Carbapenemase-Producing Escherichia coli among Humans and Backyard Animals

Background: The rapidly increasing dissemination of carbapenem-resistant Enterobacteriaceae (CRE) in both humans and animals poses a global threat to public health. However, the transmission of CRE between humans and animals has not yet been well studied. Objectives: We investigated the prevalence, risk factors, and drivers of CRE transmission between humans and their backyard animals in rural China. Methods: We conducted a comprehensive sampling strategy in 12 villages in Shandong, China. Using the household [residents and their backyard animals (farm and companion animals)] as a single surveillance unit, we assessed the prevalence of CRE at the household level and examined the factors associated with CRE carriage through a detailed questionnaire. Genetic relationships among human- and animal-derived CRE were assessed using whole-genome sequencing–based molecular methods. Results: A total of 88 New Delhi metallo-β-lactamases –type carbapenem-resistant Escherichia coli (NDM-EC), including 17 from humans, 44 from pigs, 12 from chickens, 1 from cattle, and 2 from dogs, were isolated from 65 of the 746 households examined. The remaining 12 NDM-EC were from flies in the immediate backyard environment. The NDM-EC colonization in households was significantly associated with a) the number of species of backyard animals raised/kept in the same household, and b) the use of human and/or animal feces as fertilizer. Discriminant analysis of principal components (DAPC) revealed that a large proportion of the core genomes of the NDM-EC belonged to strains from hosts other than their own, and several human isolates shared closely related core single-nucleotide polymorphisms and blaNDM genetic contexts with isolates from backyard animals. Conclusions: To our knowledge, we are the first to report evidence of direct transmission of NDM-EC between humans and animals. Given the rise of NDM-EC in community and hospital infections, combating NDM-EC transmission in backyard farm systems is needed. https://doi.org/10.1289/EHP525

Sleep behavior and depression: Findings from the China Kadoorie Biobank of 0.5 million Chinese adults.

BACKGROUND: Mixed results have shown the association between sleep behavior and depression, but evidence relating the joint effect of sleep duration and sleep disturbances is limited, especially in Chinese population. METHODS: A total of 512,891 adults aged 30-79 years from China Kadoorie Biobank (CKB) were included. Depression was defined by Composite International Diagnostic Inventory-short form (CIDI-SF). Sleep duration and sleep disturbances, including difficulty initiating and maintaining sleep (DIMS), early morning awakening (EMA), daytime dysfunction (DDF) and any sleep disturbances (ASD), were obtained by a self-reported questionnaire. Logistic regression was applied to examine the association between sleep behavior and depression. RESULTS: About 23.1% of participants reported short sleep duration (≤ 6h), and 5.1% reported long sleep duration (> 9h). Compared with normal sleep duration (7-9h), both groups were associated greater likelihood of having depression (short sleep: OR = 2.32, 95%CI: 2.14-2.51; long sleep: OR = 1.56, 96%CI: 1.34-1.81). Participants reported sleep disturbances were significantly associated with depression (odds ratios ranged from 3.31 to 4.17). Moreover, the associations tended to be stronger for those who reported both abnormal sleep duration and sleep disturbances (p for interactions < 0.05), especially for those who slept long. LIMITATIONS: The cross-sectional nature of the study design limits the interpretation of the results. CONCLUSIONS: Abnormal sleep duration and sleep disturbances were associated with depression. The associations were stronger for abnormal sleep duration accompanied with sleep disturbances, especially for a long duration. More attention should be paid on these persons in clinical practice

Potential of Pléiades and Radarsat-2 Data for Mapping Plastic-Mulched Farmland Using Object-Based Image Analysis

The increasing area of Plastic-Mulched Farmland (PMF) is aggravating the conflict between agricultural development and environmental protection. The spatial distribution of PMF requires an effective and economic technique. However, most works are currently carried out on pixel-level algorithms, which leads inevitably to mixed spectral errors. In this connection, PMF has been mapped with Pléiades and Radarsat-2 data combining object-based image analysis (OBIA) and Random Forest (RF). At first, through visual interpretation, the outcomes of various segmenting scenarios were used to select the optimum segmentation parameters. The spectral characteristics, textural and geometric features were then extracted and tailored to the best PMF mapping function subset. Finally, we map the PMF using the optimized object-level feature subset based on RF. The results show that the ability of Pléiades data to map PMF in Northern China is higher than that of Radarsat-2. The overall mapping accuracy achieved is 90.27%. In general, the precision and reliability of the mapping are the product of extensive structural data and object-level features that can reduce the reliance on spectral data

Long-Term Dynamic of Cold Stress during Heading and Flowering Stage and Its Effects on Rice Growth in China

Short episodes of low-temperature stress during reproductive stages can cause significant crop yield losses, but our understanding of the dynamics of extreme cold events and their impact on rice growth and yield in the past and present climate remains limited. In this study, by analyzing historical climate, phenology and yield component data, the spatial and temporal variability of cold stress during the rice heading and flowering stages and its impact on rice growth and yield in China was characterized. The results showed that cold stress was unevenly distributed throughout the study region, with the most severe events observed in the Yunnan Plateau with altitudes higher than 1800 m. With the increasing temperature, a significant decreasing trend in cold stress was observed across most of the three ecoregions after the 1970s. However, the phenological-shift effects with the prolonged growing period during the heading and flowering stages have slowed down the cold stress decreasing trend and led to an underestimation of the magnitude of cold stress events. Meanwhile, cold stress during heading and flowering will still be a potential threat to rice production. The cold stress-induced yield loss is related to both the intensification of extreme cold stress and the contribution of related components to yield in the three regions

Long-Term Dynamic of Cold Stress during Heading and Flowering Stage and Its Effects on Rice Growth in China

Short episodes of low-temperature stress during reproductive stages can cause significant crop yield losses, but our understanding of the dynamics of extreme cold events and their impact on rice growth and yield in the past and present climate remains limited. In this study, by analyzing historical climate, phenology and yield component data, the spatial and temporal variability of cold stress during the rice heading and flowering stages and its impact on rice growth and yield in China was characterized. The results showed that cold stress was unevenly distributed throughout the study region, with the most severe events observed in the Yunnan Plateau with altitudes higher than 1800 m. With the increasing temperature, a significant decreasing trend in cold stress was observed across most of the three ecoregions after the 1970s. However, the phenological-shift effects with the prolonged growing period during the heading and flowering stages have slowed down the cold stress decreasing trend and led to an underestimation of the magnitude of cold stress events. Meanwhile, cold stress during heading and flowering will still be a potential threat to rice production. The cold stress-induced yield loss is related to both the intensification of extreme cold stress and the contribution of related components to yield in the three regions

Assessment of the MODIS LAI Product Using Ground Measurement Data and HJ-1A/1B Imagery in the Meadow Steppe of Hulunber, China

The leaf area index (LAI) is a crucial parameter of vegetation structure. It provides key information for earth surface process simulations and climate change research on the global and regional scales. Focusing on the meadow steppe in Hulunber, Inner Mongolia, China, the present study assessed the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the study area. First, seven field campaigns collecting ground-based measurements were conducted during the growing season in 2013, and 252 pairs of LAIs and spectra were collected. Then, seven scenes of high-resolution LAI maps were obtained from the corresponding 30 m Chinese HJ-1A/1B charge-coupled diode (CCD) images by employing a regression approach. Finally, comparisons between the MODIS LAI product and the high resolution LAI maps were made to determine the accuracy of the MODIS LAI product. Moreover, the corresponding 500 m MODIS LAI maps were derived from the daily MODIS surface reflectance product to support the findings using the 1 km HJ LAI product and the ground-based comparison. The results showed that, compared to the ground data, the MODIS LAI product followed a reasonable seasonal trajectory during the growing season. However, an anomaly existed at the beginning of the growing season. Also, a slight overestimation was found for the MODIS LAI product compared to the HJ-retrieved LAI maps. The average overestimation for the LAI was approximately 0.4 m2/m2, and the relative absolute errors of the product ranged from 10%–50%. The overestimation at the beginning and end of the growing season was higher due to the interference of soil background and grass variation. The results of this study provide a comprehensive understanding of the accuracy of the regional MODIS LAI product for the Hulunber meadow steppe. This research is important for improving regional modeling and prediction of vegetation biogeochemical processes and earth system productivity

Evaluation and Intercomparison of MODIS and GEOV1 Global Leaf Area Index Products over Four Sites in North China

This study investigated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) and GEOLAND2 Version 1 (GEOV1) Leaf Area Index (LAI) products using ground measurements and LAI reference maps over four sites in North China for 2011–2013. The Terra + Aqua MODIS and Terra MODIS LAI retrieved by the main algorithm and GEOV1 LAI within the valid range were evaluated and intercompared using LAI reference maps to assess their uncertainty and seasonal variability The results showed that GEOV1 LAI is the most similar product with the LAI reference maps (R2 = 0.78 and RMSE = 0.59). The MODIS products performed well for biomes with low LAI values, but considerable uncertainty arose when the LAI was larger than 3. Terra + Aqua MODIS (R2 = 0.72 and RMSE = 0.68) was slightly more accurate than Terra MODIS (R2 = 0.57 and RMSE = 0.90) for producing slightly more successful observations. Both MODIS and GEOV1 products effectively followed the seasonal trajectory of the reference maps, and GEOV1 exhibited a smoother seasonal trajectory than MODIS. MODIS anomalies mainly occurred during summer and likely occurred because of surface reflectance uncertainty, shorter temporal resolutions and inconsistency between simulated and MODIS surface reflectances. This study suggests that further improvements of the MODIS LAI products should focus on finer algorithm inputs and improved seasonal variation modeling of MODIS observations. Future field work considering finer biome maps and better generation of LAI reference maps is still needed

Sensing of Soil Organic Matter Using Laser-Induced Breakdown Spectroscopy Coupled with Optimized Self-Adaptive Calibration Strategy

Rapid quantification of soil organic matter (SOM) is a great challenge for the health assessment and fertility management of agricultural soil. Laser-induced breakdown spectroscopy (LIBS) with appropriate modeling algorithms is an alternative tool for this measurement. However, the current calibration strategy limits the prediction performance of the LIBS technique. In this study, 563 soil samples from Hetao Irrigation District in China were collected; the LIBS spectra of the soils were recorded in the wavenumber range of 288–950 nm with a resolution of 0.116 nm; a self-adaptive partial least squares regression model (SAM–PLSR) was employed to explore optimal model parameters for SOM prediction; and calibration parameters including sample selection for the calibration database, sample numbers and sample location sites were optimized. The results showed that the sample capacity around 60–80, rather than all of the samples in the soil library database, was selected for calibration from a spectral similarity re-ordered database regarding unknown samples; the model produced excellent predictions, with R2 = 0.92, RPD = 3.53 and RMSEP = 1.03 g kg−1. Both the soil variances of the target property and the spectra similarity of the soil background were the key factors for the calibration model, and the small sample set led to poor predictions due to the low variances of the target property, while negative effects were observed for the large sample set due to strong interferences from the soil background. Therefore, the specific unknown sample depended strategy, i.e., self-adaptive modelling, could be applied for fast SOM sensing using LIBS for soils in varied scales with improved robustness and accuracy

Development of Prediction Models for Estimating Key Rice Growth Variables Using Visible and NIR Images from Unmanned Aerial Systems

The rapid and accurate acquisition of rice growth variables using unmanned aerial system (UAS) is useful for assessing rice growth and variable fertilization in precision agriculture. In this study, rice plant height (PH), leaf area index (LAI), aboveground biomass (AGB), and nitrogen nutrient index (NNI) were obtained for different growth periods in field experiments with different nitrogen (N) treatments from 2019&ndash;2020. Known spectral indices derived from the visible and NIR images and key rice growth variables measured in the field at different growth periods were used to build a prediction model using the random forest (RF) algorithm. The results showed that the different N fertilizer applications resulted in significant differences in rice growth variables; the correlation coefficients of PH and LAI with visible-near infrared (V-NIR) images at different growth periods were larger than those with visible (V) images while the reverse was true for AGB and NNI. RF models for estimating key rice growth variables were established using V-NIR images and V images, and the results were validated with an R2 value greater than 0.8 for all growth stages. The accuracy of the RF model established from V images was slightly higher than that established from V-NIR images. The RF models were further tested using V images from 2019: R2 values of 0.75, 0.75, 0.72, and 0.68 and RMSE values of 11.68, 1.58, 3.74, and 0.13 were achieved for PH, LAI, AGB, and NNI, respectively, demonstrating that RGB UAS achieved the same performance as multispectral UAS for monitoring rice growth