Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes.

There are currently limited Food and Drug Administration (FDA)-approved drugs and vaccines for the treatment or prevention of Coronavirus Disease 2019 (COVID-19). Enhanced understanding of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and pathogenesis is critical for the development of therapeutics. To provide insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2, we performed single-cell (sc) RNA sequencing (RNA-seq) of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface (ALI) cultures over a time course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target at the onset of infection, which we confirmed by electron and immunofluorescence microscopy. Over the course of infection, the cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III interferons (IFNs) and interleukin (IL)-6 but not IL-1. This results in expression of interferon-stimulated genes (ISGs) in both infected and bystander cells. This provides a detailed characterization of genes, cell types, and cell state changes associated with SARS-CoV-2 infection in the human airway

Abbreviated BioBrick Prefix and Suffix for More Efficient Primer Design

This Request for Comments (RFC) modifies the assembly standard for biological parts proposed in BBF RFC 10 by removing the NotI restriction site from the BioBrick Prefix and Suffix

FABP7 expression in normal and stab-injured brain cortex and its role in astrocyte proliferation

Reactive gliosis, in which astrocytes as well as other types of glial cells undergo massive proliferation, is a common hallmark of all brain pathologies. Brain-type fatty acid-binding protein (FABP7) is abundantly expressed in neural stem cells and astrocytes of developing brain, suggesting its role in differentiation and/or proliferation of glial cells through regulation of lipid metabolism and/or signaling. However, the role of FABP7 in proliferation of glial cells during reactive gliosis is unknown. In this study, we examined the expression of FABP7 in mouse cortical stab injury model and also the phenotype of FABP7-KO mice in glial cell proliferation. Western blotting showed that FABP7 expression was increased significantly in the injured cortex compared with the contralateral side. By immunohistochemistry, FABP7 was localized to GFAP+ astrocytes (21% of FABP7+ cells) and NG2+ oligodendrocyte progenitor cells (62%) in the normal cortex. In the injured cortex there was no change in the population of FABP7+/NG2+ cells, while there was a significant increase in FABP7+/GFAP+ cells. In the stab-injured cortex of FABP7-KO mice there was decrease in the total number of reactive astrocytes and in the number of BrdU+ astrocytes compared with wild-type mice. Primary cultured astrocytes from FABP7-KO mice also showed a significant decrease in proliferation and omega-3 fatty acid incorporation compared with wild-type astrocytes. Overall, these data suggest that FABP7 is involved in the proliferation of astrocytes by controlling cellular fatty acid homeostasis

Detection of Careless Responses in Online Surveys Using Answering Behavior on Smartphone

Some respondents make careless responses due to the “satisficing,” which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses. We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly contributed to the detection of careless responses

PATROL: Participatory Activity Tracking and Risk Assessment for Anonymous Elderly Monitoring

There has been a subsequent increase in the number of elderly people living alone, with contribution from advancement in medicine and technology. However, hospitals and nursing homes are crowded, expensive, and uncomfortable, while personal caretakers are expensive and few in number. Home monitoring technologies are therefore on the rise. In this study, we propose an anonymous elderly monitoring system to track potential risks in everyday activities such as sleep, medication, shower, and food intake using a smartphone application. We design and implement an activity visualization and notification strategy method to identify risks easily and quickly. For evaluation, we added risky situations in an activity dataset from a real-life experiment with the elderly and conducted a user study using the proposed method and two other methods varying in visualization and notification techniques. With our proposed method, 75.2% of the risks were successfully identified, while 68.5% and 65.8% were identified with other methods. The average time taken to respond to notification was 176.46 min with the proposed method, compared to 201.42 and 176.9 min with other methods. Moreover, the interface analyzing and reporting time was also lower (28 s) in the proposed method compared to 38 and 54 s in other methods

Towards Cheaper Tourists' Emotion and Satisfaction Estimation with PCA and Subgroup Analysis

Smart tourism leverages ubiquitous sensors to recognise the state of tourists and provide them with a better-tailored sightseeing experience. We previously reported on our EmoTour system [1], which uses behavioural cues and audiovisual data collected during sightseeing to estimate tourists' emotional status and satisfaction levels. Some of this data is however not exceedingly convenient to collect, as eye-gaze trackers for instance are not widely available nor usually worn by regular tourists. In this paper, we explore different possibilities to both improve our previous results and lessen the cost of data collection, to work towards a system that is better suited for real-world applications. Using Principal Component Analysis dimensionality reduction, we show how leaving out either or both of eye-gaze tracker and physiological wristband sensor data can have little to no impact on the quality of predictions, and improve on our previously reported classification and regression scores. We also apply this new method to explore differences in emotional responses according to participants' nationality, age, and gender

Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems

Proceedings of the 12th Language Resources and Evaluation ConferenceWe present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments. To this end, we introduce a dialogue system that presents arguments by means of a virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories (Interesting, Convincing, Comprehensible, Relation). The approach is applied in a user study in order to compare two state of the art argument search engines to each other and with a system based on traditional web search. The results show a significant advantage of the two search engines over the baseline. Moreover, the two search engines show significant advantages over each other in different categories, thereby reflecting strengths and weaknesses of the different underlying techniques