IV.—THE PHILOSOPHY OF SAMUEL BUTLER

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Annealing schedule from population dynamics

We introduce a dynamical annealing schedule for population-based optimization algorithms with mutation. On the basis of a statistical mechanics formulation of the population dynamics, the mutation rate adapts to a value maximizing expected rewards at each time step. Thereby, the mutation rate is eliminated as a free parameter from the algorithm.Comment: 6 pages RevTeX, 4 figures PostScript; to be published in Phys. Rev.

The statistical mechanics of a polygenic characterunder stabilizing selection, mutation and drift

By exploiting an analogy between population genetics and statistical mechanics, we study the evolution of a polygenic trait under stabilizing selection, mutation, and genetic drift. This requires us to track only four macroscopic variables, instead of the distribution of all the allele frequencies that influence the trait. These macroscopic variables are the expectations of: the trait mean and its square, the genetic variance, and of a measure of heterozygosity, and are derived from a generating function that is in turn derived by maximizing an entropy measure. These four macroscopics are enough to accurately describe the dynamics of the trait mean and of its genetic variance (and in principle of any other quantity). Unlike previous approaches that were based on an infinite series of moments or cumulants, which had to be truncated arbitrarily, our calculations provide a well-defined approximation procedure. We apply the framework to abrupt and gradual changes in the optimum, as well as to changes in the strength of stabilizing selection. Our approximations are surprisingly accurate, even for systems with as few as 5 loci. We find that when the effects of drift are included, the expected genetic variance is hardly altered by directional selection, even though it fluctuates in any particular instance. We also find hysteresis, showing that even after averaging over the microscopic variables, the macroscopic trajectories retain a memory of the underlying genetic states.Comment: 35 pages, 8 figure

TOROS optical follow-up of the advanced LIGO–VIRGO O2 second observational campaign

We present themethods and results of the optical follow-up, conducted by the Transient Optical Robotic Observatory of the South Collaboration, of gravitational wave events detected during the Advanced LIGO–Virgo second observing run (2016 November–2017 August). Given the limited field of view (∼100 arcmin) of our observational instrumentation, we targeted galaxies within the area of high localization probability that were observable from our sites. We analysed the observations using difference imaging, followed by a random forest algorithm to discriminate between real and spurious transients. Our observations were conducted using telescopes at Estacion Astrofısica de Bosque Alegre, Cerro Tololo Inter-American Observatory, the Dr. Cristina V. Torres Memorial Astronomical Observatory, and an observing station in Salta, Argentina

Evaluation of aggregate and silicone-oil counts in pre-filled siliconized syringes: An orthogonal study characterising the entire subvisible size range.

Characterisation of particulates in therapeutic monoclonal antibody (mAb) formulations is routinely extended to the sub-visible size-range (0.1–10 μm). Additionally, with the increased use of pre-filled syringes (PFS), particle differentiation is required between proteinaceous and non-proteinaceous particles such as silicone-oil droplets. Here, three orthogonal techniques: Raster Image Correlation Spectroscopy (RICS), Resonance Mass Measurements (RMM) and Micro-Flow Imaging (MFI), were evaluated with respect to their sub-visible particle measurement and characterisation capabilities. Particle formation in mAb PFS solutions was evaluated with increasing polysorbate-20 (PS-20) concentrations. All three techniques provided complementary but distinct information on protein aggregate and silicone-oil droplet presence. PS-20 limited the generation of mAb aggregates during agitation, while increasing the number of silicone-oil droplets (PS-20 concentration dependant). MFI and RMM revealed PS-20 lead to the formation of larger micron-sized droplets, with RICS revealing an increase in smaller sub-micron droplets. Subtle differences in data sets complicate the apparent correlation between silicone-oil sloughing and mAb aggregates’ generation. RICS (though the use of a specific dye) demonstrates an improved selectivity for mAb aggregates, a broader measurement size-range and smaller sample volume requirement. Thus, RICS is proposed to add value to the currently available particle measurement techniques and enable informed decisions during mAb formulation development

Chronic inflammatory arthritis drives systemic changes in circadian energy metabolism

SignificanceRheumatoid arthritis (RA) is a debilitating chronic inflammatory disease in which symptoms exhibit a strong time-of-day rhythmicity. RA is commonly associated with metabolic disturbance and increased incidence of diabetes and cardiovascular disease, yet the mechanisms underlying this metabolic dysregulation remain unclear. Here, we demonstrate that rhythmic inflammation drives reorganization of metabolic programs in distal liver and muscle tissues. Chronic inflammation leads to mitochondrial dysfunction and dysregulation of fatty acid metabolism, including accumulation of inflammation-associated ceramide species in a time-of-day-dependent manner. These findings reveal multiple points for therapeutic intervention centered on the circadian clock, metabolic dysregulation, and inflammatory signaling

Metabolomics guided pathway analysis reveals link between cancer metastasis, cholesterol sulfate, and phospholipids

Background -- Cancer cells that enter the metastatic cascade require traits that allow them to survive within the circulation and colonize distant organ sites. As disseminating cancer cells adapt to their changing microenvironments, they also modify their metabolism and metabolite production. Methods -- A mouse xenograft model of spontaneous tumor metastasis was used to determine the metabolic rewiring that occurs between primary cancers and their metastases. An “autonomous” mass spectrometry-based untargeted metabolomic workflow with integrative metabolic pathway analysis revealed a number of differentially regulated metabolites in primary mammary fat pad (MFP) tumors compared to microdissected paired lung metastases. The study was further extended to analyze metabolites in paired normal tissues which determined the potential influence of metabolites from the microenvironment. Results -- Metabolomic analysis revealed that multiple metabolites were increased in metastases, including cholesterol sulfate and phospholipids (phosphatidylglycerols and phosphatidylethanolamine). Metabolite analysis of normal lung tissue in the mouse model also revealed increased levels of these metabolites compared to tissues from normal MFP and primary MFP tumors, indicating potential extracellular uptake by cancer cells in lung metastases. These results indicate a potential functional importance of cholesterol sulfate and phospholipids in propagating metastasis. In addition, metabolites involved in DNA/RNA synthesis and the TCA cycle were decreased in lung metastases compared to primary MFP tumors. Conclusions -- Using an integrated metabolomic workflow, this study identified a link between cholesterol sulfate and phospholipids, metabolic characteristics of the metastatic niche, and the capacity of tumor cells to colonize distant sites

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

The development and validation of measures to assess cooking skills and food skills

BACKGROUND: With the increase use of convenience food and eating outside the home environment being linked to the obesity epidemic, the need to assess and monitor individuals cooking and food skills is key to help intervene where necessary to promote the usage of these skills. Therefore, this research aimed to develop and validate a measure for cooking skills and one for food skills, that are clearly described, relatable, user-friendly, suitable for different types of studies, and applicable across all sociodemographic levels. METHODS: Two measures were developed in light of the literature and expert opinion and piloted for clarity and ease of use. Following this, four studies were undertaken across different cohorts (including a sample of students, both 'Food preparation novices' and 'Experienced food preparers', and a nationally representative sample) to assess temporal stability, psychometrics, internal consistency reliability and construct validity of both measures. Analysis included T-tests, Pearson's correlations, factor analysis, and Cronbach's alphas, with a significance level of 0.05. RESULTS: Both measures were found to have a significant level of temporal stability (P < 0.001). Factor analysis revealed three factors with eigenvalues over 1, with two items in a third factor outside the two suggested measures. The internal consistency reliability for the cooking skills confidence measure ranged from 0.78 to 0.93 across all cohorts. The food skills confidence measure's Cronbach's alpha's ranged from 0.85 to 0.94. The two measures also showed a high discriminate validity as there were significant differences (P < 0.05 for cooking skills confidence and P < 0.01 for food skills confidence) between Food preparation novices' and 'Experienced food preparers.' CONCLUSIONS: The cooking skills confidence measure and the food skills confidence measure have been shown to have a very satisfactory reliability, validity and are consistent over time. Their user-friendly applicability make both measures highly suitable for large scale cross-sectional, longitudinal and intervention studies to assess or monitor cooking and food skills levels and confidence