Efficiency dynamics across segmented Bitcoin Markets: Evidence from a decomposition strategy

Heterogeneity in informational inefficiency in a cross-market virtual currency, such as Bitcoin, allows for the extraction of differential gains from a portfolio of investments over time. In this paper, we measure inefficiency in five country/region segmented Bitcoin markets based on dynamic estimation of the fractional integration order of their price series. Results reveal a time-varying and country-specific pattern of inefficiency in the five Bitcoin markets, although the degree of inefficiency in each market has declined over time. Further, we introduce a new decomposition method to disentangle components of the inefficiency degree. Results suggest that the total variation around the convergence benchmark has fallen, whilst the proportion due to the difference between convergence and efficiency has risen from approximately 77% in 2013 to almost 100% in 2020. Besides, evidence of convergence emerges until the outbreak of COVID-19, beyond which the inefficiency degree diverges measurably. We show that Bitcoin markets have become more efficient after the first-wave COVID era and then the nature of market segmentation has played a less important role, levelling the cross-market difference and thus reducing the potential for arbitrage. © 2023 Elsevier B.V.

The impact of Covid-19 restrictions in Ireland on symptom severity in mild to moderate Parkinson's disease

Introduction: The COVID pandemic and public health restrictions significantly impacted those living with neurological conditions such as Parkinson's Disease due to the curtailment of therapies. Patients attending a single centre movement disorders clinic reported reduced physical activity and quality of life during the pandemic. This study aimed to assess the impact of pandemic restrictions on Parkinson's Disease symptom severity in people with mild to moderate Parkinson's Disease. Method: A cross-sectional study design with a convenience sample of 20 people living with mild to moderate Parkinson's Disease was adopted. A telephone survey questionnaire was completed to measure changes in symptom severity on the 14 most common Parkinson's Disease symptoms. Data were analysed using descriptive statistics. Results: Nineteen participants completed the survey. Participants frequently reported a decline in nine symptoms of Parkinson's Disease;bradykinesia, rigidity, walking, sleep, mood, memory, quality of life and fatigue. Nil changes in freezing were reported. No change was reported in the nonmotor symptoms of constipation, speech and pain in 75, 65 and 95% of participants, respectively. Conclusion: Findings of this study acknowledge the negative impact of restrictions on the motor and nonmotor symptoms of Parkinson's Disease. Flexibility to access and delivery of service should be considered to mitigate any future potential restrictions.

Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action;a convolutional neural network-based module for spatial feature extraction;a triple-layer convolutional long short-term memory network for temporal feature extraction;and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods. © 2022 IEEE.

Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19

In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure. © 2022 Elsevier B.V.

Can BCG Vaccine Be a Potential Tool for COVID-19?

SARS-CoV-2 has affected essentially all countries worldwide and caused millions of people to become infected and die. Therefore, it is extremely valuable to investigate new approaches to stop the most scarring ongoing pandemic. BCG vaccine has been proposed that it could reduce the rate of new COVID cases and limit the severity of infection since TB and COVID-19 have similar dominant effects, such as cytokine storm and improper immune response. This review aimed to focus on the latest literature data on trained immunity as well as the possible cross protection effect of BCG vaccine against COVID-19. The first immune response to BCG vaccines has started with the stimulation of adaptive immune response and establishment of the immunological memory of antigen-specific T and B cells to target infectious agents. In the past years, innate immune response was thought to be not having the talent to adapt and "learn” from previous exposure to a pathogen. Trained immunity is conceivable as 'de facto' innate immune system memory. Some researches argue that there is a strong relationship between BCG immunization and COVID-19 although some are against this argument. Based on the data obtained from different research studies and ongoing clinical trials, there is still no evidence that BCG vaccine is effective against COVID-19. Besides assumptions, knowns and unknowns, the clinical efficiency of BCG vaccine against SARS-CoV-2 should be validated by accurate scientific clinical reports in different age groups to understand the potential benefits of BCG vaccine to limit COVID-19 incidence and mortality.

Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers.

In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by wearable devices (e.g., smartwatches) to judge purchase intentions through deep learning. The method of this study included a long short-term memory (LSTM) model supplemented by collective decisions. The experiment was divided into two stages. The first stage aimed to find the regularity of the ECG and verify the research by repeated measurement of a small number of subjects. A total of 201 ECGs were collected for deep learning, and the results showed that the accuracy rate of predicting purchase intention was 75.5%. Then, incremental learning was adopted to carry out the second stage of the experiment. In addition to adding subjects, it also filtered five different frequency ranges. This study employed the data augmentation method and used 480 ECGs for training, and the final accuracy rate reached 82.1%. This study could encourage online marketers to cooperate with health management companies with cross-domain big data analysis to further improve the accuracy of precision marketing.

Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey.

In this study, forecasting the number of immigrants on the Turkey's maritime line for use in a national security project carried out by Turkish Government within the scope of fight against uncontrolled immigration is discussed for the first time. Handling with the immigration problem is one of the biggest concerns of Turkey as unsupervised immigration can adversely affect the demographic and economic structure of the country. Precautions are needed as the short-, medium- and long-term impacts of undetected immigrants on the country's ecosystem are unpredictable, but due to the uncertainties inherent in immigration, the cost of using government resources such as patrol vehicles to capture undocumented immigrants can be extremely high. In order to both minimize the expenditure problem and keep immigration under control by providing a proper scan, forecasting the number of immigrants on the maritime line route is seen as an important problem and studied by probabilistic and non-probabilistic models. Since the data for 2020 and 2021 could not be attained yet due to COVID-19, in order to obtain forecasts and compare actual observations for 2019, which is the primarily focus of the research in this study, the dataset of interest on the number of daily immigrants between years 2016 and 2019 is obtained from Turkish Coast Guard Command within Ministry of Interior of Republic of Turkey. To obtain the most accurate forecasts, seven distinguished forecasting methods, from simple to complex, are implemented. Then, the forecast combination approach with meta-fuzzy functions which combines all methods is proposed. Consequently, the forecasting results are acquired and evaluated by using R. The evaluation of the results is made by using widely considered measurement accuracy metric root mean square error. According to the final assessments, the proposed approach gives more accurate forecasting results for the expected number of immigrants on the Turkey's maritime line and these results become an input to the national security project.

Evaluation of cognitive functions in adult individuals with COVID-19.

BACKGROUND AND PURPOSE: Cognitive deficits that are associated with coronavirus disease 2019 (COVID-19) and occur in the acute period are gaining importance. While most studies have focused on the elderly severely affected during acute infection, it remains unclear whether mild to moderate COVID-19 results in cognitive deficits in young patients. This study aims to evaluate the post-infection cognitive functions of young adults with mild to moderate symptoms of COVID-19. METHODS: A total of 100 adults with similar age and educational background were included in the study. Half of those had been infected with COVID-19 in the last 60 days (N = 50), and the other half had not (N = 50). Global cognitive skills of the participants were evaluated through Montreal Cognitive Assessment Scale (MoCA) and Clock-Drawing Test; memory functions with Öktem Verbal Memory Processes Test (Ö-VMPT); attention span with Digit Span Test; executive functions with Fluency Tests, Stroop Test, and Trail Making Test; visual perceptual skills with Rey Osterrieth Complex Figure Test (ROCF); and neuropsychiatric status with Neuropsychiatric Inventory (NPI). Evaluations were performed in the experimental group for 21 to 60 days from the onset of the disease, and throughout the study, in the control group. RESULTS: It was found that global cognitive skills, verbal memory, visual memory, executive function, and neuropsychiatric status were affected during COVID-19 (p < 0.05). CONCLUSION: When the cases were analyzed according to disease severity, no relationship was found between cognitive deficits and disease severity.

Queer collective memory during the time of COVID: Timelessness, isolation, and resilience in the United Kingdom.

Oral history collections both rely on and preserve community memories, and are of importance for understanding marginalized communities, particularly when they privilege minority voices. This article draws from original, video-based oral histories conducted for the United Kingdom's national LGBTQ+ (lesbian, gay, bisexual, transgender, queer/questioning, and others) museum, Queer Britain, focusing on an ongoing collection of oral histories organized around experiences related to the COVID pandemic. In order to protect the health of those interviewed and the interviewers, the researchers used virtual meeting software to record video interviews and utilized qualitative software to expand and support interview analysis. Specific oral history methodologies and concepts are explored, and museum studies content is briefly discussed, specifically as it relates to museums of marginalized people. Themes explored include isolation and timelessness, the impact of the pandemic on diverse LGBTQ+ communities, and HIV/AIDS.

Boosting compromised SARS-CoV-2-specific immunity with mRNA vaccination in liver transplant recipients.

BACKGROUND & AIMS: Liver transplant recipients (LTRs) demonstrate a reduced response to COVID-19 mRNA vaccination; however, a detailed understanding of the interplay between humoral and cellular immunity, especially after a third (and fourth) vaccine dose, is lacking. METHODS: We longitudinally compared the humoral, as well as CD4+ and CD8+ T-cell, responses between LTRs (n = 24) and healthy controls (n = 19) after three (LTRs: n = 9 to 16; healthy controls: n = 9 to 14 per experiment) to four (LTRs: n = 4; healthy controls: n = 4) vaccine doses, including in-depth phenotypical and functional characterization. RESULTS: Compared to healthy controls, development of high antibody titers required a third vaccine dose in most LTRs, while spike-specific CD8+ T cells with robust recall capacity plateaued after the second vaccine dose, albeit with a reduced frequency and epitope repertoire compared to healthy controls. This overall attenuated vaccine response was linked to a reduced frequency of spike-reactive follicular T helper cells in LTRs. CONCLUSION: Three doses of a COVID-19 mRNA vaccine induce an overall robust humoral and cellular memory response in most LTRs. Decisions regarding additional booster doses may thus be based on individual vaccine responses as well as evolution of novel variants of concern. IMPACT AND IMPLICATIONS: Due to immunosuppressive medication, liver transplant recipients (LTR) display reduced antibody titers upon COVID-19 mRNA vaccination, but the impact on long-term immune memory is not clear. Herein, we demonstrate that after three vaccine doses, the majority of LTRs not only exhibit substantial antibody titers, but also a robust memory T-cell response. Additional booster vaccine doses may be of special benefit for a small subset of LTRs with inferior vaccine response and may provide superior protection against evolving novel viral variants. These findings will help physicians to guide LTRs regarding the benefit of booster vaccinations.

A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning.

BACKGROUND: The novel coronavirus pneumonia that began to spread in 2019 is still raging and has placed a burden on medical systems and governments in various countries. For policymaking and medical resource decisions, a good prediction model is necessary to monitor and evaluate the trends of the epidemic. We used a long short-term memory (LSTM) model and the improved hybrid model based on ensemble empirical mode decomposition (EEMD) to predict COVID-19 trends; Methods: The data were collected from the Harvard Dataverse. Epidemic data from 21 January 2020 to 25 April 2021 for California, the most severely affected state in the United States, were used to develop an LSTM model and an EEMD-LSTM hybrid model, which is an LSTM model combined with ensemble empirical mode decomposition. In this study, ninety percent of the data were adopted to fit the models as a training set, while the subsequent 10% were used to test the prediction effect of the models. The mean absolute percentage error, mean absolute error, and root mean square error were used to evaluate the prediction performances of the models; Results: The results indicated that the number of confirmed cases in California was increasing as of 25 April 2021, with no obvious evidence of a sharp decline. On 25 April 2021, the LSTM model predicted 3666418 confirmed cases, whereas the EEMD-LSTM predicted 3681150. The mean absolute percentage errors for the LSTM and EEMD-LSTM models were 0.0151 and 0.0051, respectively. The mean absolute and root mean square errors were 5.58 × 104 and 5.63 × 104 for the LSTM model and 1.9 × 104 and 2.43 × 104 for the EEMD-LSTM model, respectively; Conclusions: The results showed the advantage of an EEMD-LSTM model over a single LSTM model, and the established EEMD-LSTM model may be suitable for monitoring and evaluating the epidemic situation and providing quantitative analysis evidence for epidemic prevention and control.

Optimized speaker change detection approach for speaker segmentation towards speaker diarization based on deep learning

Speaker diarization is the partitioning of an audio source stream into homogeneous segments according to the speaker's identity. It can improve the readability of an automatic speech transcription by segmenting the audio stream into speaker turns and identifying the speaker's true identity when used in combination with speaker recognition systems. Generally, the automatic speaker diarization is done based on two phases, like the transformation of audio segments into feature representation and the clustering. In this paper, clustering along with a hybrid optimization technique is carried out for performing the speaker diarization. For that, the extracted features from the audio signal is processed under speech activity prediction in order to identify the speak segments. The diarization process is done by Deep Embedded Clustering (DEC) in which the constants are trained by the developed Fractional Anticorona Whale Optimization Algorithm (FrACWOA). The FrACWOA is a hybrid optimization technique, which is designed by adapting the concept of fractional theory, precaution behaviour of COVID-19 and hunting performance of whales. DEC performs the diarization, which concurrently learns the representation of features as well as cluster assignments with neural networks. Using a mapping from the information space to a lower-dimensional feature space, DEC repeatedly discovers the most effective solution for a clustering objective. On the basis of testing accuracy, diarization error, false discovery rate (FDR), false negative rate (FNR), and false positive rate (FPR) of 0.902, 0.627, 0.276, 0.117, and 0.118, respectively, the developed FrACWOA+DEC algorithm performed much better with six speakers using the EenaduPrathidwani dataset. Comparing the accuracy of the proposed method to existing approaches such as Active learning, DE+K-means, LSTM, MCGAN, ANN-ABC-LA, and ACWOA+DFC, the accuracy of the proposed method is 12.97%, 10.31%, 9.75%, 7.53%, 4.32%, and 2.106% higher when using 6 speakers. © 2022 Elsevier B.V.

UTILIZING BERT AND CNN-LSTM IN STOCK PRICE PREDICTION USING DATA SENTIMENT ANALYSIS AND TECHNICAL ANALYSIS OF STOCK AND COMMODITY

The COVID-19 pandemic undoubtedly has affected people's lifestyles and stock investment activities. The government's policies to deal with the pandemic have an impact on increasing the number of investors in the stock market. Apart from profits, there are also risks associated with investing in stocks. To reduce the risk required analysis for stock price predictions. The data often used are stock data, commodity prices, and social media. The application of deep learning and natural language processing can help investors to process data. This paper proposes Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for technical analysis predicting stock prices using stock and commodity price data and urges BERT for sentiment analysis using social media data. The CNN-LSTM method has the best performance compared to the other four methods. The results showed that the performance of this method was the best, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were the smallest, and R Square (R2) was the largest. The BERT method has the best classification performance using 5-epochs, Weight Macro Avg, Weighted Avg, Accuracy, and the highest F1-Score. CNN-LSTM and BERT are more appropriate to predict stock prices and give investors suggestions to make stock investment decisions based on technical analysis and sentiment analysis. © 2023 ICIC International. All rights reserved.

Pressure Prediction System in Lung Circuit Using Deep Learning

A massive number of patients infected with SARS-CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated health care which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. Long short-term memory (LSTM) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems, LSTM is preferred. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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. © 2023 Tech Science Press. All rights reserved.

COVID-19 clinical medical relationship extraction based on MPNet

With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID-19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID-19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional-GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre-trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID-19 clinical text entity relation extraction task. © 2023 The Authors. IET Cyber-Physical Systems: Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

CSI-Based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.

Addendum: Predictive form of the FPM model

The article Oustaloup et al. (2021) has shown that the Fractional Power Model (FPM), A+Btm, enables well representing the cumulated data of COVID infections, thanks to a nonlinear identification technique. Beyond this identification interval, the article has also shown that the model enables predicting the future values on an unusual prediction horizon as for its range. The objective of this addendum is to explain, via an autoregressive form, why this model intrinsically benefits from such a predictivity property, the idea being to show the interest of the FPM model by highlighting its predictive specificity, inherent to non-integer integration that conditions the model. More precisely, this addendum establishes a predictive form with long memory of the FPM model. This form corresponds to an autoregressive (AR) filter of infinite order. Taking into account the whole past through an indefinite linear combination of past values, a first predictive form, said to be with long memory, results from an approach using one of the formulations of non-integer differentiation. Actually, as this first predictive form is the one of the power-law, tm, its adaptation to the FPM model, A+Btm, which generalizes the linear regression, A+Bt, is then straightforward: it leads to the predictive form of the FPM model that specifies the model in prediction. This predictive form with long memory shows that the predictivity of the FPM model is such that any predicted value takes into account the whole past, according to a weighted sum of all the past values. These values are taken into account through weighting coefficients, that, for m>−1 and a fortiori for m>0, correspond to an attenuation of the past, that the non-integer power, m, determines by itself. To confirm the specificity of the FPM model in considering the past, this model is compared with a model of another nature, also having three parameters, namely an exponential model (Liu et al. (2020);Sallahi et al. (2021)): whereas, for the FPM model, the past is taken into account globally through all past instants, for the exponential model, the past is taken into account only locally through one single past instant, the predictive form of the model having a short memory and corresponding to an AR filter of order 1. Comparative results, obtained in prediction for these two models, show the predictive interest of the FPM model.

Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach

The growth of the "Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a "Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2. © 2022 Elsevier B.V.

Patients with B-cell malignancies experience reduced antibody responses with class switching defect following BNT162b2 SARS-CoV-2 vaccination

Vaccines having aided in escaping the majority of the population from immunological naïvety, our strategies are now shifting towards an increased focus on identifying and protecting the extremely vulnerable. We here describe the results of testing 12 patients, those with lymphoid malignancies having been targeted their B-cells for therapy with rituximab-containing regimens or a Bruton tyrosine kinase inhibitor, for anti-SARS-CoV-2 spike antibodies after receiving the BNT162b2 mRNA vaccine doses. The interval from last dosing of B-cell depletion therapy to SARS-CoV-2 vaccination was at median 5.3 (range 3.1–6.6) months. Using the ‘seroprotection' threshold of 775 [BAU/mL] for the anti-spike antibody titer, our finding points out the crucial unresponsiveness of the targeted population with 0/12 (0%) achieving ‘seroprotection'. Although IgG seroconversion was observed in 4/12 (33%), supporting the overall benefit of vaccination, the figures still point out a potential need for optimization of practice. IgA was further less responsive (unsuccessful ‘seroconversion' in 11/12 (92%)), implicating an underlying class switch defect. Those with depletion on B-cells are caught at a dilemma between, being too early and too late on receiving SARS-CoV-2 vaccines. They wish to get over their immunological naïvety at the earliest, while, in order to assure quality immune memory, are also required to hold the patience for their B-cells to repopulate. Although it remains an issue whether intensified vaccine schedules and/or regimens will lead to stronger immunogenicity or more effective boosters for non-responders, we shall take advantage of every increasing evidence in order to optimize current options. © 2022 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases