Ontology-Based Nutritional Recommender System

Obesity is considered an epidemic that is continuously growing around the world. Heart diseases, diabetes, and bone and joint diseases are some of the diseases that people who are overweight or obese can develop. One of the vital causes of those disorders is poor nutrition education; there is no raising awareness about eating healthy food and practicing physical activities to burn off the excess energy. Therefore, it is necessary to use new technologies to build methods/tools that help people to overcome these avoidable nutrition disorders. For this reason, we implemented a recommendation engine capable of identifying the different levels of overweight and obesity in users and providing dietary strategies to mitigate them. To do so, we defined the Ontology of Dietary Recommendations (ODR) with axioms to model recipes, ingredients, and a set of diets to assist people who suffer from obesity. We validated the defined model by using a real set of individuals who were anonymized. A panel of advisors evaluated each individual record and suggested the most appropriate diets from those included in the ontology. Then, the proposed system was asked to also provide diet recommendations for each individual, which were compared with those proposed by the advisors (ground truth), reaching a mean accuracy of 87%.

Determinants of birthplace among middle-to lower-class women in Indonesia: A study using the Indonesian Demographic and Health Survey

Background One of the factors contributing to a high maternal mortality rate is the utilization of non-healthcare facilities as a birthplace for women. This study analyzed determinants affecting birthplace in middle-to lower-class women in Indonesia. Methods This study analyzed the 2017 Indonesian Demographic and Health Survey (IDHS) data. The total national sample size was 49,627 eligible women. Our sample included 11,104 women, aged 15–49, who had delivered babies and were of low-to-middle economic status. The type of survey dataset was individual record dataset. Data were analyzed with chi-square and multivariate logistic regression tests using Stata 16 software. Results About 64.99% middle to lower class women in Indonesia delivered in healthcare facilities. Women aged 45–49 (OR = 2.103; 95% CI = 1.13–3.93), who graduated from higher schools (OR = 2.885; 95% CI = 1.76–4.73), whose husbands had higher education (OR = 2.826; 95% CI = 1.69–4.74) and were employed (OR = 2.523; 95% CI = 1.23–5.17), who considered access to healthcare facilities was not a problem (OR = 1.528; 95% CI = 1.28–1.82), who had a single child (OR = 2.349; 95% CI = 1.97–2.80), and who lived in urban areas (OR = 2.930; 95% CI = 2.40–3.57) were determinants that significantly correlated with women giving birth in healthcare facilities. Conclusion This study provides insights for policymakers and healthcare centers in the community to strengthen access to healthcare services and devise health promotion strategies for pregnant mothers. Policy interventions designed for middle- to lower-class women should be implemented to support vulnerable groups.

Ferns and Lycophytes of Honduras: A new annotated checklist

For over two decades no attempt has been made to update the checklist of Ferns and Lycophytes (hereafter only referred to as pteridophytes) of Honduras. In 1996 Nelson-Sutherland and co-workers published the first fully annotated checklist for the country, which included 651 named species (680 taxa including varieties and hybrids). However, phylogenetic relationships have changed substantially since then. There have also been many taxonomic revisions for several groups and a considerable number of new records have since been published. Our work aimed to provide a comprehensive updated and fully annotated checklist of Honduran ferns. We undertook a comprehensive literature review of new records for Honduras and verified each individual record for the country. We followed PPGI for our taxonomic species revisions. We excluded a total of 81 taxa from the original 1996 checklist due to synonymy or lack of evidence of the species occurrence in Honduras. A total of 114 new species records were added, including four previously unpublished records. Our new updated checklist includes 713 taxa (including 8 varieties and 7 hybrids) which represents a 19% increase to the number of pteridophytes known from Honduras. Our work provides updated information regarding the pteridophytes diversity of Honduras. These data provide a crucial first step towards the first Honduran pteridophytes flora.

A Mixture Model Incorporating Individual Heterogeneity in Human Lifetimes

Analysis of some extensive individual-record data using a demographically informed model suggests constructing a general population model in which the lifetime of a person, beyond a certain threshold age, follows an extreme value distribution with a finite upper bound, and with that upper bound randomized over the population. The resulting population model incorporates heterogeneity in life-lengths, with lifetimes being finite individually, but with extremely long lifespans having negligible probability. Our findings are compared in detail with those of related studies in the literature, and used to reconcile contradictions between previous studies of extreme longevity. While being consistent with currently reported analyses of human lifetimes, we nevertheless differ with those who conclude in favour of unbounded human lifetimes.

Sunfish’s (Mola spp.) Habitat Characteristics on their Appearance at Dive Tourism Depths in Nusa Penida Waters, Bali

The sunfish or mola fish (Mola spp.) is one of marine tourism’ icon in Nusa Penida waters where they appear seasonally. This study was aimed to observe the habitat characteristics of mola fish at the depths of diving tourism where they are visible. The research was conducted from July to December 2019 using survey and descriptive methods to obtain information of mola fish animal linked to habitat conditions. On its appearance data on depths, temperatures, currents, and the presence of symbiotic fishes were observed directly assisted by dive center operators. By comparing the morphological characteristics shown photographically, it is assumed that mola fish in study area is composed by two species, M. alexandrini and M. mola. The highest individual record was in September and decreasing along the incoming months. Mola fish prefers calm deeper water with lower temperatures coincides with the presence of cleaning reef fishes.

ECG Restitution Analysis and Machine Learning to Detect Paroxysmal Atrial Fibrillation: Insight from the Equine Athlete as a Model for Human Athletes

Atrial fibrillation is the most frequent arrhythmia in both equine and human athletes. Currently, this condition is diagnosed via electrocardiogram (ECG) monitoring which lacks sensitivity in about half of cases when it presents in paroxysmal form. We investigated whether the arrhythmogenic substrate present between the episodes of paroxysmal atrial fibrillation (PAF) can be detected using restitution analysis of normal sinus-rhythm ECGs. In this work, ECG recordings were obtained during routine clinical work from control and horses with PAF. The extracted QT, TQ, and RR intervals were used for ECG restitution analysis. The restitution data were trained and tested using k-nearest neighbor (k-NN) algorithm with various values of neighbors k to derive a discrimination tool. A combination of QT, RR, and TQ intervals was used to analyze the relationship between these intervals and their effects on PAF. A simple majority vote on individual record (one beat) classifications was used to determine the final classification. The k-NN classifiers using two-interval measures were able to predict the diagnosis of PAF with area under the receiving operating characteristic curve close to 0.8 (RR, TQ with k ≥ 9) and 0.9 (RR, QT with k ≥ 21 or TQ, QT with k ≥ 25). By simultaneously using all three intervals for each beat and a majority vote, mean area under the curves of 0.9 were obtained for all tested k-values (3–41). We concluded that 3D ECG restitution analysis can potentially be used as a metric of an automated method for screening of PAF.

PubChem in 2021: new data content and improved web interfaces

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Evaluation measure for group-based record linkage

Introduction The robustness of record linkage evaluation measures is of high importance since linkage techniques are assessed based on these. However, minimal research has been conducted to evaluate the suitability of existing evaluation measures in the context of linking groups of records. Linkage quality is generally evaluated based on traditional measures such as precision and recall. As we show, these traditional evaluation measures are not suitable for evaluating groups of linked records because they evaluate the quality of individual record pairs rather than the quality of records grouped into clusters. Objectives We highlight the shortcomings of traditional evaluation measures and then propose a novel method to evaluate clustering quality in the context of group-based record linkage. Methods The proposed linkage evaluation method assesses how well individual records have been allocated into predicted groups/clusters with respect to ground-truth data. We first identify the best representative predicted cluster for each ground-truth cluster and, based on the resulting mapping, each record in a ground-truth cluster is assigned to one of seven categories. These categories reflect how well the linkage technique assigned records into groups. Results We empirically evaluate our proposed method using real-world data and show that it better reflects the quality of clusters generated by three group-based record linkage techniques. We also show that traditional measures such as precision and recall can produce ambiguous results whereas our method does not. Conclusions The proposed evaluation method provides unambiguous results regarding the assessed group-based record linkage approaches. The method comprises of seven categories which reflect how each record was predicted, providing more detailed information about the quality of the linkage result. This will help to make better-informed decisions about which linkage technique is best suited for a given linkage application.

Stress Analysis and Design Validation of Chute using DEM Software

The discrete element method (DEM) is attracting growing attention for the simulation of industrial Bulk solid flow; much of the earlier DEM modelling has considered two-dimensional (2D) flows and used circular particles. The DEM maintains the individual record (velocities, forces, etc.) of particles in flow and stress on equipment. This will enable the designer to know the problems in the design. Transfer chute is used in many industries to facilitate bulk material from one conveyor belt to another or for guide flow from a delivery point (feeder, screw conveyor) into a process or equipment (centrifuge, screener, etc.). Although the transfer chute itself may appear to be a low-cost part of the equipment train, it can easily become costly in maintenance due to plugging, abrasive wear, segregation, etc. The objective of this study is to analyse the stress distribution in a transfer chute when it is in use and to validate design is free from plugging. The modelling was done using the CREO PARAMETRIC software as per Industry standards. The Chute was modelled and simulated using the ROCKY DEM software. In the present research work, a discrete element analysis procedure is used in the ROCKY DEM simulation to predict the level of stress and velocities of particles.

θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records

The Internet of Things (IoT) is an exponentially growing emerging technology, which is implemented in the digitization of Electronic Health Records (EHR). The application of IoT is used to collect the patient’s data and the data holders and then to publish these data. However, the data collected through the IoT-based devices are vulnerable to information leakage and are a potential privacy threat. Therefore, there is a need to implement privacy protection methods to prevent individual record identification in EHR. Significant research contributions exist e.g., p+-sensitive k-anonymity and balanced p+-sensitive k-anonymity for implementing privacy protection in EHR. However, these models have certain privacy vulnerabilities, which are identified in this paper with two new types of attack: the sensitive variance attack and categorical similarity attack. A mitigation solution, the θ -sensitive k-anonymity privacy model, is proposed to prevent the mentioned attacks. The proposed model works effectively for all k-anonymous size groups and can prevent sensitive variance, categorical similarity, and homogeneity attacks by creating more diverse k-anonymous groups. Furthermore, we formally modeled and analyzed the base and the proposed privacy models to show the invalidation of the base and applicability of the proposed work. Experiments show that our proposed model outperforms the others in terms of privacy security (14.64%).