The Analysis of Oral and Fecal Virome Detects Multiple Novel Emerging Viruses in Snakes

Wild animals are considered reservoirs for emerging and reemerging viruses, such as the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous studies have reported that bats and ticks harbored variable important pathogenic viruses, some of which could cause potential diseases in humans and livestock, while viruses carried by reptiles were rarely reported. Our study first conducted snakes' virome analysis to establish effective surveillance of potential transboundary emerging diseases. Consequently, Adenoviridae, Circoviridae, Retroviridae, and Parvoviridae were identified in oral samples from Protobothrops mucrosquamatus, Elaphe dione, and Gloydius angusticeps based on sequence similarity to existing viruses. Picornaviridae and Adenoviridae were also identified in fecal samples of Protobothrops mucrosquamatus. Notably, the iflavirus and foamy virus were first reported in Protobothrops mucrosquamatus, enriching the transboundary viral diversity in snakes. Furthermore, phylogenetic analysis revealed that both the novel-identified viruses showed low genetic similarity with previously reported viruses. This study provided a basis for our understanding of microbiome diversity and the surveillance and prevention of emerging and unknown viruses in snakes.

The COVID-19 Pandemic and the International Geographical Union

Having broken out in late 2019, COVID-19 has resulted in a once-in-a-century health emergency that has rapidly evolved into a global socio-economic crisis. As of March 2022, more than 450 million people were infected by the SARS-CoV-2 virus, the cause of COVID-19, resulting in more than six million deaths (WHO, Coronavirus disease (COVID-19) situation dashboard, 2022). The medical systems of many countries have been stretched to the verge of collapse and more than half of the global labor force has stood down. Not only has the pandemic doubled the number of people at risk of starvation to 270 million (Nature, 589:329-330, 2021), but it also pushed 100 million people into poverty in 2020, triggering the worst global recession since World War II (Blake and Wadhwa, 2020 year in review: the impact of COVID-19 in 12 charts, 2020), and increasing the risk of exposure to other pandemics related to ecosystem degradation (IPBES, Workshop report on biodiversity and pandemics of the intergovernmental platform on biodiversity and ecosystem services. Retrieved from Bonn, Germany, 2020;Yin et al., Geogr Sustain 2(1):68-73, 2021). The normal functioning of many organizations has also been hampered by the pandemic and disruptions to the global travel and tourism industry have been unprecedented. By way of an example, travel restrictions led to the postponement of the 2020 34th International Geographical Congress to the following year and, ultimately, the decision was made to transition to an entirely online format for the event. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Morphological Analysis of a Collapsing Cavitation Bubble near a Solid Wall with Complex Geometry

Featured Application: Collapsing cavitation bubbles can be used in material surface cleaning, the medical field, and so on. By adjusting the micro-jet intensity of the collapsing bubbles, the cavitation phenomenon can be employed to clean irregular material surfaces, such as sections, cracks, and vegetable leaves. In the medical field, cavitation bubbles can be used as microbubble contrast agents for ultrasound diagnostic imaging or vehicles for drug or gene delivery. The growth and violent collapse of cavitation bubbles can also be employed in sterilization or killing viruses such as COVID-19. The interaction mechanism between the cavitation bubble and a solid wall is a basic problem in bubble collapse prevention and application. In particular, when bubble collapse occurs near solid walls with arbitrarily complex geometries, it is difficult to efficiently establish a model and quantitatively explore the interaction mechanism between bubbles and solid walls. Based on the advantages of the lattice Boltzmann method, a model for cavitation bubble collapse close to a solid wall was established using the pseudopotential multi-relaxation-time lattice Boltzmann model. Solid walls with arbitrarily complex geometries were introduced in the computational domain, and the fractal dimension was used to quantify the complexity of the solid wall. Furthermore, owing to the lack of periodicity, symmetry, spatial uniformity and obvious correlation in this process, the Minkowski functionals-based morphological analysis method was introduced to quantitatively describe the temporal evolution of collapsing bubble profiles and acquire effective information from the process. The interaction mechanism between the bubble and solid wall was investigated using evolutions of physical fields. In addition, the influences of the solid walls' surface conditions and the position parameter on collapsing bubbles were discussed. These achievements provide an efficient tool for quantifying the morphological changes of the collapsing bubble. © 2023 by the authors.

READMISSION RATES AMONG PATIENTS AMONG PATIENTS EXPERIENCING HOMELESSNESS IN AN INTEGRATED HEALTHCARE SYSTEM

BACKGROUND: Homelessness is a significant public health concern in the United States and is an important risk factor for poor health outcomes. There is limited data regarding the hospital utilization by this vulnerable population, especially inmanaged care settings. Historically, it has been difficult to identify patients experiencing homelessness at the population health level. In 2019, due to the passage of state law SB1152, hospitals across California now need standardized documentation policies for patients experiencing homelessness. This study assessed hospital readmission rates among hospitalized patients experiencing homelessness as identified through documentation in the electronic health record (EHR) as compared to the general hospitalized population within a large integrated health system. METHODS: This was a retrospective cohort study following adult patients (age≥18 years) hospitalized in Kaiser Permanente Northern California (KPNC) Medical Centers between 1/1/2019 through 12/1/2020. Patients were identified as homeless or housing insecure if they had SB1152 documentation, a homeless diagnosis code, or address history indicating homelessness within the extensive integrated KPNC EHR. A control group was created using 1:2 propensity score matching using Elixhauser comorbidities and demographics. Sensitivity analyses were performed to compare patients with an index hospitalization occurring during the COVID-19 shelter-in-place period between March 2020 and December 2020 with those whose index hospitalization occurred before this period. The primary outcome was 30-day readmission rate to the hospital or ED, and secondary outcomes included length of index hospitalization, and time to inpatient (IP) readmission. RESULTS: A total of 12,909 patients were included with 4,303 patients in the homeless group. Patients experiencing homelessness had increased odds for any 30-day readmission (OR 1.59;95% CI: 1.44-1.76), for inpatient readmission (OR 1.36;95% CI: 1.17-1.57), for ED readmission (OR 1.63;95% CI: 1.47-1.80), and had longer stays during their index hospitalization (IRR 1.12;95% CI: 1.04-1.21). The COVID-19 shelter-in-place period was not associated with any changes in the primary or secondary outcomes studied. CONCLUSIONS: Patients experiencing homelessness are at an increased risk for readmissions and longer hospitalizations compared to the general hospitalized population. Documentation of housing status following SB1152 has improved the ability to study hospital utilization among patients experiencing homelessness. Understanding patterns of hospital utilization in this vulnerable group will help providers to identify timely points of intervention for further social and healthcare support.

Evolutionary trend of SARS-CoV-2 inferred by the homopolymeric nucleotide repeats

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the current global COVID-19 pandemic, in which millions of lives have been lost. Understanding the zoonotic evolution of the coronavirus may provide insights for developing effective vaccines, monitoring the transmission trends, and preventing new zoonotic infections. Homopolymeric nucleotide repeats (HP), the most simple tandem repeats, are a ubiquitous feature of eukaryotic genomes. Yet the HP distributions and roles in coronavirus genome evolution are poorly investigated. In this study, we characterize the HP distributions and trends in the genomes of bat and human coronaviruses and SARS-CoV-2 variants. The results show that the SARS-CoV-2 genome is abundant in HPs, and has augmented HP contents during evolution. Especially, the disparity of HP poly-(A/T) and ploy-(C/G) of coronaviruses increases during the evolution in human hosts. The disparity of HP poly-(A/T) and ploy-(C/G) is correlated to host adaptation and the virulence level of the coronaviruses. Therefore, we propose that the HP disparity can be a quantitative measure for the zoonotic evolution levels of coronaviruses. Peculiarly, the HP disparity measure infers that SARS-CoV-2 Omicron variants have a high disparity of HP poly-(A/T) and ploy-(C/G), suggesting a high adaption to the human hosts. © 2022 Changchuan Yin, published by De Gruyter.

Developing an Integrated Data Model based on IFC and SensorML for Post-COVID Facility Management

This paper presents an integrated data model based on IFC and SensorML to facilitate the post-COVID facilities management to ensure indoor thermal comfort and human health. This paper identifies the information with the reference to different industry guidelines, including WELL building Standard and Singapore BCA post-COVID regulations, and extracts necessary information for sensor description and identification. New parameters in the BIM environment are recommended in this paper to support model-based data exchange in the future. The information required is clarified with a process map depicting the information flow, and IFC MVD to provide a structured overview of the sensor requirement. Data mapping between IFC and SensorML is performed, the result indicates that missing entities and attributes can be proposed to enrich the sensor description in the IFC schema. The integration of SensorML and IFC provides better data interoperability between both schemas, improving information standardization and openness of data exchange in sensor description. © 2021 IEEE.

THE IMPACT OF COVID-19 PANDEMIC ON THE MENTAL HEALTH OF COLLEGE AND UNIVERSITY STUDENTS

Objectives: To evaluate the impact of COVID-19 pandemic on the mental health of Malaysian university and pre-university students, especially after the shift to online academic activities, following almost one year of experiencing the pandemic Methods: A cross-sectional online survey was conducted among Malaysian preuniversity and university students, nationwide, using perceived stress scale-10 questionnaire and another validated 11-construct questionnaire. Key findings: The online questionnaires were filled out by 383 Malaysian pre-university and university students. About 40% of the respondents reported moderate to severe level of loneliness and social isolation. There was a significant correlation between suicidal thoughts and the social isolation. The prevalence of moderate to severe suicidal thoughts (14% of the respondents) was more than the reported prevalence before COVID-19 pandemic. The residential state, gender and ethnicity of the respondents did not show an association with depressive and suicidal thoughts of the respondents. Conclusion: Loneliness and feeling social isolation were the most prevalent problems, as reported by the students. Suicidal Thoughts are more prevalent, compared to pre-COVID-19 reports. The educators and institution managers must seek for appropriate methods to address the critical condition.

A Deep Learning Framework of Autonomous Pilot Agent for Air Traffic Controller Training

In this work, a deep learning-based framework is proposed to implement an autonomous pilot agent (APA), which serves as a human pseudo-pilot to assist air traffic controller (ATCO) training. A novel paradigm, including speech recognition, language understanding, pilot repetition generation (PRG), and text-to-speech (TTS), is designed to formulate the framework pipeline, which also incorporates a simulation system interface. We mainly focus on the PRG and TTS models to address the ATC specificities in this work. The neural architecture is proposed to generate the text repetition instruction by using a sequence-to-sequence text mapping. The Transformer block is improved to implement a high-efficient TTS model, in which the nonautoregressive mechanism is applied to achieve the parallel synthesis. A dedicated phoneme vocabulary is designed to cope with the multilingual issue in the ATC domain and address the out-of-vocabulary problem. With the APA framework, a virtual training mode is proposed to complete the training task without the limitation of time and location. Experimental results on a real-world dataset show that the proposed APA framework replaces the human pilot with considerable high confidence in a real-time manner during the simulation training. Most importantly, the APA framework and the virtual training system are able to cope with the dilemma of physical attendance (like COVID-19) and improve the equipment utilization capacity for the ATCO training. IEEE

Inverted repeats in coronavirus SARS-CoV-2 genome and implications in evolution

The coronavirus disease (COVID-19) pandemic, caused by the coronavirus SARS-CoV-2, has caused 60 million infections and 1.38 million fatalities. Genomic analysis of SARS-CoV-2 can provide insights on drug design and vaccine development for controlling the pandemic. Inverted repeats in a genome greatly impact the stability of the genome structure and regulate gene expression. Inverted repeats involve cellular evolution and genetic diversity, genome arrangements, and diseases. Here, we investigate the inverted repeats in the coronavirus SARS-CoV-2 genome. We find that SARS-CoV-2 genome has an abundance of inverted repeats. The inverted repeats are mainly located in the gene of the Spike protein. This result suggests the Spike protein gene undergoes recombination events, therefore, is essential for fast evolution. Comparison of the inverted repeat signatures in human and bat coronaviruses suggest that SARS-CoV-2 is mostly related SARS-related coronavirus, SARSr-CoV/RaTG13. The study also reveals that the recent SARS-related coronavirus, SARSr-CoV/RmYN02, has a high amount of inverted repeats in the spike protein gene. Besides, this study demonstrates that the inverted repeat distribution in a genome can be considered as the genomic signature. This study highlights the significance of inverted repeats in the evolution of SARS-CoV-2 and presents the inverted repeats as the genomic signature in genome analysis.

Ciphertext-policy attribute-based encryption with multi-keyword search over medical cloud data

Over the years, public health has faced a large number of challenges like COVID-19. Medical cloud computing is a promising method since it can make healthcare costs lower. The computation of health data is outsourced to the cloud server. If the encrypted medical data is not decrypted, it is difficult to search for those data. Many researchers have worked on searchable encryption schemes that allow executing searches on encrypted data. However, many existing works support single-keyword search. In this article, we propose a patient-centered fine-grained attribute-based encryption scheme with multi-keyword search (CP-ABEMKS) for medical cloud computing. First, we leverage the ciphertext-policy attribute-based technique to construct trapdoors. Then, we give a security analysis. Besides, we provide a performance evaluation, and the experiments demonstrate the efficiency and practicality of the proposed CP-ABEMKS. © 2020 IEEE.