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Transient overexpression of Slp1 in sorghum resulted in ectopic silica deposition in all leaf epidermal cell-types. Our results show that Slp1 precipitates silica in sorghum silica cells.Nonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of disease ranging from steatosis, steatohepatitis, fibrosis and eventually cirrhosis. Leukocyte cell-derived chemotaxin 2 (LECT2), a new hepatokine, may be involved in energy metabolism. This study aims to 1) evaluate the association between LECT2 and NAFLD in multiple models; and 2) investigate the role of circulating LECT2 in the development of NAFLD in a multi-center cohort study. Western blotting, qPCR and ELISA were performed to evaluate hepatic and circulating LECT2 levels. siRNA, shRNA and AAV-plasmid were used to genetically modulate LECT2 expression. Multiple models included AML12 hepatocytes exposure to palmitic acid, high fat diet/ methionine-choline deficient diet-fed C57BL/6J female mice, mice injected with liver X receptor agonist/ tunicamycin/ various inflammatory mediators, and ob/ob mice. This study shows that hepatic LECT2 expression and circulating levels are elevated in multiple rodent NAFLD models and significantly induced in response to lipid deposition in the liver. Endoplasmic reticulum stress and inflammation also promote LECT2 expression and release. Gain-and loss-of-function studies reveal that LECT2 impacts NAFLD development and progression. Furthermore, a 6-year follow-up study of 1,278 subjects confirms the association between circulating LECT2 and the risk of NAFLD. Baseline LECT2 combines with Fatty liver index shows an performance (AUROC 0.735) to predict NAFLD development during the follow-up. In conclusion, LECT2 is expressed and released in response to NAFLD and liver injury. This study suggests the potential of LECT2 to be a biomarker for NAFLD.UV-A and visible light are thought to excite endogenous photosensitizers in microbes, thereby initiating complex chemical interactions that ultimately kill cells. Natural solar-based disinfection methods have been adapted into commercial lighting technologies with varying degrees of reported efficacy and associated safety hazards for human exposure. Here we utilize a narrow-spectrum UV-A LED prototype (currently in development for health care applications) to investigate the mechanism of bacterial photoinactivation using 365 nm light. Using a combination of reverse genetics and biochemical investigation, we report mechanistic evidence that 365nm light initiates a chain-reaction of superoxide-mediated damage via auto-excitation of vitamin-based electron carriers, specifically vitamin K2 menaquinones and the FAD flavoprotein in Complex II in the electron transport chain. We observe that photoinactivation is modifiable through supplementation of the environment to bypass cell damage. Lastly, we observe that bacteria forced into metabolic dormancy by desiccation become hypersensitized to the effects of UV-A light, thereby permitting photoinactivation at fluences that are significantly lower than the industry threshold for safe human exposure. In total, these results substantiate the mechanism and potential application of narrow- spectrum UV-A light for bacterial disinfection purposes.Background and objective Affect provides contextual information about the emotional state of a person as he/she communicates in both verbal and/or non-verbal forms. While human’s are great at determining the emotional state of people while they communicate in person, it is challenging and still largely an unsolved problem to computationally determine the emotional state of a person. Methods Emotional states of a person manifest in the physiological biosignals such as electrocardiogram (ECG) and electrodermal activity (EDA) because these signals are impacted by the peripheral nervous system of the body, and the peripheral nervous system is strongly coupled with the mental state of the person. In this paper, we present a method to accurately recognize six emotions using ECG and EDA signals and applying autoregressive hidden Markov models (AR-HMMs) and heart rate variability analysis on these signals. The six emotions include happiness, sadness, surprise, fear, anger, and disgust. Results We evaluated our method on a comprehensive new dataset collected from 30 participants. Our results show that our proposed method achieves an average accuracy of 88.6% in distinguishing across the 6 emotions. Conclusions The key technical depth of the paper is in the use of the AR-HMMs to model the EDA signal and the use of LDA to enable accurate emotion recognition without requiring a large number of training samples. Unlike other studies, we have taken a hierarchical approach to classify emotions, where we first categorize the emotion as either positive or negative and then identify the exact emotion.Immobilization of microorganisms capable of degrading specific contaminants significantly promotes bioremediation processes. In this study, innovative and ecofriendly biosorbent-biodegrading biofilms have been developed in order to remediate oil-contaminated water. This was achieved by immobilizing hydrocarbon-degrading gammaproteobacteria and actinobacteria on biodegradable oil-adsorbing carriers, based on polylactic acid and polycaprolactone electrospun membranes. High capacities for adhesion and proliferation of bacterial cells were observed by scanning electron microscopy. The bioremediation efficiency of the systems, tested on crude oil and quantified by gas chromatography, showed that immobilization increased hydrocarbon biodegradation by up to 23 % compared with free living bacteria. The resulting biosorbent biodegrading biofilms simultaneously adsorbed 100 % of spilled oil and biodegraded more than 66 % over 10 days, with limited environmental dispersion of cells. Biofilm-mediated bioremediation, using eco-friendly supports, is a low-cost, low-impact, versatile tool for bioremediation of aquatic systems.Deltamethrin is used widely in Eriocheir sinensis aquaculture to remove wild fish and parasites. The residual deltamethrin greatly affects the growth and quality of E. sinensis. In this study, the LC50 of deltamethrin against E. sinensis at 24, 48 and 96 h was determined to be 6.5, 5.0 and 2.8 μg/L, respectively. The enzyme activity and gene transcription of SOD, CAT, and PO in the hepatopancreas of E. Lanifibranor solubility dmso sinensis after deltamethrin stimulation showed an increasing tendency, and these enzymes reached their maximum activities at 6-10 d. The MDA content accumulated with increased time of deltamethrin stress. After 15 d of deltamethrin stress, the hepatopancreas of E. sinensis was found to be damaged based on HE staining. These results showed that deltamethrin is highly toxic to E. sinensis. But the half-life of deltamethrin is long and mainly relies on biodegradation. To resolve the pollution of residual deltamethrin, a strain of deltamethrin-degrading bacteria, P-2, was isolated from the sediment of an E. sinensis culture pond. Through morphological observation, physiological and biochemical identification and 16S rDNA sequence analysis, we found that this strain belonged to Paracoccus sp. When the pH was 7, the substrate concentration was low, the inoculation amount was high, and the deltamethrin degradation effect of Paracoccus sp. P-2 was good. The deltamethrin residue in the hepatopancreas and muscle of E. sinensis decreased significantly when Paracoccus sp. P-2 was added at 6.0 × 108 CFU/L. The degradation efficiency of Paracoccus sp. P-2 in the hepatopancreas and muscle was more than 70%. These results showed that Paracoccus sp. P-2, the first deltamethrin-degrading bacterium in aquaculture, could be used to remove residual deltamethrin and improve the food safety of E. sinensis.Calcium alginate (CaAlg) beads were prepared using ultrasound for use in the removal of lead from natural and wastewaters by ion exchange. Ultrasound was applied in a batch mode with an ultrasonic bath or in a flow mode using an ultrasonic clamp-on device. For comparison purposes the synthesis was performed in batch mode in the absence of the ultrasound. The beads prepared using ultrasound showed a greater ion exchange capability which could be ascribed to a larger specific surface area as a result of surface roughening induced by cavitation. Scanning Electron Microscopy (SEM) images revealed that the roughening was in the form of corrugation for the product with the best ion exchange capability obtained in the flow process where preformed CaAlg droplets were subjected to ultrasound during the setting process. These beads performed 11% better for lead removal than those synthesized in the absence of ultrasound.Quite surprisingly, cisplatin and cis-[PtI2(NH3)2] were found to manifest significant differences in their reactions with the model protein lysozyme. We decided to explore whether these differences recur when reacting these two Pt compounds with other proteins. Notably, ESI-MS measurements carried out on cytochrome c nicely confirmed the reaction pattern observed for lysozyme. This prompted us to exploit a computational DFT approach to disclose the molecular basis of such behavior. We analyzed comparatively the reactions of cis-[PtCl2(NH3)2] and cis-[PtI2(NH3)2] with appropriate molecular models (Ls) of the sidechains of relevant aminoacids. We found that when Pt(II) complexes are reacted with sulfur ligands both quickly lose their halide ligands and then the resulting cis-[Pt(L)2(NH3)2] species loses ammonia upon reaction with a ligand excess. In the case of imidazole, again cis-[PtCl2(NH3)2] and cis-[PtI2(NH3)2] quickly lose their halide ligands but the resulting cis-[Pt(L)2(NH3)2] species does not lose ammonia by reaction with excess imidazole. These results imply that the two platinum complexes manifest a significantly different behavior in their reaction with representative small molecules in agreement with what observed in the case of model proteins. It follows that the protein itself must play a crucial role in triggering the peculiar reactivity of cis-[PtI2(NH3)2] and in governing the nature of the formed protein adducts. The probable reasons for the observed behavior are critically commented and discussed.Air pollution is an important issue, especially in megacities across the world. There are emission sources within and also in the regions around these cities, which cause fluctuations in air quality based on prevailing meteorological conditions. Short term air quality forecasting is used not to just possibly mitigate forthcoming high air pollution episodes, but also to plan for reduced exposures of residents. In this study, a model using Artificial Neural Networks (ANN) has been developed to forecast pollutant concentration of PM10, PM2.5, NO2, and O3 for the current day and subsequent 4 days in a highly polluted region (32 different locations in Delhi). The model has been trained using meteorological parameters and hourly pollution concentration data for the year 2018 and then used for generating air quality forecasts in real-time. It has also been equipped with Real Time Correction (RTC), to improve the quality of the forecasts by dynamically adjusting the forecasts based on the model performance during the past few days.