The α-amylases are the most widely used industrial enzymes, and are particularly useful as liquifying enzymes in industrial processes based upon starch. Since starch liquefication is carried out at evaluated temperatures, typically above 60 °C, there is substantial demand for thermostable α -amylases. Most naturally occurring α -amylases exhibit moderate thermostability, so substantial effort has been invested in attempts to increase their thermostability. One structural feature that has the potential to increase protein thermostability is the introduction of salt bridges. However, not every salt bridge contributes to protein thermostability. The salt bridges in amylases have their characteristics in terms of distribution, configuration and location. https://www.selleckchem.com/products/Lapatinib-Ditosylate.html The summary of these features helps to introduce new salt bridges based on the characteristics. This review focuses on salt bridges of α-amylases, both naturally present and introduced using mutagenesis. Its aim is to provide a bird's eye view of distribution, configuration, location of desirable salt bridges.Cyanobacteria blooms threaten water supply and are potential sources for disinfection byproducts (DBPs) formation. In this study, the underlying mechanisms for effective removal of A. spiroides and the following depression on the formation of DBPs were disclosed. Highly efficient inactivation (more than 99.99%) of A. spiroides was realized by the plasma treatment within 12 min, and 93.4% of Anatoxin-a was also removed within 12 min, with no signals of resurrection after 7 days' re-cultivation. Transcriptomic analysis demonstrated that the expressions of the genes related to cell walls and peripherals, thylakoid membranes, photosynthetic membranes, and detoxification of toxins were distinctly altered. The generated reactive oxidative species (ROS), including ·OH, O2·-, and 1O2, attacked A. spiroides and resulted in membrane damage and algae organic matter (AOM) release. EEM-PARAFAC analysis illustrated that the AOM compositions were subsequently decomposed by the ROS. As a result, the formation potentials of the C-DBPs and N-DBPs were significantly inhibited, due to the effectively removal of AOM and Anatoxin-a. This study disclosed the underneath mechanisms for the effective inactivation of A. spiroides and inhibition of the following formation of the DBPs, and supplied a prospective technique for integrated pollutant control of cyanobacterial containing drinking water.The characteristics of fecal sludge delivered to treatment plants are highly variable. Adapting treatment process operations accordingly is challenging due to a lack of analytical capacity for characterization and monitoring at many treatment plants. Cost-efficient and simple field measurements such as photographs and probe readings could be proxies for process control parameters that normally require laboratory analysis. To investigate this, we evaluated questionnaire data, expert assessments, and simple analytical measurements for fecal sludge collected from 421 onsite containments. This data served as inputs to models of varying complexity. Random forest and linear regression models were able to predict physical-chemical characteristics including total solids (TS) and ammonium (NH4+-N) concentrations, and solid-liquid separation performance including settling efficiency and filtration time (R2 from 0.51-0.66) based on image analysis of photographs (sludge color, supernatant color, and texture) and probe readings (conductivity (EC) and pH). Supernatant color was the best predictor of settling efficiency and filtration time, EC was the best predictor of NH4+-N, and texture was the best predictor of TS. Predictive models have the potential to be applied for real-time monitoring and process control if a database of measurements is developed and models are validated in other cities. Simple decision tree models based on the single classifier of containment type can also be used to make predictions about citywide planning, where a lower degree of accuracy is required.Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in this study, we predicted ARGs occurrence that are primarily found on the coast after rainfall using a conventional long short-term memory (LSTM), LSTM-convolutional neural network (CNN) hybrid model, and input attention (IA)-LSTM. To develop the models, 10 categories of environmental data collected at 30-min intervals and concentration data of 4 types of major ARGs (i.e., aac(6'-Ib-cr), blaTEM, sul1, and tetX) obtained at the Gwangalli Beach in South Korea, between 2018 and 2019 were used. When individually predicting ARGs occurrence, the conventional LSTM and IA-LSTM exhibited poor R2 values during training and testing. In contrast, the LSTM-CNN exhibited a 2-6-times improvement in accuracy over those of the conventional LSTM and IA-LSTM. However, when predicting all ARGs occurrence simultaneously, the IA-LSTM model exhibited a superior performance overall compared to that of LSTM-CNN. Additionally, the influence of environmental variables on prediction was investigated using the IA-LSTM model, and the ranges of input variables that affect each ARG were identified. Consequently, this study demonstrated the possibility of predicting the occurrence and distribution of major ARGs at the beach based on various environmental variables, and the results are expected to contribute to management of ARG occurrence at a recreational beach.A novel magnetic resonance measurement (MRM) protocol for non-invasive monitoring of fouling in spiral wound reverse osmosis (SWRO) membrane modules is demonstrated. Sodium alginate was used to progressively foul a commercial SWRO membrane at industrially relevant operating conditions in a circulating flow loop. The MRM protocol showcased the following (i) earlier, more sensitive detection and quantification of fouling in the membrane module compared to feed-channel pressure drop. This was achieved using appropriate detection of the total nuclear magnetic resonance (NMR) signal. (ii) 2D cross-sectional imaging of the location of the accumulated foulant material; this was preferentially located adjacent to the membrane spacer sheet nodes, which was subsequently confirmed by a module autopsy. This image contrast, which could also readily differentiate the membrane, feed spacer and permeate spacer regions, was realised based on differences in the NMR relaxation parameter, T2,eff. (iii) High frequency acquisition of 2D cross-sectional velocity images of the module revealing very localised flow channelling in response to gradual foulant accumulation which impacted significantly on the flow pattern within the central permeate tube.