The Gift of Ancient Times: a Breakthrough in The Tracing of Modern Human Genes

The 2022 Nobel Prize in Physiology or Medicine was awarded to Swedish biologist Svante Pääbo for his decisive contribution to paleoanthropogenomics and human origins. There are various theories about the origin of human beings, and the current mainstream view is: out of the African doctrine. In other words, ancient humans had about three times of migrations. The first time was Homo erectus, the second was Neanderthals and Denisovans, and the third was the ancestors of modern humans. All migrated from Africa to Eurasia. While pioneering a new discipline, paleoanthropogenomics, Svante Pääbo has been refining the "Out of Africa Theory”. With the help of various biological techniques, he delved into the origin of human beings from the perspective of genomics and found that some genetic imprints from ancient humans were retained in our bodies. For example, the STAT2 gene and TLR gene associated with immunity, the EPAS1 gene that contributes to hypoxic respiration and the six genes of chromosome 3 are highly positively correlated with the incidence of COVID-19. This research means that we can go back to the root of certain diseases, rather than limiting our eyes to the genes themselves, and exploring where a gene comes from will be a new way of studying diseases. We summarized his innovations in related biotechnology in the process of research, his exploration of ancient humans based on mitochondrial and nuclear genes and related results, and introduced some genes derived from ancient humans and their related information. © 2022 Institute of Biophysics,Chinese Academy of Sciences. All rights reserved.

A Meta-framework for Spatiotemporal Quantity Extraction from Text

News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic. © 2022 Association for Computational Linguistics.

Pathogenic infection characteristics and risk factors for bovine respiratory disease complex based on the detection of lung pathogens in dead cattle in northeast China.

Bovine respiratory disease complex (BRDC) involves multiple pathogens, shows diverse lung lesions, and is a major concern in calves. Pathogens from 160 lung samples of dead cattle from 81 cattle farms in northeast China from 2016 to 2021 were collected to characterize the molecular epidemiology and risk factors of BRDC and to assess the major pathogens involved in bovine suppurative or caseous necrotizing pneumonia. The BRDC was diagnosed by autopsy, pathogen isolation, PCR, or reverse transcription-PCR detection, and gene sequencing. More than 18 species of pathogens, including 491 strains of respiratory pathogens, were detected. The positivity rate of bacteria in the 160 lung samples was 31.77%, including Trueperella pyogenes (9.37%), Pasteurella multocida (8.35%), Histophilus somni (4.48%), Mannheimia haemolytica (2.44%), and other bacteria (7.13%). The positivity rate of Mycoplasma spp. was 38.9%, including M. bovis (7.74%), M. dispar (11.61%), M. bovirhinis (7.94%), M. alkalescens (6.11%), M. arginini (0.81%), and undetermined species (4.68%). Six species of viruses were detected with a positivity rate of 29.33%, including bovine herpesvirus-1 (BoHV-1; 13.25%), bovine respiratory syncytial virus (BRSV; 5.50%), bovine viral diarrhea virus (BVDV; 4.89%), bovine parainfluenza virus type-3 (BPIV-3; 4.28%), bovine parainfluenza virus type-5 (1.22%), and bovine coronavirus (2.24%). Mixed infections among bacteria (73.75%), viruses (50%), and M. bovis (23.75%) were the major features of BRDC in these cattle herds. The risk analysis for multi-pathogen co-infection indicated that BoHV-1 and H. somni; BVDV and M. bovis, P. multocida, T. pyogenes, or Mann. haemolytica; BPIV-3 and M. bovis; BRSV and M. bovis, P. multocida, or T. pyogenes; P. multocida and T. pyogenes; and M. bovis and T. pyogenes or H. somni showed co-infection trends. A survey on molecular epidemiology indicated that the occurrence rate of currently prevalent pathogens in BRDC was 46.15% (6/13) for BoHV-1.2b and 53.85% (7/13) for BoHV-1.2c, 53.3% (8/15) for BVDV-1b and 46.7% (7/15) for BVDV-1d, 29.41% (5/17) for BPIV-3a and 70.59% (12/17) for BPIV-3c, 100% (2/2) for BRSV gene subgroup IX, 91.67% (33/36) for P. multocida serotype A, and 8.33% (3/36) for P. multocida serotype D. Our research discovered new subgenotypes for BoHV-1.2c, BRSV gene subgroup IX, and P. multocida serotype D in China's cattle herds. In the BRDC cases, bovine suppurative or caseous necrotizing pneumonia was highly related to BVDV [odds ratio (OR) = 4.18; 95% confidence interval (95% CI): 1.6-10.7], M. bovis (OR = 2.35; 95% CI: 1.1-4.9), H. somni (OR = 8.2; 95% CI: 2.6-25.5) and T. pyogenes (OR = 13.92; 95% CI: 5.8-33.3). The risk factor analysis found that dairy calves <3 mo and beef calves >3 mo (OR = 5.39; 95% CI: 2.7-10.7) were more susceptible to BRDC. Beef cattle were more susceptible to bovine suppurative or caseous necrotizing pneumonia than dairy cattle (OR = 2.32; 95% CI: 1.2-4.4). These epidemiological data and the new pathogen subgenotypes will be helpful in formulating strategies of control and prevention, developing new vaccines, improving clinical differential diagnosis by necropsy, predicting the most likely pathogen, and justifying antimicrobial use.

Implementation and Evaluation of Exercise and Performance Learning Assistant System Platform for Yoga Pose Practices Using Node.js

To support people enjoying exercises or learning performances at home such as Yoga poses or traditional dances, we have developed Exercise and Performance Learning Assistant System (EPLAS). In EPLAS, the user can practice it by following the model performance of the instructor in the video. Then, the key poses of the user will be rated as the feedback by comparing the keypoints between the instructor and the user that are extracted by OpenPose. We have provided the platform using the web browser. However, OpenPose needs to run on OS directly. We developed the EPLAS platform in this paper using Node.js as the web application server. When a user requests it via the browser, the rating function is immediately executed on the server. Furthermore, we employ use Docker to simply distribute the platform. We invited 10 Okayama University students to do various Yoga positions using the EPLAS for assessments, and the results verified the success of the deployment. © 2022 IEEE.

Critique of “ Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team from Peking University

Ankit Srivastava et al. [1] proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with Centos-compatible Oracle Linux. Our experiments, covering the same three algorithms of ramBLe, evaluate its strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions in the paper and propose our explanation of the differences. IEEE

An Implementation of Exercise and Performance Learning Assistant System Platform Using Node.js

To assist practicing exercises or learning performances by themselves at home under pandemic of COVID-19, we have studied the Exercise and Performance Learning Assistant System (EPLAS) and implemented the user interface using a web browser for practicing Yoga poses. EPLAS offers a video content of model actions by an instructor to be followed by the user, and automatically takes the photos of the critical poses. Then, it rates each critical pose by differences of body key points extracted by OpenPose between the user photo and the instructor one. In this paper, we implement the EPLAS platform using Node.js as the web application server to run the rating function automatically on the server when it is requested on the browser. For evaluations, we asked 10 students in Okayama University to practice 10 Yoga poses using the interface, and confirmed its correctness. © 2022 IEEE.

A Meta-framework for Spatiotemporal Quantity Extraction from Text

News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic.(1)

DEA-UNet: a dense-edge-attention UNet architecture for medical image segmentation

Classical UNet with an encoder and decoder structure and its variants perform very well in the field of medical image segmentation. They have a key similarity of a skip-connection, which combines deep, semantic, and coarse-grained feature maps from the decoder subnetwork with shallow, low-level, and fine-grained feature maps from the encoder subnetwork. We noted that, in many cases in medical image segmentation, the boundary of the segmentation target is fuzzy and complex. Traditional UNet cannot accurately segment these details. The main purpose is to solve the fuzzy boundary problem in medical image segmentation. To solve this problem, we combine the advantages of previous models and improve them and propose a new dense edge attention U-type network (DEA-UNet) for medical image segmentation. Starting from the traditional UNet, we modified the concat and skip-connection operations in the latter part. We designed an edge guidance module that fused the features of all layers. Starting from the upsample at the deepest layer, the reverse attention module was used step by step to extract features from high to low, and the edge guidance module was combined with it, so each layer could fully extract boundary details that were difficult to be noticed by previous models, thus solving the problem of the fuzzy boundary of the lesion region. We conducted experiments on two kinds of medical datasets (chest CT and colonoscopic polyp) and compared them with the traditional network. The experimental results showed that our DEA-UNet performed better in multiple indicators. In the segmentation of coronavirus disease-19 images, the results indicate that DEA-UNet has a Dice of 74.6%, sensitivity (Sen) of 70.8%, specificity (Spe) of 96.7%, structural measure (S-alpha) of 0.766%, enhanced-alignment measure (E-phi) of 0.910%, and mean absolute error (MAE) of 0.062%. Our DEA-UNET is 31%, 16%, 3%, and 0.7 and higher than the traditional medical segmentation model UNet, UNet++, the last model Few-shot UNet, and Inf-Net in Dice. In the segmentation of colonoscopic polyp dataset Kvasir, the results indicate that DEA-UNet has a Dice of 95%, structural measure (S-alpha) of 0.953%, enhanced-alignment measure (E-phi) of 0.974%, and MAE of 0.015%. Our DEA-UNet is 13%, 13%, 23%, and 5% higher than the traditional medical segmentation model UNet, UNet++, the last model SFA, and PraNet in Dice. In other evaluation metrics, our DEA-UNet also performed better. When designing DEA-UNet, we also consider the balance between model size and prediction accuracy. Experiments show that, by proper pruning, we can greatly reduce the number of model parameters while maintaining the accuracy of prediction results with little change. This proves that our DEA-UNET has great potential in the field of medical image segmentation.

STUDY of ONLINE SHOPPING BEHAVIOR DIFFERENCES between GOAL-DIRECTED SEARCH and EXPLORATORY BROWSING in INTERMEDIATE CHOICE LIST on E-COMMERCE

With the development of the internet and shipping, and also the appearance of COVID-19, the growth of E-commerce is rising in a fast way. People change their shopping behavior from offline to online. It means there is a significant change in customers' shopping process. Lots of scholars did research about it, such as from AIDMA [1] to AISAS [2], and also from Buyer Decision Process [3] to Dynamic Buyer Decision Process [4]. Both of them talk about how customers change their behavior and process of shopping in the digital generation. So it's necessary to know clearly how customers do and think to make the serve, user interface, and user experience in websites better. But the study found that most of those existing models are for "goal-directed search"shopping (shopping with purpose). But in fact, "exploratory browsing"shopping is rising these days, it's better to know more about it, so this study focuses on the process of how people do online shopping in "goal-directed search"shopping and "exploratory browsing"shopping. Besides, this study also found because there is too much information on the internet now, people will make the two phases of the decision in the process of online shopping [5], and they need to mark and record some product information in the middle of these two phases. Hence, they need a space to save this information is called "Intermediate Choice List(ICL)"[6]. Therefore, this study also explored how ICL takes a hand in the whole online shopping process. At the end of the study built a complete shopping process model for future use in E-commerce website design. © 2022 ACM.

Poke Not Prod: Improving Quality of Life Through Non-Invasive Rejection Surveillance for Heart Transplant Recipients

Purpose: Gene expression profiling (GEP) and donor-derived cell-free DNA (dd-cfDNA) provide effective non-invasive rejection surveillance for heart transplant (HT) recipients with a trend toward improved quality of life. During the COVID-19 pandemic, rejection monitoring and titration of mediations in HT patients was difficult due to limited health-care resources for endomyocardial biopsy (EMBx). This is the first Canadian study to assess non-invasive rejection surveillance in improving patient satisfaction and reducing anxiety during HT rejection screening. Methods: Adult HT recipients, at least 6 months post-transplant, were enrolled to have surveillance EMBx replaced by non-invasive rejection testing with GEP and dd-cfDNA. Patients with multiorgan transplant, dialysis, or high rejection risk (recent acute cellular rejection ≥ grade 2R, new graft dysfunction, or heart failure) were excluded. All patients completed the Medical Outcomes Study 12-item Short Form Health Survey (SF-12) and a patient satisfaction survey. Thematic analysis was performed for open-ended responses. Results: Out of 90 patients screened, 31 had their routine EMBx replaced by non-invasive rejection testing. Based on test results, 89% of EMBx were safely eliminated. On the SF-12, participants had a median physical health score of 43 (40-53) and mental health score of 53 (46-58) out of 100. Patients’ self-reported satisfaction was 90%. Median self-reported anxiety score prior to EMBx was 50 (10-71) versus 2.5 (0-7.5) out of 100 prior to GEP/dd-cfDNA. Four codes (“emotions” (pain, anxiety, fear), “time”, “biopsy”, “accuracy”) were used to uncover two themes of “Superiority to Biopsy” and “Mental or Physical Stress”. Patients described feeling much more satisfied and less emotionally distressed with the non-invasive screening compared to EMBx. HT patients reported less fear and anxiety, reduced pain, and enjoyed the simplicity of non-invasive testing. Conclusion: Non-invasive rejection surveillance screening can positively impact patients’ mental health. In this study, non-invasive rejection surveillance eliminated the recovery time and risk of an invasive procedure for HT recipients while reducing anxiety, improving patient satisfaction, and providing an alternative way to screen patients during a period of limited resources due to a global pandemic.

Teacher Agency in Adapting to Online Teaching during COVID-19: A Case Study on Teachers of Chinese as an Additional Language in Macau

The COVID-19 university closure forced a rapid transition and adaptation to online teaching. This paper reports on a case study that examined teacher agency in response to online teaching from February to September 2020. In the study we collected multiple data from three teachers of Chinese as an additional language, including semi-structured interviews, institutional documents, and field notes, to investigate their exercise of agency in adapting to online teaching. The analysis revealed that the participants displayed strong agency to build digital competence and develop student-centered pedagogy at different stages. At the same time, the shift to online from classroom-based teaching allowed them an opportunity to transform existing practices and seek innovative pedagogy, such as a hybrid model blending asynchronous and synchronous online teaching. This study also suggests the influence of flexible and collaborative institutional culture and teacher professional digital competence in shaping the participants' agency in addressing the diverse challenges of online teaching. These findings offer insights into the value of an agency-oriented approach to professional learning and development in educational change. Educational stakeholders should pay more attention to the dynamic interaction between educational institutional systems and teacher agentic practice.

Structural basis for neutralization of SARS-CoV-2 and SARS-CoV by a potent therapeutic antibody

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented public health crisis. There are no approved vaccines or therapeutics for treating COVID-19. Here we report a humanized monoclonal antibody, H014, that efficiently neutralizes SARS-CoV-2 and SARS-CoV pseudoviruses as well as authentic SARS-CoV-2 at nanomolar concentrations by engaging the spike (S) receptor binding domain (RBD). H014 administration reduced SARS-CoV-2 titers in infected lungs and prevented pulmonary pathology in a human angiotensin-converting enzyme 2 mouse model. Cryo-electron microscopy characterization of the SARS-CoV-2 S trimer in complex with the H014 Fab fragment unveiled a previously uncharacterized conformational epitope, which was only accessible when the RBD was in an open conformation. Biochemical, cellular, virological, and structural studies demonstrated that H014 prevents attachment of SARS-CoV-2 to its host cell receptors. Epitope analysis of available neutralizing antibodies against SARS-CoV and SARS-CoV-2 uncovered broad cross-protective epitopes. Our results highlight a key role for antibody-based therapeutic interventions in the treatment of COVID-19.

Early Indicators of Human Activity During COVID-19 Period Using Digital Trace Data of Population Activities

The spread of pandemics such as COVID-19 is strongly linked to human activities. The objective of this article is to specify and examine early indicators of disease spread risk in cities during the initial stages of outbreak based on patterns of human activities obtained from digital trace data. In this study, the Venables distance ((Formula presented.)) and the activity density ((Formula presented.)) are used to quantify and evaluate human activities for 193 United States counties, whose cumulative number of confirmed cases was greater than 100 as of March 31, 2020. Venables distance provides a measure of the agglomeration of the level of human activities based on the average distance of human activities across a city or a county (less distance could lead to a greater contact risk). Activity density provides a measure of level of overall activity level in a county or a city (more activity could lead to a greater risk). Accordingly, Pearson correlation analysis is used to examine the relationship between the two human activity indicators and the basic reproduction number in the following weeks. The results show statistically significant correlations between the indicators of human activities and the basic reproduction number in all counties, as well as a significant leader-follower relationship (time lag) between them. The results also show one to two weeks’ lag between the change in activity indicators and the decrease in the basic reproduction number. This result implies that the human activity indicators provide effective early indicators for the spread risk of the pandemic during the early stages of the outbreak. Hence, the results could be used by the authorities to proactively assess the risk of disease spread by monitoring the daily Venables distance and activity density in a proactive manner. © Copyright © 2021 Gao, Fan, Yang, Lee, Li, Maron and Mostafavi.

Reflections on the Reform and Development of Higher Pharmacy and Pharmaceutical Education in China Based on Sudden Major Public Health Events

OBJECTIVE: To summarize and suggest experience and lessons proposed to promote the conjunctive development of education, so as to better play the role of pharmaceutical talents in responding to major public health emergencies. METHODS: Using expert method and literature method, online expert opinion consultation was conducted among experts from 50 pharmaceutical colleges and universities in China, and the analysis and research were carried out by combining expert opinions and literature content. RESULTS: The COVID-19 public health event sheds light on practical problems related to industry and education, including drug discovery and reserves existing weak links, pharmacists' clinical pharmacy practice ability insufficient, lack of the public health culture, as well as the pharmaceutical talents training system not conformed to the core principles of healthy China strategy, and pharmaceutical colleges and universities' activities of teaching and scientific research disconnected with innovation driven development strategy. CONCLUSION: The pharmaceutical colleges and universities should firmly establish a patient-centered talent training concept, strengthen the students' political and professional ethics qualities, continue to improve the pharmaceutical talents' knowledge structure, and deepen the collaborative innovation and cooperative education mechanism. Emphasis should be laid on continuously improving the quality of talents training and laying a solid foundation for national medical innovation in the long run.

A proposal of exercise and performance learning assistant system for self-practice at home

Due to pandemic spreads of COVID-19 and increasing populations of seniors, exercises or performance practices at home have become important to maintain healthy lives around the world. World Health Organization (WHO) has announced the physical health determines the Quality of Life (QoL) of a human. Unfortunately, a lot of people have no exercise and may be in unhealthy conditions. In this paper, we propose an Exercise and Performance Learning Assistant System (EPLAS) to assist people practicing exercises or learning performances by themselves at home. EPLAS adopts inexpensive devices and free software for low-cost implementation. It offers a video content of model actions by an instructor to be followed by the user, where the reaction is rated by comparing the feature points of the human bodies extracted by an open-source software OpenPose. For evaluations, we conduct experiments of applying EPLAS with five Yoga poses to 41 persons in Indonesia, Japan, and Taiwan, and confirm the effectiveness of the proposal. © 2020 ASTES Publishers. All rights reserved.

Repeated Nucleic Acid Tests Are Necessary for Early Diagnosis of Atypical COVID-19: A Case Report

In Wuhan, China, the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported on December 8, 2019. The patient's symptoms included fever, coughing and breathing difficulties. According to the sixth China version of 2019 coronavirus disease (COVID-19) diagnostic criteria, some patients with COVID-19 may present atypical symptoms and have negative nucleic acid tests (NATs), possibly leading to misdiagnosis and viral transmission. Our patient was a 29-year-old woman who complained of a three-day history of nasal obstruction, and no fever, coughing or breathing difficulties were noted. Physical examination revealed no obvious signs of pneumonia. On January 16, 2020, the patient flew from Wuhan to Germany for a business trip and returned to Shanghai on January 28, a passenger on her flight was tested positive for SARS-CoV-2 later. Although two consecutive NATs performed at an interval of 24 h were negative, considering her direct contact with a SARS-CoV-2-infected individual, a 64-slice computed tomography (CT) scan showed a few scattered ground-glass nodules in the left lung, suggesting possible viral pneumonia. Given the clinical characteristics, epidemiological records, CT findings and a third positive NAT, our patient was diagnosed with COVID-19. The combination of history of epidemiology, clinical symptom, lung CT scan and routine blood test will improve the clinical diagnosis of asymptomatic COVID-19, but the early diagnosis of COVID-19 can be confirmed only by the repeated NATs.