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In order to solve the existing problems such as non-standard size and inconvenient activities of the arm of dress form in current market, based on the latest collection of young male body size data, the forward and reverse modeling technology were used to build arm model. And two kinds of shoulder joint fittings were designed. The products were manufactured by rapid prototyping technology to realize free rotation of the arm at 90° coronal plane and 360° sagittal plane, which can meet the needs of draping fixation, and provide the necessary tools for garment sleeve design. The subjective and objective evaluation compared results show that the comprehensive performance of product with damping hinge by using forward modeling technology was better. In order to further improve the modeling effect, the forward and reverse hybrid modeling technology is suggested in the modeling building of the arm of dress form. This method provides technical reference for related production enterprises.

This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care. [ABSTRACT FROM AUTHOR]

Angelica dahurica var. formosana (ADF), which belongs to the Umbelliferae family, is one of the original plants of herbal raw material Angelicae Dahuricae Radix. ADF roots represent an enormous biomass resource convertible for disease treatment and bioproducts. But, early bolting of ADF resulted in lignification and a decrease in the coumarin content in the root, and roots lignification restricts its coumarin for commercial utility. Although there have been attempts to regulate the synthesis ratio of lignin and coumarin through biotechnology to increase the coumarin content in ADF and further enhance its commercial value, optimizing the biosynthesis of lignin and coumarin remains challenging. Based on gene expression analysis and phylogenetic tree profiling, AdNAC20 as the target for genetic engineering of lignin and coumarin biosynthesis in ADF was selected in this study. Early-bolting ADF had significantly greater degrees of root lignification and lower coumarin contents than that of the normal plants. In this study, overexpression of AdNAC20 gene plants were created using transgenic technology, while independent homozygous transgenic lines with precise site mutation of AdNAC20 were created using CRISPR/Cas9 technology. The overexpressing transgenic ADF plants showed a 9.28% decrease in total coumarin content and a significant 12.28% increase in lignin content, while knockout mutant plants showed a 16.3% increase in total coumarin content and a 33.48% decrease in lignin content. Furthermore, 29,671 differentially expressed genes (DEGs) were obtained by comparative transcriptomics of OE-NAC20, KO-NAC20, and WT of ADF. A schematic diagram of the gene network interacting with AdNAC20 during the early-bolting process of ADF was constructed by DEG analysis. AdNAC20 was predicted to directly regulate the transcription of several genes with SNBE-like motifs in their promoter, such as MYB46, C3H, and CCoAOMT. In this study, AdNAC20 was shown to play a dual pathway function that positively enhanced lignin formation but negatively controlled coumarin formation. And the heterologous expression of the AdNAC20 gene at Arabidopsis thaliana proved that the AdNAC20 gene also plays an important role in the process of bolting and flowering. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon‐climate feedbacks relies on understanding the environmental factors regulating soil organic carbon (SOC) storage and dynamics. Here, we employed a microbial ecological model (MEND), driven by downscaled output data from six Earth system models under two Shared Socio‐economic Pathways (SSP1‐2.6 and SSP5‐8.5) scenarios, to simulate long‐term soil biogeochemical processes. We aim to analyze the responses of soil microbial and carbon‐nitrogen (C‐N) processes to changes in environmental factors, including litter input (L), soil moisture (W) and temperature (T), and soil pH, in a broadleaf forest (BF) and a pine forest (PF). For the entire soil layer in both forests, we found that, compared to the baseline period of 2009–2020, the mean SOC during 2081–2100 increased by 40.9%–90.6% under the L or T change scenarios, versus 5.2%–31.0% under the W change scenario. However, soil moisture emerged as a key regulator of SOC, MBC and inorganic N dynamics in the topsoil of BF and PF. For example, W change led to SOC gain of 5.5%–37.2%, compared to the SOC loss of 15.5%–18.0% under L or T scenario. Additionally, a further reduction in soil pH by 0.2 units in the BF, representing the acid rain effect, significantly resulted in an additional SOC gain by 14.2%–21.3%, compared to the LTW (simultaneous changes in the three factors) scenario. These results indicate that the results derived solely from topsoil may not be extrapolated to the entire soil profile. Overall, this study significantly advances our comprehension of how different environmental factors impact the dynamics of SOC and the implications they have for climate change. Plain Language Summary: Accurately predicting carbon‐climate feedbacks relies on understanding the environmental factors regulating soil organic carbon (SOC) storage and dynamics. We aim to analyze the responses of soil microbial and carbon‐nitrogen (C‐N) processes to changes in environmental factors, including litter input (L), soil moisture (W) and temperature (T), and soil pH, in a broadleaf forest (BF) and a pine forest (PF). We found that soil moisture change would be beneficial for SOC accumulation and serves as a key regulator of MBC and inorganic N in topsoil, whereas the change in litterfall or soil temperature are favorable for SOC accumulation in the entire soil profile. Additionally, a further reduction in soil pH by 0.2 units, representing the acid rain effect, significantly resulted in an additional SOC gain by 14.2%–21.3%, compared to the scenario with simultaneous changes in L, W, and T. These results indicate that findings solely from topsoil may not be extrapolated to the entire soil profile. Overall, this study significantly advances our comprehension of how different environmental factors impact the dynamics of SOC and the implications they have for climate change. Key Points: Soil C responses to climate change are depth dependent, therefore, results from just the topsoil may not apply to the entire soil profileSoil moisture change benefits topsoil SOC accumulation, whereas litterfall and soil temperature changes favor SOC accumulation in the entire soil profileWe need to pay more attention to the effects of soil moisture and pH rather than temperature and litter‐input on soil biogeochemical processes [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs) have unique potential in the field of gas sensing due to their ultra-high specific surface area and abundant mesoporous structure. In this paper, a mesoporous Co3O4 hollow polyhedron structure with high specific surface area (141.6 m2 g−1) was synthesized by annealing at the optimum temperature. The gas response of the mesoporous Co3O4 hollow polyhedron structure to volatile organic compounds such as xylene, ethanol, methanol and acetone was systematically tested. The experimental results show that the mesoporous Co3O4 hollow polyhedron structure has the highest sensitivity (21.33) and excellent stability for xylene gas, and the detection limit is low (1 ppm). The excellent sensing performance benefits from large surface area, high oxygen defect concentration and mesoporous structure. [ABSTRACT FROM AUTHOR]

Aims. Building on conservation of resources theory, our study investigates how inclusive human resource management (IHRM) promotes nurses' innovative behavior through job crafting and further examines the moderating role of shared leadership. Background. Nurses' involvement in innovation is essential to improve nursing care delivery and accommodate changing medical environments, especially in the face of crisis events like the COVID-19 outbreak. However, knowledge about the relationship between human resource management and nurses' innovative behavior remains scarce. Methods. We collected three-wave data from 338 on-duty registered nurses at four public hospitals in China from November 2022 to January 2023. We used SPSS 22 to conduct hierarchical regressions to test our hypotheses. Results. IHRM positively predicted innovative behavior of nurses with the mediating role of job crafting. In addition, we found that IHRM was more effective in promoting job crafting and subsequent innovative behavior when nurses perceived high levels of shared leadership. Conclusion. IHRM initiated by the organization and shared leadership style are two collaborative approaches to facilitating nurses' job crafting, thereby responding to the imperative need to foster nurses' innovative behavior. Implications for Nursing Management. The present study emphasizes the important roles of IHRM and shared leadership in promoting nurses' job crafting and subsequent innovative behavior, providing theoretical and practical implications for nursing management in the current dynamic and challenging environment. [ABSTRACT FROM AUTHOR]

OBJECTIVES: This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images. METHODS: CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis. RESULTS: The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843–0.964, P < 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC). CONCLUSION: Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients. [ABSTRACT FROM AUTHOR]

Tropical and subtropical forests play a crucial role in global carbon (C) pools, and their responses to warming can significantly impact C‐climate feedback and predictions of future global warming. Despite earth system models projecting reductions in land C storage with warming, the magnitude of this response varies greatly between models, particularly in tropical and subtropical regions. Here, we conducted a field ecosystem‐level warming experiment in a subtropical forest in southern China, by translocating mesocosms (ecosystem composed of soils and plants) across 600 m elevation gradients with temperature gradients of 2.1°C (moderate warming), to explore the response of ecosystem C dynamics of the subtropical forest to continuous 6‐year warming. Compared with the control, the ecosystem C stock decreased by 3.8% under the first year of 2.1°C warming; but increased by 13.4% by the sixth year of 2.1°C warming. The increased ecosystem C stock by the sixth year of warming was mainly attributed to a combination of sustained increased plant C stock due to the maintenance of a high plant growth rate and unchanged soil C stock. The unchanged soil C stock was driven by compensating and offsetting thermal adaptation of soil microorganisms (unresponsive soil respiration and enzyme activity, and more stable microbial community), increased plant C input, and inhibitory C loss (decreased C leaching and inhibited temperature sensitivity of soil respiration) from soil drying. These results suggest that the humid subtropical forest C pool would not necessarily diminish consistently under future long‐term warming. We highlight that differential and asynchronous responses of plant and soil C processes over relatively long‐term periods should be considered when predicting the effects of climate warming on ecosystem C dynamics of subtropical forests. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) is common in patients with community-acquired pneumonia (CAP), and is associated with poor prognosis. Therefore, in this study, we evaluated whether AKI in Chinese patients with CAP could be well predicted by serum Cystatin C within 24 h after admission. Univariate and multivariate logistic regression analyses were used to investigate independent factors of AKI in patients with CAP. Totally, 2716 patients with CAP were included in this study. 766 (28%) patients developed AKI. After multivariate logistic regression analysis, serum Cystatin C (odds ratio [OR] 4.27, 95% confidence interval [CI] 3.36–5.44; p < 0.001) was an independent factor for AKI in patients with CAP. Serum Cystatin C had an area under the receiver operating characteristic curve (AUC) of 0.81 for predicting AKI, with an optimal cutoff value of 1.37 mg/L, computing 68% sensitivity, 80% specificity. Furthermore, serum Cystatin C within 24 h after admission still had a good and stable prediction efficiency for AKI in various subgroups (age, gender, hypertension, diabetes, coronary artery disease, cardiac insufficiency, cerebrovascular disease, atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, and tumor, albumin, anemia, platelet count, white blood cell count, and uric acid, confusion, uremia, respiratory rate, blood pressure, and age 65 years or older [CURB-65] score, acute respiratory failure, intensive care unit admission, and mechanical ventilation) of patients with CAP (AUCs: 0.69–0.84). Serum Cystatin C within 24 h after admission appears to be a good biomarker for predicting AKI in Chinese patients with CAP. [ABSTRACT FROM AUTHOR]

Confidence in model estimates of soil CO2 flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data‐model fusion with modified versions of the CN‐SIM model and a 571‐day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These "lagged‐peak" samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest‐performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co‐metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth‐system models. [ABSTRACT FROM AUTHOR]