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Abstract This study aimed to construct a novel pelvis-prostate model BPPP which consists of body mass index (BMI), prostate volume (PV), pelvic cavity index (PCI) and prostate-muscle index (PMI) to predict the immediate urinary continence after Retzius-sparing robot assisted laparoscopic radical prostatectomy (RS-RARP). The perioperative data of patients with prostate cancer who underwent RS-RARP in the department of urology of Nanjing Drum Tower Hospital from June 2018 to June 2022 were retrospectively analyzed. 280 patients were eligible for this study in total. Multivariate analysis showed that BMI, PV, PCI, PMI and NVB preservation were significantly associated with immediate urinary continence after RS-RARP. Subgroup analysis showed that patients with low BMI, low PV, high PCI and high PMI had a higher recovery rate of immediate urinary continence. The area under the curve of BPPP (BMI + PV + PCI + PMI) for predicting the immediate recovery of urinary continence after RS-RARP was 0.726. Delong test showed that the area under the curve of the combined test for predicting the immediate urinary continence after RS-RARP was better compared with single parameter (p

Abstract Objective To detail a novel technique of robotic‐assisted simple prostatectomy that makes handling the gland protruding into the bladder neck easier and can preserve the urethra and retain ejaculation function as much as possible. Patients and methods This is a prospective case series. Clinical data of 17 male patients who had large volume (>80 mL) benign prostatic hyperplasia (BPH) were enrolled to undergo trans‐rectovesical pouch urethral‐sparing robotic‐assisted simple prostatectomy (usRASP). We adopted the approach through the space between the bladder neck and seminal vesicle to perform a usRASP that can avoid the detrusor skirt and fibrous matrix area of the retropubic prostate. Between the transitional zone and the peripheral zone of the large prostate, the hyperplastic prostatic gland tissue can be enucleated under direct vision while preserving the prostatic urethra and retaining the ejaculatory duct and bladder neck intact. All preoperative, perioperative and postoperative clinical data were collected, and descriptive analysis was performed. Results The median intravesical prostatic protrusion was 19.3 mm (8.5–32.2). The median operative time was 100 min (75–140), and the median estimated blood loss was 100 mL (10–500). The median time to catheter removal was 7 days (5–7), with a median postoperative hospital stay of 2 days (2–4). After at least 6‐month follow‐up, the median maximum urine flow rate and postvoid residual volume were 40.1 mL/s (12.7–52.4) and 15 mL (5–23), respectively; the median International Prostate Symptom Score and Quality of Life score were 0 (0–6.3) and 1 (0–3), respectively; and the median total prostate‐specific antigen was 0.84 ng/mL (0.15–1.01). All patients successfully underwent usRASP. Fifty‐eight percent of patients with normal ejaculation function before surgery can still retain normal ejaculation function. Conclusion We described a new approach to performing usRASP. This new method remarkably improved the voiding function, maintained antegrade ejaculation and did not increase the post‐operative complications.

Abstract Viruses as the prevailing biological entities are poorly understood in underground realms. Here, we establish the first metagenomic Groundwater Virome Catalogue (GWVC) comprising 280,420 viral species ( ≥ 5 kb) detected from 607 monitored wells in seven geo-environmental zones throughout China. In expanding ~10-fold the global portfolio of known groundwater viruses, we uncover over 99% novel viruses and about 95% novel viral clusters. By linking viruses to hosts from 119 prokaryotic phyla, we double the number of microbial phyla known to be virus-infected in groundwater. As keystone ultrasmall symbionts in aquifers, CPR bacteria and DPANN archaea are susceptible to virulent viruses. Certain complete CPR viruses even likely infect non-CPR bacteria, while partial CPR/DPANN viruses harbor cell-surface modification genes that assist symbiont cell adhesion to free-living microbes. This study reveals the unknown viral world and auxiliary metabolism associated with methane, nitrogen, sulfur, and phosphorus cycling in groundwater, and highlights the importance of subsurface virosphere in viral ecology.

Background: Previous studies found that urinary phthalates (PAEs) metabolites may be associated with increased serum uric acid concentration and hyperuricemia risk. However, no population-based study has investigated the underlying biological mechanisms. Methods: This nationwide cross-sectional study analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2003–2018. Urinary PAEs metabolites were measured and 8 PAEs metabolites (MCPP, MECPP, MEHHP, MEOHP, MBzP, MiBP, MBP, and MEP) were incorporated into the analysis. Serum uric acid was determined and hyperuricemia cases were identified. Multi-variable generalized linear model, exposure-response (E-R) function and weighted quantile sum (WQS) regression were utilized to investigate the relationships of PAEs metabolites with serum uric acid concentration and hyperuricemia risk. Systemic immune inflammation (SII) was assessed using the SII index and its mediation effects were explored using causal mediation effect model. Results: Data from 10,633 US adults in the NHANES 2003–2018 was analyzed. Except for MEP, individual PAEs metabolite and total PAEs metabolites were associated with increased serum uric acid concentration and hyperuricemia risk. E-R function of PAEs metabolites with serum uric acid concentration and the risk of hyperuricemia showed significantly positive associations with most curves in a nearly linear relationship. WQS regression showed that the mixture of PAEs metabolites was related to elevated serum uric acid and hyperuricemia risk, and MBzP was identified as the most contributing PAEs metabolite. The causal mediation effect model found that SII significantly mediated the relationships of PAEs metabolites with serum uric acid and hyperuricemia risk. Conclusion: Individual and mixture of urinary PAEs metabolites were associated with increased serum uric acid concentration and the risk of hyperuricemia. MBzP exhibited the highest contribution to the overall effects. SII alteration may be an important biological mechanism underlining the impact of PAEs metabolites on serum uric acid concentration and hyperuricemia risk.

ObjectiveTo analyze changes in renal function and associated risk factors in patients with bladder cancer undergoing robot-assisted radical cystectomy (RARC) with intracorporeal or extracorporeal urinary diversion (ICUD or ECUD).MethodsClinical-pathological data was extracted from electronic medical records of 266 patients with bladder cancer who underwent RARC at our institution between August 2015 and August 2022. Postoperative renal function was assessed using the estimated glomerular filtration rate (eGFR).ResultPatients were classified into ECUD and ICUD groups based on the surgical approach. Significant differences in eGFR were observed between the two groups at 1, 2, and 3 years postoperatively. Moreover, 112 patients (42.1%) experienced long-term renal function injury. Independent risk factors for long-term renal function injury included the type of surgical approach, ureteroenteric anastomotic strictures, and pathological stage T3 or above. In terms of short-term renal function, 30 cases of acute kidney injury (AKI) were observed, with an incidence rate of 11.3%. No difference in AKI incidence was found between the groups.ConclusionsPostoperative AKI and chronic kidney injury are prevalent complications following RC. This study highlights that pathological stage, ureteroenteric anastomotic strictures, and ECUD significantly impact long-term renal function, but the type of urinary diversion (ileal conduit or orthotopic neobladder) had no effect on renal function, and ICUD was superior in terms of long-term renal injury rate. Therefore, precise preoperative assessment and the selection of appropriate surgical approach are crucial for preserving renal function in patients with bladder cancer.

Abstract Although asymmetric molecular design has been widely demonstrated effective for organic photovoltaics (OPVs), the correlation between asymmetric molecular geometry and their optoelectronic properties is still unclear. To access this issue, we have designed and synthesized several symmetric-asymmetric non-fullerene acceptors (NFAs) pairs with identical physical and optoelectronic properties. Interestingly, we found that the asymmetric NFAs universally exhibited increased open-circuit voltage compared to their symmetric counterparts, due to the reduced non-radiative charge recombination. From our molecular-dynamic simulations, the asymmetric NFA naturally exhibits more diverse molecular interaction patterns at the donor (D):acceptor (A) interface as compared to the symmetric ones, as well as higher D:A interfacial charge-transfer state energy. Moreover, it is observed that the asymmetric structure can effectively suppress triplet state formation. These advantages enable a best efficiency of 18.80%, which is one of the champion results among binary OPVs. Therefore, this work unambiguously demonstrates the unique advantage of asymmetric molecular geometry, unveils the underlying mechanism, and highlights the manipulation of D:A interface as an important consideration for future molecular design.

Abstract This paper focuses on exploring the application possibilities and optimization problems of Generative Adversarial Networks (GANs) in spatial computing to improve design efficiency and creativity and achieve a more intelligent design process. A method for icon generation is proposed, and a basic architecture for icon generation is constructed. A system with generation and optimization capabilities is constructed to meet various requirements in spatial design by introducing the concept of interactive design and the characteristics of requirement conditions. Next, the generated icons can effectively maintain diversity and innovation while meeting the conditional features by integrating multi-feature recognition modules into the discriminator and optimizing the structure of conditional features. The experiment uses publicly available icon datasets, including LLD-Icon and Icons-50. The icon shape generated by the model proposed here is more prominent, and the color of colored icons can be more finely controlled. The Inception Score (IS) values under different models are compared, and it is found that the IS value of the proposed model is 7.05, which is higher than that of other GAN models. The multi-feature icon generation model based on Auxiliary Classifier GANs performs well in presenting multiple feature representations of icons. After introducing multi-feature recognition modules into the network model, the peak error of the recognition network is only 2.000 in the initial stage, while the initial error of the ordinary GAN without multi-feature recognition modules is as high as 5.000. It indicates that the improved model effectively helps the discriminative network recognize the core information of icon images more quickly. The research results provide a reference basis for achieving more efficient and innovative interactive space design.

IntroductionDietary advanced lipoxidation end products (ALEs), which are abundant in heat-processed foods, could induce lipid metabolism disorders. However, limited studies have examined the relationship between maternal ALEs diet and offspring health.MethodsTo investigate the transgenerational effects of ALEs, a cross-generation mouse model was developed. The C57BL/6J mice were fed with dietary ALEs during preconception, pregnancy and lactation. Then, the changes of glycolipid metabolism and gut microbiota of the offspring mice were analyzed.ResultsMaternal ALEs diet not only affected the metabolic homeostasis of dams, but also induced hepatic glycolipid accumulation, abnormal liver function, and disturbance of metabolism parameters in offspring. Furthermore, maternal ALEs diet significantly upregulated the expression of TLR4, TRIF and TNF-α proteins through the AMPK/mTOR/PPARα signaling pathway, leading to dysfunctional glycolipid metabolism in offspring. In addition, 16S rRNA analysis showed that maternal ALEs diet was capable of altered microbiota composition of offspring, and increased the Firmicutes/Bacteroidetes ratio.DiscussionThis study has for the first time demonstrated the transgenerational effects of maternal ALEs diet on the glycolipid metabolism and gut microbiota in offspring mice, and may help to better understand the adverse effects of dietary ALEs.

This study leverages machine learning to enhance the diagnostic accuracy of COVID-19 using chest X-rays. The study evaluates various architectures, including efficient neural networks (EfficientNet), multiscale vision transformers (MViT), efficient vision transformers (EfficientViT), and vision transformers (ViT), against a comprehensive open-source dataset comprising 3616 COVID-19, 6012 lung opacity, 10192 normal, and 1345 viral pneumonia images. The analysis, focusing on loss functions and evaluation metrics, demonstrates distinct performance variations among these models. Notably, multiscale models like MViT and EfficientNet tend towards overfitting. Conversely, our vision transformer model, innovatively fine-tuned (FT) on the encoder blocks, exhibits superior accuracy: 95.79% in four-class, 99.57% in three-class, and similarly high performance in binary classifications, along with a recall of 98.58%, precision of 98.87%, F1 score of 98.73%, specificity of 99.76%, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.9993. The study confirms the vision transformer model’s efficacy through rigorous validation using quantitative metrics and visualization techniques and illustrates its superiority over conventional models. The innovative fine-tuning method applied to vision transformers presents a significant advancement in medical image analysis, offering a promising avenue for improving the accuracy and reliability of COVID-19 diagnosis from chest X-ray images.

Indoor microorganisms impact asthma and allergic rhinitis (AR), but the associated microbial taxa often vary extensively due to climate and geographical variations. To provide more consistent environmental assessments, new perspectives on microbial exposure for asthma and AR are needed. Home dust from 97 cases (32 asthma alone, 37 AR alone, 28 comorbidity) and 52 age- and gender-matched controls in Shanghai, China, were analyzed using high-throughput shotgun metagenomic sequencing and liquid chromatography-mass spectrometry. Homes of healthy children were enriched with environmental microbes, including Paracoccus, Pseudomonas, and Psychrobacter, and metabolites like keto acids, indoles, pyridines, and flavonoids (astragalin, hesperidin) (False Discovery Rate

China consumes 35% of the world’s fertilizer every year; however, most of the nitrogen fertilizers, which are essential for rice cultivation, are not used effectively. In this study, factors affecting the nitrogen leaching loss rate were studied in typical soil and rice varieties in South China. The effects of various irrigation measures on rice growth and nitrogen leaching loss were investigated by conducting experiments with eight groups. These groups included traditional irrigation (TI) and shallow wet irrigation (SWI). The TI is a common irrigation method for farmers in South China, maintaining a water layer of 5-8 cm depth. For SWI, after establishing a shallow water layer usually maintaining at 1-2 cm, paddy is irrigated when the field water level falls to a certain depth, then this process is then repeat as necessary. The nitrogen distribution characteristics were determined using 15N isotope tracing. In addition, the effects of nitrification, denitrification, and microbial composition on soil nitrogen transformation at different depths were studied by microbial functional gene quantification and high-throughput sequencing. The results revealed that in the SWI groups, the total nitrogen leaching loss rate reduced by 0.3-0.8% and the nitrogen use efficiency (NUE) increased by 2.18-4.43% compared with those in the TI groups. After the 15N-labeled nitrogen fertilizer was applied, the main pathways of nitrogen were found to be related to plant absorption and nitrogen residues. Furthermore, paddy soil ammonia-oxidizing archaea were more effective than ammonia-oxidizing bacteria for soil ammonia oxidation by SWI groups. The SWI measures increased the relative abundance of Firmicutes in paddy soil, enhancing the ability of rice to fix nitrogen to produce ammonium nitrogen, thus reducing the dependence of rice on chemical fertilizers. Moreover, SWI enhanced the relative abundance of nirS and nosZ genes within surface soil bacteria, thereby promoting denitrification in the surface soil of paddy fields. SWI also promoted ammonia oxidation and denitrification by increasing the abundance and activity of Proteobacteria, Nitrospirae, and Bacteroidetes. Collectively, SWI effectively reduced the nitrogen leaching loss rate and increase NUE.

Abstract Background Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy with a poor prognosis. The underlying functions and mechanisms of circular RNA and SUMOylation in the development of ICC remain poorly understood. Methods Circular RNA hsa_circ_0001681 (termed Circ-RAPGEF5 hereafter) was identified by circular RNA sequencing from 19 pairs of ICC and adjacent tissue samples. The biological function of Circ-RAPGEF5 in tumor proliferation and metastasis was examined by a series of in vitro assays. A preclinical model was used to validate the therapeutic effect of targeting Circ-RAPGEF5. RNA pull-down and dual-luciferase reporter assays were used to access the RNA interactions. Western blot and Co-IP assays were used to detect SUMOylation levels. Results Circ-RAPGEF5, which is generated from exons 2 to 6 of the host gene RAPGEF5, was upregulated in ICC. In vitro and in vivo assays showed that Circ-RAPGEF5 promoted ICC tumor proliferation and metastasis, and inhibited apoptosis. Additionally, high Circ-RAPGEF5 expression was significantly correlated with a poor prognosis. Further investigation showed that SAE1, a potential target of Circ-RAPGEF5, was also associated with poor oncological outcomes. RNA pull-down and dual-luciferase reporter assays showed an interaction of miR-3185 with Circ-RAPGEF5 and SAE1. Co-IP and western blot assays showed that Circ-RAPGEF5 is capable of regulating SUMOylation. Conclusion Circ-RAPGEF5 promotes ICC tumor progression and SUMOylation by acting as a sponge for miR-3185 to stabilize SAE1. Targeting Circ-RAPGEF5 or SAE1 might be a novel diagnostic and therapeutic strategy in ICC.

This work reports a new methodology using indentation stress relaxation to characterize the dislocation velocity–stress exponent. Through the indentation stress relaxation process, the dislocation structure builds up at the rate governed by dislocation velocity, which is a function of the externally applied stress. The relationship between the dislocation velocity and stress can thus be derived from the indentation stress relaxation data of the stress as a function of time. In this study, instrumented nanoindentation stress relaxation experiments were performed on pure aluminum samples, following three different initial displacement rates of 100, 400, and 800 nm/s. Based on the scaling properties of dislocation kinetics, the data were interpreted to derive a dislocation velocity–stress exponent of 2.5 ± 0.5 for room-temperature aluminum. Crystal plasticity finite-element simulations were performed to illustrate the sensitivity of the proposed nanoindentation stress relaxation methodology to the dislocation velocity–stress exponent value.

Neural networks have been extensively applied to a variety of tasks, achieving astounding results. Applying neural networks in the scientific field is an important research direction that is gaining increasing attention. In scientific applications, the scale of neural networks is generally moderate size, mainly to ensure the speed of inference during application. Additionally, comparing neural networks to traditional algorithms in scientific applications is inevitable. These applications often require rapid computations, making the reduction in neural network sizes increasingly important. Existing work has found that the powerful capabilities of neural networks are primarily due to their nonlinearity. Theoretical work has discovered that under strong nonlinearity, neurons in the same layer tend to behave similarly, a phenomenon known as condensation. Condensation offers an opportunity to reduce the scale of neural networks to a smaller subnetwork with a similar performance. In this article, we propose a condensation reduction method to verify the feasibility of this idea in practical problems, thereby validating existing theories. Our reduction method can currently be applied to both fully connected networks and convolutional networks, achieving positive results. In complex combustion acceleration tasks, we reduced the size of the neural network to 41.7% of its original scale while maintaining prediction accuracy. In the CIFAR10 image classification task, we reduced the network size to 11.5% of the original scale, still maintaining a satisfactory validation accuracy. Our method can be applied to most trained neural networks, reducing computational pressure and improving inference speed.

Abstract Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring the effective connectivity networks from multi-electrode array data. In this paper, we apply a novel statistical method called spectral mirror estimation to the time series of inferred effective connectivity organoid networks. This method produces a one-dimensional iso-mirror representation of the dynamics of the time series of the networks which exhibits a piecewise linear structure. A classical change point algorithm is then applied to this representation, which successfully detects a change point coinciding with the neuroscientifically significant time inhibitory neurons start appearing and the percentage of astrocytes increases dramatically. This finding demonstrates the potential utility of applying the iso-mirror dynamic structure discovery method to inferred effective connectivity time series of organoid networks.

Abstract Background Pneumonia is a common disease worldwide in preschool children. Despite its large population size, China has had no comprehensive study of the national prevalence, risk factors, and management of pneumonia among preschool children. We therefore investigated the prevalence of pneumonia among preschool children in Chinese seven representative cities, and explore the possible risk factors of pneumonia on children, with a view to calling the world's attention to childhood pneumonia to reduce the prevalence of childhood pneumonia. Methods Two group samples of 63,663 and 52,812 preschool children were recruited from 2011 and 2019 surveys, respectively. Which were derived from the cross-sectional China, Children, Homes, Health (CCHH) study using a multi-stage stratified sampling method. This survey was conducted in kindergartens in seven representative cities. Exclusion criteria were younger than 2 years old or older than 8 years old, non-permanent population, basic information such as gender, date of birth and breast feeding is incomplete. Pneumonia was determined on the basis of parents reported history of clearly diagnosed by the physician. All participants were assessed with a standard questionnaire. Risk factors for pneumonia, and association between pneumonia and other respiratory diseases were examined by multivariable-adjusted analyses done in all participants for whom data on the variables of interest were available. Disease management was evaluated by the parents’ reported history of physician diagnosis, longitudinal comparison of risk factors in 2011 and 2019. Results In 2011 and 2019, 31,277 (16,152 boys and 15,125 girls) and 32,016 (16,621 boys and 15,395 girls) preschool children aged at 2–8 of permanent population completed the questionnaire, respectively, and were thus included in the final analysis. The findings showed that the age-adjusted prevalence of pneumonia in children was 32.7% in 2011 and 26.4% in 2019. In 2011, girls (odds ratio [OR] 0.91, 95%CI [confidence interval]0.87–0.96; p = 0.0002), rural (0.85, 0.73–0.99; p = 0.0387), duration of breastfeeding ≥ 6 months(0.83, 0.79–0.88; p

Abstract Background Renal cell carcinoma (RCC) is a prevalent malignancy with a rising incidence in developing countries. Clear cell renal cell carcinoma (ccRCC) constitutes 70% of RCC cases and is prone to metastasis and recurrence, yet lacks a liquid biomarker for surveillance. Extracellular vesicles (EVs) have shown promise as biomarkers in various malignancies. In this study, we investigated the potential of serum EV-derived miRNAs as a biomarker for ccRCC metastasis and recurrence. Materials and methods Patients diagnosed with ccRCC between 2017 and 2020 were recruited in this study. In the discovery phase, high throughput small RNA sequencing was used to analyze RNA extracted from serum EVs derived from localized ccRCC (LccRCC) and advanced ccRCC (AccRCC). In the validation phase, qPCR was employed for quantitative detection of candidate biomarkers. Migration and invasion assays were performed on ccRCC cell line OSRC2. Results Serum EVs derived hsa-miR-320d was significantly up-regulated in patients with AccRCC than in patients with LccRCC (p

The pile-supported embankment has been widely adopted in highway widening projects. There is often significant differential settlement between the new and old embankments. Previous studies have not clarified the interaction mechanisms between different parts of the widening subgrade. In this research, a series of centrifuge model tests were carried out on the subgrades widened by pile-supported embankment on the soft soil base. The effect of the old embankment and piles on the subgrade deformation subject to new embankment is analyzed based on the full-field measurement results of displacement. The embankment displacement is mainly vertically downward and basically caused by the deformation of the soft soil base. The old embankment redistributes the vertical deformation of soft soil base by increasing and decreasing the vertical displacement in turn along the direction towards the slope surface. The piles exhibit supporting effect on the embankment above and restrict the vertical movement of the soil base. The piles influence the deformation of soft soil base within a certain area which expands with the increasing number of piles. The boundary of the pile influential area moves towards the subgrade axis with increasing depth. The vertical deformation of soft soil base shows peaks that are nearly linearly distributed along depth in the pile influential area. As the number of piles increases, the verticality degree of such linear distribution changes from the vertical to the slope firstly and then reverts to the vertical.

Background: Clinical trial is a crucial step in the development of a new therapy (e.g., medication) and is remarkably expensive and time-consuming. Forecasting the approval of clinical trials accurately would enable us to circumvent trials destined to fail, thereby allowing us to allocate more resources to therapies with better chances. However, existing approval prediction algorithms did not quantify the uncertainty and provide interpretability, limiting their usage in real-world clinical trial management. Methods: This paper quantifies uncertainty and improves interpretability in clinical trial approval predictions. We devised a selective classification approach and integrated it with the Hierarchical Interaction Network, the state-of-the-art clinical trial prediction model. Selective classification, encompassing a spectrum of methods for uncertainty quantification, empowers the model to withhold decision-making in the face of samples marked by ambiguity or low confidence. This approach not only amplifies the accuracy of predictions for the instances it chooses to classify but also notably enhances the model’s interpretability. Results: Comprehensive experiments demonstrate that incorporating uncertainty markedly enhances the model’s performance. Specifically, the proposed method achieved 32.37%, 21.43%, and 13.27% relative improvement on area under the precision–recall curve over the base model (Hierarchical Interaction Network) in phase I, II, and III trial approval predictions, respectively. For phase III trials, our method reaches 0.9022 area under the precision–recall curve scores. In addition, we show a case study of interpretability that helps domain experts to understand model’s outcome. The code is publicly available at https://github.com/Vincent-1125/Uncertainty-Quantification-on-Clinical-Trial-Outcome-Prediction. Conclusion: Our approach not only measures model uncertainty but also greatly improves interpretability and performance for clinical trial approval prediction.

Highlights A solid additive-assisted layer-by-layer (SAA-LBL) processing was developed to facilitate the inter-diffusion between polymer donor and acceptor and optimize the morphology of quasi-planar heterojunction for high-performance organic solar cells (OSCs). The pre-phase separation between fatty acid and polymer donor, easily tuned via controlling the cohesive energy, is critical to form the desired vertical phase-separation morphology. The SAA-LBL is generally applicable to various OSC systems, and enables the record efficiency of 19.02% among the binary OSCs.

Machine learning methods have been recognized as rapid methods for satellite-based aerosol retrievals but have not been widely applied in geostationary satellites. In this study, we developed a wide and deep learning model to retrieve the aerosol optical depth (AOD) using Himawari-8. Compared to traditional deep learning methods, we embedded a “wide” modeling component and tested the proposed model across China using independent training (2016–2018) and test (2019) datasets. The results showed that the “wide” model improves the accuracy and enhances model interpretability. The estimates exhibited better accuracy (R2 = 0.81, root-mean-square errors (RMSEs) = 0.19, and within the estimated error (EE) = 63%) than those of the deep-only models (R2 = 0.78, RMSE = 0.21, within the EE = 58%). In comparison with extreme gradient boosting (XGBoost) and Himawari-8 V2.1 AOD products, there were also significant improvements. In addition to higher accuracy, the interpretability of the proposed model was superior to that of the deep-only model. Compared with other seasons, higher contributions of spring to the AOD concentrations were interpreted. Based on the application of the wide and deep learning model, the near-real-time variation of the AOD over China could be captured with an ultrafine temporal resolution.

With the development of vehicle-road network technologies, the future traffic flow will appear in the form of hybrid network traffic flow for a long time. Due to the change in traffic characteristics, the current hard shoulder running strategy based on traditional traffic characteristics cannot effectively serve the hybrid network traffic flow scenario, and will even lead to the further deterioration of traffic congestion. In order to propose a hard shoulder running strategy suitable for a hybrid network environment, a traffic breakdown prediction method based on a hidden Markov model was established. Secondly, the characteristics of traffic breakdown in a hybrid network environment were analyzed. Finally, based on the traffic breakdown characteristics in a hybrid network environment, a dynamic hard shoulder running method based on the hidden Markov model was proposed. The effectiveness of HMMD-HSR was verified by simulation and comparison with HMM-HSR, LMD-HSR, and N-HSR. The simulation results show that the HMMD-HSR proposed in this paper can improve operation efficiency and reduce travel time in a congested expressway.

In the field of data transmission and storage, serialization formats play a crucial role by converting complex data structures into a byte stream that can be easily stored, transmitted, and reconstructed. Despite the myriad available serialization formats, ranging from JSON to Protobuf, each has limitations, particularly in balancing schema flexibility, performance, and data copying overhead. This paper introduces Lite2, a novel data serialization format that addresses these challenges by combining schemaless flexibility with the efficiency of zero-copy operations for flat or key–value pair data types. Unlike traditional formats that often require a predefined schema and involve significant data copying during serialization and deserialization, Lite2 offers a dynamic schemaless approach that eliminates unnecessary data copying, optimizing system performance and efficiency. Built upon a contiguously stored B-tree structure, Lite2 enables efficient data lookup and modification without deserialization, thereby achieving zero-copy operations.

ABSTRACT Some marine bacteria can swim at speeds exceeding 50× their body length per second as they actively seek out fleeting microscale nutrient patches. Yet, even in vast marine environments, most micro-organisms live in surface-associated biofilm communities. Such astonishing speeds can pose a serious challenge to initiating biofilm formation, and our understanding of the mechanisms that regulate deceleration is lacking. Calcium, a major constituent of marine ecosystems, promotes biofilm formation in the marine bacterium and opportunistic human pathogen Vibrio vulnificus by increasing cellular levels of the key secondary signaling molecule bis-(3´-5´)-cyclic dimeric guanosine monophosphate (c-di-GMP). Here, we show that elevated c-di-GMP levels inhibit V. vulnificus swimming motility and identify a PilZ domain protein, PlzD, that mediates this effect. PlzD is localized at the flagellar pole in slow-moving, initially adherent, and mature biofilm cells. This positioning was partly dependent on the flagellar stator protein PomA and was regulated by c-di-GMP. Mutating the conserved RXXXR c-di-GMP binding motif of the PilZ domain abrogated PlzD activity. Single-cell tracking of bacterial swimming trajectories revealed that PlzD altered the foraging behavior of cells by slowing swimming speed and by decreasing the number of directional changes, ultimately limiting exploration of the surrounding 3D space. The cumulative effects were increased biofilm formation, aggregation, oyster colonization, and attenuated virulence in mice, phenotypes underpinning the evolution of V. vulnificus as a resilient environmental organism and potent human pathogen. IMPORTANCE Many free-swimming bacteria propel themselves through liquid using rotary flagella, and mounting evidence suggests that the inhibition of flagellar rotation initiates biofilm formation, a sessile lifestyle that is a nearly universal surface colonization paradigm in bacteria. In general, motility and biofilm formation are inversely regulated by the intracellular second messenger bis-(3´-5´)-cyclic dimeric guanosine monophosphate (c-di-GMP). Here, we identify a protein, PlzD, bearing a conserved c-di-GMP binding PilZ domain that localizes to the flagellar pole in a c-di-GMP-dependent manner and alters the foraging behavior, biofilm, and virulence characteristics of the opportunistic human pathogen, Vibrio vulnificus. Our data suggest that PlzD interacts with components of the flagellar stator to decrease bacterial swimming speed and changes in swimming direction, and these activities are enhanced when cellular c-di-GMP levels are elevated. These results reveal a physical link between a second messenger (c-di-GMP) and an effector (PlzD) that promotes transition from a motile to a sessile state in V. vulnificus.

Polymeric materials have multiple potential applications in nuclear power reactors including advanced and small modulus reactors, in addition to their extensive usage in safety electronic cables in the existing light water reactors. Through the qualification programs, knowledge accumulates about the polymer degradation kinetics under gamma-irradiation environment to facilitate the development of service-life predictive models. This paper aims to promote mechanistic-based predictability as a new approach to the qualification of polymeric components. Reviewed in this article are the current qualification standards and procedures, the current understandings of the degradation mechanisms, and kinetic models applicable to accelerated experiments for qualification.

Urban ecological space (UES) is crucial to the sustainable development of cities and the improvement of human well-being. Rapid urbanization tends to lead to the fragmentation of urban ecological space and functional decline. China's coastal cities are developed regions with high urbanization levels and the unique geographical location makes them have two kinds of ecological spaces, namely marine ecological spaces (MES) and terrestrial ecological spaces (TES). During rapid urbanization, the spatiotemporal changes of the urban ecological space availability (UESA) and its driving factors in China's coastal cities are still unclear. For this, this work selected 53 coastal cities in China as the research samples and applied remote sensing monitoring data of land use to analyze the spatiotemporal patterns of the UESA under different travel modes (walking, public transport, and driving) from 1990 to 2015. Then, the spatial econometric model was applied to reveal the impact of urbanization on urban ecological space availability (UESA). The main research results show: (1) generally, the marine ecological spaces availability (MESA), terrestrial ecological spaces availability (TESA), and marine and terrestrial ecological spaces availability (MTESA) of the coastal cities in China showed an increasing trend from 1990 to 2015. With the increase in travel distance, the MESA, TESA, and MTESA in the coastal cities increased accordingly. (2) however, the spatial pattern of MESA in the coastal cities showed a significant heterogeneity, The hot spots of the MESA mostly appeared in coastal cities from Zhejiang to Fujian, and the cold spots were concentrated in the Liaoning, Hebei, and Shandong. (3) The TESA and MTESA in southern coastal cities were generally higher than that in northern coastal cities. (4) The expansion of the urban built-up area (UBUA) led to the improvement of the UESA in China's coastal cities. GDP and population density were positive factors affecting the TESA and MTESA. Our research can provide scientific planning basis for coordinating marine and terrestrial ecological space and optimizing territorial space planning, which is conducive to improving the life quality of residents and promoting sustainable urban development.

Background & Aims: Nonalcoholic steatohepatitis (NASH), a leading cause of cirrhosis, strongly associates with the metabolic syndrome, an insulin-resistant proinflammatory state that disrupts energy balance and promotes progressive liver degeneration. We aimed to define the role of Smoothened (Smo), an obligatory component of the Hedgehog signaling pathway, in controlling hepatocyte metabolic homeostasis and, thereby, susceptibility to NASH. Methods: We conditionally deleted Smo in hepatocytes of healthy chow-fed mice and performed metabolic phenotyping, coupled with single-cell RNA sequencing (RNA-seq), to characterize the role of hepatocyte Smo in regulating basal hepatic and systemic metabolic homeostasis. Liver RNA-seq datasets from 2 large human cohorts were also analyzed to define the relationship between Smo and NASH susceptibility in people. Results: Hepatocyte Smo deletion inhibited the Hedgehog pathway and promoted fatty liver, hyperinsulinemia, and insulin resistance. We identified a plausible mechanism whereby inactivation of Smo stimulated the mTORC1-SREBP1c signaling axis, which promoted lipogenesis while inhibiting the hepatic insulin cascade. Transcriptomics of bulk and single Smo-deficient hepatocytes supported suppression of insulin signaling and also revealed molecular abnormalities associated with oxidative stress and mitochondrial dysfunction. Analysis of human bulk RNA-seq data revealed that Smo expression was (1) highest in healthy livers, (2) lower in livers with NASH than in those with simple steatosis, (3) negatively correlated with markers of insulin resistance and liver injury, and (4) declined progressively as fibrosis severity worsened. Conclusions: The Hedgehog pathway controls insulin sensitivity and energy homeostasis in adult livers. Loss of hepatocyte Hedgehog activity induces hepatic and systemic metabolic stress and enhances susceptibility to NASH by promoting hepatic lipoxicity and insulin resistance.

ObjectiveA spherical bulkhead with openings has many structural parameters, and it is time-consuming and laborious to directly use the finite element method to carry out parameter research. Therefore, a combined surrogate model method for predicting characteristic stresses is proposed. MethodThe geometric characteristic parameters of an opening spherical bulkhead are extracted and used as design variables to construct a surrogate model for the characteristic stresses of a spherical bulkhead with a single opening. After analyzing the correlation between the characteristic stresses and structural parameters using the surrogate model, a combined surrogate model method with lower dimensions of design variables is proposed. The combined surrogate model consists of a surrogate model of characteristic stresses based on the parameters of a cylindrical-toroidal-spherical shell and a surrogate model of characteristic stress correction coefficients based on the opening fence parameters and thickness of the spherical shell. ResultsThe results show that the accuracy of the combined surrogate model is significantly improved due to the lower dimensions of design variables. Compared with the results of the directly constructed surrogate model, the maximum error and average error of the characteristic stresses are reduced by 4.96%–22.95% and 0.59%–5.43% respectively. The single-opening combination surrogate model is used to predict the characteristic stresses of a typical spherical bulkhead with multiple openings, and the prediction errors of the characteristic stresses are 0.12%–11.42%. ConclusionThe proposed combined surrogate model for the strength of a spherical bulkhead with a single opening can be used for the rapid prediction of the characteristic stresses of spherical bulkheads with multiple openings.

The most widely applied doubly-fed induction generator (DFIG) leads to insufficient frequency regulation ability of power system. The virtual inertia control can adjust the active power of DFIG according to frequency fluctuation. However, the frequent changes in power flow and operating point bring great challenges to traditional virtual inertia controller with fixed parameters. Direct Heuristic Dynamic Programming (Direct HDP) based control parameter regulator is proposed in this paper to obtain the optimal control parameters when the operation condition changes. Compared with the initial parameters, the response has been significantly improved after adaptive optimization by the proposed method when the system operating point changes.

Pressure on the balance of power grid peak-shaving and the consumption of renewable energy is increasing year by year. If the peak-shaving balance problem of the power grid cannot be coordinated efficiently, it will lead to waste of resources such as abandon water and renewable energy. It is not difficult to conclude from historical data that the difficult moments of peak shaving in the regional power grids are usually not at the same period. For a given moment, if area A is lack of peak-shaving ability while area B has plenty of peak-shaving ability. We name the peak-shaving ability borrowed from area B to area A through the transmission line as inter-provincial mutual aid. The overall peak-shaving capacity of the regional power grid can be optimized through the inter-provincial mutual aid measures during the difficult peak-shaving period of each province. This will lead to the minimum abandon of renewable energy. Based on the conception of inter-provincial mutual aid, the authors studied the sensitivity of different inter-provincial mutual aid models to the impact of regional power grid peak shaving balance. Firstly, a method of obtaining the sensitivity of the regional power grid’s peak-shaving balance capability to the inter-provincial mutual aid capability is proposed. Then, the correctness of the raised method is verified through several examples of the Central China Power Grid. The last but not least, the above research results can be used to guide which transmission section has the first priority to be strengthened under a given investment scale.

In the paper, an electric vehicle (EV) charging load prediction approach considering vehicle types is developed. The GM (1,1) model is initially introduced to estimate the ownership of electric vehicles (EVs), and the model is validated by examining the difference between the forecast results and the actual situation. The Monte Carlo algorithm is also used to build the probability distribution model of travel laws and charging characteristics to predict the load demand when the scale of EVs is connected to the grid. For various types of EVs, the charging behavior is analyzed by considering charging start moment, charging duration, the initial state of charge (SOC), daily mileage, and charging mode. The charging load curves for each type of EV are superimposed to obtain the total load demand curve. Then it is compared with the original load curve in a certain region to analyze the impact of EV charging load on the original load curve of the grid, and to provide theoretical guidance for grid planning and optimal dispatching.

Ground states of local Hamiltonians are of key interest in many-body physics and also in quantum information processing. Efficient verification of these states are crucial to many applications, but very challenging. Here we propose a simple, but powerful recipe for verifying the ground states of general frustration-free Hamiltonians based on local measurements. Moreover, we derive rigorous bounds on the sample complexity by virtue of the quantum detectability lemma (with improvement) and quantum union bound. Notably, the number of samples required does not increase with the system size when the underlying Hamiltonian is local and gapped, which is the case of most interest. As an application, we propose a general approach for verifying Affleck-Kennedy-Lieb-Tasaki (AKLT) states on arbitrary graphs based on local spin measurements, which requires only a constant number of samples for AKLT states defined on various lattices. Our work is of interest not only to many tasks in quantum information processing, but also to the study of many-body physics.

Abstract Following significant advances in image acquisition, synapse detection, and neuronal segmentation in connectomics, researchers have extracted an increasingly diverse set of wiring diagrams from brain tissue. Neuroscientists frequently represent these wiring diagrams as graphs with nodes corresponding to a single neuron and edges indicating synaptic connectivity. The edges can contain “colors” or “labels”, indicating excitatory versus inhibitory connections, among other things. By representing the wiring diagram as a graph, we can begin to identify motifs, the frequently occurring subgraphs that correspond to specific biological functions. Most analyses on these wiring diagrams have focused on hypothesized motifs—those we expect to find. However, one of the goals of connectomics is to identify biologically-significant motifs that we did not previously hypothesize. To identify these structures, we need large-scale subgraph enumeration to find the frequencies of all unique motifs. Exact subgraph enumeration is a computationally expensive task, particularly in the edge-dense wiring diagrams. Furthermore, most existing methods do not differentiate between types of edges which can significantly affect the function of a motif. We propose a parallel, general-purpose subgraph enumeration strategy to count motifs in the connectome. Next, we introduce a divide-and-conquer community-based subgraph enumeration strategy that allows for enumeration per brain region. Lastly, we allow for differentiation of edges by types to better reflect the underlying biological properties of the graph. We demonstrate our results on eleven connectomes and publish for future analyses extensive overviews for the 26 trillion subgraphs enumerated that required approximately 9.25 years of computation time.

Abstract SARS-CoV-2 infection triggers profound and variable immune responses in human hosts. Chromatin remodeling has been observed in individuals severely ill or convalescing with COVID-19, but chromatin remodeling early in disease prior to anti-spike protein IgG seroconversion has not been defined. We performed the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and RNA-seq on peripheral blood mononuclear cells (PBMCs) from outpatients with mild or moderate symptom severity at different stages of clinical illness. Early in the disease course prior to IgG seroconversion, modifications in chromatin accessibility associated with mild or moderate symptoms were already robust and included severity-associated changes in accessibility of genes in interleukin signaling, regulation of cell differentiation and cell morphology. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif accessibility for individual PBMC cell types over time. The most extensive remodeling occurred in CD14+ monocytes, where sub-populations with distinct chromatin accessibility profiles were observed prior to seroconversion. Mild symptom severity was marked by upregulation of classical antiviral pathways, including those regulating IRF1 and IRF7, whereas in moderate disease, these classical antiviral signals diminished, suggesting dysregulated and less effective responses. Together, these observations offer novel insight into the epigenome of early mild SARS-CoV-2 infection and suggest that detection of chromatin remodeling in early disease may offer promise for a new class of diagnostic tools for COVID-19.

Abstract Interface phenomena refer to the phenomena caused by various physical and chemical processes occurring at phase interfaces. These phenomena can include adhesion, friction, lubrication, evaporation, condensation, adsorption, monolayer formation, and other phenomena and are applied in many fields, such as food, papermaking, rubber, material science, energy, and biomedicine. However, traditional detection equipment cannot meet the interface phenomena observation needs because the information detected by techniques such as electrochemistry, spectroscopy, chromatography, and mass spectrometry is limited, as are the sensitivity and minimum detection limit. Surface plasmon resonance (SPR) combined technology is a powerful tool for investigating interface phenomena. This paper reviews all combination technologies of SPR with various conventional detection systems, emphasizing the combination with electrochemistry, Raman spectroscopy, and mass spectrometry. These technologies can observe interface phenomena through SPR and provide redox, molecular structure, functional group change, and other information. Through this information, the reactions on the interface can be made clearer and more controllable, which plays an important role in practical detection. In the future, SPR combined technology will mainly develop toward signal enhancement, light source coupling mode, and sensor chip design, and SPR will be combined with various detection technologies to achieve real‐time in situ detection of interface phenomena.

Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim of this study is to apply remote sensing technology for lithological classification and attempt to enhance the accuracy of classification. Taking a study area in Jixi, Heilongjiang Province, China, as an example, lithological classification is conducted using high-resolution satellite remote sensing data from GF-2 and texture data based on gray-level co-occurrence matrix (GLCM). By comparing the accuracy of lithological classification using different methods, the support vector machine (SVM) method with the highest overall accuracy is selected for further investigation. Subsequently, this study compares the effects of combining GF-2 data with different texture data, and the results indicate that combining textures can effectively improve the accuracy of lithological classification. In particular, the combination of GF-2 and the Dissimilarity index performs the best among single-texture combinations, with an overall accuracy improvement of 7.8630% (increasing from 74.6681% to 82.5311%) compared to using only GF-2 data. In the multi-texture combination dataset, the Mean index is crucial for enhancing classification accuracy. Selecting appropriate textures for combination can effectively improve classification accuracy, but it is important to note that excessive overlaying of textures may lead to a decrease in accuracy. Furthermore, this study employs principal component analysis (PCA) and independent component analysis (ICA) to process the GF-2 data and combines the resulting PCA and ICA datasets with different texture data for lithological classification. The results demonstrate that combining PCA and ICA with texture data further enhances classification accuracy. In conclusion, this study demonstrates the application of remote sensing technology in lithological classification, with a focus on exploring the application value of different combinations of multispectral data, texture data, PCA data, and ICA data. These findings provide valuable insights for future research in this field.

Abstract The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic.

This work presents a stable and reliable turnkey continuous-wave laser system for a Yb/Ba multispecies trapped-ion quantum computer. The compact and rack-mountable optics system exhibits high robustness, operating over a year without realignment, regardless of temperature changes in the laboratory. The overall optical system is divided into a few isolated modules interconnected by optical fibers for easy maintenance. The light sources are frequency-stabilized by comparing their frequencies with two complementary references: 1) a commercial Fizeau wavelength meter and 2) a high-finesse optical cavity. This scheme enables automatic frequency stabilization for days with a sub-MHz precision.

The mass loss of the Antarctic Ice Sheet (AIS) is an important contributor to global sea-level rise in response to the warming ocean and atmospheric temperatures as well as the changes in current systems and precipitation patterns. In this study, a regional mascon method is developed to squeeze more mass change signals, in which the pseudo-observations of the geopotential are generated from the unfiltered GRACE Level-2 data whereas the regularization matrix is constructed with the prior information derived from filtered GRACE Level-2 data. A series of mascon solutions with 1°×1° equal-area resolution over AIS is derived from the updated Tongji-Grace2018 model spanning April 2002 to December 2016. Compared to the filtering results from P4M6 decorrelation and 100 km Gaussian filtering, our mascon solutions can effectively suppress the strips, improve the spatial resolution over AIS, and get a stronger signal with an improvement of 116.86% in the Antarctic Peninsula Ice Sheet (APIS), and more coincide with the features of glaciers and ice streams, such as the most striking ice mass loss in Totten, Getz, Thwaites and Pine Island, and the ice mass gain in Kamb Ice Stream. During the period from 2002 to 2016, the mass change rates from our mascon solution are −103.6 ± 5.6 Gt/yr, 63.0 ± 4.3 Gt/yr, −143.3 ± 4.9 Gt/yr and −23.29 ± 1.2 Gt/yr in AIS, East AIS, West AIS, and APIS, respectively. The mass change signals at the basin scale are with even more distinguishing features, with the highest mass gain rates of 18.03 ± 1.88 Gt/yr and 14.55 ± 0.60 Gt/yr at Basin 7 and Basin 18, and the highest mass loss rates of −58.57 ± 2.48 Gt/yr and −44.12 ± 2.27 Gt/yr at Basin 21 and Basin 22. Relative to the cumulated surface mass balance from the regional atmospheric climate model, the correlation coefficients of our mascon solutions are 0.91, 0.94, and 0.96 in East AIS, West AIS, and APIS.

Daily travel distance in urban China has substantially increased. The spatial layout of the 15-minute neighborhood, which supports local living and encourages walking and biking, was detailed in the Urban Residential District Planning and Design Standards in China in 2018. This study investigates the impacts of the 15-minute neighborhood described in the 2018 standards on activity space, using mobile network data in Qingdao, China. A total of 42,991 subscribers of China Mobile are randomly sampled. The 15-minute neighborhood attributes are objectively measured for sampled residents individually. Our study shows that not all 15-minute neighborhood attributes are associated with smaller activity space. Commercial retail services and green open space, which were found to increase walking and physical activity, do not reduce activity space. On the other hand, public services such as primary school and middle school, bus stops, neighborhood centers, and sports facilities within walking distance are positively associated with smaller activity space.

Abstract High‐performance perovskite solar cells (PVSCs) with low energy consumption and green processing are highly desired, but constrained by the difficulty in morphology control and the poor understanding on morphology evolution mechanisms. To address this issue, here we studied the effect of antisolvents on the perovskite film formation. We found that both the antisolvents and the perovskite composition affect the perovskite film morphology greatly via influencing the intermediate phase, and different perovskite compositions require different antisolvents to reach the optimal morphology. This provides the opportunity to achieve high‐performance PVSCs with green antisolvent, that is, isopropanol (iPA) by changing the perovskite compositions, and leads to a power conversion efficiency (PCE) of 21.50% for PVSCs based on MA0.6FA0.4PbI3. Further, we fabricated “fully green” PVSCs with all layers prepared by green solvents, and the optimal PCE can reach 19%, which represents the highest among PVSCs with full‐green processing. This work provides insight into the perovskite morphology evolution and paves the way toward “green” processing PVSCs.

Individuals with type 2 diabetes mellitus (T2DM) have an increased risk of bone metabolic disorders and bone fracture due to disease progression and clinical treatment. The effect of sodium-glucose cotransporter 2 (SGLT2) inhibitors, now greatly prescribed for the treatment of T2DM, on bone metabolism is not clear. This study aimed to explore the possible influence of bone metabolic disorder and the underlying mechanism through a comparison of three different SGLT2 inhibitors (canagliflozin, dapagliflozin, and empagliflozin) in the treatment of type 2 diabetic mice. For the in vivo experiments, four groups (DM, DM+Cana, DM+Dapa, and DM+Empa) were established using micro-CT to detect the bone microarchitecture and bone-related parameters. The study results indicated that canagliflozin, but not dapagliflozin or empagliflozin, increased bone mineral density (p

ObjectiveObese or overweight is a risk factor for some chronic diseases, and oxidative stress and inflammation may be one of the molecular mechanisms leading to the persistence of these chronic diseases. Discovering interventions to alleviate oxidative stress and inflammation in the overweight/obese population, is very important for public health and health education.MethodsA two-week panel intervention study (Run 0-Run 1-Run 2) was conducted. The subjects were 77 overweight/obese undergraduates attending Dali University, with a BMI>24 kg/m2. The physical indices measured at the end of each run included BMI, waist circumference, serum ROS, TNF-α, IL-1β and urinary 8-OHdG. Students were allocated to one of four intervention groups: No intervention (control); walking; taking vitamin C; and walking + taking vitamin C.ResultsThe results demonstrated (1) Walking significantly alleviated ROS levels, and this was consistent in Run 1 and Run 2; (2) During Run1, all three intervention modes reduced levels of 8-OHdG, but there was a statistically insignificant increase during Run 2; (3) No alleviating effects of the three intervention modes on TNF-α levels during Run 1 and Run 2 were observed; (4) The alleviating effects of the three intervention modes on IL-1β levels during Run 1 and Run 2 were clear.ConclusionWalking and taking vitamin C can reduce levels of ROS, 8-OHdG and IL-1β, but not TNF-α, in overweight/obese participants. These interventions may become potential preventive measures for the overweight against obese-induced oxidative stress and inflammation.

The frequency of chronic cutaneous wounds are sharply increasing in aging populations. Patients with age-related diseases, such as diabetes, tumors, renal failure and stroke are prone to soft tissue and skin injury, compounded by slowed healing in aging. Imbalance of wound inflammation, loss of growth factor secretion, and impairment of tissue repair abilities are all possible reasons for failed healing. Therefore, it is vital to explore novel approaches to accelerate wound healing. Platelet-rich plasma (PRP) as a cell therapy has been widely applied for tissue repair and regeneration. PRP promotes wound healing by releasing antimicrobial peptides, growth factors and micro-RNAs. Medical evidence indicates that autologous platelet-rich plasma (au-PRP) can promote wound healing effectively, safely and rapidly. However, its clinical application is usually restricted to patients with chronic cutaneous wounds, generally because of other severe complications and poor clinical comorbidities. Allogeneic platelet-rich plasma (al-PRP), with abundant sources, has demonstrated its superiority in the field of chronic wound treatment. Al-PRP could overcome the limitations of au-PRP and has promising prospects in clinical applications. The aim of this review is to summarize the current status and future challenges of al-PRP in chronic cutaneous wound management. We also summarized clinical cases to further describe the application of al-PRP for chronic wounds in clinical practice.

Background: Increasing prevalence of childhood allergic rhinitis(AR) needs a deeper understanding on the potential adverse effects of early life exposure to air pollution. Objectives: The main aim was to evaluate the effects of maternal exposure to PM2.5 and chemical constituents during pregnancy on preschool children’s AR, and further to explore the modification effects of regions and exclusive breastfeeding. Methods: A multi-center population-based study was performed in 6 cities from 3 regions of China in 2011–2012. Maternal exposure to ambient PM2.5 and main chemical constituents(BC, OM, SO42−, NO3−, NH4+) during pregnancy was assessed and a longitudinal prospective analysis was applied on preschool children’s AR. The modification effects of regions and exclusive breastfeeding were investigated. Results: A total of 8.8% and 9.8% of children reported doctor-diagnosed allergic rhinitis(DDAR) and current hay fever, respectively, and 48.6% had less than 6 months of exclusive breastfeeding. The means of PM2.5 during pregnancy were 52.7 μg/m3, 70.3 μg/m3 and 76.4 μg/m3 in the east, north and central south of China, respectively. Multilevel log-binomial model regression showed that each interquartile range(IQR) increase of PM2.5 during pregnancy was associated with an average increase in prevalence ratio (PR) of DDAR by 1.43(95% confidence interval(CI): 1.11, 1.84) and current hay fever by 1.79(95% CI: 1.26, 2.55), respectively. Among chemical constituents, black carbon (BC) had the strongest associations. Across 3 regions, the eastern cities had the highest associations, followed by those in the central south and the north. For those equal to or longer than 6 months of exclusive breastfeeding, the associations were significantly reduced. Conclusions: Children in east of China had the highest risks of developing AR per unit increase of maternal exposure to PM2.5 during pregnancy, especially BC constituent. Remarkable decline was found in association with an increase in breastfeeding for ≥6 months, in particular in east of China.

Geoscientists use observations and descriptions of the rock record to study the origins and history of our planet, which has resulted in a vast volume of scientific literature. Recent progress in natural language processing (NLP) has the potential to parse through and extract knowledge from unstructured text, but there has, so far, been only limited work on the concepts and vocabularies that are specific to geoscience. Herein we harvest and process public geoscientific reports (i.e., Canadian federal and provincial geological survey publications databases) and a subset of open access and peer-reviewed publications to train new, geoscience-specific language models to address that knowledge gap. Language model performance is validated using a series of new geoscience-specific NLP tasks (i.e., analogies, clustering, relatedness, and nearest neighbour analysis) that were developed as part of the current study. The raw and processed national geological survey corpora, language models, and evaluation criteria are all made public for the first time. We demonstrate that non-contextual (i.e., Global Vectors for Word Representation, GloVe) and contextual (i.e., Bidirectional Encoder Representations from Transformers, BERT) language models updated using the geoscientific corpora outperform the generic versions of these models for each of the evaluation criteria. Principal component analysis further demonstrates that word embeddings trained on geoscientific text capture meaningful semantic relationships, including rock classifications, mineral properties and compositions, and the geochemical behaviour of elements. Semantic relationships that emerge from the vector space have the potential to unlock latent knowledge within unstructured text, and perhaps more importantly, also highlight the potential for other downstream geoscience-focused NLP tasks (e.g., keyword prediction, document similarity, recommender systems, rock and mineral classification).

Colorectal cancer is a leading cause of cancer deaths. Most colorectal cancer patients eventually develop chemoresistance to the current standard-of-care therapies. Here, we used patient-derived colorectal cancer organoids to demonstrate that resistant tumor cells undergo significant chromatin changes in response to oxaliplatin treatment. Integrated transcriptomic and chromatin accessibility analyses using ATAC-Seq and RNA-Seq identified a group of genes associated with significantly increased chromatin accessibility and upregulated gene expression. CRISPR/Cas9 silencing of fibroblast growth factor receptor 1 (FGFR1) and oxytocin receptor (OXTR) helped overcome oxaliplatin resistance. Similarly, treatment with oxaliplatin in combination with an FGFR1 inhibitor (PD166866) or an antagonist of OXTR (L-368,899) suppressed chemoresistant organoids. However, oxaliplatin treatment did not activate either FGFR1 or OXTR expression in another resistant organoid, suggesting that chromatin accessibility changes are patient-specific. The use of patient-derived cancer organoids in combination with transcriptomic and chromatin profiling may lead to precision treatments to overcome chemoresistance in colorectal cancer.

The impoundment of the Three Gorges Reservoir (TGR) has greatly altered the hydrological regime and thus formed a distinctive riparian zone with anti-seasonal inundation and exposure, which may affect the soil aggregate properties in this riparian zone. Yet, the soil aggregate size distribution and stability influenced by the hydrological regime along the step-impounded elevation have rarely been documented. This study aimed to elucidate how the hydrological regime of the TGR affected the aggregate size distribution and stability in the riparian zone. Based on the step-impounded elevation, topsoil samples were collected from four elevation-dependent transects in a middle section of the TGR. Dry-sieving and wet-sieving methods were employed. The results showed that, with a decrease in the elevation gradient, the mass percentage of the >5 mm aggregates significantly decreased, while the proportions of the other size classes presented an increasing trend. Additionally, the mean weight diameter (MWD), geometric mean diameter (GMD), aggregate stability rate (ASR), and percentage of aggregate destruction (PAD) of the fractal dimension showed a successive decrease with a decrease in the elevation gradient, whereas PADMWD, PADGMD, PADASR, and the fractal dimension demonstrated a reverse trend. It can thus be deduced that the hydrological regime of the TGR significantly modified the aggregate size distribution and dramatically reduced the aggregate stability, which may provide a crucial basis for assessing the soil erosion in similar riparian zones.