Transient pressure analysis of a volume fracturing well in fractured tight oil reservoirs

This research was supported by the Ministry of Land and Resources Special Geological Survey: Upper Paleozoic Marine Shale Gas Geological Survey in Yunnan, Guizhou, Guangxi Region (DD20160178), The Key Laboratory of Unconventional Petroleum Geology of Geological Survey of China Open Fund and the Major National R&D Projects: Study on the Test Method for Shale Structure and Composition at Different Scales with project number: 2016ZX05034-003-006.Peer reviewedPostprin

UDTIRI: An Open-Source Road Pothole Detection Benchmark Suite

It is seen that there is enormous potential to leverage powerful deep learning methods in the emerging field of urban digital twins. It is particularly in the area of intelligent road inspection where there is currently limited research and data available. To facilitate progress in this field, we have developed a well-labeled road pothole dataset named Urban Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this dataset will enable the use of powerful deep learning methods in urban road inspection, providing algorithms with a more comprehensive understanding of the scene and maximizing their potential. Our dataset comprises 1000 images of potholes, captured in various scenarios with different lighting and humidity conditions. Our intention is to employ this dataset for object detection, semantic segmentation, and instance segmentation tasks. Our team has devoted significant effort to conducting a detailed statistical analysis, and benchmarking a selection of representative algorithms from recent years. We also provide a multi-task platform for researchers to fully exploit the performance of various algorithms with the support of UDTIRI dataset.Comment: Database webpage: https://www.udtiri.com/, Kaggle webpage: https://www.kaggle.com/datasets/jiahangli617/udtir

A retrospective comparative study on the diagnostic efficacy and the complications: between CassiII rotational core biopsy and core needle biopsy

Accurate pathologic diagnosis and molecular classification of breast mass biopsy tissue is important for determining individualized therapy for (neo)adjuvant systemic therapies for invasive breast cancer. The CassiII rotational core biopsy system is a novel biopsy technique with a guide needle and a “stick-freeze” technology. The comprehensive assessments including the concordance rates of diagnosis and biomarker status between CassiII and core needle biopsy were evaluated in this study. Estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki67 were analyzed through immunohistochemistry. In total, 655 patients with breast cancer who underwent surgery after biopsy at Sir Run Run Shaw Hospital between January 2019 to December 2021 were evaluated. The concordance rates (CRs) of malignant surgical specimens with CassiII needle biopsy was significantly high compared with core needle biopsy. Moreover, CassiII needle biopsy had about 20% improvement in sensitivity and about 5% improvement in positive predictive value compared to Core needle biopsy. The characteristics including age and tumor size were identified the risk factors for pathological inconsistencies with core needle biopsies. However, CassiII needle biopsy was associated with tumor diameter only. The CRs of ER, PgR, HER2, and Ki67 using Cassi needle were 98.08% (kappa, 0.941; p<.001), 90.77% (kappa, 0.812; p<.001), 69.62% (kappa, 0.482; p<.001), and 86.92% (kappa, 0.552; p<.001), respectively. Post-biopsy complications with CassiII needle biopsy were also collected. The complications of CassiII needle biopsy including chest stuffiness, pain and subcutaneous ecchymosis are not rare. The underlying mechanism of subcutaneous congestion or hematoma after CassiII needle biopsy might be the larger needle diameter and the effect of temperature on coagulation function. In summary, CassiII needle biopsy is age-independent and has a better accuracy than CNB for distinguishing carcinoma in situ and invasive carcinoma

BNP facilitates NMB-encoded histaminergic itch via NPRC-NMBR crosstalk

Histamine-dependent and -independent itch is conveyed by parallel peripheral neural pathways that express gastrin-releasing peptide (GRP) and neuromedin B (NMB), respectively, to the spinal cord of mice. B-type natriuretic peptide (BNP) has been proposed to transmit both types of itch via its receptor NPRA encoded b

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms