The Effect of Sleep Deprivation and Subsequent Recovery Period on the Synaptic Proteome of Rat Cerebral Cortex

Sleep deprivation (SD) is commonplace in the modern way of life and has a substantial social, medical, and human cost. Sleep deprivation induces cognitive impairment such as loss of executive attention, working memory decline, poor emotion regulation, increased reaction times, and higher cognitive functions are particularly vulnerable to sleep loss. Furthermore, SD is associated with obesity, diabetes, cardiovascular diseases, cancer, and a vast majority of psychiatric and neurodegenerative disorders are accompanied by sleep disturbances. Despite the widespread scientific interest in the effect of sleep loss on synaptic function, there is a lack of investigation focusing on synaptic transmission on the proteome level. In the present study, we report the effects of SD and recovery period (RP) on the cortical synaptic proteome in rats. Synaptosomes were isolated after 8 h of SD performed by gentle handling and after 16 h of RP. The purity of synaptosome fraction was validated with western blot and electron microscopy, and the protein abundance alterations were analyzed by mass spectrometry. We observed that SD and RP have a wide impact on neurotransmitter-related proteins at both the presynaptic and postsynaptic membranes. The abundance of synaptic proteins has changed to a greater extent in consequence of SD than during RP: we identified 78 proteins with altered abundance after SD and 39 proteins after the course of RP. Levels of most of the altered proteins were upregulated during SD, while RP showed the opposite tendency, and three proteins (Gabbr1, Anks1b, and Decr1) showed abundance changes with opposite direction after SD and RP. The functional cluster analysis revealed that a majority of the altered proteins is related to signal transduction and regulation, synaptic transmission and synaptic assembly, protein and ion transport, and lipid and fatty acid metabolism, while the interaction network analysis revealed several connections between the significantly altered proteins and the molecular processes of synaptic plasticity or sleep. Our proteomic data implies suppression of SNARE-mediated synaptic vesicle exocytosis and impaired endocytic processes after sleep deprivation. Both SD and RP altered GABA neurotransmission and affected protein synthesis, several regulatory processes and signaling pathways, energy homeostatic processes, and metabolic pathways.

The Impact of COVID-19 on the Socio-economic Indicators of the Selected Asian Countries

The COVID-19 pandemic originated in Wuhan, China, in December 2019. The virus has spread across the globe over the last 20 months. In the interest of public health, the World Health Organization (WHO) has declared a public health emergency to harmonise international responses to the virus. In a strongly interconnected world, the effect of the pandemic goes beyond mortality and morbidity. The unprecedented outbreak of COVID-19 has also resulted in a global economic crisis. Almost every sector of the economy has been gravely affected by the pandemic to various degrees. In an attempt to curb the spread of the virus many countries have initiated measures such as lockdowns, travel restrictions, ban on public and private transportation, closure of schools and colleges and restrictions on public and social gatherings. These initiatives have led to the decline in GDP, foreign trade and foreign exchange reserves, the rise of unemployment, the crash of stock markets and the depreciation of national currencies among other things. This study assesses the impact of COVID-19 on selected macroeconomic parameters of various Asian countries to present insights on the economic and health crisis caused due to COVID-19. The study analyses the effect of the pandemic on the macroeconomic factors listed above as well as the human cost of the pandemic during the last 20 months. The research finds that the outbreak adversely affected the economy and lives of people in India when compared to selected Asian nations.

New “Snakebot” Could Map Cambodian Minefields

By navigating under dense vegetation, an innovative robot could significantly reduce the monetary, environmental, and human cost of demining Cambodia.

Cost-Utility Analysis of the Integrated Care Models for the Management of Hypertension Patients: A Quasi-Experiment in Southwest Rural China

Background: Hypertension has become the second-leading risk factor for death worldwide. However, the fragmented three-level “county–township–village” medical and healthcare system in rural China cannot provide continuous, coordinated, and comprehensive health care for patients with hypertension, as a result of which rural China has a low rate of hypertension control. This study aimed to explore the costs and benefits of an integrated care model using three intervention modes—multidisciplinary teams (MDT), multi-institutional pathway (MIP), and system global budget and performance-based payments (SGB-P4P)—for hypertension management in rural China.Methods: A Markov model with 1-year per cycle was adopted to simulate the lifetime medical costs and quality-adjusted life-years (QALYs) for patients. The interventions included Option 1 (MDT + MIP), Option 2 (MDT + MIP + SGB–P4P), and the Usual practice (usual care). We used the incremental cost-effectiveness ratio (ICER), net monetary benefit (NMB), and net health benefit (NHB) to make economic decisions and a 5% discount rate. One-way and probability sensitivity analyses were performed to test model robustness. Data on the blood pressure control rate, transition probability, utility, annual treatment costs, and project costs were from the community intervention trial (CMB-OC) project.Results: Compared with the Usual practice, Option 1 yielded an additional 0.068 QALYs and an additional cost of $229.99, resulting in an ICER of $3,373.75/QALY, the NMB was –$120.97, and the NHB was −0.076 QALYs. Compared with the Usual practice, Option 2 yielded an additional 0.545 QALYs, and the cost decreased by $2,007.31, yielding an ICER of –$3,680.72/QALY. The NMB was $2,879.42, and the NHB was 1.801 QALYs. Compared with Option 1, Option 2 yielded an additional 0.477 QALYs, and the cost decreased by $2,237.30, so the ICER was –$4,688.50/QALY, the NMB was $3,000.40, and the NHB was 1.876 QALYs. The one-way sensitivity analysis showed that the most sensitive factors in the model were treatment cost of ESRD, human cost, and discount rate. The probability sensitivity analysis showed that when willingness to pay was $1,599.16/QALY, the cost-effectiveness probability of Option 1, Option 2, and the Usual practice was 0.008, 0.813, and 0.179, respectively.Conclusions: The integrated care model with performance-based prepaid payments was the most beneficial intervention, whereas the general integrated care model (MDT + MIP) was not cost-effective. The integrated care model (MDT + MIP + SGB-P4P) was suggested for use in the community management of hypertension in rural China as a continuous, patient-centered care system to improve the efficiency of hypertension management.

Digital Drilling in Holographic World

Nowadays oil & gas industry is receiving a bulk of data than ever before from its onsite wells where may hundred miles away from operator's headquarter, which benefits us monitoring and analyzing those digital fortune in a data hub, saving a lot of expenditure and improving the efficiency compared to old-fashioned approach which requires senior engineers with rich experience working on wellsite. In this way, the oil & gas operators save money tremendously on human cost under the booming of drilling operations. While, could we do more to dig out further values from those data? Make our operations less dependable on limited resources, the senior drilling engineers, especially when the oil and gas industry face the chasm of human resources sustainability after the hit of downturn, also make the plain real-time data more intuitive and self-explanatory to the operation decision makers in an unprecedented way. What's more, could we make our drilling activities more visible and interactive? This paper is going to introduce using augmented reality technology to create an intuitive platform to integrate and present real-time operation parameters and data. Like any revolutionary method or technology, it could improve the industry efficiency in a non-negligible way, help us manage massive real-time data more effectively and efficiently. The 3D holographic projection presents dynamic models or systems based on the data stream and graphic algorithm, which evolves our industry from 2D world to 3D world, combining the reality environment with the digital world, creating a digital reflection of the real wellsite, bottom hole assembly (BHA), well trajectories, lithological layers, etc. Thanks to the visualization technologies and augmented reality, we can create a digital twin of physic world for those engineers, technicians, managers using holographic method to interact with, scale up and down, analyzing in a better awareness. In this paper, we will describe a digital drilling wellsite which is established on operator's Real Time Operation Center (RTOC)office to monitor and analyze live field operations, the operator could have an overview of their on-site operations, tracking the equipment performance, engineering parameters and downhole status to enhance the understanding and interaction with the on-going field operations. The wellbore trajectory model gives the team a superior knowledge by combining the engineering data or geological data. Not only help well placement in desired reservoir but also improve the anti-collision concept in direction drilling. This model is extreme meaningful when engineers need a discussion to optimize or change their drilling plan as it is 3D visible and able to interact with. We will continues digging out further more value of the real-time data collected from wellsite to educate us find the cost-saving ways which improve our performance and eliminate the complicated conditions that normally resulted in Non production time (NPT) event. For our oil & gas industry, we are just start to have a more adventure and prosperous journey in digitalizing transforming.

Insight: leadership

Jon G. Hall CITP FBCS writes about the human cost to the mountains of accumulated technical debt — and what, if anything, can be done about it.

Ice on Suspension Bridge Cables

In this report we study mathematical models for predicting when ice may form and fall from vertical steel hangers of suspension bridges down onto the road below. This is an important problem to study, not only because of the economic costs related to closing a bridge due to the risk of falling ice, but also the human cost if the bridge is still open to traffic when ice falls down. In the report we present two main categories of models for predicting falling times: 1) models based on heat transfer from the surrounding air, and 2) models based on heating due to radiation from the sun. A flow chart is furthermore presented together with tables for determining which model to use and quickly estimating time of failure based on a set of simple conditions.

Deep Learning for Component Fault Detection in Electricity Transmission Lines.

Fault identification is one of the most significant bottlenecks faced by electricity transmission and distribution utilities in developing countries to deliver efficient services to the customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In view of this, we exploited the use of oblique UAV imagery with a high spatial resolution and a fine-tuned and deep Convolutional Neural Networks (CNNs) to monitor four major Electric power transmission network (EPTN) components. This study explored the capability of the Single Shot Multibox Detector (SSD), a one-stage object detection model on the electric transmission power line imagery to localize, detect and classify faults. The fault considered in this study include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. Our adapted neural network is a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision (mAP) of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels of F1-score and representation. Finally, comparable to other works in literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood.