Structural and functional insights into the lipid regulation of human anion exchanger 2.

Anion exchanger 2 (AE2) is an electroneutral Na+-independent Cl-/HCO3- exchanger belongs to the SLC4 transporter family. The widely expressed AE2 participates in a variety of physiological processes, including transepithelial acid-base secretion and osteoclastogenesis. Both the transmembrane domains (TMDs) and the N-terminal cytoplasmic domain (NTD) are involved in regulation of AE2 activity. However, the regulatory mechanism remains unclear. Here, we report a 3.2 Å cryo-EM structure of the AE2 TMDs in complex with PIP2 and a 3.3 Å full-length mutant AE2 structure in the resting state without PIP2. We demonstrate that PIP2 at the TMD dimer interface is involved in the substrate exchange process. Mutation in the PIP2 binding site leads to the displacement of TM7 and further stabilizes the interaction between the TMD and the NTD. Reduced substrate transport activity and conformation similar to AE2 in acidic pH indicating the central contribution of PIP2 to the function of AE2.

A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model.

A micro air quality monitor can realize grid monitoring and real-time monitoring of air pollutants. Its development can effectively help human beings to control air pollution and improve air quality. However, affected by many factors, the measurement accuracy of micro air quality monitors needs to be improved. In this paper, a combined calibration model of Multiple Linear Regression, Boosted Regression Tree and AutoRegressive Integrated Moving Average model (MLR-BRT-ARIMA) is proposed to calibrate the measurement data of the micro air quality monitor. First, the very widely used and easily interpretable multiple linear regression model is used to find the linear relationship between various pollutant concentrations and the measurement data of the micro air quality monitor to obtain the fitted values of various pollutant concentrations. Second, we take the measurement data of the micro air quality monitor and the fitted value of the multiple regression model as the input, and use the boosted regression tree to find the nonlinear relationship between the concentrations of various pollutants and the input variables. Finally, the autoregressive integrated moving average model is used to extract the information hidden in the residual sequence, and finally the establishment of the MLR-BRT-ARIMA model is completed. Root mean square error, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of the MLR-BRT-ARIMA model and other commonly used models such as multilayer perceptron neural network, support vector regression machine and nonlinear autoregressive models with exogenous input. The results show that no matter what kind of pollutant, the MLR-BRT-ARIMA combined model proposed in this paper has the best performance of the three indicators. Using this model to calibrate the measurement value of the micro air quality monitor can improve the accuracy by 82.4-95.4%.

Exploring the effects of online learning on EFL learners' motivation, anxiety, and attitudes during the COVID-19 pandemic: a focus on Iran.

With the emergence and dissemination of the COVID-19 pandemic and the transformation of education to online classes, it is important to explore the effects of online learning on EFL learners' psychological factors in the Iranian EFL learners. Hence, this study aims to explore the effects of online learning on EFL learners' motivation, anxiety, and attitude. For this purpose, using a convenience sampling method, a total of 293 upper-intermediate EFL learners at Iran Language Institute in Ahvaz, Iran, took the Oxford Quick Placement Test, and 200 EFL learners whose scores fell around the mean were selected and assigned randomly to an experimental group and a control group. The participants' motivation, anxiety, and attitudes were measured before and after the treatments (lasting 10 one-hour sessions held once a week) using validated questionnaires. The collected data were analyzed through a one-way MANOVA test and a one-sample t-test. The findings evidenced that online learning positively affected the participants' motivation, anxiety, and attitudes. That is, due to the online learning, their motivation increased, their anxiety lowered, and positive attitudes were shaped toward L2 learning. Finally, based on the findings, some implications are proposed.

Exploring the role of content and language integrated learning approach in developing transversal skills in university students with respect to the mediating role of emotional intelligence.

Although a range of studies has explored the effects of the content and language integrated learning (CLIL) approach on students' achievement, its impact on improving university students' transversal skills (TSs) has remained largely unexplored in Iran. Thus, the present study has a two-fold purpose. First, it aims to investigate the role of the CLIL approach in improving Iranian university students' TSs. Second, it purports to explore the mediating role of emotional intelligence (EI) in the relationship between the CLIL approach and TSs. For this purpose, a survey design was used wherein a research-made questionnaire was distributed among 123 university students to gather the required data. Findings disclosed that the CLIL approach had a positive role in developing the university students' TSs. Further, the results indicated that EI positively affected the development of TSs in university students. Moreover, the findings evidenced that EI significantly mediated the relationship between the CLIL approach and the development of the university students' TSs. The study concludes that applying the CLIL approach involves an entirely new paradigm, offering valuable implications for relevant stakeholders.