Fabrication of SiC Composites with Synergistic Toughening of Carbon Whisker and In Situ

The SiC composites with synergistic toughening of carbon whisker and in situ 3C-SiC nanowire have been fabricated by hot press sinter technology and annealed treatment technology. Effect of annealed time on the morphology of SiC nanowires and mechanical properties of the Cw/SiC composites was surveyed in detail. The appropriate annealed time improved mechanical properties of the Cw/SiC composites. The synergistic effect of carbon whisker and SiC nanowire can improve the fracture toughness for Cw/SiC composites. The vapor-liquid-solid growth (VLS) mechanism was proposed. TEM photo showed that 3C-SiC nanowire can be obtained with preferential growth plane ({111}), which corresponded to interplanar spacing about 0.25 nm

Mitochondrial respiration defects in cancer cells cause activation of Akt survival pathway through a redox-mediated mechanism

Cancer cells exhibit increased glycolysis for ATP production due, in part, to respiration injury (the Warburg effect). Because ATP generation through glycolysis is less efficient than through mitochondrial respiration, how cancer cells with this metabolic disadvantage can survive the competition with other cells and eventually develop drug resistance is a long-standing paradox. We report that mitochondrial respiration defects lead to activation of the Akt survival pathway through a novel mechanism mediated by NADH. Respiration-deficient cells (ρ-) harboring mitochondrial DNA deletion exhibit dependency on glycolysis, increased NADH, and activation of Akt, leading to drug resistance and survival advantage in hypoxia. Similarly, chemical inhibition of mitochondrial respiration and hypoxia also activates Akt. The increase in NADH caused by respiratory deficiency inactivates PTEN through a redox modification mechanism, leading to Akt activation. These findings provide a novel mechanistic insight into the Warburg effect and explain how metabolic alteration in cancer cells may gain a survival advantage and withstand therapeutic agents

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis

Novel Role of NOX in Supporting Aerobic Glycolysis in Cancer Cells with Mitochondrial Dysfunction and as a Potential Target for Cancer Therapy

NAD(P)H oxidase plays a role in cancer metabolism by providing NAD+ to support increased glycolysis

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Computational Detection and Functional Analysis of Human Tissue-Specific A-to-I RNA Editing

A-to-I RNA editing is a widespread post-transcriptional modification event in vertebrates. It could increase transcriptome and proteome diversity through recoding the genomic information and cross-linking other regulatory events, such as those mediated by alternative splicing, RNAi and microRNA (miRNA). Previous studies indicated that RNA editing can occur in a tissue-specific manner in response to the requirements of the local environment. We set out to systematically detect tissue-specific A-to-I RNA editing sites in 43 human tissues using bioinformatics approaches based on the Fisher's exact test and the Benjamini & Hochberg false discovery rate (FDR) multiple testing correction. Twenty-three sites in total were identified to be tissue-specific. One of them resulted in an altered amino acid residue which may prevent the phosphorylation of PARP-10 and affect its activity. Eight and two tissue-specific A-to-I RNA editing sites were predicted to destroy putative exonic splicing enhancers (ESEs) and exonic splicing silencers (ESSs), respectively. Brain-specific and ovary-specific A-to-I RNA editing sites were further verified by comparing the cDNA sequences with their corresponding genomic templates in multiple cell lines from brain, colon, breast, bone marrow, lymph, liver, ovary and kidney tissue. Our findings help to elucidate the role of A-to-I RNA editing in the regulation of tissue-specific development and function, and the approach utilized here can be broadened to study other types of tissue-specific substitution editing

Ventricular flow analysis and its association with exertional capacity in repaired tetralogy of Fallot: 4D flow cardiovascular magnetic resonance study

Background: Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) allows quantification of biventricular blood flow by flow components and kinetic energy (KE) analyses. However, it remains unclear whether 4D flow parameters can predict cardiopulmonary exercise testing (CPET) as a clinical outcome in repaired tetralogy of Fallot (rTOF). Current study aimed to (1) compare 4D flow CMR parameters in rTOF with age- and gender-matched healthy controls, (2) investigate associations of 4D flow parameters with functional and volumetric right ventricular (RV) remodelling markers, and CPET outcome. Methods: Sixty-three rTOF patients (14 paediatric, 49 adult; 30 ± 15 years; 29 M) and 63 age- and gender-matched healthy controls (14 paediatric, 49 adult; 31 ± 15 years) were prospectively recruited at four centers. All underwent cine and 4D flow CMR, and all adults performed standardized CPET same day or within one week of CMR. RV remodelling index was calculated as the ratio of RV to left ventricular (LV) end-diastolic volumes. Four flow components were analyzed: direct flow, retained inflow, delayed ejection flow and residual volume. Additionally, three phasic KE parameters normalized to end-diastolic volume (KEi EDV), were analyzed for both LV and RV: peak systolic, average systolic and peak E-wave. Results: In comparisons of rTOF vs. healthy controls, median LV retained inflow (18% vs. 16%, P = 0.005) and median peak E-wave KEi EDV (34.9 µJ/ml vs. 29.2 µJ/ml, P = 0.006) were higher in rTOF; median RV direct flow was lower in rTOF (25% vs. 35%, P < 0.001); median RV delayed ejection flow (21% vs. 17%, P < 0.001) and residual volume (39% vs. 31%, P < 0.001) were both greater in rTOF. RV KEi EDV parameters were all higher in rTOF than healthy controls (all P < 0.001). On multivariate analysis, RV direct flow was an independent predictor of RV function and CPET outcome. RV direct flow and RV peak E-wave KEi EDV were independent predictors of RV remodelling index. Conclusions: In this multi-scanner multicenter 4D flow CMR study, reduced RV direct flow was independently associated with RV dysfunction, remodelling and, to a lesser extent, exercise intolerance in rTOF patients. This supports its utility as an imaging parameter for monitoring disease progression and therapeutic response in rTOF. Clinical Trial Registrationhttps://www.clinicaltrials.gov. Unique identifier: NCT03217240

Clouds and convective self-aggregation in a multi-model ensemble of radiative-convective equilibrium simulations

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique amongst intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than un-aggregated simulations