Review on the conceptual framework of teacher resilience

Resilience is the ability to bounce back from setbacks and adapt to new circumstances. Resilient teachers can handle these issues. In this case, it’s proposed to interpret the recent decade’s resilience research on teachers. Provide a conceptual framework for teacher resilience factors. The Scopus database was used to collect articles. The titles and abstracts of articles were read one by one. As a result, 22 articles were included in the data analysis. The country where the data were collected, the aims of the study, the education level which the participants working, the sample size, the scale used, and the variables included in the study are marked in the full text. Most studies were effect determination, correlation, or exploratory. Initially, age and gender inequalities among instructors were examined. Postgraduate instructors are more resilient than undergraduates. Psychological factors, workplace variables, and teacher competency and attributes are used to study teacher resilience. Teachers’ resilience negatively impacts depression, stress, anxiety, well-being, and mood. Quality of life and well-being are positively connected. Job crafting, work engagement, and working environment are favorably connected, whereas job burnout and turnover intention are adversely correlated. Resilience was positively connected with emotion regulation, empathy, others’ emotion evaluation, teacher competence, teacher self-efficacy, and self-esteem in teachers. Anger, anxiety, mindfulness, pleasure, social support, fear, and training affect teachers’ resilience. Teachers’ resilience affects stress, depersonalization, personal accomplishment, emotional exhaustion, children’s resilience, job engagement, happiness, well-being, self-care, and success

Development and application of a hydroclimatological stream temperature model within the Soil and Water Assessment Tool

We develop a stream temperature model within the Soil and Water Assessment Tool (SWAT) that reflects the combined influence of meteorological (air temperature) and hydrological conditions (streamflow, snowmelt, groundwater, surface runoff, and lateral soil flow) on water temperature within a watershed. SWAT currently uses a linear air-stream temperature relationship to determine stream temperature, without consideration of watershed hydrology. As SWAT uses stream temperature to model various in-stream biological and water quality processes, an improvement of the stream temperature model will result in improved accuracy in modeling these processes. The new stream temperature model is tested on seven coastal and mountainous streams throughout the western United States for which high quality flow and water temperature data were available. The new routine does not require input data beyond that already supplied to the model, can be calibrated with a limited number of calibration parameters, and achieves improved representation of observed daily stream temperature. For the watersheds modeled, the Nash-Sutcliffe (NS) coefficient and mean error (ME) for the new stream temperature model averaged 0.81 and −0.69°C, respectively, for the calibration period and 0.82 and −0.63°C for the validation period. The original SWAT stream temperature model averaged a NS of −0.27 and ME of 3.21°C for the calibration period and a NS of −0.26 and ME of 3.02°C for the validation period. Sensitivity analyses suggest that the new stream temperature model calibration parameters are physically reasonable and the model is better able to capture stream temperature changes resulting from changes in hydroclimatological conditions

Method for Prioritizing Urban Pesticides for Monitoring

Environmental Monitoring Branch (EM) is to monitor pesticide residues in surface waters with urban runoff inputs. Recent monitoring efforts have identified urban runoff as a major contributor of pesticides to California surface waters (Ensminger et al., 2012). Pesticide use is i

Integrated mRNA Sequence Optimization Using Deep Learning

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5\u27 untranslated region (UTR) and 3\u27UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random field) is employed to determine the codon for each amino acid. In UTR generation, RNA-Bart (bidirectional auto-regressive transformer) is proposed to output the corresponding UTR. The results show that the optimized sequences of exogenous genes acquired the pattern of human endogenous gene sequence. In experimental validation, the mRNA sequence optimized by our method, compared with conventional method, shows higher protein expression. To the best of our knowledge, this is the first study by introducing deep-learning methods to integrated mRNA sequence optimization, and these results may contribute to the development of mRNA therapeutics

Stemdriver: a Knowledgebase of Gene Functions for Hematopoietic Stem Cell Fate Determination

StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis

Environmental Modeling and Exposure Assessment of Sediment-Associated Pyrethroids in an Agricultural Watershed

Synthetic pyrethroid insecticides have generated public concerns due to their increasing use and potential effects on aquatic ecosystems. A modeling system was developed in this study for simulating the transport processes and associated sediment toxicity of pyrethroids at coupled field/watershed scales. The model was tested in the Orestimba Creek watershed, an agriculturally intensive area in California' Central Valley. Model predictions were satisfactory when compared with measured suspended solid concentration (R2 = 0.536), pyrethroid toxic unit (0.576), and cumulative mortality of Hyalella azteca (0.570). The results indicated that sediment toxicity in the study area was strongly related to the concentration of pyrethroids in bed sediment. Bifenthrin was identified as the dominant contributor to the sediment toxicity in recent years, accounting for 50–85% of predicted toxicity units. In addition, more than 90% of the variation on the annual maximum toxic unit of pyrethroids was attributed to precipitation and prior application of bifenthrin in the late irrigation season. As one of the first studies simulating the dynamics and spatial variability of pyrethroids in fields and instreams, the modeling results provided useful information on new policies to be considered with respect to pyrethroid regulation. This study suggested two potential measures to efficiently reduce sediment toxicity by pyrethroids in the study area: [1] limiting bifenthrin use immediately before rainfall season; and [2] implementing conservation practices to retain soil on cropland