An ethically guided preclinical device for phenotyping H_(2)production in laboratory rodents

Background:Dihydrogen(H_(2))is produced endogenously by the intestinal microbiota through the fermentation of diet carbohydrates.Over the past few years,numer-ous studies have demonstrated the significant therapeutic potential of H_(2)in various pathophysiological contexts,making the characterization of its production in labora-tory species of major preclinical importance.Methods:This study proposes an innovative solution to accurately monitor H_(2)pro-duction in free-moving rodents while respecting animal welfare standards.The devel-oped device consisted of a wire rodent cage placed inside an airtight chamber in which the air quality was maintained,and the H_(2)concentration was continuously analyzed.After the airtightness and efficiency of the systems used to control and maintain air quality in the chamber were checked,tests were carried out on rats and mice with different metabolic phenotypes,over 12 min to 1-h experiments and repeatedly.H_(2)production rates(HPR)were obtained using an easy calculation algorithm based on a first-order moving average.Results:HPR in hyperphagic Zucker rats was found to be twice as high as in control Wistar rats,respectively,2.64 and 1.27 nmol.s^(−1)per animal.In addition,the ingestion of inulin,a dietary fiber,stimulated H_(2)production in mice.HPRs were 0.46 nmol.s^(−1)for animals under control diet and 1.99 nmol.s^(−1)for animals under inulin diet.Conclusions:The proposed device coupled with our algorithm enables fine analysis of the metabolic phenotype of laboratory rats or mice with regard to their endogenous H_(2)production.

Massive Power Device Condition Monitoring Data Feature Extraction and Clustering Analysis using MapReduce and Graph Model

Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.

A Comparison of Airborne Formaldehyde Field Measurements Collected in an Anatomic Pathology Laboratory

Environmental monitoring of airborne formaldehyde (FA) using sensitive methodologies is fundamental to prevent health risks. The objective of this study was to compare three different FA monitoring methods during the daily activities of an anatomic pathology laboratory. Daily eight-hour measurements deriving from Radiello® passive diffusive samplers (PDS), NEMo XT continuous optical sensor (COS), and multi-gas 1512 photoacoustic monitor (MPM) were simultaneously compared over a period of 14 working days. Given the different daily distributions of the measurements performed by the three devices, all measurements were time-aligned for comparison purposes. The 95% limit of agreement (LOA) method was applied to estimate the degree of concordance of each device with respect to the others. Formaldehyde arithmetic mean measured using PDS was 32.6 ± 10.4 ppb (range: 19.8 - 62.7). The simultaneous measures performed by COS and MPM were respectively 42.4 ± 44.8 ppb (range: 7.0 - 175.0) and 189.0 ± 163.7 ppb (range: 40.0 - 2895.4). The MPM geometric mean (171.3 ppb) was approximately five times higher than those derived from COS (32.3 ppb) and PDS (31.4 ppb). The results of the LOA method applied to log-transformed FA data showed the same systematic discrepancies between MPM and the other two devices. A good agreement between PDS and COS could lead to a tailored approach according to the individual specificity of these techniques. This tool may be useful for accurately assessing the risk of FA exposure among healthcare workers. However, the limited specificity of the MPM does not support its use as a monitoring method for FA in the workplace.

The accuracy of arrhythmia detection and its clinical significance of intelligent wearable electrocardiogram monitoring devices in asymptomatic myocardial ischemia patients during daily activities.

This study focuses on the accuracy of arrhythmia detection by intelligent wearable electrocardiogram(ECG)monitoring devices in asymptomatic myocardial ischemia patients during daily activities.It elaborates on the technical principles,features,and working modes of such devices and makes a comparison with traditional ECG monitoring methods.Through a well-designed experimental approach involving data collection and analysis using specific evaluation metrics and standards,the accuracy of arrhythmia detection is evaluated.The relationship between arrhythmia and myocardial ischemia is explored,along with its impact on diagnosis,prognosis,and treatment strategy development.The application of these devices in daily activities,including feasibility,compliance,and analysis during different activity states and long-term trends,is also examined.Despite the potential benefits,technical limitations and barriers to clinical acceptance are identified,and future research directions are proposed.The findings contribute to a better understanding of the role and value of intelligent wearable ECG monitoring devices in the management of asymptomatic myocardial ischemia patients.

A Systematic Review on the Internet of Medical Things:Techniques,Open Issues,and Future Directions

IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network.Advancement in IoMT makes human lives easy and better.This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications,methodologies,and techniques to ensure the sustainability of IoMT-driven systems.The limitations of existing IoMTframeworks are also analyzed concerning their applicability in real-time driven systems or applications.In addition to this,various issues(gaps),challenges,and needs in the context of such systems are highlighted.The purpose of this paper is to interpret a rigorous review concept related to IoMT and present significant contributions in the field across the research fraternity.Lastly,this paper discusses the opportunities and prospects of IoMT and discusses various open research problems.

Cross-Validation of a Mobile N20 Flux Prototype （IPNOA） Using Micrometeorological and Chamber Methods

Within a LIFE＋ project IPNOA （improved flux prototype for n2o emission from agriculture）, LIFE11 ENV/IT/302 is a mobile prototype was developed to evaluate at field scale N20 emissions using a fast chamber technique. Main challenge was to develop a mobile system capable of moving on various field surfaces, equipped with very reliable N20 gas analyser （Los Gatos Research Inc.）, electrically autonomous （with batteries） and enough robust to face up to field conditions. In this paper, we report the major features of this prototype studied during two field campaigns. The N20 flux IPNOA prototype was compared with other methodological implementations： first, during an INGOS （integrated non-CO2 greenhouse gas observing systems） campaign on a grazed grassland at Easter Bush （Scotland） by Eddy correlation method, and then after on an arable crop at Grignon （France） using automatic and manual chambers fitted with QC-TILDAS （Quantum Cascade Tunable Infrared Laser Differential Absorption Spectrometer, Aerodyne Research Inc.）, with the 46C model of thermo-instrument analyser or with a GC （gas chromatography） analysis.

Harmonic Data Recovery Method Based on Multivariate Norm Matrix

During state perception of a power system, fragments of harmonic data are inevitably lost owing to the loss of synchronization signals, transmission delays, instrument failures, or other factors. A harmonic data recovery method is proposed based on multivariate norm matrix in this paper. The proposed method involves dynamic time warping for correlation analysis of harmonic data, normalized cuts for correlation clustering of power-quality monitoring devices, and adaptive alternating direction method of multipliers for multivariable norm joint optimization. Compared with existing data recovery methods, our proposed method maintains excellent recovery accuracy without requiring prior information or optimization of the power-quality monitoring device. Simulation results on the IEEE 39-bus and IEEE 118-bus test systems demonstrate the low computational complexity of the proposed method and its robustness against noise. In addition, the application of the proposed method to field data from a real-world system provides consistent results with those obtained from simulations.