Efficient non-collinear antiferromagnetic state switching induced by orbital Hall effect in chromium

Recently orbital Hall current has attracted attention as an alternative method to switch the magnetization of ferromagnets. Here we present our findings on electrical switching of antiferromagnetic state in Mn3Sn/Cr, where despite the much smaller spin Hall angle of Cr, the switching current density is comparable to heavy metal based heterostructures. On the other hand, the inverse process, i.e., spin-to-charge conversion in Cr-based heterostructures is much less efficient than the Pt-based equivalents, as manifested in the almost one order of magnitude smaller terahertz emission intensity and spin current induced magnetoresistance in Cr-based structures. These results in combination with the slow decay of terahertz emission against Cr thickness (diffusion length of ~11 nm) suggest that the observed magnetic switching can be attributed to orbital current generation in Cr, followed by efficient conversion to spin current. Our work demonstrates the potential of light metals like Cr as an efficient orbital/spin current source for antiferromagnetic spintronics.Comment: 19 pages, 4 figure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Soil moisture variability in a temperate deciduous forest: insights from electrical resistivity and throughfall data

Hydrogeophysics is a growing discipline that holds significant promise to help elucidate details of dynamic processes in the near surface, built on the ability of geophysical methods to measure properties from which hydrological and geochemical variables can be derived. For example, bulk electrical conductivity is governed by, amongst others, interstitial water content, fluid salinity, and temperature, and can be measured using a range of geophysical methods. In many cases, electrical resistivity tomography (ERT) is well suited to characterize these properties in multiple dimensions and to monitor dynamic processes, such as water infiltration and solute transport. In recent years, ERT has been used increasingly for ecosystem research in a wide range of settings; in particular to characterize vegetation-driven changes in root-zone and near-surface water dynamics. This increased popularity is due to operational factors (e.g., improved equipment, low site impact), data considerations (e.g., excellent repeatability), and the fact that ERT operates at scales significantly larger than traditional point sensors. Current limitations to a more widespread use of the approach include the high equipment costs, and the need for site-specific petrophysical relationships between properties of interest. In this presentation we will discuss recent equipment advances and theoretical and methodological aspects involved in the accurate estimation of soil moisture from ERT results. Examples will be presented from two studies in a temperate climate (Michigan, USA) and one from a humid tropical location (Tapajos, Brazil)

Experimental and Numerical Investigation of Dustfall Effect on Remote Sensing Retrieval Accuracy of Chlorophyll Content

Chlorophyll is the dominant pigment in the photosynthetic light-harvesting complexes that is related to the physiological function of leaves and is responsible for light absorption and energy transfer. Dust pollution has become an environmental problem in many areas in China, indicating that accurately estimating chlorophyll content of vegetation using remote sensing for assessing the vegetation growth status in dusty areas is vital. However, dust deposited on the leaf may affect the chlorophyll content retrieval accuracy. Thus, quantitatively studying the dustfall effect is essential. Using selected vegetation indices (VIs), the medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI), and the double difference index (DD), we studied the retrieval accuracy of chlorophyll content at the leaf scale under dusty environments based on a laboratory experiment and spectra simulation. First, the retrieval accuracy under different dustfall amounts was studied based on a laboratory experiment. Then, the relationship between dustfall amount and fractional dustfall cover (FDC) was experimentally analyzed for spectra simulation of dusty leaves. Based on spectral data simulated using a PROSPECT-based mixture model, the sensitivity of VIs to dust under different chlorophyll contents was analyzed comprehensively, and the MTCI was modified to reduce its sensitivity to dust. The results showed that (1) according to experimental investigation, the DD model provides low retrieval accuracy, the MTCI model is highly accurate when the dustfall amount is less than 80 g/m2, and the retrieval accuracy decreases significantly when the dustfall amount is more than 80 g/m2; (2) a logarithmic relationship exists between FDC and dustfall amount, and the PROSPECT-based mixture model can simulate the leaf spectra under different dustfall amounts and different chlorophyll contents with a root mean square error of 0.015; and (3) according to numerical investigation, MTCI’s sensitivity to dust in the chlorophyll content range of 25 to 60 μg/cm2 is lower than in other chlorophyll content ranges; DD’s sensitivity to dust was generally high throughout the whole chlorophyll content range. These findings may contribute to quantitatively understanding the dustfall effect on the retrieval of chlorophyll content and would help to accurately retrieve chlorophyll content in dusty areas using remote sensing

Inequalities in Older age and Primary Health Care Utilization in Low- and Middle-Income Countries:A Systematic Review

The objective of this research was to systematically review and synthesize quantitative studies that assessed the association between socioeconomic inequalities and primary health care (PHC) utilization among older people living in low- and middle- income countries (LMICs). Six databases were searched, including Embase, Medline, Psych Info, Global Health, Latin American and Caribbean Health Sciences Literature (LILACS), and China National Knowledge Infrastructure, CNKI, to identify eligible studies. A narrative synthesis approach was used for evidence synthesis. A total of 20 eligible cross-sectional studies were included in this systematic review. The indicators of socioeconomic status (SES) identified included income level, education, employment/occupation, and health insurance. Most studies reported that higher income, higher educational levels and enrollment in health insurance plans were associated with increased PHC utilization. Several studies suggested that people who were unemployed and economically inactive in older age or who had worked in formal sectors were more likely to use PHC. Our findings suggest a pro-rich phenomenon of PHC utilization in older people living in LMICs, with results varying by indicators of SES and study settings

Dictionary Learning for Few-Shot Remote Sensing Scene Classification

With deep learning-based methods growing (even with scarce data in some fields), few-shot remote sensing scene classification (FSRSSC) has received a lot of attention. One mainstream approach uses base data to train a feature extractor (FE) in the pre-training phase and employs novel data to design the classifier and complete the classification task in the meta-test phase. Due to the scarcity of remote sensing data, obtaining a suitable feature extractor for remote sensing data and designing a robust classifier have become two major challenges. In this paper, we propose a novel dictionary learning (DL) algorithm for few-shot remote sensing scene classification to address these two difficulties. First, we use natural image datasets with sufficient data to obtain a pre-trained feature extractor. We fine-tune the parameters with the remote sensing dataset to make the feature extractor suitable for remote sensing data. Second, we design the kernel space classifier to map the features to a high-dimensional space and embed the label information into the dictionary learning to improve the discrimination of features for classification. Extensive experiments on four popular remote sensing scene classification datasets demonstrate the effectiveness of our proposed dictionary learning method

Dictionary Learning for Few-Shot Remote Sensing Scene Classification

With deep learning-based methods growing (even with scarce data in some fields), few-shot remote sensing scene classification (FSRSSC) has received a lot of attention. One mainstream approach uses base data to train a feature extractor (FE) in the pre-training phase and employs novel data to design the classifier and complete the classification task in the meta-test phase. Due to the scarcity of remote sensing data, obtaining a suitable feature extractor for remote sensing data and designing a robust classifier have become two major challenges. In this paper, we propose a novel dictionary learning (DL) algorithm for few-shot remote sensing scene classification to address these two difficulties. First, we use natural image datasets with sufficient data to obtain a pre-trained feature extractor. We fine-tune the parameters with the remote sensing dataset to make the feature extractor suitable for remote sensing data. Second, we design the kernel space classifier to map the features to a high-dimensional space and embed the label information into the dictionary learning to improve the discrimination of features for classification. Extensive experiments on four popular remote sensing scene classification datasets demonstrate the effectiveness of our proposed dictionary learning method

NGAL protects against endotoxin-induced renal tubular cell damage by suppressing apoptosis

Abstract Background We sought to confirm that neutrophil gelatinase-associated lipocalin (NGAL) protects against apoptosis during endotoxemia. Methods Endotoxemia was induced in rats with lipopolysaccharide (LPS; 3.5 mg/kg) and serum creatinine (SCr), urinary NGAL (uNGAL), renal histopathology confirmed acute kidney injury (AKI). Renal caspase 3 and NGAL were assayed with immunohistochemistry 6 h later. A HK-2 cell model was used in which NGAL and caspase 3 mRNA were evaluated by qRT-PCR within 6 h after LPS (50 μM) treatment, and correlations were studied. NGAL and caspase 3 mRNA expression were measured after delivering NGAL siRNA in HK-2 cells and apoptosis was measured with TUNEL and flow cytometry. Results SCr and uNGAL were significantly increased after LPS treatment and renal morphology data indicated AKI and renal tubular epithelial cell apoptosis. Caspase 3 and NGAL were predominantly expressed in the tubular epithelial cells and there was a correlation between caspase 3 and NGAL protein (r = 0.663, p = 0.01). In vitro, there was a strong correlation between caspase 3 and NGAL mRNA in LPS-injured HK-2 cells within 24 h (r = 0.448, p < 0.05). Suppressing the NGAL gene in HK-2 cells increased caspase 3 mRNA 4.5-fold and apoptosis increased 1.5-fold after LPS treatment. Conclusions NGAL is associated with caspase 3 in renal tubular cells with endotoxin-induced kidney injury, and may regulate its expression and inhibit apoptosis