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The pandemic changed how we viewed professional development. Our teachers reported needing support in the affective domain in addition to traditional professional development. To address this, we piloted Professional Development in Pajamas ("PD in PJs") that focused on sharing peer-to-peer technological pedagogical knowledge and added "wellness pedagogical knowledge" that addressed teachers' affective wellness needs. This approach reflects a significant change in our understanding of useful professional learning. We share the structure of our approach in the hope that other educators and administrators may adapt it to support teacher and teacher candidates in a variety of post-pandemic contexts at their institutions.

The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein-protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

The purpose of this research paper is to encourage school teachers (with an emphasis on elementary teachers) to recognize and explore the literacy potential of their classroom libraries as a powerful critical literacy tool/asset that when designed and utilized strategically has the potential to reposition their classroom pedagogy and curriculum design to be empowering, transformative, inclusive and progressive. To that effect, this paper offers/proposes a framework/model for critical text selection' that can be readily adapted by teachers across various grade levels within the K-12 classrooms to identify the specific critical literacy needs of their learners (including the learning environment) and then employ their classroom library as a critical literacy tool that facilitates learner empowerment both inside and outside their learning spaces. The paper also presents interested teachers with an alternate way to traverse the proposed framework in the hope that it will provide an impetus to teacher scholars and educators interested in adapting the framework into their classroom to innovatively modify the framework we have presented to better work with their unique curricular structure and design.

The purpose of this research paper is to offer/propose a framework/model of specific imaginative practices, classroom engagement ideas, and implementation pathways that can be adapted across various grade levels within the K-12 (preferably language and literacy) classrooms to utilize the inherent diversity (of lived experiences) amongst the learners as a resources for fostering equity, diversity, and inclusion (EDI) in the classroom while simultaneously generating 'new' knowledge for learning and sense-making. Essentially, the framework proposed will afford expressive outlets into the curriculum via imaginative practices that are designed to allow opportunities for critical literacy learning by bringing common experiences and issues to the fore (utilizing dialogic comprehension-as-sensemaking pedagogy) and thus perpetuating diversity consciousness. The paper also includes a mock-up (i.e. present an example for each step in the framework when possible) of the proposed model in the hope that it will provide an impetus to teacher scholars and educators interested in adapting/including the framework into their classroom.

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences that provides a single access point to 44 RNA resources and >18 million ncRNA sequences from a wide range of organisms and RNA types. RNAcentral now also includes secondary (2D) structure information for >13 million sequences, making RNAcentral the world's largest RNA 2D structure database. The 2D diagrams are displayed using R2DT, a new 2D structure visualization method that uses consistent, reproducible and recognizable layouts for related RNAs. The sequence similarity search has been updated with a faster interface featuring facets for filtering search results by RNA type, organism, source database or any keyword. This sequence search tool is available as a reusable web component, and has been integrated into several RNAcentral member databases, including Rfam, miRBase and snoDB. To allow for a more fine-grained assignment of RNA types and subtypes, all RNAcentral sequences have been annotated with Sequence Ontology terms. The RNAcentral database continues to grow and provide a central data resource for the RNA community. RNAcentral is freely available at https://rnacentral.org.

Institutional withdrawal policies allow students with academic and personal difficulties to avert or minimize damage to the GPA by dropping a course in which they may risk a grade of "D" or "F," or to reduce credit hours to better manage those that remain. Offsetting costs involve lost tuition, delay of progress, and perhaps wasted effort. Yet in the pressures of the moment, how likely are students to weigh competing factors before acting? Do they consider the implications for financial aid, satisfactory academic progress, admission to the major, or timely progress toward a degree? What duties may the institution share with students to promote informed judgments? The authors explore the rewards and risks of dropping a course in view of the literature on the "W." Further, they explicate their own case study of withdrawal behaviors in an introductory course to the Informatics major to address questions raised in light of the growing literature on student retention and persistence. "What ties may exist between dropping a course and dropping out of college?" "How may student success studies guide institutions and faculty to assist students facing 'W' decisions?" "Of what benefit may this kind of intervention be for them?" For example, "How may proactive assistance in the 'W' process encourage students to pursue the competencies of a college degree amid discomfort and frustration likely having both cognitive and affective elements?" "How may students who persist through this challenge be better prepared for the complexities of their lives and work?" (Kuh, Kinzie, Schuh, & Whitt, 2010).

This study examines the effectiveness of Neurocognitive Based Concept Mapping (NBCM) on students' learning in a science course. A total of 32 grade IX of high school Central Board of Secondary Education (CBSE) students were involved in this study by pre-test and post-test measurements. They were divided into two groups: NBCM group as an experimental group and non- NBCM group as a control group. Both the groups were pre-tested with the basic knowledge on the topics "The fundamental unit of life & Is matter around us pure" before instruction. The control group was treated with routine traditional method of teaching whereas the experimental group was instructed with NCBC for three weeks. The researchers were developed a new strategy of teaching science through advanced concept mapping called Neurocognitive Based Concept Mapping for the first time and this strategy was adopted to the experimental group in the post test. The results were analysed using Sciences statistical techniques by Statistical Package for Social Science (SPSS) software. The experimental study revealed that extremely statistically significant between pre-test and post test at 0.01 level of significance. Hence neurocognitive based concept mapping strategy enhances the students to secure good scores and this strategy can be implemented to various level of subjects as well as students.

Advanced and precise forecasting of infectious diseases plays a critical role in planning and providing resources effectively. Time series forecasting for non-linear issues are accessible using deep learning techniques. The association between climatic parameters and dengue occurrences was investigated in this work, and a forecasting model was constructed using a deep learning approach called long short-term memory (LSTM). Univariate and multivariate LSTM time series forecast models were developed using meteorological and dengue incidence data from January 2006 to December 2019. For univariate data, the Pycharm/Google Colab platform was implemented, as the deep learning framework Keras, which is one of the models in the machine learning library based on Tensorflow. The Pandas Python package with builtin support for time series data was used for multivariate data. The final model was chosen using the mae loss and the Adam optimizer. Once the model had been fixed, predictions were made using the model. The research showed that the meteorological factors such as maximum temperature at lag 3, minimum temperature at lag 3, maximum vapour pressure (lag 0,1 and 2), minimum vapour pressure at lag 1, and vapour pressure daily mean at lag 0,1,4 are all significant predictors of dengue along with RMSE value of 1.121 . The results indicated that LSTM network has higher prediction accuracy than any other traditional forecasting methods. Timely management of seasonal diseases such as dengue along with meteorological parameters can predict epidemics in the future. [ABSTRACT FROM AUTHOR]