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Abstract This article proposes an in-situ steam-assisted thermal decomposition method for oil shale using long-distance horizontal wells.Firstly, this article establishes a coupled thermal–hydraulic hydrothermal mechanical model considering the anisotropy of oil shale to study the in-situ pyrolysis process of oil shale by steam injection in horizontal wells. Then Optimization analysis was conducted on the effects of fracture spacing, fracture length, and horizontal well length on extraction efficiency. Finally, An economic analysis was conducted on the new extraction method. The research results indicate that: (1) Compared with the traditional vertical well mining mode (1injection and 8 production well, with a well spacing of 50 m), the newly proposed long-distance horizontal well configuration (1 injection and 2 production well) has higher heating efficiency. (2) When the fracture spacing is less than 25 m, the heating efficiency is no longer significantly improved. Increasing the length of fractures has a significant effect on improving heating efficiency. (3) When the length of the horizontal well exceeds 450 m, the extraction cost no longer significantly decreases. Increasing the length of fracturing fractures can significantly reduce extraction costs. When the fracture spacing is less than 30 m, the extraction cost no longer significantly decreases. This study provides theoretical reference and data support for the efficient in-situ thermal decomposition of oil shale.

Abstract Spatial proteomics is a multidimensional technique that studies the spatial distribution and function of proteins within cells or tissues across both spatial and temporal dimensions. This field multidimensionally reveals the complex structure of the human proteome, including the characteristics of protein spatial distribution, dynamic protein translocation, and protein interaction networks. Recently, as a crucial method for studying protein spatial localization, spatial proteomics has been applied in the clinical investigation of various diseases. This review summarizes the fundamental concepts and characteristics of tissue-level spatial proteomics, its research progress in common human diseases such as cancer, neurological disorders, cardiovascular diseases, autoimmune diseases, and anticipates its future development trends. The aim is to highlight the significant impact of spatial proteomics on understanding disease pathogenesis, advancing diagnostic methods, and developing potential therapeutic targets in clinical research.

Abstract Background Gestational age (GEAA) estimated by newborn DNA methylation (GAmAge) is associated with maternal prenatal exposures and immediate birth outcomes. However, the association of GAmAge with long-term overweight or obesity (OWO) trajectories is yet to be determined. Methods GAmAge was calculated for 831 children from a US predominantly urban, low-income, multi-ethnic birth cohort based on cord blood DNA methylation profile using Illumina EPIC array. Repeated anthropometric measurements aligned with pediatric primary care schedule allowed us to calculate body-mass-index percentiles (BMIPCT) at specific age and to define long-term weight trajectories from birth to 18 years. Results GAmAge was associated with BMIPCT trajectories, defined by 4 groups: stable (consistent OWO: “early OWO”; constant normal weight: “NW”) or non-stable (OWO by year 1 of follow-up: “late OWO”; OWO by year 6 of follow-up: “NW to very late OWO”). GAmAge differentiated between the group with consistently normal BMIPCT pattern and the non-stable groups with late and very late OWO development. Such differentiation was observed in the age periods of birth to 1year, 3years, 6years, 10years, and 14years (p

The overall strength of the surrounding rock of “three-soft” thick coal seam mining roadway is low, and the combination of rock structure generally changes greatly, resulting in obvious differences in the stability of hanging roof of driving face, and some areas with poor roof stability cannot directly adopt bolter miners and supporting construction technology, resulting in frequent stoppage of driving face and a decrease in the overall driving efficiency. In view of such problems, taking “three-soft” thick coal seam mining roadway of Shajihai Coal Mine as engineering background, and using comprehensive research methods such as on-site research, theoretical analysis and numerical simulation, it was clarified that roof leakage of driving face, bolter miner falling into the floor or mistracking, and rib spalling are the key constraints restricting the application of the bolter miner in “three-soft” thick coal seam roadway. Considering the influence of parameters such as the thickness of the hanging roof, the influence coefficient of disturbance, the strength of each rock layer of the roof and the integrity of each rock layer, the calculation formula of the limit span of hanging roof of driving face was established and derived. A stability grading method for surrounding rocks of roadway was formed dominated by the calculation of the limit span of hanging roof of driving face, and surrounding rocks of “three-soft” thick coal seam roadway is divided into 5 grades: extremely stable, stable, moderately stable, unstable and extremely unstable. Classification and optimization scheme of bolter miner equipment and construction process under different stability levels of surrounding rock is proposed, and the application countermeasures of bolter miner under difficult operating conditions are given from the aspects of cutting drum position adjustment, floor grounding treatment, and addition of roadway operation platform. The research results were applied in the roadway of Shajihai Coal Mine No.B1003W05 working face, and its classification optimization scheme can effectively ensure the normal operation of the bolter miner in “three-soft” thick coal seams, and the driving speed is increased by 1.0−1.8 times compared with fully-mechanized driving of boom-type roadheader.

Abstract Background Gestational DNA methylation age (GAmAge) has been developed and validated in European ancestry samples. Its applicability to other ethnicities and associations with fetal stress and newborn phenotypes such as inflammation markers are still to be determined. This study aims to examine the applicability of GAmAge developed from cord blood samples of European decedents to a racially diverse birth cohort, and associations with newborn phenotypes. Methods GAmAge based on 176 CpGs (Haftorn GAmAge) was calculated for 940 children from a US predominantly urban, low-income, multiethnic birth cohort. Cord blood DNA methylation was profiled by Illumina EPIC array. Newborn phenotypes included anthropometric measurements and, for a subset of newborns (N = 194), twenty-seven cord blood inflammatory markers (sandwich immunoassays). Results GAmAge had a stronger correlation with GEAA in boys (r = 0.89, 95% confidence interval (CI) [0.87,0.91]) compared with girls (r = 0.83, 95% CI [0.80,0.86]), and was stronger among extremely preterm to very preterm babies (r = 0.91, 95% CI [0.81,0.96]), compared with moderate (r = 0.48, 95% CI [0.34,0.60]) and term babies (r = 0.58, 95% CI [0.53,0.63]). Among White newborns (N = 51), the correlation between GAmAge vs. GEAA was slightly stronger (r = 0.89, 95% CI [0.82,0.94]) compared with Black/African American newborns (N = 668; r = 0.87, 95% CI [0.85,0.89]) or Hispanic (N = 221; r = 0.79, 95% CI [0.74,0.84]). Adjusting for GEAA and sex, GAmAge was associated with anthropometric measurements, cord blood brain-derived neurotrophic factor (BDNF), and monocyte chemoattractant protein-1 (MCP-1) (p

Abstract A self-made triaxial testing machine with thermal–hydraulic–mechanical–chemical (THMC) coupling and a tubular heating furnace, combined with in situ (IS) micro-computed-tomography technology was utilized in this study. The evolution of pore-fissure (PF) structure parameters (porosity, PF scale distribution, effective PF volume ratio, and permeability) of bituminous coal under stress-free (SF) and IS conditions with temperature was investigated, and then the mechanism of experimental results was analyzed. Results showed that (1) under SF conditions, at 300–550 °C, the coal samples after pyrolysis are dominated by elongated large fissures, with PF structure parameters positively correlating with temperature. After 400 °C, the number of PFs increases, with most PFs having equivalent diameter (R) ≤ 100 μm. (2) Under IS conditions, coal sample fissures are dominated by elongated large fissures at 300–350 °C and by holes at 350–600 °C. (3) Under IS conditions at 300–600 °C, the PF structure parameters of coal samples initially decrease with temperature and subsequently increase. The number of PFs fluctuates within a certain range, and the PF scale distribution dynamically shifts with temperature. (4) After 300 °C, the PF structure parameters of bituminous coal under SF and IS conditions show a bipolar distribution with temperature. Therefore, the weakening effect of stress on the PF structure of coal samples should not be overlooked during IS pyrolysis mining of coal bodies.

Based on digital cores of oil shale obtained from high-temperature steam in-situ pyrolysis and micro-CT scanning experiments, the real structures of oil shale after pyrolysis at different temperatures were seamlessly integrated into COMSOL through precise grid partitioning. This enabled the simulation of in-situ steam-assisted oil recovery two-phase flow fields. The study examines the dynamic evolution of phase interfaces, pressure fields, velocity fields, and oil displacement efficiency during the in-situ two-phase flow process in pore structures with varying degrees of development. The research indicates that: Firstly, the development and connectivity of pore structures significantly influence the advancement speed of the phase interface — the better the pore structure development, the faster the phase interface advances. Secondly, as seepage progresses, the stability of phase interface advancement improves, with the difference between the peak δa value before stabilization and the stabilized δa value decreasing over time. Thirdly, at the moment of steam injection, a surge in Pa within the seepage zone occurs. The complexity of the pore structure effectively mitigates the surge in Pa caused by the instantaneous gas drive effect. Finally, the total seepage volumetric flow rate Qtotal increases with time. The oil production ratio α at the outlet decreases slightly with time, but remains above 97.6 %, demonstrating the effectiveness of steam-assisted oil recovery.

Abstract When high-temperature steam is used as a medium to pyrolyze organic-rich shale, water steam not only acts as heat transfer but also participates in the chemical reaction of organic matter pyrolysis, thus affecting the generation law and release characteristics of gas products. In this study, based on a long-distance reaction system of organic-rich shale pyrolysis via steam injection, the effects of steam temperature and reaction distance on gas product composition are analyzed in depth and compared with other pyrolysis processes. The advantages of organic-rich shale pyrolysis via steam injection are then evaluated. The volume concentration of hydrogen in the gas product obtained via the steam injection pyrolysis of organic-rich shale is the highest, which is more than 60%. The hydrogen content increases as the reaction distance is extended; however, the rate of increase changes gradually. Increasing the reaction distance from 800 to 4000 mm increases the hydrogen content from 34.91% to 69.68% and from 63.13% to 78.61% when the steam temperature is 500 °C and 555 °C, respectively. However, the higher the heat injection temperature, the smaller the reaction distance required to form a high concentration hydrogen pyrolysis environment (hydrogen concentration > 60%). When the steam pyrolysis temperature is increased from 500 °C to 555 °C, the reaction distance required to form a high concentration of hydrogen is reduced from 3800 to 800 mm. Compared with the direct retorting process, the volume concentration of hydrogen obtained from high-temperature steam pyrolysis of organic-rich shale is 8.82 and 10.72 times that of the commonly used Fushun and Kivite furnaces, respectively. The pyrolysis of organic-rich shale via steam injection is a pyrolysis process in a hydrogen-rich environment.

The elevated levels of antibiotic resistance genes (ARGs) in livestock manure represent a significant threat to both the environment and human health. Composting has been recognized as an effective strategy to mitigate the abundance of ARGs in manure. However, notable rebounds in ARGs abundance have been observed during this process. This study explored the changes in ARGs abundance and the underlying influencing factors during the composting of carnivore (chicken and pig) and herbivore (sheep and cow) manures, along with mushroom residues. The findings revealed that the total relative abundance of ARGs increased by 6.96 and 10.94 folds in chicken and pig manure composts, respectively, whereas it decreased by a remarkable 91.72% and 98.37% in sheep and cow manure composts. Nitrogen content emerged as the primary physicochemical factors governing the abundance of ARGs in chicken and pig manure composts. Conversely, carbon content played a pivotal role in determining ARGs abundance in chicken and pig manure composts. Furthermore, the presence of dominant hosts, such as Corynebacterium, Bacillus, and Clostridium, along with emerging bacteria like Thermobifida, Saccharomonospora, and Actinomadura, contributed significantly to the enrichment of total ARGs, including tetG, tetO, tetX, and sul2, in chicken and pig manure composts. The coexistence of these genes with mobile genetic elements and a plethora of host bacteria, coupled with their high abundance, renders them particularly high-risk ARGs. On the other hand, the observed decrease in the abundance of total ARGs in sheep and cow manure composts can be attributed to the decline in the population of host bacteria, specifically Atopostipes, Psychrobacter, and Corynebacterium. Collectively, these results provide crucial insights into the management of ARGs risks and offer essential theoretical support for enhancing the safe utilization of organic fertilizer in agriculture.

Abstract Introduction Regorafenib remains the standard and widely used second‐line strategy for advanced hepatocellular carcinoma (HCC). There is still a lack of large‐scale multicenter real‐world evidence concerning the concurrent use of regorafenib with immune checkpoint inhibitors (ICI). This study aims to evaluate whether combining regorafenib with ICI provides greater clinical benefit than regorafenib monotherapy as second‐line therapy for advanced HCC under real‐world circumstances. Patients and Methods The study included 208 patients from five medical facilities. One hundred forty‐three patients received regorafenib plus ICI combination therapy, while 65 patients received regorafenib monotherapy. Propensity score matching (PSM) analysis was employed. Results The regorafenib plus ICI group demonstrated significantly higher objective response rate (24.3% vs. 10.3%, after PSM, p = 0.030) and disease control rate (79.4% vs. 50.0%, after PSM, p

Thioredoxin reductase (TXNRD) is a selenoprotein that plays a crucial role in cellular antioxidant defense. Previously, a distinctive guiding bar motif was identified in TXNRD1, which influences the transfer of electrons. In this study, utilizing single amino acid substitution and Excitation-Emission Matrix (EEM) fluorescence spectrum analysis, we discovered that the guiding bar communicates with the FAD and modulates the electron flow of the enzyme. Differential Scanning Fluorimetry (DSF) analysis demonstrated that the aromatic amino acid in guiding bar is a stabilizer for TXNRD1. Kinetic analysis revealed that the guiding bar is vital for the disulfide reductase activity but hinders the selenocysteine-independent reduction activity of TXNRD1. Meanwhile, the guiding bar shields the selenocysteine residue of TXNRD1 from the attack of electrophilic reagents. We also found that the inhibition of TXNRD1 by caveolin-1 scaffolding domain (CSD) peptides and compound LCS3 did not bind to the guiding bar motif. In summary, the obtained results highlight new aspects of the guiding bar that restrict the flexibility of the C-terminal redox motif and govern the transition from antioxidant to pro-oxidant.

Background: The coronavirus 2019 (COVID-19) pandemic fundamentally changed how populations interface with the healthcare system. Despite historical spikes in US mortality during the pandemic, emergency department (ED) visits were paradoxically low. This is a concerning phenomenon that raises a red flag regarding access to care, especially among vulnerable populations. In this study we sought to understand how ED utilization evolved during the COVID-19 pandemic among traditionally understudied, low-income, racially diverse US- and foreign-born mothers. Methods: This is a secondary analysis of a pre-existing dataset of 3,073 participants enrolled in the Boston Birth Cohort at birth and followed prospectively. We obtained ED visit diagnoses from 2019 and 2020 via electronic health records, categorized according to the International Classification of Diseases, 10th Revision, and compared them using graph plots, chi-square, and negative binomial regression. Results: The number of ED visits decreased by 29.1% (P US born) and mental health- (US-born only) related visits. Our findings demonstrate that EDs remain a critical access point for care for minority populations and have implications for preparedness, resources, and services of EDs in urban settings to better address the needs of communities. However, alternative avenues for healthcare services for these populations, particularly during health crises, warrant further investigation.

With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately categorize them. Land cover classification has practical application value in various fields, such as environmental monitoring and protection, urban and rural planning and management, and climate change research. In recent years, remote sensing image classification methods based on deep learning have been rapidly developed, in which semantic segmentation technology has become one of the mainstream methods for land cover classification using remote sensing image. Traditional semantic segmentation algorithms tend to ignore the edge information, resulting in poor classification of the edge part in land cover classification, and there are numerous attention mechanisms to make improvements for these problems. In this paper, a triple attention mechanism, BETAM (Border-Enhanced Triple Attention Mechanism), for edge feature enhancement of high-resolution remote sensing images is proposed. Furthermore, a new model on the basis of the semantic segmentation network model DeeplabV3+ is also introduced, which is called DeepBETAM. The triple attention mechanism BETAM is able to capture feature dependencies in three dimensions: position, space, and channel, respectively. Through feature importance weighting, modeling of spatial relationships, and adaptive learning capabilities, the model BETAM pays more attention to edge features, thus improving the accuracy of edge detection. A remote sensing image dataset SMCD (Subject Meticulous Categorization Dataset) is constructed to verify the robustness of the attention mechanism BETAM and the model DeepBETAM. Extensive experiments were conducted on the two self-built datasets FRSID and SMCD. Experimental results showed that the mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and mean Recall (mRecall) of DeepBETAM are 63.64%, 71.27%, and 71.31%, respectively. These metrics are superior to DeeplabV3+, DeeplabV3+(SENet), DeeplabV3+(CBAM), DeeplabV3+(SAM), DeeplabV3+(ECANet), and DeeplabV3+(CAM), which are network models that incorporate different attention mechanisms. The reason is that BETAM has better edge segmentation results and segmentation accuracy. Meanwhile, on the basis of the self-built dataset, the four main classifications of buildings, cultivated land, water bodies and vegetation were subdivided and detected, and good experimental results were obtained, which verified the robustness of the attention mechanism BETAM and the model DeepBETAM. The method has broad application prospects and can provide favorable support for research and application in the field of surface classification.

Pests have caused significant losses to agriculture, greatly increasing the detection of pests in the planting process and the cost of pest management in the early stages. At this time, advances in computer vision and deep learning for the detection of pests appearing in the crop open the door to the application of target detection algorithms that can greatly improve the efficiency of tomato pest detection and play an important technical role in the realization of the intelligent planting of tomatoes. However, in the natural environment, tomato leaf pests are small in size, large in similarity, and large in environmental variability, and this type of situation can lead to greater detection difficulty. Aiming at the above problems, a network target detection model based on deep learning, YOLONDD, is proposed in this paper. Designing a new loss function, NMIoU (Normalized Wasserstein Distance with Mean Pairwise Distance Intersection over Union), which improves the ability of anomaly processing, improves the model’s ability to detect and identify objects of different scales, and improves the robustness to scale changes; Adding a Dynamic head (DyHead) with an attention mechanism will improve the detection ability of targets at different scales, reduce the number of computations and parameters, improve the accuracy of target detection, enhance the overall performance of the model, and accelerate the training process. Adding decoupled head to Head can effectively reduce the number of parameters and computational complexity and enhance the model’s generalization ability and robustness. The experimental results show that the average accuracy of YOLONDD can reach 90.1%, which is 3.33% higher than the original YOLOv5 algorithm and is better than SSD, Faster R-CNN, YOLOv7, YOLOv8, RetinaNet, and other target detection networks, and it can be more efficiently and accurately utilized in tomato leaf pest detection.

Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefore, this paper proposes a lightweight flower counting and positioning model, Light-FC-YOLO, based on YOLOv8s. By integrating lightweight convolution, the model is more portable and deployable. At the same time, a new efficient detection head, Efficient head, and the integration of the LSKA large kernel attention mechanism are proposed to enhance the model’s feature detail extraction capability and change the weight ratio of the shallow edge and key point information in the network. Finally, the SIoU loss function with target angle deviation calculation is introduced to improve the model’s detection accuracy and target positioning ability. Experimental results show that Light-FC-YOLO, with a model size reduction of 27.2% and a parameter reduction of 39.0%, has a Mean Average Precision (mAP) and recall that are 0.8% and 1.4% higher than YOLOv8s, respectively. In the counting comparison experiment, the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of Light-FC-YOLO reached 0.9577 and 8.69, respectively, both superior to lightweight models such as YOLOv8s. The lightweight flower detection method proposed in this paper can efficiently complete flower positioning and counting tasks, providing technical support and reference solutions for automated flower production management.

Target detection algorithms can greatly improve the efficiency of tomato leaf disease detection and play an important technical role in intelligent tomato cultivation. However, there are some challenges in the detection process, such as the diversity of complex backgrounds and the loss of leaf symmetry due to leaf shadowing, and existing disease detection methods have some disadvantages in terms of deteriorating generalization ability and insufficient accuracy. Aiming at the above issues, a target detection model for tomato leaf disease based on deep learning with a global attention mechanism, TDGA, is proposed in this paper. The main idea of TDGA includes three aspects. Firstly, TDGA adds a global attention mechanism (GAM) after up-sampling and down-sampling, as well as in the SPPF module, to improve the feature extraction ability of the target object, effectively reducing the interference of invalid targets. Secondly, TDGA uses a switchable atrous convolution (SAConv) in the C3 module to improve the model’s ability to detect. Thirdly, TDGA adopts the efficient IoU loss (EIoU) instead of complete IoU loss (CIoU) to solve the ambiguous definition of aspect ratio and sample imbalance. In addition, the influences of different environmental factors such as single leaf, multiple leaves, and shadows on the performance of tomato disease detection are extensively experimented with and analyzed in this paper, which also verified the robustness of TDGA. The experimental results show that the average accuracy of TDGA reaches 91.40%, which is 2.93% higher than that of the original YOLOv5 network, which is higher than YOLOv5, YOLOv7, YOLOHC, YOLOv8, SSD, Faster R-CNN, RetinaNet and other target detection networks, so that TDGA can be utilized for the detection of tomato leaf disease more efficiently and accurately, even in complex environments.

Abstract Background Maternal pre-pregnancy obesity is an established risk factor for childhood obesity. Investigating epigenetic alterations induced by maternal obesity during fetal development could gain mechanistic insight into the developmental origins of childhood obesity. While obesity disproportionately affects underrepresented racial and ethnic mothers and children in the USA, few studies investigated the role of prenatal epigenetic programming in intergenerational obesity of these high-risk populations. Methods This study included 903 mother–child pairs from the Boston Birth Cohort, a predominantly urban, low-income minority birth cohort. Mother-infant dyads were enrolled at birth and the children were followed prospectively to age 18 years. Infinium Methylation EPIC BeadChip was used to measure epigenome-wide methylation level of cord blood. We performed an epigenome-wide association study of maternal pre-pregnancy body mass index (BMI) and cord blood DNA methylation (DNAm). To quantify the degree to which cord blood DNAm mediates the maternal BMI-childhood obesity, we further investigated whether maternal BMI-associated DNAm sites impact birthweight or childhood overweight or obesity (OWO) from age 1 to age 18 and performed corresponding mediation analyses. Results The study sample contained 52.8% maternal pre-pregnancy OWO and 63.2% offspring OWO at age 1–18 years. Maternal BMI was associated with cord blood DNAm at 8 CpG sites (genome-wide false discovery rate [FDR]

Adenosquamous carcinoma (ASC) is a rare histological type of intrahepatic cholangiocarcinoma, which includes both adenocarcinoma and squamous cell carcinoma. The clinical features, physical examination, routine laboratory tests, and imaging examinations of patients with ASC are nonspecific. ASC is easily misdiagnosed as hepatocellular carcinoma, and patients with ASC always have a poor prognosis. This study reports a patient with ASC who was diagnosed based on pathological results, underwent surgical resection, and received postoperative chemotherapy (gemcitabine plus cisplatin) combined with immunotherapy (sintilimab). During the 1-year follow-up, the patient was in good condition, and no signs of cancer recurrence were noted. This case highlights that surgical resection and chemotherapy combined with immunotherapy may be feasible for patients with ASC.

Land cover classification is of great value and can be widely used in many fields. Earlier land cover classification methods used traditional image segmentation techniques, which cannot fully and comprehensively extract the ground information in remote sensing images. Therefore, it is necessary to integrate the advanced techniques of deep learning into the study of semantic segmentation of remote sensing images. However, most of current high-resolution image segmentation networks have disadvantages such as large parameters and high network training cost. In view of the problems above, a lightweight land cover classification model via semantic segmentation, DeepGDLE, is proposed in this paper. The model DeepGDLE is designed on the basis of DeeplabV3+ network and utilizes the GhostNet network instead of the backbone feature extraction network in the encoder. Using Depthwise Separable Convolution (DSC) instead of dilation convolution. This reduces the number of parameters and increases the computational speed of the model. By optimizing the dilation rate of parallel convolution in the ASPP module, the “grid effect” is avoided. ECANet lightweight channel attention mechanism is added after the feature extraction module and the pyramid pooling module to focus on the important weights of the model. Finally, the loss function Focal Loss is utilized to solve the problem of category imbalance in the dataset. As a result, the model DeepGDLE effectively reduces the parameters of the network model and the network training cost. And extensive experiments compared with several existing semantic segmentation algorithms such as DeeplabV3+, UNet, SegNet, etc. show that DeepGDLE improves the quality and efficiency of image segmentation. Therefore, compared to other networks, the DeepGDLE network model can be more effectively applied to land cover classification. In addition, in order to investigate the effects of different factors on the semantic segmentation performance of remote sensing images and to verify the robustness of the DeepGDLE model, a new remote sensing image dataset, FRSID, is constructed in this paper. This dataset takes into account more influences than the public dataset. The experimental results show that on the WHDLD dataset, the experimental metrics mIoU, mPA, and mRecall of the proposed model, DeepGDLE, are 62.29%, 72.85%, and 72.46%, respectively. On the FRSID dataset, the metrics mIoU, mPA, and mRecall are 65.89%, 74.43%, and 74.08%, respectively. For the future scope of research in this field, it may focus on the fusion of multi-source remote sensing data and the intelligent interpretation of remote sensing images.

Introduction: Monitoring surface water through the extraction of water bodies from high-resolution remote sensing images is of significant importance. With the advancements in deep learning, deep neural networks have been increasingly applied to high-resolution remote sensing image segmentation. However, conventional convolutional models face challenges in water body extraction, including issues like unclear water boundaries and a high number of training parameters.Methods: In this study, we employed the DeeplabV3+ network for water body extraction in high-resolution remote sensing images. However, the traditional DeeplabV3+ network exhibited limitations in segmentation accuracy for high-resolution remote sensing images and incurred high training costs due to a large number of parameters. To address these issues, we made several improvements to the traditional DeeplabV3+ network: Replaced the backbone network with MobileNetV2. Added a Channel Attention (CA) module to the MobileNetV2 feature extraction network. Introduced an Atrous Spatial Pyramid Pooling (ASPP) module. Implemented Focal loss for balanced loss computation.Results: Our proposed method yielded significant enhancements. It not only improved the segmentation accuracy of water bodies in high-resolution remote sensing images but also effectively reduced the number of network parameters and training time. Experimental results on the Water dataset demonstrated superior performance compared to other networks: Outperformed the U-Net network by 3.06% in terms of mean Intersection over Union (mIoU). Outperformed the MACU-Net network by 1.03%. Outperformed the traditional DeeplabV3+ network by 2.05%. The proposed method surpassed not only the traditional DeeplabV3+ but also U-Net, PSP-Net, and MACU-Net networks.Discussion: These results highlight the effectiveness of our modified DeeplabV3+ network with MobileNetV2 backbone, CA module, ASPP module, and Focal loss for water body extraction in high-resolution remote sensing images. The reduction in training time and parameters makes our approach a promising solution for accurate and efficient water body segmentation in remote sensing applications.

Abstract Background In utero exposure to diabetes has been shown to contribute to preterm birth, though the underlying biological mechanisms are yet to be fully elucidated. Fetal epigenetic variations established in utero may be a possible pathway. This study aimed to investigate whether in utero exposure to diabetes was associated with a change in newborn DNA methylation, and whether the identified CpG sites mediate the association between diabetes and preterm birth in a racially diverse birth cohort population. Methods This study included 954 mother–newborn pairs. Methylation levels in the cord blood were determined using the Illumina Infinium MethylationEPIC BeadChip 850 K array platform. In utero exposure to diabetes was defined by the presence of maternal pregestational or gestational diabetes. Preterm birth was defined as gestational age at birth less than 37 weeks. Linear regression analysis was employed to identify differentially methylated CpG sites. Differentially methylated regions were identified using the DMRcate Package. Results 126 (13%) newborns were born to mothers with diabetes in pregnancy and 173 (18%) newborns were born preterm, while 41 newborns were born both preterm and to mothers with diabetes in pregnancy. Genomic-wide CpG analysis found that eighteen CpG sites in cord blood were differentially methylated by maternal diabetes status at an FDR threshold of 5%. These significant CpG sites were mapped to 12 known genes, one of which was annotated to gene Major Histocompatibility Complex, Class II, DM Beta (HLA-DMB). Consistently, one of the two identified significant methylated regions overlapped with HLA-DMB. The identified differentially methylated CpG sites mediated the association between diabetes in pregnancy and preterm birth by 61%. Conclusions In this US birth cohort, we found that maternal diabetes was associated with altered fetal DNA methylation patterns, which substantially explained the link between diabetes and preterm birth.

Abstract Background The mother–child inheritance of DNA methylation (DNAm) variations could contribute to the inheritance of disease susceptibility across generations. However, no study has investigated patterns of mother–child associations in DNAm at the genome-wide scale. It remains unknown whether there are sex differences in mother–child DNAm associations. Results Using genome-wide DNAm profiling data (721,331 DNAm sites, including 704,552 on autosomes and 16,779 on the X chromosome) of 396 mother–newborn pairs (54.5% male) from the Boston Birth Cohort, we found significant sex differences in mother–newborn correlations in genome-wide DNAm patterns (Spearman’s rho = 0.91–0.98; p = 4.0 × 10–8), with female newborns having stronger correlations. Sex differences in correlations were attenuated but remained significant after excluding X-chromosomal DNAm sites (Spearman’s rho = 0.91–0.98; p = 0.035). Moreover, 89,267 DNAm sites (12.4% of all analyzed, including 88,051 [12.5% of analyzed] autosomal and 1,216 [7.2% of analyzed] X-chromosomal sites) showed significant mother–newborn associations in methylation levels, and the top autosomal DNAm sites had high heritability than the genome-wide background (e.g., the top 100 autosomal DNAm sites had a medium h 2 of 0.92). Additionally, significant interactions between newborn sex and methylation levels were observed for 11 X-chromosomal and 4 autosomal DNAm sites that were mapped to genes that have been associated with sex-specific disease/traits or early development (e.g., EFHC2, NXY, ADCYAP1R1, and BMP4). Finally, 18,769 DNAm sites (14,482 [77.2%] on the X chromosome) showed mother–newborn differences in methylation levels that were significantly associated with newborn sex, and the top autosomal DNAm sites had relatively small heritability (e.g., the top 100 autosomal DNAm sites had a medium h 2 of 0.23). These DNAm sites were mapped to 2,532 autosomal genes and 978 X-chromosomal genes with significant enrichment in pathways involved in neurodegenerative and psychological diseases, development, neurophysiological process, immune response, and sex-specific cancers. Replication analysis in the Isle of Wight birth cohort yielded consistent results. Conclusion In two independent birth cohorts, we demonstrated strong mother–newborn correlations in whole blood DNAm on both autosomes and ChrX, and such correlations vary substantially by sex. Future studies are needed to examine to what extent our findings contribute to developmental origins of pediatric and adult diseases with well-observed sex differences.

Abstract In this paper, the organic pigments indigo and isatin were detected by high performance liquid chromatography (HPLC) in The Mural of Four Buddhas which is in Cave 3 from the ancient Chinese Tiantishan grottoes (Ming Dynasty, East Slope). By analysing the preservation conditions of the mural and the environmental conditions of the place where the Tiantishan grottoes are located, we speculated that the isatin detected in this mural was mainly produced by the oxidative decomposition of indigo by ozone (O3), rather than by photodegradation of indigo. We have used theoretical calculation software Gaussian09 (G09) and Amsterdam Density Functional (ADF) module in the Amsterdam Modeling Suite (AMS) software to simulate the reaction mechanism of O3 oxidation of indigo, and the end products of the oxidation of the natural plant dye indigo by O3 were identified as isatin, C16H10N2O3 and C8H6NO3 using HPLC, fluorescence spectroscopy and high performance liquid chromatography-mass spectrometry(HPLC–MS). This finding confirmed the accuracy of the mechanism of indigo fading by O3 oxidation. These findings provided a theoretical basis for subsequent research into the derivation of natural organic dyes in the face of increasing O3 pollution and for better protection of valuable historical and cultural heritage such as ancient Chinese grottoes murals.

Currently, the classification of grapevine black rot disease relies on assessing the percentage of affected spots in the total area, with a primary focus on accurately segmenting these spots in images. Particularly challenging are cases in which lesion areas are small and boundaries are ill-defined, hampering precise segmentation. In our study, we introduce an enhanced U-Net network tailored for segmenting black rot spots on grape leaves. Leveraging VGG as the U-Net’s backbone, we strategically position the atrous spatial pyramid pooling (ASPP) module at the base of the U-Net to serve as a link between the encoder and decoder. Additionally, channel and spatial dual-attention modules are integrated into the decoder, alongside a feature pyramid network aimed at fusing diverse levels of feature maps to enhance the segmentation of diseased regions. Our model outperforms traditional plant disease semantic segmentation approaches like DeeplabV3+, U-Net, and PSPNet, achieving impressive pixel accuracy (PA) and mean intersection over union (MIoU) scores of 94.33% and 91.09%, respectively. Demonstrating strong performance across various levels of spot segmentation, our method showcases its efficacy in enhancing the segmentation accuracy of black rot spots on grapevines.

Plant phenotypic traits play an important role in understanding plant growth dynamics and complex genetic traits. In phenotyping, the segmentation of plant organs, such as leaves and stems, helps in automatically monitoring growth and improving screening efficiency for large-scale genetic breeding. In this paper, we propose an AC-UNet stem and leaf segmentation algorithm based on an improved UNet. This algorithm aims to address the issues of feature edge information loss and sample breakage in the segmentation of plant organs, specifically in Betula luminifera. The method replaces the backbone feature extraction network of UNet with VGG16 to reduce the redundancy of network information. It adds a multi-scale mechanism in the splicing part, an optimized hollow space pyramid pooling module, and a cross-attention mechanism in the expanding network part at the output end to obtain deeper feature information. Additionally, Dice_Boundary is introduced as a loss function in the back-end of the algorithm to circumvent the sample distribution imbalance problem. The PSPNet model achieves mIoU of 58.76%, mPA of 73.24%, and Precision of 66.90%, the DeepLabV3 model achieves mIoU of 82.13%, mPA of 91.47%, and Precision of 87.73%, on the data set. The traditional UNet model achieves mIoU of 84.45%, mPA of 91.11%, and Precision of 90.63%, and the Swin-UNet model achieves . The mIoU is 79.02%, mPA is 85.99%, and Precision is 88.73%. The AC-UNet proposed in this article achieved excellent performance on the Swin-UNet dataset, with mIoU, mPA, and Precision of 87.50%, 92.71%, and 93.69% respectively, which are better than the selected PSPNet, DeepLabV3, traditional UNet, and Swin-UNet. Commonly used semantic segmentation algorithms. Experiments show that the algorithm in this paper can not only achieve efficient segmentation of the stem and leaves of Betula luminifera but also outperforms the existing state-of-the-art algorithms in terms of both speed. This can provide more accurate auxiliary support for the subsequent acquisition of plant phenotypic traits.

Background Pregnancy complications are risk factors for cardiovascular disease (CVD). Little is known about the role of renal biomarkers measured shortly after delivery, individually or in combination with pregnancy complications, in predicting subsequent severe maternal CVD. Methods and Results This study included 566 mothers of diverse races and ethnicities from the Boston Birth cohort, enrolled at delivery and followed prospectively. Plasma creatinine and CysC (cystatin C) were measured 1 to 3 days after delivery. CVD during follow‐up was defined by physician diagnoses in electronic medical records. Associations of renal biomarkers and pregnancy complications with time‐to‐CVD events were assessed using Cox proportional hazards models. During an average of 10.3±3.2 years of follow‐up, 30 mothers developed 1 or more CVDs. Only a modest association was observed between creatinine and risk of CVD. In comparison, we found that per 0.1 mg/L increase of CysC was associated with a hazard ratio (HR) of 1.2 (95% CI, 1.1–1.4) for CVD after adjusting for covariates. Compared with those without preeclampsia and with normal CysC level (≤75th percentile), mothers with preeclampsia and elevated CysC (>75th percentile) had the highest risk of CVD (HR, 4.6 [95% CI, 1.7–17.7]), whereas mothers with preeclampsia only or with elevated CysC only did not have significantly increased CVD risk. Similar synergistic effects for CVD were observed between CysC and preterm delivery. Conclusions In this sample of US, traditionally underrepresented multiracial and multiethnic high‐risk mothers, elevated maternal plasma CysC, independently and jointly with pregnancy complications, increased risk of CVD later in life. These findings warrant further investigation. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03228875.

Cardiac fibrosis is a common pathophysiological remodeling process that occurs in a variety of cardiovascular diseases and greatly influences heart structure and function, progressively leading to the development of heart failure. However, to date, few effective therapies for cardiac fibrosis exist. Abnormal proliferation, differentiation, and migration of cardiac fibroblasts are responsible for the excessive deposition of extracellular matrix in the myocardium. Acetylation, a widespread and reversible protein post-translational modification, plays an important role in the development of cardiac fibrosis by adding acetyl groups to lysine residues. Many acetyltransferases and deacetylases regulate the dynamic alterations of acetylation in cardiac fibrosis, regulating a range of pathogenic conditions including oxidative stress, mitochondrial dysfunction, and energy metabolism disturbance. In this review, we demonstrate the critical roles that acetylation modifications caused by different types of pathological injury play in cardiac fibrosis. Furthermore, we propose therapeutic acetylation-targeting strategies for the prevention and treatment of patients with cardiac fibrosis.

Blurry scenarios, such as light reflections and water ripples, often affect the clarity and signal-to-noise ratio of fish images, posing significant challenges for traditional deep learning models in accurately recognizing fish species. Firstly, deep learning models rely on a large amount of labeled data. However, it is often difficult to label data in blurry scenarios. Secondly, existing deep learning models need to be more effective for the processing of bad, blurry, and otherwise inadequate images, which is an essential reason for their low recognition rate. A method based on the diffusion model and attention mechanism for fish image recognition in blurry scenarios, DiffusionFR, is proposed to solve these problems and improve the performance of species recognition of fish images in blurry scenarios. This paper presents the selection and application of this correcting technique. In the method, DiffusionFR, a two-stage diffusion network model, TSD, is designed to deblur bad, blurry, and otherwise inadequate fish scene pictures to restore clarity, and a learnable attention module, LAM, is intended to improve the accuracy of fish recognition. In addition, a new dataset of fish images in blurry scenarios, BlurryFish, was constructed and used to validate the effectiveness of DiffusionFR, combining bad, blurry, and otherwise inadequate images from the publicly available dataset Fish4Knowledge. The experimental results demonstrate that DiffusionFR achieves outstanding performance on various datasets. On the original dataset, DiffusionFR achieved the highest training accuracy of 97.55%, as well as a Top-1 accuracy test score of 92.02% and a Top-5 accuracy test score of 95.17%. Furthermore, on nine datasets with light reflection noise, the mean values of training accuracy reached a peak at 96.50%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 90.96% and 94.12%, respectively. Similarly, on three datasets with water ripple noise, the mean values of training accuracy reached a peak at 95.00%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 89.54% and 92.73%, respectively. These results demonstrate that the method showcases superior accuracy and enhanced robustness in handling original datasets and datasets with light reflection and water ripple noise.

Most genetic studies of autism spectrum disorder (ASD) have focused on the nuclear genome. Here, the authors show that variations in mitochondrial DNA, detectable at birth, are also associated with risk of ASD.

Abstract Alterations in tryptophan and serotonin have been implicated in various mental disorders; but studies are limited on child neurodevelopmental disabilities such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). This prospective cohort study examined the associations between levels of tryptophan and select metabolites (5-methoxytryptophol (5-MTX), 5-hydroxytryptophan (5-HTP), serotonin, N-acetyltrytophan) in cord plasma (collected at birth) and physician-diagnosed ASD, ADHD and other developmental disabilities (DD) in childhood. The study sample (n = 996) derived from the Boston Birth Cohort, which included 326 neurotypical children, 87 ASD, 269 ADHD, and 314 other DD children (mutually exclusive). These participants were enrolled at birth and followed-up prospectively (from October 1, 1998 to June 30, 2018) at the Boston Medical Center. Higher levels of cord 5-MTX was associated with a lower risk of ASD (aOR: 0.56, 95% CI: 0.41, 0.77) and ADHD (aOR: 0.79, 95% CI: 0.65, 0.96) per Z-score increase, after adjusting for potential confounders. Similarly, children with cord 5-MTX ≥ 25th percentile (vs.

Background: Gestational diabetes mellitus (GDM) is associated with increased risk of cardiovascular disease (CVD). Racial/ethnic differences in GDM prevalence have been described, but disparities by nativity and duration of US residence are not well studied. Methods: We analyzed data from 6088 women (mean age: 27.5 years [standard deviation: 6.3 years]) from the Boston Birth Cohort who self-identified as non-Hispanic Black (NHB; n = 2697), Hispanic (n = 2395), or non-Hispanic White (NHW; n = 996). Using multivariable logistic regression, we examined the cross-sectional association of nativity and duration of US residence (< 10 vs ≥ 10 years) with GDM within each race/ethnicity group. Results: Foreign-born NHB, NHW, and Hispanic women with a duration of US residence of < 10 years had a lower prevalence of CVD risk factors than those with US residence of ≥ 10 years, respectively, as follows: smoking (NHB: 1.7% vs 3.1%; NHW: 5.7% vs 8.1%; Hispanic: 0.4% vs 2.6%); obesity (NHB: 17.1% vs 23.4%; NHW: 3.8% vs 15.6%; Hispanic: 10.9% vs 22.7%); and severe stress (NHB: 8.7% vs 11.9%; NHW: 5.7% vs 28.1%; Hispanic: 3.8% vs 7.3%). In analyses adjusting for sociodemographic characteristics and CVD risk factors, foreign-born NHB women with a duration of US residence of < 10 years had higher odds of having GDM (adjusted odds ratio: 1.60, 95% confidence interval: 0.99-2.60), compared with their US-born counterparts, whereas foreign-born Hispanic women with a duration of US residence of < 10 years had lower odds of having GDM (adjusted odds ratio: 0.54, 95% confidence interval: 0.32-0.91). The odds of having GDM in Hispanic and NHB women with a duration of US residence of ≥ 10 years were not significantly different from those of their US-born counterparts. Conclusions: The “healthy immigrant effect” and its waning with longer duration of US residence apply to the prevalence of GDM among Hispanic women but not NHB women. Further research on the intersectionality of race and nativity-based disparities is needed. Résumé: Introduction: Le diabète sucré gestationnel (DSG) est associé à l’augmentation du risque de maladies cardiovasculaires (MCV). Les différences raciales/ethniques dans la prévalence du DSG ont été décrites, mais les disparités selon le lieu de naissance et la durée de résidence aux É.-U font l’objet de peu d’études. Méthodes: Nous avons analysé les données de 6 088 femmes (âge moyen : 27,5 ans [écart type : 6,3 ans]) de la Boston Birth Cohort qui ont déclaré être noires non hispaniques (NNH; n = 2 697), hispaniques (n = 2 395) ou blanches non hispaniques (BNH; n = 996). À l’aide de la régression logistique multivariée, nous avons examiné l’association transversale entre le lieu de naissance et la durée de résidence aux É.-U. (< 10 vs ≥ 10 ans), et le DSG dans chaque groupe racial/ethnique Résultats: Les femmes NNH, BNH et hispaniques nées à l’étranger qui avaient une durée de résidence aux É.-U. de < 10 ans avaient une prévalence plus faible des facteurs de risque de MCV que celles qui avaient une résidence aux É.-U. de ≥ 10 ans, et ce, de façon respective comme suit : le tabagisme (NNH : 1,7 % vs 3,1 %; BNH : 5,7 % vs 8,1 %; hispaniques : 0,4 % vs 2,6 %); l’obésité (NNH : 17,1 % vs 23,4 %; BNH : 3,8 % vs 15,6 %; hispaniques : 10,9 % vs 22,7 %); le stress important (NNH : 8,7 % vs 11,9 %; BNH : 5,7 % vs 28,1 %; hispaniques : 3,8 % vs 7,3 %). Lors de l’ajustement des caractéristiques sociodémographiques et des facteurs de risque de MCV, les femmes NNH nées à l’étranger qui avaient une durée de résidence aux É.-U. de < 10 ans montraient une plus grande probabilité d’avoir le DSG (rapport de cotes ajusté : 1,60, intervalle de confiance à 95 % : 0,99-2,60) que leurs homologues nées aux É.-U., alors que les femmes hispaniques nées à l’étranger qui avaient une durée de résidence aux É.-U. de < 10 ans montraient une plus faible probabilité d’avoir le DSG (rapport de cotes ajusté : 0,54, intervalle de confiance à 95 % : 0,32-0,91). La probabilité que les femmes hispaniques et NNH qui avaient une durée de résidence aux É.-U. de ≥ 10 ans aient le DSG n’était pas significativement différente de celles de leurs homologues nées aux É.-U. Conclusions: L’« effet de l’immigrant en bonne santé » et son déclin associé à la plus longue durée de résidence aux É.-U. s’appliquent à la prévalence du DSG chez les femmes hispaniques, mais non chez les femmes NNH. D’autres recherches sur l’intersectionnalité entre la race et les disparités selon le lieu de naissance sont nécessaires.

Immune checkpoint inhibitors are generally contraindicated for post-transplant patients. However, we report two patients with metastatic hepatocellular carcinoma (HCC) treated with camrelizumab (SHR-1210), an anti-programmed cell death 1 (PD-1) agent, after liver transplant. Before undergoing immunotherapy, both patients underwent liver allograft biopsy and obtained negative PD-L1 expression in tumor and liver graft specimens by immunohistochemistry. Then, camrelizumab (200 mg) was administered once every 3 weeks. During immunotherapy, the targeted therapy was continued, and the immunosuppression regimen was adjusted to a low-dose level. No graft rejection or other severe adverse reactions were observed. The disease remained stable (SD, mRECIST) for 3 months in one patient and 10 months in the other. Therefore, camrelizumab may have safety and potential benefits in advanced HCC after liver transplant.

Summary: Background: DNA methylation (DNAm) in cord blood has been associated with various prenatal factors and birth outcomes. This study sought to fill an important knowledge gap: the link of cord DNAm with child postnatal growth trajectories from birth to age 18 years (y). Methods: Using data from a US predominantly urban, low-income, multi-ethnic birth cohort (N = 831), we first applied non-parametric methods to identify body-mass-index percentile (BMIPCT) trajectories from birth to age 18 y (the outcome); then, conducted epigenome-wide association study (EWAS) of the outcome, interrogating over 700,000 CpG sites profiled by the Illumina Infinium MethylationEPIC BeadChip. Multivariate linear regression models and likelihood ratio tests (LRT) were applied to examine the DNAm-outcome association in the overall sample and sex strata. Findings: We identified four distinct patterns of BMIPCT trajectories: normal weight (NW), Early overweight or obesity (OWO), Late OWO, and normal to very late OWO. DNAm at CpG18582997 annotated to TPGS1, CpG15241084 of TLR7, and cg24350936 of RAB31 were associated with BMIPCT at birth-to-3 y, 10 y, and 14 y, respectively (LRT FDR < 0.05 for all). Interpretation: In this prospective birth cohort study, we identified 4 distinct and robust patterns of growth trajectories from birth to 18 y, which were associated with variations in cord blood DNAm at genes implicated in inflammation induction pathways. These findings, if further replicated, raise the possibility that these DNAm markers along with early assessment of BMIPCT trajectories may help identify young children at high-risk for obesity later in life. Funding: Detailed in the Acknowledgements section.

Static facultative heap composting of animal manure is widely used in China, but there is almost no systematic research on the phytotoxicity of the produced compost. Here, we evaluated the phytotoxic variation in compost produced by facultative heap composting of four types of animal manure (chicken manure, pig manure, sheep manure, and cattle manure) using different plant seeds (cucumber, radish, Chinese cabbage, and oilseed rape) to determine germination index (GI). The key factors that affected GI values were identified, including the dynamics of the phytotoxicity and microbial community during heap composting. Sensitivity to toxicity differed depending on the type of plant seed used. Phytotoxicity during facultative heap composting, evaluated by the GI, was in the order: chicken manure (0–6.6 %)

Abstract Background Given the trend of increasing maternal age and associated adverse reproductive outcomes in the US, this study aimed to assess whether this association is due to an independent aging or confounded by sociodemographic, biomedical, or behavioral determinants in a predominantly Black US population. Methods Data was from 8509 women enrolled in the Boston Birth Cohort. Adverse reproductive outcomes included spontaneous preterm delivery, cesarean delivery, and low birth weight. Covariates included sociodemographic (parity, race/ethnicity, education, marital status, income, receipt of public assistance, nativity); biomedical (obesity, hypertensive disorders, diabetes mellitus); and behavioral (consistent intake of multivitamin supplements, support from father of baby, support from family, major stress in pregnancy, cigarette smoking, alcohol intake). Analysis included Lowess and marginal probability plots, crude and adjusted sequential logistic regression models to examine age-outcome associations and to what degree the association can be explained by the above covariables. Result Overall, the study sample had high levels of spontaneous preterm birth (18%), cesarean delivery (33%) and low birth weight (26%). Unadjusted models showed no significant difference odds of spontaneous preterm birth by maternal age but higher odds of cesarean section (aOR: 1.77, 95% CI: 1.60, 1.95) and low birth weight (aOR: 1.15, 95% CI: 1.04, 1.28) among women 30 years or older. Adjustment for sociodemographic factors, biomedical conditions and behavioral factors revealed higher odds of spontaneous preterm birth: (aOR: 1.30, 95% CI: 1.14, 1.49), cesarean section deliveries (aOR: 1.68, 95% CI: 1.51, 1.87) and low birth weight (aOR: 1.36, 95% CI: 1.21, 1.53). Across all ages, optimal BMI status and consistent multivitamin supplement intake were protective of spontaneous preterm birth and low birth weight. Conclusion In this high-risk minority population, we demonstrated that the association between increasing maternal age and adverse pregnancy outcomes was due to an independent aging effect and the presence of confounding by sociodemographic, biomedical, and behavioral factors. Some modifiable risk factors to counteract aging effect, include optimizing BMI and consistent intake of multivitamin supplement. A fundamental change in how care is provided to women, particularly low income Black women, is needed with emphasis on the protective role of optimal nutritional status. Trial registration ClinicalTrials.gov Identifier: NCT03228875

The stability of the casing is a crucial prerequisite for implementing the in situ high-temperature steam injection method in oil shale reservoirs. In order to address the issues of substantial expansion, concentrated thermal stresses, and susceptibility to damage observed in traditional straight casings under high temperatures, this paper proposes the utilization of a corrugated casing structure. In this regard, to investigate the impact of the shape and structure of the wellbore casing on its mechanical properties, identical corrugated and straight casings were selected and studied. Uniaxial compression and tensile tests were conducted on the casings, along with coordination deformation experiments between the casing and cement sheath under varying temperatures. Numerical simulations were employed to obtain the deformation characteristics of the corrugated and straight casings under axial compression and tension loads, as well as the stress distribution on the outer casing wall. The results showed that when subjected to the same amount of deformation under axial loading, the corrugated casing experienced lower compressive and tensile loads compared to the straight casing. Moreover, under the sole constraint of cement sheath, increasing the temperature led to lower vertical strains (perpendicular to the ground) at all measuring points of the corrugated casing as compared to the corresponding strains in the straight casing. Numerical simulations revealed that, under the same temperatures, the deformation at the interface between the corrugated casing and the cement sheath was smaller, while the vertical stress at the interface of the corrugated casing was also lower than the straight casing. Overall, the corrugated casing, with its corrugated structure that enabled micro-deformation, effectively mitigated the axial deformation of the casing caused by thermal expansion. Consequently, the corrugated casing reduced the extrusion of wellbore casing on the cement sheath, thereby preserving the integrity and stability of the wellbore cementing structure.

Leaf spot disease and brown spot disease are common diseases affecting maple leaves. Accurate and efficient detection of these diseases is crucial for maintaining the photosynthetic efficiency and growth quality of maple leaves. However, existing segmentation methods for plant diseases often fail to accurately and rapidly detect disease areas on plant leaves. This paper presents a novel solution to accurately and efficiently detect common diseases in maple leaves. We propose a deep learning approach based on an enhanced version of DeepLabV3+ specifically designed for detecting common diseases in maple leaves. To construct the maple leaf spot dataset, we employed image annotation and data enhancement techniques. Our method incorporates the CBAM-FF module to fuse gradual features and deep features, enhancing the detection performance. Furthermore, we leverage the SANet attention mechanism to improve the feature extraction capabilities of the MobileNetV2 backbone network for spot features. The utilization of the focal loss function further enhances the detection accuracy of the affected areas. Experimental results demonstrate the effectiveness of our improved algorithm, achieving a mean intersection over union (MIoU) of 90.23% and a mean pixel accuracy (MPA) of 94.75%. Notably, our method outperforms traditional semantic segmentation methods commonly used for plant diseases, such as DeeplabV3+, Unet, Segnet, and others. The proposed approach significantly enhances the segmentation performance for detecting diseased spots on Liquidambar formosana leaves. Additionally, based on pixel statistics, the segmented lesion image is graded for accurate detection.

Drought is one of the most critical abiotic stresses influencing maize yield. Improving maize cultivars with drought tolerance using marker-assisted selection requires a better understanding of its genetic basis. In this study, a doubled haploid (DH) population consisting of 217 lines was created by crossing the inbred lines Han 21 (drought-tolerant) and Ye 478 (drought-sensitive). The population was genotyped with a 6 K SNP assay and 756 SNP (single nucleotide polymorphism) markers were used to construct a linkage map with a length of 1344 cM. Grain yield (GY), ear setting percentage (ESP), and anthesis–silking interval (ASI) were recorded in seven environments under well-watered (WW) and water-stressed (WS) regimes. High phenotypic variation was observed for all traits under both water regimes. Using the LSMEAN (least-squares mean) values from all environments for each trait, 18 QTL were detected, with 9 associated with the WW and 9 with the WS regime. Four chromosome regions, Chr. 3: 219.8–223.7 Mb, Chr. 5: 191.5–194.7 Mb, Chr. 7: 132.2–135.6 Mb, and Chr. 10: 88.2–89.4 Mb, harbored at least 2 QTL in each region, and QTL co-located in a region inherited favorable alleles from the same parent. A set of 64 drought-tolerant BC3F6 lines showed preferential accumulation of the favorable alleles in these four regions, supporting an association between the four regions and maize drought tolerance. QTL-by-environment interaction analysis revealed 28 edQTL (environment-dependent QTL) associated with the WS regime and 22 associated with the WW regime for GY, ESP, and ASI. All WS QTL and 55.6% of WW QTL were located in the edQTL regions. The hotspot genomic regions identified in this work will support further fine mapping and marker-assisted breeding of drought-tolerant maize.

Abstract Background The maize inbred line A188 is an attractive model for elucidation of gene function and improvement due to its high embryogenic capacity and many contrasting traits to the first maize reference genome, B73, and other elite lines. The lack of a genome assembly of A188 limits its use as a model for functional studies. Results Here, we present a chromosome-level genome assembly of A188 using long reads and optical maps. Comparison of A188 with B73 using both whole-genome alignments and read depths from sequencing reads identify approximately 1.1 Gb of syntenic sequences as well as extensive structural variation, including a 1.8-Mb duplication containing the Gametophyte factor1 locus for unilateral cross-incompatibility, and six inversions of 0.7 Mb or greater. Increased copy number of carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. High ccd1 expression in seeds together with low expression of yellow endosperm 1 (y1) reduces carotenoid accumulation, accounting for the white seed phenotype of A188. Furthermore, transcriptome and epigenome analyses reveal enhanced expression of defense pathways and altered DNA methylation patterns of the embryonic callus. Conclusions The A188 genome assembly provides a high-resolution sequence for a complex genome species and a foundational resource for analyses of genome variation and gene function in maize. The genome, in comparison to B73, contains extensive intra-species structural variations and other genetic differences. Expression and network analyses identify discrete profiles for embryonic callus and other tissues.

Abstract Background ABO-incompatible liver transplantation (ABO-i LT) has become a rescue therapeutic option for patients with severe hepatic failure. Although the use of rituximab greatly reduces the morbidity of antibody-mediated rejection (AMR), severe adverse effects, such as infection and biliary complications, still seriously threaten the survival of transplant recipients. The aim of this study was to evaluate the safety and feasibility of using mesenchymal stem cells (MSCs) to replace rituximab in ABO-i LT. Methods Twenty-two patients with severe hepatic failure undergoing ABO-i LT were enrolled and randomly divided into two groups: the MSC group and the rituximab group. The safety of the application of MSCs and the incidence of allograft rejection, including antibody-mediated rejection (AMR) and acute cellular rejection (ACR), were evaluated in both groups at the 2-year follow-up period as primary endpoints. Recipients and graft survival and other postoperative complications were compared as secondary endpoints. Results No severe MSC-related adverse events were observed during the trial. MSC treatment yielded comparable, if not better, results than rituximab at decreasing the incidence of acute rejection (9.1% vs 27.3%). Inspiringly, compared to those in the rituximab group, the rates of biliary complications (0% vs 45.5%) and infection (9.1% vs 81.8%) were significantly decreased in the MSC group. In addition, there were no significant differences in 2-year graft and recipient survival between the two groups (81.8% vs 72.7%). Conclusions Our data show that MSC transfusion is comparable to rituximab treatment for AMR prophylaxis following ABO-i LT. Additionally, the results indicate that MSCs are more beneficial to the prevention of infection and biliary complications and may be introduced as a novel immunosuppressive approach for ABO-i LT. Trial registration Trial registration: chictr.org.cn , ChiCTR2000037732. Registered 31 August 2020- Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=57074 .

Abstract Background As is well recognized that inflammation plays a crucial role in the genesis and progression of various cancer. Here we investigate the prognostic value of a novel index: the combination of neutrophil to lymphocyte ratio and platelet distribution width (coNLR-PDW) in post-operation patients with resectable hepatocellular carcinoma (HCC). Methods The receiver operating characteristic (ROC) curve was utilized to determine the optimal cutoff values of continuous variables, including the neutrophil-lymphocyte ratio (NLR) and platelet distribution width (PDW). Kaplan-Meier method and the Log-rank test were used to compare survival differences across three groups stratified by the coNLR-PDW score. Univariate and multivariate Cox proportional hazard regression analyses were adopted to identify independent factors of HCC patient’s prognosis. Results 1.59 and 13.0 were perceived as the optimal cutoff value for NLR and PDW based on the ROC curve, respectively. Kaplan-Meier method revealed that a higher coNLR-PDW score predicts poorer overall survival (OS) and disease-free survival (DFS) (P

With the development of computer vision technology, its applications in forestry are steadily becoming wider. To address the problems of inconvenience in transporting unmanned aerial vehicles (UAVs), as well as the complex operation of large instruments for measurement, a new method based on multi-vision image segmentation and coordinate fusion (MISF) is proposed in this paper for measuring the size of standing trees. In MISF, after images of a standing tree are captured using a camera from multiple angles, a semantic segmentation method based on deep learning is used to segment the main body of the standing tree and automatically detect the edge feature points. Next, the effects of visual field splicing and fusion are analyzed collaboratively using the correlations among images, so as to restore the three-dimensional spatial information of the feature points of the tree to be measured. Lastly, the size attributes of the standing tree, such as height, diameter at breast height (DBH), and crown width, are automatically measured. The urban environment measurement experiment showed that the relative errors of tree height, DBH, and crown width measured using the proposed method, i.e., MISF, were 1.89%, 2.42%, and 3.15%, respectively, representing a significant enhancement compared with binocular measurement. On the one hand, the experimental results exhibited a high degree of measurement accuracy; therefore, MISF can be used for the management inventory of typical forests. On the other hand, MISF cannot be used if a tree’s images cannot be acquired due to environmental or other reasons.

Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sensing data, which often performs poorly in multidimensional data scenarios due to weak computational scalability and the problem of dimensional catastrophe, ignoring potential correlations between sensing data and some important information of certain characteristics. In order to capture the correlation of multidimensional sensing data and improve the accuracy of anomaly detection effectively, GTAF, an anomaly detection model for multivariate sequences based on an improved graph neural network with a transformer, a graph attention mechanism and a multi-channel fusion mechanism, is proposed in this paper. First, we added a multi-channel transformer structure for intrinsic pattern extraction of different data. Then, we combined the multi-channel transformer structure with GDN’s original graph attention network (GAT) to attain better capture of features of time series, better learning of dependencies between time series and hence prediction of future values of adjacent time series. Finally, we added a multi-channel data fusion module, which utilizes channel attention to integrate global information and upgrade anomaly detection accuracy. The results of experiments show that the average accuracies of GTAF, the anomaly detection model proposed in this paper, are 92.83% and 96.59% on two datasets from unmanned systems, respectively, which has higher accuracy and computational efficiency compared with other methods.

With a greater amount of attention paid to the pollution of the environment caused by coal processing, methods using soil microorganism repairing to improve coking contaminated soil becomes more and more significant. The biodegradation of toluene or benzoate and the nitrate-reducing process were analyzed in coking contaminated soil by the addition of toluene or benzoate as the sole carbon source as well as energy source in enrichment solution under nitrate-reducing, then the structural diversity of anaerobic denitrification microbial community of degradation toluene or benzoate was compared by high throughput sequencing. The molar ratio of toluene or benzoate to nitrate was 1∶8.7 or 1∶5.9, respectively, a reasonable approximation of the theoretical stoichiometric ratios. After 120 days of enrichment, the main bacteria in the coking soil is Proteobacteria, and the advantage phylum of phenol degradation in soil enrichment under the condition of nitrate reduction is Firmicutes, while for benzoate it is Proteobacteria. α-, and γ-proteobacteria, Bacilli is the dominant class in toluene-degrading bacteria, and the dominant class in the degradation of benzoate is β-proteobacteria. The results show that Exiguobacterium is the predominant genus of toluene-degrading bacteria, while Azoarcus is the dominant species in benzoate.

Background Preeclampsia is a major cause of maternal and fetal morbidity and mortality. Given its large public health burden, there is a need to identify modifiable factors that can be targeted for preeclampsia prevention. In this study, we examined whether a Mediterranean‐style diet is protective for preeclampsia in a large cohort of racially and ethnically diverse, urban, low‐income women. Methods and Results We used data from the Boston Birth Cohort. Maternal sociodemographic and dietary data were obtained via interview and food frequency questionnaire within 24 to 72 hours postpartum, respectively. Additional clinical information, including physician diagnoses of preexisting conditions and preeclampsia, were extracted from medical records. We derived a Mediterranean‐style diet score from the food frequency questionnaire and performed logistic regression to examine the association of the Mediterranean‐style diet score with preeclampsia. Of 8507 women in the sample, 848 developed preeclampsia. 47% were Black, 28% were Hispanic, and the remaining were White/Other. After multivariable adjustment, greatest adherence with MSD was associated with lower preeclampsia odds (adjusted odds ratio comparing tertile 3 to tertile 1, 0.78; 95% CI, 0.64–0.96). A subgroup analysis of Black women demonstrated a similar benefit with an adjusted odds ratio comparing tertile 3 to tertile 1 of 0.74 (95% CI, 0.76–0.96). Conclusions Self‐report of higher adherence to a Mediterranean‐style diet is associated with lower preeclampsia odds, and benefit of this diet is present among Black women as well.

Introduction Pharmacist-participated medication reconciliation proved an effective strategy to decrease the risk of medication discrepancy-related errors. However, it is still under pilot in China and its effectiveness in the Chinese healthcare system remains unclear. This study aims to conduct a pharmacist-participated medication reconciliation intervention for elderly patients in county hospitals in China and to evaluate its effect.Methods and analysis This is a multicentre, prospective, open-label, assessor-blinded, cluster, non-randomised, controlled study for elderly patients. The study will be conducted in seven county hospitals, and the clusters will be hospital wards. In each hospital, two internal medicine wards will be randomly allocated into either intervention group or control group. Patients in the intervention group will receive pharmacist-participated medication reconciliation, and those in the control group will receive standard care. The primary outcome is the incidence of medication discrepancy, and the secondary outcomes are patients’ medication adherence, healthcare utilisation and medical costs within 30 days after discharge.Ethics and dissemination Ethics committee approval of this study was obtained from Peking University Institution Review Board (IRB00001052-21016). We have also obtained ethical approvals from all the participating centres. The findings will be published in scientific and conference presentations.Trail registration number ChiCTR2100045668.

Abstract Background Preeclampsia and preterm delivery (PTD) are believed to affect women’s long-term health including cardiovascular disease (CVD), but the biological underpinnings are largely unknown. We aimed to test whether maternal postpartum metabolomic profiles, especially CVD-related metabolites, varied according to PTD subtypes with and without preeclampsia, in a US urban, low-income multi-ethnic population. Methods This study, from the Boston Birth Cohort, included 980 women with term delivery, 79 with medically indicated PTD (mPTD) and preeclampsia, 52 with mPTD only, and 219 with spontaneous PTD (sPTD). Metabolomic profiling in postpartum plasma was conducted by liquid chromatography-mass spectrometry. Linear regression models were used to assess the associations of each metabolite with mPTD with preeclampsia, mPTD only, and sPTD, respectively, adjusting for pertinent covariates. Weighted gene coexpression network analysis was applied to investigate interconnected metabolites associated with the PTD/preeclampsia subgroups. Bonferroni correction was applied to account for multiple testing. Results A total of 380 known metabolites were analyzed. Compared to term controls, women with mPTD and preeclampsia showed a significant increase in 36 metabolites, mainly representing acylcarnitines and multiple classes of lipids (diacylglycerols, triacylglycerols, phosphocholines, and lysophosphocholines), as well as a decrease in 11 metabolites including nucleotides, steroids, and cholesteryl esters (CEs) (P

Background: Exposure to metals lead (Pb), mercury (Hg), and cadmium (Cd) and trace elements selenium (Se) and manganese (Mn) has been linked to the developmental origins of cardiometabolic diseases, but the mechanisms are not well-understood. Objective: Conduct a metabolome-wide association study to understand how in utero exposure to Pb, Hg, Cd, Se, and Mn affects the metabolic programming of fetuses. Methods: We used data from the Boston Birth Cohort, which enrolled mother-child pairs from Boston, MA. We measured metals and trace elements in maternal red blood cells (RBCs) collected 24–72 h after delivery, and metabolites in cord blood collected at birth. We used multivariable linear regression to examine associations of metals and trace elements with metabolites and Bonferroni correction to account for multiple comparisons. We assessed non-linear associations of metals and trace elements with metabolites using restricted cubic spline plots. Results: This analysis included 670 mother-child pairs (57% non-Hispanic Black and 24% Hispanic). After Bonferroni correction, there were 25 cord metabolites associated with at least one of the metals or trace elements. Pb was negatively associated with the xenobiotic piperine, Cd was positively associated with xenobiotics cotinine and hydroxycotinine, and Hg was associated with 8 lipid metabolites (in both directions). Se and Mn shared associations with 6 metabolites (in both directions), which mostly included nucleotides and amino acids; Se was additionally associated with 7 metabolites (mostly amino acids, nucleotides, and carnitines) and Mn was additionally associated with C36:4 hydroxy phosphatidylcholine. Restricted cubic spline plots showed that most associations were linear. Discussion: Maternal RBC metal and trace element concentrations were associated in a dose-dependent fashion with cord blood metabolites. What remains to be determined is whether these metals- and trace elements-associated changes in cord metabolites can influence a child’s risk of cardiometabolic diseases.