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Abstract Background The rumen is a crucial digestive organ for dairy cows. The rumen microbiota assists in the digestion of plant feed through microbe-mediated fermentation, during which the plant feed is transformed into nutrients for the cow's use. Variations in the composition and function of the rumen microbiome affect the energy utilization efficiency of dairy cows, which is one of the reasons for the varying body condition scores (BCSs). This study focused on prepartum Holstein dairy cows to analyze differences in rumen microbiota and metabolites among cows with different BCSs. Twelve prepartum dairy cows were divided into two groups, low BCS (LBCS, BCS = 2.75, n = 6) and high BCS (HBCS, BCS = 3.5, n = 6), to explore differences in microbial composition and metabolites. Results In the HBCS group, the genera within the phylum Firmicutes exhibited stronger correlations and greater abundances. Phyla such as Firmicutes, Patescibacteria, Acidobacteriota, Euryarchaeota, and Desulfobacterota, in addition to most of their constituent microbial groups, were significantly more abundant in the HBCS group than in the LBCS group. At the genus level, the abundances of Anaerovibrio, Veillonellaceae_UCG_001, Ruminococcus_gauvreauii_group, Blautia, Eubacterium, Prevotellaceae_YAB2003_group, Schwartzia, and Halomonas significantly increased in the HBCS group. The citrate cycle, involved in carbohydrate metabolism, exhibited a significant enrichment trend, with a notable increase in the abundance of its key substrate, citrate, in the HBCS group. This increase was significantly positively correlated with the differential bacterial genera. Conclusion In this study, prepartum dairy cows with higher BCS exhibited greater abundance of Firmicutes. This study provides theoretical support for microbiological research on dairy cows with different BCSs and suggests that regulating the rumen microbiome could help maintain prepartum dairy cows within an optimal BCS range.

Abstract Background Increased level of serum cholic acid (CA) is often accompanied with decreased CYP2E1 expression in hepatocellular carcinoma (HCC) patients. However, the roles of CA and CYP2E1 in hepatocarcinogenesis have not been elucidated. This study aimed to investigate the roles and the underlying mechanisms of CYP2E1 and CA in HCC cell growth. Methods The proteomic analysis of liver tumors from DEN-induced male SD rats with CA administration was used to reveal the changes of protein expression in the CA treated group. The growth of CA-treated HCC cells was examined by colony formation assays. Autophagic flux was assessed with immunofluorescence and confocal microscopy. Western blot analysis was used to examine the expression of CYP2E1, mTOR, AKT, p62, and LC3II/I. A xenograft tumor model in nude mice was used to examine the role of CYP2E1 in CA-induced hepatocellular carcinogenesis. The samples from HCC patients were used to evaluate the clinical value of CYP2E1 expression. Results CA treatment significantly increased the growth of HCC cells and promoted xenograft tumors accompanied by a decrease of CYP2E1 expression. Further studies revealed that both in vitro and in vivo, upregulated CYP2E1 expression inhibited the growth of HCC cells, blocked autophagic flux, decreased AKT phosphorylation, and increased mTOR phosphorylation. CYP2E1 was involved in CA-activated autophagy through the AKT/mTOR signaling. Finally, decreased CYP2E1 expression was observed in the tumor tissues of HCC patients and its expression level in tumors was negatively correlated with the serum level of total bile acids (TBA) and gamma-glutamyltransferase (GGT). Conclusions CYP2E1 downregulation contributes to CA-induced HCC development presumably through autophagy regulation. Thus, CYP2E1 may serve as a potential target for HCC drug development.

Abstract Aiming at the acid mine drainage (AMD) in zinc, copper and other heavy metals treatment difficulties, severe pollution of soil and water environment and other problems. Through the ultrasonic precipitation method, this study prepared fly ash-loaded nano-FeS composites (nFeS-F). The effects of nFeS-F dosage, pH, stirring rate, reaction time and initial concentration of the solution on the adsorption of Zn(II) and Cu(II) were investigated. The data were fitted by Lagergren first and second-order kinetic equations, Internal diffusion equation, Langmuir and Freundlich isotherm models, and combined with SEM, TEM, FTIR, TGA, and XPS assays to reveal the mechanism of nFeS-F adsorption of Zn(II) and Cu(II). The results demonstrated that: The removal of Zn(II) and Cu(II) by nFeS-F could reach 83.36% and 70.40%, respectively (The dosage was 8 g/L, pH was 4, time was 150 min, and concentration was 100 mg/L). The adsorption process, mainly chemical adsorption, conforms to the Lagergren second-order kinetic equation (R2 = 0.9952 and 0.9932). The adsorption isotherms have a higher fitting degree with the Langmuir model (R2 = 0.9964 and 0.9966), and the adsorption is a monolayer adsorption process. This study can provide a reference for treating heavy metals in acid mine drainage and resource utilization of fly ash.

Background: This study investigated the effects of purple sweet potato anthocyanins (PSPA) in a type 2 diabetes mellitus (T2DM) mouse model. Methods: Sixty-five male mice were randomly divided into one control group and four experimental groups, which were fed with a high-fat diet and intraperitoneally injected with streptozotocin (STZ) to induce T2DM. The model mice were treated with 0 (M), 227.5 (LP), 455 (MP), or 910 (HP) mg/kg PSPA for ten days. ELISA, 16S rRNA sequencing, and hematoxylin and eosin staining were used to assess blood biochemical parameters, gut microbial composition, and liver tissue structure, respectively. Results: The FBG concentration was significantly decreased in the LP (6.32 ± 1.05 mmol/L), MP (6.32 ± 1.05 mmol/L), and HP (5.65 ± 0.83 mmol/L) groups; the glycosylated hemoglobin levels were significantly decreased in the HP group (14.43 ± 7.12 pg/mL) compared with that in the M group (8.08 ± 1.04 mmol/L; 27.20 ± 7.72 pg/mL; P

Neuroscientists have recognized the importance of astrocytes in regulating neurological function and their influence on the release of glial transmitters. Few studies, however, have focused on the effects of general anesthetic agents on neuroglia or astrocytes. Astrocytes can also be an important target of general anesthetic agents as they exert not only sedative, analgesic, and amnesic effects but also mediate general anesthetic-induced neurotoxicity and postoperative cognitive dysfunction. Here, we analyzed recent advances in understanding the mechanism of general anesthetic agents on astrocytes, and found that exposure to general anesthetic agents will destroy the morphology and proliferation of astrocytes, in addition to acting on the receptors on their surface, which not only affect Ca2+ signaling, inhibit the release of brain-derived neurotrophic factor and lactate from astrocytes, but are even involved in the regulation of the pro- and anti-inflammatory processes of astrocytes. These would obviously affect the communication between astrocytes as well as between astrocytes and neighboring neurons, other neuroglia, and vascular cells. In this review, we summarize how general anesthetic agents act on neurons via astrocytes, and explore potential mechanisms of action of general anesthetic agents on the nervous system. We hope that this review will provide a new direction for mitigating the neurotoxicity of general anesthetic agents.

Abstract Background Prolonged length of stay in post-anesthesia care unit (PLOS in PACU) is a combination of risk factors and complications that can compromise quality of care and operating room efficiency. Our study aimed to develop a nomogram to predict PLOS in PACU of patients undergoing elective surgery. Methods Data from 24017 patients were collected. Least absolute shrinkage and selection operator (LASSO) was used to screen variables. A logistic regression model was built on variables determined by a combined method of forward selection and backward elimination. Nomogram was designed with the model. The nomogram performance was evaluated with the area under the receiver operating characteristic curve (AUC) for discrimination, calibration plot for consistency between predictions and actuality, and decision curve analysis (DCA) for clinical application value. Results A nomogram was established based on the selected ten variables, including age, BMI < 21 kg/m2, American society of Anesthesiologists Physical Status (ASA), surgery type, chill, delirium, pain, naloxone, operation duration and blood transfusion. The C-index value was 0.773 [95% confidence interval (CI) = 0.765 - 0.781] in the development set and 0.757 (95% CI = 0.744-0.770) in the validation set. The AUC was > 0.75 for the prediction of PLOS in PACU. The calibration curves revealed high consistencies between the predicted and actual probability. The DCA showed that if the threshold probability is over 10% , using the models to predict PLOS in PACU and implement intervention adds more benefit. Conclusions This study presented a nomogram to facilitate individualized prediction of PLOS in PACU for patients undergoing elective surgery.

In this study, a novel type of green and highly efficient Na-CDs corrosion inhibitor was successfully prepared using a hydrothermal method, and its properties were thoroughly characterized. The results revealed that the Na-CDs possessed a uniform nanoscale size distribution and a well-defined graphene lattice structure. The corrosion inhibition performance of Na-CDs at varying concentrations was assessed, and it was found that the corrosion inhibition efficiency initially increased with the increase of concentration, peaking at over 96 % when the concentration reached 150 mg/L, before subsequently declining. Analysis of the adsorption behavior of Na-CDs on the metal surface confirmed that the adsorption process followed the R-P adsorption isotherm, indicative of an almost ideal homogeneous monolayer adsorption. The corrosion inhibition mechanism is the result of forming a protective film on the metal surface by physical adsorption and chemical adsorption, with the bridging role of Cl- further reinforcing the structure of this film. However, at excessively high concentrations of Na-CDs, desorption occurred, compromising the integrity of the protective film. This study enriches the field of metal-doped carbon dots as corrosion inhibitors and provides insights into the synthesis of carbon dots and the development of carbon dot-based corrosion inhibitors.

The issue of environmental pollution has received considerable critical attention in recent years. It is necessary to develop the green and efficient corrosion inhibitors. In this paper, the inhibition of doxazosin mesylate (DM) and terazosin hydrochloride (TH), as green corrosion inhibitors, were explored systematically by electrochemical impedance, polarization curves, SEM, AFM and contact angle. The effect of different heterocyclic amounts on the corrosion inhibition efficiency for Q235 steel in 1 M HCl was also discussed. It was found that DM showed the better performance than TH, with the best corrosion inhibition efficiency of 96.4 % at 1 mg/L. The FTIR and XPS analysis of the corroded Q235 steel surface revealed that the two drugs formed adsorption films on the steel surface and the types of adsorption were chemical and physical adsorption. Finally, the mechanism of the two drugs was analyzed in detail by isothermal adsorption simulations, quantum chemical calculations and molecular dynamics simulations.

ABSTRACT Heat stress is one of the major stressors affecting dairy cow production, which causes great losses in animal husbandry production and economic development. This study combined 16S rDNA sequencing, metagenomic sequencing, and metabolomic analysis to investigate the effects of heat stress on the rumen microbiome and metabolism of cows. To achieve this, 10 cows each of growing heifers, heifers, and lactating cows were selected for sample collection in April and August (a total of 60 cows). Ruminal fluid was collected, filtered through gauze, and immediately transferred to liquid nitrogen prior to metagenomic, 16S rDNA sequencing, and metabolomic analyses. Heat stress increased the abundance of pathogenic bacteria, such as Treponema_2, Sphingobacterium, and Streptococcus, in cattle at all growth stages, with the structure and composition of rumen microbial communities being the most affected by heat stress in growing heifers and the least affected by heat stress in lactating cows. Heat stress led to a significant decrease in carbohydrate metabolic function in growing heifers and heifers, whereas microbial function was the most stable in lactating cows, suggesting that changes in microbial function are not only more conserved than microbial composition but are also affected by age. Heat stress led to significant enrichment of fatty acyl metabolites in growing heifers, heifers, and lactating cows, and the heat stress indicator species Bacillus, Lactococcus, Streptococcus, and Enterococcus were significantly associated with fatty acyl compounds in growing heifers and heifers. Therefore, heat stress alters the metabolic status of cows by modulating fatty acid biosynthesis, the PPAR signaling pathway, and arachidonic acid metabolism through the regulation of long-chain fatty acids and other biomarkers in cows at three growth stages. IMPORTANCE Heat stress is one of the main causes of economic losses in the dairy industry worldwide; however, the mechanisms associated with the metabolic and microbial changes in heat stress remain unclear. Here, we characterized both the changes in metabolites, rumen microbial communities, and their functional potential indices derived from rumen fluid and serum samples from cows at different growth stages and under different climates. This study highlights that the rumen microbe may be involved in the regulation of lipid metabolism by modulating the fatty acyl metabolites. Under heat stress, the changes in the metabolic status of growing heifers, heifers, and lactating cows were closely related to arachidonic acid metabolism, fatty acid biosynthesis, and energy metabolism. Moreover, this study provides new markers for further research to understand the effects of heat stress on the physiological metabolism of Holstein cows and the time-dependent changes associated with growth stages.

Introduction: Cadmium (Cd) is a hazardous environmental pollutant present in soil, water, and food. Accumulation of Cd in organisms can cause systematic injury and damage to the kidney. The Masson pine pollen aqueous extract (MPPAE) has attracted increasing attention due to its antioxidant activity and ability to enhance immunity.Methods: In this study, we investigated the potential of MPPAE to protect against Cd-induced kidney damage in rats and the underlying mechanism. The transcriptome and metabolome of rats with Cd-induced kidney damage, following treatment with MPPAE, were explored.Results: The concentrations of superoxide dismutase (SOD) and malondialdehyde (MDA) were both significantly altered after treatment with MPPAE. Furthermore, sequencing and analysis of the transcriptome and metabolome of rats with Cd-induced kidney damage, following treatment with MPPAE, revealed differential expression of numerous genes and metabolites compared with the untreated control rats. These differentially expressed genes (DEGs) included detoxification-related genes such as cytochrome P450 and the transporter. The differentially expressed metabolites (DEMs) included 4-hydroxybenzoic acid, L-ascorbate, and ciliatine. Conjoint transcriptome and metabolome analysis showed that several DEGs were correlated with DEMs.Conclusion: These preliminary findings indicate the potential of MPPAE for the treatment of toxic metal poisoning.

Four multi-catheterized lactating goats were used in a 4 × 4 Latin square experiment to investigate the responses of amino acid metabolism in portal-drained viscera (PDV), liver, and mammary glands to short-term varying supplies of methionine (Met). During the last 45 h in each experimental period, goats were fasted for 12 h and then abomasally infused with an amino acid (AA) mixture plus glucose for 33 h. Treatments consisted of graded removal of Met from an infused AA mixture to achieve Met content in the infusate of 100% (complete), 60%, 30%, or 0% that in casein. Graded Met removal decreased the production of milk, milk protein, lactose, and fat linearly whilst also decreasing arterial Met concentration linearly (P 0.1) was maintained as Met supply declined. This was achieved through increased mammary affinity (P

To investigate the effects of individual essential amino acids (EAA) on growth and the underlying mechanisms, EAA individually supplemented a low-protein (LP) diet fed to young rats in the present study. Treatments were an LP diet that contained 6% crude protein (CP), a high-protein (HP) diet that contained 18% CP, and 10 LP diets supplemented with individual EAA to achieve an EAA supply equal to that of the HP diet. The CP concentration of the LP diet was ascertained from the results of the first experiment, which examined the effects of dietary CP concentrations on growth rates, with CP ranging from 2% to 26%. Weight gain was increased with the supplementation of His, Ile, Lys, Thr, or Trp as compared to the LP diet (p < 0.05). Feed intake was greater for the His-, Lys-, and Thr-supplemented treatments as compared to the LP group (p < 0.05). Protein utilization efficiency was lower for the HP group than other groups (p < 0.01). The supplementation of Leu, Lys, and Val led to reduced protein utilization efficiency (p < 0.05), but the supplementation of Thr and Trp led to greater efficiency than the LP group (p < 0.05). Compared to the LP group, plasma urea concentrations were elevated with individual EAA supplementation, with the exception of the Thr addition. The added EAA resulted in increased concentrations of the corresponding EAA in plasma, except for Arg and Phe supplementation. The supplementation of Arg, His, Leu, Lys, and Met individually stimulated mTORC1 pathway activity (p < 0.05), and all EAA resulted in the decreased expression of ATF4 (p < 0.05). In summary, the supplementation of His, Ile, Lys, Thr, or Trp to an LP diet improved the growth performance of young rats. Responses to His and Lys additions were related to the activated mTORC1 pathway and feed intake increases. The improved growth performance resulting from the addition of a single EAA is not solely attributed to the increased plasma availability of EAA. Rather, it may be the consequence of a confluence of factors encompassing signaling pathways, the availability of amino acids, and other associated elements. The additivity of these factors results in independent responses to several EAA with no order of limitation, as is universally encoded in growth models for all production animal species.

The efficacy of credit risk assessment models is pivotal to the risk management capacity of financial institutions. Traditional credit risk models often suffer from inadequate predictive accuracy due to overlooked feature combinations and weak supervisory signals. Addressing these limitations, we present a novel approach for credit default prediction that integrates feature interactions and contrastive learning. Specifically, we introduce second-order interactions atop standard linear models to achieve low-order feature interplay. Concurrently, the integration of deep neural networks and attention mechanisms facilitates the learning of concealed high-order features, thus enhancing the model’s non-linear modeling capabilities and illuminating latent feature associations. Further, to ameliorate the issues of noise and diminished supervisory signals, we embed slight noise in feature embeddings for data augmentation and construct contrastive views, ultimately refining feature quality. To attest to the effectiveness of our approach, we conducted experiments on two real-world datasets, benchmarking against eight predictive methods including LR, XGBoost, and FiBiNET. The results unequivocally demonstrate the superior performance of our method across various metrics, underscoring its promise and excellence in the realm of credit risk assessment.

Traditional manual visual detection methods are inefficient, subjective, and costly, making them prone to false and missed detections. Deep-learning-based defect detection identifies the types of defects and pinpoints their locations. By employing this approach, we could enhance the production workflow, boost production efficiency, minimize company expenses, and lessen the workload on workers. In this paper, we propose a lightweight tile-defect detection algorithm that strikes a balance between model parameters and accuracy. Firstly, we introduced the mobile-friendly vision transformer into the backbone network to capture global and local information. This allowed the model to comprehend the image content better and enhance defect feature extraction. Secondly, we designed a lightweight feature fusion network. This design amplified the network’s detection capability for defects of different scales and mitigated the blurriness and redundancy in the feature maps while reducing the model’s parameter count. We then devised a convolution module incorporating the normalization-based attention module, to direct the model’s focus toward defect features. This reduced background noise and filtered out features irrelevant to defects. Finally, we utilized a bounding box regression loss with a dynamic focusing mechanism. This approach facilitated the prediction of more precise object bounding boxes, thereby improving the model’s convergence rate and detection precision. Experimental results demonstrated that the improved algorithm achieved a mean average precision of 71.9%, marking a 3.1% improvement over the original algorithm. Furthermore, there was a reduction of 26.2% in the model’s parameters and a 20.9% decrease in the number of calculations.

Measurement(s) Metabolite abundances in serum • Metabolite abundances in rumen fluid • taxonomic diversity assessment by targeted gene surveyin rumen fluid • metagenome in rumen fluid Technology Type(s) Liquid Chromatography Mass Spectrometry • next generation DNA sequencing • Next Generation Sequencing Factor Type(s) heat stress and growth stages Sample Characteristic - Organism Bos taurus Sample Characteristic - Environment growing heifers, heifers, and lactating cows were selected in April and August of Shandong Province

This paper outlines the methods, results, and statistical analysis of a model we developed to demonstrate the feasibility of applying remote sensor meteorological data to navigation by using meteorological contour matching (METCOM). Terrain contour matching (TERCOM), a contemporary navigation system, possesses inherent performance flaws that may be resolved and improved by METCOM for subsonic and hypersonic missile or aircraft navigation. Remote sensor imagery data for this model was accessed from the Geostationary Operational Environmental Satellites-R Series operated by the National Oceanic and Atmospheric Administration by using Amazon Web Services through a script we developed in Python. Data processed for the model included imagery data and corresponding geospatial data from the legacy atmospheric profile products: legacy vertical temperature and legacy vertical moisture. Our analysis of the model included an error assessment to determine model accuracy, geostatistical analysis through semivariograms, meteorological signal of model data, and a combinatorial analysis to evaluate navigation performance. We conducted a model assessment which indicated an accuracy of 66.2% in the data used as a combined result of instrument error and interference of cloud formations. Results of the remaining analysis offered methods to evaluate METCOM performance and compare different meteorological data products. These results allowed us to statistically compare METCOM and TERCOM, yielding several indications of improved performance including an increase by a factor of at least 13.5 in data variability and contourability. The analysis we conducted served as a proof of concept to justify further research into the feasibility and application of METCOM.

Measurement(s) metabolomic profiling Technology Type(s) Ultra High-performance Liquid Chromatography/Tandem Mass Spectrometry Factor Type(s) arginine • histidine • isoleucine • leucine • lysine • methionine • phenylalanine • threonine • tryptophan • valine Sample Characteristic - Organism Rattus norvegicus

At present, the development of efficient green corrosion inhibitors has become one of the important directions of metal corrosion protection. In this paper, biomass carbon quantum dots (BCQDs) were prepared by hydrothermal reaction using biomass precursors. The optical properties and structural composition of the synthesized BCQDs were characterized in detail. After that, the corrosion inhibition performance of BCQDs was evaluated by EIS, Tafel, SEM and TEM. The electrochemistry and surface tests demonstrate that the aggregates can effectively prevent Q235 steel from corrosion in 1 M HCl medium at 298 K-328 K, with the maximum inhibition efficiency of 94.1 % at the concentration of 200 mg/L at 308 K. Finally, the corrosion inhibition mechanism was analyzed by FTIR, CA and XPS on the surface of the corroded carbon steel samples. The inhibition mechanism suggests that BCQDs can inhibit metal corrosion by self-aggregation and adsorption on metal surface. This is owing to its small size effect and functional groups containing heteroatoms, easily forming dense protective film.

Diet and species are important factors affecting the rumen microbiota, with roughage stimulating rumen development and concentrate feeds being broken down by the decomposition of Ruminal flora to provide the organism with a large amount of energy. This study aimed to explore the effects of host and dietary factors on rumen flora composition and diversity, as well as on host metabolism. The study reports the research conducted on 5-month-old male Small-tail Han sheep and 5-month-old male Boer goat, each with an average weight of 33.87 ± 1.70 kg. Five animals of each species were divided into two groups, namely, the S group (Small-tail Han sheep) and the B group (Boer goat). The experiment was carried out in two various periods, namely, X and Y for groups S and B, respectively. The rations were fed with concentrate-to-roughage ratios of 3:7 and 5:5, respectively. Growth performance was measured by the weight increase index. The results showed that, under the same raising conditions, the ratio between body weight increases and the amount of feed was lower in the S group than in the B group, but the differences were not significant. According to the analysis of the apparent digestibility ratio of nutrition ingredients, the XS group had a significantly higher apparent digestibility ratio for acid detergent fiber than the XB group (p < 0.05). Even though the analysis of rumen fermentation parameters showed that the rumen pH has no significant differences between the XS and XB groups, it was significantly lower in the YS group than in the YB group. The XS group contained a significantly lower content of total volatile fatty acids than the XB group (p < 0.05). Analysis of the 16S rDNA sequencing results revealed that, compared to the B group, the S group was highly enriched with the following bacteria: Proteobacteria, γ-proteobacteria, Aeromonadales, and Succinivibrionaceae. Thus, the host species affected the abundance and diversity of rumen bacteria. Feed utilization efficiency of Small-tail Han sheep was higher than Boer goats, which might be specifically associated with Succinivibrionaceae. The results from this study show that animals belonging to the same family but different genera and species can differ in metabolic pathways even when they are provided with the same animal feed.

Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and load scheduling for power supply departments. Various complex and ever-changing factors such as weather, seasons, regional economic structures, and enterprise production cycles exert uncontrollable effects on the electric grid load. While the causal convolutional neural network can significantly enhance long-term sequence prediction, it may suffer from problems such as vanishing gradients and overfitting due to extended time series. To address this issue, this paper introduces a new power load data anomaly detection method, which leverages a convolutional neural network (CNN) to extract temporal and spatial information from the load data. The features extracted are then processed using a bidirectional long short-term memory network (BiLSTM) to capture the temporal dependencies in the data more adeptly. An enhanced random forest (RF) classifier is employed for anomaly detection in electric load data. Furthermore, the paper proposes a new model framework for electricity load forecasting that combines a dilated causal convolutional neural network with ensemble learning. This combination addresses issues such as vanishing gradients encountered in causal convolutional neural networks with long time series. Extreme gradient boosting (XGBoost), category boosting (CATBoost), and light gradient boosting machine (LightGBM) models act as the base learners for ensemble modeling to comprehend deep cross-features, and the prediction results generated by ensemble learning serve as a new feature set for secondary ensemble modeling. The dilated convolutional neural network broadens the receptive field of the convolutional kernel. All acquired feature values are concatenated and input into the dilated causal convolutional neural network for training, achieving short-to-medium-term electric load forecasting. Experimental results indicate that compared to existing models, its root mean squared error (RMSE) and mean squared error (MSE) in short-term and mid-term electricity load forecasting are reduced by 4.96% and 12.31%, respectively, underscoring the efficacy of the proposed framework.

Leak detection of an oil pipeline can prevent environmental and financial losses. A method for the cyber-physical system of pipeline leak detection is proposed based on the empirical mode decomposition (EMD) and deep belief network (DBN). Experiment data are acquired from an oil pipeline company. The EMD is suitable for noise removal and signal reconstruction from raw pressure signals, and the reconstructed signals are used to establish a DBN model of pipeline leakage. Our proposed method obtains higher-recognition-accuracy results (98% accuracy) and can more effectively identify leak detection than the twin support vector machine (TWSVM), support vector machine (SVM), and back-propagation neural network (BPNN).

Several epidemiological studies have suggested an association between low vitamin D status and increased risk for type 2 diabetes (T2D). This study aimed to explore the dose-response relationship of serum 25-hydroxyvitamin D [25(OH)D] concentrations with incident T2D and the interaction between serum 25(OH)D with individual factors on T2D risk. A total of 1,926 adults without diabetes (mean age: 52.08 ± 13.82 years; 42% men) were prospectively followed for 36 months. Cox proportional hazards model and restricted cubic spline analysis were performed to assess the association and dose-response relationship between serum 25(OH)D and T2D incidence. Both additive and multiplicative interactions were calculated between serum 25(OH)D and individual factors. The net reclassification index (NRI) was used to evaluate the improvement of risk prediction of T2D by adding serum 25(OH)D to traditional risk factors. There were 114 new T2D cases over a mean follow-up of 36 months. Serum 25(OH)D was not associated with T2D incidence, and no significant dose-response relationship was found in the total population. However, stratified analyses suggested a non-linear inverse relationship among individuals with baseline fasting plasma glucose (FPG)

The mechanistic target of rapamycin complex 1 (mTORC1) integrates various types of signal inputs, such as energy, growth factors, and amino acids to regulate cell growth and proliferation mainly through the 2 direct downstream targets, eukaryotic translation initiation factor 4E-binding protein 1 (4EBP1) and ribosomal protein S6 kinase 1 (S6K1). Most of the signal arms upstream of mTORC1 including energy status, stress signals, and growth factors converge on the tuberous sclerosis complex (TSC) − Ras homologue enriched in brain (Rheb) axis. Amino acids, however, are distinct from other signals and modulate mTORC1 using a unique pathway. In recent years, the transmission mechanism of amino acid signals upstream of mTORC1 has been gradually elucidated, and some sensors or signal transmission pathways for individual amino acids have also been discovered. With the help of these findings, we propose a general picture of recent advances, which demonstrates that various amino acids from lysosomes, cytoplasm, and Golgi are sensed by their respective sensors. These signals converge on mTORC1 and form a huge and complicated signal network with multiple synergies, antagonisms, and feedback mechanisms.

Grammar error correction (GEC) is a crucial task in the field of Natural Language Processing (NLP). Its objective is to automatically detect and rectify grammatical mistakes in sentences, which possesses immense application research value. Currently, mainstream grammar-correction methods primarily rely on sequence labeling and text generation, which are two kinds of end-to-end methods. These methods have shown exemplary performance in areas with low error density but often fail to deliver satisfactory results in high-error density situations where multiple errors exist in a single sentence. Consequently, these methods tend to overcorrect correct words, leading to a high rate of false positives. To address this issue, we researched the specific characteristics of the Chinese grammar error correction (CGEC) task in high-error density situations. We proposed a grammar-correction method based on prompt templates. Firstly, we proposed a strategy for constructing prompt templates suitable for CGEC. This strategy transforms the CGEC task into a masked fill-in-the-blank task compatible with the masked language model BERT. Secondly, we proposed a method for dynamically updating templates, which incorporates already corrected errors into the template through dynamic updates to improve the template quality. Moreover, we used the phonetic and graphical resemblance knowledge from the confusion set as guiding information. By combining this with BERT’s prediction results, the model can more accurately select the correct characters, significantly enhancing the accuracy of the model’s prediction correction results. Our methods were validated through experiments on a public grammar-correction dataset. The results indicate that our method achieves higher correction performance and lower false correction rates in high-error density scenarios.

Abstract The fatigue strength of a component is known to highly depend on its surface quality, and it is thus necessary to develop a reliable and appropriate mathematical model for fatigue strength assessment that consider the effect of surface roughness. In this paper, different underlying physical mechanisms of the roughness effect at different regions of specimens were studied by fatigue testing of 7N01 aluminum alloy. For a quantitative analysis of the surface roughness effect, a revised stress field intensity approach for a fatigue strength assessment of microsized notches was proposed as a theoretical support. In the new model, a new form of weight function was built to adapt the characteristics of microsized notches. In addition, the effect of the field radius was fundamentally weakened on solution of the stress field intensity and the difficulty of fatigue failure region definition in the traditional method was overcome correspondingly in the proposed model, which made the calculated field strength accurate and objective. Finally, to demonstrate the validity of the revised approach quantitatively, specimens with conventionally sized notches were subjected to stress field intensity calculations. The results showed that the revised approach has satisfactory accuracy compared with the other two traditional approaches from the perspective of quantitative analysis.

Research on COVID-19 has drawn the attention of scholars around the world since the outbreak of the pandemic. Several literature reviews of research topics and themes based on scientometric indicators or bibliometric analyses have already been conducted. However, topics and themes in linguistic-specific research on COVID-19 remain under-studied. With the help of the CiteSpace software, the present study reviewed linguistic research published in SSCI and A&HCI journals to address the identified gap in the literature. The overall performance of the documents was described and document co-citations, keyword co-occurrence, and keyword clusters were visualized via CiteSpace. The main topic areas identified in the reviewed studies ranged from the influences of COVID-19 on language education, and speech-language pathology to crisis communication. The results of the study indicate not only that COVID-19-related linguistic research is topically limited but also that insufficient attention has been accorded by linguistic researchers to Conceptual Metaphor Theory, Critical Discourse Analysis, Pragmatics, and Corpus-based discourse analysis in exploring pandemic discourses and texts.

Quercetin has a wide range of biological properties that can be used to prevent or decrease particular inflammatory diseases. In this study, we aimed to investigate the gene expression profile and metabolic pathway of the gut microbiota of an antibiotic-treated mouse model administered quercetin. Blood, feces, and intestinal tissue samples were collected and metagenomic sequencing, enzyme-linked immunosorbent assay, and western blot analysis were used to detect variations. The results showed that the quercetin-treated group exhibited increased levels of health beneficial bacterial species, including Faecalibaculum rodentium (103.13%), Enterorhabdus caecimuris (4.13%), Eggerthella lenta (4%), Roseburia hominis (1.33%), and Enterorhabdus mucosicola (1.79%), compared with the model group. These bacterial species were positively related to butyrate, propionate, and intestinal tight junction proteins (zonula occludens-1 and occludin) expression, but negatively related to serum lipopolysaccharide and tumor necrosis factor-α level. In addition, the metabolic pathway analysis showed that dietary quercetin significantly enhanced spliceosomes (111.11%), tight junctions (62.96%), the citrate cycle (10.41%), pyruvate metabolism (6.95%), and lysine biosynthesis (5.06%), but decreasing fatty acid biosynthesis (23.91%) and N-glycan (7.37%) biosynthesis. Furthermore, these metabolic pathway changes were related to relative changes in the abundance of 10 Kyoto Encyclopedia of Genes and Genomes genes (K00244, K00341, K02946, K03737, K01885, k10352, k11717, k10532, K02078, K01191). In conclusion, dietary quercetin increased butyrate-producing bacterial species, and the acetyl-CoA-mediated increased butyrate accelerated carbohydrate, energy metabolism, reduced cell motility and endotoxemia, and increased the gut barrier function, thereby leading to healthy colonic conditions for the host.

Abstract Pectin is a kind of natural and complex carbohydrates which is extensively used in food, chemical, cosmetic, and pharmaceutical industries. Fresh sunflower (Helianthus annuus L.) heads were utilized as a novel source of pectin extracted by ammonium oxalate. The conditions of the extraction process were optimized implementing the response surface methodology. Under optimal extraction parameters (extraction time 1.34 h, liquid–solid ratio 15:1 mL/g, ammonium oxalate concentration 0.76% (w/v)), the maximum experimental yield was 7.36%. The effect of spray-drying and freeze-drying on the physiochemical properties, structural characteristics, and antioxidant activities was investigated by FT-IR spectroscopy, high performance size exclusion chromatography, and X-ray diffraction. The results showed freeze-drying lead to decrease in galacturonic acid (GalA) content (76.2%), molecular weight (M w 316 kDa), and crystallinity. The antioxidant activities of pectin were investigated utilizing the in-vitro DPPH and ABTS radical-scavenging systems. This study provided a novel and efficient extraction method of sunflower pectin, and confirmed that different drying processes had an effect on the structure and properties of pectin.

The effect of heat stress on ruminants is an important issue. In recent years, the growth of the Chinese dairy industry has rapidly increased, generating RMB 468,738 million revenue in 2021. A decreased milk yield is the most recognized impact of heat stress on dairy cows and results in significant economic loss to dairy producers. Heat stress also lowers immunity and antioxidant capacity and changes the bacterial composition and metabolites of the rumen. The purpose of this study was to investigate the effect of addition Saccharomyces cerevisiae culture on heat-stressed cows. The impact of S. cerevisiae culture on microbiota composition, functional profiles, and metabolomics was assessed in heat-stressed cows. A total of 45 Holstein cows in mid-lactation were selected and randomly divided into three groups (15 cows per group). Groups D-C, D-A, and D-B were fed with the basal diet, the basal diet + first S. cerevisiae culture 100 g/day, and the basal diet + second S. cerevisiae culture 30 g/day, respectively. The trial lasted 60 days. There was an increased abundance of the Phylum Firmicutes in the rumen of heat-stressed dairy cows fed with S. cerevisiae, of which four genera had significantly higher abundance, Ruminococcus_gauvreauii_group, Butyrivibrio_2, Moryella, and Ruminiclostridium_6. At the functional level, ten pathways differed significantly between the three groups (P < 0.05), with an increase in fatty acid biosynthesis, fatty acid metabolism, PPAR signaling pathway, ferroptosis, and biotin metabolism in the treatment groups. More differential metabolites were found in the D-C and D-A groups than in the D-C and D-B groups. These results indicate that S. cerevisiae cultures can influence the health status of heat-stressed cows by modulating rumen microbial composition, function, and metabolites, thereby improving rumen cellulolytic capacity. This study can provide or offer suggestions or recommendations for the development and utilization of feed additives.

Mastitis results in great economic loss to the dairy goat industry. Many approaches have attempted to decrease the morbidity associated with this disease, and among these, transgenic strategy have been recognized as a potential approach. A previous mammalian study reports that interferon-gamma (IFN-γ) has potential anti-bacterial bioactivity against infection in vitro; however, its capacity in vivo is ambiguous. In this study, we initially constructed targeting and homologous recombination vectors (containing the IFN-γ gene) and then transferred the vectors into goat mammary gland epithelial cells (GMECs). Enzyme digestion and sequencing analysis indicated that the vectors used in this study were built correctly. Subsequently, monoclonal cells were selected using puromycin and the polymerase chain reaction (PCR) test indicated that IFN-γ was correctly inserted downstream of the casein promoter. Monoclonal cells were then assessed for reducible expression, and reverse transcriptase-PCR (RT-PCR) and Western blot tests confirmed that monoclonal cells could express IFN-γ. Finally, anti-bacterial capacity was evaluated using bacterial counts and flow cytometry analysis. Decreased bacterial counts and cell apoptosis rates in transgenic GMECs demonstrated that the secretion of IFN-γ could inhibit bacterial proliferation. Therefore, IFN-γ gene transfection in goat mammary epithelial cells could inhibit bacterial proliferation and reduce the risk of mammary gland infection in goats.

To investigate the responses of amino acid metabolism in portal-drained viscera (PDV), liver, and mammary glands (MGs) to a graded gradual decrease of post-ruminal Lys supply, four multi-catheterized lactating goats were used in a 4 × 4 Latin square experiment. Goats were fasted for 12 h and then received a 33-h abomasal infusion of an amino acid mixture and glucose. Treatments consisted of a graded decrease of Lys content in the infusate to 100 (complete), 60, 30, or 0% as in casein. Lys-removed infusions decreased the production of milk, milk protein, fat, and lactose linearly and also decreased arterial Lys concentrations linearly (p < 0.05). Net PDV uptake decreased linearly (p < 0.05) with decreasing PDV loss ratio (p < 0.05). Although liver removal of Lys decreased linearly (p < 0.05), the removal ratio relative to portal absorption changed small, which was about 10% in all four treatments. Reduced Lys supply resulted in a linear decrease in the utilization of Lys in the peripheral tissues (except mammary, p < 0.05) and the release of more Lys in MGs. Although net mammary uptake of Lys declined linearly (p < 0.05), lactating goats can partially offset the negative effect of decreased circulating Lys concentrations by increasing mammary affinity (p < 0.05) and increasing mammary blood flow (p < 0.05). Graded removal of Lys from the infusate linearly decreased mammary uptake-to-output ratios of Lys (p < 0.05) suggesting that mammary catabolism of Lys decreased. Meanwhile, the treatments linearly increased circulating concentrations of glucagon and linearly decreased prolactin (p < 0.05). In conclusion, the results of the present study indicated that there were several mechanisms used to mitigate a Lys deficiency, including reduced catabolism of Lys in PDV and peripheral tissues (including MGs) and linearly increased mammary blood flow and mammary affinity together with increased mammary uptake and U:O of branched-chain amino acids (BCAA). Given these changes, the decline in milk protein production could be attributed to the combined effect of mass action with Lys and hormonal status.

BackgroundSevoflurane exposure at brain developmental stages has been reported to induce neurotoxicity and, subsequently, results in learning deficits at the juvenile age. In this study, we aimed to investigate the effects of prior early-age sevoflurane exposure on locomotor activity, anxiety, CA1-dependent learning, and spatial memory, as well as synapse changes in mice.MethodsTotally, 3% sevoflurane was given to neonatal mice at postnatal day 7 for 4 h. These sevoflurane-treated mice were later subjected to open field and Morris water maze tests at their adult age (postnatal days 60–90) to assess their motor activity and spatial learning ability, respectively. The brain slices of sevoflurane-treated and control mice were examined for dendritic spine density and long-term potentiation (LTP) features following behavior tests (postnatal day 60). Protein levels of N-methyl-D-aspartate (NMDA) receptor subtypes and PSD95 in brain lysate were measured by using immunoblotting at the same age (postnatal day 60).ResultsPrior early-age sevoflurane exposure increased the overall moving distance, prolonged the central-area lingering time, and increased the central-area entries of adult mice. Sevoflurane-treated mice spent more time in the target quadrant during the probe test. An increase of the spine density of pyramidal neurons in the CA1 region was observed in sevoflurane-treated mice. NMDA receptor GluN2A subunit, but not the GluN2B or PSD95, was increased in the brain lysate of sevoflurane-treated mice compared with that of control mice. LTP in the hippocampus did not significantly differ between sevoflurane-treated and control mice.ConclusionExposure to sevoflurane for mice during an early brain developmental stage (P7) induces later-on hyperactivity, anxiety-free, and enhancement of memory retention. These observations shed light on future investigations on the underlying mechanisms of sevoflurane’s effect on neuronal development.

We aimed to investigate the effect of different levels of nutritional restriction on mammary gland development during the embryonic period by gradient nutritional restriction in pregnant female mice. We started the nutritional restriction of 60 female CD-1(ICR) mice from day 9 of gestation based on 100%, 90%, 80%, 70% and 60% of ad libitum intake. After delivery, the weight and body fat of the offspring and the mother were recorded (n = 12). Offspring mammary development and gene expression were explored by whole mount and qPCR. Mammary development patterns of in offspring were constructed using Sholl analysis, principal component analysis (PCA) and regression analysis. We found that: (1) Mild maternal nutritional restriction (90–70% of ad libitum intake) did not affect offspring weight, while body fat percentage was more sensitive to nutritional restriction (lower at 80% ad libitum feeding). (2) A precipitous drop in mammary development and altered developmental patterns occurred when nutritional restriction ranged from 80% to 70% of ad libitum intake. (3) Mild maternal nutritional restriction (90% of ad libitum intake) promoted mammary-development-related gene expression. In conclusion, our results suggest that mild maternal nutritional restriction during gestation contributes to increased embryonic mammary gland development. When maternal nutritional restriction reaches 70% of ad libitum intake, the mammary glands of the offspring show noticeable maldevelopment. Our results help provide a theoretical basis for the effect of maternal nutritional restriction during gestation on offspring mammary development and a reference for the amount of maternal nutritional restriction.

To explore the versatility of speakers, a piezoelectric micro-electro-mechanical system (MEMS) speaker combining the function of a silent alarm is proposed, which mainly comprises a lead zirconate titanate (PZT) actuation layer and a rigid–flexible coupling supporting layer. Measurements performed on encapsulated prototypes mounted to an artificial ear simulator have revealed that, compared to a speaker with a rigid supporting layer, the sound pressure level (SPL) of the proposed piezoelectric MEMS speaker with a rigid–flexible coupling supporting layer is significantly higher and is especially higher by 4.1–20.1 dB in the frequency range from 20 Hz to 4.2 kHz, indicating that the rigid–flexible coupling supporting layer can improve the SPL significantly in low frequency. Moreover, the spectral distribution characteristic of its playback audio is similar to that of the commercial electromagnetic type. The device can also function as a silent alarm based on oral airflows in dangerous situations, as it performs well at recognizing words according to their unique voltage-signal characteristics, and can avoid the effects of external sound noise, body movement, long distance, and occlusion. This strategy provides inspiration for functional diversification of piezoelectric MEMS speakers.

BackgroundHealth policy and services is a continuously evolving field of research that can inform prevention and control efforts for a variety of health conditions. The “Healthy China” strategy reflects the demand to formulate health policy that suits China's national needs and goals. Applying bibliometric analysis to grasp the general situation of health policy and services research globally will be conducive to informing China's designated health plans and initiatives.MethodA bibliometric analysis of 58,065 articles on “Health Policy and Services” topics was conducted. The document type was restricted to journal articles that were published in the Web of Science database between the time parameter of January 1, 2009 to December 31, 2018. Data was collected on indicators such as the annual number of publications in the field of health policy and services, the country where the publication is issued, the publication organization, the source journal, the frequency of citations, research hotspots, and academic areas.ResultsThe overall number of articles published in Web of Science on health policy and services research has increased over time. The United States has the largest number of articles in the field. The institution with the highest number of citations in the field is Harvard University and the journal with the most published articles in the field is Health Affairs. Research hotspots in the health policy and services field include topics such as “HIV Infections,” “Primary Health Care,” “Delivery of Health Care,” and “Health Services Accessibility.”ConclusionExperts in the field of health policy and services globally are dedicated to researching the most effective ways to improve people's health and living standards. There is a certain gap in the depth of health policy and services research between China and developed countries and regions such as Europe or America. China must learn from foreign experience to conduct meaningful and informative research that can aid in the formulation of multi-dimensional health policies in specific areas such as environmental infectious diseases, where attention is needed in areas beyond the medical and health system.

The dairy farming industry is facing massive economic losses as heat stress continues to rise. The purpose of this study was to see how feeding Saccharomyces cerevisiae culture (SC) influences productive performance, lactation performance, serum biochemical indexes, hormonal level, antioxidant capacity, and immune function in mid-lactating cows during heat stress. Forty-five healthy mid-lactation dairy cows with comparable milk yield, lactation days, and parity were randomly divided into 3 groups (15 cows in each group). The control group (CON) was fed the basal diet, while the treatment groups were fed the basal diet + first Saccharomyces cerevisiae culture 100 g/d (SC-1) and the basal diet + second Saccharomyces cerevisiae culture 30 g/d (SC-2), respectively. The SC-1 and SC-2 groups with SC added in the treatment groups reduced rectal temperature and respiratory rate in heat-stressed cows (P < 0.05). The milk yield of SC-1 and SC-2 treatment groups was significantly higher than that of CON (P < 0.05). Except for somatic cell count, which was significantly lower in SC-1 and SC-2 than in CON (P < 0.05), there were no significant differences in the milk components. The addition of SC: (i) increased serum urea levels (P < 0.05), but there was no significant difference in glucose, total cholesterol, alanine transaminase, aspartate aminotransferase, total protein, albumin and alkaline phosphatase levels (P > 0.05); (ii) increased serum levels of immunoglobulin-A, immunoglobulin-G, immunoglobulin M, interleukin-4, interleukin-10 and heat shock protein-70 (P < 0.05), while decreasing serum levels of interleukin-1β, interleukin-6, interleukin-2, interferon-γ and tumor necrosis factor-α (P < 0.05); (iii) increased total antioxidant capacity, glutathione peroxidase and superoxide dismutase in serum (P < 0.05), while decreasing malondialdehyde; (iv) increased serum levels of glucocorticoids, insulin, cortisol and prolactin (P < 0.05), while decreasing the serum levels of triiodothyronine and thyroxine (P < 0.05). In conclusion, under the current experimental conditions, the addition of SC can reduce rectal temperature and respiratory rate in heat-stressed mid-lactation cows, reduce the number of somatic cells in milk and improve the mid-lactation cow performance. In addition, SC addition to the diet can raise serum urea levels, regulate serum hormone levels, boost antioxidant capacity in mid-lactation cows, and boost overall immunity.

Pitting corrosion of rotating machinery is one of the most common faults in industrial engineering. The convolutional neural network (CNN) is increasingly applied to the fault diagnosis. However, the conventional CNN method will reduce the feature dimension of the collected signal and cause the loss of information during the pooling process. In this paper, a new method based on dilated CNN combined with spatial dropout (DCSD) is proposed to diagnose the early faults of rotating machinery. By filling the convolution kernel, the DCSD method can increase the receptive field of the CNN without increasing the number of parameters while retaining more features of the raw vibration signal of the rotating machine. To avoid the dropout method eliminates the adjacent elements with a strong correlation, the Spatial Dropout method is adopted to reduce the overfitting problem of deep networks. The early pitting gears experiment was designed to verify the DCSD method in this paper. The raw vibration signal data of 6 different healthy states were collected to verify the effectiveness of the method. The experimental results show that the DCSD method proposed can effectively distinguish the different early gears pitting, and the diagnostic accuracy is better than other popular deep learning methods.

This research investigated effects of dietary phytosterols supplementation on growth performance and cecal gut microflora in yellow-feather broilers. A total of 360 yellow-feather broilers (1-day-old) were randomly assigned to 3 treatment groups: control group (basal diet), antibiotic group (basal diet supplemented with 200 mg/kg oxytetracycline calcium and 250 mg/kg nosiheptide), and phytosterols groups (basal diet supplemented with 25 mg/kg phytosterols). Each treatment group had 6 replicates, and there were 20 broilers within each replicate. No treatment effects on average daily feed intake, average daily gain, and food conversion rate were observed. The antibiotic group had a lower liver index compared with control group and phytosterols group. Other visceral indexes including bursa of Fabricius, spleen, and heart were not different among the 3 treatment groups. In terms of alpha diversity, no treatment effects on Shannon and Simpson indexes were observed. Supplementation of phytosterols significantly decreased the Chao1 and Ace indexes, indicating lower community richness of the gut microflora. At phylum level, the phytosterols group had a higher abundance of Bacteroidetes compared with the control group. At genus level, no treatment effect was observed on the top 10 genera. Overall, supplementation of phytosterols at 25 mg/kg level did not affect the growth performance of yellow-feather broilers, and its effect on gut microflora was limited.

The post-00s (the Generation after 00s) college students in the new era show the characteristics of slow employment. In order to promote the employment of contemporary college students, this paper analyzes the psychological and ideological problems of slow employment of post-00s college students through questionnaire survey on the dynamic employment thought, value orientation, employment location, professional satisfaction and other issues of contemporary college students. A series of employment promotion strategies adapted to the characteristics of college students under the new situation are put forward from the aspects of perfecting the curriculum system of employment guidance, improving the guidance ability of employment guidance teachers, perfecting the employment guidance and service system of graduates.

AbstractAiming at the situation that the bottom of the pile foundation contains sediment defects, the theoretical analysis of the vertical bearing characteristics of the pile foundation is carried out. Based on the load transfer mechanism of the pile-soil system and based on the load transfer function of the elastoplasticity of the soil on the side of the pile and the weakening of the trifold line of the soil at the end of the pile, a load transfer calculation model for bored cast-in-place piles with slag defects under different conditions was established. The corresponding analytical formulas are deduced, and the accuracy and reliability of load transfer derivation are verified in combination with calculation examples, which are suitable for indoor and field tests. The influence coefficient of sedimentary defect in single pile is defined, and the parameters are analyzed. The law about the influence coefficient of length-diameter ratio, the thickness of sedimentary defect, and the modulus of elasticity of sedimentary defect in single pile are obtained: With the increase of the ratio of length and diameter, the defect coefficient of single piles gradually increases and tends to ease, and the bearing effect of pile foundations is more convergent. As the thickness of the sediment increases, the influence factor of the single pile defect gradually decreases, and the influence factor of the bearing capacity of pile foundation becomes more discrete. With the increase of the elastic modulus of sediment, the influence factor of single pile defect gradually increases, and the ultimate bearing capacity loss decreases. The vertical bearing characteristics of the pile foundation are evaluated in this way, which provides an important reference for further analysis of the bearing characteristics of pile foundations with slag defects.

The problems of acid mine drainage (AMD) in coal mine acidic wastewaters arise from a range of sources, including severe pollution with heavy metals and SO42- and difficulties during treatment. Based on the ability of Maifan stone to adsorb heavy metals and the dissimilatory reduction of SO42- by sulfate-reducing bacteria (SRB), Maifan stone-sulfate-reducing bacterium-immobilized particles were prepared via immobilization techniques using Shandong Maifan stone as the experimental material. The effects of Maifan stones containing SRB on mitigating AMD were investigated by constructing Dynamic Column 1 with Maifan stone-sulfate-reducing bacterium-immobilized particles and by constructing Dynamic Column 2 with SRB mixed with Maifan stones. By the use of adsorption isotherms, adsorption kinetics, a reduction kinetics model and X-ray diffraction (XRD) and scanning electron microscopy (SEM) studies, the mechanism by which Maifan stone-sulfate-reducing bacterium-immobilized particles mitigate AMD was revealed. The results showed that the total effect of Maifan stone-sulfate-reducing bacterium-immobilized particles on AMD was better than that of biological Maifan stone carriers. The highest rates for the removal of Fe2+, Mn2+, and SO42- in AMD were 90.51%, 85.75% and 93.61%, respectively, and the pH value of the wastewater increased from 4.08 to 7.64. The isotherms for the adsorption of Fe2+ and Mn2+ on Maifan stone-sulfate-reducing bacterium-immobilized particles conformed to the output of the Langmuir model. The adsorption kinetics were in accordance with Lagergren first-order kinetics, and the kinetics for the reduction of SO42- conformed to those of a first-order reaction model.

Mancozeb (MZ), an antibacterial pesticide, has been linked to reproductive toxicity, neurotoxicity, and endocrine disruption. However, whether MZ has cardiactoxicity is unclear. In this study, the cardiotoxic effects of exposure to environment-related MZ concentrations ranging from 1.88 μM to 7.52 μM were evaluated at the larval stage of zebrafish. Transcriptome sequencing predicted the mechanism of MZ-induced cardiac developmental toxicity in zebrafish by enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Consistent with morphological changes, the osm, pfkfb3, foxh1, stc1, and nrarpb genes may effect normal development of zebrafish heart by activating NOTCH signaling pathways, resulting in pericardial edema, myocardial fibrosis, and congestion in the heart area. Moreover, differential gene expression analysis indicated that cyp-related genes (cyp1c2 and cyp3c3) were significantly upregulated after MZ treatment, which may be related to apoptosis of myocardial cells. These results were verified by real-time quantitative RT-qPCR and acridine orange staining. Our findings suggest that MZ-mediated cardiotoxic development of zebrafish larvae may be related to the activation of Notch and apoptosis-related signaling pathways.

Computer modeling and simulation is fast-moving towards clinical applications to advance cardiac diagnosis and aid treatment planning. As cardiac models become more popular and useful, the need to tailor these models for individual subjects has grown. Therefore, model identification becomes an important step of cardiac modeling. Cardiac model identification is a challenging task, particularly for simulation in high organizational scales such as tissue and organ scales, as the models are nonlinear and involve hidden ion channel gating variables. In this study, we proposed a two-stage model calibration algorithm to estimate the parameters of cardiac tissue model using membrane potential data. Specifically, an ensemble Kalman Filter (EnKF) is used in the first stage to track the membrane potentials and the hidden ion channel gating variables; the outputs from the EnKF are used as the initial values to calculate forward prediction in the second stage. The optimization algorithm minimizes the sum of squared errors in both stages through a coarse and a fine optimization. The proposed method is validated through multiple simulation designs, which shows good accuracy and efficiency.

The camera tracking systems based on visual image processing face a problem that they are completely ineffective in their blind zones. To address this problem, a design of acoustic enhanced tracking system combining visual and auditory target tracking methods is reported in this article. The system holds the abilities of performing sound direction estimation and target tracking in real-time. Estimating direction of arrival of the sound accompanied with the target helps the camera turn towards the target outside the field of view. This sound-triggered mode of camera operation makes a significant supplement to conventional cameras' working state. Considering the embedded system is necessary in consideration of the cost and size of the system in practical application, we designed a small aperture array with 7 digital omnidirectional MEMS microphones and built the overall system based on FPGA and ARM. The experiments were carried out in a normal indoor environment and the results confirmed that the system can perform auditory and visual tracking in real-time.