Abstract Motivation Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra and inter class variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge. Results We present iPromoter-BnCNN for identification and accurate classification of six types of promoters - σ24, σ28, σ32, σ38, σ54, σ70. It is a CNN based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with six state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset. Availability Our proposed tool iPromoter-BnCNN web server is freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source code can be found https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ Supplementary information Supplementary data (benchmark dataset, independent test dataset, model files, structural property information, attention mechanism details and web server usage) are available at Bioinformatics. online.

Federated Learning (FL) is a new technology that has been a hot research topic. It enables training an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. There are many application domains where large amounts of properly labeled and complete data are not available in a centralized location, for example, doctors' diagnosis from medical image analysis. There are also growing concerns over data and user privacy as Artificial Intelligence is becoming ubiquitous in new application domains. As such, very recently, a lot of research has been conducted in several areas within the nascent field of FL. A variety of surveys on different subtopics exist in current literature, focusing on specific challenges, design aspects and application domains. In this paper, we review existing contemporary works in the related areas in order to understand the challenges and topics that are emphasized by each type of FL surveys. Furthermore, we categorize FL research in terms of challenges, design factors and applications, conducting a holistic review of each and outlining promising research directions.

Considering the flat rotation curves as input and treating the matter content in the galactic halo region as quark matter, we have found out a background spacetime metric for the region of the galactic halo. We obtain fairly general conditions that ensure that gravity in the halo region is attractive. We also investigate the stability of circular orbits, along with a different role for quark matter. Bag-model quark matter meeting these conditions therefore provides a suitable model for dark matter.Comment: 5 Latex pages, Accpted in Phys. Lett. B (available in Online

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