Influence of sound and light combined conditions in urban environments on residents' tolerance limits in pre sleep state

To determine the sound and light combined conditions pollution in urban residential environments at night, this paper comprehensively evaluates cross-visual and auditory sensory channels in the laboratory. Experimental variables include extremum and gradient, and the working state of the participants was determined and verified. A subjective evaluation experiment on 18 combined conditions was carried out by synthesizing real-world data. Results from the sound and light combined conditions experiment show that there are significant differences in the tolerance limits of participants to different content sound variables (p = 0.000 0.05, p = 0.122 > 0.05, p = 0.146 > 0.05) at different color temperatures. The tolerance limits of participants will not be reduced due to the superposition of two interference variables: sound pollution and light pollution. Adding light pollution to sound pollution can increase the tolerance limits of participants, while adding sound pollution to light pollution has no significant effect on the tolerance limits. The study also found that adding light with different color temperatures to the human voice can increase participants’ tolerance limit to human voice (1% -2%), indicating that visual elements can change individuals’ perception of sound. In addition, the physiological and psychological differences between participants may affect the performance differences of individual participants in sound and light combined conditions

A Markov Process Inspired Cellular Automata Model of Road Traffic

To provide a more accurate description of the driving behaviors in vehicle queues, a namely Markov-Gap cellular automata model is proposed in this paper. It views the variation of the gap between two consequent vehicles as a Markov process whose stationary distribution corresponds to the observed distribution of practical gaps. The multiformity of this Markov process provides the model enough flexibility to describe various driving behaviors. Two examples are given to show how to specialize it for different scenarios: usually mentioned flows on freeways and start-up flows at signalized intersections. The agreement between the empirical observations and the simulation results suggests the soundness of this new approach.Comment: revised according to the helpful comments from the anonymous reviewer

Synthetic Datasets for Autonomous Driving: A Survey

Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their expensive and time-consuming experimental and labeling costs. Therefore, more and more researchers are turning to synthetic datasets to easily generate rich and changeable data as an effective complement to the real world and to improve the performance of algorithms. In this paper, we summarize the evolution of synthetic dataset generation methods and review the work to date in synthetic datasets related to single and multi-task categories for to autonomous driving study. We also discuss the role that synthetic dataset plays the evaluation, gap test, and positive effect in autonomous driving related algorithm testing, especially on trustworthiness and safety aspects. Finally, we discuss general trends and possible development directions. To the best of our knowledge, this is the first survey focusing on the application of synthetic datasets in autonomous driving. This survey also raises awareness of the problems of real-world deployment of autonomous driving technology and provides researchers with a possible solution.Comment: 19 pages, 5 figure

Preserving Insulin Secretion in Diabetes by Inhibiting VDAC1 Overexpression and Surface Translocation in beta Cells

Type 2 diabetes (T2D) develops after years of prediabetes during which high glucose (glucotoxicity) impairs insulin secretion. We report that the ATP-conducting mitochondrial outer membrane voltage-dependent anion channel-1 (VDAC1) is upregulated in islets from T2D and non-diabetic organ donors under glucotoxic conditions. This is caused by a glucotoxicity-induced transcriptional program, triggered during years of prediabetes with suboptimal blood glucose control. Metformin counteracts VDAC1 induction. VDAC1 overexpression causes its mistargeting to the plasma membrane of the insulinsecreting beta cells with loss of the crucial metabolic coupling factor ATP. VDAC1 antibodies and inhibitors prevent ATP loss. Through direct inhibition of VDAC1 conductance, metformin, like specific VDAC1 inhibitors and antibodies, restores the impaired generation of ATP and glucose-stimulated insulin secretion in T2D islets. Treatment of db/db mice with VDAC1 inhibitor prevents hyperglycemia, and maintains normal glucose tolerance and physiological regulation of insulin secretion. Thus, beta cell function is preserved by targeting the novel diabetes executer protein VDAC1.Peer reviewe

Mapping of host-parasite-microbiome interactions reveals metabolic determinants of tropism and tolerance in Chagas disease

Chagas disease (CD) is a parasitic disease caused by Trypanosoma cruzi protozoa, presenting with cardiomyopathy, megaesophagus, and/or megacolon. To determine the mechanisms of gastrointestinal (GI) CD tissue tropism, we systematically characterized the spatial localization of infection-induced metabolic and microbiome alterations, in a mouse model of CD. Notably, the impact of the transition between acute and persistent infection differed between tissue sites, with sustained large-scale effects of infection in the esophagus and large intestine, providing a potential mechanism for the tropism of CD within the GI tract. Infection affected acylcarnitine metabolism; carnitine supplementation prevented acute-stage CD mortality without affecting parasite burden by mitigating infection-induced metabolic disturbances and reducing cardiac strain. Overall, results identified a previously-unknown mechanism of disease tolerance in CD, with potential for new therapeutic regimen development. More broadly, results highlight the potential of spatially resolved metabolomics to provide insight into disease pathogenesis and infectious disease drug development.Open Access fees paid for in whole or in part by the University of Oklahoma Libraries. This work was supported by start-up funds from the University of Oklahoma to L.-I.M. and the National Institute of Allergy and Infectious Diseases of the NIH under award number R21AI148886 to L.-I.M. Initial tissue collection was supported by a postdoctoral fellowship to L.-I.M. from the Canadian Institutes of Health Research (award number 338511; www.cihr-irsc.gc.ca/). Microbial community analysis was supported, in part, by an NIH grant (award number NIH 2R01-GM089886 to K.S). Immunological characterization was performed on instrumentation from the OU Protein Production and Characterization Core facility, supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the NIH under grant number P20GM103640. Histology samples were processed by the University of Oklahoma Health Sciences Center, Stephenson Cancer Center Tissue Pathology Shared Resource, supported by the National Cancer Institute Cancer Center Support Grant P30CA225520 and COBRE P20GM103639. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.Ye

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits

Umbilical artery ultrasound haemodynamics combined with serum adiponectin levels can aid in predicting adverse pregnancy outcomes in patients with severe pre-eclampsia

Severe pre-eclampsia is a leading cause of maternal and perinatal morbidity and mortality. This retrospective study explored pregnancy outcome predictive values of umbilical artery Doppler with serum adiponectin in severe pre-eclampsia. Fasting elbow venous blood was collected from 118 severe pre-eclampsia patients [maternal systolic pressure ≥ 160 mmHg and/or diastolic pressure ≥ 110 mmHg + minimal proteinuria, 56; mild hypertension + heavy proteinuria (≥2 g/24 h or random urinary protein ≥ 2+), 42; no proteinuria but new-onset hypertension + diseases of heart/lung/liver/kidney/other organs or abnormalities in blood/digestive/nervous systems, placental foetus involved, 20] and 90 controls (18.5-24.9 kg/m2) in the first morning of admission. Serum adiponectin and resistance/pulsatility indexes were separately measured and correlatively analysed by Pearson’s coefficient analysis. Adverse outcomes included maternal primary postpartum haemorrhage and placental abruption, neonatal asphyxia, low birth weight, foetal distress, foetal growth restriction. In severe pre-eclampsia, serum adiponectin (downregulated) was negatively-correlated with resistance/pulsatility indexes (upregulated). The area under the curve of umbilical artery Doppler with serum adiponectin for predicting adverse outcomes of severe pre-eclampsia was 0.6545 (specificity 60.27%, sensitivity 60.00%). In conclusion, umbilical artery Doppler with serum adiponectin predicts adverse pregnancy outcomes in severe pre-eclampsia.Impact statement What is already known on this subject? Sad levels were lowered in sPE patients. UA ultrasound hemodynamic parameters can predict adverse pregnancy outcomes. What do the results of this study add? Our study revealed that ultrasonic hemodynamic indexes of UA combined with Sad levels had better efficacy in predicting pregnancy outcomes in patients with sPE, and our study is expected to improve the accuracy of clinical prediction of adverse outcomes in sPE patients. What are the implications of these findings for clinical practice and/or further research? Through the combined detection of multiple indicators of the foetus in the mother, our study expects to be able to monitor and predict the growth of the foetus in the mother more accurately in clinical practice, avoid excessive intervention or untimely intervention, and reduce the incidence of perinatal adverse pregnancy outcomes

A Neural Network Model for Urban Traffic Volumes Compression

Traffic data are the information source for traffic control and management. With the development and integration of Intelligent Transportation Systems, many applications and their respective sensors and detectors are a rich source of data about transportation system characteristics and performance. However, because of the limitation of databases and devices, the huge amounts of traffic data can not be stored without reduction. In this paper, an approach for urban traffic volume compression based on artificial neural network is proposed. The lossy compression of data is realized by using a set of three-layer back-propagation neural networks to remove the correlation within traffic volumes. The model has both a small reproduction error and a relatively high compression ratio