Biological membranes are highly ordered structures consisting of mosaics of lipids and proteins. Elevated temperatures can directly and effectively change the properties of these membranes, including ...their fluidity and permeability, through a holistic effect that involves changes in the lipid composition and/or interactions between lipids and specific membrane proteins. Ultimately, high temperatures can alter microdomain remodeling and instantaneously relay ambient cues to downstream signaling pathways. Thus, dynamic membrane regulation not only helps cells perceive temperature changes but also participates in intracellular responses and determines a cell's fate. Moreover, due to the specific distribution of extra- and endomembrane elements, the plasma membrane (PM) and membranous organelles are individually responsible for distinct developmental events during plant adaptation to heat stress. This review describes recent studies that focused on the roles of various components that can alter the physical state of the plasma and thylakoid membranes as well as the crucial signaling pathways initiated through the membrane system, encompassing both endomembranes and membranous organelles in the context of heat stress responses.
Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order ...to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strategies. Considering the long history and the extensive literature on the change-point detection, we provide an in-depth discussion on a normal mean change-point model from aspects of regression analysis, hypothesis testing, consistency and inference. In particular, we present a strategy to gather and aggregate local information for change-point detection that has become the cornerstone of several emerging methods because of its attractiveness in both computational and theoretical properties.
A rolling bearing fault diagnosis method based on whale gray wolf optimization algorithm-variational mode decomposition-support vector machine (WGWOA-VMD-SVM) was proposed to solve the unclear fault ...characterization of rolling bearing vibration signal due to its nonlinear and nonstationary characteristics. A whale gray wolf optimization algorithm (WGWOA) was proposed by combining whale optimization algorithm (WOA) and gray wolf optimization (GWO), and the rolling bearing signal was decomposed by using variational mode decomposition (VMD). Each eigenvalue was extracted as eigenvector after VMD, and the training and test sets of the fault diagnosis model were divided accordingly. The support vector machine (SVM) was used as the fault diagnosis model and optimized by using WGWOA. The validity of this method was verified by two cases of Case Western Reserve University bearing data set and laboratory test. The test results show that in the bearing data set of Case Western Reserve University, compared with the existing VMD-SVM method, the fault diagnosis accuracy rate of the WGWOA-VMD-SVM method in five repeated tests reaches 100.00%, which preliminarily verifies the feasibility of this algorithm. In the laboratory test case, the diagnostic effect of the proposed fault diagnosis method is compared with backpropagation neural network, SVM, VMD-SVM, WOA-VMD-SVM, GWO-VMD-SVM, and WGWOA-VMD-SVM. Test results show that the accuracy rate of WGWOA-VMD-SVM fault diagnosis is the highest, the accuracy rate of a single test reaches 100.00%, and the accuracy rate of five repeated tests reaches 99.75%, which is the highest compared with the above six methods. WGWOA plays a good optimization role in optimizing VMD and SVM. The signal decomposed by VMD is optimized by using the WGWOA algorithm without mode overlap. WGWOA has the better convergence performance than WOA and GWO, which further verifies its superiority among the compared methods. The research results can provide an effective improvement method for the existing rolling bearing fault diagnosis technology.
Earth's terrestrial near‐subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the ...thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high‐resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsec (∼1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. We anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models.
Key Points:
We have quantified the thicknesses of permeable layers above bedrock for Earth System Models
We distinguish among uplands and lowlands, using optimal models for each to predict depth to bedrock
The data set honors the geologic, topographic, and climatic controls on permeable layer thicknesses
Accumulation of snowfall during winter and snowmelt in the subsequent spring or earlier summer provides a dominant water source in alpine regions. Most land surface and hydrological models use ...near‐surface air temperature (Ta) thresholds to partition precipitation into snow and rain, underestimating snowfall over drier regions. We developed a snow‐rain partitioning scheme using the wet‐bulb temperature (Tw), which is closer to the surface temperature of a falling hydrometeor than Ta. Tw becomes more depressed in drier environments as derived from Tw depression equation using Ta and surface air humidity, resulting in a greater fraction of snowfall. We implemented this new Tw scheme in the Noah‐MP land surface model and evaluated the model against a high‐quality ground‐based snow product over the contiguous United States. The results suggest that the new Tw scheme substantially improves the model skill in simulating snow depth and snow water equivalent over most snow‐covered grids, especially the higher and drier continental mountain ranges in the Western United States, while it retains the modeling accuracy over the more humid Eastern United States.
Plain Language Summary
The partitioning between rainfall and snowfall is important for understanding the impacts of climate change and water resource availability. Most land surface and hydrological models use surface air temperature to partition precipitation into rain and snow and thus underestimate snowfall and snow mass accumulated on the ground in the drier Western United States. A falling hydrometeor evaporates or sublimates at its surface depending on the humidity of the surrounding air and cools off, resulting in a surface temperature that is cooler than the air temperature. The depressed surface temperature is close to the wet‐bulb temperature. We developed a scheme using the wet‐bulb temperature and tested it with a physically based snow model over the contiguous United States. The testing results strongly support the use of wet‐bulb temperature, which enhances snowfall and the snow mass on the ground more significantly over the higher and drier mountains in the Western United States, while it retains the modeling accuracy in the more humid Eastern United States.
Key Points
We developed a snow‐rain partitioning scheme using the wet‐bulb temperature and tested it with a physically based snow model over CONUS
The new scheme produces more snowfall and snow mass on the ground that agree better with a ground‐based snow product over the drier Western CONUS
•Various dietary factors interact genetically with cardiovascular metabolic diseases.•Dietary fat intake is closely associated with susceptibility to heart failure but not with coronary artery ...disease, diabetes, or stroke.•Unsaturated fatty acids found in dietary lipids exhibit anti-inflammatory, antioxidant, and anti-thrombotic properties, which are closely linked to cardiovascular health.•Adopting a healthy dietary pattern can be an effective measure for preventing cardiovascular metabolic diseases.
When specific nutrients are inadequate, vulnerability to cardiovascular and metabolic illnesses increases. The data linking dietary nutrition with these illnesses, however, has been sparse in the past observational research and randomized controlled trials.
A Mendelian randomization (MR) analysis was performed to assess the influence of macronutrients (fat, protein, sugar, and carbohydrates) and micronutrients (β-carotene, folate, calcium, iron, copper, magnesium, phosphorus, selenium, zinc, vitamin C, vitamin D, vitamin B, and vitamin B12) on the susceptibility to cardiovascular metabolic disorders, including coronary artery disease, heart failure, ischemic stroke, and type 2 diabetes.
We employed a two-sample Mendelian randomization (MR) analysis, utilizing inverse variance weighting and conducting comprehensive sensitivity assessments. We obtained publicly accessible summary data from separate cohorts comprising individuals of European ancestry. The level of statistical significance was established at a threshold of P < 0. 00074.
Based on our research findings, we have established a causal association between the consumption of circulating fat and the development of cardiovascular and metabolic diseases. The study found that an increase of one standard deviation in fat consumption was associated with a decreased risk of heart failure, with an odds ratio of 0. 56 (95 % CI: 0. 40–0. 79; p = 0. 0007). Notably, various sensitivity analyses confirmed the robustness of this association. Conversely, we did not find any significant correlation between other dietary components and the risk of cardiovascular and metabolic disorders.
Our research findings demonstrate a conspicuous impact of dietary fat consumption on the susceptibility to heart failure, independent of coronary artery disease, diabetes, and stroke. Consequently, it is indicated that dietary factors are unrelated to the predisposition to cardiovascular metabolic disorders.
Naturally occurring van der Waals materials Frisenda, Riccardo; Niu, Yue; Gant, Patricia ...
NPJ 2D materials and applications,
10/2020, Volume:
4, Issue:
1
Journal Article
Peer reviewed
Open access
Abstract
The exfoliation of two naturally occurring van der Waals minerals, graphite and molybdenite, arouse an unprecedented level of interest by the scientific community and shaped a whole new ...field of research: 2D materials research. Several years later, the family of van der Waals materials that can be exfoliated to isolate 2D materials keeps growing, but most of them are synthetic. Interestingly, in nature, plenty of naturally occurring van der Waals minerals can be found with a wide range of chemical compositions and crystal structures whose properties are mostly unexplored so far. This Perspective aims to provide an overview of different families of van der Waals minerals to stimulate their exploration in the 2D limit.
Groundwater-based irrigation has dramatically expanded over the past decades. It has important implications for terrestrial water, energy fluxes, and food production, as well as local to regional ...climates. However, irrigation water use is hard to monitor at large scales due to various constraints, including the high cost of metering equipment installation and maintenance, privacy issues, and the presence of illegal or unregistered wells. This study estimates irrigation water amounts using machine learning to integrate in situ pumping records, remote sensing products, and climate data in the Kansas High Plains. We use a random forest regression to estimate the annual irrigation water amount at a reprojected spatial resolution of 6 km based on various data, including remotely sensed vegetation indices and evapotranspiration (ET), land cover, near-surface meteorological forcing, and a satellite-derived irrigation map. In addition, we assess the value of ECOSTRESS ET products for irrigation water use estimation and compare with the baseline results by using MODIS ET. The random forest regression model can capture the temporal and spatial variability of irrigation amounts with a satisfactory accuracy (R2 = 0.82). It performs reasonably well when it is calibrated on the western portion of the study area and tested on the eastern portion that receives more rain than the western one, suggesting its potential transferability to other regions. ECSOTRESS ET and MODIS ET yield a similar irrigation estimation accuracy.
Despite plentiful evidence of frozen ground effects on snowmelt infiltration from lab experiments at pedon scales, streamflow observations show a weaker or no effect in terms of timing and magnitude ...at larger scales. We aim to understand this conflicting phenomenon through modeling using the Noah land surface model with multi‐physics (MP; Noah‐MP) options and the Routing Application for Parallel computatIon of Discharge (RAPID) over the Mississippi River Basin. We conduct 16 experiments with combinations of two supercooled liquid water (SLW) parameterization schemes and four soil hydraulic property schemes in Noah‐MP driven by two gridded precipitation products of the North American Land Data Assimilation System (NLDAS) and the Integrated Multi‐satellitE Retrievals for GPM (IMERG) Final. We then use RAPID to route Noah‐MP modeled surface runoff and groundwater discharge to predict daily streamflow at 52 United States Geological Survey gauges from 2015 to 2019. A model with the highest permeability performs better than other schemes on daily streamflow predictions by 20%–57% throughout a water year and 29%–113% for the spring as measured by the mean Kling‐Gupta Efficiency of the 52 gauges. Different SLW schemes demonstrate negligible effects on streamflow predictions. Models forced by IMERG show a better prediction skill compared with those forced by NLDAS at most of the gauges. Both precipitation products confirm that a scheme of higher permeability yields more accurate streamflow predictions over frozen ground. Future models should represent preferential flows through macropore networks to improve the understanding of frozen soil effects on infiltration and discharge across scales.
Plain Language Summary
Frozen ground presumably affects the discharge of snowmelt water into rivers during winter and spring due to the apparent effects of ice “blockage.” The presence of ice in the soil affects the release of soil liquid water and the time to release the water to local streams and rivers through the effects of soil ice on water flow and capacity to hold snowmelt water. At present, it is not fully understood how the soil ice affects the soil's capability of holding and releasing liquid water to rivers at river‐basin to continental scales. We use a computer model to test competing hypotheses through combinations of optional schemes of water holding capacity and water flow. The modeling results over major sub‐basins in the Mississippi River show that a model with higher permeable frozen soil results in higher skill in streamflow predictions at river basin scales. This study highlights the need to represent water flow through macropores that may be formed due to ice expansion during freezing/thawing cycles.
Key Points
Streamflow predictions are substantially sensitive to the choice of frozen soil hydraulic property parameterizations
Irrespective of precipitation product used, a scheme of higher frozen soil permeability yields more skillful streamflow predictions
Streamflow prediction improvement with a scheme of higher frozen soil permeability is pronounced in basins dominated by frozen ground
Accurate estimation of the spatio‐temporal distribution of snow water equivalent is essential given its global importance for understanding climate dynamics and climate change, and as a source of ...fresh water. Here, we explore the potential of using the Long Short‐Term Memory (LSTM) network for continental and regional scale modeling of daily snow accumulation and melt dynamics at 4‐km pixel resolution across the conterminous US (CONUS). To reduce training costs (data are available for ∼0.31 million snowy pixels), we combine spatial sampling with stagewise model development, whereby the network is first pretrained across the entire CONUS and then subjected to regional fine‐tuning. Accordingly, model evaluation is focused on out‐of‐sample predictive performance across space (analogous to the prediction in ungauged basins problem). We find that, given identical inputs (precipitation, temperature, and elevation), a single CONUS‐wide LSTM provides significantly better spatio‐temporal generalization than a regionally calibrated version of the physical‐conceptual temperature‐index‐based SNOW17 model. Adding more meteorological information (dew point temperature, vapor pressure deficit, longwave radiation, and shortwave radiation) further improves model performance, while rendering redundant the local information provided by elevation. Overall, the LSTM exhibits better transferability than SNOW17 to locations that were not included in the training data set, reinforcing the advantages of structure learning over parameter learning. Our results suggest that an LSTM‐based approach could be used to develop continental/global‐scale systems for modeling snow dynamics.
Plain Language Summary
Understanding the spatio‐temporal distribution of water in the snowpack (known as snow water equivalent) is very important for understanding climate dynamics and climate change, and for forecasting and management of global water supplies. In this study, we use Deep Learning (DL) to model snow accumulation and melt at 4‐km pixel‐scale resolution across the conterminous US (CONUS). Long Short‐Term Memory (LSTM) networks are developed at both continental‐ and regional‐scale, by combining spatial pixel sampling and stagewise network pre‐training/fine‐tuning. We benchmark out‐of‐sample predictive performance against the physical‐conceptual temperature‐index‐based SNOW17 model, and find that LSTM networks significantly outperform calibrated versions of the SNOW17 model when given identical information. Further, when provided with additional meteorological information, performance of the LSTM is improved. The LSTM models also exhibits better transferability than the SNOW17, indicating the potential for future development of a DL‐based system for modeling continental/global‐scale snow dynamics.
Key Points
A trained continental‐scale Long Short‐Term Memory (LSTM) network is capable of providing almost as good performance as a regionally trained one
The continent‐scale LSTM outperforms a regionally trained SNOW17, and a SNOW17 model calibrated locally to each pixel across the domain
The LSTM exhibits better spatial transferability than SNOW17, and exhibits a trade‐off between transferability and model complexity
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