In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers\' answer searching process. We first classified studies conducted for the topic \"Frontiers in Neuroinformatics\" according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.

Transformer is an attention-based architecture proven the state-of-the-art model in natural language processing (NLP). To reduce the difficulty of beginning to use transformer-based models in medical language understanding and expand the capability of the scikit-learn toolkit in deep learning, we proposed an easy to learn Python toolkit named transformers-sklearn. By wrapping the interfaces of transformers in only three functions (i.e., fit, score, and predict), transformers-sklearn combines the advantages of the transformers and scikit-learn toolkits.
In transformers-sklearn, three Python classes were implemented, namely, BERTologyClassifier for the classification task, BERTologyNERClassifier for the named entity recognition (NER) task, and BERTologyRegressor for the regression task. Each class contains three methods, i.e., fit for fine-tuning transformer-based models with the training dataset, score for evaluating the performance of the fine-tuned model, and predict for predicting the labels of the test dataset. transformers-sklearn is a user-friendly toolkit that (1) Is customizable via a few parameters (e.g., model_name_or_path and model_type), (2) Supports multilingual NLP tasks, and (3) Requires less coding. The input data format is automatically generated by transformers-sklearn with the annotated corpus. Newcomers only need to prepare the dataset. The model framework and training methods are predefined in transformers-sklearn.
We collected four open-source medical language datasets, including TrialClassification for Chinese medical trial text multi label classification, BC5CDR for English biomedical text name entity recognition, DiabetesNER for Chinese diabetes entity recognition and BIOSSES for English biomedical sentence similarity estimation. In the four medical NLP tasks, the average code size of our script is 45 lines/task, which is one-sixth the size of transformers\' script. The experimental results show that transformers-sklearn based on pretrained BERT models achieved macro F1 scores of 0.8225, 0.8703 and 0.6908, respectively, on the TrialClassification, BC5CDR and DiabetesNER tasks and a Pearson correlation of 0.8260 on the BIOSSES task, which is consistent with the results of transformers.
The proposed toolkit could help newcomers address medical language understanding tasks using the scikit-learn coding style easily. The code and tutorials of transformers-sklearn are available at https://doi.org/10.5281/zenodo.4453803 . In future, more medical language understanding tasks will be supported to improve the applications of transformers_sklearn.

With the impacts of climate disruption becoming more evident there has been an increase in the uptake of climate change adaptation \"toolkits\" to assist local governments build community resilience and adapt to the impacts of climate change. There is increasing attention and call for practitioners to adopt proactive and participatory approaches to help in the adaptive response planning process. One such toolkit is the International Council for Local Environmental Initiatives (ICLEI) Asian Cities Climate Change Resilience Network (ACCRN) Process (IAP). This is a simple but rigorous toolkit developed to help local governments in Asian cities build resilience to the impacts of climate change. This paper outlines the application of the toolkit to determine its versatility in the rural context and was trialled in the Himalayan rural enclave of Ramgad in the Indian state of Uttarakhand. Given the differences between urban and rural environments, the outcomes highlighted the need for further investigation and analysis into the process to ensure that the methodology truly reflects the nature of rural systems and their level of vulnerability and adaptive capacity. Overall, the toolkit proved to be a simple but versatile toolkit to assess the vulnerability and adaptive capacity of communities in rural Himalaya. Over 40 resilience intervention strategies were developed for the Ramgad enclave and these were prioritized according to their technical, political, social and economic feasibility.

We have developed EumicrobeDBLite-a lightweight comprehensive genome resource and sequence analysis platform for oomycete organisms. EumicrobeDBLite is a successor of the VBI Microbial Database (VMD) that was built using the Genome Unified Schema (GUS). In this version, GUS has been greatly simplified with the removal of many obsolete modules and the redesign of others to incorporate contemporary data. Several dependences, such as perl object layers used for data loading in VMD, have been replaced with independent lightweight scripts. EumicrobeDBLite now runs on a powerful annotation engine developed at our laboratory, called \'Genome Annotator Lite\'. Currently, this database has 26 publicly available genomes and 10 expressed sequence tag (EST) datasets of oomycete organisms. The browser page has dynamic tracks presenting comparative genomics analyses, coding and non-coding data, tRNA genes, repeats and EST alignments. In addition, we have defined 44 777 core conserved proteins from 12 oomycete organisms which form 2974 clusters. Synteny viewing is enabled by the incorporation of the Genome Synteny Viewer (GSV) tool. The user interface has undergone major changes for ease of browsing. Queryable comparative genomics information, conserved orthologous genes and pathways are among the new key features updated in this database. The browser has been upgraded to enable user upload of GFF files for quick view of genome annotation comparisons. The toolkit page integrates the EMBOSS package and has a gene prediction tool. Annotations for the organisms are updated once every 6 months to ensure quality. The database resource is available at www.eumicrobedb.org.