Spatial distribution and driving factors of the associations between temperature and influenza-like illness in the United States: a time-stratified case-crossover study.

BACKGROUND: Several previous studies investigated the associations between temperature and influenza in a single city or region without a national picture. The attributable risk of influenza due to temperature and the corresponding driving factors were unclear. This study aimed to evaluate the spatial distribution characteristics of attributable risk of Influenza-like illness (ILI) caused by adverse temperatures and explore the related driving factors in the United States. METHODS: ILI, meteorological factors, and PM2.5 of 48 states in the United States were collected during 2011-2019. The time-stratified case-crossover design with a distributed lag non-linear model was carried out to evaluate the association between temperature and ILI at the state level. The multivariate meta-analysis was performed to obtain the combined effects at the national level. The attributable fraction (AF) was calculated to assess the ILI burden ascribed to adverse temperatures. The ordinary least square model (OLS), spatial lag model (SLM), and spatial error model (SEM) were utilized to identify driving factors. RESULTS: A total of 7,716,115 ILI cases were included in this study. Overall, the temperature was negatively associated with ILI risk, and lower temperature gave rise to a higher risk of ILI. AF ascribed to adverse temperatures differed across states, from 49.44% (95% eCI: 36.47% ~ 58.68%) in Montana to 6.51% (95% eCI: -6.49% ~ 16.46%) in Wisconsin. At the national level, 29.08% (95% eCI: 27.60% ~ 30.24%) of ILI was attributable to cold. Per 10,000 dollars increase in per-capita income was associated with the increment in AF (OLS: ß = -6.110, P = 0.021; SLM: ß = -5.496, P = 0.022; SEM: ß = -6.150, P = 0.022). CONCLUSION: The cold could enhance the risk of ILI and result in a considerable proportion of ILI disease burden. The ILI burden attributed to cold varied across states and was higher in those states with lower economic status. Targeted prevention programs should be considered to lower the burden of influenza.

Tyrosylprotein sulfotransferase suppresses ABA signaling via sulfation of SnRK2.2/2.3/2.6.

Phytohormone abscisic acid (ABA) plays vital roles in stress tolerance, while long-term overactivation of ABA signaling suppresses plant growth and development. However, the braking mechanism of ABA responses is not clear. Protein tyrosine sulfation catalyzed by tyrosylprotein sulfotransferase (TPST) is a critical post-translational modification. Through genetic screening, we identified a tpst mutant in Arabidopsis that was hypersensitive to ABA. In-depth analysis revealed that TPST could interact with and sulfate SnRK2.2/2.3/2.6, which accelerated their degradation and weakened the ABA signaling. Taken together, these findings uncovered a novel mechanism of desensitizing ABA responses via protein sulfation.

Transcriptional investigation of the toxic mechanisms of perfluorooctane sulfonate in rats based on an RNA-Seq approach.

Perfluorooctane sulfonate (PFOS) was widely used in industrial applications before it was listed as a persistent organic pollutant by the Conference of the Parties in the Stockholm Convention in 2009. Although the potential toxicity of PFOS has been studied, its toxic mechanisms remain largely undefined. Here, we investigated novel hub genes and pathways affected by PFOS to gain new conceptions of the toxic mechanisms of PFOS. Reduced body weight gain and abnormal ultra-structures in the liver and kidney tissues were spotted in PFOS-exposed rats, indicating successful establishment of the PFOS-exposed rat model. The transcriptomic alterations of blood samples upon PFOS exposure were analysed using RNA-Seq. GO analysis indicates that the differentially expressed gene-enriched GO terms are related to metabolism, cellular processes, and biological regulation. Kyoto encyclopaedia of gene and genomes (KEGG) and gene set enrichment analysis (GSEA) were conducted to identify six key pathways: spliceosome, B cell receptor signalling pathway, acute myeloid leukaemia, protein processing in the endoplasmic reticulum, NF-kappa B signalling pathway, and Fc gamma R-mediated phagocytosis. The top 10 hub genes were screened from a protein-protein interaction network and verified via quantitative real-time polymerase chain reaction. The overall pathway network and hub genes may provide new insights into the toxic mechanisms of PFOS exposure states.

Enhancement of sludge dewaterability by electrolysis coupled with peroxymonosulfate oxidation process: Performance, mechanisms and implications.

With the rapid increase in waste activated sludge (WAS), it is urgent to develop appropriate dewatering processes to diminish sludge volume and improve disposal efficiency. In this study, an advanced oxidation process using electrolysis coupled with peroxymonosulfate (E/PMS) was applied to improve the dewaterability of WAS. The results indicated that the sludge water content (WC) and capillary suction time (CST) dropped from 98.4 ± 0.2% and 220.1 ± 2.3 s to 70.7 ± 0.8% and 63.0 ± 1.2 s, respectively, under the following conditions: an electrolysis voltage of 20 V, an electrolysis time of 20 min, and 200 mg/g TS PMS. The increase in sludge zeta potential, surface hydrophobicity, and flowability indicated a significant improvement in sludge dewaterability. SO4•-, O•H, and O21 generated in the E/PMS process were responsible for the improvement of WAS dewaterability. These reactive oxygen species damaged extracellular polymeric substances (EPS), decreased fluorescent EPS components, and transformed the extracellular protein secondary structures by influencing the H-bond actions that maintain the α-helix. The bound water content, and apparent viscosity of WAS were found to be reduced, which was also attributed to an increase in dewatering capacity. Additionally, E/PMS treatment enhanced the degradation of organic matter in sludge and reduced the toxicity of the filtrate as well as the bioavailability of heavy metals. The cost analysis found that the E/PMS process was relatively economical and has great potential for practical application.

Host Metabolic Disorders Induced by Alterations in Intestinal Flora under Dietary Pesticide Exposure.

A dietary pesticide residue causes underestimated influences on body health. In this work, experimental mice were exposed to commonly used pesticides that cause insulin resistance, inflammation, and non-alcoholic fatty liver diseases. Alterations in intestinal flora were detected in the exposure groups. The abundance of the flora causing high endotoxin production was intensively increased and led to body inflammation. High Firmicutes/Bacteroidetes and obesity-related flora characteristics were also found. The metabolisms of intestinal flora and host circulation were investigated through metabolomics. The associations of flora with their metabolites and host circulation were also established. Association analysis can determine the influences of pesticide exposure on such a complex system. The affected metabolic pathways in the liver were also determined to clarify the mechanism underlying the effect of pesticide exposure on host physiology. Interventions with fructooligosaccharides and fecal microbiota transplantation alleviated the metabolic disorders, thus directly confirming that the intestinal flora mediates the effects of pesticide exposure on host circulation. This work elucidated the intestinal-flora-mediated effects of dietary pollutant exposure on body health and provided potential measures for regulating flora and host circulation.

Application of a cellulose filter aid in municipal sewage sludge dewatering and drying: Jar, pilot, and factory scale.

Cellulose filter aid (CFA), a skeleton builder, has been studied for sludge conditioning at laboratory scale because of its function in reducing compressibility of sludge cake. However, there are few practical applications of CFA at the pilot and factory scales. In this study, we combined CFA with an advanced sludge dewatering technology, in practical applications. For jar test, the optimum CFA dose was 3 wt.% dry solid (DS); furthermore, the combination of CFA with polyaluminum chloride (PAC) and polyacrylamide (PAM) resulted in the lowest moisture content (73.92%) and the highest calorific value (13,628 kJ/kg) of the sludge cake. In the pilot test, the 5 wt.% DS of the CFA resulted in the optimal sludge feeding amount. In the factory test, the optimal CFA dose was further increased to 10%, and the amount of sludge feeding in a single batch was 20.5% higher than that without CFA. The optimal dose of CFA in the jar-pilot-factory test increased, which may occur because the CFA more easily penetrated the cake and performed better in building the skeleton when the sludge cake was thin. This study could provide a reference for the application of CFA and other skeleton builders in sludge dewatering and drying. PRACTITIONER POINTS: An advanced sludge treatment process-low-temperature vacuum dewatering and drying was tested. Cellulose filter aid showed good effect in jar test, pilot test, and factory test. When the scale of the experiment was from jar to factory, the optimal dosage of the cellulose filter aid was magnified. The amplification phenomenon of optimal dosage was related to the distribution pattern of cellulose filter aid in different thickness mud cakes.

A New Time-Frequency Feature Extraction Method for Action Detection on Artificial Knee by Fractional Fourier Transform.

With the aim of designing an action detection method on artificial knee, a new time-frequency feature extraction method was proposed. The inertial data were extracted periodically using the microelectromechanical systems (MEMS) inertial measurement unit (IMU) on the prosthesis, and the features were extracted from the inertial data after fractional Fourier transform (FRFT). Then, a feature vector composed of eight features was constructed. The transformation results of these features after FRFT with different orders were analyzed, and the dimensions of the feature vector were reduced. The classification effects of different features and different orders are analyzed, according to which order and feature of each sub-classifier were designed. Finally, according to the experiment with the prototype, the method proposed above can reduce the requirements of hardware calculation and has a better classification effect. The accuracies of each sub-classifier are 95.05%, 95.38%, 91.43%, and 89.39%, respectively; the precisions are 78.43%, 98.36%, 98.36%, and 93.41%, respectively; and the recalls are 100%, 93.26%, 86.96%, and 86.68%, respectively.