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Based on the EPA Method 1603, 519 presumptive E. coli isolates were obtained from the fecal samples of 13 different host species and 192 isolates from surface water samples taken at four locations in a watershed of mid-Missouri. As indicated by the eccPCR amplification, the overall prevalence of C-II through C-VIII in the presumptive E. coli isolates was estimated to be about 0.6 % in the fecal samples and about 1.6 % in the water samples. Therefore, the potential impact of cryptic clades on water quality monitoring may be limited if EPA Method 1603 is used. Furthermore, clades C-II through C-VIII in stream water samples were found repeatedly only at a single sampling site, but neither at the upstream sites nor five kilometers downstream of the site. The data do not support nor reject the environmental hypothesis about cryptic clades. Further study is needed to determine the implication of the observation.In Morocco, droughts are an increasing threat affecting water availability, agricultural production and producers' livelihoods. Moreover, water demand for irrigation has led to overexploitation of the groundwater table causing significant natural resource management challenges. The combination of groundwater changes and increasing drought risk raises concerns about the ability of agricultural producers to be resilient against drought. In this study, we describe the interactions of environmental and socioeconomic processes which influence farmers' livelihoods involved in tomato production in Morocco. Building on system dynamics modelling tools, we aim to improve the understanding of the long-term dynamic behavior of water management and to explore plausible policy scenarios necessary for sustainable and resilient water resource management and agricultural development. Our results show that tomato production is not yet severely impacted by droughts. However, droughts are accelerating the process of groundwater depletion, impacting farmers' livelihoods, by decreasing crop productivity and reducing farmer's revenue over a longer time period, especially since tomatoes are a high-value crop. Therefore, integrated and effective policies are presented as a set of measures for a systemic enhancement of resilience. We conclude that a more radical approach toward water resource conservation and upholding the most vulnerable producers has to be adopted in order to enhance a sustainable and inclusive resilience of the tomato production in Morocco.Many ecological restoration programs have been implemented in China during the last two decades. At the same time, the vegetation has turned green significantly in China. However, few studies have directly evaluated the contribution of the ecological restoration programs to vegetation greening in comparison with the contribution of climate change using high-resolution data of afforestation areas at the national scale. We used newly compiled high-resolution data on yearly forest plantation and mountain closure, the daily climate data from the 2480 meteorological stations and GIMMS 3g NDVI data. We used a multiple linear regression model to compare the influence of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We then used the hierarchical variance partitioning method to evaluate the relative contribution of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We found a significant greening trend in China from 1999 to 2015 with an annual increaserams to vegetation greening and provided insights facilitating policy makers to prioritize future restoration planning.The technology of flue gas CO2 fixation by microalgae is highly attractive in the era of CO2 neutrality. However, CO2 emission along the whole process has yet to be sufficiently evaluated. Here, a life-cycle assessment was performed to evaluate the energy conversion characteristics and environmental impacts of flue gas CO2 fixation from coal-fired power plant (Case 1) and coal chemical plant (Case 2) by microalgae. The results show that total energy consumption and CO2 gas emissions for Case 1 are 27.5-38.0 MJ/kg microalgae power (MP) and 5.7-7.7 kg CO2 equiv/kg MP, respectively, which are lower than that for Case 2 (122.5-181.3 MJ/kg MP and 32.7-48.6 kg CO2 equiv/kg MP). The CO2 gas aeration rate and microalgae growth rate are the two most sensitive parameters for the energy conversion and net CO2 emission. Therefore, increasing the CO2 aeration efficiency and microalgae growth rate are key to advance the technology of flue gas CO2 fixation by microalgae which will contribute to carbon naturality.Di(2-ethylhexyl) phthalate (DEHP) is an omnipresent environmental pollutant. It has been determined that DEHP is involved in multiple health disorders. Lycopene (Lyc) is a natural carotenoid pigment, with anti-inflammatory and antioxidant properties. However, it is not clear whether Lyc can protect the spleen from DEHP-induced oxidative damage. A total of 140 mice were randomly divided into seven groups (n = 20) and continuously gavaged with corn oil, distilled water, DEHP (500 or 1000 mg/kg BW/day) and/or Lyc (5 mg/kg BW/day) for 28 days. Histopathological and ultrastructural results showed a DEHP-induced inflammatory response and mitochondrial injuries. Moreover, DEHP exposure induced redox imbalance, which resulted in the up-regulation of ROS activity and MDA content, and the down-regulation of T-AOC, T-SOD and CAT in the DEHP groups. Simultaneously, our results also demonstrated that DEHP-induced kelch-like ECH-associated protein 1 (Keap1) expression was downregulated, and the expression levels of P62, nuclear factor erythroid 2-related factor (NRF2) and their downstream target genes were up-regulated. However, the supplementary Lyc reverted these changes to normal levels. Together, Lyc prevented DEHP-induced splenic injuries by regulating the P62-Keap1-NRF2 signaling pathway. Hence, the protective effects of Lyc might be a therapeutic strategy to ameliorate DEHP-induced splenic damage.Pollution due to heavy metals is a global issue in recent years. Initially, there were fewer contaminants, which has increased exponentially owing to rapid industrialization and various anthropogenic activities. Toxicity due to heavy metals causes a lot of health problems and organ system failure in human beings. It also affects other forms of living beings such as plants, animals and even the microbiota. This has been reported by various press reports and research findings. In this review, the production of heavy metals, associated effects on the environment and the technologies employed for detecting these heavy metals are comprehensively discussed. The analytical instruments, including biosensors, have been found to be more beneficial than other techniques. Biosensor exhibits numerous special features, such as reproducibility, reusability, linearity, sensitivity, selectivity, and stability. Over the last three years, biosensors have also had a detection limit of 65.36 ng/mL for heavy metals. The design of biosensors, features and types were also explained in detail. The limit of detection for the heavy metals in wastewater using biosensors was also included with recent references up to the last five years.Despite of the crucial role of eye region in nonverbal communication, how and when the brain responds to affective signals projected from this region remains unclear. This study explored the temporal dynamics in the processing of emotionally valenced eye regions (happy, neutral, and fearful) and the influence of attentional resources using a dual-target rapid serial visual presentation task and event-related potential (ERP) technique. Behaviorally, the recognition accuracy of happy and fearful eye regions was higher than that for neutral eye regions in the deficient attentional resources condition (lag2), indicating reduced attentional blink for emotional eye regions. The ERP findings denote that fearful and happy eye regions modulated the N170. The early posterior negativity (EPN) was influenced by the interaction between lag and emotional valence, which was reflected by larger amplitudes for fearful rather than neutral eye regions in the lag2 condition. The amplitude of the late positive potential (LPP) also increased for the happy and fearful eye regions. These outcomes suggest that the human brain is highly sensitive to isolated eye regions. Moreover, fearful signals emitted from the eye region are processed automatically and are unaffected by attentional resource availability.High throughput technologies used in experimental biological sciences produce data with a vast number of variables at a rapid pace, making large volumes of high-dimensional data available. The exploratory analysis of such high-dimensional data can be aided by human interpretable low-dimensional visualizations. This work investigates how both discrete and continuous structures in biological data can be captured using the recently proposed dimensionality reduction method SONG, and compares the results with commonly used methods UMAP and PHATE. Using simulated and real-world datasets, we observe that SONG produces insightful visualizations by preserving various patterns, including discrete clusters, continuums, and branching structures in all considered datasets. More importantly, for datasets containing both discrete and continuous structures, SONG performs better at preserving both the structures compared to UMAP and PHATE. Furthermore, our quantitative evaluation of the three methods using downstream analysis validates the on par quality of the SONG's low-dimensional embeddings compared to the other methods.In this paper, a novel continual learning classification method (SCLM) in small sample cases is proposed, which inspired by the immune system's continuous improvement of immunity through injecting vaccines. Data-driven classification method requires a large number of historical data to establish a pattern recognition model with good generalization performance. However, in practice, the data that can be used for training is usually small and unbalanced, which lead to poor classification accuracy. In addition, batch learning method cannot improve continually classification performance by learning test phase data. In view of the above problems, SCLM generates sample as vaccine by finding the group center of training samples, so that B cells mature and activate memory cells in the train phase. In the test phase, the recognition ability of SCLM is further improved by learning new samples and updating memory cells. In order to evaluate its performance under the condition of less training samples and its possible advantages, the experiments on well-known datasets in UCI repository and reciprocating compressor faults diagnose were performed. The results show that SCLM has better classification performance than other methods when the number of training samples is insufficient. At the same time, the method of generating data has significantly improved the classification performance of other methods.Control of infections with Dictyocaulus viviparus is difficult due to its volatile epidemiology. In the absence of predictive models, 'vigilance and treatment' is today's mainstay for control. In order to evaluate the potential of predictive model development to support a more preventative approach, this longitudinal study aimed at understanding the influence of weather factors on D. viviparus bulk tank milk antibody ELISA results. Bulk tank milk samples were analysed with a Major Sperm Protein-based ELISA (expressed as an optical density ratio) twice monthly on 717 Flemish dairy farms during the grazing season (April-October) in 2018. PI3K inhibitor Meteorological data of the sampled farms were obtained at 1 km spatial scale using the ALARO-SURFEX climate model. A mixed effects model showed that the bulk tank milk optical density ratio was significantly associated with the month of sampling, evapotranspiration, temperature and its quadratic term, the number of hot days and the number of rainy days in the 7-8 weeks prior to sampling.