With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

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The fragile X mental retardation (FMR1) gene contains an expansion-prone CGG repeat within its 5′ UTR. Alleles with 55–200 repeats are known as premutation (PM) alleles and confer risk for one or more of the FMR1 premutation (PM) disorders that include Fragile X-associated Tremor/Ataxia Syndrome (FXTAS), Fragile X-associated Primary Ovarian Insufficiency (FXPOI), and Fragile X-Associated Neuropsychiatric Disorders (FXAND). PM alleles expand on intergenerational transmission, with the children of PM mothers being at risk of inheriting alleles with > 200 CGG repeats (full mutation FM) alleles) and thus developing Fragile X Syndrome (FXS). PM alleles can be somatically unstable. This can lead to individuals being mosaic for multiple size alleles. Here, we describe a detailed evaluation of somatic mosaicism in a large cohort of female PM carriers and show that 94% display some evidence of somatic instability with the presence of a series of expanded alleles that differ from the next allele by a single repeat unit. Using two different metrics for instability that we have developed, we show that, as with intergenerational instability, there is a direct relationship between the extent of somatic expansion and the number of CGG repeats in the originally inherited allele and an inverse relationship with the number of AGG interruptions. Expansions are progressive as evidenced by a positive correlation with age and by examination of blood samples from the same individual taken at different time points. Our data also suggests the existence of other genetic or environmental factors that affect the extent of somatic expansion. Importantly, the analysis of candidate single nucleotide polymorphisms (SNPs) suggests that two DNA repair factors, FAN1 and MSH3, may be modifiers of somatic expansion risk in the PM population as observed in other repeat expansion disorders.

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Traditional target detection algorithms have difficulty to adapt complex environmental changes and have limited applicable scenarios. However, the deep learning-based target detection model can automatically learn with strong generalization capability. In this paper, we choose a single-stage deep learningbased target detection model for research based on the model's real-time processing requirements and to improve the accuracy and robustness of target detection in remote sensing images, In addition, we improves the YOLOv4 network and present a new approach. Firstly, proposes a classification setting of the Nonmaximum suppression (NMS) threshold to increase accuracy without affecting the speed. Secondly, we study the anchor frame allocation problem in YOLOv4 and proposes two allocation schemes. The proposed anchor frame scheme also improves the detection performance, and experimental results on Dota dataset validate their effectiveness.

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