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Diver’s disease is a complication that the human body suffers from after being in a high-pressure space and then quickly entering a low-pressure space. These pressures are very harmful for the tissues of the body, especially for the brain. Decompression sickness occurs when nitrogen bubbles up in the bloodstream, caused by staying underwater too long or surfacing too quickly. Decompression sickness symptoms usually appear with a delay after diving. By processing electroencephalography, information about neurophysiological disorders can be extracted precisely. Since the diagnosis of divers’ diseases has not been dealt with using a unique model, this paper deals with the diagnosis of brain abnormalities caused by diving. In this paper, an intelligent model using the electroencephalography of divers will be presented to diagnose the brain disorders. The proposed model based on the brain function can show the connections of the divers’ brain regions using cellular neural networks. First, the effective features are extracted from the signals, and then based on the proposed architecture based on the brain, the differences in the brain connections of divers compared to non-divers are expressed. The obtained results show that there are differences in intra-regional connections of some brain regions of divers including T7, T8, O1 and O2 ($p$ < 0.05) compared to nondivers.

We develop simple models for the global spread of infectious diseases, emphasizing human mobility via air travel and the variation of public health infrastructure from region to region. We derive formulas relating the total and peak number of infections in two countries to the rate of travel between them and their respective epidemiological parameters.

The Leslie–Gower predator–prey model with logistic growth in prey is here modified to include an SI parasitic infection affecting the prey population only. Thresholds are identified for the predator population to survive, and the conditions for the disease to die out naturally are given. The behavior of the system around each equilibrium is investigated, showing that the disease incidence may have a relevant influence on the dynamics of complex ecosytems, assuming at times the role of a biological control parameter.

The new idea of group defense as recently introduced by the author in the context of two interacting populations is in this paper applied to communities subject also to a disease. The system is formulated with the bare minimum of interactions among all the populations involved in order to highlight the effects of the nonlinearity describing the defense mechanism. A key parameter identified in the purely demographic model, which completely describes its outcomes, is seen here to have an important role also, in that it is dropping below a threshold prevents the disease from invading the environment and causes the healthy prey and predators to coexist via persistent oscillations.

In ecological systems, the fear of predation risk asserts a privilege to the prey species by restricting their exposure to the potential predators. It also imposes costs by constraining the exploration of optimal resources. Additional foods for predators play a pivotal role in the biological conservation programs. The predators have ability to distinguish between the susceptible and infected prey items, and they avoid the latter ones to reduce their fitness cost. A predator-prey model with disease in prey is investigated in this study with an aim to explore the effects of fear factor, additional foods and selective predation on the ecological systems. We also investigate the spatio-temporal model to incorporate the facts that the prey and predator populations perform active movements in the spatial directions for their biological relevance. Both the temporal and spatio-temporal models are analyzed through noteworthy mathematical as well as numerical techniques. Our simulation results show that the level of fear responsible for the reduction in the birth rate of susceptible prey, rate of disease transmission and the selective feeding behavior of predators have potentials to create instability in the ecosystem. In contrast, the level of fear responsible for reduction in the disease prevalence can restore stability in the ecosystem by killing the persistent oscillations. Our eco-epidemic system exhibits chaotic nature if the growth of predators due to additional food sources is very low. We find that the spatio-temporal model demonstrates different spatial patterns of the prey and predator populations in the ecosystem.

Our current research is based on the investigation of an eco-epidemiological model that solely includes illness in predators. Predators, both healthy and diseased, are thought to consume prey and breed; however, the offsprings are expected to be vulnerable. To achieve a more realistic and explicit outcome of the existing phenomena correlated with our model system, we consider that the process of disease transmission is mediated by some time lag and the intensity of disease prevalence is seasonally forced. Our simulation results show that the disease dies out for lower intensity of disease prevalence or higher rate of consumption of prey by susceptible predator. The system undergoes subcritical/supercritical Hopf bifurcation as the parameters representing the intensity of disease prevalence, consumption rate of prey by susceptible/infected predator vary. The system exhibits two types of bistabilities: the first one between stable coexistence and oscillating coexistence, and the second one between two oscillatory coexistence cycles. Moreover, we see that with gradual increase in the incubation delay, the system shows transitions from stable focus to limit cycle oscillations to period doubling oscillations to chaotic dynamics. Chaotic dynamics is also observed for the periodic changes in the intensity of disease prevalence if it takes much time for the pathogens to develop sufficiently inside body of the susceptible predators.

To have a better understanding of the mechanisms of disease development, knowledge of mutations and the genes on which the mutations occur is of crucial importance. Information on disease-related mutations can be accessed through public databases or biomedical literature sources. However, information retrieval from such resources can be problematic because of two reasons: manually created databases are usually incomplete and not up to date, and reading through a vast amount of publicly available biomedical documents is very time-consuming. In this paper, we describe an automated system, MuGeX (Mutation Gene eXtractor), that automatically extracts mutation–gene pairs from Medline abstracts for a disease query. Our system is tested on a corpus that consists of 231 Medline abstracts. While recall for mutation detection alone is 85.9%, precision is 95.9%. For extraction of mutation–gene pairs, we focus on Alzheimer's disease. The recall for mutation–gene pair identification is estimated at 91.3%, and precision is estimated at 88.9%. With automatic extraction techniques, MuGeX overcomes the problems of information retrieval from public resources and reduces the time required to access relevant information, while preserving the accuracy of retrieved information.

We have analyzed codon usage for poly-Q stretches of different lengths for the human proteome. First, we have obtained that all long poly-Q stretches in Protein Data Bank (PDB) belong to the disordered regions. Second, we have found the bias for codon usage for glutamine homo-repeats in the human proteome. In the cases when the same codon is used for poly-Q stretches only CAG triplets are found. Similar results are obtained for human proteins with glutamine homo-repeats associated with diseases. Moreover, for proteins associated with diseases (from the HraDis database), the fraction of proteins for which the same codon is used for glutamine homo-repeats is less (22%) than for proteins from the human proteome (26%). We have demonstrated for poly-Q stretches in the human proteome that in some cases (28) the splicing sites correspond to the homo-repeats and in 11 cases, these sites appear at the $C$-terminal part of the homo-repeats with statistical significance 10${}^{-8}$.

MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since the experimental techniques used to find that new miRNA-disease relationships are time-consuming and expensive, many computational techniques have been developed. In this study, Weighted $K$-Nearest Known Neighbors and Network Consistency Projection techniques were suggested to predict new miRNA-disease relationships using various types of knowledge such as known miRNA-disease relationships, functional similarity of miRNA, and disease semantic similarity. An average AUC of 0.9037 and 0.9168 were calculated in our method by 5-fold and leave-one-out cross validation, respectively. Case studies of breast, lung, and colon neoplasms were applied to prove the performance of our proposed technique, and the results confirmed the predictive reliability of this method. Therefore, reported experimental results have shown that our proposed method can be used as a reliable computational model to reveal potential relationships between miRNAs and diseases.

Circular RNAs (circRNAs) are endogenous non-coding RNAs with a covalently closed loop structure. They have many biological functions, mainly regulatory ones. They have been proven to modulate protein-coding genes in the human genome. CircRNAs are linked to various diseases like Alzheimer’s disease, diabetes, atherosclerosis, Parkinson’s disease and cancer. Identifying the associations between circular RNAs and diseases is essential for disease diagnosis, prevention, and treatment. The proposed model, based on the variational autoencoder and genetic algorithm circular RNA disease association (VAGA-CDA), predicts novel circRNA-disease associations. First, the experimentally verified circRNA-disease associations are augmented with the synthetic minority oversampling technique (SMOTE) and regenerated using a variational autoencoder, and feature selection is applied to these vectors by a genetic algorithm (GA). The variational autoencoder effectively extracts features from the augmented samples. The optimized feature selection of the genetic algorithm effectively carried out dimensionality reduction. The sophisticated feature vectors extracted are then given to a Random Forest classifier to predict new circRNA-disease associations. The proposed model yields an AUC value of 0.9644 and 0.9628 under 5-fold and 10-fold cross-validations, respectively. The results of the case studies indicate the robustness of the proposed model.

Physiological status and pathological changes in an individual can be captured by metabolic state that reflects the influence of both genetic variants and environmental factors such as diet, lifestyle and gut microbiome. The totality of environmental exposure throughout lifetime – i.e., exposome – is difficult to measure with current technologies. However, targeted measurement of exogenous chemicals and untargeted profiling of endogenous metabolites have been widely used to discover biomarkers of pathophysiologic changes and to understand functional impacts of genetic variants. To investigate the coverage of chemical space and interindividual variation related to demographic and pathological conditions, we profiled 169 plasma samples using an untargeted metabolomics platform. On average, 1,009 metabolites were quantified in each individual (range 906 – 1,038) out of 1,244 total chemical compounds detected in our cohort. Of note, age was positively correlated with the total number of detected metabolites in both males and females. Using the robust Q_n estimator, we found metabolite outliers in each sample (mean 22, range from 7 to 86). A total of 50 metabolites were outliers in a patient with phenylketonuria including the ones known for phenylalanine pathway suggesting multiple metabolic pathways perturbed in this patient. The largest number of outliers (N=86) was found in a 5-year-old boy with alpha-1-antitrypsin deficiency who were waiting for liver transplantation due to cirrhosis. Xenobiotics including drugs, diets and environmental chemicals were significantly correlated with diverse endogenous metabolites and the use of antibiotics significantly changed gut microbial products detected in host circulation. Several challenges such as annotation of features, reference range and variance for each feature per age group and gender, and population scale reference datasets need to be addressed; however, untargeted metabolomics could be immediately deployed as a biomarker discovery platform and to evaluate the impact of genomic variants and exposures on metabolic pathways for some diseases.

The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of mutations or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.