The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

In view of the urgency and multiple targets for biodiversity conservation, and the scarcity of financial resources and political will, it is important to reach a consensus on which actions should be given priority. Multicriteria techniques can contribute to the improvement of the conservation prioritization process and different initiatives and approaches could benefit from their use. Here we present an exercise in prioritizing and seeking consensus using a multicriteria technique to rank the objectives proposed by three national action plans in Brazil. This ranking allowed: 1) to compare whether the order of appearance of the actions in these plans are in fact considered to be priorities by the public that drafted these documents; 2) to identify idiosyncrasies between what an official public policy document says and what the people responsible for execution think of these actions; and 3) to identify priority actions common to more than one plan – which can optimize the biodiversity conservation process. We identified a lack of congruence between the order of objectives identified in the action plans and the ranking of priorities made by the specialists who prepared these same plans. A set of common objectives was identified as a priority among different Plans. However, these common objectives were not always a priority in their respective Action Plans. We warn that the lack of congruence observed can compromise the execution of some of the general objectives, humpering or even preventing the conservation of the target species of these documents.

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 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.

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.

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 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.

Low indoor humidity has been shown to influence the transmission of respiratory diseases via air. A certain proportion of sick leave in offices is therefore attributable to dryness of air. An improvement in these conditions thus means a reduction in sick leave, which is accompanied by cost savings for companies. Vertical indoor greening has a verifiable positive effect on air humidity, especially in winter months. In this article, the correlation between improved air humidity in greened rooms and reduction of sick leave due to improved air humidity was described. The resulting indirect economic effect was determined by comparing the costs for construction, green care, and technical maintenance of indoor greenery with savings due to lower sick leave. Based on long-term measurement data on air humidity and temperature, and actual cost values for three buildings, located in Vienna, Austria, with 6 greened and 3 reference rooms without greenery, the correlation of the method was derived and finally formulated in a generalized way using dimensioning factors. Only considering the influence on air humidity, profitability of 6.6 m2 vertical greening installed in an example office with six workplaces equipped with technical ventilation and saving of two sick days already results after about 4.5 years.

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 [...]

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.

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.