Monintoring Student Depression by Mobile Devices with Autoencoder Networks

The timely assessment of mental health is difficult since we lack the objective measurements of symptoms, especially for the Covid-19 pandemic quarantined students. Fortunately, smart phones can capture the real-world data such as the GPS traces and the phone active time et.al that link the behavioral patterns to the mental health. However, recent studies are based on a very small size of participants and only collect fewer phone features, which means that the effective predicting models which require various features are hardly adopted. In this paper, we develop an android application to record multidimensional data of users as well as a PHQ-9 and a SAS questionary, and we distribute it to 176 college students to collect larger scale data when in quarantine period. To address the unprecise problem of handcrafted feature extraction, we design an autoencoder machine learning model to monitor the student mental health. Extensive experiments indicate that the performance of the proposed method improves its F-1 score for PHQ-9 and SAS by 5% and 6% to the state of the current studies, respectively. © 2023 IEEE.

Epidemiology and evolution of novel deltacoronaviruses in birds in central China

BIRDS : The variety and widespread of coronavirus in natural reservoir animals is likely to cause epidemics via interspecific transmission, which has attracted much attention due to frequent coronavirus epidemics in recent decades. Birds are natural reservoir of various viruses, but the existence of coronaviruses in wild birds in central China has been barely studied. Some bird coronaviruses belong to the genus of Deltacoronavirus. To explore the diversity of bird deltacoronaviruses in central China, we tested faecal samples from 415 wild birds in Hunan Province, China. By RT-PCR detection, we identified eight samples positive for deltacoronaviruses which were all from common magpies, and in four of them, we successfully amplified complete deltacoronavirus genomes distinct from currently known deltacoronavirus, indicating four novel deltacoronavirus stains (HNU1-1, HNU1-2, HNU2 and HNU3). Comparative analysis on the four genomic sequences showed that these novel magpie deltacoronaviruses shared three different S genes among which the S genes of HNU1-1 and HNU1-2 showed 93.8% amino acid (aa) identity to that of thrush coronavirus HKU12, HNU2 S showed 71.9% aa identity to that of White-eye coronavirus HKU16, and HNU3 S showed 72.4% aa identity to that of sparrow coronavirus HKU17. Recombination analysis showed that frequent recombination events of the S genes occurred among these deltacoronavirus strains. Two novel putative cleavage sites separating the non-structural proteins in the HNU coronaviruses were found. Bayesian phylogeographic analysis showed that the south coast of China might be a potential origin of bird deltacoronaviruses existing in inland China. In summary, these results suggest that common magpie in China carries diverse deltacoronaviruses with novel genomic features, indicating an important source of environmental coronaviruses closed to human communities, which may provide key information for prevention and control of future coronavirus epidemics

LCFNet: A Lightweight Context Fusion Network for COVID-19 Segmentation

The rapid and uncontrollable spread of COVID-19 has seriously threatened global public health. Rapid and accurate diagnosis of COVID-19 is the main key to control and manage the epidemic. COVID-19 segmentation can provide great insights to accelerate clinical decisions. COVID-19 segmentation based on deep learning method has attracted extensive research in the field of medical image analysis. However, most existing networks are heavyweight networks, which causes structural redundancy and expensive computational cost. Moreover, the segmentation problem of low-contrast COVID-19 image and the obscure boundary between the infected area and normal tissues affects accuracy of segmentation. To solve the problems, we propose a lightweight contextual information fusion network, LCFNet, for COVID-19 segmentation. We introduce a contextual information fusion strategy combining multiple global pyramid guidance (GPG) with scale-aware pyramid fusion (SAPF) module and deep supervision (DS) module, which can capture more fine-grained image features. We conduct experiments on two COVID-19 datasets. We perform the ablation studies, demonstrating the effectiveness of key components of the proposed method. Compared with traditional segmentation methods, LCFNet model match more consistently with the ground-truth boundary, which shows the superiority of the proposed method. Moreove, LCFNet has 1.68M parameters, which demonstrates the robustness of the proposed method. Our proposed method can further induce the number of model parameters. Compared with the state-of-the-art methods, the proposed approach has achieved significant improvements and is also superior to other segmentation methods. © 2022 SPIE.

Early Detection of SARS-CoV-2 Seroconversion in Humans with Aggregation-Induced Near-Infrared Emission Nanoparticle-Labeled Lateral Flow Immunoassay

An outbreak of coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses great threats to human health and the international economy. To reduce large-scale infection and transmission risk of SARS-CoV-2, a simple, rapid, and sensitive serological diagnostic method is urgently needed. Herein, an aggregation-induced emission (AIE) nanoparticle (AIE₈₁₀NP, lambda_em = 810 nm)-labeled lateral flow immunoassay was designed for early detection of immunoglobulin M (IgM) and immunoglobulin G (IgG) against SARS-CoV-2 in clinical serum samples. Using a near-infrared (NIR) AIE nanoparticle as the fluorescent reporter (lambda = 145 nm), the autofluorescence from the nitrocellulose membrane and biosample and the excitation background noise were effectively eliminated. After optimization, the limit of detection of IgM and IgG is 0.236 and 0.125 mug mL^-1, respectively, commensurate with that of the enzyme-linked immunosorbent assay (ELISA) (0.040 and 0.039 mug mL^-1). The sensitivity of the proposed AIE₈₁₀NP-based test strip for detecting IgM and IgG is 78 and 95% (172 serum samples), commensurate with that of ELISA (85 and 95%) and better than that of a commercial colloidal gold nanoparticle (AuNP)-based test strip (41 and 85%). Importantly, the time of detecting IgM or IgG with an AIE₈₁₀NP-based test strip in sequential clinical samples is 1-7 days after symptom onset, which is significantly earlier than that with a AuNP-based test strip (8-15 days). Therefore, the NIR-emissive AIE nanoparticle-labeled lateral flow immunoassay holds great potential for early detection of IgM and IgG in a seroconversion window period.

One-Year Real-Time Measurement of Black Carbon in the Rural Area of Qingdao, Northeastern China: Seasonal Variations, Meteorological Effects, and the COVID-19 Case Analysis

In this paper, we report the results obtained from one year of real-time measurement (i.e., from December 2019 to November 2020) of atmospheric black carbon (BC) under a rural environment in Qingdao of Northeastern China. The annual average concentration of BC was 1.92 +/- 1.89 mu g m(-3). The highest average concentration of BC was observed in winter (3.65 +/- 2.66 mu g m(-3)), followed by fall (1.73 +/- 1.33 mu g m(-3)), spring (1.53 +/- 1.33 mu g m(-3)), and summer (0.83 +/- 0.56 mu g m(-3)). A clear weekend effect was observed in winter, which was characterized by higher BC concentration (4.60 +/- 2.86 mu g m(-3)) during the weekend rather than that (3.22 +/- 2.45 mu g m(-3)) during weekdays. The influence of meteorological parameters, including surface horizontal wind speed, boundary layer height (BLH), and precipitation, on BC, was investigated. In particular, such BLH influence presented evidently seasonal dependence, while there was no significant seasonality for horizontal wind speed. These may reflect different roles of atmospheric vertical dilution on affecting BC in different seasons. The oBC/oCO ratio decreased with the increase of precipitation, indicative of the influence of below-cloud wet removal of BC, especially during summertime where rainfall events more frequently occurred than any of other seasons. The bivariate-polar-plot analysis showed that the high BC concentrations were mainly associated with low wind speed in all seasons, highlighting an important BC source originated from local emissions. By using concentration-weighted trajectory analysis, it was found that regional transports, especially from northeastern in winter, could not be negligible for contributing to BC pollution in rural Qingdao. In the coronavirus disease 2019 (COVID-19) case analysis, we observed an obvious increase in the BC/NO2 ratio during the COVID-19 lockdown, supporting the significant non-traffic source sector (such as residential coal combustion) for BC in rural Qingdao.

Plasminogen improves lung lesions and hypoxemia in patients with COVID-19.

BACKGROUND: Lungs from patients with coronavirus disease 2019 (COVID-19) have shown typical signs of acute respiratory distress syndrome (ARDS), formation of hyaline membrane mainly composed of fibrin and 'ground-glass' opacity. Previously, we showed plasminogen itself is a key regulator in fibrin degradation, wound healing and infection. AIM: We aimed to investigate whether plasminogen can improve lung lesions and hypoxemia of COVID-19. DESIGN: Thirteen clinically moderate, severe or critical COVID-19 patients were treated with atomization inhalation of freeze-dried plasminogen. METHODS: Levels of their lung lesions, oxygen saturation and heart rates were compared before and after treatment by computed tomography scanning images and patient monitor. RESULTS: After plasminogen inhalation, conditions of lung lesions in five clinically moderate patients have quickly improved, shown as the decreased range and density of 'ground glass' opacity. Improvements of oxygen saturation were observed in six clinically severe patients. In the two patients with critical conditions, the oxygen levels have significantly increased from 79-82% to 91% just about 1 h after the first inhalation. In 8 of 13 patients, the heart rates had slowed down. For the five clinically moderate patients, the difference is even statistically significant. Furthermore, a general relief of chest tightness was observed. CONCLUSION: Whereas it is reported that plasminogen is dramatically increased in adults with ARDS, this study suggests that additional plasminogen may be effective and efficient in treating lung lesions and hypoxemia during COVID-19 infections. Although further studies are needed, this study highlights a possible hope of efficiently combating this rapid epidemic emergency.