Digestibility in selected rainbow trout families and modelling of growth from the specific intake of digestible protein

The experiments aimed to clarify variations in digestibility of dietary nutrients in rainbow trout. Furthermore, the objective was to study how differences in digestibility might be related to growth and feed utilisation at various growth rates. When comparing the results from the experiments it appeared that particularly protein digestibility was closely related to specific growth rate and feed conversion ratio at high growth rates. As a tool to visualise the relationship between protein digestibility and growth of rainbow trout a growth model was developed based on the specific intake of digestible protein, and general assumptions on protein content and protein retention efficiency in rainbow trout. The model indicated that increased protein digestibility only partly explained growth increase and that additional factors were important for growth increment

Reconstruction of Hydraulic Data by Machine Learning

Numerical simulation models associated with hydraulic engineering take a wide array of data into account to produce predictions: rainfall contribution to the drainage basin (characterized by soil nature, infiltration capacity and moisture), current water height in the river, topography, nature and geometry of the river bed, etc. This data is tainted with uncertainties related to an imperfect knowledge of the field, measurement errors on the physical parameters calibrating the equations of physics, an approximation of the latter, etc. These uncertainties can lead the model to overestimate or underestimate the flow and height of the river. Moreover, complex assimilation models often require numerous evaluations of physical solvers to evaluate these uncertainties, limiting their use for some real-time operational applications. In this study, we explore the possibility of building a predictor for river height at an observation point based on drainage basin time series data. An array of data-driven techniques is assessed for this task, including statistical models, machine learning techniques and deep neural network approaches. These are assessed on several metrics, offering an overview of the possibilities related to hydraulic time-series. An important finding is that for the same hydraulic quantity, the best predictors vary depending on whether the data is produced using a physical model or real observations.Comment: Submitted to SimHydro 201

Ownership-dependent mating tactics of minor males of the beetle Librodor japonicus (Nitidulidae) with intra-sexual dimorphism of mandibles

Intra-sexual dimorphism is found in the weapons of many male beetles. Different behavioral tactics to access females between major and minor males, which adopt fighting and alternative tactics, respectively, are thought to maintain the male dimorphism. In these species major males have enlarged weapons that they use in fights with rival males. Minor males also have small weapons in some of these species, and it is unclear why these males possess weapons. We examined the hypothesis that minor males might adopt a fighting tactic when their status was relatively high in comparison with that of other males (e.g., ownership of a territory). We observed the behavioral tactics of major and minor males of the beetle Librodor japonicus, whose males have a dimorphism of their mandibles. Major males fought for resources, whereas minor males adopted two status-dependent tactics, fighting and sneaking, to access females, depending on their ownership of a sap site. We suggest that ownership status-dependent mating tactics in minor males may maintain the intra-sexual dimorphism in this beetle.</p

Status of Pandemic Influenza Vaccination and Factors Affecting It in Pregnant Women in Kahramanmaras, an Eastern Mediterranean City of Turkey

BACKGROUND: Pregnant women are a target group for receipt of influenza vaccine because there appears to be an elevated mortality and morbidity rate associated with influenza virus infection in pregnant women. The goal of this study is to determine the factors affecting the decisions of pregnant women in Turkey to be vaccinated or not for 2009 H1N1 influenza. METHODOLOGY: We enrolled 314 of 522 (60.2%) pregnant women who attended to the antenatal clinics of the Medical Faculty of Kahramanmaras Sutcuimam University's Department of Gynecology and Obstetrics between December 23, 2009, and February 1, 2010. We developed a 48-question survey which was completed in a face-to-face interview at the clinic with each pregnant woman. PRINCIPAL FINDINGS: Of the 314 pregnant women, 27.4% were in the first trimester, 33.8% were in the second trimester, and 38.8% were in the third trimester. Twenty-eight pregnant women (8.9%) got vaccinated. Of all the women interviewed, 68.5% stated that they were comfortable with their decisions about the vaccine, 7.3% stated they were not comfortable, and 24.2% stated that they were hesitant about their decisions. The probability of receiving the 2009 H1N1 vaccine was 3.46 times higher among working women than housewives, 1.85 times higher among women who have a child than those who do not, and 1.29 times higher among women with a high-school education or higher than those with only a secondary-school education and below. Correct knowledge about the minimal risks associated with receipt of influenza vaccine were associated with a significant increase in the probability of receiving the 2009 H1N1 vaccine. CONCLUSIONS/SIGNIFICANCE: The number of pregnant women in the study group who received the 2009 H1N1 vaccine was very low (8.9%) and two-thirds of them stated that they were comfortable with their decisions concerning the vaccine. Our results may have implications for public health measures to increase the currently low vaccination rate among pregnant women. Further studies are required to confirm whether our findings generalize to other influenza seasons and other settings

Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Modelling informative time points: an evolutionary process approach

Real time series sometimes exhibit various types of "irregularities": missing observations, observations collected not regularly over time for practical reasons, observation times driven by the series itself, or outlying observations. However, the vast majority of methods of time series analysis are designed for regular time series only. A particular case of irregularly spaced time series is that in which the sampling procedure over time depends also on the observed values. In such situations, there is stochastic dependence between the process being modelled and the times of the observations. In this work, we propose a model in which the sampling design depends on all past history of the observed processes. Taking into account the natural temporal order underlying available data represented by a time series, then a modelling approach based on evolutionary processes seems a natural choice. We consider maximum likelihood estimation of the model parameters. Numerical studies with simulated and real data sets are performed to illustrate the benefits of this model-based approach.- The authors acknowledge Foundation FCT (FundacAo para a Ciencia e Tecnologia) as members of the research project PTDC/MAT-STA/28243/2017 and Center for Research & Development in Mathematics and Applications of Aveiro University within project UID/MAT/04106/2019

Poor survival outcomes in HER2 positive breast cancer patients with low grade, node negative tumours

We present a retrospective analysis on a cohort of low-grade, node-negative patients showing that human epidermal growth factor receptor 2 (HER2) status significantly affects the survival in this otherwise very good prognostic group. Our results provide support for the use of adjuvant trastuzumab in patients who are typically classified as having very good prognosis, not routinely offered standard chemotherapy, and who as such do not fit current UK prescribing guidelines for trastuzumab

Why do Particle Clouds Generate Electric Charges?

Grains in desert sandstorms spontaneously generate strong electrical charges; likewise volcanic dust plumes produce spectacular lightning displays. Charged particle clouds also cause devastating explosions in food, drug and coal processing industries. Despite the wide-ranging importance of granular charging in both nature and industry, even the simplest aspects of its causes remain elusive, because it is difficult to understand how inert grains in contact with little more than other inert grains can generate the large charges observed. Here, we present a simple yet predictive explanation for the charging of granular materials in collisional flows. We argue from very basic considerations that charge transfer can be expected in collisions of identical dielectric grains in the presence of an electric field, and we confirm the model's predictions using discrete-element simulations and a tabletop granular experiment

System Theoretic Process Analysis: a literature survey on the approaches used for improving the safety in complex systems

Computer systems are becoming increasingly complex, specially interactive software systems, namely software user interfaces. The scientic community relies on dierent methods to assess their safety. This article provides an updated literature survey on hazard analysis approaches used to improve the safety of complex systems. To support the survey, we conceptualise complex systems, highlighting the challenge in terms of assessing their safety. We provide a brief overview on the approaches historically available to tackle issues in those systems, along with their most common methods. Finally, the article focuses in one method of a non-traditional approach, which is described in more details, along with some of its extensions, which seeks to improve the hazard analysis in complex systems