EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network

Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems.

Study on the protective effect of the cichoric acid to piglet infected with porcine deltacoronavirus

To evaluate the protective effect of cichoric acid against PDCoV, 36 8-day-old suckling piglets were randomly divided into 4 groups in this study, with 9 pigs in each group. Each piglet in the cichoric acid group and cichoric acid+challenge group was given orally cichoric acid (3.6 mg/kg body weight) every day;on the 3rd day of the experiment, each piglet in the challenge group and the cichoric acid+challenge group was orally administered 10mL of PDCoV NC isolate (10~5TCID50/mL) for challenge, while each piglet in the blank group and cichoric acid group was orally administered 10mL DMEM;on the 7th day, all piglets were subjected to necropsy. Statistics analysis was performed for the average daily weight gain and the degree of diarrhea in piglets in each group, and the results showed that during the experiment, the diarrhea degree of the piglets in the challenge group was more serious than that in the cichoric acid+challenge group, and the average daily gain of the piglets in the challenge group was significantly lower than that in the cichoric acid+challenge group, control group and cichoric acid group (P<0.05), while the average daily gains of cichoric acid+challenge group, control group and cichoric acid group have no significant difference. Pathological and histopathological examinations showed that the small intestinal tissue lesions of the piglets in the challenge group were more serious than those in cichoric acid+challenge group, and a large number of intestinal villi were shed. The real time PCR was used to detect the PDCoV load and the amount of antiviral gene in the small intestine of piglets in each group, results showed that compared with the challenge group, the amounts of antiviral genes OAS,Mx1 and PKR in the jejunum and ileum of the cichoric acid+challenge group were increased, while the PDCoV were decreased significantly. The above results indicate that the damage of PDCoV to the intestinal tract caused by PSCoV was reduced for the piglets given orally cichoric acid. This study first reported that cichoric acid has a protective effect on the piglets with PDCoV challenge, providing theoretical basis for the studies of cichoric acid's antiviral mechanism and clinical prevention and control of PDCoV.

Characterization of COVID-19 Vaccine Clinical Trial Discussions on the Social Question-and-Answer Site Quora (preprint)

Introduction: Throughout the COVID-19 pandemic underserved populations, such as racial and ethnic minorities, were disproportionately impacted by illness, hospitalization, and death. Equity in clinical trials means that the participants in clinical trials represent the people who are most likely to have the health condition and need the treatment that the trial is testing. Infodemiology approaches examining user conversations on social media platforms have the potential to elucidate specific barriers and challenges related to clinical trial participation.Methodology: The study retrospectively collected and analyzed user question and answer posts from Quora in October 2021 using an inductive content coding approach. We also examined user’s publicly available profile metadata to identify racial and ethnic minority populations to capture their experiences, attitudes, topics, and barriers to COVID-19 vaccine trials.Result A total of 1,073 questions and 7,479 answers were collected based on structured automated keyword queries and data mining. A total of 763 questions and 2,548 answers were identified as related to COVID-19 vaccine clinical trials. The majority of these online interactions focused on asking questions and sharing knowledge and opinions about COVID-19 vaccine trials, including major topics related to: (a) interpreting whether clinical trial results could be trusted; (b) questions about vaccine efficacy and safety; (c) understanding trial design, regulatory considerations, and vaccine platforms; and (d) questions about trial enrollment, length, and adequate representation. Additionally, four major barriers discussed included: (i) disagreement from users regarding whether clinical trials require representation from different racial and ethnic minorities; (ii) concerns regarding the safety of trials when participating; (iii) lack of knowledge on how to register for a trial; and (iv) whether participants could withdraw from a trial to receive a more rapidly approved COVID-19 vaccine.Conclusions Our study found active user discussions related to COVID-19 vaccine clinical trials on Quora, including those specific to minority health topics and posted by self-identified racial and ethnic minority online users. Results from this study can help identify near real-time barriers to participation among underrepresented groups and support the design of future outreach strategies to help with recruitment and inclusive participation.

SnapUnlock: A Contrastive Learning-Based Contactless Authentication via Heterogeneous Sensors

Contactless authentication is crucial to keep social distance and prevent bacterial infection. However, existing authentication approaches, such as fingerprinting and face recognition, leverage sensors to verify static biometric features. They either increase the probability of indirect infection or inconvenience the users wearing masks. To tackle these problems, we propose a contactless behavioral biometric authentication mechanism that makes use of heterogeneous sensors. We conduct a preliminary study to demonstrate the feasibility of finger snapping as a natural biometric behavior. A prototype-SnapUnlock system was designed and implemented for further real-world evaluation in various environments. SnapUnlock adopts the principle of contrastive-based representation learning to effectively encode the features of heterogeneous readings. With the representations learned, enrolled samples trained with the classifier can achieve superior performances. We extensively evaluate SnapUnlock involving 50 participants in different experimental settings. The results show that SnapUnlock can achieve a 4.2% average false reject rate and 0.73% average false accept rate. Even in a noisy environment, our system performs similar results.

The Distribution of Emergency Logistics Centers under the COVID-19 Lockdown: The Case of Yangtze River Delta Area

The regular lockdown policy adopted in controlling the pandemic of COVID-19 has caused logistic disruptions in some areas that have a great impact on the living standards of residents and the production of enterprises. Given that the construction of emergency logistics centers is an effective solution, this paper takes the Yangtze River Delta Area (YRDA) of China as an example and discusses the site selection and material distribution of the emergency logistics centers in the region via a two-stage model. The first stage is the selection of candidate emergency logistics centers in the YRDA. A comprehensive evaluation index system is built with 4 primary and 15 secondary indexes to evaluate the logistic infrastructure capacity of the 41 cities in the YRDA. Further, through a principal component analysis, 12 cities are selected as candidate construction sites for emergency logistics centers. In the second stage, a biobjective site selection model with uncertain demand is established and calculated via the NSGA-II algorithm. According to the time sensitivity of emergency logistics, six cities are filtered from the optimal solution set, including Hefei, Hangzhou, Xuzhou, Wenzhou, Changzhou, and Shanghai, ensuring that all 41 cities are within their service scope.

Artificial intelligence-based CT metrics used in predicting clinical outcome of COVID-19 in young and middle-aged adults.

BACKGROUND: Currently, most researchers mainly analyzed coronavirus disease 2019 (COVID-19) pneumonia visually or qualitatively, probably somewhat time-consuming and not precise enough. PURPOSE: This study aimed to excavate more information, such as differences in distribution, density, and severity of pneumonia lesions between males and females in a specific age group using artificial intelligence (AI)-based computed tomography (CT) metrics. Besides, these metrics were incorporated into a clinical regression model to predict the short-term outcome. MATERIALS AND METHODS: The clinical, laboratory information and a series of HRCT images from 49 patients, aged from 20 to 50 years and confirmed with COVID-19, were collected. The volumes and percentages of infection (POIs) among bilateral lungs and each bronchopulmonary segment were extracted using uAI-Discover-NCP software (version R001). The POI in three HU ranges (i.e., <-300, -300-49, and ≥50 HU representing ground-glass opacity [GGO], mixed opacity, and consolidation) were also extracted. Hospital stay was predicted with several POI after adjusting days from illness onset to admission, leucocytes, lymphocytes, C-reactive protein, age, and gender using a multiple linear regression model. A total of 91 patients aged 20-50 from public database were selected. RESULTS: Right lower lobes had the highest POI, followed by left lower lobes, right upper lobes, middle lobes, and left upper lobes. The distributions in lung lobes and segments were different between the sexes. Men had a higher total POI and GGO of the lungs, but less consolidation than women in initial CT (all p < 0.05). The total POI, percentage of consolidation on initial CT, and changed POI were positively correlated with hospital stay in the model. A total of 91 patients aged 20-50 years in the public database were selected, and AI segmentation was performed. The POI of the lower lobes was obviously higher than that in the upper lobes; the POI of each segment of the right upper lobe in the males was higher than that in the females, which was consistent with the result of the 49 patients previously. CONCLUSION: Both men and women had characteristic distributions in lung lobes and bronchopulmonary segments. AI-based CT quantitative metrics can provide more precise information regarding lesion distribution and severity to predict clinical outcome.

Molecular basis of SARS-CoV-2 Omicron variant receptor engagement and antibody evasion and neutralization (preprint)

The SARS-CoV-2 Omicron variant exhibits striking immune evasion and is spreading globally at an unprecedented speed. Understanding the underlying structural basis of the high transmissibility and greatly enhanced immune evasion of Omicron is of high importance. Here through cryo-EM analysis, we present both the closed and open states of the Omicron spike, which appear more compact than the counterparts of the G614 strain, potentially related to the Omicron substitution induced enhanced protomer-protomer and S1-S2 interactions. The closed state showing dominant population may indicate a conformational masking mechanism of immune evasion for Omicron spike. Moreover, we capture two states for the Omicron S/ACE2 complex with S binding one or two ACE2s, revealing that the substitutions on the Omicron RBM result in new salt bridges/H-bonds and more favorable electrostatic surface properties, together strengthened interaction with ACE2, in line with the higher ACE2 affinity of the Omicron relative to the G614 strain. Furthermore, we determine cryo-EM structures of the Omicron S/S3H3 Fab, an antibody able to cross-neutralize major variants of concern including Omicron, elucidating the structural basis for S3H3-mediated broad-spectrum neutralization. Our findings shed new lights on the high transmissibility and immune evasion of the Omicron variant and may also inform design of broadly effective vaccines against emerging variants.

A Longitudinal and Geospatial Analysis of COVID-19 Tweets During the Early Outbreak Period in the United States (preprint)

Introduction: Local rates of COVID-19 cases and deaths may not accurately convey the variability in community-level concern about COVID-19 during the early outbreak period in the United States. Social media interaction may elucidate communication about COVID-19 in this critical period, during which communities may have formulated initial conceptions pertaining to the perceived gravity of the disease and potential behavioral strategies for prevention.Methods: Scripts were written to obtain tweets related to COVID-19 from Twitter. Using manually-annotated tweets about symptom-related concerns from a prior study, a machine learning classifier was applied to obtain a subset of tweets about concerns relating to COVID-19. The longitudinal relationship between these social media posts and active COVID-19 cases was assessed using linear and exponential regression. Changes in the geospatial clustering of tweets was assessed for the top five most populous cities in the United States.Results: Social media posts relating to COVID-19 concerns appeared more predictive of active COVID-19 cases as temporal distance increased. The distribution of tweets in New York City and Phoenix appeared concentrated in city centers, whereas tweets from other cities were more residential. Tweets from New York City became more highly concentrated, but the opposite trend was observed in tweets from Los Angeles.Conclusion: Clustering of social media posts about COVID-19 revealed discrepancies across major US cities. General concern about the COVID-19 pandemic may moderate the relationship between behavioral/environmental factors and COVID-19 transmission. The degree and modality of this moderating effect may differ across US areas.

Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study (preprint)

Key pointsO_ST_ABSQuestionC_ST_ABSHow do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID-19 patients at hospital admission perform? FindingsThis model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244. MeaningThe findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission. IMPORTANCEThe outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality for severely and critically ill patients. However, the availability of validated nomograms and the machine-learning model to predict severity risk and triage of affected patients is limited. OBJECTIVETo develop and validate nomograms and machine-learning models for severity risk assessment and triage for COVID-19 patients at hospital admission. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort of 299 consecutively hospitalized COVID-19 patients at The Central Hospital of Wuhan, China, from December 23, 2019, to February 13, 2020, was used to train and validate the models. Six cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020, were used to prospectively validate the models. MAIN OUTCOME AND MEASURESThe main outcome was the onset of severe or critical illness during hospitalization. Model performances were quantified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTSOf the 299 hospitalized COVID-19 patients in the retrospective cohort, the median age was 50 years ((interquartile range, 35.5-63.0; range, 20-94 years) and 137 (45.8%) were men. Of the 426 hospitalized COVID-19 patients in the prospective cohorts, the median age was 62.0 years ((interquartile range, 50.0-72.0; range, 19-94 years) and 236 (55.4%) were men. The model was prospectively validated on six cohorts yielding AUCs ranging from 0.816 to 0.976, with accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off values of the low, medium, and high-risk probabilities were 0.072 and 0.244. The developed online calculators can be found at https://covid19risk.ai/. CONCLUSION AND RELEVANCEThe machine learning models, nomograms, and online calculators might be useful for the prediction of onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Further prospective research and clinical feedback are necessary to evaluate the clinical usefulness of this model and to determine whether these models can help optimize medical resources and reduce mortality rates compared with current clinical practices.

Artificial intelligence-based CT metrics in relation with clinical outcome of COVID-19 in young and middle-aged adults (preprint)

Purposes: Currently, most researchers mainly analyzed COVID-19 pneumonia visually or qualitatively, probably somewhat time-consuming and not precise enough. This study aimed to excavate more information, such as differences in distribution, density, and severity of pneumonia lesions between males and females in a specific age group using artificial intelligence (AI)-based CT metrics. Besides, these metrics were incorporated into a clinical regression model to predict the short-term outcome.Methods: The clinical, laboratory information and a series of HRCT images from 49 patients, aged from 20 to 50 years and confirmed with COVID-19, were collected. The volumes and percentages of infection (POI) among bilateral lungs and each bronchopulmonary segment were extracted using uAI-Discover-NCP software (version R001). The POI in three HU ranges, (i.e. <-300, -300~49 and ≥50 HU representing ground-glass opacity (GGO), mixed opacity and consolidation), were also extracted. Hospital stay was predicted with several POIs after adjusting days from illness onset to admission, leucocytes, lymphocytes, c-reactive protein, age and gender using a multiple linear regression model.Results: Right lower lobes had the highest POI, followed by left lower lobes, right upper lobes, middle lobes and left upper lobes. The distributions in lung lobes and segments were different between the sexes. Men had a higher total POI and GGO of the lungs, but less consolidation than women in initial CT (all p<0.05). The total POI, percentage of consolidation on initial CT and changed POI were positively correlated with hospital stay in the model.Conclusion: Both men and women had characteristic distributions in lung lobes and bronchopulmonary segments. AI-based CT quantitative metrics can provide more precise information regarding lesion distribution and severity to predict clinical outcome.