structure and metabolic potential, particularly with regard to nitrogen (N) cycling, in integrated multitrophic aquaculture (IMTA) ponds with shrimp remain unclear. In this study, an analysis of microbial community taxonomic diversity and a metagenomic analysis of N-related genes were performed in a shrimp-crab pond (Penaeus japonicus-Portunus trituberculatus, SC) and a shrimp-crab-clam pond (P. japonicus-P. trituberculatus-Sinonovacula constricta, SCC) to evaluate microbial structure and N transformation capacities in these two shrimp IMTA ponds. The composition of the microbial communities was similar between SC and SCC, but the water and sediments shared few common members in either pond. The relative abundances of N cycling genes were significantly higher in sediment than in water in both SC and SCC, except for assimilatory nitrate reduction genes. The main drivers of the differences in the relative abundances of N cycling genes in SC and SCC were salinity and pH in water and the NO2− and NH4+ contents of pore water in sediment. These results indicate that the coculture of S. constricta in a shrimp-crab pond may result in decreased N cycling in sediment. The reduced N flux in the shrimp IMTA ponds primarily originates within the sediment, except for assimilatory nitrate reduction.

The development of modern networking requires that high-performance network processors be designed quickly and efficiently to support new protocols. As a very important part of the processor, the parser parses the headers of the packets—this is the precondition for further processing and finally forwarding these packets. This paper presents a framework designed to transform P4 programs to VHDL and to generate parsers on Field Programmable Gate Arrays (FPGAs). The framework includes a pipeline-based hardware architecture and a back-end compiler. The hardware architecture comprises many components with varying functionality, each of which has its own optimized VHDL template. By using the output of a standard frontend P4 compiler, our proposed compiler extracts the parameters and relationships from within the used components, which can then be mapped to corresponding templates by configuring, optimizing, and instantiating them. Finally, these templates are connected to output VHDL code. When a prototype of this framework is implemented and evaluated, the results demonstrate that the throughputs of the generated parsers achieve nearly 320 Gbps at a clock rate of around 300 MHz. Compared with state-of-the-art solutions, our proposed parsers achieve an average of twice the throughput when similar amounts of resources are being used.

Corrosion of steel bar is one of key factors undermining reinforced concrete (RC) structures in a harsh environment. This paper attempts to review the non-destructive procedures from the aspect of the corrosion measurement techniques, especially their advantages and limitations. Systematical classification of diagnostic methods is carried out to determine any probable corrosion issues before the structures become severe, and helps choose the suitable method according to different construction features. Furthermore, the three electrochemical factors method is introduced to inspire researchers to combine various techniques to improve corrosion evaluation accuracy. The recommendations for future work are summarized, in conclusion.

Data concerning the effects of phthalate exposure on preterm delivery and fetal growth are limited in humans. In this paper, we assessed the relationship between 15 phthalate levels in cord blood and preterm delivery and fetal growth parameters in 207 Chinese women going into labor. Exposure to phthalates except DCHP was associated with gestational age reduction and preterm delivery (p