Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data

In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse,noisy velocity data,where equationbased constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated.Specifically,a Bayesian deep neural network is trained on sparse measurement data to capture the flow field.In the meantime,the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available.A non-parametric variational inference approach is applied to enable efficient physicsconstrained Bayesian learning.Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.

Effect of Xuebijing injection on hematopoietic homeostasis of LPS induced sepsis in mice

目的:研究血必净注射液对脓毒症小鼠骨髓造血稳态的影响及其机制。方法:血必净注射液刺激离体小鼠骨髓细胞,观察其对骨髓细胞增殖分化功能的影响。采用20mg/kg 剂量的LPS 构建脓毒症小鼠模型,流式细胞术检测LPS 对小鼠骨髓造血干细胞及祖细胞含量的影响。腹腔注射LPS 后,采用尾静脉注射的方法每天给予20ml/kg 的血必净注射液治疗,第5 天处死动物,从腿骨提取骨髓细胞,利用流式细胞术检测血必净注射液对脓毒症小鼠骨髓造血干细胞及免疫细胞的影响,通过荧光定量PCR 检测血必净注射液对小鼠骨髓细胞转录因子的影响。结果:体外实验结果显示,血必净注射液可通过上调CD11B 基因的表达,下调GFI1B、GATA2 及FOS 基因的表达,促进小鼠骨髓细胞的分化。动物实验结果发现,LPS 诱导了小鼠骨髓造血干细胞LSK 的病理性增殖,导致多能祖细胞MPP、粒系-巨噬系祖细胞GMP 及巨核系-红系祖细胞MEP 的含量显著降低,从而改变了小鼠骨髓的造血稳态。而血必净注射液可通过抑制LSK 的病理性增殖,抑制髓源性抑制细胞MDSC 的含量,改善脓毒症小鼠的免疫功能。其机制可能与其上调LSD1 及PU.1 基因的表达有关。结论:血必净注射液可通过调节脓毒症小鼠骨髓细胞的造血稳态改善免疫功能,其机制可能与其上调LSD1 及PU.1 基因的表达有关。

Lactate metabolism in human health and disease

The current understanding of lactate extends from its origins as a byproduct of glycolysis to its role in tumor metabolism,as identified by studies on the Warburg effect.The lactate shuttle hypothesis suggests that lactate plays an important role as a bridging signaling molecule that coordinates signaling among different cells,organs and tissues.Lactylation is a posttranslational modification initially reported by Professor Yingming Zhao’s research group in 2019.Subsequent studies confirmed that lactylation is a vital component of lactate function and is involved in tumor proliferation,neural excitation,inflammation and other biological processes.An indispensable substance for various physiological cellular functions,lactate plays a regulatory role in different aspects of energy metabolism and signal transduction.Therefore,a comprehensive review and summary of lactate is presented to clarify the role of lactate in disease and to provide a reference and direction for future research.This review offers a systematic overview of lactate homeostasis and its roles in physiological and pathological processes,as well as a comprehensive overview of the effects of lactylation in various diseases,particularly inflammation and cancer.

Post-translational modification of Parkin and its research progress in cancer

Clinical practice has shown that Parkin is the major causative gene found in an autosomal recessive juvenile parkin-sonism(AR-JP)via Parkin mutations and that the Parkin protein is the core expression product of the Parkin gene,which itself belongs to an E3 ubiquitin ligase.Since the discovery of the Parkin gene in the late 1990s,researchers in many countries have begun extensive research on this gene and found that in addition to AR-JP,the Parkin gene is associated with many diseases,including type 2 diabetes,leprosy,Alzheimer’s,autism,and cancer.Recent studies have found that the loss or dysfunction of Parkin has a certain relationship with tumorigenesis.In general,the Parkin gene,a well-established tumor suppressor,is deficient and mutated in a variety of malignancies.Parkin overexpres-sion inhibits tumor cell growth and promotes apoptosis.However,the functions of Parkin in tumorigenesis and its regulatory mechanisms are still not fully understood.This article describes the structure,functions,and post-transla-tional modifications of Parkin,and summarizes the recent advances in the tumor suppressive function of Parkin and its underlying mechanisms.

Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium

Phonon Boltzmann transport equation(BTE)is a key tool for modeling multiscale phonon transport,which is critical to the thermal management of miniaturized integrated circuits,but assumptions about the system temperatures(i.e.,small temperature gradients)are usually made to ensure that it is computationally tractable.To include the effects of large temperature non-equilibrium,we demonstrate a data-free deep learning scheme,physics-informed neural network(PINN),for solving stationary,mode-resolved phonon BTE with arbitrary temperature gradients.This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables.Numerical experiments suggest that the proposed PINN can accurately predict phonon transport(from 1D to 3D)under arbitrary temperature gradients.Moreover,the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.