In the course of evaluating the patient with spinal disease, a myriad of measurements need to be performed before determining the diagnosis and the severity of the disease process. This text explicitly outlines the measurement of the spine from a clinical, laboratory, and radiographic approach. A detailed description of the actual technique of measurement and the clinical implication are presented with accompanying illustrations. This amalgamation of measurement tools for the spine is a beneficial reference for a wide spectrum of healthcare providers: students, nurses, residents, fellows, and established surgeons. In addition to its detailed illustrated presentation, each measurement technique has been graded for scientific and clinical utility with a score that specifically grades: Interobserver reliability Intraobserver reliability Universality Disease specificity Ease of application Simplicity Patient tolerability Expense The detail presented in this text will not only serve as a reference, but will also allow the reader to accurately reproduce measurement techniques, thus enhancing inter-physician communication, research of the spine, and improvement of patient care.
Many quantum algorithms for numerical linear algebra assume black-box access to a block-encoding of the matrix of interest, which is a strong assumption when the matrix is not sparse. Kernel matrices, which arise from discretizing a kernel function $k(x,x')$, have a variety of applications in mathematics and engineering. They are generally dense and full-rank. Classically, the celebrated fast multipole method performs matrix multiplication on kernel matrices of dimension $N$ in time almost linear in $N$ by using the linear algebraic framework of hierarchical matrices. In light of this success, we propose a block-encoding scheme of the hierarchical matrix structure on a quantum computer. When applied to many physical kernel matrices, our method can improve the runtime of solving quantum linear systems of dimension $N$ to $O(κ\operatorname{polylog}(\frac{N}{\varepsilon}))$, where $κ$ and $\varepsilon$ are the condition number and error bound of the matrix operation. This runtime is near-optimal and, in terms of $N$, exponentially improves over prior quantum linear systems algorithms in the case of dense and full-rank kernel matrices. We discuss possible applications of our methodology in solving integral equations and accelerating computations in N-body problems.
Background: Although "social isolation" protects the life and health of Vietnamese citizens from the adverse effects of the COVID-19 pandemic, it also triggers massive reductions in the economic activities of the country. Objective: our study aimed to identify negative impacts of COVID-19 on occupations of Vietnamese people during the first national lockdown, including the quality and quantity of jobs as well as adverse problems at work due to COVID-19. Methods: A cross-sectional study using web-based platforms was conducted during the first time of social isolation in Vietnam at the beginning of April 2020. We utilized a respondent-driven sampling technique to select 1423 respondents from 63 cities and provinces over Vietnam. Exploratory factor analysis (EFA) was used to define sub-domains of perceived impacts of COVID-19 on occupations. Findings: Approximately two-thirds of respondents reported decreases in their income (61.6%), and 28.2% reported that their income deficit was 40% and above. The percentage of female individuals having decreased revenue due to COVID-19 was higher than that of male respondents (65.2% and 54.7%, respectively). "Worry that colleagues exposed to COVID-19 patients" and "Being alienated because employment-related to COVID-19" accounted for the highest score in each factor. Compared to healthcare workers, being self-employed/unemployed/retired were less likely to suffer from "Increased workload and conflicts due to COVID-19" and "Disclosure and discrimination related to COVID-19 work exposure." Conclusion: Our study revealed a drastic reduction in both the quality and quantity of working, as well as the increased fear and stigmatization of exposure to COVID-19 at workplaces. Health protection and economic support are immediate targets that should be focused on when implementing policies and regulations.
Vietnam has had one of the fastest growing economies in Asia over the years. However, the COVID-19 pandemic has proven to be a major hindrance to this growth as the country's GDP plummeted significantly. Air pollution can further amplify the impact of the pandemic since residents exposed to high levels of pollution are likely to increasingly suffer from respiratory illnesses, such as asthma. This paper investigates the impact of COVID-19 on air quality and how air quality can influence the spread of the virus. Finally, the paper proposes suitable machine learning practices for predicting air quality, based on historical trends, using spatial and temporal data.
Cardiovascular disease (CVD) is the world's number one cause of mortality. Research in recent years has begun to illustrate a significant association between CVD and air pollution. As most of these studies employed traditional statistics, cross-sectional or meta-analysis methods, a study undertaken by the authors was designed to investigate how a geographical information system (GIS) could be used to develop a more efficient spatio-temporal method of analysis than the currently existing methods mainly based on statistical inference. Using Bangalore, India, as a case study, demographic, environmental and CVD mortality data was sought from the city. However, critical deficiencies in the quality of the environmental data and mortality records were identified and quantified. This paper discusses the shortcomings in the quality of mortality data, together with the development of a framework based on WHO guidelines to improve the defects, henceforth considerably improving data quality.
This study uses novel household survey data that are representative of Bangladesh's large cities, and of slum and nonslum areas within the cities, to investigate the effects of demographic and socioeconomic factors on early child growth in 2013. The study also decomposes the difference in mean child growth between slum and nonslum areas in 2013, and the increase in mean child growth in slum and nonslum areas from 2006 to 2013. Mother's education attainment and household wealth largely explain the cross-sectional difference and intertemporal change in child growth. Although positive in some cases, the effects of maternal and child health services, and potential health-protective household amenities, differ by the type of health facility, household amenity, and urban area. The results suggest that a focus on nutrition-sensitive programs for slum residents and the urban poor is appropriate.
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