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Diabetic wounds present significant burdens to both patients and the healthcare system due to their prolonged inflammatory phase and adverse microenvironment. Traditional Chinese medicine (TCM), particularly Scutellaria baicalensis extract (SE), has shown promise in wound healing. Herein, sesbania gum (SG) was oxidized and formed hydrogel with carboxymethyl chitosan (CMCS) through the imine bond. Then, SE was loaded into the hydrogel as a wound dressing (CMCS−OSG@SE hydrogel). In vitro experiments demonstrated the mechanical properties and ROS scavenging efficiency of the hydrogel, as well as the release of SE and its biocompatibility. In an vivo study, diabetic mice with S. aureus infection were used, and the CMCS−-OSG@SE hydrogel dressing accelerated wound healing by promoting epidermal regeneration and collagen deposition. This composite polysaccharide hydrogel loaded with SE shows great potential for diabetic wound treatment.

Diet selection, a core problem of foraging behavior, is a nutritional adaptation strategy formed in the long-term natural selection process by grazing herbivores and is significant for the sustainable management of grassland. Studies have mainly focused on the impacts of the individual and whole community spatial characteristics and herbivore body status on herbivore foraging behavior; thus, the response and mechanism of forage plants in different terrains to the diet selection of grazing herbivores remains unclear. Therefore, in this study, forage plants (gramineae, cyperaceae, legume, forbs, edible shrubs, and community) in different topographies (terrace, riparian zones, shady slope, half shady slope, half sunny slope, sunny slope) on the eastern Tibetan plateau were selected to study changes in nutrient and mineral content characteristics of forage plants, as well as the difference in feeding bias of yaks for forage plants in different terrains by using an indoor cafeteria trial. A structural equation model was used to illustrate the impact of the forage plants in different terrains on the feeding bias of yak. The multi-criterion decision model TOPSIS showed that the nutritional value of gramineae was highest for the shaded slope, and that of cyperaceae and leguminosae was the highest for the terraces. The nutrient value of forbs and the whole community was highest for the sunny slope. Dry matter intake by yaks of leguminosae, forbs, and the whole plant community was significantly higher for terraces than for grasslands with other topographies, and all were significantly lower in riparian zones. Yak forage preference of leguminosae, forbs, and the whole community was the highest for the terrace and the lowest for the riparian zones. Structural equation modeling showed that for functional groups, the interactions between topography and functional groups were the drivers influencing yak forage preferences. Our study highlights the propensity of yaks to forage for plants in areas with different topographies. These results have provided a scientific basis for understanding the relationship between herbivores and plants in grasslands and for formulating scientific grazing management strategies, which are of considerable importance for sustainable grassland livestock husbandry.

Poultry is a source of meat that is in great demand in the world. The quality of meat is an imperative point for shoppers. To explore the genes controlling meat quality characteristics, the growth and meat quality traits and muscle transcriptome of two indigenous Yunnan chicken breeds, Wuding chickens (WDs) and Daweishan mini chickens (MCs), were compared with Cobb broilers (CBs). The growth and meat quality characteristics of these two indigenous breeds were found to differ from CB. In particular, the crude fat (CF), inosine monophosphate content, amino acid (AA), and total fatty acid (TFA) content of WDs were significantly higher than those of CBs and MCs. In addition, it was found that MC pectoralis had 420 differentially expressed genes (DEGs) relative to CBs, and WDs had 217 DEGs relative to CBs. Among them, 105 DEGs were shared. The results of 10 selected genes were also confirmed by qPCR. The differentially expressed genes were six enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways including lysosomes, phagosomes, PPAR signaling pathways, cell adhesion molecules, cytokine–cytokine receptor interaction, and phagosome sphingolipid metabolism. Interestingly, four genes (LPL, GK, SCD, and FABP7) in the PPAR signal pathway related to fatty acid (FA) metabolism were elevated in WD muscles, which may account for higher CF, inosine monophosphate content, and AA and FA contents, key factors affecting meat quality. This work laid the foundation for improving the meat quality of Yunnan indigenous chickens, especially WD. In future molecular breeding, the genes in this study can be used as molecular screening markers and applied to the molecular breeding of chicken quality characteristics.

The aim of this study is to produce a biodegradable food packaging material that reduces environmental pollution and protects food safety. The effects of total solids content, substrate ratio, polyphenol content, and magnetic stirring time on bovine bone gelatin/sodium carboxymethylcellulose nanoemulsion (BBG/SCMC–NE) were investigated using particle size, PDI, turbidity, rheological properties, and zeta potential as evaluation indexes. The micro, structural, antioxidant, encapsulation, and release properties were characterized after deriving its optimal preparation process. The results showed that the nanoemulsion was optimally prepared with a total solids content of 2%, a substrate ratio of 9:1, a polyphenol content of 0.2%, and a magnetic stirring time of 60 min. SEM showed that the nanoemulsion showed a dense and uniform reticulated structure. FTIR and XRD results showed that covalent cross-linking of proteins and polysaccharides altered the structure of gelatin molecular chains to a more compact form but did not change its semi-crystalline structure. DSC showed that the 9:1 BBG/SCMC–NE had a higher thermal denaturation temperature and greater thermal stability, and its DPPH scavenging rate could reach 79.25% and encapsulation rate up to 90.88%, with excellent slow-release performance. The results of the study provide basic guidance for the preparation of stable active food packaging with excellent properties.

BackgroundWe aimed to explore the independent associations between screen time and the risk of stroke among Chinese adults based on the China Health and Nutrition Survey (CHNS).MethodsData on Chinese adults aged older than 40 years from the CHNS in during 2004–2009 were selected. A total of 4,587 individuals were included in 2009, including screen time and the risk of stroke. Simultaneously, we traced the previous screen time to 2004 for those with outcome measures in 2009 (n = 2,100). Basic information, lifestyle, and screen behavior were obtained through face-to-face interviews and self-completed questionnaires. Anthropometric data collected included blood pressure, body weight, height, hip circumference, and waist circumference. Fasting blood was obtained for measurements of lipid and glucose levels. Cross-sectional analysis and cohort analysis were both performed using multivariate logistic regression.ResultsOf all participants, 3,004 (65.49%) participants spent more than 2 h per day on screen time. Taking the men who spent less than 2 h on screen per day as reference, the crude odds ratio (OR) of the high risk of stroke was 1.53 [95% confidence interval (CI), 1.20–1.95] for the men who spent 2–3 h per day on screen and 2.37 (95% CI, 1.78–3.16) for the men who spent more than 3 h per day on screen. This difference remained significant after adjusting for confounding factors. No association was observed among women. However, in the cohort analysis with screen time in 2006 as the independent variable, the association between screen time and stroke risk was found both in men [OR, 1.83 (95% CI, 1.19–2.82)] and women [OR, 1.48 (95% CI, 1.10–1.99)]).ConclusionWe found that the high screen time was associated with an increased stroke risk, which was pronounced in men, warranting a universal need to limit screen time in order to improve health.

ObjectiveUnsaturated fatty acids (UFA) may be related to glycometabolism. While associations between UFA intake (especially their subtype) and prediabetes or type 2 diabetes mellitus (T2DM) need to be further studied. In this study, we aimed to evaluate the potential relation of UFA with prediabetes and T2DM.MethodsA total of 16,290 adults aged older than 18 years from the National Health and Nutrition Examination Survey (NHANES) from 2005 to March 2020 were included in the present analysis. Dietary intake was assessed by two day, 24-hour dietary recalls and daily intake of total monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA); four specific fatty acids of MUFA and seven specific fatty acids of PUFA were calculated. Prediabetes and T2DM were diagnosed by fasting glucose, glycohemoglobin, and self-reported medication or insulin. Rao–Scott modified chi-square tests, the Taylor series linearization method, and multivariable logistic regression analyses were applied to analyze the associations of dietary MUFA and PUFA intake with diabetes risk.ResultsOf the participants, 44.34% had prediabetes and 13.16% had T2DM patients. From multivariate analysis, we found that intake of MUFA, PUFA, and some subtypes was negatively associated with the risk of prediabetes and T2DM in Americans. Compared with adults in the lowest tertile, those in the highest MUFA (PUFA) tertile had an approximately 50% (49%) and 69% (68%) lower risk of prediabetes and T2DM, respectively. Moreover, the effects of the subtypes of MUFA and PUFA on prediabetes and T2DM were different. Higher intakes of MFA 18:1, MFA 20:1, PFA 18:2, and PFA 18:3 and higher tertile intakes of MFA 16:1 and PFA 20:4 were related to a lower risk of prediabetes and T2DM. Similarly, the effects of MUFA, PUFA, and subtype on prediabetes and T2DM varied among different age groups, being weakened along with age.ConclusionOur study suggested that total MUFA and PUFA intake might be essential in preventing prediabetes and T2DM, especially in Americans. However, this protective effect may decrease with age. Moreover, the effects of the specific UFA on prediabetes and T2DM need further consideration.

This study aimed to explore the underlying reasons for the differences in egg production in Chinese Chahua chickens. The Chahua chicken population was divided into two groups, namely a high-yielding group (H) and a low-yielding group (L). The egg-laying performance, ovarian morphology, histological characteristics, and serum hormone concentrations in the H and L groups were analysed. The results revealed that there were significant differences (P < 0.01) between the two groups for the age at the first egg and the number of eggs. The H group showed significantly more (P < 0.01) hierarchical follicles and small yellow follicles than the L group. While several secondary follicles and primary follicles were observed in the ovaries of the H group, only a few secondary follicles were observed in the ovaries of the L group. The serum concentrations of the follicle-stimulating hormone, the luteinising hormone, and oestradiol were significantly higher in the H group compared to that in the L group (P < 0.01). Next, we sequenced and analysed the ovarian expression profiles in the two groups using RNA-seq. The transcriptome sequencing revealed 875 differentially expressed genes (DEGs) between the H and L groups. Of the 875 DEGs, 624 were up-regulated, and 251 were down-regulated. The DEGs were mapped to 31 Gene Ontology (GO) terms and 15 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for the ovarian tissue. The KEGG pathway analysis mainly revealed the involvement of focal adhesion, cytokine-cytokine receptor interaction, and the TGF-beta signalling pathway, while the GO analysis mainly showed involvement of the hormonal activity, extracellular matrix, and extracellular region. Our results showed that understanding the differences in the gene expressions between the ovarian tissues of high- and low-yielding hens may provide a useful reference for improving the egg-laying performance in Chahua chickens.

Abstract Morphometric estimates of mean or individual glomerular volume (MGV, IGV) have biological implications, over and above qualitative histologic data. However, morphometry is time‐consuming and requires expertise limiting its utility in clinical cases. We evaluated MGV and IGV using plastic‐ and paraffin‐embedded tissue from 10 control and 10 focal segmental glomerulosclerosis (FSGS) mice (aging and 5/6th nephrectomy models) using the gold standard Cavalieri (Cav) method versus the 2‐profile and Weibel–Gomez (WG) methods and a novel 3‐profile method. We compared accuracy, bias and precision, and quantified results obtained when sampling differing numbers of glomeruli. In both FSGS and controls, we identified an acceptable precision for MGV of 10‐glomerular sampling versus 20‐glomerular sampling using the Cav method, while 5‐glomerular sampling was less precise. In plastic tissue, 2‐ or 3‐profile MGVs showed greater concordance with MGV when using Cav, versus MGV with WG. IGV comparisons using the same glomeruli reported a consistent underestimation bias with both 2‐ or 3‐profile methods versus the Cav method. FSGS glomeruli showed wider variations in bias estimation than controls. Our 3‐profile method offered incremental benefit to the 2‐profile method in both IGV and MGV estimation (improved correlation coefficient, Lin's concordance and reduced bias). In our control animals, we quantified a shrinkage artifact of 52% from tissue processed for paraffin‐embedded versus plastic‐embedded tissue. FSGS glomeruli showed overall reduced shrinkage albeit with variable artifact signifying periglomerular/glomerular fibrosis. A novel 3‐profile method offers slightly improved concordance with reduced bias versus 2‐profile. Our findings have implications for future studies using glomerular morphometry.

Aspergillus carbonarius is known to produce the carcinogenic ochratoxin A (OTA) in grapes. The metabolism process before OTA biosynthesis influences the content and composition of the volatile compounds in grapes. In this study, a self-established method based on QuEChERS coupled with high-performance liquid chromatography–fluorescence detection (HPLC-FLD) was used to determine the OTA levels during a seven-day contamination period. The results showed that OTA was detected on the second day after contamination with A. carbonarius. Thus, the first day was considered as the critical sampling timepoint for analyzing the volatiles in grapes before OTA biosynthesis. Additionally, the volatile compounds in grapes were analyzed using headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) and dispersive liquid–liquid microextraction gas chromatography–mass spectrometry (DLLME-GC-MS). The corresponding data were evaluated via multivariate data analysis using projection methods, including PCA and OPLS-DA. The results indicated significant differences in the nine volatile compounds in grapes contaminated with A. carbonarius before OTA biosynthesis. The results of the Pearson correlation analysis showed positive correlations between ethyl acetate, styrene, 1-hexanol and OTA; (E)-2-hexenal and nerolic acid were negatively correlated with OTA. Overall, these findings provide a theoretical basis for the early prediction of OTA formation in grape and grape products using GC-MS technology.

Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the baseline anomaly detector by analyzing the high-response regions in anomaly maps. We introduce an SVM-based false positive classifier as a post-processing module, which identifies false alarms from positive predictions at the object level. Moreover, we devise a sample synthesis strategy that generates synthetic false positives from the trained baseline detector while producing synthetic defect patch features from fuzzy domain knowledge. Following comprehensive evaluations, we showcase substantial performance enhancements in two advanced out-of-distribution anomaly detection models, Cflow and Fastflow, across image and pixel-level anomaly detection performance metrics. Substantive improvements are observed in two distinct industrial applications, with notable instances of elevating the image-level F1-score from 46.15% to 78.26% in optimal scenarios and boosting pixel-level AUROC from 72.36% to 94.74%.

Unsupervised defect detection methods have garnered substantial attention in industrial defect detection owing to their capacity to circumvent complex fault sample collection. However, these models grapple with establishing a robust boundary between normal and abnormal conditions in intricate scenarios, leading to a heightened frequency of false-positive predictions. Spurious alerts exacerbate the work of reconfirmation and impede the widespread adoption of unsupervised anomaly detection models in industrial applications. To this end, we delve into the sole available data source in unsupervised defect detection models, the unsupervised training dataset, to introduce a solution called the False Alarm Identification (FAI) method aimed at learning the distribution of potential false alarms using anomaly-free images. It exploits a multi-layer perceptron to capture the semantic information of potential false alarms from a detector trained on anomaly-free training images at the object level. During the testing phase, the FAI model operates as a post-processing module applied after the baseline detection algorithm. The FAI algorithm determines whether each positive patch predicted by the normalizing flow algorithm is a false alarm by its semantic features. When a positive prediction is identified as a false alarm, the corresponding pixel-wise predictions are set to negative. The effectiveness of the FAI method is demonstrated by two state-of-the-art normalizing flow algorithms on extensive industrial applications.

This study primarily compares the carcass and meat quality traits of yaks of different ages and genders. Measurements of carcass weight and characteristics were taken for male and female Gannan yaks aged 1–2, 2–4, 4–6, and 6 years and older. Additionally, the weight and meat quality of 9 major cuts were measured. The results indicated significant variations in various aspects of yak carcasses around the age of 4, and substantial differences between male and female yaks after the age of 4. Different body regions showed distinct meat quality. The tenderness of the tenderloin is the highest (P < 0.05), while the striploin is the lowest (P < 0.05), and the protein content of the hindquarter cuts is higher. Principal component analysis (PCA) was employed to identify key factors significantly affecting total meat yield. Subsequently, predictive models for total meat yield were developed for yaks aged 1–4 years, 4+ years for males, and 4+ years for females based on these factors. Furthermore, the meat from the 9 major cuts was classified according to its intrinsic meat quality traits, which holds significant reference value for the assessment of yak carcass yield grades and the grading of quality in meat cuts, ultimately contributing to the realization of the full potential of the yak meat industry.

In data analysis and modeling, least squares and total least squares are both mathematical optimization techniques. It is noteworthy that both the least squares method and the total least squares method are used to deal with precise and random data. However, when the given data are not random, or when the data are imprecise, and only the range of the data is available, the traditional linear regression method cannot be used. This paper presents an uncertain total least squares estimation method and an uncertain robust total least squares linear regression method based on uncertainty theory and total least squares method. The uncertain total least squares estimation can fully consider errors in the given data and the uncertain robust total least squares linear regression method can effectively eliminate outliers in the data. It is possible to obtain a more reasonable fitting effect with both of these methods, as well as to solve the predicted value and the confidence interval with these two methods. In terms of robust total least squares linear regression estimation, both uncertain total least squares regression estimation and uncertain robust total least squares regression estimation are feasible based on numerical examples. There are more accurate fitting equations and more reliable results with uncertain robust least squares linear regression estimation.

The effects of reduced glutathione (GSH) on non-volatile and volatile metabolites of Chardonnay wine during storage under simulated oxidation were investigated. The metabolites of GSH, which play a key role in the storage of white wine, were identified. In this study, GSHs at 0, 10, and 20 mg/L were added to wine samples and stored at 45 °C for 45 days. Wine samples supplemented with 0 mg/L GSH were used as controls (CK). The samples stored for 45 days were analyzed via ultra-high performance liquid chromatography–tandem mass spectrometry and gas chromatography–tandem mass spectrometry. A total of 1107 non-volatile metabolites were detected, and 617 volatile metabolites were identified. Variable Importance in Projection (VIP) of >1.0 and Fold Change (FC) of ≧2.0 were used to screen differential metabolites. A total of 59 important non-volatile and 39 differential volatile metabolites were screened. Among the non-volatile metabolites, 17 substances were down-regulated, whereas 16 substances were up-regulated. Among the volatile metabolites, 3 substances were down-regulated, while 19 substances were up-regulated. After analysis, some lipids were found to play an important role in the changes to non-volatile substances. This study provides theoretical support for further application of GSH in increasing the oxidation stability of white wine.

Abstract Yaks and Tibetan sheep are important and renowned livestock of the Qinghai-Tibetan Plateau (QTP). Both host genetics and environmental factors can shape the composition of gut microbiota, however, there is still no consensus on which is the more dominant factor. To investigate the influence of hosts and seasons on the gut microbiome diversity component, we collected fecal samples from yaks and Tibetan sheep across different seasons (summer and winter), during which they consumed different diets. Using 16S rRNA sequencing, principal component analysis (PCoA) data showed that PCo1 explained 57.4% of the observed variance (P = 0.001) and clearly divided winter samples from summer ones, while PCo2 explained 7.1% of observed variance (P = 0.001) and mainly highlighted differences in host species. Cluster analysis data revealed that the gut microbiota composition displayed a convergence caused by season and not by genetics. Further, we profiled the gut microbial community and found that the more dominant genera in yak and Tibetan sheep microbiota were influenced by seasonal diets factors rather than genetics. This study therefore indicated that seasonal diet can trump host genetics even at higher taxonomic levels, thus providing a cautionary note for the breeding and management of these two species.

ABSTRACT Escherichia coli serine hydroxymethyltransferase (GlyA) converts serine to glycine, and glyA mutants are auxotrophic for glycine. CycA is a transporter that mediates glycine uptake. Deleting glyA in E. coli strain W3110 led to activation of CysB, which was related to novobiocin (NOV) susceptibility. Moreover, deleting glyA resulted in increased sensitivity to NOV, and this could be reversed by high concentrations of glycine. Reverse mutants of ΔglyA were selected and one of them had a mutation in yrdC, the gene encoding threonylcarbamoyl-AMP synthase. Subsequent proteome analysis showed that deleting glyA led to increased expression of TcyP and TdcB, making this bacterium dependent on CycA for glycine assimilation. Furthermore, deleting cycA in a ΔglyA background caused a severe growth defect on Luria-Bertani medium, which could be complemented by high concentrations of exogenous glycine. Mutation of yrdC led to decreased expression of TdcB but increased expression of ThrA/B/C and LtaE, which favored the conversion of threonine to glycine and thus avoided the dependence on CycA. Correspondingly, deleting of tcyP, tdcB, or gshA could reverse the NOV-sensitive phenotype of ΔglyA mutants. Overexpression of cycA resulted in increased sensitivity to NOV, whereas deleting this gene caused NOV resistance. Moreover, overexpression of cycA led to increased accumulation of NOV upon drug treatment. Therefore, inactivation of glyA in E. coli led to CycA-dependent glycine assimilation, which enhanced the accumulation of NOV and then made the bacterium more sensitive to this drug. These findings broaden our understanding of glycine metabolism and mechanisms of NOV susceptibility. IMPORTANCE Novobiocin (NOV) has been used in clinical practice as an ATPase inhibitor for decades. However, because it has been withdrawn from the market, pharmaceutical companies are searching for other ATPase inhibitors. Thus, probing the mechanisms of susceptibility to NOV will be beneficial to those efforts. In this study, we showed that inactivation of glyA in E. coli led to CycA-dependent glycine assimilation, which accompanied the accumulation of NOV and thereby increased the sensitivity to this drug. To date, this is the first report demonstrating the linkage between glycine assimilation and NOV susceptibility, and it is also the first report showing that YrdC is able to modulate the metabolic flux of threonine.

A magnetic solid-phase extraction technique based on magnetic dendritic structured nanoparticles (Fe3O4@SiO2-NH2-G5) as adsorbent coupled with ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) has been developed to detect diazepam, midazolam, zolpidem, and zaleplon in human urine. With Fe3O4@SiO2-NH2 as the central core, dendrimer (G5) grafted alternately with cyanuric chloride and imidazole were bonded to the surface of the core to synthesize Fe3O4@SiO2-NH2-G5. The morphology and structure of the magnetic materials were characterized by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), vibrating sample magnetometer (VSM), and dynamic light scattering (DLS). The key parameters affecting the extraction efficiency were optimized. A satisfactory performance was obtained under the optimum extraction conditions. The proposed method was validated, and the limits of detection of zaleplon, diazepam, zolpidem, and midazolam were 0.05, 0.05, 0.02, and 0.02 ng mL−1, respectively. The linear correlation coefficients r of the four analytes were > 0.996, the intra-day precision was between 2.4 % and 9.4 % with the recoveries between 88.3 % and 104.8 %, and the inter-day precision ranged from 3.9 % to 15.2 % with the recovery in the range of 94.1 %−108.3 %. The magnetic dendritic structure nanomaterial Fe3O4@SiO2-NH2-G5 was successfully used to extract sedative-hypnotic drugs from human urine samples. The Fe3O4@SiO2-NH2-G5-based magnetic solid-phase extraction method eliminates centrifugation and filtration steps as in conventional extraction. Only one step of vortex dispersion extraction could achieve the separation and purification of the target compounds. The proposed method was simple, rapid, environment-friendly, and suitable for the analysis of sedative-hypnotic drugs in human urine.

CTX-M extended spectrum beta-lactamase-producing Escherichia coli cause severe health hazards in livestock breeding. To date, little is known about antibiotic resistance differences among bacterial isolates from yaks, cows, and beef cattle; therefore, the aims of this study were to analyse the prevalence of CTX-M-producing E. coli in yak, beef cattle, and dairy cattle feces from different provinces in China. A total of 790 fecal samples from yaks, beef cattle, and dairy cows were used. Among all the samples, 523 non duplicate E. coli isolates were identified, and 29.6% of samples harbored CTX-M producers. The results showed that these E. coli strains harbored 15 clusters of CTX-M genes: CTX-M-79, CTX-M-55, CTX-M-15, CTX-M-14, CTX-M-28, CTX-M-179, CTX-M-65, CTX-M-24, CTX-M-27, CTX-M-102, CTX-M-105, CTX-M-173, CTX-M-238, CTX-M-196, and CTX-M-10. The dominant resistance genes were CTX-M-15, CTX-M-14, and CTX-M-55. Moreover, the distribution of CTX-M genes was related to geographical region. Based on the above findings, we reasoned that bovines are potential reservoirs of antibiotic resistance, and this problem should be given adequate attention.

The traditional combination forecasting model has good forecasting effect, but it needs precise historical data. In fact, many random events are uncertain, and much of the data are imprecise; sometimes, historical data are lacking. We need to study combination forecasting problems by means of uncertainty theory. Uncertain least squares estimation is an important technique of uncertain statistics, an important way to deal with imprecise data, and one of the best methods to solve the unknown parameters of uncertain linear regression equations. On the basis of the traditional combination forecasting method and uncertain least squares estimation, this paper proposes two kinds of uncertain combination forecasting models, which are the unary uncertain linear combination forecasting model and the uncertain relative error combination forecasting model, respectively. We set up several piecewise linear regression models according to the data of different periods and, according to certain weights, These piecewise linear regression models are combined into a unary uncertain linear combination forecasting model with a better forecasting effect. The uncertain relative error combination forecasting model is a new forecasting model that combines the traditional relative error linear forecasting model and the uncertain least squares estimation. Compared with the traditional forecasting model, the model can better deal with the forecasting problem of imprecise data. We verify the feasibility of the uncertain combination forecasting model through a numerical example. According to the data analysis, compared with the existing model, the forecasting effect of the proposed model is better.

Abstract Background DELLAs play key roles in plant gibberellin signaling pathways and are generally important in plant development and growth. However, DELLAs in many plant taxa have not yet been systematically analyzed. Results In our study, we searched for DELLA genes across 58 green plant genomes and found 181 DELLAs. Structure analysis showed some DELLA domains do not contain “D-E-L-L-A” sequences and instead contain similar domains, including DGLLA and DSLLH domains. “VHYNP” motifs in plant DELLAs comprise 23 types of sequences, while some DELLAs did not contain GRAS domains. In grape, we found that the DELLA protein GSVIVT01015465001 contains an F-box domain, while apple DELLA proteins MDP0000220512 and MDP0000403162 contain a WW domain and a BCIP domain, respectively. These DELLAs can be divided into 22 homologous groups and 17 orthologous groups, and 35 paralogous genes were identified. In total, 35 positively selected genes (PSGs) and 121 negatively selected genes (NSGs) were found among DELLAs based on selective pressure analysis, with an average K s of NSGs that was significantly higher than that of PSGs (P

Abstract Background LncRNAs are key regulators in cancer. The current study explored the role of lncRNA LINC00261 (LINC00261) in lung cancer (LC). Methods Expression of LINC00261 in LC tissues and cells was determined by quantitative real-time polymerase chain reaction (qRT-PCR). Pearson’s Chi square test and Kaplan–Meier analysis were performed to evaluate the correlations between LINC00261 expression and clinical characteristics, and overall survival time. A549 and SPC-A1 cells were transfected with LINC00261 overexpression plasmid, cell viability, cell number, and apoptosis were detected by CCK-8 assay, colony formation, and flow cytometry. Moreover, wound-healing and transwell assay were performed to detect cell metastasis and invasion. Expressions of proteins related to cell proliferation and metastasis were determined by Western blot. Xenograft was constructed, and tumor size and weight were measured and the effects of LINC00261 overexpression on tumor growth were detected. Bioinformatics analysis, dual-luciferase reporter assay, qRT-PCR, correlation analysis, and functional rescue experiments were conducted on clinical cases and LC cells to explore the molecular mechanism of LINC00261 in LC. Results In LC, LINC00261 expression was down-regulated, and was associated with more advanced TNM stage, metastasis and a shorter survival time. LINC00261 overexpression inhibited the growth and metastasis of LC cells in vitro and tumor growth in vivo. Furthermore, miR-1269a directly interacted with LINC00261 and FOXO1. The expressions of miR-1269a and FOXO1 were dysregulated by LINC00261 in LC. Additionally, miR-1269a promoted the progression of LC through targeting FOXO1. Conclusions Down-regulation of LINC00261 expression has a prognostic value in LC, and overexpression LINC00261 inhibits LC progression via targeting miR-1269a/FOXO1 axis.

Efficient extractions of trace antibiotic residues in the environment are a key factor for accurate quantification of the residues. A new nanoporous material, namely, magnetically covalent organic polymer (MCOP, Fe3O4@SiO2-NH2-COP) was synthesized in this work and was used for magnetic solid-phase extraction (MSPE). The combination of MSPE with high-performance liquid chromatography separation together with ultraviolet detection (HPLC-UV) was established as an effective method for the determination of four sulfonamide (SA) residues in surface water surrounding a cattle farm. The synthesized magnetic material was characterized by SEM, TEM, FT-IR, magnetic properties measurement system (MPMS), and nitrogen gas porosimetry. The material possessed many attractive features, such as a unique microporous structure, a larger specific surface area (137.93 m2·g−1) than bare Fe3O4 (24.84 m2·g−1), high saturation magnetization (50.5 emu·g−1), open adsorption sites, and high stability. The influencing parameters, including pH, the used amount of MCOPs, the type of eluent, adsorption solution, and desorption time, were optimized. Under the optimized conditions, the method conferred good linearity ranges (R2 ≥ 0.9990), low detection limits (S/N = 3, LOD, 0.10–0.25 μg·L−1), and satisfactory recoveries (79.7% to 92.2%). The enrichment factor (EF) for the four SAs was 34.13–38.86. The relative standard deviations of intraday (n = 5) and of interday (n = 3) were less than 4.8% and 8.9%, respectively. The equilibria between extraction and desorption for SAs could be reached within 150 s. The proposed method was sensitive and convenient for detecting SA residues in complex environmental matrices, and the successful application of the new MCOPs as an adsorbent was demonstrated.

Sexual maturation provides economically important traits in poultry production. Research on the initiation mechanism of sexual maturity is of great significance for breeding high-yield laying hens. However, the underlying mechanisms are not fully clear. Here, one hundred and fifty Chahua No. 2 laying hens (the CH2 group, which has precocious puberty) and one hundred and fifty Wu Liang Shan black-bone laying hens (the WLS group, a late-maturing chicken breed) with similar weights and ages were randomly selected. ELISA was used to determine the secretion levels of luteinizing hormone (LH), estradiol (E2), and progesterone (P4) in 150-day-old serum and small yellow follicle (SYF) tissues. A histology examination, immunohistochemistry, and quantitative real-time PCR (qPCR) were used to explore the molecular mechanism of how some genes related to oxidative stress affect sexual maturation. The results showed that the secretion levels of LH, E2, and P4 in the CH2 group serum and SYF were higher than those in the WLS group. The results of the real-time PCR of all genes showed that the expression levels of cytochrome P450 family 11 subfamily A member 1, steroidogenic acute regulatory protein, follicle-stimulating hormone receptor, and cytochrome P450 family 19 subfamily A member 1 in the CH2 group were significantly higher than those in the WLS groups (p < 0.001). Untargeted metabolomics combined with multivariate statistical analysis was used to identify biomarkers of SYF tissues in the CH2 and WLS groups. A trajectory analysis of the principal component analysis (PCA) results showed that the samples within the group were clustered and that the samples were dispersed between the CH2 and the WLS groups, indicating that the results of the measured data were reliable and could be used for further research. Further analysis showed that a total of 319 metabolites in small yellow follicles of the CH2 and WLS groups were identified, among which 54 downregulated differential metabolites were identified. These 54 metabolites were found as potential CH2 biomarkers compared with WLS at 150 days, and the different expressions of L-arginine, L-prolinamide, (R)-4-hydroxymandelate, glutathione, and homovanillic acid were more significant. Twenty metabolic pathways were found when significantly differential metabolites were queried in the KEGG database. According to the impact values of the metabolic pathways, eighteen differential metabolites belonged to the mTOR signaling pathway, glutathione metabolism, ABC transporters, the cell ferroptosis pathway, and D-arginine and D-ornithine metabolism. Interestingly, we identified that the cell ferroptosis pathway played an important role in chicken follicle selection for the first time. The histology and immunohistochemistry of SYF showed that the number of granulosa cells increased in the CH2 groups and the expression levels of glutathione peroxidase 4, tumor protein p53, ribosomal protein S6 kinase, and sterol regulatory element binding protein 1 in the granulosa cell layer were upregulated in the CH2 group at the time of sexual maturation. Furthermore, we also speculated that the antioxidant system may play an indispensable role in regulating sexual maturity in chickens. Overall, our findings suggest differentially expressed metabolites and metabolic pathways between CH2 and WLS chickens, providing new insights into the initiation mechanism of sexual maturation.

To obtain high-quality yellow peach wines of varying characteristics, different fermentation strategies, including various pre-fermentative treatments, were applied. This study aimed to determine the effect of different fermentation strategies on the physicochemical properties, monomer phenol content, in vitro antioxidant activity, and volatile compounds of yellow peach wine. The results showed that peach wine P12, fermented with pulp, had higher total phenolic content (TPC), total flavonoid content (TFC), monomer phenol and volatile compound content, and antioxidant activity. Ten monomeric phenols were detected in peach wines, and the content of catechin was the highest. Juice fermentation wine, J12, had a strong floral aroma, and some volatile compounds were released during fermentation when water was added to the pulp, and low alcohol content did not reduce the variety of volatile compounds. The main aroma and common characteristics of the fermented yellow peach wine were dominated by esters, with a relative odor activity value (ROAV) ≥ 1, namely, isoamyl acetate, ethyl hexanoate, and ethyl octanoate. Our results demonstrated that the application of the described fermentation strategies significantly affected the quality and volatile compound content of yellow peach wines. This might assist in the development of a highly diverse yellow peach wine flavor.

Herein, we report that Shroom3 knockdown, via Fyn inhibition, induced albuminuria with foot process effacement (FPE) without focal segmental glomerulosclerosis (FSGS) or podocytopenia. Interestingly, knockdown mice had reduced podocyte volumes. Human minimal change disease (MCD), where podocyte Fyn inactivation was reported, also showed lower glomerular volumes than FSGS. We hypothesized that lower glomerular volume prevented the progression to podocytopenia. To test this hypothesis, we utilized unilateral and 5/6th nephrectomy models in Shroom3-KD mice. Knockdown mice exhibited less glomerular and podocyte hypertrophy after nephrectomy. FYN-knockdown podocytes had similar reductions in podocyte volume, implying that Fyn was downstream of Shroom3. Using SHROOM3 or FYN knockdown, we confirmed reduced podocyte protein content, along with significantly increased phosphorylated AMPK, a negative regulator of anabolism. AMPK activation resulted from increased cytoplasmic redistribution of LKB1 in podocytes. Inhibition of AMPK abolished the reduction in glomerular volume and induced podocytopenia in mice with FPE, suggesting a protective role for AMPK activation. In agreement with this, treatment of glomerular injury models with AMPK activators restricted glomerular volume, podocytopenia, and progression to FSGS. Glomerular transcriptomes from MCD biopsies also showed significant enrichment of Fyn inactivation and Ampk activation versus FSGS glomeruli. In summary, we demonstrated the important role of AMPK in glomerular volume regulation and podocyte survival. Our data suggest that AMPK activation adaptively regulates glomerular volume to prevent podocytopenia in the context of podocyte injury.

This study primarily explored the internal mechanism underlying the ultrasonication-induced release of antioxidant peptides. An oxhide gelatin solution was treated ultrasonically (power = 200, 300, and 400 W), followed by enzymatic hydrolysis and structural and morphological analysis. The results showed that ultrasonication increased not only the degree of hydrolysis (DH) and protein recovery rate of the oxhide gelatin but also the ABTS radical scavenging, DPPH radical scavenging, ferrous chelating, and ferric reducing activities of its hydrolysate. The oxhide gelatin hydrolysate treated with 300-W ultrasonication had the maximum antioxidant activities. Ultrasonication inhibited hydrogen bond formation, reduced the crosslinking between collagen molecules, transformed part of the folded structure into a helical structure, and lowered the thermal stability of collagen molecules. The micromorphological analysis revealed that ultrasonication caused the gelatin surface to become loose and develop cracks, and as the power of the ultrasonication increased, the repetition interval distance (dÅ) also increased. Moreover, ultrasonication improved the solubilization, surface hydrophobicity, and interface characteristics and increased the content of basic and aromatic amino acids in the hydrolysate. In conclusion, ultrasonication modifies the protein structure, which increases the enzyme’s accessibility to the peptide bonds and further enhances antioxidant peptide release. These findings provide new insights into the application of ultrasonication in the release of antioxidant peptides.

After several decades of use, trimethoprim (TMP) remains one of the key access antimicrobial drugs listed by the World Health Organization. To circumvent the problem of trimethoprim resistance worldwide, a better understanding of drug-resistance mechanisms is required. In this study, we screened the single-gene knockout library of Escherichia coli, and identified mgrB and other several genes involved in trimethoprim resistance. Subsequent comparative transcriptional analysis between ΔmgrB and the wild-type strain showed that expression levels of phoP, phoQ, and folA were significantly upregulated in ΔmgrB. Further deleting phoP or phoQ could partially restore trimethoprim sensitivity to ΔmgrB, and co-overexpression of phoP/Q caused TMP resistance, suggesting the involvement of PhoP/Q in trimethoprim resistance. Correspondingly, MgrB and PhoP were shown to be able to modulated folA expression in vivo. After that, efforts were made to test if PhoP could directly modulate the expression of folA. Though phosphorylated PhoP could bind to the promotor region of folA in vitro, the former only provided a weak protection on the latter as shown by the DNA footprinting assay. In addition, deleting the deduced PhoP box in ΔmgrB could only slightly reverse the TMP resistance phenotype, suggesting that it is less likely for PhoP to directly modulate the transcription of folA. Taken together, our data suggested that, in E. coli, MgrB affects susceptibility to trimethoprim by modulating the expression of folA with the involvement of PhoP/Q. This work broadens our understanding of the regulation of folate metabolism and the mechanisms of TMP resistance in bacteria.

Abstract Background Chinese wild grapevine (Vitis amurensis) has remarkable cold stress tolerance, exceeding that of the common cultivated grapevine (Vitis vinifera L.). Result Here, we surveyed the expression dynamics of microRNAs (miRNAs) across Chinese wild grapevine (cv. Beibinghong) and cultivated grapevine (cv. Cabernet Sauvignon) under cold stress using high-throughput sequencing. We identified 186 known miRNAs in cultivated grape and 427 known miRNAs in Beibinghong. Of the identified miRNAs, 59 are conserved miRNAs orthologous in Cabernet Sauvignon and Beibinghong. In addition, 105 and 129 novel miRNAs were identified in Cabernet Sauvignon and Beibinghong, respectively. The expression of some miRNAs was related to cold stress both in Cabernet Sauvignon and Beibinghong. Many cold-related miRNAs in Cabernet Sauvignon and Beibinghong were predicted to target stress response-related genes such as MYB, WRKY, bHLH transcription factor genes, and heat shock protein genes. However, the expression tendency under cold treatment of many of these miRNAs was different between Cabernet Sauvignon and Beibinghong. Different modes of expression of cultivated and Chinese wild grape miRNAs were indicated in key pathways under cold stress by degradome, target prediction, GO, and KEGG analyses. Conclusion Our study indicated three likely reasons that led to the different cold stress tolerance levels of Cabernet Sauvignon and Beibinghong. Specifically, there may be (1) differential expression of orthologous miRNAs between cultivated grapevine and Chinese wild grape; (2) species-specific miRNAs or target genes; or (3) different regulatory models of miRNAs in cultivated and Chinese wild grape in some key pathways.

Molybdenum (Mo) is a component of the Mo cofactor (Moco) of nitrate reductase (NR) and is therefore essential for nitrate metabolism. However, little is known about Mo deficiency phenotypes or about how physiological and molecular mechanisms of Mo uptake and transport influence nitrate uptake and utilization in strawberry. Here, we used physiological and cytological techniques to identify Mo deficiency phenotypes in strawberry. Seedlings cultured with MoO42- grew well and exhibited normal microstructure and ultrastructure of leaves and roots. By contrast, seedlings cultivated under Mo-deficient conditions showed yellow leaf blades and ultrastructural changes such as irregular chloroplasts and unclear membrane structures that were similar to the symptoms of nitrogen deficiency. We cloned and analyzed a putative molybdate transporter, FaMOT1, which may encode a molybdate transporter involved in the uptake and translocation of molybdate. Interestingly, the addition of the molybdate analog tungstate led to lower tissue Mo concentrations, reduced the translocation of Mo from roots to shoots, and increased the plants’ sensitivity to Mo deficiency. Seedlings cultivated with MoO42- altered expression of genes in Moco biosynthesis. As expected, NR activity was higher under sufficient MoO42- levels. Furthermore, seedlings grown on Mo-deficient medium exhibited decreased 15NO3- translocation and lower 15NO3- use efficiency. These findings represent an important step towards understanding how molybdate transport, concentration, and deficiency symptoms influence nitrate uptake and utilization in strawberry.

As a polymicrobial disease, sour rot decreases grape berry yield and wine quality. The diversity of microbial communities in sour rot-affected grapes depends on the cultivation site, but the microbes responsible for this disease in eastern coastal China, has not been reported. To identify the microbes that cause sour grape rot in this important grape-producing region, the diversity and abundance of bacteria and fungi were assessed by metagenomic analysis and cultivation-dependent techniques. A total of 15 bacteria and 10 fungi were isolated from sour rot-affected grapes. High-throughput sequencing of PCR-amplicons generated from diseased grapes revealed 1343 OTUs of bacteria and 1038 OTUs of fungi. Proteobacteria and Firmicutes were dominant phyla among the 19 bacterial phyla identified. Ascomycota was the dominant fungal phylum and the fungi Issatchenkia terricola, Colletotrichum viniferum, Hanseniaspora vineae, Saprochaete gigas, and Candida diversa represented the vast majority ofmicrobial species associated with sour rot-affected grapes. An in vitro spoilage assay confirmed that four of the isolated bacteria strains (two Cronobacter species, Serratia marcescens and Lysinibacillus fusiformis) and five of the isolated fungi strains (three Aspergillus species, Alternaria tenuissima, and Fusarium proliferatum) spoiled grapes. These microorganisms, which appear responsible for spoiling grapes in eastern China, appear closely related to microbes that cause this plant disease around the world.

The cross-sectional geometry of a rail is complex, and numerous guided wave modes can be propagated in rails. In order to select a mode which is the most suitable for detecting a specific crack on a rail, a mathematical model of guided wave mode selection is constructed. The model is composed of a modal vibration factor and a modal orthogonal factor. By setting a reasonable vibration coefficient and orthogonal coefficient, the mode with the highest sensitivity to cracks is selected for crack detection. Taking a vertical crack on the rail bottom as an example, mode 1 at a frequency of 60 kHz is selected as the most suitable detection mode. At the same time, mode 7 and mode 11 are selected as comparative modes, and these three modes are simulated to detect rail cracks. Among them, mode 1 is the best, which verifies the correctness of the mode selection model. In addition, vertical cracks are manufactured artificially on the side of the rail bottom. The cracks are successfully detected by mode 1, and the positioning error is 0.07 m. After correction, the error is reduced to 0.02 m. The model can effectively select guided wave modes suitable for detecting arbitrary cracks on rails, which provides a theoretical solution for rail crack detection.

Background: Antibiotics releasing from the manufacturing sites to the surrounding environment has been identified as a risk factor for the development of antibiotic resistance of bacterial pathogens. However, the knowledge of the abundance and distribution of antibiotic resistance genes (ARGs) influenced by antibiotic pollution is still limited. Methods: In this work, the contamination by resistance genes of the environmental media including an urban river and soil along the river located near the sewage outlet of a veterinary antibiotic manufacturing site in Shijiazhuang, China, was assessed. The abundance and dynamic distribution of ARGs in different sampling points and during different seasons were analyzed using fluorescent quantitative PCR method (qPCR). Results: A total of 11 resistance genes, one integron and one transposon were detected in water and soils around the pharmaceutical factory, and among which, the sulfonamide resistance genes sul1 and β-lactam resistance genes blaSHV were the most abundant genes. The relative abundance of ARGs in both river water and soil samples collected at the downstream of the sewage outlet was higher than that of samples collected at the upstream, non-polluted areas (p < 0.05). The mobile genetic elements (MGEs) integron in river was significantly correlated (p < 0.05) with the relative abundance of ARGs. Conclusions: The results indicate that the discharge of waste from antibiotic manufacturing site may pose a risk of horizontal transfer of ARGs.

Taint in grapes and wine following vineyard exposure to bushfire smoke continues to challenge the financial viability of grape and wine producers worldwide. In response, researchers are studying the chemical, sensory and physiological consequences of grapevine smoke exposure. However, studies involving winemaking trials are often limited by the availability of suitable quantities of smoke-affected grapes, either from vineyards exposed to smoke or from field trials involving the application of smoke to grapevines. This study compared the accumulation of volatile phenol glycosides (as compositional markers of smoke taint) in Viognier and Cabernet Sauvignon grapes exposed to smoke pre- vs. post-harvest, and found post-harvest smoke exposure of fruit gave similar levels of volatile phenol glycosides to fruit exposed to smoke pre-harvest. Furthermore, wines made from smoke-affected fruit contained similar levels of smoke-derived volatile phenols and their glycosides, irrespective of whether smoke exposure occurred pre- vs. post-harvest. Post-harvest smoke exposure therefore provides a valid approach to generating smoke-affected grapes in the quantities needed for winemaking trials and/or trials that employ both chemical and sensory analysis of wine.

Rail corrugation often occurs on the high-speed railway, which will affect ride comfort and even the train operation safety in severe condition. Detection of rail corrugation wavelength and depth is absolutely essential for maintenance and safety. A novel method using wheel vibration acceleration is proposed in this paper, in which ensemble empirical mode decomposition (EEMD) is employed to estimate the wavelength, and bispectrum features are extracted to recognize the depth with support vector machine (SVM). Firstly, a vehicle-track coupling model considering the rail corrugation of high-speed railway is established to calculate the wheel vibration acceleration. Secondly, the estimation algorithm of wavelength is studied by analyzing the main frequency with EEMD. The optimal parameters of EEMD are selected according to the orthogonal coefficient of decomposition results and the distribution of the extreme points of signal. The depth detection is transformed to a classification problem with SVM. Bispectrum features, which are extracted from the reconstructed signal using the high-frequency components of wheel vibration acceleration, combining with train speed and corrugation wavelength are input into SVM to recognize the rail corrugation depth. Finally, numerical simulation is carried out to verify the accuracy of the proposed estimation method. The simulation results show that the proposed detection algorithm can accurately identify rail corrugation, the estimation error of rail corrugation wavelength is less than 0.25%, and the classification accuracy of rail corrugation depth is more than 99%.

Cement asphalt mortar (CA) disengagement of a ballastless track will induce changes of dynamic response of a passing vehicle, which can accordingly be used to estimate the disengagement degree. In this paper, a novel method called CA mortar disengagement degree estimation algorithm (CMDEA) is proposed through an analysis of wheel acceleration of a passing vehicle. The disengagement degree estimation is transformed into an optimization problem by regarding the CA mortar disengagement degree as a parameter of a vehicle-track coupling model. An improved genetic algorithm with a shifting window is employed for the parameter optimization, which is split into a number of phases and whose initial values are given in terms of a priori probabilities. The accuracy and robustness of the estimation are discussed, and the results are compared with regular genetic algorithm. The simulation results show that CMDEA can estimate CA mortar degrees with an acceptable accuracy. Meanwhile, the proposed algorithm has the advantages of a lower error value and much shorter computation time. Moreover, the robustness of the algorithm under different vehicle speeds, track irregularities, and signal noise levels is also verified.

Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

Amblyopia results from inadequate visual experience during the critical period of visual development. Abnormal binocular interactions are believed to play a critical role in amblyopia. These binocular deficits can often be resolved, owing to the residual visual plasticity in amblyopes. In this study, we quantitatively measured the sensory eye dominance in treated anisometropic amblyopes to determine whether they had fully recovered. Fourteen treated anisometropic amblyopes with normal or corrected to normal visual acuity participated, and their sensory eye dominance was assessed by using a binocular phase combination paradigm. We found that the two eyes were unequal in binocular combination in most (11 out of 14) of our treated anisometropic amblyopes, but none of the controls. We concluded that the treated anisometropic amblyopes, even those with a normal range of visual acuity, exhibited abnormal binocular processing. Our results thus suggest that there is potential for improvement in treated anisometropic amblyopes that may further enhance their binocular visual functioning.

Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate foreign object intrusion detection is particularly important. As a result of the lack of intruding foreign object samples during the operational period, artificially generated ones will greatly benefit the development of the detection methods. In this paper, we propose a novel method to generate railway intruding object images based on an improved conditional deep convolutional generative adversarial network (C-DCGAN). It consists of a generator and multi-scale discriminators. Loss function is also improved so as to generate samples with a high quality and authenticity. The generator is extracted in order to generate foreign object images from input semantic labels. We synthesize the generated objects to the railway scene. To make the generated objects more similar to real objects, on scale in different positions of a railway scene, a scale estimation algorithm based on the gauge constant is proposed. The experimental results on the railway intruding object dataset show that the proposed C-DCGAN model outperforms several state-of-the-art methods and achieves a higher quality (the pixel-wise accuracy, mean intersection-over-union (mIoU), and mean average precision (mAP) are 80.46%, 0.65, and 0.69, respectively) and diversity (the Fréchet-Inception Distance (FID) score is 26.87) of generated samples. The mIoU of the real-generated pedestrian pairs reaches 0.85, and indicates a higher scale of accuracy for the generated intruding objects in the railway scene.

A solid phase membrane adsorbent—a nylon 6 nanofibers membrane coated by polypyrrole (PPy-PA6-NFsM)—was firstly synthesized and used for extraction of two β-lactam antibiotics (oxacillin and cloxacillin) in urban river water. Then the analytes were detected by capillary electrophoresis with a diode array detector (CE-DAD). The synthesized nanofibers membrane was characterized by scanning electron microscopy and a Fourier transform infrared spectrometer. The experimental conditions were optimized, including the amount used of PPy-PA6-NFsM, pH of the sample solutions, adsorption volume, and desorption conditions. Under the optimal extraction and separation conditions, the detection limits were found to be 2.0 ng/mL for both oxacillin and cloxacillin. The proposed method was applied to the determination of the two β-lactams in water samples of an urban river. The recoveries of these two β-lactams were found to be in the range 84.2−96.4%, demonstrating that PPy-PA6-NFsM has a high extraction capability for these two antibiotics. The relative standard deviations, ranging from 2.26% to 5.29% for intraday measurements and from 2.38% to 7.02% for inter-day determinations, were derived respectively.

The cross-section of a rail has a complex geometry, and there are many propagating modes of ultrasonic guided waves in a rail. The analysis of mode shapes or the cross-sectional wave structure is of high significance to the design of an appropriate wave excitation approach for long-range defect detection of a rail. Traditionally, the semi-analytical finite elements (SAFE) method is used to obtain ultrasonic guided waves’ dispersion curves of a rail. Then, through solving the eigenvectors, it is able to calculate the displacement values of discrete nodes in three degrees of freedom (DOFs) and further obtain the wave structures. In this paper, a graphical analysis method of guided wave mode shapes is proposed. The displacements of each node in three DOFs are converted into Red Green Blue (RGB) image pixels, and the complex vibration vector data is expressed by an image. Therefore, the graphical analysis of mode shapes can be realized by using conventional image processing methods without the design of special data processing algorithms. This will improve the processing efficiency, and it is more intuitive and easier to analyze the vibration displacements represented by the image. The simulation results show that the proposed graphical analysis method can quickly and precisely locate the excitation position of the guided wave mode in the rail. By adopting image processing methods, such as the K-means clustering algorithm, the guided wave modes at a 35 kHz frequency in a rail are classified according to their mode shapes. Classification is essential for exploring the relations and fundamentals of vibrations in modes. The graphical analysis method proposed in this paper provides a novel method for the mode analysis of guided waves in rails.

This paper proposes a rail defect location method based on a single mode extraction algorithm (SMEA) of ultrasonic guided waves. Simulation analysis and verification were conducted. The dispersion curves of a CHN60 rail were obtained using the semi-analytical finite element method, and the modal data of the guided waves were determined. According to the inverse transformation of the excitation response algorithm, modal identification under low-frequency and high-frequency excitation was realized, and the vibration displacements at other positions of a rail were successfully predicted. Furthermore, an SMEA for guided waves is proposed, through which the single extraction results of four modes were successfully obtained when the rail was excited along different excitation directions at a frequency of 200 Hz. In addition, the SMEA was applied to defect location detection, and the single reflection mode waveform of the defect was extracted. Based on the group velocity of the mode and its propagation time, the distance between the defect and the excitation point was measured, and the defect location was predicted as a result. Moreover, the SMEA was applied to locate the railhead defect. The detection mode, the frequency, and the excitation method Were selected through the dispersion curves and modal identification results, and a series of signals of the sampling nodes were obtained using the three-dimensional finite element software ANSYS. The distance between the defect and the excitation point was calculated using the SMEA result. When compared with the structure of the simulated model, the errors obtained were all less than 0.5 m, proving the efficacy of this method in precisely locating rail defects, thus providing an innovated solution for rail defect location.

Different guided wave mode has different sensitivity to the defects of rail head, rail web and rail base in the detection of rail defects using ultrasonic guided wave. A novel guided wave mode selection and excitation method is proposed, which is effective for detection and positioning of the three parts of rail defects. Firstly, the mode shape data in a CHN60 rail is obtained at the frequency of 35 kHz based on SAFE method. The guided wave modes are selected, combining the strain energy distribution diagrams with the phase velocity dispersion curves of modes, which are sensitive to the defects of the rail head, rail web and rail base. Then, the optimal excitation direction and excitation node of the modes are calculated with the mode shape matrix. Phase control and time delay technology are employed to achieve the expected modes enhancement and interferential modes suppression. Finally, ANSYS is used to excite the specific modes and detect defects in different rail parts to validate the proposed methods. The results show that the expected modes are well acquired. The selected specific modes are sensitive to the defects of different positions and the positioning error is small enough for the maintenance staff to accept.

Veterinary drug residues, particularly traces of β2-agonists, can cause various kinds of harmful impact to the environment and public health. Here, a sensitive chemiluminescence (CL) method incorporated with a flow injection analysis is developed for the determination of two β2-agonists [i.e., salbutamol (SAL) and terbutaline (TEB)]. The system is based on the CL reaction of Ni(IV) complex with luminol in alkaline solutions, whereas SAL and TEB can significantly enhance CL intensities. Under optimum conditions, CL intensities are proportional to the SAL and TEB concentration in the range of 1.0 × 10−9 M to 5.0 × 10−7 M and 1.0 × 10−9 M to 1.0 × 10−7 M, respectively. The limits of detection (3σ) are 1.0 × 10−11 M for TEB, and 1.3 × 10−11 for SAL respectively. Relative standard deviations (n = 11) are less than 2% for 5.0 × 10−8 M SAL and TEB. Possible reaction mechanisms for the CL system are suggested based on the CL system spectra, Ni(IV) complex oxidation characteristics, and electron spin resonance (ESR) techniques. The proposed method has been applied to the analysis of urine and swine feed samples with satisfactory results.