The preterm birth, low birth weight, and infant survival rates were 70.7% (29/41), 68.3% (28/41), and 78.0% (32/41), respectively. RRT does not reduce renal recovery time compared with non-RRT. Overall, the prognoses of both mothers and their fetuses are good following treatment for PPAKI. RRT does not reduce renal recovery time compared with non-RRT. Overall, the prognoses of both mothers and their fetuses are good following treatment for PPAKI. and have been well studied for their roles in tumorigeneis, plus cancer diagnosis and treatment, but their prognostic value in colon cancer, especially for early-stage cancer, has not been fully illuminated. https://www.selleckchem.com/products/biib129.html This study examined the expression levels of BRCA1 and BRCA2 proteins in sporadic colon cancer cases and investigated their value in prognosis. The expression levels of and in 275 colon cancer patients who underwent radical surgeries were assayed by immunohistochemical staining in dissected tumor samples. Also, its correlation with clinicopathological characteristics, disease-free survival, and overall survival was investigated. Tumors with low expression levels of , and both were 19.6%, 17.8%, and 6.5%, respectively. The levels of expression were not associated with clinicopathological parameters (gender, age, histological differentiation, and tumor node metastasis stage). Patients with low-levels of BRCA1 protein in their tumors demonstrated a lower chance of 5-year disease-free survival (55.6% vs. 69.7%, =0.046), which was more obvious in the patients with stage I-II tumors without chemotherapy (52.6% vs. 82.6%, =0.006). Neither BRCA1 nor BRCA2 affected overall survival in this cohort. Multivariate analysis revealed that pathologic stage and the level of BRCA1 protein were independent factors of long-term disease-free survival. This study highlights BRCA1 as an independent prognosticator of early-stage colon cancer. This study highlights BRCA1 as an independent prognosticator of early-stage colon cancer. Deep venous thrombosis (DVT) is known to occur preferentially on the left lower extremity. The renowned surgeon Denis Burkitt advanced the theory that a heavy sigmoid colon would compress the left pelvic veins and predispose to DVT. Our study aimed to evaluate this hypothesis by comparing the laterality distributions with and without a prior colectomy. We conducted a retrospective analysis of the 2016 National Inpatient Sample database by stratifying the patients at any age with acute DVT of lower extremity by history of prior colectomy, thereby eliminating local gut mechanical factors in the development of DVT. We compared the laterality distribution (i.e., left, right, bilateral, and unspecified) between the patients with and without a prior colectomy. We also conducted a subgroup analysis by the sex category to examine the difference in laterality distribution for male and female patients. Chi-square test for independence was used. P value ≤0.05 was considered statistically significant. We found an estimated total of 342,525 cases. Among patients without a prior colectomy, 136,605 (41.6%) were left-sided DVT versus 119,555 (36.4%) right-sided, with 55,555 bilateral and 16,865 unspecified. Among patients with a prior colectomy, 5,750 (41.2%) were left-sided, 5,000 (35.9%) were right-sided, 2,345 were bilateral and 850 were unspecified. The laterality distribution between the two groups was not significantly different (  = .167). The left-side predominance disappeared only in males with a prior colectomy (37.1% for left vs. 38.9% for right,  = .027). Our findings did not confirm the Burkitt's hypothesis. The left-side predominance of lower extremity DVT was attenuated only in male patients with a prior colectomy. Our findings did not confirm the Burkitt's hypothesis. The left-side predominance of lower extremity DVT was attenuated only in male patients with a prior colectomy.Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting-describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time-series data. This article is part of the theme issue 'Machine learning for weather and climate modelling'.Recent advances in computing algorithms and hardware have rekindled interest in developing high-accuracy, low-cost surrogate models for simulating physical systems. The idea is to replace expensive numerical integration of complex coupled partial differential equations at fine time scales performed on supercomputers, with machine-learned surrogates that efficiently and accurately forecast future system states using data sampled from the underlying system. One particularly popular technique being explored within the weather and climate modelling community is the echo state network (ESN), an attractive alternative to other well-known deep learning architectures. Using the classical Lorenz 63 system, and the three tier multi-scale Lorenz 96 system (Thornes T, Duben P, Palmer T. 2017 Q. J. R. Meteorol. Soc.143, 897-908. (doi10.1002/qj.2974)) as benchmarks, we realize that previously studied state-of-the-art ESNs operate in two distinct regimes, corresponding to low and high spectral radius (LSR/HSR) for the sparsexact computing has emerged as a novel approach to helping with scaling. In this paper, we evaluate the performance of three models (LSR-ESN, HSR-ESN and D2R2) by varying the precision or word size of the computation as our inexactness-controlling parameter. For precisions of 64, 32 and 16 bits, we show that, surprisingly, the least expensive D2R2 method yields the most robust results and the greatest savings compared to ESNs. Specifically, D2R2 achieves 68 × in computational savings, with an additional 2 × if precision reductions are also employed, outperforming ESN variants by a large margin. This article is part of the theme issue 'Machine learning for weather and climate modelling'.