COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

Fractional analysis of radiative blood transport through a porous channel containing multishaped cobalt nanoparticles: An application to hemodynamics

In this work, impacts of dispersing nonspherical shaped cobalt nanoparticles in the blood are analyzed for magnetohydrodynamic radiative transport of blood inside a vertical porous channel. An Oldroyd‐B model is used to feature flow characteristics of blood along with Fourier's principle of heat transmission for the mathematical modeling of the problem. A fractional system is constructed by employing the idea of the Caputo–Fabrizio derivative on subsequent differential equations. The Laplace transform method is adopted to solve the fractional flow and energy equations subject to generalized boundary conditions, which involve time‐dependent functions and , respectively. Instead of promoting the analytic velocity and energy expressions, Zakian's numerical algorithm is operated to achieve the reverse transformation purpose of Laplace domain functions. To certify the obtained solutions, two additional numerical algorithms named Stehfest's algorithm and Durbin's algorithm are inculcated in this study, and comparative illustrations are drawn. For the extensive investigation of shear stress and heat transfer phenomenon, numerical simulations for the coefficient of skin friction and Nusselt number are performed, and outcomes are communicated through various tables. The impacts of shape‐dependent viscosity and other significant parameters on flow patterns are investigated through graphs for multiple motion types of the left channel wall. Meanwhile, the thermal performance of nanofluid is examined for platelet, brick, cylinder, and blade shape nanoparticles, along with other thermal parameters. In addition, some recently reported results and flow profiles for Maxwell, second‐grade, and viscous fluids are deduced graphically as special cases of the current study.

Exact solutions via Prabhakar fractional approach to investigate heat transfer and flow features of hybrid nanofluid subject to shape and slip effects

The core devotion of this study is to develop a generalized model by means of a recently proposed fractional technique in order to anticipate the enhancement in the thermal efficiency of engine oil because of the dispersion of graphene and magnesia nanoparticles. In addition to investigating the synergistic attributes of the foregoing particles, this work evaluates shape impacts for column, brick, tetrahedron, blade, and lamina-like shapes. In the primary model, the flow equation is coupled with concentration and energy functions. This classical system is transmuted into a fractional environment by generalizing mathematical expressions of thermal and diffusion fluxes by virtue of the Prabhakar fractional operator. In this study, ramped flow and temperature slip conditions are simultaneously applied for the first time to examine the behavior of a hybrid nanofluid. The mathematical analysis of this problem involves the incorporation of dimension-independent parameters into the model and the execution of the Laplace transform for the consequent equations. By doing so, exact solutions are derived in the form of Mittag-Leffler functions. Multiple illustrations are developed by dint of exact solutions to chew over all aspects of temperature variations and flow dynamics. For the preparation of these illustrations, the details of parametric ranges are as follows: [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. The contribution of differently shaped nanoparticles, volume proportions, and fractional parameters in boosting the heat-transferring attributes of engine oil is also anticipated. In this regard, results for Nusselt number are provided in tabular form. Additionally, a brief analysis of shear stress is carried out for fractional parameters and various combinations of magnesia, graphene, and engine oil. This investigation anticipates that engine oil's hybridization with magnesia and graphene would result in a 33% increase in its thermal performance, which evidently improves its industrial significance. The enhancement in Schmidt number yields an improvement in the mass transfer rate. An increment in collective volume fraction leads to raising the profile of the thermal field. However, the velocity indicates a decreasing behavior. Nusselt number reaches its highest value ([Formula: see text]) for the lamina shape of considered particles. When the intensity of the buoyancy force augments, it causes the velocity to increase.

Vehicle make and model recognition using mixed sample data augmentation techniques

<span lang="EN-US">Vehicle identification based on make and model is an integral part of an intelligent transport system that helps traffic monitoring and crime control. Much research has been performed in this regard, but most of them used manual feature extraction or ensemble convolution neural networks that result in increased execution time during inference. This paper compared three deep learning models and utilized different augmentation techniques to achieve state-of-the-art performance without ensembling or fusing the models. Experimentations are made without any augmentation, with standard augmentation, and by mixed sample data augmentation techniques. Gradient accumulation and stochastic weighted averaging with mixed precision are used to have a large batch size that helped to reduce training time. The dataset comprised 48 vehicles’ models running on the road of Pakistan. The highest accuracy and F1 score of 97% and 95% using the FMix augmentation technique with EfficientNetV2-S architecture gave the confidence that the proposed solution can be implemented in production. </span>

Comprehensive Investigation of Thermal and Flow Features of Alloy Based Nanofluid Considering Shape and Newtonian Heating Effects via New Fractional Approach

The core purpose of this work is the formulation of a mathematical model by dint of a new fractional modeling approach to study the dynamics of flow and heat transfer phenomena. This approach involves the incorporation of the Prabhakar fractional operator in mathematical analysis to transform the governing system from a conventional framework to a generalized one. This generalized model evaluates the improvement in thermal efficacy of vacuum pump oil because of the inclusion of aluminum alloy nanoparticles. The flow of the under-observation nanofluid starts due to the combined effects of natural convection and the ramped velocity function at the boundary. Meanwhile, an analysis of the energy equation is conducted by taking the Newtonian heating mechanism into consideration. The characteristics of platelet-, brick-, cylinder-, and blade-shaped alloy nanoparticles are incorporated into the primary system using shape-dependent relations for thermal conductivity and viscosity. Both the classical and generalized models are solved to derive the exact solutions by first inserting some dimension-independent quantities and then operating the Laplace transform on the succeeding equations. These solutions are utilized for the development of graphical illustrations to serve the purpose of covering all features of the problem under consideration. Furthermore, changes in energy and flow functions due to the dominant influences of the relevant contributing factors are delineated with appropriate physical arguments. In addition, the numerical results of the skin friction coefficient and Nusselt number are displayed via multiple tables to analyze the disturbance in shear stress and discuss the contribution of the fractional parameters, the volume concentration of the considered nanoparticles, and the shape factor in the boost of the thermal potential of the considered nanofluid. The findings imply that aluminum alloy nanoparticles have the ability to produce a 44% enhancement in the thermal effectiveness of vacuum pump oil. Moreover, the flow velocity is reduced as the loading range of the nanoparticles rises.

SEnsembleNet: A Squeeze and Excitation based Ensemble Network for COVID-19 Infection Percentage Estimation from CT-Scans.. [DOI: 10.36227/techrxiv.19497467.v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Coronavirus (COVID-19) is a contagious disease caused by SARS-CoV-2 virus. Usually, COVID-19 is diagnosed by PCR test, which requires less human expertise, but this test's false-negative ratio is high. COVID can also be diagnosed from radiographs such as CT-scan and X-ray, but it requires expert radiologists. So there is a need for an automated way to interpret chest radiographs using artificial intelligence. Several labelled datasets and deep learning algorithms are available to diagnose corona patients using radiographs. These algorithms classify the images into predefined categories such as healthy or infected. But there is no way to know how much area of chest radiograph is infected by COVID. This paper proposed an ensemble network to predict COVID-19 percentage infection from a chest CT scan. The proposed ensemble network used squeeze and excitation bock to learn individual models' weights during the training process. On validation data and test data, the proposed approach obtained a mean absolute error of 4.469 and 3.64, respectively. Implementation is publicly available at https://github.com/talhaanwarch/Covid-Infection-Estimation<br><br>

SEnsembleNet: A Squeeze and Excitation based Ensemble Network for COVID-19 Infection Percentage Estimation from CT-Scans.. [DOI: 10.36227/techrxiv.19497467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Coronavirus (COVID-19) is a contagious disease caused by SARS-CoV-2 virus. Usually, COVID-19 is diagnosed by PCR test, which requires less human expertise, but this test's false-negative ratio is high. COVID can also be diagnosed from radiographs such as CT-scan and X-ray, but it requires expert radiologists. So there is a need for an automated way to interpret chest radiographs using artificial intelligence. Several labelled datasets and deep learning algorithms are available to diagnose corona patients using radiographs. These algorithms classify the images into predefined categories such as healthy or infected. But there is no way to know how much area of chest radiograph is infected by COVID. This paper proposed an ensemble network to predict COVID-19 percentage infection from a chest CT scan. The proposed ensemble network used squeeze and excitation bock to learn individual models' weights during the training process. On validation data and test data, the proposed approach obtained a mean absolute error of 4.469 and 3.64, respectively. Implementation is publicly available at https://github.com/talhaanwarch/Covid-Infection-Estimation<br><br>

Global and intertuberal epileptic networks in tuberous sclerosis based on stereoelectroencephalographic (sEEG) findings: a quantitative EEG analysis in pediatric subjects and surgical implications

OBJECTIVE

Recent evidence favors a network concept in tuberous sclerosis (TSC) with seizure generation and propagation related to changes in global and regional connectivity between multiple, anatomically distant tubers. Direct exploration of network dynamics in TSC has been made possible through intracranial sampling with stereoelectroencephalography (sEEG). The objective of this study is to define epileptic networks in TSC using quantitative analysis of sEEG recordings. We also discuss the impact of the definition of these epileptic networks on surgical decision-making.

METHODS

Intracranial sEEG recordings were obtained from four pediatric patients who presented with medically refractory epilepsy secondary to TSC and subjected to quantitative signal analysis methods. Cortical connectivity was quantified by calculating pairwise coherence between all contacts and constructing an association matrix. The global coherence, defined as the ratio of the largest eigenvalue to the sum of all the eigenvalues, was calculated for each frequency band (delta, theta, alpha, beta, gamma). Spatial distribution of the connectivity was identified by plotting the leading principal component (product of the largest eigenvalue and its corresponding eigenvector).

RESULTS

Four pediatric subjects with TSC underwent invasive intracranial monitoring with sEEG, comprising 31 depth electrodes and 250 contacts, for localization of the epileptogenic focus and guidance of subsequent surgical intervention. Quantitative connectivity analysis revealed a change in global coherence during the ictal period in the beta/low gamma (14-30 Hz) and high gamma (31-80 Hz) bands. Our results corroborate findings from existing literature, which implicate higher frequencies as a driver of synchrony and desynchrony.

CONCLUSIONS

Coordinated high-frequency activity in the beta/low gamma and high gamma bands among spatially distant sEEG define the ictal period in TSC. This time-dependent change in global coherence demonstrates evidence for intra-tuberal and inter-tuberal connectivity in TSC. This observation has surgical implications. It suggests that targeting multiple tubers has a higher chance of seizure control as there is a higher chance of disrupting the epileptic network. The use of laser interstitial thermal therapy (LITT) allowed us to target multiple disparately located tubers in a minimally invasive manner with good seizure control outcomes.

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

A Generic Approach for Classification of Psychological Disorders Diagnosis using EEG

Electroencephalogram (EEG) is a widely used technique to diagnose psychological disorders. Until now, most of the studies focused on the diagnosis of a particular psychological disorder using EEG. We propose a generic approach to diagnose the different type of psychological disorders with high accuracy. The proposed approach is tested on five different datasets and three psychological disorders. Electrodes having higher signal to noise ratio are selected from the raw EEG signals. Multiple linear and non-linear features are then extracted from the selected electrodes. After feature selection, machine learning is used to diagnose the psychological disorders. We kept the same generic approach for all the datasets and diseases and achieved 93%, 85% and 80% F1 score on Schizophrenia, Epilepsy and Parkinson disease, respectively.

A fractional model for thermal investigation of MoS2‐Fe3O4/engine oil hybrid nanofluid under double ramped conditions and shape factor influence: The Atangana–Baleanu approach

In recent times, the study of diathermal oils is an area of interest for multiple researchers because of their numerous pivotal applications in industrial and engineering operations. The core aim of this work is the formulation of a fractional model to anticipate improvement in thermal and flow characteristics of a particular kind of diathermal oils named engine oil due to the dispersion of two different types of nanoparticles. Molybdenum‐disulfide (MoS2) and iron oxide (Fe3O4) nanoparticles are considered to form hybrid nanofluid, and combined impacts of their particular features on the thermal efficiency of engine oil are investigated. The ramped movement of an unbounded vertically inclined wall initiates the flow of hybrid nanofluid and some supplementary physical phenomena such as heat radiation, uniform magnetic field, and ramped heating also influence this flow. Additionally, the significant role of nanoparticles' shape factor in augmenting the heat transfer capacity of engine oil is examined. Initially, the flow of hybrid nanofluid is described through the Brinkman‐type fluid model, which is developed in light of Maxwell equations and Boussinesq approximation. Later, this mathematical model is transmuted to a fractional framework by incorporating the time‐fractional Atangana–Baleanu derivative. Laplace transformation is employed to procure exact solutions of the generalized model. These solutions are portrayed through several graphical illustrations to analyze the influence of various involved physical parameters. Special attention is given to heat transfer rate and shear stress, and a comprehensive tabular study is performed in terms of Nusselt number and skin friction coefficient. It is concluded that the heat‐transferring potential of observed hybrid nanofluid is 17.4% higher than pure engine oil. The combination of fractional model and ramping technique is found to be more effective for increasing the heat transfer rate and reducing the shear stress.

COVID19 Diagnosis using AutoML from 3D CT scans.. [DOI: 10.36227/techrxiv.14914851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Coronavirus is a pandemic that affects the respiratory system causing cough, shortness of breath, and death in severe cases. Polymerase chain reaction (PCR) tests are used to diagnose coronavirus. The false-negative rate of these tests is high, so there needs a supporting method for an accurate diagnosis. CT scan provides a detailed examination of the chest to diagnose COVID but a single CT scan comprises hundreds of slices. Expert and experienced radiologists and pulmonologists can diagnose COVID from these hundreds of slices, but this is very time-consuming. So an automatic artificial intelligence (AI) based method is required to diagnose coronavirus with high accuracy. Developing this AI-based technique requires a lot of resources and time, but once it is developed, it can significantly help the clinicians. This paper used an Automated machine learning (AutoML) technique that requires fewer resources (optimal architecture trials) and time to develop, resulting in the best diagnosis. The AutoML models are trained on 2D slices instead of 3D CT scans, and the predictions on unknown data (slices of CT scan) are aggregated to form a prediction of 3D CT scan. The aggregation process picked the most occurred case, whether COVID or non-COVID from all CT scan slices and labeled the 3D CT scan accordingly. Different thresholds are also used to label COVID or non-COVID 3D CT scans from 2D slices. The approach resulted in accuracy and F1-score of 89% and 88%, respectively. Implementation is available at github.com/talhaanwarch/mia-covid19

COVID19 Diagnosis using AutoML from 3D CT scans.. [DOI: 10.36227/techrxiv.14914851.v1]

Coronavirus is a pandemic that affects the respiratory system causing cough, shortness of breath, and death in severe cases. Polymerase chain reaction (PCR) tests are used to diagnose coronavirus. The false-negative rate of these tests is high, so there needs a supporting method for an accurate diagnosis. CT scan provides a detailed examination of the chest to diagnose COVID but a single CT scan comprises hundreds of slices. Expert and experienced radiologists and pulmonologists can diagnose COVID from these hundreds of slices, but this is very time-consuming. So an automatic artificial intelligence (AI) based method is required to diagnose coronavirus with high accuracy. Developing this AI-based technique requires a lot of resources and time, but once it is developed, it can significantly help the clinicians. This paper used an Automated machine learning (AutoML) technique that requires fewer resources (optimal architecture trials) and time to develop, resulting in the best diagnosis. The AutoML models are trained on 2D slices instead of 3D CT scans, and the predictions on unknown data (slices of CT scan) are aggregated to form a prediction of 3D CT scan. The aggregation process picked the most occurred case, whether COVID or non-COVID from all CT scan slices and labeled the 3D CT scan accordingly. Different thresholds are also used to label COVID or non-COVID 3D CT scans from 2D slices. The approach resulted in accuracy and F1-score of 89% and 88%, respectively. Implementation is available at github.com/talhaanwarch/mia-covid19

Radiative MHD unsteady Casson fluid flow with heat source/sink through a vertical channel suspended in porous medium subject to generalized boundary conditions

Unsteady, incompressible flow of Casson fluid between two infinitely long upward heated walls nested in a porous medium is analyzed in this work. The mass diffusion and heat transfer phenomena are also studied in the presence of thermal radiation, magnetic field, and heat source/sink. The generalized boundary conditions in terms of continuous time-dependent functions are considered for mass, energy, and momentum fields. Fick’s law, Fourier’s law, and momentum conservation principle are adopted to formulate the mathematical equations. Analytic solution for the concentration equation is established first by adding certain unit-less quantities and then by using the Laplace method of transformation. Semi-analytic solutions are calculated by means of Stehfest’s numerical Laplace inversion algorithm for energy and velocity equations. To demonstrate the verification of those solutions, a tabular comparison is drawn. Graphical illustrations along with physical descriptions are provided to discuss the essential contribution of thermo-physical parameters in heat and mass transfer and flow of the Casson fluid. The numerical computations of Sherwood number, Nusselt number, and skin friction for various inputs of related parameters are organized in tables to investigate mass transfer rate, heat transfer rate, and shear stress respectively. It is observed that porosity of the medium and buoyancy force tend to accelerate the flow. The heat and mass transfer rates are appreciated by Prandtl and Schmidt numbers respectively. Furthermore, radiation parameter and Grashof number significantly minimize the shear stress.

Magnetohydrodynamic mixed convective peristaltic slip transport of carbon nanotubes dispersed in water through an inclined channel with Joule heating

Carbon nanotubes are considered to be the latest nanotechnology innovation because of their remarkable physical and mechanical properties. Recently, researchers have shown great interest in the peristaltic transport of nanotube‐based nanofluid as this process involves a wide range of uses in the bioengineering, biomechanics, and medical fields. In this investigation, influence of single‐walled carbon nanotubes (SWCNTs) on magnetohydrodynamic mixed convective peristalsis through an inclined and asymmetric channel is analyzed. The additional physical mechanisms such as velocity slip, viscous dissipation, thermal slip, Joule heating, and heat consumption/injection are also encountered. The principal equations are formulated under the estimation of long wavelength and low Reynolds number. Perturbation method is operated to evaluate the solutions of subsequent nonlinear system of equations for small Brinkman number. To deeply analyze the characteristics of embedded parameters, graphs are presented and comprehensive interpretation is provided. Rate of heat transfer is augmented for higher proportion of SWCNTs in base fluid water. At the center of channel, increasing volume fraction of SWCNTs and strong Lorentz force retard the motion of fluid while flow is accelerated in more inclined channel. Volume fraction of SWCNTs, Grashof number, and inclination parameter encourage the pressure gradient at wider part of the channel. The size of bolus is contracted by strong Lorentz force and large volume fraction of SWCNTs. Three basic models named as Maxwell's, Hamilton‐Crosser's, and Xue's model are utilized to forecast the thermal conductivity of nanofluid and succeeding numerical computations for heat transfer rate are presented through table. It is found that the Xue's model is most effective to anticipate the thermal conductivity of nanofluids. Moreover, the addition of a heat sink in the system significantly influences the heat transfer process and plays a supportive role to rapidly cool down the channel.

An analysis of the continuing professional development needs of radiographers and radiation therapists in Singapore

INTRODUCTION

Continuing Professional development (CPD) is deemed essential for the Radiographers (DR) and Radiation Therapists (RT) after Singapore commenced state registration. Diagnostic imaging and radiotherapy treatment services are constantly revolutionizing and those working in this field requires sufficient knowledge of the uptrends for training and development. The purpose of this survey is to identify the current training needs of the registered DR and RT in Singapore, and to understand their views about CPD activities.

METHODS

An online questionnaire was disseminated by the Singapore Society of Radiographers (SSR) to all registered DR and RT in Singapore, and all practicing in restructured and private hospitals were included. Data collection took place from January 2018 to April 2018.

RESULTS

102 responses were analysed, where 89 were DR and 13 were RT. CPD was provided in 72.5% (n = 74) of the participants' institutions, and 69.6% (n = 71) of participants were aware of CPD. Interestingly, participants were significantly more likely to be unaware of CPD when working in an institution which do not offer CPD. Training programme objective was the most important factor for selecting a programme. 93.1% (n = 95) preferred SSR to support them for CPD. There were a few constraints to CPD engagements identified such as financial factors, lack of time, and institution availability.

CONCLUSION

There was significant intrinsic motivation in a quality CPD activity. CPD activities should be current, accessible and relevant for the healthcare professionals to increase participation, which directly contributes to high standards of clinical care.

IMPLICATIONS FOR PRACTICE

Local healthcare institutions should be aware and address needs, gaps and aspirations of the local DR and RT community to ensure adequate preparation has been made upon initiation of mandatory CPD.

Multiple comparisons of diversity indices invaded by Lantana camara

Current study assessed the impact of Lantana camara invasion on native plant diversity in Pothohar region of Pakistan. The approach used for study was random samplings and comparisons of diversity indices [number of species (S), abundance (N), species richness (R), evenness (Jꞌ), Shannon diversity index (Hꞌ) and Simpson index of dominance (λ)] with two categorical factors i.e., invaded and non-invaded (control). Control plots harboured by an average of 1.74 more species/10m2. The control category was diverse (Hꞌ=2.56) than invaded category (Hꞌ=1.56). The higher value of species richness in control plots shows heterogeneous nature of communities and vice versa in invaded plots. At multivariate scale, ordination (nMDS) and ANOSIM showed significant magnitude of differences between invaded and control plots at all sites. The decrease in studied diversity indices in invaded over control sites indicated that plant communities become less productive due to Lantana invasion.

Radiative heat transfer enhancement in MHD porous channel flow of an Oldroyd-B fluid under generalized boundary conditions

This study explains the transient free convection phenomenon in a vertical porous channel subject to nonlinear thermal radiation. The infinite vertical channel encloses magnetohydrodynamic (MHD) flow of an Oldroyd-B fluid. The left channel wall possesses time-dependent velocity u 0 g ( t ˜ ) , while the right wall exhibits no motion. The momentum and temperature field equations are developed on the bases of momentum conservation law and Fourier’s principle of heat transfer. Laplace transformation technique and Durbin’s numerical inversion method are jointly incorporated to compute the solutions of the formulated problem. The influences of flow and material parameters on heat transfer and fluid velocity are graphically scrutinized with physical aspects. The numerical computations for skin friction and temperature gradient are tabularized to comprehensively examine the wall shear stress and heat transfer rate. Finally, velocity fields for Maxwell fluid, second grade fluid, and viscous fluid are traced out as limiting cases and their comparison is drawn with the velocity field of an Oldroyd-B fluid. Besides this, some newly published results are also deduced from the acquired solutions. It is observed that increasing the magnitude of radiation parameter Rd rapidly enhances the rate of heat transfer at the right channel wall while an inverse behavior of Nusselt number is witnessed at the left channel wall. The Maxwell fluid and second grade fluid indicate the swiftest and slowest channel flow rates respectively. The shear stress specifies dual nature for relaxation and retardation parameters subject to static and moving wall. Additionally, it is found that the flow of an Oldroyd-B fluid is retarded by a magnetic field.

Prediction of promiscuous epitopes in NSP2 of Chikungunya virus: An in-silico approach

This study is focused towards developing a global consensus sequence of nonstructural protein 2 (NSP2), a protease of Chikungunya Virus (CHIKV) and predict immunogenic promiscuous T-cell epitopes based on various bioinformatics tools. To date, no epitope data is available for the Chikungunya virus in the IEDB database. In this study, 100 available nucleotide sequences of NSP2-CHIKV belonging to different strains were downloaded from the National Centre for Biotechnology Information (NCBI) database. The nucleotide sequences were subjected to translated sequencing using the EXPASY tool followed by protein alignment using the CLC workbench and a global consensus sequence for the respective protein was developed. IEDB tool was used to predict HLA-I and HLA-II binding promiscuous epitopes from the consensus sequence of NSP2-CHIKV. Thirty-four B-cell based epitopes are predicted and the promiscuous epitope is VVDTTGSTKPDPGD at position 341-354. Twenty-six MHC-I short peptide epitopes are predicted to bind with HLA-A. The promiscuous epitopes predicted to bind with HLA-A*01:01 are VTAIVSSLHY, SLSESATMVY, FSKPLVYY, QPTDHVVGEY at positions 317-326, 84-93, 535-544 and 15-24 with percentile ranks 0.17, 0.39, 0.51 and 0.81, respectively. Twenty-four MHC-II short peptide epitopes are predicted for HLA-DRB. The promiscuous epitope predicted to bind with HLA-DRB*01:01 is VVGEYLVLSPQTVLRS from 20-35 with a lowest percentile rank of 0.01. These predicted epitopes can be effective targets towards development of vaccine against CHIKV. Epitopes predicted in this study displayed good binding affinity, antigenicity and promiscuity for the HLA classes. These predicted epitopes can prove to be translationally important towards the development of CHIKV.

Mesenchymal Stem/Stromal Cell Engulfment Reveals Metastatic Advantage in Breast Cancer

Twenty percent of breast cancer (BC) patients develop distant metastasis for which there is no cure. Mesenchymal stem/stromal cells (MSCs) in the tumor microenvironment were shown to stimulate metastasis, but the mechanisms are unclear. Here, we identified and quantified cancer cells engulfing stromal cells in clinical samples of BC metastasis by dual immunostaining for EZH2 and ALDH1 expression. Using flow cytometry and a microfluidic single-cell paring and retrieval platform, we show that MSC engulfment capacity is associated with BC cell metastatic potential and generates cells with mesenchymal-like, invasion, and stem cell traits. Whole-transcriptome analyses of selectively retrieved engulfing BC cells identify a gene signature of MSC engulfment consisting of WNT5A, MSR1, ELMO1, IL1RL2, ZPLD1, and SIRPB1. These results delineate a mechanism by which MSCs in the tumor microenvironment promote metastasis and provide a microfluidic platform with the potential to predict BC metastasis in clinical samples.

Deep learning based diagnosis of COVID-19 using chest CT-scan images.. [DOI: 10.36227/techrxiv.12328061.v1]

The Coronavirus disease (COVID-19) is an infectious disease that primarily affects lungs. This virus has spread in almost every continent. Countries are racing to slow down the spread by testing and treating patients. To diagnose the infected people, reverse transcription-polymerase chain reaction (RT-PCR) test is used. Because of colossal demand; PCR kits are under shortage, and to overcome this; radiographic techniques such as X-rays and CT-scan can be used for diagnostic purpose. In this paper, deep learning technology is used to diagnose COVID-19 in subjects through chest CT-scan. EfficientNet deep learning architecture is used for timely and accurate detection of coronavirus with an accuracy 0.897, F1 score 0.896, and AUC 0.895. Three different learning rate strategies are used, such as reducing the learning rate when model performance stops increasing (reduce on plateau), cyclic learning rate, and constant learning rate. Reduce on plateau strategy achieved F1-score of 0.9, cyclic learning rate and constant learning rate resulted in F1-score of 0.86 and 0.82, respectively. Implementation is available at github.com/talhaanwarch/Corona\_Virus/tree/master/CT\_scan

Deep learning based diagnosis of COVID-19 using chest CT-scan images.. [DOI: 10.36227/techrxiv.12328061]

The Coronavirus disease (COVID-19) is an infectious disease that primarily affects lungs. This virus has spread in almost every continent. Countries are racing to slow down the spread by testing and treating patients. To diagnose the infected people, reverse transcription-polymerase chain reaction (RT-PCR) test is used. Because of colossal demand; PCR kits are under shortage, and to overcome this; radiographic techniques such as X-rays and CT-scan can be used for diagnostic purpose. In this paper, deep learning technology is used to diagnose COVID-19 in subjects through chest CT-scan. EfficientNet deep learning architecture is used for timely and accurate detection of coronavirus with an accuracy 0.897, F1 score 0.896, and AUC 0.895. Three different learning rate strategies are used, such as reducing the learning rate when model performance stops increasing (reduce on plateau), cyclic learning rate, and constant learning rate. Reduce on plateau strategy achieved F1-score of 0.9, cyclic learning rate and constant learning rate resulted in F1-score of 0.86 and 0.82, respectively. Implementation is available at github.com/talhaanwarch/Corona\_Virus/tree/master/CT\_scan

Heat Transfer Enhancement in Unsteady MHD Natural Convective Flow of CNTs Oldroyd-B Nanofluid under Ramped Wall Velocity and Ramped Wall Temperature

This article analyzes heat transfer enhancement in incompressible time dependent magnetohydrodynamic (MHD) convective flow of Oldroyd-B nanofluid with carbon nanotubes (CNTs). Single wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes (MWCNTs) are immersed in a base fluid named Sodium alginate. The flow is restricted to an infinite vertical plate saturated in a porous material incorporating the generalized Darcy’s law and heat suction/injection. The governing equations for momentum, shear stress and energy are modelled in the form of partial differential equations along with ramped wall temperature and ramped wall velocity boundary conditions. Laplace transformation is applied to convert principal partial differential equations to ordinary differential equations first and, later, complex multivalued functions of Laplace parameter are handled with numerical inversion to obtain the solutions in real time domain. Expression for Nusselt number is also obtained to clearly examine the difference in rate of heat transfer. A comparison for isothermal wall condition and ramped wall condition is also made to analyze the difference in both profiles. A graphical study is conducted to analyze how the fluid profiles are significantly affected by several pertinent parameters. Rate of heat transfer increases with increasing volume fraction of nanoparticle while shear stress reduces with elevation in retardation time. Moreover, flow gets accelerated with increase in Grashof number and Porosity parameter. For every parameter, a comparison between solutions of SWCNTs and MWCNTs is also presented.

Unsteady Radiative Natural Convective MHD Nanofluid Flow Past a Porous Moving Vertical Plate with Heat Source/Sink

In this research article, we investigated a comprehensive analysis of time-dependent free convection electrically and thermally conducted water-based nanofluid flow containing Copper and Titanium oxide (Cu and TiO 2 ) past a moving porous vertical plate. A uniform transverse magnetic field is imposed perpendicular to the flow direction. Thermal radiation and heat sink terms are included in the energy equation. The governing equations of this flow consist of partial differential equations along with some initial and boundary conditions. The solution method of these flow interpreting equations comprised of two parts. Firstly, principal equations of flow are symmetrically transformed to a set of nonlinear coupled dimensionless partial differential equations using convenient dimensionless parameters. Secondly, the Laplace transformation technique is applied to those non-dimensional equations to get the close form exact solutions. The control of momentum and heat profile with respect to different associated parameters is analyzed thoroughly with the help of graphs. Fluid accelerates with increasing Grashof number (Gr) and porosity parameter (K), while increasing values of heat sink parameter (Q) and Prandtl number (Pr) drop the thermal profile. Moreover, velocity and thermal profile comparison for Cu and TiO 2 -based nanofluids is graphed.