Transmission Distortion of MCT1 rs1049434 among Polish Elite Athletes

Background: To date, nearly 300 genetic markers were linked to endurance and power/strength traits. The current study aimed to compare genotype distributions and allele frequencies of the common polymorphisms: MCT1 rs1049434, NRF2 rs12594956, MYBPC3 rs1052373 and HFE rs1799945 in Polish elite athletes versus nonathletes. Methods: The study involved 101 male elite Polish athletes and 41 healthy individuals from the Polish population as a control group. SNP data were extracted from whole-genome sequencing (WGS) performed using the following parameters: paired reads of 150 bps, at least 90 Gb of data per sample with 300 M reads and 30x mean coverage. Results: All the analyzed polymorphisms conformed to Hardy-Weinberg equilibrium (HWE) in athletes and the control group, except the MCT1 rs1049434, where allele T was over-represented in the elite trainers' group. No significant between-group differences were found for analyzed polymorphisms. Conclusions: The MCT1 rs1049434 transmission distortion might be characteristic of Polish athletes and the effect of strict inclusion criteria. This result and the lack of statistically significant changes in the frequency of other polymorphisms between the groups might result from the small group size

Measurement Challenges for Cyber Cyber Digital Twins: Experiences from the Deployment of Facebook's WW Simulation System

A cyber cyber digital twin is a deployed software model that executes in tandem with the system it simulates, contributing to, and drawing from, the systems behaviour. This paper outlines Facebooks cyber cyber digital twin, dubbed WW, a twin of Facebooks WWW platform, built using web-enabled simulation. The paper focuses on the current research challenges and opportunities in the area of measurement. Measurement challenges lie at the heart of modern simulation. They directly impact how we use simulation outcomes for automated online and semi-Automated offline decision making. Measurements also encompas how we verify and validate those outcomes. Modern simulation systems are increasingly becoming more like cyber cyber digital twins, effectively moving from manual to automated decision making, hence, these measurement challenges acquire ever greater significance

Testing web enabled simulation at scale using metamorphic testing

We report on Facebook's deployment of MIA (Metamorphic Interaction Automaton). MIA is used to test Facebook's Web Enabled Simulation, built on a web infrastructure of hundreds of millions of lines of code. MIA tackles the twin problems of test flakiness and the unknowable oracle problem. It uses metamorphic testing to automate continuous integration and regression test execution. MIA also plays the role of a test bot, automatically commenting on all relevant changes submitted for code review. It currently uses a suite of over 40 metamorphic test cases. Even at this extreme scale, a non-trivial metamorphic test suite subset yields outcomes within 20 minutes (sufficient for continuous integration and review processes). Furthermore, our offline mode simulation reduces test flakiness from approximately 50% (of all online tests) to 0% (offline). Metamorphic testing has been widely-studied for 22 years. This paper is the first reported deployment into an industrial continuous integration system

Genetics of Exercise and Diet-Induced Fat Loss Efficiency: A Systematic Review

Physical exercise and dieting are well-known and effective methods for fat loss and improving cardiovascular health. However, different individuals often react differently to the same exercise regimen or dietary plan. While specific individuals may undergo substantial fat loss, others may observe only limited effects. A wide range of inter-individual variability in weight gain and changes in body composition induced by physical exercises and diets led to an investigation into the genetic factors that may contribute to the individual variations in such responses. This systematic review aimed at identifying the genetic markers associated with fat loss resulting from diet or exercise. A search of the current literature was performed using the PubMed database. Forty-seven articles met the inclusion criteria when assessing genetic markers associated with weight loss efficiency in response to different types of exercises and diets. Overall, we identified 30 genetic markers of fat-loss efficiency in response to different kinds of diets and 24 in response to exercise. Most studies (n = 46) used the candidate gene approach. We should aspire to the customized selection of exercise and dietary plans for each individual to prevent and treat obesity

Facebook’s Cyber–Cyber and Cyber–Physical Digital Twins

A cyber-cyber digital twin is a simulation of a software system. By contrast, a cyber-physical digital twin is a simulation of a non-software (physical) system. Although cyber-physical digital twins have received a lot of recent attention, their cyber-cyber counterparts have been comparatively overlooked. In this paper we show how the unique properties of cyber-cyber digital twins open up exciting opportunities for research and development. Like all digital twins, the cyber-cyber digital twin is both informed by and informs the behaviour of the twin it simulates. It is therefore a software system that simulates another software system, making it conceptually truly a twin, blurring the distinction between the simulated and the simulator. Cyber-cyber digital twins can be twins of other cyber-cyber digital twins, leading to a hierarchy of twins. As we shall see, these apparently philosophical observations have practical ramifications for the design, implementation and deployment of digital twins at Facebook

Supporting medical decisions for treating rare diseases through genetic programming

Bakurov, I., Castelli, M., Vanneschi, L., & Freitas, M. J. (2019). Supporting medical decisions for treating rare diseases through genetic programming. In P. Kaufmann, & P. A. Castillo (Eds.), Applications of Evolutionary Computation: 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Proceedings (pp. 187-203). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11454 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-16692-2_13. ISBN: 978-3-030-16691-5; Online ISBN: 978-3-030-16692-2Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centers like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.authorsversionpublishe

Inhibition of classical and alternative modes of respiration in Candida albicans leads to cell wall remodelling and increased macrophage recognition

The human fungal pathogen Candida albicans requires respiratory function for normal growth, morphogenesis and virulence. Mitochondria therefore represent an enticing target for the development of new antifungal strategies. This possibility is bolstered by the presence of characteristics specific to fungi. However, respiration in C. albicans, as in many fungal organisms, is facilitated by redundant electron transport mechanisms, making direct inhibition a challenge. In addition, many chemicals known to target the electron transport chain are highly toxic. Here we make use of chemicals with low toxicity to efficiently inhibit respiration in C. albicans. We find that use of the Nitric Oxide donor, Sodium Nitroprusside (SNP), and the alternative oxidase inhibitor, SHAM, prevents respiration, leads to a loss of viability and to cell wall rearrangements that increase the rate of uptake by macrophages in vitro and in vivo. We propose that SNP+SHAM treatment leads to transcriptional changes that drive cell wall re-arrangement but which also prime cells to activate transition to hyphal growth. In line with this we find that pre-treatment of C. albicans with SNP+SHAM leads to an increase in virulence. Our data reveals strong links between respiration, cell wall remodelling and activation of virulence factors. Our findings demonstrate that respiration in C. albicans can be efficiently inhibited with chemicals, which are not damaging to the mammalian host, but that we need to develop a deeper understanding of the roles of mitochondria in cellular signalling if they are to be developed successfully as a target for new antifungals

A Zebrafish Compound Screen Reveals Modulation of Neutrophil Reverse Migration as an Anti-Inflammatory Mechanism

Diseases of failed inflammation resolution are common and largely incurable. Therapeutic induction of inflammation resolution is an attractive strategy to bring about healing without increasing susceptibility to infection. However, therapeutic targeting of inflammation resolution has been hampered by a lack of understanding of the underlying molecular controls. To address this drug development challenge, we developed an in vivo screen for proresolution therapeutics in a transgenic zebrafish model. Inflammation induced by sterile tissue injury was assessed for accelerated resolution in the presence of a library of known compounds. Of the molecules with proresolution activity, tanshinone IIA, derived from a Chinese medicinal herb, potently induced inflammation resolution in vivo both by induction of neutrophil apoptosis and by promoting reverse migration of neutrophils. Tanshinone IIA blocked proinflammatory signals in vivo, and its effects are conserved in human neutrophils, supporting a potential role in treating human inflammation and providing compelling evidence of the translational potential of this screening strategy

A comparison of machine learning techniques for survival prediction in breast cancer

<p>Abstract</p> <p>Background</p> <p>The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established <it>70-gene signature</it>.</p> <p>Results</p> <p>We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection.</p> <p>Conclusions</p> <p>Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.</p