A Novel InSe-FET Biosensor based on Carrier-Scattering Regulation Derived from the DNA Probe Assembly-Determined Electrostatic Potential Distribution

Low-dimensional material field-effect transistor (FET)-based biosensors have the advantages of high sensitivity, high detection speed, small size, low cost, and excellent compatibility with integrated circuits. The sensing mechanism is extremely important in the design and fabrication of high-performance FET biosensors in practical applications. Herein, an InSe-FET biosensor is designed and its dominant sensing mechanism during detection and (mi)RNA detection performance are investigated. Finite element analysis reveals the electrostatic potential distribution in the InSe channel with DNA probe assembly showing that Coulomb scattering is the dominant sensing mechanism for carrier scattering-sensitive InSe. The simulation and experimental results indicate that carriers in InSe are extremely sensitive to the scattering of surface impurities because of their small electron mass. The firstly reported back-gate bias working mode of an InSe-FET biosensor has a linear relationship with an extra-large detectable range of 1 fM-10 nM, high specificity for identifying 1-nucleotide polymorphisms, and excellent repeatability and reusability. The detection of biomarker miRNAs in clinical serum samples and specific RNA in SARS-CoV-2 pseudovirus samples indicate promising applications of InSe-FET biosensors in critical disease screening and the fast diagnoses of infectious diseases. This study can be useful for the design and fabrication of high-performance FET biosensors.

MaskOut: A Data Augmentation Method for Masked Face Recognition

Deep learning methods have achieved great performances in face recognition. However, the performances of deep learning methods deteriorate in case of wearing a mask. Recently, due to the world-wide COVID-19 pandemic, masked face recognition attracts more attention. It is non-trivial and urgent to improve the performances in masked face recognition. In this work, a simple and effective data augmentation method, named MaskOut, is proposed. MaskOut replaces a random region below the nose of a face with a random mask template to mask out original face features. Our method is computing and memory efficient and convenient to combine with other methods. The experimental results show that the performances in masked face recognition are improved by a large margin with MaskOut. Besides, we construct a real-life masked face dataset, named MCPRL-Mask, to evaluate the performance of masked face recognition models.

PROJECT PORTFOLIO SELECTION BASED ON MULTI-PROJECT SYNERGY

To date, the selection of a project portfolio that maximises the decision-making outcome remains essential. However, existing research on project synergy has mainly focused on two projects, while there are multiple projects in some cases. Two kinds of synergies among multiple projects are proposed. First, multiple projects must be selected together, in order to produce synergy. Second, some projects depend on synergy with other projects, leading to a synergetic increase in performance. Furthermore, we present strategic synergy, with benefits, resources, and technology, which is quantified for a procurement project concerning a COVID-19 pandemic recovery plan. A design structure matrix is used to describe the technology diffusion among the projects. Then, strategic alignment is utilised to measure the strategic contribution of projects. Next, a portfolio selection model considering uncertainty is established, based on the strategic utility. Finally, our results indicate that selecting projects considering multi-project synergy is more advantageous.

Recognizing COVID-19 positive: through CT images

Since the beginning of 2020, the COVID-19 infection caused by a virus called SARS-CoV-2 has spread rapidly around the world. Recently, researchers and public health officials from different disciplines studied the pathogenesis of SARS CoV 2 and found that the imaging pattern of patients with SARS CoV 2 infection had been observed on computed tomography (CT). This article is to measure whether the traditional deep learning algorithm can rely solely on lung CT images as a basis for the presence of new coronary pneumonia. Using the classic deep learning algorithms of AlexNet, VGG, ResNet, SqueezeNet and DenseNet as the basis, using the lung CT data of patients with new coronary pneumonia published on Kaggle as training and testing, and testing whether the pretraining migration learning method will Make the algorithm get a higher accuracy rate. According to the results, the accuracy rate of all algorithms without the pre-training model is more than 70%, and the accuracy rate of some algorithms reaches 82%. It shows that the deep learning algorithm, driven by a small amount of data, can not be completely used as a means of identification, but the algorithm using deep learning can help doctors identify. Moreover, with the increase of data, a more optimized learning algorithm can also obtain higher accuracy.

[Impact of the COVID-19 epidemic on anxiety among the elderly in community].

Objective: To compare the prevalence of anxiety among old people before and during the COVID-19 epidemic in China, and to provide scientific evidence for psychological intervention of the elderly during public health emergencies. Methods: In 2019, the National Psychological Care Project for the Elderly was launched, covering 818 communities across the country, and 188 407 subjects received psychological assessment. In April and May 2020, a convenient sample of 6 467 aged 65 and above subjects were followed up on the anxiety status and its influencing factors during the epidemic period by using structurized questionnaire. Data collection and management were carried out using the national elderly psychological care project data collection platform. McNemar test was used to compare the difference of the prevalence of anxiety among elderly before (October 2019 to January 23, 2020) and during the epidemic (April-May 2020). The difference of the prevalence of anxiety among elderly with different characteristics was compared by chi square test. The influencing factors of anxiety before and during the epidemic situation were analyzed by multivariate logistic regression model. Results: The prevalence of anxiety symptoms in the elderly population was 4.95% (95%CI: 4.42%-5.48%) before the outbreak of COVID-19, and 10.10% (95%CI: 9.36%-10.83%) during the epidemic which was twice as high as before the outbreak. The difference was statistically significant (P<0.05). Multivariate logistic regression analysis showed that the risk factors of anxiety symptoms before the outbreak were with one underlying disease (OR=1.57, 95%CI: 1.05-2.37), with two or more underlying diseases (OR=3.10, 95%CI: 2.13-4.51), and the protective factors were with hobbies, good relationship between children, good relationship with spouse, positive aging attitude and good psychological resilience (all P<0.05). The risk factors of developing anxiety symptoms during the epidemic were living in rural areas (OR=1.77, 95%CI: 1.42-2.20), participating in social activities regularly (OR=1.23, 95%CI: 1.02-1.48), having a good relationship with friends (OR=1.42, 95%CI: 1.11-1.82) and were quarantined or people around were quarantined for medical observation (OR=2.80, 95% CI: 1.90-4.13). Conclusion: The COVID-19 epidemic leads to a double increase in anxiety among the elderly. We should pay more attention to the psychological state of the elderly in rural area and who is being quarantined or people around being quarantined for medical observation.

[Suggestions for thoracic surgery clinical practice in non-epidemic area of 2019 coronavirus disease].

In this paper, the mechanism of destroying human alveolar epithelial cells and pulmonary tissue by 2019 novel coronavirus (2019-nCoV) was discussed firstly. There may be multiple mechanisms including killing directly the target cells and hyperinflammatory responses. Secondly, the clinical features, CT imaging, short-term and long-term pulmonary function damage of the 2019 coronavirus disease (COVID-19) was analyzed. Finally, some suggestions for thoracic surgery clinical practice in non-epidemic area during and after the epidemic of COVID-19 were provided, to help all the thoracic surgery patients receive active and effective treatment.

Tocilizumab in the treatment of a critical COVID-19 patient: a case report.

In December 2019, Coronavirus disease 2019 (COVID-19) emerged in Wuhan and rapidly spread throughout China and the rest of the world. COVID-19 is currently a global pandemic. There are cytokine storms in severe COVID-19 patients. Interleukin-6 plays an important role in cytokine storm. Tocilizumab is a blocker of interleukin-6 receptor, which is likely to become an effective drug for patients with severe COVID-19. Here, we reported a case in which tocilizumab was effective for a critical COVID-19 patient.

Diagnosis and treatment strategies for acute biliary infection during the epidemic period of COVID-19

Acute biliary infection is one of the common causes of acute abdomen, easily causes severe infection and even death. The reasonable treatment of the acute phase is very important. The sudden outbreak of corona virus disease (COVID-19) has posed severe challenges to the country's economic and social life, and has also led to an extreme shortage of medical resources. The diagnosis and treatment of acute biliary infections disease also face challenges. In order to control the epidemic of infectious diseases, we write this article from the perspective of prevention and controlling of COVID-19, referring to the Tokyo Guidelines 2018 to express our views on the diagnosis and treatment strategies for acute biliary infection, hoping to do our best to prevent and control the epidemic of COVID-19.