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Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

Abstract Radiomics offers a noninvasive avenue for predicting clinicopathological factors. However, thorough investigations into a robust breast cancer outcome-predicting model and its biological significance remain limited. This study develops a robust radiomic model for prognosis prediction, and further excavates its biological foundation and transferring prediction performance. We retrospectively collected preoperative dynamic contrast-enhanced MRI data from three distinct breast cancer patient cohorts. In FUSCC cohort (n = 466), Lasso was used to select features correlated with patient prognosis and multivariate Cox regression was utilized to integrate these features and build the radiomic risk model, while multiomic analysis was conducted to investigate the model’s biological implications. DUKE cohort (n = 619) and I-SPY1 cohort (n = 128) were used to test the performance of the radiomic signature in outcome prediction. A thirteen-feature radiomic signature was identified in the FUSCC cohort training set and validated in the FUSCC cohort testing set, DUKE cohort and I-SPY1 cohort for predicting relapse-free survival (RFS) and overall survival (OS) (RFS: p = 0.013, p = 0.024 and p = 0.035; OS: p = 0.036, p = 0.005 and p = 0.027 in the three cohorts). Multiomic analysis uncovered metabolic dysregulation underlying the radiomic signature (ATP metabolic process: NES = 1.84, p-adjust = 0.02; cholesterol biosynthesis: NES = 1.79, p-adjust = 0.01). Regarding the therapeutic implications, the radiomic signature exhibited value when combining clinical factors for predicting the pathological complete response to neoadjuvant chemotherapy (DUKE cohort, AUC = 0.72; I-SPY1 cohort, AUC = 0.73). In conclusion, our study identified a breast cancer outcome-predicting radiomic signature in a multicenter radio-multiomic study, along with its correlations with multiomic features in prognostic risk assessment, laying the groundwork for future prospective clinical trials in personalized risk stratification and precision therapy.

It is of great interest to overcome the problem of high-temperature oxidation of press-hardened steels (PHS) in press hardening with a cost-effective Cr alloying strategy. Therefore, since Cr is one of the expensive metals, the effect of Cr (Cr ≤ 3 wt%) content on the oxidation resistance of Cr–Si–Mn alloyed steels was investigated. It was found that Cr–Si–Mn alloyed PHS with a Cr content of about 1.5 wt% or less cannot form a continuous Cr2O3 layer during press hardening. However, when the Cr content reaches around 2.5 wt%, the PHS shows excellent oxidation resistance due to the formation of continuous Cr2O3 and amorphous SiO2 layers, as well as an Mn-rich oxide layer. However, further addition of 0.5 wt% could not significantly improve the oxidation resistance of this PHS. Moreover, the increase in Cr content simultaneously enhances the positive role of Si and Mn in high-temperature oxidation resistance. This work provides new insights into the design of the next generation of PHSs with economical and superior performance.

Abstract Aim Nutritional characteristics and additives in ultra-processed foods (UPF) are directly related to bone health. Physical activity as a modifiable lifestyle intervention also plays a possible role in bone mineral density (BMD), but effect of physical activity on association between UPF and osteoporosis is not fully understood. Herein, this study aims to explore the association of UPF with osteoporosis, and assess the potential mediating effects of some related factors on this pathway. Methods Data of adults were extracted from the National Health and Nutrition Examination Survey (NHANES) database in this cross-sectional study. Associations of unprocessed/minimally processed food (MPF), processed culinary ingredient (PCI), processed foods (PF) and UPF with femur neck BMD, total femur BMD and osteoporosis were investigated using linear regression and weighted univariate and multivariate logistic regression analyses respectively. Subgroup analyses of age, gender, physical activity, poverty income ratio (PIR), hypertension, diabetes mellitus (DM), cardiovascular disease (CVD), and dyslipidemia were performed. The potential mediating and interaction effects of physical activity and related factors on association of UPF with osteoporosis were also assessed. The evaluation indexes were β, odds ratios (ORs) and 95% confidence intervals (CIs). Results Among 10,678 eligible persons, 454 had osteoporosis. After adjusting for covariates, elevated UPF intake was associated with decreased demur neck and total femur BMD (β=-0.003). A higher UPF intake level (> 57.51%) was linked to higher odds of osteoporosis (OR = 1.789). These relationships were also significant in different subgroups. Physical activity had a potential mediating effect on the association between UPF and osteoporosis (OR = 0.47, mediating proportion = 21.54%). Conclusion UPF intake levels were associated with BMD and osteoporosis. Physical activity had an interaction effect with UPF, and had a potential mediating effect on relationship between UPF and osteoporosis.

Abstract Copy number variation (CNV) tends to occur in genetically enriched regions and is likely associated with a number of complex diseases such as skin aging. In this study, we investigated the genome-wide CNVs in 20 wrinkled skin cases (WSC) of Xiang pigs and 63 controls, and identified 7893 copy number variable regions (CNVRs). We estimated the F-statistic (Fst) at each locus and identified that 93 case-controls stratified CNVRs (Fst > = 0.15) overlapped with 87 known genes. Functional enrichment analysis showed that most of these genes were predominantly enriched in pathways and terms related to the extracellular matrix. Finally, we found that some CNVs were predicted to have high effects on genes such as VCAN, TIMP1 and FOXO1 through transcriptional amplification, transcript ablation and so on. Most of the genes overlapped with those CNVRs have been reported to be related to aging in human or animals. The copy numbers presented the positive correlations with the transcript level of the genes in skins between the cases and controls. Our results suggested that those 22 CNVRs, including 19 CNV losses and 3 CNV gains, were putatively associated with the skin wrinkle of Xiang pigs.

Abstract Background Populus spp. is a crucial fast-growing and productive tree species extensively cultivated in the mid-latitude plains of the world. However, the impact of intensive cultivation management on gene expression in plantation remains largely unexplored. Results Precision water and fertilizer-intensive management substantially increased key enzyme activities of nitrogen transport, assimilation, and photosynthesis (1.12–2.63 times than CK) in Populus × euramericana 'Neva' plantation. Meanwhile, this management approach had a significant regulatory effect on the gene expression of poplar plantations. 1554 differential expression genes (DEGs)were identified in drip irrigation (ND) compared with conventional irrigation. Relative to ND, 2761–4116 DEGs, predominantly up-regulated, were identified under three drip fertilization combinations, among which 202 DEGs were mainly regulated by fertilization. Moreover, drip irrigation reduced the expression of cell wall synthesis-related genes to reduce unnecessary water transport. Precision drip and fertilizer-intensive management promotes the synergistic regulation of carbon and nitrogen metabolism and up-regulates the expression of major genes in nitrogen transport and assimilation processes (5 DEGs), photosynthesis (15 DEGs), and plant hormone signal transduction (11 DEGs). The incorporation of trace elements further enhanced the up-regulation of secondary metabolic process genes. In addition, the co-expression network identified nine hub genes regulated by precision water and fertilizer-intensive management, suggesting a pivotal role in regulating the growth of poplar. Conclusion Precision water and fertilizer-intensive management demonstrated the ability to regulate the expression of key genes and transcription factor genes involved in carbon and nitrogen metabolism pathways, plant hormone signal transduction, and enhance the activity of key enzymes involved in related processes. This regulation facilitated nitrogen absorption and utilization, and photosynthetic abilities such as light capture, light transport, and electron transport, which faintly synergistically regulate the growth of poplar plantations. These results provide a reference for proposing highly efficient precision intensive management to optimize the expression of target genes.

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N2O and CH4) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

Abstract Structural superlubricity (SSL) is a state of contact with no wear and ultralow friction. SSL has been characterized at contact with van der Waals (vdW) layered materials, while its stability under extreme loading conditions has not been assessed. By designing both self-mated and non-self-mated vdW contacts with materials chosen for their high strengths, we report outstanding robustness of SSL under very high pressures in experiments. The incommensurate self-mated vdW contact between graphite interfaces can maintain the state of SSL under a pressure no lower than 9.45 GPa, and the non-self-mated vdW contact between a tungsten tip and graphite substrate remains stable up to 3.74 GPa. Beyond this critical pressure, wear is activated, signaling the breakdown of vdW contacts and SSL. This unexpectedly strong pressure-resistance and wear-free feature of SSL breaks down the picture of progressive wear. Atomistic simulations show that lattice destruction at the vdW contact by pressure-assisted bonding triggers wear through shear-induced tearing of the single-atomic layers. The correlation between the breakdown pressure and material properties shows that the bulk modulus and the first ionization energy are the most relevant factors, indicating the combined structural and electronic effects. Impressively, the breakdown pressures defined by the SSL interface could even exceed the strength of materials in contact, demonstrating the robustness of SSL. These findings offer a fundamental understanding of wear at the vdW contacts and guide the design of SSL-enabled applications.

The rapid detection of grid voltage faults is crucial to achieve grid voltage fault ride-through. However, conventional grid voltage detection methods, including Fourier transform, dq axis transform, wavelet transform, and voltage peak methods, generally yield varying degrees of distortions in the detection results of short-time disturbances in grid voltage. To address this, this paper proposes an approach for quickly detecting short-time disturbances in grid voltage. Leveraging the distinctive high harmonic content present in grids, the suggested strategy involves a transformation of grid voltage calculated using the Fourier transform method, known for its robust anti-interference performance, into high-frequency signals through coordinate conversion. The integration of the rolling sliding window method addresses the extended calculation time issue associated with the Fourier transform, enabling the detection of grid voltage disturbance signals within a single high-frequency signal cycle using this method. The simulation experiment results demonstrated the wide applicability of the proposed method compared with other methods. It is not only effective for short-time grid voltage disturbances, but also suitable for long-time grid voltage disturbances. Regardless of whether high-frequency harmonics are present or absent in the grid voltage, this method allows for the rapid detection of grid voltage disturbances (within about 2 ms). Moreover, its accuracy in detecting grid voltage amplitudes aligns with requirements in engineering applications.

Abstract Background Computed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, it is critical to correctly determine false-negative results. Therefore, the current study was designed to construct a predictive model for predicting false-negative cases among patients receiving CTB for PNs who receive non-malignant results. Materials and methods From January 2016 to December 2020, consecutive patients from two centers who received CTB-based non-malignant pathology results while undergoing evaluation for PNs were examined retrospectively. A training cohort was used to discover characteristics that predicted false negative results, allowing the development of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy. Results The training cohort included 102 patients with PNs who showed non-malignant pathology results based on CTB. Each patient underwent CTB for a single nodule. Among these patients, 85 and 17 patients, respectively, showed true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUVmax, P = 0.001) and CTB-based findings of suspected malignant cells (P = 0.043) were identified as being predictive of false negative results. Following that, these two predictors were combined to produce a predictive model. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.945. Furthermore, it demonstrated sensitivity and specificity values of 88.2% and 87.1% respectively. The testing cohort included 62 patients, each of whom had a single PN. When the developed model was used to evaluate this testing cohort, this yielded an AUC value of 0.851. Conclusions In patients with PNs, the predictive model developed herein demonstrated good diagnostic effectiveness for identifying false-negative CTB-based non-malignant pathology data.

Abstract Background Skeletal muscle development plays a crucial role in yield and quality of pork; however, this process is influenced by various factors. In this study, we employed whole-genome bisulfite sequencing (WGBS) and transcriptome sequencing to comprehensively investigate the longissimus dorsi muscle (LDM), aiming to identify key genes that impact the growth and development of Duroc pigs with different average daily gains (ADGs). Results Eight pigs were selected and divided into two groups based on ADGs: H (774.89 g) group and L (658.77 g) group. Each pair of the H and L groups were half-siblings. The results of methylation sequencing revealed 2631 differentially methylated genes (DMGs) involved in metabolic processes, signalling, insulin secretion, and other biological activities. Furthermore, a joint analysis was conducted on these DMGs and the differentially expressed genes (DEGs) obtained from transcriptome sequencing of the same individual. This analysis identified 316 differentially methylated and differentially expressed genes (DMEGs), including 18 DMEGs in promoter regions and 294 DMEGs in gene body regions. Finally, LPAR1 and MEF2C were selected as candidate genes associated with muscle development. Bisulfite sequencing PCR (BSP) and quantitative real-time PCR (qRT–PCR) revealed that the promoter region of LPAR1 exhibited significantly lower methylation levels (P < 0.05) and greater expression levels (P < 0.05) in the H group than in the L group. Additionally, hypermethylation was observed in the gene body region of MEF2C, as was a low expression level, in the H group (P < 0.05). Conclusions These results suggest that the differences in the ADGs of Duroc pigs fed the same diet may be influenced by the methylation levels and expression levels of genes related to skeletal muscle development.

Forests have experienced unprecedented decline and mortality beyond their historical range in past decades, which is attributed to disturbances like drought, fire, insects and disease. Traditional disturbance detection methods that typically employ a time series perspective to identify discrete disturbance events within continuous tree growth signals and were mainly designed for gap-scale often fail to identify disturbances across populations. To more accurately identify growth suppression and release clusters based on the perspective of forest population dynamics, here we applied a novel method of detecting forest disturbances using a Gaussian mixture model with an expectation maximisation algorithm (DGE), to fit annual distributions of growth indicators, i.e., tree-ring index. We further compared our novel approach of DGE with five traditional methods based on two sets of real tree-ring data and simulated tree-ring data. The results show an improvement of accuracy (35.5 %–48.1 %), sensitivity (36.6 %–58.1 %), precision (21.2 %–51.5 %) and specificity (11.1 %–20.6 %) for the average of two real radial growth datasets and an improvement of accuracy (7.6 %–13.9 %), sensitivity (70 %), precision (100 %) and specificity (9.1 %–9.7 %) for the simulation radial growth dataset, indicating that our DGE approach performs much better than the traditional methods. This study contributes to an improved strategy and understanding for sustainable forest management in the context of climate warming, demonstrating that our DGE approach can be better applied to global forest disturbance detection under global change.

Ubiquitination is a crucial post-translational modification of proteins that mediates the degradation or functional regulation of specific proteins. This process participates in various biological processes such as cell growth, development, and signal transduction. E3 ubiquitin ligases play both positive and negative regulatory roles in osteogenesis and differentiation by ubiquitination-mediated degradation or stabilization of transcription factors, signaling molecules, and cytoskeletal proteins. These activities affect the proliferation, differentiation, survival, and bone formation of osteoblasts (OBs). In recent years, advances in genomics, transcriptomics, and proteomics have led to a deeper understanding of the classification, function, and mechanisms of action of E3 ubiquitin ligases. This understanding provides new insights and approaches for revealing the molecular regulatory mechanisms of bone formation and identifying therapeutic targets for bone metabolic diseases. This review discusses the research progress and significance of the positive and negative regulatory roles and mechanisms of E3 ubiquitin ligases in the process of osteogenic differentiation. Additionally, the review highlights the role of E3 ubiquitin ligases in bone-related diseases. A thorough understanding of the role and mechanisms of E3 ubiquitin ligases in osteogenic differentiation could provide promising therapeutic targets for bone tissue engineering based on stem cells.

Abstract Background Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) is one of the causes of tumor immune tolerance and failure of cancer immunotherapy. Here, we found that bladder cancer (BCa)-derived exosomal circRNA_0013936 could enhance the immunosuppressive activity of PMN-MDSCs by regulating the expression of fatty acid transporter protein 2 (FATP2) and receptor-interacting protein kinase 3 (RIPK3). However, the underlying mechanism remains largely unknown. Methods BCa-derived exosomes was isolated and used for a series of experiments. RNA sequencing was used to identify the differentially expressed circRNAs. Western blotting, immunohistochemistry, immunofluorescence, qRT-PCR, ELISA and Flow cytometry were performed to reveal the potential mechanism of circRNA_0013936 promoting the immunosuppressive activity of PMN-MDSC. Results CircRNA_0013936 enriched in BCa-derived exosomes could promote the expression of FATP2 and inhibit the expression of RIPK3 in PMN-MDSCs. Mechanistically, circRNA_0013936 promoted the expression of FATP2 and inhibited the expression of RIPK3 expression via sponging miR-320a and miR-301b, which directly targeted JAK2 and CREB1 respectively. Ultimately, circRNA_0013936 significantly inhibited the functions of CD8+ T cells by up-regulating FATP2 through the circRNA_0013936/miR-320a/JAK2 pathway, and down-regulating RIPK3 through the circRNA_0013936/miR-301b/CREB1 pathway in PMN-MDSCs. Conclusions BCa-derived exosomal circRNA_0013936 promotes suppressive immunity by up-regulating FATP2 through the circRNA_0013936/miR-320a/JAK2 pathway and down-regulating RIPK3 through the circRNA_0013936/miR-301b-3p/CREB1 pathway in PMN-MDSCs. These findings help to find new targets for clinical treatment of human bladder cancer.

The occurrence and development of gastric cancer (GC) is determined by many factors. In recent years, the development of molecular biology has provided new ideas for the diagnosis and treatment of GC. Microsatellite instability (MSI) GC is a special type of GC caused by the body 's DNA mismatch repair (MMR) gene defect. Studies of the clinical characteristics, molecular mechanisms and prognosis of MSI GC found specific clinicopathological features and better prognosis in MSI GC compared with ordinary types of GC. However, there are still some controversies in the study of MSI gastric cancer at home and abroad. This article aims to review the concept of MSI, the relationship between MSI and clinicopathological features of gastric cancer, and the treatment progress of MSI gastric cancer.

Abstract As the largest transporter family impacting on tumor genesis and development, the prognostic value of solute carrier (SLC) members has not been elucidated in colorectal cancer (CRC). We aimed to identify a prognostic signature from the SLC members and comprehensively analyze their roles in CRC. Firstly, we downloaded transcriptome data and clinical information of CRC samples from GEO (GSE39582) and TCGA as training and testing dataset, respectively. We extracted the expression matrix of SLC genes and established a prognostic model by univariate and multivariate Cox regression. Afterwards, the low-risk and high-risk group were identified. Then, the differences of prognosis traits, transcriptome features, clinical characteristics, immune infiltration and drug sensitivity between the two groups were explored. Furthermore, molecular subtyping was also implemented by non-negative matrix factorization (NMF). Finally, we studied the expression of the screened SLC genes in CRC tumor tissues and normal tissues as well as investigated the role of SLC12A2 by loss of function and gain of function. As a result, we developed a prognostic risk model based on the screened 6-SLC genes (SLC39A8, SLC2A3, SLC39A13, SLC35B1, SLC4A3, SLC12A2). Both in the training and testing sets, CRC patients in the high-risk group had the poorer prognosis and were in the more advanced pathological stage. What’s more, the high-risk group were enriched with CRC progression signatures and immune infiltration. Two groups showed different drug sensitivity. On the other hand, two distinct subclasses (C1 and C2) were identified based on the 6 SLC genes. CRC patients in the high-risk group and C1 subtype had a worse prognosis. Furthermore, we found and validated that SLC12A2 was steadily upregulated in CRC. A loss-of-function study showed that knockdown of SLC12A2 expression restrained proliferation and stemness of CRC cells while a gain-of-function study showed the contrary results. Hence, we provided a 6-SLC gene signature for prognosis prediction of CRC patients. At the same time, we identified that SLC12A2 could promote tumor progression in CRC, which may serve as a potential therapeutic target.

Cropland carbon budget depicts the amount of carbon flowing in and out of agroecosystems and the changes in carbon stocks of soil and living biomass during the same period. Soil carbon credit is the additional change in soil carbon stock under certain farming practices compared with the business-as-usual practices. Accurately calculating cropland carbon budget and soil carbon credit is critical to assessing climate change mitigation potential in agroecosystems. The calculation of cropland carbon budget and soil carbon credit is sensitive to local soil and climatic conditions, especially initial soil organic carbon (SOC) stock, which is determined by both SOC concentration (SOC%) and bulk density (Bulk_Density). SOC stock data are either from soil sampling or gridded public survey data. In agroecosystem models, SOC stock data are a key model input for quantifying cropland carbon budget and soil carbon credit. However, various types and degrees of uncertainties exist in SOC stock datasets, which propagate to the quantification of SOC stock change. In particular, a large discrepancy is found in two widely used SOC stock datasets — Rapid Carbon Assessment dataset (RaCA) and Gridded Soil Survey Geographic Database (gSSURGO) — in the U.S. Midwest, with a relative difference (quantified using Normalized Root Mean Square Error, NRMSE) of 48.0% for 0–30 cm SOC stock between the two datasets. It remains largely unclear how uncertainty in SOC stocks affects the calculation of cropland carbon budget and soil carbon credit. To address this question, we used a well-validated process-based agroecosystem model, ecosys, to assess the impacts of SOC stock uncertainty on carbon budget and soil carbon credit calculation in the U.S. Midwestern corn-soybean rotation systems. Our results reveal the following findings: (1) A sizable discrepancy exists in simulated cropland carbon budget between using gSSURGO and using RaCA for their SOC% and Bulk_Density as model inputs, with a Pearson correlation coefficient (r) of only 0.4 for simulated change of SOC stock (ΔSOC) using these two different soil datasets. (2) Simulated cropland carbon budget components were more sensitive to initial SOC% than to Bulk_Density. For example, the upper and lower quartiles of multi-year averaged ΔSOC were −29.8 and 4.8 gC/m2/year for the selected counties respectively, with an uncertainty of 13.7 and 0.7 gC/m2/year induced by uncertainties in initial SOC% and Bulk_Density, respectively. (3) Both simulated ΔSOC and its uncertainty were negatively correlated with initial SOC%, whereas ΔSOC was negatively correlated with air temperature, and ΔSOC uncertainty was positively correlated with air temperature. (4) The uncertainty of calculated soil carbon credits was much smaller compared with the uncertainty of calculated absolute carbon budgets assuming the same SOC stock uncertainty level in the inputs. Specifically, in our assessment comparing planting cover crops vs no cover crop, the uncertainty of calculated soil carbon credits induced by initial SOC% uncertainty was less than 4% (relative to the quantified value of the soil carbon credits) for 90% of the cases. Our analysis highlights that high accuracy measurement of SOC% as inputs is needed for the calculation of cropland carbon budgets; however, soil carbon credit quantification is much less sensitive to the initial SOC% inputs, and the current publicly available soil datasets (e.g., gSSURGO) are largely suitable for the calculation of soil carbon credits.

Objective: The present study aimed to explore the function of human bone marrow mesenchymal stem cells (hBMMSCs)-derived exosomal long noncoding RNA histocompatibility leukocyte antigen complex P5 (HCP5) in the osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs) to improve chronic periodontitis (CP). Methods: Exosomes were extracted from hBMMSCs. Alizarin red S staining was used to detect mineralised nodules. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to measure HCP5 and miR-24-3p expression. The mRNA and protein levels of alkaline phosphatase (ALP), osteocalcin, osterix, runt-related transcription factor 2, bone morphogenetic protein 2, osteopontin, fibronectin, collagen 1, heme oxygenase 1 (HO1), P38, and ETS transcription factor ELK1 (ELK1) were detected using RT-qPCR and Western blot. Enzyme-linked immunosorbent assay (ELISA) kits were used to determine the HO1 and carbon monoxide concentrations. Heme, biliverdin, and Fe2+ levels were determined using detection kits. Micro-computed tomography, hematoxylin and eosin staining, ALP staining, tartrate-resistant acid phosphatase staining, ELISA, and RT-qPCR were conducted to evaluate the effect of HCP5 on CP mice. Dual luciferase, RNA immunoprecipitation, and RNA pulldown experiments were performed to identify the interactions among HCP5, miR-24-3p, and HO1. Results: The osteogenic ability of hPDLSCs significantly increased when co-cultured with hBMMSCs or hBMMSCs exosomes. Overexpression of HCP5 and HO1 in hBMMSCs exosomes promoted the osteogenic differentiation of hPDLSCs, and knockdown of HCP5 repressed the osteogenic differentiation of hPDLSCs. HCP5 knockdown enhanced the inflammatory response and repressed osteogenesis in CP mice. MiR-24-3p overexpression diminished the stimulatory effect of HCP5 on the osteogenic ability of hPDLSCs. Mechanistically, HCP5 acted as a sponge for miR-24-3p and regulated HO1 expression, and HO1 activated the P38/ELK1 pathway. Conclusion: HBMMSCs-derived exosomal HCP5 promotes the osteogenic differentiation of hPDLSCs and alleviates CP by regulating the miR-24-3p/HO1/P38/ELK1 signalling pathway.

Abstract Background To date, carbohydrate antigen 19‐9 (CA19‐9) and carcinoembryonic antigen (CEA) have been widely used for the screening, diagnosis and prediction of biliary tract cancer (BTC) patients. However, few studies with large sample sizes of carbohydrate antigen 50 (CA50) were reported in BTC patients. Methods A total of 1121 patients from the Liver Cancer Clin‐Bio Databank of Anhui Hepatobiliary Surgery Union between January 2017 and December 2022 were included in this study (673 in the training cohort and 448 in the validation cohort): among them, 458 with BTC, 178 with hepatocellular carcinoma (HCC), 23 with combined hepatocellular‐cholangiocarcinoma, and 462 with nontumor patients. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the diagnostic efficacy and clinical usefulness. Results ROC curves obtained by combining CA50, CA19‐9, and AFP showed that the AUC value of the diagnostic MODEL 1 was 0.885 (95% CI 0.856–0.885, specificity 70.3%, and sensitivity 84.0%) in the training cohort and 0.879 (0.841–0.917, 76.7%, and 84.3%) in the validation cohort. In addition, comparing iCCA and HCC (235 in the training cohort, 157 in the validation cohort), the AUC values of the diagnostic MODEL 2 were 0.893 (95% CI 0.853–0.933, specificity 96%, and sensitivity 68.6%) in the training cohort and 0.872 (95% CI 0.818–0.927, 94.2%, and 64.6%) in the validation cohort. Conclusion The model combining CA50, CA19‐9, and AFP not only has good diagnostic value for BTC but also has good diagnostic value for distinguishing iCCA and HCC.

Small intestinal lipomatosis is a rare condition that presents a diagnostic challenge due to the absence of identifiable clinical symptoms and limitations of small intestine examination methods. Consequently, preoperative diagnosis is difficult and only a limited number of cases have been documented in the scientific literature. Here, we report a rare case of volvulus caused by small intestinal lipomatosis. A 58-year-old female patient was tentatively diagnosed with acute ileus. The whirl sign was detected using abdominal three-dimensional enhanced computed tomography, along with marked local intestinal dilation and multiple irregular fat-like containing lesions. During surgery, abnormal dilation of the small intestine between 80 and 220 cm from the ileocecal valve was detected and the affected intestine displayed a folded and twisted configuration. Examination of the resected intestine showed that the inner wall of the diseased intestinal lumen was covered with more than 100 lipomas of different sizes, the largest of which measured ~8.0 cm in diameter. Based on clinical symptoms alone, it was difficult to identify the cause of intestinal volvulus before surgery. Complete resection of the affected small intestine and subsequent pathological analysis yielded a definitive diagnosis of small intestinal lipomatosis. While small intestinal lipomatosis is a rare condition, prognosis is favorable if diagnosed early and treated appropriately. The application of three-dimensional enhanced computed tomography imaging can aid in accurate diagnosis, while complete resection of the affected small intestine is crucial to improve patient prognosis.

This paper aims to develop a distributed channel estimation (CE) algorithm for spatially non-stationary (SNS) channels in extremely large aperture array systems, addressing the issues of high communication cost and computational complexity associated with traditional centralized algorithms. However, SNS channels differ from conventional spatially stationary channels, presenting new challenges such as varying sparsity patterns for different antennas. To overcome these challenges, we propose a novel distributed CE algorithm accompanied by a simple yet effective hard thresholding scheme. The proposed algorithm is not only suitable for uniform antenna arrays but also for irregularly deployed antennas. Simulation results demonstrate the advantages of the proposed algorithm in terms of estimation accuracy, communication cost, and computational complexity.

Abstract Background Schools are high incidence places for public health emergencies. Good health literacy helps students cope with public health emergencies. Overall, the health literacy of young students is relatively low. Health education can promote health literacy, but the health education related to public health emergencies for Chinese junior middle school students needs to be improved. To design and implement health education courses related to public health emergencies for junior middle school students and examine the impact on their health literacy, emotions, and coping styles. Methods From March to December 2022, 724 students in Grade 7 and Grade 8 of two junior middle schools in Changzhou were randomly divided into a course group (n = 359) and a control group (n = 365). The course group received an age-appropriate health education course that addressed public health emergencies; there were 12 classes, one per week. The control group received general health education. One week before and after the courses, the two groups of students were assessed with the Adolescent Health Literacy Evaluation Scale under Public Health Emergencies (AHLES-PHE), the Depression Self-Rating Scale for Children (DSRSC), the Generalized Anxiety Disorder 7-item scale (GAD-7), and the Simplified Coping Style Questionnaire (SCSQ). Results After the courses were completed, the scores of AHLES-PHE [156.0 (45.0,180.0) vs. 165.0 (54.0,180.0), P 0.05). Conclusion This suggests that the health education courses related to public health emergencies designed in this study has an effect on improving health literacy, depression and anxiety in junior middle school students.

Abstract Background We investigated the associations between the different doses of tigecycline, its efficacy and safety, and the role of tigecycline therapeutic drug monitoring for patients in the intensive care unit. Methods This study was a single-center cohort including patients infected with multidrug-resistant Acinetobacter baumannii (MDR-AB) and multidrug-resistant Klebsiella pneumoniae (MDR-KP) causing pulmonary infections. The steady-state plasma concentration after tigecycline administration was determined by High-Performance Liquid Chromatography (HPLC) in patients admitted to the ICU between October 2020 and December 2021. Multivariate analyses of tigecycline’s clinical efficacy and safety were performed to control confounding factors. Results For this study, we included 45 patients and 45 blood samples to determine steady-state trough concentrations of tigecycline. All patients were divided into the High Dose (HD) and Standard Dose (SD) groups. The median trough concentration of tigecycline was 0.56 μg/mL in the HD group, which was higher than in the SD group (0,21 μg/mL), p = 0.000. There was no significant difference between the two groups of patients in terms of bacterial eradication rate, mortality rate, and clinical efficacy. Multiple regression analysis showed that the ICU days were correlated with mortality OR 1.030(1.005–1.056), p = 0.017. APACHE II was significantly associated with clinical efficacy OR 0.870(0.755–1.002), p = 0.045. The level of fibrinogen decline in the HD group was significantly higher than in the SD group (-3.05 ± 1.67 vs -1.75 ± 1.90), p = 0.038. We identified that age and tigecycline treatment duration influenced fibrinogen decline. Conclusions Tigecycline plasma concentrations are significantly increased when using a high dose. However, the plasma concentration of tigecycline is not correlated with clinical efficacy and adverse reactions. Fibrinogen decline appears to be related to the patient’s age and days of tigecycline. Large sample data are still needed to confirm the clinical guidance significance of tigecycline TDM.