地下水引發自由端順向坡土體滑動特性分析

Retrogressive sliding can be observed in groundwater-induced landslides. The major inducing factor of this phenomenon is the fluidization in the down-hillslope and the soil of the bottom-layers. Moreover, when the water content in the area around the groundwater input pipe surpasses 29%, sag in the upper soil layer can also occur. As the groundwater discharge increases in experiments with the same slope, the equilibrium time needed to achieve landslide equilibrium gets shorter and the durations of landslides also decrease. As the slope in experiments with the same level of groundwater discharge increases, the equilibrium time required to achieve landslide equilibrium gets shorter, the landsliding area in a specific time period gets larger and the total landsliding area also decreases.由地下水誘發自由端邊坡滑動現象，崩塌型態屬於後退型崩塌。主要誘發因子為滲透水分造成下層土壤流動化及地下水輸入管鄰近區域因土壤土體底層水分飽和度超過29%而產生土壤下陷現象。在相同坡度的邊坡滑動實驗中，地下水量越增加，達到邊坡滑動平衡的時間越短且發生滑動次數越少；而在相同輸入地下水量的邊坡滑動實驗中，坡度越增加則達邊坡滑動平衡時間越短、有越高比例的滑動面積集中在特定時段，且總滑動面積也會因此減少

From carbon nanotubes and silicate layers to graphene platelets for polymer nanocomposites

In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy–graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.5

Indication and adverse event profiles of denosumab and zoledronic acid: based on U.S. FDA adverse event reporting system (FAERS)

Objective: To investigate adverse events (AEs) associated with denosumab (Dmab) and zoledronic acid (ZA), compare their association strengths, and explore potential applications to provide clinical reference.Methods: We collected data from FAERS from January 2004 to November 2022 and mined AE signals for Dmab and ZA using ROR values. We compared signal intensity for same AEs and investigated off-label use. We also examined their AEs in adjuvant therapy for breast and prostate cancer.Results: 154,735 reports of primary suspect drugs were analyzed in the FAERS database (Dmab: 117,857; ZA: 36,878). Dmab and ZA had 333 and 1,379 AE signals, with 189 overlaps. The AEs of Dmab included death (ROR:3.478), osteonecrosis of jaw (ROR:53.025), back pain (ROR:2.432), tooth disorder (ROR:16.18), bone pain (ROR:6.523). For ZA, the AEs included osteonecrosis (ROR:104.866), death (ROR: 3.645), pain (ROR:3.963), osteonecrosis of jaw (ROR: 91.744), tooth extraction (ROR: 142.143). Among overlap signals, Dmab showed higher strength in exostosis of the jaw (ROR: 182.66 vs. 5.769), atypical fractures (ROR: 55.589 vs. 9.123), and atypical femur fractures (ROR:49.824 vs. 4.968). And ZA exhibited stronger associations in abscess jaw (ROR: 84.119 vs. 11.12), gingival ulceration (ROR: 74.125 vs. 4.827), increased bone formation (ROR: 69.344 vs. 3.218). Additionally, we identified 528 off-label uses for Dmab and 206 for ZA, with Dmab mainly used in prostate cancer (1.04%), breast cancer (1.03%), and arthritis (0.42%), while ZA in breast cancer (3.21%), prostate cancer (2.48%), and neoplasm malignant (0.52%). For Dmab in breast cancer treatment, AEs included death (11.6%), disease progression (3.3%), and neutropenia (2.7%), while for ZA included death (19.8%), emotional disorder (12.9%), osteomyelitis (11.7%). For prostate cancer treatment, Dmab`s AEs were death (8.9%), prostate cancer metastatic (1.6%), renal impairment (1.7%), while ZA`s included death (34.4%), general physical health deterioration (19.9%), and hemoglobin decreased (18.9%).Conclusion: Our analysis of FAERS database provided postmarketing surveillance data and revealed different strengths of reported AE signals between Dmab and ZA in some of their common AEs. It’s also worth noting that both drugs have potential off-label applications, which could introduce new AEs. This highlights the necessity for safety monitoring when using Dmab and ZA off-label

Metabolic syndrome and abdominal fat are associated with inflammation, but not with clinical outcomes, in peritoneal dialysis patients

BACKGROUND: In the general population, metabolic syndrome (MetS) is correlated with visceral fat and a risk factor for cardiovascular disease (CVD); however, little is known about the significance of abdominal fat and its association with inflammation and medication use in peritoneal dialysis (PD) patients. We investigated the relationship of visceral fat area (VFA) with C-reactive protein (CRP) levels and medication use in PD patients and followed their clinical outcomes. METHODS: In a prospective study from February 2009 to February 2012, we assessed diabetes mellitus (DM) status, clinical and PD-associated characteristics, medication use, CRP levels, components of MetS, and VFA in 183 PD patients. These patients were categorized into 3 groups based on MetS and DM status: non-MetS (group 1, n = 73), MetS (group 2, n = 65), and DM (group 3, n = 45). VFA was evaluated by computed tomography (CT) and corrected for body mass index (BMI). RESULTS: Patients in group 1 had smaller VFAs than patients in groups 2 and 3 (3.2 ± 1.8, 4.6 ± 1.9, and 4.9 ± 2.0 cm(2)/[kg/m(2)], respectively, P < 0.05) and lower CRP levels (0.97 ± 2.31, 1.27 ± 2.57, and 1.11 ± 1.35 mg/dL, respectively, P < 0.05). VFA increased with the number of criteria met for MetS. After adjusting for age, body weight, and sex, CRP and albumin levels functioned as independent positive predictors of VFA; on other hand, the use of renin-angiotensin system blockers was inversely correlated with VFA in PD patients without DM. In the survival analysis, DM patients (group 3) had the poorest survival among the 3 groups, but no significant differences were found between groups 1 and 2. CONCLUSION: This study showed that VFA and MetS are associated with CRP levels but cannot predict survival in PD patients without DM. The complex relationship of nutritional parameters to VFA and MetS may explain these results. The type of antihypertensive medication used was also associated with the VFA. The mechanisms behind these findings warrant further investigation

An Autoencoder-Based Deep Learning Method For Genotype Imputation

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses

PyPose v0.6: The Imperative Programming Interface for Robotics

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code