17 research outputs found
RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins
Advance access publication date: 2 June 2022RNA binding proteins (RBPs) are critical for the post-transcriptional control of RNAs and play vital roles in a myriad of biological processes, such as RNA localization and gene regulation. Therefore, computational methods that are capable of accurately identifying RBPs are highly desirable and have important implications for biomedical and biotechnological applications. Here, we propose a two-stage deep transfer learning-based framework, termed RBP-TSTL, for accurate prediction of RBPs. In the first stage, the knowledge from the self-supervised pre-trained model was extracted as feature embeddings and used to represent the protein sequences, while in the second stage, a customized deep learning model was initialized based on an annotated pre-training RBPs dataset before being fine-tuned on each corresponding target species dataset. This two-stage transfer learning framework can enable the RBP-TSTL model to be effectively trained to learn and improve the prediction performance. Extensive performance benchmarking of the RBP-TSTL models trained using the features generated by the self-supervised pre-trained model and other models trained using hand-crafting encoding features demonstrated the effectiveness of the proposed two-stage knowledge transfer strategy based on the self-supervised pre-trained models. Using the best-performing RBP-TSTL models, we further conducted genome-scale RBP predictions for Homo sapiens, Arabidopsis thaliana, Escherichia coli, and Salmonella and established a computational compendium containing all the predicted putative RBPs candidates. We anticipate that the proposed RBP-TSTL approach will be explored as a useful tool for the characterization of RNA-binding proteins and exploration of their sequence-structure-function relationships.Xinxin Peng, Xiaoyu Wang, Yuming Guo, Zongyuan Ge, Fuyi Li, Xin Gao and Jiangning Son
Improving somatic health for outpatients with severe mental illness: the development of an intervention
Objective: Patients with severe mental illness (SMI) suffer from more somatic illness than the general population. Possible causes are side effects of neuropsychiatric medication, genetic vulnerability, insufficient health care and lifestyle. This co-morbidity is potentially reversible and augments the costs for health care and diminishes quality of life. Screening on symptoms and risks of somatic diseases and coordination of care are proposed to improve SMI-patients' somatic health status. Methods: A clinical facility was started to improve the somatic health status of patients in an outpatient centre in southern Netherlands. This outpatient centre was added to the specialized care for severe and enduring SMI. The intervention consisted of the inventarisation of side-effects and the detection of gaps in health care provision for 72 patients. This was based on interviewing the patients, laboratory screening, collecting information from their general practitioner and pharmacy. A list was compiled of possible diagnosis and health risks, and a plan of action was made for the treatment. Healthcare consumption, quality of life and general functioning were assessed to analyze cost-effectiveness. Evaluations were performed with the psychiatric care team on the process. Results: Mean annual cost of GP's and medical specialist's consultations were E492. There existed a negative relation between EQ5D VAS and the number of self reported chronic diseases. Conclusion: The authors conclude that the procedure is well feasible, but should be set up in close collaboration with all health care professionals of these patients to make tailor made solutions possible
Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp