Numerical Simulation of Foam Flow in Annulus With Wellbore Heat Transfer

In the study of hydraulic parameters in foam drilling, most foam hydraulic models were built based on the assumption that wellbore temperature was equal to formation temperature. But energy exchange takes place among annular foam fluid, formation and drill string, there may be a discrepancy between wellbore temperature and formation temperature. The effects of heat transfer on foam flow in annulus were investigated by numerical simulation in this paper. Simulation results show that due to the influence of temperature on pressure, the changes of annular pressure are transient. And foam fluid density, viscosity and other physical parameters are change with pressure. During foam fluid transports the solid particles from bottom to surface, solid particles accelerate and foam decelerates. When high solid particles concentration zone is reached, foam velocity decreases to a minimum value, and then increases due to the decrease of solid particle concentration. When cuttings velocity increases to a certain value, cuttings transport with a constant velocity.Key words: Heat transfer; Foam drilling; Annulus pressure; Foam velocity; Numerical simulatio

PerAF-based resting-state fMRI classifier for minimal hepatic encephalopathy

BackgroundMinimal hepatic encephalopathy (MHE) is a common cognitive impairment in patients with end-stage liver cirrhosis. However, the selection of sensitive biomarkers and the establishment of reliable diagnostic methods are currently challenging. We aimed to explore the abnormal spontaneous brain activity in patients with MHE and evaluate the clinical diagnostic value of four indicators for MHE using the support vector machine (SVM) method.MethodsA total of 45 MHE patients and 40 healthy controls were enrolled. Amplitude of low frequency fluctuation (ALFF), fractional amplitude of low frequency fluctuation (fALFF), percentage amplitude of low frequency fluctuation (PerAF), and regional homogeneity (ReHo) were used to evaluate local spontaneous brain activity. SVM analysis was used to construct the classification model and evaluate the diagnostic value.ResultsTwo-sample t-test and SVM analysis showed that, compared with the healthy control group, MHE patients had decreased ALFF values in the left angular gyrus, right inferior temporal gyrus, left postcentral gyrus, precentral gyrus, and right supplementary motor area. These regions indicated moderate classification efficacy (AUC = 0.75). Decreased ReHo metrics in the right anterior cingulate and paracingulate gyri also showed general discriminative power (AUC = 0.72). fALFF metrics, whether analyzed independently or combined with other indicators, exhibited limited classification performance (AUC < 0.70). Decreased PerAF metrics in the right superior parietal lobule, right dorsolateral prefrontal cortex, and right middle frontal gyrus achieved a good classification accuracy rate (AUC value 0.83; accuracy 81.18%; sensitivity 75.56%; specificity 87.50%), outperforming other functional metrics.ConclusionWe found that decreased mean PerAF in the right supramarginal gyrus, right dorsolateral superior frontal gyrus, and right middle frontal gyrus may serve as potential neuroimaging indicators for early identification of cognitive impairment in MHE patients, providing critical evidence for clinical screening protocols

Early detection of secondary damage in ipsilateral thalamus after acute infarction at unilateral corona radiata by diffusion tensor imaging and magnetic resonance spectroscopy

<p>Abstract</p> <p>Background</p> <p>Traditional magnetic resonance (MR) imaging can identify abnormal changes in ipsilateral thalamus in patients with unilateral middle cerebral artery (MCA) infarcts. However, it is difficult to demonstrate these early changes quantitatively. Diffusion tensor imaging (DTI) and proton magnetic resonance spectroscopy (MRS) are potentially sensitive and quantitative methods of detection in examining changes of tissue microstructure and metabolism. In this study, We used both DTI and MRS to examine possible secondary damage of thalamus in patients with corona radiata infarction.</p> <p>Methods</p> <p>Twelve patients with unilateral corona radiata infarction underwent MR imaging including DTI and MRS at one week (W1), four weeks (W4), and twelve weeks (W12) after onset of stroke. Twelve age-matched controls were imaged. Mean diffusivity (MD), fractional anisotropy (FA), N-acetylaspartate (NAA), choline(Cho), and creatine(Cr) were measured in thalami.</p> <p>Results</p> <p>T1-weighted fluid attenuation inversion recovery (FLAIR), T2-weighted, and T2-FLAIR imaging showed an infarct at unilateral corona radiate but no other lesion in each patient brain. In patients, MD was significantly increased at W12, compared to W1 and W4 (all <it>P</it>< 0.05). NAA was significantly decreased at W4 compared to W1, and at W12 compared to W4 (all <it>P</it>< 0.05) in the ipsilateral thalamus. There was no significant change in FA, Cho, or Cr in the ipsilateral thalamus from W1 to W12. Spearman's rank correlation analysis revealed a significant negative correlation between MD and the peak area of NAA, Cho, and Cr at W1, W4, and W12 and a significant positive correlation of FA with NAA at W1.</p> <p>Conclusions</p> <p>These findings indicate that DTI and MRS can detect the early changes indicating secondary damage in the ipsilateral thalamus after unilateral corona radiata infarction. MRS may reveal the progressive course of damage in the ipsilateral thalamus over time.</p

Early stage transplantation of bone marrow cells markedly ameliorates copper metabolism and restores liver function in a mouse model of Wilson disease

<p>Abstract</p> <p>Background</p> <p>Recent studies have demonstrated that normal bone marrow (BM) cells transplantation can correct liver injury in a mouse model of Wilson disease (WD). However, it still remains unknown when BM cells transplantation should be administered. The aim of this study was to investigate the potential impact of normal BM cells transplantation at different stages of WD to correct liver injury in toxic milk (tx) mice.</p> <p>Methods</p> <p>Recipient tx mice were sublethally irradiated (5 Gy) prior to transplantation. The congenic wild-type (DL) BM cells labeled with CM-DiI were transplanted via caudal vein injection into tx mice at the early (2 months of age) or late stage (5 months of age) of WD. The same volume of saline or tx BM cells were injected as controls. The DL donor cell population, copper concentration, serum ceruloplasmin oxidase activity and aspartate aminotransferase (AST) levels in the various groups were evaluated at 1, 4, 8 and 12 weeks post-transplant, respectively.</p> <p>Results</p> <p>The DL BM cells population was observed from 1 to 12 weeks and peaked by the 4^thweek in the recipient liver after transplantation. DL BM cells transplantation during the early stage significantly corrected copper accumulation, AST across the observed time points and serum ceruloplasmin oxidase activity through 8 to 12 weeks in tx mice compared with those treated with saline or tx BM cells (all <it>P </it>< 0.05). In contrast, BM cells transplantation during the late stage only corrected AST levels from 4 to 12 weeks post-transplant and copper accumulation at 12 weeks post-transplant (all <it>P </it>< 0.05). No significant difference was found between the saline and tx BM cells transplantation groups across the observed time points (<it>P </it>> 0.05).</p> <p>Conclusions</p> <p>Early stage transplantation of normal BM cells is better than late stage transplantation in correcting liver function and copper metabolism in a mouse model of WD.</p

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

Classification method of IPv6 traffic based on convolutional neural network

Abstract This paper proposes a method of IPv6 traffic classification based on convolutional neural network. It aims to solve the problems of low accuracy, high dependence on traffic feature selection, inefficiency in IPv6 traffic classification technology based on machine learning, and the lack of research on IPv6 traffic classification methods. The method transforms IPv6 traffic into two-dimensional matrix as input of convolution neural network. Then, it constructs a traffic classification model of feature autonomous learning. The model consist of one input layer, two convolutional pooling layers, one full connection layer and one output layer. The experimental results show that the designed model has a good performance in IPv6 traffic classification.</jats:p