Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

Book Review: Taking Literature and Language Learning Online: New Perspectives on Teaching, Research, and Technology

The creation of beautiful literature and art is one of humanity's most essential endeavours. The importance of literature as a component of the language-teaching curriculum has fluctuated over the last century with the popularity of various language-teaching pedagogies. Notwithstanding, it has recently seen a resurrection of appreciation for its effective utility in language acquisition. Covid-19 lockdown combined with the further progress of computer-assisted language learning has led to a gradual shift in the provision of literature-based language education to an online setting. Under this trend, Sandra Stadler-Heer and Amos Paran's edited chapter book Taking Literature and Language Learning Online: New Perspectives on Teaching, Research and Technology concentrates on a particular component of this transfer process, namely the interaction between literature and language learning. This book review provides an overview of this volume.

Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray Imagesg±

The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. Thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. The article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. The authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.

FAIR TAXATION UNDER THE GLOBAL ANTI-BASE EROSION RULES?

In 2021 the OECD launched the Global Minimum Company Tax to implement the Action 1 of the BEPS Project. This instrument has seen as a good mechanism to prevent company avoiding taxes at the global level and to stop existence of the harmful tax regimes worldwide, as well as a good mechanism to achieve fair taxation in the era of global digitalization. However, the broke-out of the COVID-19 pandemic and, consequently, the close of the national borders, then armed conflict between Russia and Ukraine, boost financial crisis and the crises in almost all social and industrial spheres at the global level. Such unwilling trend, between all, has influenced behavior of the companies and the initial optimism of the OECD and other international organizations that the global minimum company tax, at the very end, would end existence of the harmful tax regimes, tax avoidance and unfair taxation, dropped significantly. Therefore, at the very end of the 2022 and the beginning of the 2023, the OECD launched consultation document on tax certainty in the application of the Pillar Two of the global minimum tax known as a GloBE (Global Anti-Base Erosion) Model Rules. This paper deals with mentioned issue and actual problems that the application of the GLoBE rules is faced with.

Thermo-Electro-Fluidic Simulation Study of Impact of Blower Motor Heat on Performance of Peltier Cooler for Protective Clothing

The necessity for portable cooling devices to prevent thermal-related diseases in workers wearing protective clothing in hot outdoor weather conditions, such as COVID-19 quarantine sites, is increasing. Coolers for such purposes require a compact design and low-power consumption characteristics to maximize wearability and operating time. Therefore, a thermoelectric device based on the Peltier effect has been widely used rather than a relatively bulky system based on a refrigeration cycle accompanying the phase change of a refrigerant. Despite a number of previous experimental and numerical studies on the Peltier cooling device, there remains much research to be conducted on the effect and removal of motor-related internal heat sources deteriorating the cooling performance. Specifically, this paper presents thermo-electro-fluidic simulations on the impact of heat from an air blower on the coefficient of performance of a Peltier cooler. In addition, a numerical study on the outcome of heat source removal is also evaluated and discussed to draw an improved design of the cooler in terms of cooling capacity and coefficient of performance. The simulation results predicted that the coefficient of performance could be raised by 10.6% due to the suppression of heat generation from a blower motor. Accordingly, the cooling capacity of the specific Peltier cooler investigated in this study was expected to be considerably improved by 80.6% from 4.68 W to 8.45 W through the design change.

Proposed Convolution Architecture for Monkeypox Detection using Dermoscopy Images

Monkeypox is a skin disease that spreadsfrom animals to people and then people to people, the class of the monkeypox is zoonotic and its genus are othopoxvirus. There is no special treatment for monkeypox but the monkeypox and smallpox symptoms are almost similar, so the antiviral drug developed for prevent from smallpox virus may be used for monkeypox Infected person, the Prevention of monkeypox is just like COVID-19 proper hand wash, Smallpox vaccine, keep away from infected person, used PPE kits. In this paper Deep learning is use for detection of monkeypox with the help of CNN model, The Original Images contains a total number of 228 images, 102 belongs to the Monkeypox class and the remaining 126 represents the normal. But in deep learning greater amount of data required, data augmentation is also applied on it after this the total number of images are 3192. A variety of optimizers have been used to find out the best result in this paper, a comparison is usedbased on Loss, Accuracy, AUC, F1 score, Validation loss, Validation accuracy, validation AUC, Validation F1 score of each optimizer. after comparing alloptimizer, the Adam optimizer gives the best result its total testing accuracy is 92.21%, total number of epochs used for testing is 100. With the help of deep learning model Doctors are easily detect the monkeypox virus with the single image of infected person. © 2023 IEEE.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset.

Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands

The energy-efficient plate heat exchanger (PHE) and refrigerant R1234yf, which has a low global warming potential (GWP), can be used to realize an energy efficient heat pump (HP) system for electric vehicles (EV), extending their driving range. Therefore, the characteristics of R1234yf in an offset-fin strip (OSF) flowstructured PHE are critical for heat-exchanger design. This study investigates the condensation heat transfer coefficient (C-HTC) and two-phase frictional pressure drop (2P-FPD) of R1234yf during condensation in an OSF flow-structured PHE under various operating conditions. First, a modified Wilson plot method was used to determine the multiplier (C) and Reynolds number exponential (n) for the coolant side as -0.426 and 0.494, respectively. When the heat flux (q), average vapor quality (xa), and mass flux (G) increased, the C-HTC increased, whereas it decreased with saturation temperature (Tsat). Despite the force-convective condensation flow regime, the C-HTC increment was minimal with G at lower xa owing to the lesser significance of the shear effect. Additionally, the 2P-FPD was unaffected by q but increased considerably with an increase in xa and G and a decrease in Tsat. Based on the current experimental database, empirical correlations for forecasting friction factor and Nusselt number were developed with a 91% predictability.

Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model's performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model's ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.

Addressing the Key Pitfalls Hindering Technology Transfer Success

The key challenges that are commonly faced by companies undertaking a tech transfer include: * Client expectations and initial project scope definition: the initial assumptions of the drug developer or marketing authorization holder (MHA) based on their initial information relating to the product can be a significant challenge. Typical examples include product stability issues (typically on legacy products) being evidenced due to a change to more compliant analytical methods;change to product brought about by compliance-related requirements on legacy products, such as nitrosamines, and elemental impurities guidelines, etc.;and regulatory requirements being misjudged at the onset of the project, among other factors. * Product knowledge management: particularly when undertaking tech transfers of legacy products or products in the initial discovery phase, there may be a lack of technical knowledge on the product itself to make a comprehensive and robust tech transfer process. All this may contribute to slowing down the transfer of knowledge, with implications for tech transfer timelines. * Standardization at receiving site: another challenge typically faced by receiving sites of CDMOs is a lack of standardization of their internal processes and or documentation brought about by multiple tech transfers with varying types of clients with multiple requirements. The originating site-particularly if it belongs to a small start-up-may not have team members with specialist experience in handling a transfer, so may need additional support in collating the required information to hand over to the receiving site.

X-rays imaging analysis for early diagnosis of the thoracic disorders using Capsule Neural Network with Transfer learning

Over the past few years, millions of people around the world have developed thoracic ailments. MRI, CT scan, reverse transcription, and other methods are among those used to detect thoracic disorders. These procedures demand medical knowledge and are exceedingly pricy and delicate. An alternate and more widely used method to diagnose diseases of the chest is X-ray imaging. The goal of this study was to increase detection precision in order to develop a computationally assisted diagnostic tool. Different diseases can be identified by combining radiological imaging with various artificial intelligence application approaches. In this study, transfer learning (TL) and capsule neural network techniques are used to propose a method for the automatic detection of various thoracic illnesses utilizing digitized chest X-ray pictures of suspected patients. Four public databases were combined to build a dataset for this purpose. Three pre trained convolutional neural networks (CNNs) were utilized in TL with augmentation as a preprocessing technique to train and evaluate the model. Pneumonia, COVID19, normal, and TB (Tb) were the four class classifiers used to train the network to categorize. © 2023 IEEE.

Developing a transfer risk assessment tool for the Midlands Critical Care Network

Introduction: Inter-facility critical care transfers are a high-risk activity, with a significant reported critical incident rate.1 The 2019 ICS Transfer of the Critically Ill Adult Patient guideline2 recommends a consultant-led risk assessment is performed in order to provide a rationale for the make-up of the transfer team. Prior to our project, there was no formalised risk assessment process at our unit. Objective(s): We wished to assess whether any 'informal' risk assessment process was already being performed prior to transfers. We then aimed to implement a clear assessment process, initially for our unit but ultimately for our critical care network. Method(s): We performed a baseline audit of adult inter-facility critical care transfers undertaken by a team from our unit between 1st December 2019 and 28th February 2020. Notes were analysed for evidence of any risk assessment performed in discussion with the responsible consultant We then locally piloted a new risk assessment tool for our Critical Care Network's transfer documentation. It included the required elements from ICS guidance, and followed a systems-based approach to facilitate completion in time-critical situations. Colour coding enabled easy identification of potential high-risk transfers and guided team formation. Initial re-audit of the new tool was performed between 16th September and 16th October 2020, after which it was implemented across the network. A further re-audit was performed between 1st October and 31st December 2021. Result(s): Fifteen transfers occurred during the initial audit period. All were clinical. No risk assessments were documented (0% compliance), although all were accompanied by a transfer-trained, airway competent doctor and all but one by an ODP. Our second audit cycle identified 10 transfers, of which 4 had risk assessments completed (40% compliance). All transfers had been undertaken with a dual doctor/ODP team. We identified that there was limited knowledge of the risk assessment process among clinicians, so introduced the topic into our unit's transfer training programme. Assessment completion was made a key performance indicator, fed back to team members following each transfer. Our final cycle covered 14 clinical transfers. Eight had a fully completed risk assessment (57% compliance), 2 had partially completed risk assessments (14% partial compliance), 4 had no risk assessment and 2 cases were excluded due to incomplete data. Conclusion(s): Our tool is now used for all inter-hospital transfers across the Midlands Critical Care Network. It enabled risk assessments to be performed appropriately for transfers originating from our unit. Introduction was initially hampered by limited training for clinicians during the first wave of the Covid pandemic, and compliance improved once this was implemented. The recent introduction of a regional critical care transfer service means that the majority of transfers undertaken by our unit's staff are now time-critical clinical transfers. This may contribute to the failure to complete risk assessments in some cases, however these assessments are likely to be of higher importance since such transfers may be higher risk. We now aim to collect feedback from transferring staff to identify any barriers to correct completion.

Classifying Chest X-Ray Images using Residual Neural Network

This unprecedented time of the COVID-19 outbreak challenged the status-quo whether it is on business operation, political leadership, scientific capability, engineering implementation, data analysis, and strategic thinking, in terms of resiliency, agility, and innovativeness. Due to some identified constraints, while addressing the issue of global health, human ingenuity has proven again that in times of crisis, it is our best asset. Constraints like limited testing capacity and lack of real-time information regarding the spread of the virus, are the highest priority in the mitigation process, aside from the development of vaccines and the pushing through of vaccination programs. Using the available Chest X-Ray Images dataset and an AI-Computer Vision Technique called Convolutional Neural Network, features of the images were extracted and classified as COVID-19 positive or not. This paper proposes the usage of the 18-layer Residual Neural Network (ResNet-18) as an architecture instead of other ResNet with a higher number of layers. The researcher achieves the highest validation accuracy of 99.26%. Moving forward, using this lower number of layers in training a model classifier, resolves the issue of device constraints such as storage capacity and computing resources while still assuring highly accurate outputs. © 2022 IEEE.

An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19

In this proposal, a study based on deep-learned features via transfer learning was developed to obtain a set of features and techniques for pattern recognition in the context of COVID-19 images. The proposal was based on the ResNet-50, DenseNet-201 and EfficientNet-b0 deep-learning models. In this work, the chosen layer for analysis was the avg pool layer from each model, with 2048 features from the ResNet-50, 1920 features from the DenseNet0201 and 1280 obtained features from the EfficientNet-b0. The most relevant descriptors were defined for the classification process, applying the ReliefF algorithm and two classification strategies: individually applied classifiers and employed an ensemble of classifiers using the score-level fusion approach. Thus, the two best combinations were identified, both using the DenseNet-201 model with the same subset of features. The first combination was defined via the SMO classifier (accuracy of 98.38%) and the second via the ensemble strategy (accuracy of 97.89%). The feature subset was composed of only 210 descriptors, representing only 10% of the original set. The strategies and information presented here are relevant contributions for the specialists interested in the study and development of computer-aided diagnosis in COVID-19 images. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Automating Biopharma Manufacturing

"With the advance of data science enabling factors such as easy access to scalable memory and computing resources;our growing competence in collecting, storing, and contextualizing data;advances in robotics;[and] the quickly evolving method landscape driven by the open-source community, the benefits of automation and simulation are becoming accessible in the notoriously complicated realm of biopharma manufacturing," says Marcel von der Haar, head of product strategy data analytics at Sartorius. "Plug-and-play" capabilities of automation systems, which enable flexible manufacturing and faster technology transfer, are more important than ever, he says. Walvax Biotech's new COVID-19 mRNA vaccine plant in China is another example of an intelligent and digital plant;it uses Honeywell's batch process control, building and energy management solution systems, and digital twins to monitor assets (5). "Automation brings in the data for machine learning to model the dynamic processes of cell growth and map it against the multiple dimensions provided by advanced sensors," explains Brandl.

Digitalization Moves Forward in Pharma Equipment and Processes

Digital technologies that could meet these new challenges and aid manufacturing scale-up and speed to market, such as automated digital data collection and augmented and virtual reality (AR/VR) remote collaboration tools, were already available and had been adopted by some, but the new demand spurred greater adoption. "There is a cultural aspect to digitalization because it's a significant investment that results in changes to the operational structure of a facility;it is beneficial when the digitalization comes from the top," explains Yvonne Duckworth, automation engineer and Industry 4.0 subject matter expert at the CRB Group, a life sciences engineering and construction company. Machine sensors and process analytical technology (PAT) instruments can communicate directly with data collection systems using the NoT. Efficient development and tech transfer for mRNA vaccine manufacturing The data analysis and clear communication allowed by digital tools has demonstrated its benefits for process development and technical transfer, making time to market faster.

Masked Face Recognition System using Extended VGG-19

The scientific community has observed several issues as a result of COVID-19, both directly and indirectly. The use of face mask for health protection is crucial in the current COVID-19 scenario. Besides, ensuring the security of all people, from individuals to the state system, financial resources, diverse establishments, government, and non-government entities, is an essential component of contemporary life. Face recognition system is one of the most widely used security technology in modern life. In the presence of face masks, the performance of the current face recognition systems is not satisfactory. In this paper, we investigate a flexible solution that could be employed to recognize masked faces effectively. To do this, we develop a unique dataset to recognize the masked face, consisting of a frontal and lateral face with a mask. We propose an extended VGG19 deep model to improve the accuracy of the masked face recognition system. Then, we compare the accuracy of the proposed framework to that of well-known deep learning techniques, such as the standard Convolutional Neural Network (CNN) and the original VGG19. The experimental results demonstrate that the proposed extended VGG19 outperforms the investigated approaches. Quantitatively, the proposed model recognizes the frontal face with the mask with high accuracy of 96%. © 2022 IEEE.

Cash transfer in the Covid-19 pandemic in Latin America and the Caribbean

Este artigo apresenta resultados parciais de uma investigação em desenvolvimento por pesquisadores de universidades brasileiras, argentinas e uruguaias. Os programas de transferência de renda são vistos como medidas sociais para mitigar a pobreza, bem como para diminuir o aumento do desemprego, do trabalho informal e do desperdício de renda. A metodologia de pesquisa foram estudos bibliográficos e documentais;dados secundários;acesso a sites e dados da Comissão Econômica para a América Latina e o Caribe. A discussão enfoca concepções, modalidades e o debate sobre Programas Focalizados de Transferência de Renda e Renda Básica Universal como referência para discutir a realidade dos programas de transferência de renda na América Latina e Caribe. Os resultados apontaram para a ampliação dos programas focalizados de transferência de renda;criação de programas emergenciais para atender as consequências econômicas e sociais geradas pela pandemia de Covid-19, mas não foi identificada a implementação da Renda Básica Universal e Incondicional.Alternate :Este artículo presenta resultados parciales de una investigación en desarrollo por investigadores de universidades brasileñas, argentinas y uruguayas. Los programas de transferencias monetarias son vistos como medidas sociales para mitigar la pobreza, así como para disminuir el aumento del desempleo, el trabajo informal y el desperdicio de ingresos. La metodología de investigación fueron estudios bibliográficos y documentales;Datos secundarios;acceso a sitios web y datos de la Comisión Económica para América Latina y el Caribe. La discusión se centra en las concepciones, modalidades y el debate sobre los Programas de Transferencias Monetarias Focalizadas y la Renta Básica Universal como referencia para discutir la realidad de los programas de transferencias monetarias en América Latina y el Caribe. Los resultados señalaron la ampliación de los programas de transferencias monetarias focalizadas;creación de programas de emergencia para atender las consecuencias económicas y sociales generadas por la pandemia del Covid-19, pero no se identificó la implementación de la Renta Básica Universal e Incondicional.Alternate :This article presents partial results of an investigation under development by researchers at Brazilian, Argentine and Uruguayan Universities. The cash transfer programs are seen as social measures to mitigate poverty, as well as to decrease the rise of unemployment, informal work and waste of income. The research methodology were bibliographic and documental studies;secondary data;access to websites and data from the Economic Commission for Latin America and the Caribbean. The discuss focus on conceptions, modalities and the debate on Focalized Cash Transfer Programs and Universal Basic Income as reference to discuss the reality of cash transfer programs in Latin America and the Caribbean. The outcomes pointed out the enlargement of the focalized cash transfer programs;creation of emergence programs to meet the economic and social consequences generated by the Covid-19 pandemic, but it was not identified the implementation of the Universal and Unconditional Basic Income.

FaST-Net: Face Style Self-Transfer Network for Masked Face Inpainting

During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods. © 2023 SPIE.

CD4+ T cells from preeclamptic patients cause hypertension, autoantibodies and cognitive dysfunction in a pregnant rat model

CD4+ T Cells from Preeclamptic patients with or without a history of COVID-19 during pregnancy cause hypertension, autoantibodies and cognitive dysfunction in a pregnant rat model Objective: Preeclampsia (PE) new onset hypertension (HTN) during pregnancy, is associated with increased autoantibodies, cerebral blood flow (CBF) impaired cognitive function and memory loss. We have shown adoptive transfer of placentalCD4+T cells from PE women into athymic nude pregnant rats causesHTNand autoantibodies associated with PE.COVID-19 (CV) during pregnancy is associated with increased diagnosis of PE. However, we do not know the role of CD4+ T cells stimulated in response to CV in contributing to the PE phenotype seen patients with a Hx of CV during pregnancy. Therefore, we hypothesize that adoptive transfer of placental CD4+ T cells from patients with a CV History (Hx) during pregnancy with PE causes HTN, increased CBF and cognitive dysfunction in pregnant athymic nude recipient rats. Study Design: Placental CD4+ T cells isolated from normotensive (NP), PE, Hx of CV normotensive (CV Hx NT), and Hx of CV with PE (CV Hx+PE) at delivery. One million CD4+ T cells were injected i.p. into nude athymic rats on gestational day (GD) 12. The Barnes maze and the novel object recognition behavioral assays were used to assess cognitive function on GDs 15-19. Blood pressure (MAP) and CBF were measured by carotid catheter and laser Doppler flowmetry on GD19, respectively. A two-way ANOVA was used for statistical analysis. Result(s):MAPincreased inCVHx+PE (111 +/- 4, n = 4) and PE recipient rats (115 +/- 2 mmHg, n = 5) compared to CV Hx NT (100 +/- 4, n = 5) and NP (99 +/- 3 mmHg, n = 4, P < .05). CV Hx+PE and PE exhibited latency with errors navigating in the Barnes maze compared to CV Hx NT and NP groups. Locomotor activity was decreased in CV Hx+PE (P < .05) compared to PE, CV Hx NT, and NP groups. CV Hx+PE and PE spent more time exploring identical objects compared to CV Hx NT and NP groups. PE and CV Hx+ PE had increased CBF compared to CV Hx NT and NP rats. Conclusion(s): Our findings indicate that pregnant recipients of CD4+ T cells from PE with or without a Hx CV during pregnancy cause HTN, increased CBF and cognitive dysfunction compared to recipients of NP or NT Hx COVID-19 CD4+ T cells.