ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network

With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.Comment: 13 pages, 5 figure

Association between urinary albumin-to-creatinine ratio and all-cause and cardiovascular-cause mortality among MASLD: NHANES 2001–2018

BackgroundUrinary albumin-to-creatinine ratio (UACR) is an established biomarker for assessing kidney damage, but recent studies suggest it may also reflect broader health risks. This study aimed to investigate the association between UACR and all-cause and cardiovascular disease (CVD)-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease (MASLD).MethodsIn this prospective cohort study, we included sample of 3,412 MASLD enrolled in the National Health and Nutrition Examination Survey 2001–2018. The study population was divided into three different risk categories based on urinary UACR: low level (<4.67 mg/g), intermediate level (4.67–7.67 mg/g), and high level (7.68–30 mg/g). Cox proportional hazards models were used to estimate the hazard ratios (HR) for the association between UACR level and both all-cause and CVD-cause mortality. Restricted cubic spline (RCS) curve analysis was employed to assess the non-linear association between UACR and mortality. Kaplan-Meier (KM) survival curves were used to evaluate survival rates across UACR groups.ResultsThe study found that higher UACR levels, even within the normal range, were independently associated with increased risks of both all-cause and CVD-cause mortality. Each 1 mg/g increase in UACR was associated with a 4% higher risk of all-cause mortality (HR 1.04, 95% CI 1.03–1.05) and a 5% higher risk of cardiovascular mortality (HR 1.05, 95% CI 1.02–1.08). Compared with the low UACR group, high UACR both showed an increased all-cause mortality risk [HR, 2.69 (95% CI, 2.07–3.50)] and CVD-cause mortality risk [HR, 2.97 (95% CI, 1.76–4.99)]. RCS curve analysis revealed a non-linear positive correlation between UACR and both all-cause and CVD-cause mortality, identifying UACR thresholds of 7.467 mg/g for all-cause mortality and 7.195 mg/g for CVD-cause mortality. The KM survival curves confirmed that participants with lower UACR levels had higher survival rates.ConclusionElevated UACR levels within the normal range, are associated with increased all-cause and cardiovascular mortality in patients with MASLD. UACR may serve as a useful early biomarker for identifying individuals at higher risk of mortality, supporting more proactive clinical interventions to manage MASLD-related risks

A high-order, high-efficiency adaptive time filter algorithm for shale reservoir model based on coupled fluid flow with porous media flow

In this paper, a third-order time adaptive algorithm with less computation, low complexity is provided for shale reservoir model based on coupled fluid flow with porous media flow. The algorithm combines the three-step linear time filters method for simple post-processing and the second-order backward differential formula (BDF2), is third-order accurate and provides, at no extra computational complexity. At the same time, the time filter method can also be used to damp non-physical oscillations inherent in the BDF2 method, ensuring stability. We proves the variable time stepsize second-order backward differential formula plus time filter (BDF2-TF) algorithm's stability and the convergence properties of the fluid velocity u and hydraulic head $\phi$ in the $L^2$ norm with an order of $O(k_{n+1}^3 + h^3)$. In the experiments, the adaptive algorithm automatically adjusts the time step in response to the varying characteristics of different models, ensuring that errors are maintained within acceptable limits. This algorithm addresses the issue that high-order algorithms may select inappropriate time steps, resulting in instability or reduced precision of the numerical solution, thereby enhancing calculation accuracy and efficiency. We perform three-dimensional numerical experiments to verify the BDF2-TF algorithm's effectiveness, stability, and third-order convergence. Simultaneously, a simplified model is employed to simulate the process of shale oil extraction from reservoirs, further demonstrating the algorithm's practical applicability

A simulation-based method to determine the coefficient of hyperbolic decline curve for tight oil production

 Tight oil reservoirs are characterized by the ultra low porosity and permeability, making it a great challenge to enhance oil production. Owing to the fast development in hydraulic fracturing technology of horizontal wells, tight oil has been widely explored in North America. Individual wells have a long term of low production after a rapid production decline. This causes low cumulative production in tight oil reservoirs. A rate decline curve is the most common method to forecast their production rates. The forecast can provide useful information during decision making on future development of production wells. In this paper, a relationship is developed between the parameters of a hyperbolic decline curve and the reservoir/fracture properties when a reservoir simulation model is used based on the data from a real field. Understanding of this relationship improves the application of the hyperbolic decline curve and provides a useful reference to forecast production performance in a more convenient and efficient way.Cited as: Yu, Y., Chen, Z., Xu, J. A simulation-based method to determine the coefficient of hyperbolic decline curve for tight oil production. Advances in Geo-Energy Research, 2019, 3(4): 375-380, doi: 10.26804/ager.2019.04.0

Hydraulic fracturing-induced seismicity characterization through coupled modeling of stress and fracture-fault systems

This work summarizes our recent findings on hydraulic fracturing-induced seismicity nucleated in the Duvernay shale reservoirs within the Western Canada Sedimentary Basin. A coupled model of in-situ stress and fracture-fault systems was built to quantify four-dimensional stress and pressure changes and spatiotemporal seismicity nucleation during hydraulic fracturing. Five triggering mechanisms were successfully recognized in seismicity-frequent areas, including a direct hydraulic connection between impermeable faults and hydraulic fractures, fault slip owing to downward pressure diffusion, fault reactivation due to upward poroelastic stress perturbation, aftershocks of mainshock events, and reactivation of natural fractures surrounding the faults. This work shed light on how fracturing operations triggered the induced seismicity, providing a solid foundation for the investigation of controlling factors and mitigation strategies for hydraulic fracturing-induced seismicity.Cited as: Hui, G., Chen, Z., Chen, S., Gu, F. Hydraulic fracturing-induced seismicity characterization through coupled modeling of stress and fracture-fault systems. Advances in Geo-Energy Research, 2022, 6(3): 269-270. https://doi.org/10.46690/ager.2022.03.1