The exposure of the body to stress, regardless of whether it comes from physical, chemical or emotional stimuli from the environment, causes an inadequate adaptation of the organisms which can contribute to the development of various diseases. Abnormally high blood concentrations of cortisol, known as stress hormone, lead to the development of a hormonal disorder called hyperadrenocorticism or Cushing’s syndrome. In the majority of cases, Cushing’s syndrome is diagnosed when symptoms are apparent, and screening endocrinological test confirms the existence either of increased cortisol production or decreased sensitivity of the hypothalamic-pituitary-adrenal axis to negative glucocorticoid feedback. In our research, we examined a total of 23 male and 7 female dogs that were suspected to have Cushing’s syndrome, based on history and clinical signs. A total of 15 male and 5 female dogs were positive for Cushing’s syndrome (HAC group), whereas the remaining dogs were used to form non-HAC group. Using the apparatus IDEXX “Vet Test 8008”, the following biochemical parameters were determined: glucose, urea, creatinine, phosphorus, calcium, total protein, albumin, globulin, alanine aminotransferase, alkaline phosphatase, bilirubin, cholesterol, and amylase. Regarding haematological parameters, the following parameters were investigated: erythrocytes, leukocytes, platelets, erythrocyte indices (MCV, MCH, MCHC, RDW), white blood cell count, haemoglobin and haematocrit, using “Laser cite vet lab Station” (IDEXX). No significant differences in haematological and biochemical blood parameters were noticed between the HAC and the non-HAC group of dogs. However, dogs suffering from Cushing’s syndrome had a higher value in the number of erythrocytes compared to the control group. The finding that has to be payed attention to is the difference in platelet count between the control group of dogs and dogs suffering from Cushing’s syndrome.

BACKGROUND Post-traumatic stress disorder (PTSD) is a mental health condition that is triggered by a terrifying event either experiencing it or witnessing it. Although the pathogenesis is still unknown, some researches indicate inflammatory background and liver dysfunction as a part of the disease. We wanted to determine inflammatory markers' levels and investigate the correlation with liver enzymes in PTSD patients. METHODS This cross-sectional study included 60 male subjects aged between 40 - 60 years. Subjects were divided into two groups: a group of veterans with combat exposure and PTSD according to DSM-IV criteria and a control group of healthy subjects without combat exposure. WBC count, leucocytes ratios, levels of inflammatory markers (C reactive protein- CRP, fibrinogen, and erythrocyte sedimentation rateESR), and liver enzymes (aspartate aminotransferase- AST, alanine aminotransferase- ALT, creatine kinase- CK, and gamma-glutamyl transferase- GGT) were determined in all respondents. RESULTS The concentrations of CRP, fibrinogen, ESR, platelet-lymphocyte ratio and monocytelymphocyte ratio in subjects with PTSD were statistically significantly higher than those in the control group. Levels of AST and GGT in PTSD subjects were statistically significantly higher than of those in the control group subjects. Statistically significant positive correlation was found between serum AST and CRP concentration (Rho = 0.416; P = 0.022), as well as GGT and CRP concentration (Rho = 0.395; P = 0.031). CONCLUSIONS Results indicate the relationship between liver pathology and inflammation in the complex pathogenesis of PTSD. These can be used in future researches and development of a new diagnostic approach and treatment that may lead to a longer lifespan of PTSD patients. KEY WORDS PTSD, Inflammation, Liver Enzymes

The Bosnian and Herzegovinian market lacks data about the percentage of genetically modified soy products placed on the domestic market. There has been research on the issue of the presence of GMO products in our domestic market, but neither of the results is used as a reference for this occurrence. Therefore, this research topic tends to contribute to this issue, by examining genetically modified soy in processed food. The sample of seven products containing soya is examined by the methods of DNA isolation and real-time PCR for CP4 EPSPS. The results showed positive results for the presence of CP4 gene in certain products without an appropriate label. This mislabeling was confirmed since a couple of samples were labeled as GMO-free but contained CP4 gene, indicating GMO product.

Background Although men are more prone to developing cardiovascular disease (CVD) than women, risk factors for CVD, such as nicotine abuse and diabetes mellitus, have been shown to be more detrimental in women than in men. Objective We developed a method to systematically investigate population-wide electronic health records for all possible associations between risk factors for CVD and other diagnoses. The developed structured approach allows an exploratory and comprehensive screening of all possible comorbidities of CVD, which are more connected to CVD in either men or women. Methods Based on a population-wide medical claims dataset comprising 44 million records of inpatient stays in Austria from 2003 to 2014, we determined comorbidities of acute myocardial infarction (AMI; International Classification of Diseases, Tenth Revision [ICD-10] code I21) and chronic ischemic heart disease (CHD; ICD-10 code I25) with a significantly different prevalence in men and women. We introduced a measure of sex difference as a measure of differences in logarithmic odds ratios (ORs) between male and female patients in units of pooled standard errors. Results Except for lipid metabolism disorders (OR for females [ORf]=6.68, 95% confidence interval [CI]=6.57-6.79, OR for males [ORm]=8.31, 95% CI=8.21-8.41), all identified comorbidities were more likely to be associated with AMI and CHD in females than in males: nicotine dependence (ORf=6.16, 95% CI=5.96-6.36, ORm=4.43, 95% CI=4.35-4.5), diabetes mellitus (ORf=3.52, 95% CI=3.45-3.59, ORm=3.13, 95% CI=3.07-3.19), obesity (ORf=3.64, 95% CI=3.56-3.72, ORm=3.33, 95% CI=3.27-3.39), renal disorders (ORf=4.27, 95% CI=4.11-4.44, ORm=3.74, 95% CI=3.67-3.81), asthma (ORf=2.09, 95% CI=1.96-2.23, ORm=1.59, 95% CI=1.5-1.68), and COPD (ORf=2.09, 95% CI 1.96-2.23, ORm=1.59, 95% CI 1.5-1.68). Similar results could be observed for AMI. Conclusions Although AMI and CHD are more prevalent in men, women appear to be more affected by certain comorbidities of AMI and CHD in their risk for developing CVD.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

We developed measures of relational beliefs and expectations among single young gay and bisexual men (YGBM). Data come from an online cross-sectional study YGBM, which ran from July 2012 until January 2013. There were 50 items on relational beliefs and 25 items on relational expectations. We used random split samples and a priori analysis to group items together and applied principal axis factoring with varimax orthogonal rotation. We had a total N = 1582 in our analytical sample and identified six constructs of relational expectations (restrictions, negative break up, masculine and gender norms, optimism, cheating, immediacy) and two constructs of relational beliefs (sex beliefs, equality). Our findings highlight specific relational cognitions among YGBM and offer insight into the beliefs and expectations that may inform their relationships. Findings may be useful for health professionals to help YGBM reflect and understand the health implications of their beliefs and expectations about same-sex relationships to promote healthy decision-making as they seek future partners.

As exploitation of low and medium airspace for air traffic management (ATM) is gaining more attention, aerial vehicles’ security issues pose a major challenge to the air–ground-integrated vehicle networks (AGIVNs). Traditional surveillance technology lacks the capacity to support the intensive ATM of the future. Therefore, an advanced automatic-dependent surveillance-broadcast (ADS-B) technique is applied to track and monitor aerial vehicles in a more effective manner. In this article, we propose a grouping-based conflict detection algorithm based on the preprocessed ADS-B data set, and analyze the experimental results and visualize the detected conflicts. Then, in order to further improve flight safety and conflict detection, the trajectories of the aerial vehicles are predicted based on machine learning-based algorithms. The results are fed into the conflict detection algorithm to execute conflict prediction. It was shown that the trajectory prediction model using long short-term memory (LSTM) can achieve better prediction performance, especially when predicting the long-term trajectory of aerial vehicles. The conflict detection results based on the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds.

This paper presents accounts in the Ottoman language on the movement of Hamzevis and their activities written by the famous mufti and muderis of Belgrade Munīrī Belġrādī by the beginning of the 17th century. In two of his seventeen works that are known to us, Belġrādī speaks directly about the Hamzevis, their founder and activities, and in two works, writing about other topics, he indirectly touches on the Hamzevis, their teachings and behaviour. Belġrādī’s accounts are important because they give a picture of the Ottoman State’s attitude towards religious movements as well as the positioning of a scholar, such as Belġrādī, regarding these movements. The paper also points out that the Hamzevi movement was not only of a religious character, but also had a potential to be a carrier of socio-political changes. This paper provides basic academic literature on Hamzevis, a brief overview of the author’s biography, a Latin transcription of Belġrādī’s reports with a paraphrased translation into Bosnian, and an analysis of these data in the light of socio-political events in the Ottoman Empire during the second half of the 16th century.

Stability has always been the main safety issue for all marine vessels, and static stability evaluation is adequate for ship service [...]

Intra-tumour genetic heterogeneity (ITH) fuels cancer evolution. The role of clonal diversity and genetic complexity in the progression of clear-cell renal cell carcinomas (ccRCCs) has been characterised, but the ability to predict clinically relevant evolutionary trajectories remains limited. Here, towards enhancing this ability, we investigated spatial features of clonal diversification through a combined computational modelling and experimental analysis in the TRACERx Renal study. We observe through modelling that spatial patterns of tumour growth impact the extent and trajectory of subclonal diversification. Moreover, subpopulations with high clonal diversity, and parallel evolution events, are frequently observed near the tumour margin. In-silico time-course studies further showed that budding structures on the tumour surface could indicate future steps of subclonal evolution. Such structures were evident radiologically in 15 early-stage ccRCCs, raising the possibility that spatially resolved sampling of these regions, when combined with sequencing, may enable identification of evolutionary potential in early-stage tumours.

We introduce a sequential learning algorithm to address a robust controller tuning problem, which in effect, finds (with high probability) a candidate solution satisfying the internal performance constraint to a chance-constrained program which has black-box functions. The algorithm leverages ideas from the areas of randomised algorithms and ordinal optimisation, and also draws comparisons with the scenario approach; these have all been previously applied to finding approximate solutions for difficult design problems. By exploiting statistical correlations through black-box sampling, we formally prove that our algorithm yields a controller meeting the prescribed probabilistic performance specification. Additionally, we characterise the computational requirement of the algorithm with a probabilistic lower bound on the algorithm's stopping time. To validate our work, the algorithm is then demonstrated for tuning model predictive controllers on a diesel engine air-path across a fleet of vehicles. The algorithm successfully tuned a single controller to meet a desired tracking error performance, even in the presence of the plant uncertainty inherent across the fleet. Moreover, the algorithm was shown to exhibit a sample complexity comparable to the scenario approach.