Even affective changes induced by the global health crisis are insufficient to perturb the hyper-stability of visual long-term memory.

Past studies of emotion and mood on memory have mostly focused on the learning of emotional material in the laboratory or on the consequences of a punctate catastrophic event. However, the influence of a long-lasting global condition on memory and learning has not been studied. The COVID-19 pandemic unfortunately offered a unique situation to observe the effects of prolonged, negative events on human memory for visual information. One thousand online subjects were asked to remember the details of real-world photographs of objects to enable fine-grained visual discriminations from novel within-category foils. Visual memory performance was invariant across time, regardless of the infection rate in the local or national population, or the subjects' self-reported affective state using the Positive and Negative Affect Schedule (PANAS). Thus, visual memory provides the human brain with storage that is particularly resilient to changes in emotional state, even when those changes are experienced for months longer than any imaginable laboratory procedure.

A Comparison on LSTM Deep Learning Method and Random Walk Model Used on Financial and Medical Applications: An Example in COVID-19 Development Prediction.

This study aims to establish the model of the cryptocurrency price trend based on a financial theory using the Long Short-Term Memory (LSTM) networks model with multiple combinations between the window length and the predicting horizons. The Random Walk model is also applied with different parameter settings. The object of this study is the cryptocurrency and medical issues, primarily the Bitcoin and Ethereum and the COVID-19. Quantitative analysis is adopted as the method of this dissertation. The research tool is Python programming language, and the TensorFlow package is employed to model and analyze research topics. The results of this study show the limitations of the LSTM and Random Walk model for price prediction while demonstrating the different characteristics of both models with different parameter settings, providing a balance between the model's accuracy and the model's practicality.

Long-term memory CD8+ T cells specific for SARS-CoV-2 in individuals who received the BNT162b2 mRNA vaccine.

Long-term memory T cells have not been well analyzed in individuals vaccinated with a COVID-19 vaccine although analysis of these T cells is necessary to evaluate vaccine efficacy. Here, investigate HLA-A*24:02-restricted CD8+ T cells specific for SARS-CoV-2-derived spike (S) epitopes in individuals immunized with the BNT162b2 mRNA vaccine. T cells specific for the S-QI9 and S-NF9 immunodominant epitopes have higher ability to recognize epitopes than other epitope-specific T cell populations. This higher recognition of S-QI9-specific T cells is due to the high stability of the S-QI9 peptide for HLA-A*24:02, whereas that of S-NF9-specific T cells results from the high affinity of T cell receptor. T cells specific for S-QI9 and S-NF9 are detectable >30 weeks after the second vaccination, indicating that the vaccine induces long-term memory T cells specific for these epitopes. Because the S-QI9 epitope is highly conserved among SARS-CoV-2 variants, S-QI9-specific T cells may help prevent infection with SARS-CoV-2 variants.

Implementing a Hand Gesture Recognition System Based on Range-Doppler Map.

There have been several studies of hand gesture recognition for human-machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.

A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture.

Recognizing various abnormal human activities from video is very challenging. This problem is also greatly influenced by the lack of datasets containing various abnormal human activities. The available datasets contain various human activities, but only a few of them contain non-standard human behavior such as theft, harassment, etc. There are datasets such as KTH that focus on abnormal activities such as sudden behavioral changes, as well as on various changes in interpersonal interactions. The UCF-crime dataset contains categories such as fighting, abuse, explosions, robberies, etc. However, this dataset is very time consuming. The events in the videos occur in a few seconds. This may affect the overall results of the neural networks that are used to detect the incident. In this article, we create a dataset that deals with abnormal activities, containing categories such as Begging, Drunkenness, Fight, Harassment, Hijack, Knife Hazard, Normal Videos, Pollution, Property Damage, Robbery, and Terrorism. We use the created dataset for the training and testing of the ConvLSTM (convolutional long short-term memory) neural network, which we designed. However, we also test the created dataset using other architectures. We use ConvLSTM architectures and 3D Resnet50, 3D Resnet101, and 3D Resnet152. With the created dataset and the architecture we designed, we obtained an accuracy of classification of 96.19% and a precision of 96.50%.

Episodic long-term memory in post-infectious SARS-CoV-2 patients.

BACKGROUND: Episodic long-term memory (LTM) difficulties/deficits are frequent in COVID-19-recovered patients and negatively impact on prognosis and outcome. However, little is known about their semiology and prevalence, also being still debated whether they arise from primary amnesic features or are secondary to dysexecutive/inattentive processes and disease-related/premorbid status. Hence, this study aimed at (1) assessing LTM functioning in post-infectious SARS-CoV-2 patients by accounting for premorbid and disease-related confounders and (2) exploring its cognitive etiology. METHODS: Measures of global cognition (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)) and LTM (Babcock Memory Test (BMT)) of fifty-four COVID-19-recovered patients were retrospectively collected. Patients were subdivided into those being already at risk or not for cognitive decline (RCD + ; RCD -). Cognitive measures were converted into equivalent scores (ESs). RESULTS: LTM sub-clinical/clinical deficits (ESs = 0/1) were mildly-to-moderately prevalent in both RCD + (MoCA-Memory, 31.8%; BMT, 31.8%) and RCD - (MoCA-Memory, 28.6%; BMT, 39.3%) patients. MMSE and MoCA total scores, but not the MoCA-Attention subtest, were associated with the BMT. RCD + asymptomatic patients performed better on the BMT (p = .033) than those requiring O2 therapy (but not ventilation). DISCUSSION: COVID-19-recovered individuals might show LTM deficits of both primary and secondary etiology and should be thus screened for them, especially those having suffered mid-to-moderate COVID-19 and those already being at risk for cognitive decline. Both I- and II-level measures of verbal LTM can be adopted, although the former might be more sensitive.

Feasibility of remote Memory Clinics using the plan, do, study, act (PDSA) cycle.

INTRODUCTION: A timely diagnosis of dementia is crucial for initiating and maintaining support for people living with dementia. The coronavirus disease (COVID) pandemic temporarily halted Memory Clinics, where this is organised, and rate of dementia diagnosis has fallen. Despite increasing use of alternatives to face-to-face (F2F) consultations in other departments, it is unclear whether this is feasible within the traditional Memory Clinic model. AIMS: The main aim of this service improvement project performed during the pandemic was to explore feasibility of telephone (TC) and videoconference (VC) Memory Clinic consultations. METHODS: Consecutive patients on the Memory Clinic waiting list were telephoned and offered an initial appointment by VC or TC. Data extracted included: age, internet-enabled device ownership, reason for and choice of Memory Clinic assessment. We noted Montreal Cognitive Assessment-Blind (TC) and Addenbrooke's Cognitive Examination-III (VC via Attend Anywhere) scores, and feasibility of consultation. RESULTS: Out of 100 patients, 12 had a home assessment, moved away, been hospitalised, or died. 45, 21 and 6 preferred F2F, VC and TC assessments respectively. 16 were not contactable and offered a F2F appointment. The main reason for preferring F2F was non-ownership, or inability to use an internet-enabled device (80%). VC and TC preference reasons were unwillingness to come to hospital (59%), and convenience (41%). Attendance rate was 100% for VC and TC, but 77% for F2F. Feasibility (successful consultations) was seen in 90% (VC) and 67% (TC) patients. CONCLUSION: For able and willing patients, remote Memory Consultations can be both feasible and beneficial. This has implications for future planning in dementia services.