DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip - deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware

Altered gene-expression profile in rat plasma and promoted body and brain development by environmental enrichment

Environmental enrichment (EE) refers to the exposure of laboratory animals to physical and social stimulation, which can improve animals’ well-being. The study was aimed to explore how the prenatal EE impacts affect the development, behavior, hormones and gene expression of the offspring. 28 pregnant rats were randomized into an EE group (EEG) housed in cages with EE or a control group (CG) housed in normal cages. Measurements included offspring development parameters (body weight, body length, and tail length) and behavior (open-field test, OFT), hormone levels (cortisol, dopamine, 5-HT, and growth hormone) and gene expression profile. Results showed that the development parameters of EEG offspring were statistically superior to the CG offspring. OFT count of EEG offspring was more than CG. EEG and CG offspring did not differ on cortisol, dopamine, 5-HT or growth factor. Gene expression profile chip test showed that 25 genes were up-regulated and 23 genes down-regulated in the EEG vs CG comparison, among which five GO annotations and four KEGG pathways were annotated. Findings indicate that EE during pregnancy could positively promote the body and nervous system development of offspring, involving the evidence for altered gene expression profile.Keywords: Environmental enrichment, rats, gene expression, behavior, developmentAfrican Journal of Biotechnology Vol. 12(20), pp. 3071-308

RNAa-mediated overexpression of WT1 induces apoptosis in HepG2 cells

<p>Abstract</p> <p>Aim</p> <p>Recent studies have reported that double-stranded RNA (dsRNA) can activate gene expression by targeting promoter sequence in a process termed RNA activation. The present study was conducted to evaluate the potential of WT1 induction by small activating RNA targeting the WT1 promoter (dsWT1) in the treatment of hepatocellular carcinoma.</p> <p>Methods</p> <p>The human hepatocellular carcinoma cell line HepG2 was transfected with dsRNA by liposomes. The expression of mRNA and protein in cells were investigated using real-time reverse real-time quantitative PCR and Western blot, respectively. Cell viability and clonogenicity were determined by MTT assay and clonogenicity assay, respectively. Cell apoptosis was evaluated by flow-cytometric analysis.</p> <p>Results</p> <p>Expressions of WT1 mRNA and protein in dsWT1 treated HepG2 cells were significantly elevated. Inhibition of cell viability by dsWT1 was dose-dependent and time-dependent. Reduction of the number and size of colonies formed were found in dsWT1 treated cells. dsWT1 induced significant apoptosis in HepG2 cells. The decreased anti-apoptotic protein Bcl-2 and elevated pro-apoptotic protein Bak expression were detected in dsWT1 treated cells. The level of pro-caspase-3 remarkably decreased and cleaved caspase-3 and PARP fragment were also detected in dsWT1 treated cells.</p> <p>Conclusion</p> <p>These data show that RNAa-mediated overexpression of WT1 may have therapeutic potential in the treatment of hepatocellular carcinoma.</p

Pressure-stabilized divalent ozonide CaO3 and its impact on Earth's oxygen cycles.

High pressure can drastically alter chemical bonding and produce exotic compounds that defy conventional wisdom. Especially significant are compounds pertaining to oxygen cycles inside Earth, which hold key to understanding major geological events that impact the environment essential to life on Earth. Here we report the discovery of pressure-stabilized divalent ozonide CaO3 crystal that exhibits intriguing bonding and oxidation states with profound geological implications. Our computational study identifies a crystalline phase of CaO3 by reaction of CaO and O2 at high pressure and high temperature conditions; ensuing experiments synthesize this rare compound under compression in a diamond anvil cell with laser heating. High-pressure x-ray diffraction data show that CaO3 crystal forms at 35 GPa and persists down to 20 GPa on decompression. Analysis of charge states reveals a formal oxidation state of -2 for ozone anions in CaO3. These findings unravel the ozonide chemistry at high pressure and offer insights for elucidating prominent seismic anomalies and oxygen cycles in Earth's interior. We further predict multiple reactions producing CaO3 by geologically abundant mineral precursors at various depths in Earth's mantle

The role of the Basic Public Health Service program in the control of hypertension in China: results from a cross-sectional health service interview survey

Objectives Non-communicable diseases (NCDs) have become the main cause of mortality in China. In 2009, the Chinese government introduced the Basic Public Health Service (BPHS) program to relieve the rising burden of NCDs through public health measures and delivery of essential medical care. The primary aim of this study was to evaluate the impact of the BPHS program on hypertension control. Methods The China National Health Development Research Center (CNHDRC) undertook a Cross-sectional Health Service Interview Survey (CHSIS) of 62,097 people from primary healthcare reform pilot areas across 17 provinces from eastern, central, and western parts of China in 2014. The current study is based on responses to the CHSIS survey from 7,867 participants, who had been diagnosed with hypertension. Multi-variable mixed logit regression analysis was used to estimate the association between BPHS management and uncontrolled hypertension. In a follow-up analysis, generalized structural equation modelling (GSEM) was used to test for mediation of the BPHS program effect through patient compliance with medication. Findings The estimated proportion of patients with uncontrolled hypertension was 30% lower (23.2% vs 31.5%) in those participants who were adequately managed under the BPHS program. Other predictors of hypertension control included compliance with medication, self-reported wellbeing, income, educational attainment and exercise; smoking was associated with reduced hypertension control. The significant inverse association between uncontrolled hypertension and age indicates poor outcomes for younger patients. Additional testing suggested that nearly 40% of the effect of BPHS management (95% CI: 28.2 to 51.7) could be mediated by improved compliance with medication; there was also an indication that the effect of management was 30% stronger in districts/counties with established digital information management systems (IMS). Conclusion Hypertension control improved markedly following active management through the BPHS program. Some of that improvement could be explained by greater compliance with medication among program participants. This study also identified the need to tailor the BPHS program to the needs of younger patients to achieve higher levels of control in this population. Future investigations should explore ways in which existing healthcare management influences the success of the BPHS program

A new score system for predicting response to cardiac resynchronization therapy

Background: The aim of this study was to establish a score system derived from clinical, echocardiographic and electrocardiographic indexes and evaluate its clinical value for cardiac resynchronization therapy (CRT) patient selection. Methods: Ninety-three patients receiving CRT were enrolled. A patient selection score system was generated by the clinical, echocardiographic and electrocardiographic parameters achieving a significant level by univariate and multivariate Cox regression model. The positive response to CRT was a left ventricular end systolic volume decrease of ≥ 15% and not reaching primary clinical endpoint (death or re-hospitalization for heart failure) at the end of follow-up. Results: Thirty-nine patients were CRT non-responders (41.94%) and 54 were responders (58.06%). A 4-point score system was generated based on tricuspid annular plane systolic ex­cursion (TAPSE), longitudinal strain (LS), and complete left bundle branch block (CLBBB) combined with a wide QRS duration (QRSd). The sensitivity and specificity for prediction of a positive response to CRT at a score &gt; 2 were 0.823 and 0.850, respectively (AUC: 0.92295% CI 0.691–0.916, p&lt; 0.001). Conclusions: A patient selection score system based on the integration of TAPSE, LS and CLBBB combined with a wide QRSd can help to predict positive response to CRT effectively and reliably

An experimental study and axial tensile constitutive model of the toughness of PP-SACC for rapid repairs

To improve the economic benefits of engineered cementitious composites and control the repair cycle, repair materials were designed, with the key components of the mixture being low-cost polypropylene (PP) fibers and fast-setting sulfoaluminate cement. The effects of water/binder ratio, fiber content, and aggregate particle size on the flowability, mechanical properties, and toughness of the polypropylene fiber-reinforced sulfoaluminate cementitious composite (PP-SACC) were explored. Based on experimentally measured axial tensile stress–strain curves, a constitutive model of PP-SACC was derived in terms of fiber content and water/binder ratio. Additionally, the correlation coefficients representing the relationships of the mixture indices with the tensile properties were explored based on revised gray relational analysis. Test results indicated that fiber content and water/binder ratio were the most important factors affecting the mechanical properties, toughness, and fluidity of the material; in contrast, the influence of aggregate size was slight. The PP-SACC mixture with an aggregate size of 75 µm, a water/binder ratio of 0.30, and a fiber content of 3.0% demonstrated an excellent degree of toughness and exhibited a flexural hardening phenomenon under bending load

PEELER: Learning to Effectively Predict Flakiness without Running Tests

—Regression testing is a widely adopted approach to expose change-induced bugs as well as to verify the correctness/robustness of code in modern software development settings. Unfortunately, the occurrence of flaky tests leads to a significant increase in the cost of regression testing and eventually reduces the productivity of developers (i.e., their ability to find and fix real problems). State-of-the-art approaches leverage dynamic test information obtained through expensive re-execution of test cases to effectively identify flaky tests. Towards accounting for scalability constraints, some recent approaches have built on static test case features, but fall short on effectiveness. In this paper, we introduce PEELER, a new fully static approach for predicting flaky tests through exploring a representation of test cases based on the data dependency relations. The predictor is then trained as a neural network based model, which achieves at the same time scalability (because it does not require any test execution), effectiveness (because it exploits relevant test dependency features), and practicality (because it can be applied in the wild to find new flaky tests). Experimental validation on 17,532 test cases from 21 Java projects shows that PEELER outperforms the state-of-the-art FlakeFlagger by around 20 percentage points: we catch 22% more flaky tests while yielding 51% less false positives. Finally, in a live study with projects in-the-wild, we reported to developers 21 flakiness cases, among which 12 have already been confirmed by developers as being indeed flaky