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General General

Analyzing hCov Genome Sequences: Predicting Virulence and Mutation

bioRxiv Preprint

Covid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. We present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. We have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest. In a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.

Sawmya, S.; Saha, A.; Tasnim, S.; Toufikuzzaman, M.; Anjum, N.; Rafid, A. H. M.; Rahman, M. S.; Rahman, M. S.

2021-04-20

General General

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains

bioRxiv Preprint

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. Machine learning was used to identify functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strain interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.

Rynkiewicz, P.; Babbitt, G. A.; Cui, F.; Hudson, A. O.; Lynch, M. L.

2021-04-20

General General

Algoritmos de minería de datos en la industria sanitaria

ArXiv Preprint

In this paper, we review data mining approaches for health applications. Our focus is on hardware-centric approaches. Modern computers consist of multiple processors, each equipped with multiple cores, each with a set of arithmetic/logical units. Thus, a modern computer may be composed of several thousand units capable of doing arithmetic operations like addition and multiplication. Graphic processors, in addition may offer some thousand such units. In both cases, single instruction multiple data and multiple instruction multiple data parallelism must be exploited. We review the principles of algorithms which exploit this parallelism and focus also on the memory issues when multiple processing units access main memory through caches. This is important for many applications of health, such as ECG, EEG, CT, SPECT, fMRI, DTI, ultrasound, microscopy, dermascopy, etc.

Marta Li Wang

2021-04-19

General General

Masked Face Recognition using ResNet-50

ArXiv Preprint

Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.

Bishwas Mandal, Adaeze Okeukwu, Yihong Theis

2021-04-19

General General

Masked Face Recognition using ResNet-50

ArXiv Preprint

Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.

Bishwas Mandal, Adaeze Okeukwu, Yihong Theis

2021-04-19

General General

Leaf-inspired homeostatic cellulose biosensors.

In Science advances

An incompatibility between skin homeostasis and existing biosensor interfaces inhibits long-term electrophysiological signal measurement. Inspired by the leaf homeostasis system, we developed the first homeostatic cellulose biosensor with functions of protection, sensation, self-regulation, and biosafety. Moreover, we find that a mesoporous cellulose membrane transforms into homeostatic material with properties that include high ion conductivity, excellent flexibility and stability, appropriate adhesion force, and self-healing effects when swollen in a saline solution. The proposed biosensor is found to maintain a stable skin-sensor interface through homeostasis even when challenged by various stresses, such as a dynamic environment, severe detachment, dense hair, sweat, and long-term measurement. Last, we demonstrate the high usability of our homeostatic biosensor for continuous and stable measurement of electrophysiological signals and give a showcase application in the field of brain-computer interfacing where the biosensors and machine learning together help to control real-time applications beyond the laboratory at unprecedented versatility.

Kim Ji-Yong, Yun Yong Ju, Jeong Joshua, Kim C-Yoon, Müller Klaus-Robert, Lee Seong-Whan

2021-Apr