A Survey of Human-Computer Interaction Technology in Intelligent Home for Children's Learning

The development of the COVID-19 epidemic has increased the home learning time of children. More researchers began to pay attention to children's learning in home. This survey reviewed the frontier and classic cases in the field of interactive design of children's home learning in the past five years, analyzed tangible user interface, augmented reality, and multimodal interaction in human-computer interaction of children's home learning. This paper reviewed the application of interactive system in children's learning and points out its positive side in development of ability, process of learning, habits of learning, and environment of learning of children. Through analysis, we advise that it is necessary to create home learning applications, link smart home systems, and build an interactive learning environment for smart home learning environment design. Finally, we point out the technical and ethical problems existing in the current research, proposes that intelligent perception, emotion recognition, and expression technologies should be introduced in the future, and looks forward to the development of this field. © 2023 Institute of Computing Technology. All rights reserved.

Analytical and biomedical applications of microfluidics in traditional Chinese medicine research

Traditional Chinese medicine (TCM) has significant benefits in the prevention and treatment of diseases due to its unique theoretical system and research techniques. However, there are still key issues to be resolved in the full interpretation and use of TCM, such as vague active compounds and mechanism of action. Therefore, it is promising to promote the research on TCM through innovative strategies and advanced cutting-edge technologies. Microfluidic chips have provided controllable unique platforms for biomedical applications in TCM research with flexible composition and large-scale integration. In this review, the analysis and biomedical applications of microfluidics in the field of TCM are highlighted, including quality control of Chinese herbal medicines (CHMs), delivery of CHMs, evaluation of pharmacological activity as well as disease diagnosis. Finally, potential challenges and prospects of existing microfluidic technologies in the inheritance and innovation of TCM are discussed.Copyright © 2022 Elsevier B.V.

Potential Mechanism of Colchicine against COVID-19 and Non-Small Cell Lung Cancer based on Network Pharmacology and Bioinformatics Analysis

Colchicine is an alkaloid with antitumor effect isolated from Colchicum autumnale plants, it has been reported in multiple studies as a potential treatment for coronavirus disease-19 and this study applied network pharmacology and bioinformatics analysis to explore the potential mechanism of colchicine against non-small cell lung cancer and coronavirus disease-19. The R software was used to determine the differentially expressed genes of non-small cell lung cancer/coronavirus disease-19, and carry out prognostic analysis and clinical analysis of the differentially expressed genes, the targets of colchicine were obtained from various databases, the protein-protein interaction network of intersection targets of colchicine and non-small cell lung cancer/coronavirus disease-19 was constructed, enrichment analysis of gene ontology and kyoto encyclopedia of genes and genomes pathways was performed by Metascape database and the molecular docking and molecular dynamics simulation were completed. We obtained a total of 716 differentially expressed genes and identified 13 potential prognostic genes associated with the clinical characterization of non-small cell lung cancer/coronavirus disease-19 patients. C-C motif chemokine ligand 2, toll like receptor 4, intercellular adhesion molecule 1, peroxisome proliferator activated receptor gamma, interleukin 17A, interferon gamma, angiotensin I converting enzyme, fos proto-oncogene, nuclear factor kappa B inhibitor alpha, TIMP metallopeptidase inhibitor 1 and secreted phosphoprotein 1 were core targets. The intersection targets of colchicine and non-small cell lung cancer/coronavirus disease-19 were mainly enriched in biological processes such as inflammatory response, response to cytokine and response to lipopolysaccharide, as well as signal pathways such as interleukin 17 signaling pathway, hypoxia inducible factor 1 signaling pathway and nucleotide binding oligomerization domain-like receptor signaling pathway. The results of molecular docking showed that the colchicine is better combined with the core targets. Analysis of molecular dynamics simulation showed that the protein and ligand form a stabilizing effect. Based on bioinformatics analysis and network pharmacology, we obtained biomarkers that may be used for the prognosis of non-small cell lung cancer/coronavirus disease-19 patients and revealed that colchicine may play a potential role in the treatment of non-small cell lung cancer/coronavirus disease-19 by regulating multiple targets and multiple signaling pathways and participating in multiple biological processes.

Risk signals mining of ribavirin and interferon-alpha based on the US FDA Adverse Event Reporting System database.

Objective: To identify which induced the symptoms/signs and laboratory abnormal findings occurred in patients with novel coronavirus pneumonia, by disease itself or by ribavirin and interferon-alpha treatments, through mining the adverse events (AEs) signals of the 2 antivirus agents. Method(s): According to the symptoms/signs and laboratory abnormal findings of novel coronavirus pneumonia mentioned in the literature and "Diagnosis and Treatment scheme of Novel Coronavirus Pneumonia (trial version 5)", AEs in this study were selected. Related data were collected from the U.S. FDA Adverse Events Reporting System (FARES) from Jan 1, 2004 to Dec 31, 2019, and the reporting odds ratio (ROR) method was used for signals detection for the above-mentioned 2 drugs. Result(s): A total of 7 582 463 AEs related to drugs were reported in the FAERS database, of which 31 775 related to ribavirin and 2 345 related to interferon-alpha. The results showed that AEs related to ribavirin in respiratory, thoracic, and mediastinal disorders were nasal congestion, cough, laryngeal pain, pharyngeal oedema, productive cough, and dyspnoea;AEs related to interferon-alpha were laryngeal pain and haemoptysis. In other system organ class, AEs related to above 2 drugs were pyrexia, feeling cold, pyrexia, nausea, vomiting, diarrhoea, headache, arthralgia, myalgia, and rash. AEs of laboratory abnormal results related to ribavirin were white blood cell/platelet count decrease and aspartate/alanine aminotransferase increase;AEs related to interferon-alpha were white blood cell/platelet count decrease, aspartate/alanine aminotransferase increase, and lymphocyte count decrease. Conclusion(s): Some AEs induced by ribavirin and interferon-alpha were similar to symptoms/signs and laboratory abnormal findings of novel coronavirus pneumonia, which should be distinguished in the clinical practice.Copyright © 2020 by the Chinese Medical Association.

Out-of-hospital management strategies for gastric cancer patients during the COVID-19 outbreak.

Since the outbreak of Corona Virus Disease 2019 occurred in December 2019, the reduction of population mobility has curbed the spread of the epidemic to some extent but also prolonged the waiting time for the treatment of patients with gastric cancer. Based on fully understanding the different staging characteristics of gastric cancer, clinical departments should develop reasonable out-of-hospital management strategies. On one hand, reasonable communication channels should be established to allow patients to receive adequate guidance out of the hospital. On the other hand, shared decisions with patients should be made to adjust treatment strategies, and education on viral prevention should be implemented to minimize the impact of the epidemic on tumor treatment.Copyright © 2020 by the Chinese Medical Association.

Integrated smart analytics of nucleic acid amplification tests via paper microfluidics and deep learning in cloud computing

Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dynamics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research.Copyright © 2023 Elsevier Ltd

Implementation of radiotherapy in a tertiary hospital in Zhejiang province during the epidemic of COVID-19.

Objective: To explore the clinical practice of delivering radiotherapy during the outbreak of 2019 novel coronavirus disease(COVID-19). Method(s): During this epidemic period, available method including but not limited to: strict disinfection, body temperature monitoring and staff training of relevant knowledge, were used to ensure the safety of radiotherapy treatment. Statistical analysis was performed to study the relevant data including proportion of patients receiving radiotherapy for different purposes, time from scanning to the first time of radiation delivery and degree of satisfaction in the view of staffs and patients, respectively. Result(s): A total of 60 patients received radiation therapy in the department of radiotherapy of Zhejiang Provincial People's Hospital (2020-02-11). Compared with the same period in 2019 (after the Spring Festival), the total number of patients receiving radiotherapy was decreased from 72 to 60(83.3%). Among them, the number of patients receiving palliative radiation therapy decreased significantly, while the proportion of radical, preoperative and/or postoperative radiotherapy/radiochemotherapy did not significantly decrease. There was significant difference between two years (chi2=6.967, P<0.05). The median time for newly admitted patients to receive radiotherapy was two days, which was not significantly longer than the interval in 2019 (P>0.05). Staff and patients were generally satisfied with the current prevention measures. Conclusion(s): Using a variety of prevention and control method, and taking full account of medical safety and patient benefits, radiation-related activities can be carried out during the epidemic.Copyright © 2020 by the Chinese Medical Association.

Toxicity of the disinfectant benzalkonium chloride (C14) towards cyanobacterium Microcystis results from its impact on the photosynthetic apparatus and cell metabolism

Quaternary ammonium compounds (QACs) are commonly used in a variety of consumer and commercial products, typically as a component of disinfectants. During the COVID-19 pandemic, QACs became one of the primary agents utilized to inactivate the SARS-CoV-2 virus on surfaces. However, the ecotoxicological effects of QACs upon aquatic organisms have not been fully assessed. In this study, we examined the effects of a widely used QAC (benzalkonium chloride-C14, BAC-14) on two toxigenic Microcystis strains and one non-toxigenic freshwater Microcystis strain and carried out an analysis focused on primary, adaptive and compensatory stress responses at apical (growth and photosynthesis) and metabolic levels. This analysis revealed that the two toxic Microcystis strains were more tolerant than the non-toxic strain, with 96 hr-EC50 values of 0.70, 0.76, and 0.38 mg/L BAC-14 for toxigenic M. aeruginosa FACHB-905, toxigenic M. aeruginosa FACHB-469, and non-toxigenic M. wesenbergii FACHB-908, respectively. The photosynthetic activities of the Microcystis, assessed via Fv/Fm values, were significantly suppressed under 0.4 mg/L BAC-14. Furthermore, this analysis revealed that BAC-14 altered 14, 12, and 8 metabolic pathways in M. aeruginosa FACHB-905, M. aeruginosa FACHB-469, and M. wesenbergii FACHB-908, respectively. It is noteworthy that BAC-14 enhanced the level of extracellular microcystin production in the toxigenic Microcystis strains, although cell growth was not significantly affected. Collectively, these data show that BAC-14 disrupted the physiological and metabolic status of Microcystis cells and stimulated the production and release of microcystin, which could result in damage to aquatic systems. © 2022

Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff.

OBJECTIVES: Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital. DESIGN: Single-site, observational cohort study. SETTING: An urban, academic hospital in Boston, Massachusetts, USA. PARTICIPANTS: We enrolled adult hospital staff entering the hospital at a key ingress point. INTERVENTIONS: Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence. OUTCOME MEASURES: Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm. RESULTS: One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important.

A new PASS approach for tourism policymaking against COVID-19: learning from China’s experiences at early stages of the pandemic

The COVID-19 pandemic has caused unprecedented and far-reaching impacts on the tourism industry worldwide. To fight against COVID-19 while maintaining economic development, China has adopted its unique policymaking system and made certain achievements in preventing the pandemic and promoting the recovery of domestic tourism, which may have reference values to other countries. By collecting the national-level tourism policies on COVID-19 in China and analyzing the connections between the keywords of these policies, this study conceptually proposes a new PASS (P: Pause-promote;A: Avoid-alternate;S: Supervise-stabilize;S: Support-sustain) approach for comprehensive and seamless policymaking for the public health crisis management in the tourism context. Even though the new PASS is derived based on China’s pandemic policymaking experiences, its general values are well supported by the practices in other countries. Policy recommendations from perspectives of key stakeholders are summarized. Policymaking challenges are finally discussed. © 2022 Asia Pacific Tourism Association.

Birds of a Feather Flock Together: How Set Bias Helps to Deanonymize You via Revealed Intersection Sizes

Secure two-party protocols that compute intersection-related statistics have attracted much attention from the industry. These protocols enable two organizations to jointly compute a function (e.g., count and sum) over the intersection of their sets without explicitly revealing this intersection. However, most of such protocols will reveal the intersection size of the two sets in the end. In this work, we are interested in how well an attacker can leverage the revealed intersection sizes to infer some elements' membership of one organization's set. Even disclosing an element's membership of one organization's set to the other organization may violate privacy regulations (e.g., GDPR) since such an element is usually used to identify a person between two organizations. We are the first to study this set membership leakage in intersection-size-revealing protocols. We propose two attacks, namely, baseline attack and feature-aware attack, to evaluate this leakage in realistic scenarios. In particular, our feature-aware attack exploits the realistic set bias that elements with specific features are more likely to be the members of one organization's set. The results show that our two attacks can infer 2.0 similar to 72.7 set members on average in three realistic scenarios. If the set bias is not weak, the feature-aware attack will outperform the baseline one. For example, in COVID-19 contact tracing, the feature-aware attack can find 25.9 tokens of infected patients in 135 protocol invocations, 1.5 x more than the baseline attack. We discuss how such results may cause negative real-world impacts and propose possible defenses against our attacks.

AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease

Global pandemics such as COVID-19 have resulted in significant global social and economic disruption. Although polymerase chain reaction (PCR) is recommended as the standard test for identifying the SARS-CoV-2, conventional assays are time-consuming. In parallel, although artificial intelligence (AI) has been employed to contain the disease, the implementation of AI in PCR analytics, which may enhance the cognition of diagnostics, is quite rare. The information that the amplification curve reveals can reflect the dynamics of reactions. Here, we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices (mu PADs) to detect synthetic RNA templates of the SARS-CoV-2 ORFlab gene. The mu PADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments. During analysis, real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks, including RNN, LSTM, and GRU. Of these, the GRU is found to be most effective and accurate. Based on the experimentally obtained datasets, qualitative forecasting can be made as early as 13 cycles, which significantly enhances the efficiency of the PCR tests by 67.5% (similar to 40 min). Also, an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles. To further improve PCR testing efficiency, we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers, which enables real-time quantitative analysis of PCR tests. The presented approach is the first to integrate AI for on-chip PCR data analysis. It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation. It is also capable of fully exploring the dynamics and intrinsic features of each reaction. This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field. It is universally applicable and can be extended to multiple areas of fundamental research.

Is Zoom viable for sociophonetic research? A comparison of in-person and online recordings for vocalic analysis

In this study, we explore whether Zoom is a viable method for collecting data for sociophonetic research, focusing on vocalic analysis. We investigate whether recordings collected through Zoom yield different acoustic measurements than recordings collected through in-person recording equipment, for the exact same speech. We analyze vowel formant data from 18 speakers who recorded Zoom conversations at the same time as they recorded themselves with portable recording equipment. We find that, overall, Zoom recordings yield lower raw F1 values and higher F2 values than recording equipment. We also tested whether normalization affects discrepancies between recording methods and found that while discrepancies still appear after normalizing with the Watt and Fabricius modified method, Lobanov normalization largely minimizes discrepancies between recording methods. Discrepancies are also mitigated with a Zoom recording setup that involves the speaker wearing headphones and recording with an external microphone.

Deep Learning-Based COVID-19 Twitter Analysis

Since the outbreak of COVID-19, the pandemic has impacted billions of people's lives around the world. Social media, such as Twitter, has been one of the major platforms where people express their emotions and thoughts about the unprecedented pandemic. In this paper, we perform Twitter sentiment analysis to gain insights into the development of Twitter users' sentiments during the period from February 1 to December 31, 2020. We use Long Short-term Memory (LSTM), a deep learning-based Natural Language Processing (NLP) method, to detect multiple sentiments out of eleven kinds. We also picked a number of topics of interest, such as social justice, mental health, vaccines, and misinformation, and conducted theme-specific sentiment analysis. In order to delve deeper into the meaning behind the sentiment trends, we used the Latent Dirichlet Allocation (LDA) algorithm to perform theme-specific topic modeling, which reveals interesting results. © 2021 ACM.

Laryngoscope8: Laryngeal image dataset and classification of laryngeal disease based on attention mechanism

Laryngeal disease is a common disease worldwide. However, currently there are no public laryngeal image datasets, which hinders the development of automatic classification of laryngeal disease. In this work, we build a new laryngeal image dataset called Laryngoscope8, which comprises 3057 images of 1950 unique individuals, and the images have been labeled with one of eight labels (including seven pathological labels and one normal label) by professional otolaryngologists. We also propose a laryngeal disease classification method, which uses attention mechanism to obtain the critical area under the supervision of image labels for laryngeal disease classification. That is, we first train a CNN model to classify the laryngeal images. If the classification result is correct, the region with strong response is most likely a critical area. The regions with strong responses are used as training data to train an object localization model that can automatically locate the critical area. Given an image for classification, the trained object localization model is employed to locate the critical area. Then, the located critical area is employed for image classification. The entire process only requires image-level labels and does not require manual labeling of the critical area. Experiment results show that the proposed method achieves promising performance in laryngeal disease classification. (C) 2021 Elsevier B.V. All rights reserved.

Reliability and validity of the Chinese version of fear of coronavirus disease 2019 scale

Objective To translate the English version of fear of coronavirus disease 2019 (COVID-19) scale (FCV-19S) into Chinese and assess its reliability and validity. Methods FCV-19S was translated and culture-adapted to form a Chinese version of fear of COVID-19 scale (FCV-19S-C). A total of 334 questionnaires were sent out online, including FCV-19S-C, Chinese version of depression, anxiety and stress scale (DASS-C21), impact of event scale-revised (IES-R) and public stigma scale. The reliability and validity of FCV-19S-C and the influencing factors of COVID-19 fear were analyzed with the survey data. Results FCV-19S-C contained 7 items. One common factor was extracted by exploratory factor analysis, reflecting that all items in the scale belonged to the common factor, which could explain 69.5% of total variation. The load of item factors ranged from 0.780 to 0.873, showing good construct validity. The total score of FCV-19S-C was positively correlated with the total scores of DASS-C21 and IES-R (r＝0.403 and 0.471, both P＜0.01), indicating that the scale had good concurrent validity. The Cronbach’s α coefficient of FCV-19S-C was 0.924, showing good reliability. Linear regression analysis showed that the influences of COVID-19 on the psychological level and family income could predict the total score of FCV-19S-C (β＝0.62 and 0.20, both P＜0.01). The total score of FCV-19S-C could predict the total score of the public stigma scale (β＝0.37, P＜0.01). Conclusion FCV-19S-C has good reliability and validity, and can be used as a tool to understand the public fear of COVID-19 in China. The fear of COVID-19 is related to the loss of family income and the public stigma of COVID-19 patients.

A retrospective study of the clinical characteristics of COVID-19 patients with gastrointestinal symptoms

Objective: To investigate the gastrointestinal (GI) manifestations of COVID-19 and retrospectively analyze the clinical characteristics of COVID-19 patients with GI symptoms. Methods: Data of 137 COVID-19 inpatients treated in Renmin Hospital of Wuhan University from February 1 to February 29, 2020 were collected. Patients were divided into GI group and Non-GI group according to the presence of digestive system abnormalities and gastrointestinal symptoms before and during admission. General data, clinical data, and relevant laboratory examination results of 137 patients were collected. SPSS 23.0 software was used for statistical analysis to compare the differences of various indicators between the two groups. Results: There was no statistically significant difference between the clinical data and the clinical manifestations of fever and dry cough between the GI group and the Non-GI group (P>0.05). The proportion of patients with fatigue in the GI group was higher than that in the Non-GI group (P<0.001). The proportion of critically ill patients was greater than that of the Non-GI group (P=0.011), and the proportion of GI combined with hypertension, diabetes, cardiovascular disease and chronic liver disease was higher than that of the Non-GI group (all P<0.05). GI group mortality rate was much higher than that of Non-GI group (P<0.001). Patients with GI group had higher white blood cell count and neutrophil count than Non-GI (P<0.001). The proportion of neutrophils, lymphocytes as well as lymph between the two groups had no statistical difference (P>0.05). The proportion of monocytes in the GI group was lower than that in the Non-GI group (P=0.033). There was no statistical difference in platelet count and C-reactive protein level between the two groups (P>0.05). LDH, TBIL, and Urea levels of GI group were higher than those of Non-GI group (P<0.05). There was no statistical difference in other cardiac, liver and kidney function tests, PT and APTT values between the two groups (P>0.05), but D-dimer in GI group was higher than in Non-GI group (P<0.001). Conclusion: Gastrointestinal symptoms are common in COVID-19 patients, and patients with other underlying diseases are at greater risk for developing gastrointestinal symptoms. COVID-19 patients with gastrointestinal symptoms progress more rapidly, have a higher mortality rate, and exhibit certain concomitant symptoms and laboratory tests that are specific to COVID-19. Therefore, more attention should be paid to the digestive system abnormalities and gastrointestinal symptoms in COVID-19 patients during clinical work. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.