Palindromic Vectors, Symmetropy and Symmentropy as Symmetry Descriptors of Binary Data

Today, the palindromic analysis of biological sequences, based exclusively on the study of “mirror” symmetry properties, is almost unavoidable. However, other types of symmetry, such as those present in friezes, could allow us to analyze binary sequences from another point of view. New tools, such as symmetropy and symmentropy, based on new types of palindromes allow us to discriminate binarized 1/f noise sequences better than Lempel–Ziv complexity. These new palindromes with new types of symmetry also allow for better discrimination of binarized DNA sequences. A relative error of 6% of symmetropy is obtained from the HUMHBB and YEAST1 DNA sequences. A factor of 4 between the slopes obtained from the linear fits of the local symmentropies for the two DNA sequences shows the discriminative capacity of the local symmentropy. Moreover, it is highlighted that a certain number of these new palindromes of sizes greater than 30 bits are more discriminating than those of smaller sizes assimilated to those from an independent and identically distributed random variable.

Exploratory Graph Analysis for Factor Retention: Simulation Results for Continuous and Binary Data

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and binary data, as evidence regarding the accuracy of such criteria in the latter case is scarce. Simulation results, based on scenarios resulting from varying sample size, communalities from major factors, interfactor correlations, skewness, and correlation measure, show that EGA outperforms the traditional factor retention criteria considered in most cases in terms of bias and accuracy. In addition, we show that factor retention decisions for binary data are preferably made using Pearson, instead of tetrachoric, correlations, which is contradictory to popular belief.

A Framework for Reversible Data Embedding into Base45 and Other Non-Base64 Encoded Strings

Printable string encodings are widely used in several applications that cannot deal with binary data, the most known example being the mail system. In this paper, we investigate the potential of some of the proposed encodings to hide and carry extra information. We describe a framework for reversibly embedding data in printable string encodings, like Base45. The method leverages the characteristic of some encodings that are not surjective by using illegal configurations to embed one bit of information. With the assumption of uniformly distributed binary input data, an estimation of the expected payload can be computed easily. Results are reported for Base45 and Base85 encodings.

C-reactive Protein to Albumin Ratio among Patients Admitted to Intensive Care Unit of a Tertiary Care Hospital: A Descriptive Crosssectional Study

Introduction: Sepsis is the most common cause of mortality among patients admitted to intensive care unit. There is emerging evidence on the role of C-reactive protein to albumin ratio (C-reactive protein/Albumin) in predicting outcomes in patients with critical illness and sepsis, admitted to intensive care unit. We aimed to find out the median value of C-reactive protein/Albumin ratio among patients admitted to intensive care unit of a tertiary care hospital. Methods: We conducted a descriptive cross-sectional study of 110 critically ill patients (>18 years old) admitted to intensive care unit of Dhulikhel Hospital from April, 2014 to June, 2016. The ethical approval (Reference number.51/16) was obtained from Institutional Review Committee at Kathmandu University School of Medical Sciences. C-reactive protein/albumin ratio was calculated from records of patients admitted to the intensive care unit. Convenience sampling was done. Data were entered into Microsoft Excel and analysed using Statistical Package for Social Sciences version 20. Point estimate at 95% Confidence Interval was calculated along with frequencies and percentages for binary data. Results: Among 110 patients admitted to the intensive care unit, the median value of C-reactive protein/Albumin ratio was found to be 3.4 (Interquartile range: 3.1-4.5). Conclusions: Our study showed higher median C-reactive protein /Albumin similar to toher studies. Sepsis is a common finding among patients admitted to intensive care unit. Monitoring of C-reactive protein/albumin level in a patient admitted to intensive care unit could be useful for stratifying patients with a high risk of developing sepsis.

Ocular Morbidity among Patients Visiting the Department of Ophthalmology during the Coronavirus Disease 2021 Pandemic at a Tertiary Care Hospital: A Descriptive Cross-sectional Study

Introduction: Due to the ongoing coronavirus disease 2021 pandemic and lockdown, eye care services have been compromised globally. The magnitude of ocular diseases across all populations in Nepal are few and far between and rare during this pandemic. This study was aimed to find out the prevalence of ocular morbidity among patients visiting the department of Ophthalmology of a tertiary care hospital during the pandemic. Methods: A descriptive cross-sectional study was conducted among the patients visiting thedepartment of Ophthalmology of a tertiary care hospital from 18 August 2021 to 30 September 2021. Ethical clearance was taken from the Institutional Review Committee (Reference: 078/079-023). Convenience sampling was done. Basic demographic data, clinical characteristics, visual status and prevalence of ocular morbidities were noted. Data entry was done using Statistical Package for the Social Sciences version 26. Point estimate at 95% Confidence Interval was calculated along with frequency and percentage for binary data. Results: Out of 650 study subjects examined, 454 (69.8%) (66-73.0 at 95% Confidence Interval) study subjects had at least one ocular morbidity in at least one eye. Refractive error 153 (33.7%) was the commonest ocular morbidity followed by headache 52 (11.5%), dry eyes 50 (11%), non-communicable diseases related ocular morbidity 41 (9%), and age related cataract 37 (8.1%). Conclusions: The prevalence of ocular morbidity in our study was higher than findings from other similar studies done at national and international levels, though the causes of ocular morbidity was similar.

Long-term Health Ailments among COVID-19 recovered patients in South Asian Countries: A Descriptive Cross-sectional Study

Introduction: COVID-19 pandemic declared as Global Health Emergency by World Health Organization. The novel SARS-CoV-2 virus is the major cause of COVID-19.Although the recovery rate of COVID-19 is higher, the recovered patients experience mild to severe health ailments post recovery. These health ailments affect their routine day to day life and also their quality of life. The key objective of this study is to find out the prevalence of various health ailments among COVID recovered population from south Asian countries.Methods: A descriptive cross-section study was conducted among 384 COVID-19 recovered population in South Asian Countries through randomized survey. Ethical approval of the institution was obtained and a convenient sampling technique was done. Statistical package for Social Sciences is used for the analysis of the data. . Point estimate at 95% Confidence Interval was calculated along with frequency and proportion for binary dataResults: Among 384 samples, 68% of patients had post COVID-19 long term extreme tiredness and 64% of patients reported with sleepless ness. 73% of patients had fever and smell loss during the COVID19. 64% had reported body pain and cough when they had the infection. 42% of the patients were healthy ones without any comorbidity prior to COVID.Conclusions: The study concludes that there was high prevalence of long term illness among COVID-19 recovered patients and the prevalence was reported even in patients who had no comorbidities prior to COVID19 are the dominant health disorders prevalent among COVID-19 recovered population.

Differentially private synthetic mixed-type data generation for unsupervised learning

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that will satisfy similar statistical properties as the original data. This learned model can generate an arbitrary amount of synthetic data, which can then be freely shared due to the post-processing guarantee of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both binary data (MIMIC-III) and mixed-type data (ADULT), and compare its performance with existing private algorithms on metrics in unsupervised settings. We also introduce a new quantitative metric able to detect diversity, or lack thereof, of synthetic data.