Mitochondria, the powerhouse of the cell, are dynamic organelles that undergo constant morphological changes. Increasing evidence indicates that mitochondria morphologies and functions can be modulated by mechanical cues. However, the mechano-sensing and -responding properties of mitochondria and the relation between mitochondrial morphologies and functions are unclear due to the lack of methods to precisely exert mechano-stimulation on and deform mitochondria inside live cells. Here, we present an optogenetic approach that uses light to induce deformation of mitochondria by recruiting molecular motors to the outer mitochondrial membrane via light-activated protein-protein hetero-dimerization. Mechanical forces generated by motor proteins distort the outer membrane, during which the inner mitochondrial membrane can also be deformed. Moreover, this optical method can achieve subcellular spatial precision and be combined with different optical dimerizers and molecular motors. This method presents a mitochondria-specific mechano-stimulator for studying mitochondria mechanobiology and the interplay between mitochondria shapes and functions.
  The influence of outdoor green space on microbial communities indoors has scarcely been investigated. Here, we study the associations between nearby residential green space and residential indoor microbiota.
 
  We collected settled dust from 176 living rooms of participants of the ENVIRONAGE birth cohort. We performed 16S and ITS amplicon sequencing, and quantitative PCR measurements of total bacterial and fungal loads to calculate bacterial and fungal diversity measures (Chao1 richness, Shannon and Simpson diversity indices) and relative abundance of individual taxa. Green spaces were estimated within 50m and 100m buffers around the residential address. We defined total residential green space using high-resolution land-cover data, further stratified in low-growing (height<3m) and high-growing green (height>3m). We used land-use data to calculate the residential nature. We ran linear regression models, adjusting for confounders and other potential determinants. Results are expressed as units change fonges in indoor microbiota diversity and to explore their contribution to beneficial health effects associated with green space exposure.
 Nearby green space determines the diversity of indoor environment microbiota, and the type of green differently impacts bacterial and fungal diversity.  https://www.selleckchem.com/products/grl0617.html  Further research is needed to investigate in more detail possible microbial taxa compositions underlying the observed changes in indoor microbiota diversity and to explore their contribution to beneficial health effects associated with green space exposure.Burial of organic carbon (OC) in rift lakes on plateau is an important part of the global cycle. It is affected by natural and anthropogenic factors. In this study, we selected the sediment records of 7 rift lakes on the Yunnan-Guizhou Plateau to study spatial-temporal variation, sources and driving factors of organic carbon burial since 1850. The analysis of the temporal and spatial trend of carbon burial shows that the TOC concentration, TOC flux, C N and mass accumulate rate have increased significantly since 1850. Co-occurrence network analysis indicated that a strong correlation between the TOC concentration and silty. TOC concentration were identified as core genera due to their high concentration. Carbon isotope tracing results show that before 1950, endogenous OC input played a dominant role, and after 1950, the proportion of exogenous OC increased. Canonical correlation analysis indicated that after 1950, agriculture intensification and population increase become one of the factors affecting the carbon burial of lakes in this area. The result of this study indicate that anthropogenic factors have become the main factors promoting carbon burial in rift lakes on the plateau.
  While temperature changes have been confirmed as one of the contributory factors affecting human health, the association between high-frequency temperature variability (HFTV, i.e., temperature variation at short time scales such as 1, 2, and 5 days) and the hospitalization of chronic obstructive pulmonary disease (COPD) was rarely reported.
 
  To evaluate the associations between high-frequency temperature variabilities (i.e., at 1, 2, and 5-day scales) and daily COPD hospitalization.
 
  We collected daily records of COPD hospitalization and meteorological variables from 2013 to 2017 in 21 cities of Guangdong Province, South China. A quasi-Poisson regression with a distributed lag nonlinear model was first employed to quantify the effects of two HFTV measures, i.e., the day-to-day (DTD) temperature change and the intraday-interday temperature variability (IITV), on COPD morbidity for each city. Second, we used multivariate meta-analysis to pool the city-specific estimates, and stratified analyses were performe significant adverse impacts on COPD hospitalization. As climate change intensifies, precautions need to be taken to mitigate the impacts of high-frequency temperature changes.
 The increases of DTD and IITV have significant adverse impacts on COPD hospitalization. As climate change intensifies, precautions need to be taken to mitigate the impacts of high-frequency temperature changes.The water resource is an essential field of economic growth, social progress, and environmental integrity. A novel solution is offered to meet water needs, distribution, and IoT-based quality management requirements. With technological growth, this paper presents an IoT-enabled Water Resource Management and Distribution Monitoring System (IWRM-DMS) using sensors, gauge meters, flow meters, ultrasonic sensors, motors to implement in rural cities. Thus, research proposes that the IWRM-DMS establish the rural demand for water and the water supply system to minimize water demand. The system proposed includes different sensors, such as the water flow sensor, the pH sensor, the water pressure valve, the flow meters, and ultrasound sensors. This water system has been developed, which addresses the demand for domestic water in the village. Machine Intelligence has been designed for demand prediction in the decision support system. The simulation results confirm the applicability of the proposed framework in real-time environments.