A nanofiber Murray membrane with antibacterial properties for high efficiency oily particulate filtration

Particulate air pollution represented by PM2.5 is one of the biggest environmental challenges in the 21st century. Especially in 2020, the global outbreak of COVID-19 has brought new challenges to melt-blown filter materials, such as the attenuation of filtration efficiency with breathing, even no filtration effect for viruses as their smaller diameter, the sharp decline of filter efficiency after oily filtration cycle, and its limit in some explosive occasions. Here, using the diameter difference of polystyrene (PS), polyvinylidene fluoride (PVDF) and nylon 6(PA6) fibers, we report a multistage structure nanofiber membrane (PS/PVDF/PA6&Ag MSNMs) with high efficiency, low resistance and antibacterial effect by constructing gradient pore structure and introducing silver nanoparticles (Ag NPs), overcoming the above defects. The average filtration efficiency of PS/PVDF/PA6&Ag MSNMs for diisooctyl sebacate (DEHS) monodisperse particles from 0.2 μm to 4.9 μm was 99.88%, and the pressure drop was only 128 Pa. After repeated circulation for 100 times, the filtration efficiency and pressure drop remained stable. Above all, the antibacterial nanofiber membrane with high efficiency and low resistance has been preliminarily constructed, the future research will further focus on the performance after circulation. © 2023 Elsevier Ltd

Virus-ex-machina and Political Revolution

Slavoj Žižek's Pandemic!: Covid-19 Shakes the World is his latest intervention into the politics of virus and disaster. Pandemic! is the collection of essays written in the middle of viral outbreak, a philosophical and political reflection on the pandemic itself and its global implication in the post-viral era to come. Žižek recommends that what is to be done is not to panic or be disappointed at the irrevocability of the normal: that normality is actually what produced the viral outbreak. Throughout this book, Žižek is both a theorist of ecological justice insisting on the human culpability as the main culprit of the viral disaster and a political materialist who never tires himself of configuring the viability of a certain kind of communism beyond neoliberal capitalism in the post-viral global world. In this respect, virus should not be dealt with as enemy, malignant bacteria like cancer cells: the viral problem is totally cultural and up to the host organism. This problématique continues in his theoretical polemics with Han Byung-chul's myopic diagnosis of the hard reality under capitalism and Giorgio Agamben's pre-meditated, unrealistic critique of the possibility of biopolitics and "surveillance” in the state-driven quarantine and lockdown. Žižek dreams in this reflection on the signs of the times of viral catastrophe the appearance of new form of communism or "co-immunism,” which will be radically different from the Chinese authoritarian state-capitalism and the neoliberal re-normalization of disaster capitalism. © 2020 Journal of English Language and Literature. All rights reserved.

Learning upper patch attention using dual-branch training strategy for masked face recognition

In the context of pandemic, COVID-19, recognition of masked face images is a challenging problem, as most of the facial components become invisible. By utilizing prior information that mask-occlusion is located in the lower half of the face, we propose a dual-branch training strategy to guide the model to focus on the upper half of the face to extract robust features for Masked face recognition (MFR). During training, the features learned at the intermediate layers of the global branch are fed to our proposed attention module, named Upper Patch Attention (UPA), which acts as a local branch. Both branches are jointly optimized to enhance the feature extraction from non-occluded regions. We also propose a self-attention module, which integrates into the backbone network to enhance the interaction between the channels and spatial locations in the learning process. Extensive experiments on synthetic and real-masked face datasets demonstrate the effectiveness of our method. © 2022 Elsevier Ltd

An Ensemble Approach for the Diagnosis of COVID-19 from Speech and Cough Sounds

Mass COVID-19 infections detection has shown to be a very hard problem. In this work, we describe our systems developed to diagnose COVID-19 cases based on coughing sounds and speech. We propose a hybrid configuration that employs Convolution Neural Network (CNN), Time Delay Neural Network (TDNN) and Long Short-Term Memory (LSTM) for the extraction of coughing sound and speech embeddings. Moreover, the proposed framework utilizes SpecAugment-based on-the-fly data augmentation and multi-level statistics pooling for mapping frame level information into utterance level embedding. We employ classical support vector machines, random forests, AdaBoost, decision trees, and logistic regression classifiers for the final decision making, to determine whether the given feature is from a COVID-19 negative or positive patient. We also adopt an end-to-end approach employing ResNet model with a one-class softmax loss function for making positive versus negative decision over the high resolution hand-crafted features. Experiments are carried out on the two subsets, denoted as COVID-19 Speech Sounds (CSS) and COVID-19 Cough Sounds (CCS), from the Cambridge COVID-19 Sound database and experimental results are reported on the development and test sets of these subsets. Our approach outperforms the baselines provided by the challenge organizers on the development set, and shows that using speech to help remotely detect early COVID-19 infections and eventually other respiratory diseases is likely possible, which opens a new opportunity for a promising cheap and scalable pre-diagnosis way to better handle pandemics. © 2021, Springer Nature Switzerland AG.

Effects of simulation training on COVID-19 control ability and psychological states of nurses in a children's hospital.

OBJECTIVE: This study aims to discuss the effects of simulation training on improving the pre-examination, triage, prevention, and control of coronavirus disease 2019 (COVID-19), explain the psychological states of pre-examination and triage staff in general hospitals during the COVID-19 epidemic, and analyze relevant influencing factors. This study may serve as a reference of psychological consultation services to medical staff. SUBJECTS AND METHODS: This study included 60 nurses assigned in the pre-examination department, fever clinics, and isolation wards of a general hospital from January 26th to February 1st, 2020. Before assuming the posts, the nurses received simulation training on the clinical reception of suspected patients with COVID-19. Operation skills of the nurses before and after the training were assessed. RESULTS: The percent of passing significantly increased from 65% before the training to 98.33% after training (p<0.05). Training also significantly relieved the anxiety and depression of the nurses (p<0.05). CONCLUSIONS: Scenario-simulation training can increase the emergency abilities of pediatric nurses in the prevention and control of the COVID-19 epidemic and relieve the anxiety of nurses.

[Clinical characteristics and influencing factors of patients with novel coronavirus pneumonia combined with liver injury in Shaanxi region].

Objective: To understand the clinical characteristics, change of liver function, influencing factors and prognosis in hospitalized patients with coronavirus disease-19 (COVID-19) combined with liver injury. Methods: The general conditions, biochemical indicators of liver, blood clotting mechanism, routine blood test, UGT1A1 * 28 gene polymorphism and other data of 40 cases with COVID-19 admitted to the isolation ward of Tangdu Hospital were retrospectively analyzed. The clinical characteristics, influencing factors and prognosis of liver injury in patients with liver injury group and those with normal liver function group were compared. The mean of two samples in univariate analysis was compared by t-test and analysis of variance. The counting data was measured by χ(2) tests. The non-normal distribution measurement data were described by the median, and the non-parametric test was used. Statistically significant influencing factors were used as the independent variables in univariate analysis. Multiple logistic regression analysis was used to analyze the main influencing factors of liver injury. Results: Of the 40 cases, 25 were male (62.5%) and 15 were female (37.5%), aged 22 to 83 (53.87 ± 15.84) years. Liver injury was occurred in 22 cases (55%) during the course of the disease. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) level was initially increased (4.4 to 3.5 times of the normal value) along with decrease of albumin in the second week, and the difference was statistically significant (P < 0.001). Ten cases (43.5%) had highest abnormal total blood bilirubin (54.1 µmol/ L). There was no correlation between the increase in transaminase and the increase in total blood bilirubin (R = -0.006, P = 0.972). Three cases had prothrombin activity (PTA) of ≤50%, 10 cases had elevated FDP, and 13 cases had elevated D-dimer, all of whom were severe or critically ill. Liver function injury was more likely to occur in patients who used many types of drugs and large amounts of hormones (P = 0.002, P = 0.031), and there was no correlation with the TA6TA7 mutation in the UGT1A1 * 28 gene locus. Multiple regression analysis showed that the occurrence of liver injury was only related to critical illness. The liver function of all patients had recovered within one week after conventional liver protection treatment. Conclusion: COVID-19 combined with liver function injury may be due to the slight elevation of transaminase, mostly around the second week of the disease course. Severe patients have a higher proportion of liver injury, and critical type is an independent risk factor for liver injury.