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Abstract Background Gestational diabetes mellitus (GDM) is a complication of pregnancy associated with numerous adverse outcomes. There may be a potential link between GDM and arsenic (As) exposure, but this hypothesis remains controversial. This meta-analysis summarizes the latest studies evaluating the association between As and GDM. Methods A comprehensive search of the PubMed, Embase, and Scopus databases up to September 2023 was performed. The pooled estimates with 95% CIs were presented using forest plots. Estimates were calculated with random effects models, and subgroup and sensitivity analyses were conducted to address heterogeneity. Results A total of 13 eligible studies involving 2575 patients with GDM were included in this meta-analysis. The results showed that women exposed to As had a significantly increased risk of GDM (OR 1.47, 95% CI: 1.11 to 1.95, P = 0.007). Subgroup analyses suggested that the heterogeneity might be attributed to the years of publication. In addition, sensitivity analysis confirmed the robust and reliable results. Conclusions This analysis suggested that women exposed to As have a greater risk of GDM. However, the significant heterogeneity across studies requires careful interpretation. Registration The PROSPERO registration ID is CRD42023461820.

Abstract Background MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. This study aimed to investigate its feasibility of clinical application in Parkinson’s disease (PD). Methods 27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning. All image reconstruction and processing were automatically performed with in-house C + + programs on the Automatic Differentiation using Expression Template platform. According to the HybraPD atlas consisting of 12 human brain subcortical nuclei, the region-of-interest (ROI) based analysis was conducted to extract quantitative parameters, then identify PD-related abnormalities from the T1, T2* and proton density maps and quantitative susceptibility mapping (QSM), by comparing patients and HCs. Results The ROI-based analysis revealed significantly decreased mean T1 values in substantia nigra pars compacta and habenular nuclei, mean T2* value in subthalamic nucleus and increased mean QSM value in subthalamic nucleus in PD patients, compared to HCs (all p values

Abstract This paper presents a groundbreaking method for predicting the compressive strength (F c ) of recycled aggregate concrete (RAC) through the application of K-nearest neighbors (KNN) analysis. The task of designing mixture proportions to achieve the desired F c can be remarkably intricate, owing to the intricate interplay among the components involved. Machine learning (ML) algorithms have exhibited considerable promise in tackling this complexity effectively. In pursuit of enhanced prediction accuracy, this research introduces a semi-empirical approach that seamlessly integrates strategies, including optimization techniques. This study incorporates two meta-heuristic methods, the Fire Hawk optimizer (FHO) and Runge–Kutta optimization (RUK) to enhance model accuracy. The research results reveal three separate models: KNFH, KNRK, and a single KNN model, each providing valuable insights for precise F c prediction. Remarkably, the KNFH model stands out as a top performer, boasting an impressive R 2 value of 0.994 and a meager RMSE value of 1.122. These findings not only validate the accuracy and reliability of the KNFH model but also highlight its effectiveness in predicting F c outcomes. This approach holds great promise for precise F c forecasting in the construction industry. Integrating meta-heuristic algorithms significantly improves model accuracy, leading to more reliable forecasts with profound implications for construction projects and their outcomes. This research marks a significant advancement in predicting F c using ML, offering valuable tools for engineers and builders.

According to its excellent properties, polyethylene terephthalate (PET) is widely used as food containers in mineral water, soft drinks, beers, etc., resulting in a huge waste of PET bottles which increases the environmental burden. With the development of the recycling technology of PET bottles, the discarded PET bottles after consumption have been reused as food containers in many countries successfully. This paper is mainly focused on the new developments of chemical and physical PET recycling technology and its application in enterprises and outlines regulations in the United States, Canada, the European Union, Britain, Japan, South Korea and other countries. The relevant laws and regulations of PET recycling as food containers in our country have also been reviewed. The purpose of this paper is to provide reference opinions on recycling PET to produce food contact materials in China, including promoting waste classification management, improving the risk assessment systems, and formulating relevant national food safety standards.

Building an intelligent supervision system through information technology for quality and safety of food contact materials and products is one important measure to promote the precision supervision and improve supervision efficiency. In this paper, the development of intelligent supervision in the current smart society is studied, and the current supervision modes and situation of food contact materials and products in China are analyzed. Based on this, this paper gives out the smart regulatory system framework construction suggestion for quality and safety of food contact materials and products, and provides the reference for government departments to supervise the quality and safety of food contact materials and products under new situations.

l-threonine is an essential amino acid in human and animal nutrition. It is widely used in food, medicine, feed, and other fields. The global market scale exceeds 700,000 tons per year. Corynebacterium glutamicum, as a chassis cell for industrial amino acid production, has the advantages of biological safety and strong environmental adaptability, and is a potential strain for the efficient production of l-threonine. This article systematically reviewed the function and application of l-threonine, the pathway of C. glutamicum to synthesize l-threonine, and the use of metabolic engineering technology to improve the production of l-threonine.

Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control.

The escalating issue of air pollution in China’s rapidly developing urban areas has prompted increased attention to the role of meteorological conditions in PM2.5 pollution. This study examines the spatiotemporal distribution of PM2.5 concentrations and their relationship with meteorological factors in six major Chinese urban agglomerations from 2017 to 2020, using daily average data. Statistical and spatial analysis techniques are employed, alongside the construction of eight machine learning models for prediction purposes. The study also compares the feature importance of various meteorological factors impacting PM2.5 concentrations. Results reveal significant regional differences in both average PM2.5 levels and meteorological influences. The Multilayer Perceptron (MLP) model demonstrates the highest prediction accuracy for PM2.5 concentrations. According to the MLP model’s feature importance identification, temperature is the most significant factor affecting PM2.5 concentrations across all urban agglomerations, while wind speed and precipitation have the least impact. Contributions from air pressure and dew point temperature, however, vary among different urban agglomerations. This research considers the impact of urban agglomerations and meteorological conditions on PM2.5 and also offers valuable artificial intelligence-based insights into the key meteorological factors influencing PM2.5 concentrations in diverse regions, thereby informing the development of effective air pollution control policies.

PurposeTo examine whether the cerebral blood flow (CBF) and CBF connectivity differences are sex-specific and whether these differences are correlated with cognitive impairment in mTBI.MethodsResting-state perfusion magnetic resonance imaging was performed in 40 patients with acute mTBI and 40 healthy controls by using pseudocontinuous arterial spin labeling within 14 days following injury. The differences in normalized CBF were first compared and CBF connectivity of the brain regions with significant CBF differences were compared next. The association between the normalized CBF and CBF connectivity differences and cognitive function were further investigated.ResultsMen patients had lower normalized CBF in the frontal gyrus, temporal gyrus and hippocampus and decreased negative CBF connectivity between brain regions including the hippocampus, temporal gyrus, postcentral gyrus and lenticular nucleus, putamen, compared with men controls. Women patients had lower normalized CBF in the frontal gyrus, however had higher normalized CBF in the temporal gyrus and hippocampus, compared with women controls. Additionally, women patients showed increased positive CBF connectivity between the seed region of interest (ROI) of the right inferior temporal gyrus and temporal gyrus and frontal gyrus, and had increased positive CBF connectivity between the seed ROI of the right hippocampus and the temporal gyrus. Furthermore, men patients had higher CBF in the right middle temporal gyrus and left precentral gyrus than women patients.ConclusionThis study provides evidence of sex differences in both decreased and increased CBF and CBF connectivity and association with cognitive outcome in the acute stage after mTBI.

Liquid crystals (LC) have been found to have many unique characteristics during the last few decades. The liquid crystal phase is ubiquitous in the biological world, as well as in cellulose and its derivatives. Cellulose nanocrystals (CNC), which can obtain the chiral LC phase in aqueous suspension, have been attracting much attention. The unique size and properties of CNC, such as their light weight, special optical properties, non-toxicity, and biocompatibility, etc., have made them widely applicable in diverse fields. This brief literature review introduces the relationship between the LC phase and CNC. Advantages of CNC as a liquid crystal forming material and the preparation of CNC are discussed. The left-handed cholesteric phase structure and the corresponding unique optical properties of CNC-based LC are described in detail. The CNC-based LC aqueous suspension and three stages of forming LC phase are also described. Then, the main reasons accounting for the brittleness and non-uniformity of pristine CNC-based LC are summarized, as well as ways to overcome these problems. Finally, some optical applications of CNC-based LC films in anti-counterfeiting, colorimetric sensors, and composite devices are considered.

Klebsiella pneumoniae invasion syndrome (KPIS) is a critical multi-site infection that is usually caused by highly virulent Klebsiella pneumonia . It is relatively common in Asian patients with diabetes and leads to sepsis, which has a high mortality rate. We report the case of a man in his early 40s who presented to the hospital with blurred vision in his left eye of 7 days’ duration and fever of 1 day’s duration. After a complete examination, he was diagnosed with KPIS on the basis of his liver abscessation, lung abscessation, endophthalmitis of the left eye and brain abscessation. After needle puncture and drainage of the left eye and liver abscess and anti-bacterial treatment with meropenem, the patient recovered well. When KPIS is suspected, attention should be paid to the sites of infection and the selection of the most appropriate antibiotics, but the most important aim should be to drain the lesions in a timely manner to improve the patient’s prognosis.

It is widely known that root morphology has different response mechanisms at different phosphorus (P) levels. However, the effects of P application times on wheat (Triticum aestivum L.) root morphology and, therefore, on growth and nutrient uptake are still unclear. In this study, we investigated the feedback from the physiological indexes of wheat on different P application times after planting. Compared with the P application at planting, the shoot biomass with delayed P application for 21 days (d) increased by 11.8% (p < 0.05). Compared with the P application at planting, the shoot P uptake with delayed P application for 14–21 days increased by 38.4–71.2%, while the shoot N uptake and K uptake with delayed P application for 21 days increased by 16.0% and 14.1% (p < 0.05). Compared with the P application at planting, P use efficiency, N use efficiency, and K use efficiency with delayed P application for 21 days increased by 16.4%, 12.4%, and 12.4%. Delayed P application for 14–21 days promoted wheat shoot growth, nutrient (P, N, and K) uptake, and their use efficiencies. This is particularly important for optimizing the P fertilizer input and nutrient management for wheat growth.

Abstract Background The nonstructural carbohydrates (NSCs) of plants are posited to be crucial traits for the resistance and resilience of plants to climate change-induced drought and flooding. However, the potential effects of persistent drought and waterlogging on the dynamics of the NSCs and the underlying mechanisms are still poorly understood. Methods We measured the NSCs concentrations and pool size, photosynthetic rate and biomass of Robinia pseudoacacia L. seedlings for both 2015 and 2016 under five soil water treatments: 3%, 8%, 17%, 24% and 34% soil water content, representing extreme drought (ED), moderate drought (MD), the control group (CG), field capacity (FC) and waterlogging (WL) stresses, respectively. We observed the relationship between the pool size of NSCs and the survival of seedlings under water stress (drought and waterlogging) for 30 days in greenhouse. Results Compared with CG, the net photosynthetic rate decreased 91%, 67%, 34% and 71%, and the biomass decreased by 37%, 15%, 16% and 33% under ED, MD, FC and WL, respectively. The total NSC (TNSC) concentration was significantly increased by 154% under ED after 10 days and sharply decreased by 50% under ED after 30 days. The concentrations of soluble sugars (SS) were significantly increased by 100% under MD after 10 days and sharply decreased by 60% under ED after 30 days. Compared with GC, the response of NSCs, photosynthetic rate and biomass under ED were more dramatic than that under WL. The pool sizes of fructose and sucrose were larger under ED and WL, but the maximum pool size of starch occurred under the CG. The depletion of NSCs was not observed under ED at the end of the experiments in both 2015 and 2016. Conclusions Our results indicate that the dynamics of NSCs is an important physiological feature of plant adaptation and resistance to drought and waterlogging. In addition, high sugars concentrations are beneficial for the plants during the short-term extreme drought and the longer term mild drought or waterlogging.

Human-related factors are a crucial inducement of traffic accidents. Understanding the influence of freeway environments on the driving behavior and workload experienced by drivers has been demonstrated to be of primary importance for improving traffic safety. To study the effect of alignment, traffic flow, and sign information on drivers’ mental workload and behavior, 16 scenarios were constructed using the orthogonal design method, and simulated driving experiments were carried out with 45 participants. During driving, indicators such as the mean and standard deviation of vehicle speed and lane departure were collected, and the NASA-TLX questionnaire was adopted to measure workload. Analysis of variance results indicated that the radius of the horizontal curve, gradient, flow, and sign information level have a significant influence on drivers’ workload and speed keeping ability. In addition, the horizontal curve radius has a significant effect on lane keeping ability. The importance of safety influencing factors on driving workload and performance was quantitatively ranked by integrating the trend of Deng’s correlation degree, comprehensive correlation degree, and similar correlation degree, whose weight was calculated using the entropy method. Traffic sign information was found to have the greatest impact on workload. In terms of driving performance, traffic volume has the greatest influence on the mean and standard deviation of vehicle speed, followed by the amount of sign information. Lane departure is most affected by the radius of the horizontal curve. These findings provide guidance for freeway traffic safety regulation, including workload control and road facility optimization.

Soil-dissolved organic matter (DOM) drives the carbon (C) and nitrogen (N) cycles in agroecosystems. Despite many studies on DOM dynamics, hardly any attention has been directed toward DOM quality, particularly DOM composition. The aim of this study was to elucidate how C and N management practices alter soil water-extractable organic matter (WEOM) in a loess soil agroecosystem. Field experiments were conducted with a winter wheat monoculture. Three N fertilization rates (0, 120, and 240 kg ha−1 year−1) were applied for 17 years (2002–2019), combined with five C practices (zero, low, and high rates of sheep manure or wheat straw) for three years (2016–2019). The results reveal that soil organic carbon (SOC) and water-extractable organic carbon (WEOC) concentrations in the topsoil (0–20 cm) were increased by organic amendments considerably but were not affected by N fertilization. The fluorescence excitation–emission matrix spectra (EEM) of WEOM were resolved to two humic-like components (C1 and C2) and two soluble microbial byproduct-like components (C3 and C4). The proportions of C1 and C2 were increased, while the proportion of C3 was decreased by both C and N management practices. In conclusion, organic amendments increased both WEOM quality and its proportion of humic-like components, whereas N fertilization increased the proportion of humic-like components without variations of WEOM quality in the topsoil of loess soil.

With regard to the inefficient application of a food processing information system due to shortage of the knowledge acquisition measure and self-updating function of knowledge, a method of constructing an online aided decision making knowledge base for quality and security of food processing, based on regular expression, is discussed in the paper. Firstly, the method establishes an online aided decision making knowledge base for quality and security of food processing based on regular expression; and then an automatic knowledge inference engine is applied to update the knowledge in the base, combined with industry experts’ experience knowledge. Continuous deriving of food processing knowledge can be realized based on the inference engine. The research will greatly enhance the efficiency and applicability of obtaining knowledge from an online aided decision making system for quality and security of food processing.

In connection with the problem that the food processing information system is poor due to the absence of knowledge acquisition and a knowledge selfupdating function, a knowledge acquisition approach, based on a Support Vector Machine (SVM) is proposed. First, the approach establishes a set of predicted samples for the relationship between the food processing parameters and product quality; then it uses discretization of the continuous attributes, attributes reduction and a rule extraction algorithm of SVM to automatically acquire predicted knowledge from a large number of predicted sample sets. After that it saves the predicted knowledge in the knowledge base of an expert system; finally, the method realizes extraction of the knowledge about the food processing process based on the inference engine, which greatly enhances the efficiency and applicability of the acquired knowledge in an online aided decision system of food processing quality and safety.

Through analysis of cold-induced transcriptome, a novel gene encoding a putative MYB transcription factor was isolated and designated Cold induced MYB 1 (CMYB1). Tissue-specific gene expression analysis revealed that CMYB1 was highly expressed in rice stems and nodes. qRT-PCR assay indicated that CMYB1 was dramatically induced by cold stress (>100-folds) and induced by exogenous ABA and osmotic stress. Interestingly, CMYB1 showed rhythmic expression profile in rice leaves at different developmental stages. Subcellular localization assay suggested that CMYB1-GFP (green fluorescent protein) fusion protein was localized in the nuclei. Moreover, CMYB1 exhibited the transcriptional activation activity when transiently expressed in rice protoplast cells. Taken together, CMYB1 probably functions as a transcriptional activator in mediating stress and rhythm responsive gene expression in rice.

Homeodomain-leucine zipper type I (HD-Zip I) proteins are involved in the regulation of plant development and response to environmental stresses. In this study, OsSLI1 (Oryza sativa stress largely induced 1), encoding a member of the HD-Zip I subfamily, was isolated from rice. The expression of OsSLI1 was dramatically induced by multiple abiotic stresses and exogenous abscisic acid (ABA). In silico sequence analysis discovered several cis-acting elements including multiple ABREs (ABA-responsive element binding factors) in the upstream promoter region of OsSLI1. The OsSLI1-GFP fusion protein was localized in the nucleus of rice protoplast cells and the transcriptional activity of OsSLI1 was confirmed by the yeast hybrid system. Further, it was found that OsSLI1 expression was enhanced in an ABI5-Like1 (ABL1) deficiency rice mutant abl1 under stress conditions, suggesting that ABL1 probably negatively regulates OsSLI1 gene expression. Moreover, it was found that OsSLI1 was regulated in panicle development. Taken together, OsSLI1 may be a transcriptional activator regulating stress-responsive gene expression and panicle development in rice.