LitRev: A data driven method for quick literature review from PubMed

LitRev is a novel robust data driven approach, devel-oped for quick literature review on a particular topic of interest. This method identifies common biological phrases that follow a power law distribution and important phrases which have the normalized point wise mutual information score greater than zero.

Importance of Variability Pattern Analysis for Malnutrition Information Score to Improve Treatment of Chronic Kidney Disease

Background: Morbidity and mortality envisaged by chronic kidney disease (CKD) remains a health menace throughout the world. Complications, incidence, prevalence, the impact of dietary recommendations, risk factors, outcome, and management strategies have not been rationalized due to several adversities resulting in escalated death rates. The objective of this study was to evaluate and establish a malnutrition information score (MIS) as a means of ease of CKD prevention and progression. MIS underlies the consistencies in findings through MIS show higher values can be corroborated to recommend the augmentation parameters in utilizing MICS techniques and other healthcare types of equipments. Methods: A randomized, non-biased sampling of patients presenting to dialysis unit with their maintenance schedule program in the nephrology department of Medical Unit-3 of PIMS, Islamabad were introspected for inclusions. Personal history, BSF thickness and MAC values were measured, and MAMC was calculated by = MAC – (3.1416 x TSF). The study was conducted in the Department of Nephrology, PIMS, Islamabad for a duration of six months. Results: The study conducted across 59 male patients and 33female patients indicate that males are more susceptible to CKD than females as evidenced by clinical stability of 67%. Appetite loss, degree of severity, Co-morbidity, BMI, MIS range, and clinical stability status of 100 study participants show variable indices indicating the stress on personal and family history of the patients. MIS pose as an indicator for determining the apt Malnutrition-Inflammation Complex Syndrome (MICS) and other types of equipments for treating CKD. Conclusion: CKD patients undergoing maintenance hemodialysis analyzed for dietary balance affirm the utility of MIS in determining the MICS and other types of healthcare equipments indirectly in ensuring reduction in morbidity and mortality.

Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features

With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration.

SICEM: A Generation Approach of Band Combination for Hyperspectral Imagery Reconstitution Based on Space and Information Analyses

A band selection algorithm named space and information comprehensive evaluation model (SICEM) is proposed in this paper, which reconstitutes the hyperspectral imagery by building an optimal subset to replace the original spectrum. SICEM reduces the dimensions while keeping the vital information of an image, and these are accomplished through two phases. Specifically, the improved fast density peaks clustering (I-FDPC) algorithm is employed to pick out the scattered bands in geometric space to generate a candidate set U at first. Then, we conduct pruning in U through iterative information analysis until the target set Ω is built. In this phase, we need to calculate comprehensive information score (CIS) for every member in U after assigning weights to the amount of information (AoI) and correlation. In each iteration, the band with highest score is selected into Ω , and the ones highly related to it will be removed out of U via a threshold. Compared with the four state-of-the-art unsupervised algorithms on real-world HSI datasets (IndianP and PaviaU), we find that SICEM has strong ability to form an optimal reduced-dimension combination with low correlation and rich information and it performs well in discrete band distribution, accuracy, consistency, and stability.

Bt corn growing information system in Cagayan Province, Philippines: An analysis for enhanced extension delivery service

The adoption of Bacillus thuringiensis (Bt) corn gene has increased production and agricultural information exchange plays a vital role towards sustained production growth. The study examined the information system by assessing the usefulness of information, frequency of consultations and strength of information exchange in Bt corn production in Cagayan Province, Philippines. Using descriptive correlation design, it was revealed that both technical and economic information from all sources were rated useful. Technical information on seed variety, land preparation, weather forecast, soil management and fertilizer application are frequently utilized while on the economic information only current market prices was frequently used. Frequency of Consultation with personal information sources gained the highest mean value of 5.02 signifying a two to three times exchange of information. Public information sources obtained an average mean of 2.80 which means four or five times contact in a year. This reveals the gap between the corn growers and public information sources. For the mass media sources, both radio (4.56) and television (4.72) are frequently used at two to three times a month. Personal information sources showed strong (224.25 IS) exchange of information between and among the Bt corn growers which comprise their communication network. Total information scores from public and mass media information sources revealed weak exchange of information. The total information score is significantly correlated with membership to organization, years of membership, nature of membership, land ownership and position in the farm. Total information score from friends, radio and newspaper showed significant relationship with income. Thus, information exchange with friends, listening to radio and reading newspaper had a significant influence on the increase of corn growers’ income.

DDOS Detection on Internet of Things Using Unsupervised Algorithms

The increase in the deployment of IOT networks has improved productivity of humans and organisations. However, IOT networks are increasingly becoming platforms for launching DDOS attacks due to inherent weaker security and resource-constrained nature of IOT devices. This paper focusses on detecting DDOS attack in IOT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDOS attacks. Emphasis was laid on exploitation based DDOS attacks which include Transmission Control Protocol SYN-Flood attacks and UDP-Lag attacks. Mirai, BASHLITE and CICDDOS2019 datasets were used in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.

Social network analysis and community detection on spread of COVID-19

This paper explains the epidemic spread using social network analysis, based on data from the first three months of the 2020 COVID-19 outbreak across the world and in Canada. A network is defined and visualization is used to understand the spread of coronavirus among countries and the impact of other countries on the spread of coronavirus in Canada. The degree centrality is used to identify the main influencing countries. Exponential Random Graph Models (ERGM) are used to identify the processes that influence link creation between countries. The community detection is done using Infomap, Label propagation, Spinglass, and Louvain algorithms. Finally, we assess the community detection performance of the algorithms using adjusted rand index and normalized mutual information score.

Translation of the Amsterdam Preoperative Anxiety and Information Score (APAIS) into the Amharic Version and Its Validation for Evaluation of Preoperative Anxiety

BACKGROUND፡ Peri-operative anxiety is a vague, unpleasant feeling, the source of which is often nonspecific and unknown to the individual. It affects all aspects of anesthesia. Although the magnitude and consequences of preoperative anxiety are well documented in the developed world, there are limited studies conducted in Ethiopia. The primary aim of this study is to produce the Amharic version of APAIS and evaluate its validity in assessing the prevalence of preoperative anxiety in surgical patients.METHODS: A cross-cultural adaptation process using a forward/backward translation of the APAIS scale was performed. The Amharic version was then tested in 365 sampled elective adult surgical patients scheduled for surgery at Tikur Anbessa specialized Hospital. The validity of the translated version was also checked by evaluating its psychometric properties of internal validity and acceptability.Result: The results showed that the reliability of the APAISAmharic was high (Cronbach's alpha of 0.87) and the data collected was a good fit (RMSEA of 0.04). In addition, the questionnaire was well-accepted 100% with no missing values for each dimension of the APAIS. The mean APAIS scores for total anxiety and desire for information were 11.6 and 6.0 respectively and 58.9% of the study participants had anxiety and those patients, who had some form of formal education, came from urban areas, had previous anesthesia experience and complications and who had average or high information requirement was more likely to be anxious.CONCLUSION: APAIS-Amharic is a reliable and acceptable tool for measuring patients’ preoperative anxiety and their need for information. It can be used routinely as a screening instrument at pre-anesthesia clinics to assess patients’ level of anxiety.