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Photometric differences are widely used as supervision signals to train neural networks for estimating depth and camera pose from unlabeled monocular videos. However, this approach is detrimental for model optimization because occlusions and moving objects in a scene violate the underlying static scenario assumption. In addition, pixels in textureless regions or less discriminative pixels hinder model training. To solve these problems, in this paper, we deal with moving objects and occlusions utilizing the difference of the flow fields and depth structure generated by affine transformation and view synthesis, respectively. Secondly, we mitigate the effect of textureless regions on model optimization by measuring differences between features with more semantic and contextual information without adding networks. In addition, although the bidirectionality component is used in each sub-objective function, a pair of images are reasoned about only once, which helps reduce overhead. Extensive experiments and visual analysis demonstrate the effectiveness of the proposed method, which outperform existing state-of-the-art self-supervised methods under the same conditions and without introducing additional auxiliary information., accepted by the Transactions on Intelligent Transportation Systems on February 20, 2023

Patients with cancer are at higher risk of developing venous thromboembolism (VTE) compared with the general population. This elevated risk is due to several risk factors and multiple, overlapping thrombotic and hemostatic pathophysiological pathways that are specific to this patient population. Hence, the management of cancer-associated VTE can be challenging for clinicians. Patients with cancer-associated VTE are at higher risk of both recurrent events despite anticoagulation and bleeding complications due to the anticoagulant regimens. Direct oral anticoagulants have recently been shown to be effective, safe, and more convenient than parenteral low-molecular-weight heparin for the management of cancer-associated VTE. Despite these recent advances in anticoagulant therapy, many unmet needs remain in these patients (increased risk of bleeding with specific cancer types, drug-drug interactions, liver dysfunction). Factor XI inhibitors are currently being assessed for the management of cancer-associated VTE and may help clinicians address these important knowledge gaps.

Forests are crucial to mitigation efforts, especially for carbon sequestration, as climate change poses a growing global threat. Based on the Forestry for Carbon Sequestration problem, this paper established a model to estimate Carbon Sequestration. And about forest management plan, a series of value indicators are established. Finally, amount of carbon dioxide can be sequestered by forests and their products over time and the results of the forest man-agement plan model are given.

The traditional dynamic analysis of the finite element structure is mostly focused on the mode analysis. In the paper, the finite element method is combined with the power flow analysis to analyze and optimize the vibration transmission characteristics of the shared bearing bore, so that the vibration energy transmitted to the mounting flange is minimized. The analysis shows that the elastic ring and the outer case play an important role in vibration transmission and reduction. An optimization model is established to improve the vibration reduction rate of the shared bearing bore. The results show that the optimization can effectively improve the vibration reduction rate of the shared bearing bore, approximately 15 % reduction of peak response is achieved.

Four new sesquiterpenoids, dstramonins A-D (1–4), and one new natural product (5), together with three known compounds (6–8), were isolated from the leaves of Datura stramonium L. The structures of new compounds were elucidated by extensive spectroscopic analysis and comparison with the literature. The cytotoxicity of isolates against LN229 cells was assessed and compounds 2–4, and 7 displayed cytotoxic activity with IC50 values ranging from 8.03 to 13.83 μM.

Qi Meng,1,2,* Jing Yang,1,2 Fei Wang,1,2 Cheng Li,3 Guoyao Sang,4 Hua Liu,1,2 Di Shen,1,2 Jinxia Zhang,1,2 Sheng Jiang,1,2,* Aibibai Yusufu,1,2,* Guoli Du1,2,* 1State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, People’s Republic of China; 2Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 3Laboratory Medicine Diagnostic Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 4Data Statistics and Analysis Center of Operation Management Department, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guoli Du; Aibibai Yusufu, Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, People’s Republic of China, Email genemagic@126.com; hebibe@163.comPurpose: Cardiovascular disease is the leading cause of mortality in patients with type 2 diabetes mellitus (T2DM). This study aimed to develop and validate a nomogram for predicting the risk factors for coronary heart disease (CHD) in T2DM in the population of northwestern China.Patients and Methods: The records of 2357 T2DM patients who were treated in the First Affiliated Hospital of Xinjiang Medical University from July 2021 to July 2022 were reviewed. After some data (n =239) were excluded, 2118 participants were included in the study and randomly divided into a training set (n =1483) and a validation set (n = 635) at a ratio of 3:1. Univariate and stepwise regression analysis was performed to screen risk factors and develop predictive models. The results of logistic regression are presented through a nomogram. The C-index, receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA) were employed to verify the distinction, calibration, and clinical practicality of the model.Results: The stepwise logistic regression analysis suggested that independent factors in patients with T2DM combined with CHD were age, gender, hypertension (HTN), glycated hemoglobin (HbA1c), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), and Uygur, which were associated with the occurrence of CHD. The nomogram demonstrated good discrimination with a C-index of 0.771 (95% CI, 0.741, 0.800) in the training set and 0.785 (95% CI, 0.743, 0.828) in the validation set. The area under curve (AUC) of the ROC curves were 0.771 (95% CI, 0.741, 0.800) and 0.785 (95% CI, 0.743, 0.828) in the training and validation sets, respectively. The nomogram was well-calibrated. The DCA revealed that the nomogram was clinically valuable.Conclusion: A nomogram based on 7 clinical characteristics was developed to predict CHD in patients with T2DM.Keywords: nomogram, coronary heart disease, type 2 diabetes mellitus, ethnicity, risk factor