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PLoS one 11(1), e0147725 (2016). doi:10.1371/journal.pone.0147725, Published by PLoS, Lawrence, Kan.

This paper presents regulation of an asynchronous induction motor so as to create a stable vacuum milk pump using Variable Frequency Drive (VFD). Contribution includes providing information about the usage of the VFD, which regulates the activity of an asynchronous induction motor so that the vacuum pump milking machine creates stable vacuum. The paper describes the functional and time dependence of input values and output parameters of frequency converters at changing electric motor speed. For simulation and verification the milking process a mathematical model of the milking machine was created. The simulation was verified in Matlab/Simulink software. The constructed mathematical model showed symmetric regulation. Control model symmetry was verified at the laboratory of milking machine. The possibility to remove the control valve from milking equipment was proven using the measured data. It was found that constant vacuum values can be maintained. A constant vacuum can be maintained by changing vacuum pump speed. This control is of an accepted standard (ISO 5707: 2007). The power saving control values (on the milking equipment) of the VFD were positive throughout the measuring range. The performance of the milking vacuum pump is normally designed from the maximum air consumption of the milking machine at nominal vacuum (50 kPa), and a performance reserve is added to this. This means that the pump is operated between the ranges 7.53 and 15.06 dm3 s−1. By using a vacuum pump controlled by a VFD, power savings can be achieved from 32.50% to 54.02% compared to a control valve.

[Abstract] Our current knowledge on the crocodyliform evolution is strongly biased towards the skull morphology, and the postcranial skeleton is usually neglected in many taxonomic descriptions. However, it is logical to expect that it can contribute with its own phylogenetic signal. In this paper, the changes in the tree topology caused by the addition of the postcranial information are analysed for the family Allodaposuchidae, the most representative eusuchians in the latest Cretaceous of Europe. At present, different phylogenetic hypotheses have been proposed for this group without reaching a consensus. The results of this paper evidence a shift in the phylogenetic position when the postcranium is included in the dataset, pointing to a relevant phylogenetic signal in the postcranial elements. Finally, the phylogenetic relationships of allodaposuchids within Eusuchia are reassessed; and the internal relationships within Allodaposuchidae are also reconsidered after an exhaustive revision of the morphological data. New and improved diagnoses for each species are here provided. This work was supported by postdoctoral grants funded by the program “Axudas á etapa postdoutoral da Xunta de Galicia 2017 – Modalidade A” (ED481B 2017/027) (https://www.xunta.gal/) and the program “Ayudas Juan de la Cierva – Formación 2019” (FJC2019-042583-I) (https://www.ciencia.gob.es/). Additional support to visit palaeontological collections was received from the program “Axudas á investigación 2017 e 2018” of the Universidade da Coruña (https://www.udc.es/), and from the SYNTHESYS+ Project (DE-TAF-2604) which is financed by the European Commission via the H2020 Research Infrastructure programme (https://www.synthesys.info/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Xunta de Galicia; ED481B 2017/027

Objective Digital nudging has been mooted as a tool to alter user privacy behavior. However, empirical studies on digital nudging have yielded divergent results: while some studies found nudging to be highly effective, other studies found no such effects. Furthermore, previous studies employed a wide range of digital nudges, making it difficult to discern the effectiveness of digital nudging. To address these issues, we performed a systematic review of empirical studies on digital nudging and information disclosure as a specific privacy behavior. Method The search was conducted in five digital libraries and databases: Scopus, Google Scholar, ACM Digital Library, Web of Science, and Science Direct for peer-reviewed papers published in English after 2006, examining the effects of various nudging strategies on disclosure of personal information online. Results The review unveiled 78 papers that employed four categories of nudge interventions: presentation, information, defaults, and incentives, either individually or in combination. A meta-analysis on a subset of papers with available data (n = 54) revealed a significant small-to-medium sized effect of the nudge interventions on disclosure (Hedges’ g = 0.32). There was significant variation in the effectiveness of nudging (I2 = 89%), which was partially accounted for by interventions to increase disclosure being more effective than interventions to reduce disclosure. No evidence was found for differences in the effectiveness of nudging with presentation, information, defaults, and incentives interventions. Conclusion Identifying ways to nudge users into making more informed and desirable privacy decisions is of significant practical and policy value. There is a growing interest in digital privacy nudges for disclosure of personal information, with most empirical papers focusing on nudging with presentation. Further research is needed to elucidate the relative effectiveness of different intervention strategies and how nudges can confound one another.

In ecology, species can mitigate their extinction risks in uncertain environments by diversifying individual phenotypes. This observation is quantified by the theory of bet-hedging, which provides a reason for the degree of phenotypic diversity observed even in clonal populations. Bet-hedging in well-mixed populations is rather well understood. However, many species underwent range expansions during their evolutionary history, and the importance of phenotypic diversity in such scenarios still needs to be understood. In this paper, we develop a theory of bet-hedging for populations colonizing new, unknown environments that fluctuate either in space or time. In this case, we find that bet-hedging is a more favorable strategy than in well-mixed populations. For slow rates of variation, temporal and spatial fluctuations lead to different outcomes. In spatially fluctuating environments, bet-hedging is favored compared to temporally fluctuating environments. In the limit of frequent environmental variation, no opportunity for bet-hedging exists, regardless of the nature of the environmental fluctuations. For the same model, bet-hedging is never an advantageous strategy in the well-mixed case, supporting the view that range expansions strongly promote diversification. These conclusions are robust against stochasticity induced by finite population sizes. Our findings shed light on the importance of phenotypic heterogeneity in range expansions, paving the way to novel approaches to understand how biodiversity emerges and is maintained., Author summary Ecological populations are often exposed to unpredictable and variable environmental conditions. A number of strategies have evolved to cope with such uncertainty. One of them is stochastic phenotypic switching, by which some individuals in the community are enabled to tackle adverse conditions, even at the price of reducing overall growth in the short term. In this paper, we study the effectiveness of these “bet-hedging” strategies for a population in the process of colonizing new territory. We show that bet-hedging is more advantageous when the environment varies spatially rather than temporally, and infrequently rather than frequently.

The interaction between phenotypic plasticity, e.g. learning, and evolution is an important topic both in Evolutionary Biology and Machine Learning. The evolution of learning is commonly studied in Evolutionary Biology, while the use of an evolutionary process to improve learning is of interest to the field of Machine Learning. This paper takes a different point of view by studying the effect of learning on the evolutionary process, the so-called Baldwin effect. A well-studied result in the literature about the Baldwin effect is that learning affects the speed of convergence of the evolutionary process towards some genetic configuration, which corresponds to the environment-induced plastic response. This paper demonstrates that learning can change the outcome of evolution, i.e., lead to a genetic configuration that does not correspond to the plastic response. Results are obtained both analytically and experimentally by means of an agent-based model of a foraging task, in an environment where the distribution of resources follows seasonal cycles and the foraging success on different resource types is conditioned by trade-offs that can be evolved and learned. This paper attempts to answer a question that has been overlooked: whether learning has an effect on what genotypic traits are evolved, i.e. the selection of a trait that enables a plastic response changes the selection pressure on a different trait, in what could be described as co-evolution between different traits in the same genome., PLoS ONE, 14 (9), ISSN:1932-6203

PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson’s Disease, using symptom measurements captured continuously by wearables (passive monitoring) or by high-use-frequency smartphone apps (active monitoring). The goal of the toolkit is to help address the current lack of algorithmic and model transparency in this area by facilitating open sharing of standardised methods that allow the comparison of results across multiple centres and hardware variations. PDkit adopts the information-processing pipeline abstraction incorporating stages for data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. Additionally, a dataflow programming framework is provided to support high performance computations. The practical use of PDkit is demonstrated in the context of the CUSSP clinical trial in the UK. The toolkit is implemented in the python programming language, the de facto standard for modern data science applications, and is widely available under the MIT license., Author summary Parkinson’s Disease is the fastest growing neurological condition affecting millions of people across the world. People with Parkinson’s suffer from a variety of symptoms that result in diminished ability to move, eat, remember or sleep. Research in new treatments are limited because the clinical tools used to assess its symptoms are subjective, require considerable time to perform and specialised skills and can only detect coarse-grain changes. To address this situation, clinicians are turning to smartphone apps and wearables to create new ways to assess symptoms that are more sensitive to change and can be applied frequently at home by patients and their carers. In this paper, we discuss PDkit, an open source toolkit that we developed to help address this current lack of algorithmic and model transparency. Adopting PDkit facilitates the open sharing of standardised methods and can accelerate the development of new methods and system to assess Parkinson’s and enables research groups to innovate. The toolkit provides funcionality that support data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. The practical use of PDkit is demonstrated via its use by the CUSSP clinical trial conducted in the UK.

Seabirds are one of the most at-risk groups, with many species in decline. In Scandinavia, seabirds are at a heightened risk of extinction due to accelerated global warming. Norway is home to significant portion of the European Atlantic Puffin (Fratercula arctica) populations, but Norwegian populations have declined significantly during the last decades. In this paper we use biometric data from modern and archaeologicalF.arcticaspecimens to investigate patterns in body size variation over time of this iconic species. We aimed to set out a baseline for our archaeological comparison by firstly investigating whether modern subspecies ofF.arcticaare reflected in the osteological characters and are enough to distinguish subspecies from the bones alone. We then investigated if archaeological remains ofF.arcticadiffer in size from the modern subspecies. Our results show that the subspeciesFratercula arctica naumanniwas distinctly larger than the other subspecies. However,Fratercula arctica arcticaandFratercula arctica grabaewere difficult to separate based on size. This generally supports ornithological observations. Post-MedievalF.arcticabones from Måsøy were similar to modernF.a.arcticapopulations. The mid-Holocene remains from Dollsteinhola overlaps with the modern size ranges ofF.a.arcticaandF.a.grabaebut are generally shorter and more robust. Dollsteinhola is located close to the borders of the modern breeding ranges of bothF.a.arcticaandF.a.grabae. We consider it therefore likely that given the mid-Holocene climatic oscillations, breeding ranges of the two subspecies shifted north or south accordingly. However, this does not explain the different proportions of the Dollsteinhola specimens. Our data provide the first evidence for shifting distributions in ancient Atlantic Puffins and represent the first osteological analysis ofFratercula arcticasubspecies.

Copyright: © 2021 Lampert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., Introduction: There has been growing interest in community gardens as an effective and affordable health promotion strategy. However, most available evidence is derived from qualitative studies, whereas quantitative research on this subject is limited. Objectives: To synthetize the literature about physical and mental health outcomes associated with community gardening. Two main questions were addressed: a) is there evidence, from quantitative studies, that community gardening is associated to physical and mental health and well-being of non-institutionalized individuals? b) Does community gardening provokes any discomfort in terms of physical health, i.e., bodily pain, to their beneficiaries? Methods: A systematic review of the literature was carried out following PRISMA guidelines by searching relevant electronic databases (PubMed, Scopus, and Web of Science). Empirical, quantitative studies published in English with no restrictions concerning the date of publication were considered eligible. The quality of the evidence was appraised using the tool developed by the National Heart, Lung, and Blood Institute of the National Institutes of Health for Observational Cohort and Cross-Sectional Studies. Results: Overall, 8 studies were considered eligible, of which seven studies were rated as having good methodological quality (one scored as fair). Community gardeners had significantly better health outcomes than their neighbours not engaged in gardening activities in terms of life satisfaction, happiness, general health, mental health, and social cohesion. Conclusion: Community gardens are associated to health gains for their users, irrespective of age, being an affordable and efficient way of promoting physical and mental health and well-being. To encourage the design, maintenance, and prospective evaluation of supportive urban environments promoting healthy and, at the same time, sustainable lifestyles, is essential to achieve public health gains and environmental sustainability., This literature review was funded by the Fundação para a Ciência e a Tecnologia and by the Empresa Municipal de Ambiente de Cascais through a doctoral grant to the first author of this paper (PDE/BDE/122672/2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The purpose of this paper is to examine, whether and under which conditions humans are able to predict the putting distance of a robotic device. Based on the "flash-lag effect" (FLE) it was expected that the prediction errors increase with increasing putting velocity. Furthermore, we hypothesized that the predictions are more accurate and more confident if human observers operate under full vision (F-RCHB) compared to either temporal occlusion (I-RCHB) or spatial occlusion (invisible ball, F-RHC, or club, F-B). In two experiments, 48 video sequences of putt movements performed by a BioRob robot arm were presented to thirty-nine students (age: 24.49±3.20 years). In the experiments, video sequences included six putting distances (1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 m; experiment 1) under full versus incomplete vision (F-RCHB versus I-RCHB) and three putting distances (2. 0, 3.0, and 4.0 m; experiment 2) under the four visual conditions (F-RCHB, I-RCHB, F-RCH, and F-B). After the presentation of each video sequence, the participants estimated the putting distance on a scale from 0 to 6 m and provided their confidence of prediction on a 5-point scale. Both experiments show comparable results for the respective dependent variables (error and confidence measures). The participants consistently overestimated the putting distance under the full vision conditions; however, the experiments did not show a pattern that was consistent with the FLE. Under the temporal occlusion condition, a prediction was not possible; rather a random estimation pattern was found around the centre of the prediction scale (3 m). Spatial occlusion did not affect errors and confidence of prediction. The experiments indicate that temporal constraints seem to be more critical than spatial constraints. The FLE may not apply to distance prediction compared to location estimation.

The paper deals with the impact of chosen geometric and material factors on maximal stresses in carotid atherosclerotic plaque calculated using patient-specific finite element models. These stresses are believed to be decisive for the plaque vulnerability but all applied models suffer from inaccuracy of input data, especially when obtained in vivo only. One hundred computational models based on ex vivo MRI are used to investigate the impact of wall thickness, MRI slice thickness, lipid core and fibrous tissue stiffness, and media anisotropy on the calculated peak plaque and peak cap stresses. The investigated factors are taken as continuous in the range based on published experimental results, only the impact of anisotropy is evaluated by comparison with a corresponding isotropic model. Design of Experiment concept is applied to assess the statistical significance of these investigated factors representing uncertainties in the input data of the model. The results show that consideration of realistic properties of arterial wall in the model is decisive for the stress evaluation; assignment of properties of fibrous tissue even to media and adventitia layers as done in some studies may induce up to eightfold overestimation of peak stress. The impact of MRI slice thickness may play a key role when local thin fibrous cap is present. Anisotropy of media layer is insignificant, and the stiffness of fibrous tissue and lipid core may become significant in some combinations.

We study the literature on willingness to pay (WTP) for local food by applying meta-regression analysis to a set of 35 eligible research papers that provide 86 estimates on consumers’ WTP for the attribute “local.” An analysis of the distribution of WTP measures suggests the presence of publication selection bias that favors larger and statistically significant results. The analyzed literature provides evidence for statistically significant differences among consumers’ WTP for various types of product. Moreover, we find that the methodological approach (choice experiments vs. other approaches) and the analyzed country can have a significant influence on the generated WTP for local., PLoS ONE, 14 (5), ISSN:1932-6203

Dual Energy X-Ray Absorptiometry (DXA) is currently the most widely adopted non-invasive clinical technique to assess bone mineral density and bone mineral content in human research and represents the primary tool for the diagnosis of osteoporosis. DXA measures areal bone mineral density, BMD, which does not account for the three-dimensional structure of the vertebrae and for the distribution of bone mass. The result is that longitudinal DXA can only predict about 70% of vertebral fractures. This study proposes a complementary tool, based on Finite Element (FE) models, to improve the DXA accuracy. Bone is simulated as elastic and inhomogeneous material, with stiffness distribution derived from DXA greyscale images of density. The numerical procedure simulates a compressive load on each vertebra to evaluate the local minimum principal strain values. From these values, both the local average and the maximum strains are computed over the cross sections and along the height of the analysed bone region, to provide a parameter, named Strain Index of Bone (SIB), which could be considered as a bone fragility index. The procedure is initially validated on 33 cylindrical trabecular bone samples obtained from porcine lumbar vertebrae, experimentally tested under static compressive loading. Comparing the experimental mechanical parameters with the SIB, we could find a higher correlation of the ultimate stress, σULT, with the SIB values (R2adj = 0.63) than that observed with the conventional DXA-based clinical parameters, i.e. Bone Mineral Density, BMD (R2adj = 0.34) and Trabecular Bone Score, TBS (R2adj = -0.03). The paper finally presents a few case studies of numerical simulations carried out on human lumbar vertebrae. If our results are confirmed in prospective studies, SIB could be used-together with BMD and TBS-to improve the fracture risk assessment and support the clinical decision to assume specific drugs for metabolic bone diseases.

A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the ‘true’ underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated ‘ground-truth’ dynamical systems as well as on experimental fMRI time series, and demonstrate that the learnt dynamics harbors task-related nonlinear structure that a linear dynamical model fails to capture. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research., PLoS Computational Biology, 15 (8), ISSN:1553-734X, ISSN:1553-7358

Mitochondrial disorders associated with genetic defects of the ATP synthase are among the most deleterious diseases of the neuromuscular system that primarily manifest in newborns. Nevertheless, the number of established animal models for the elucidation of the molecular mechanisms behind such pathologies is limited. In this paper, we target the Drosophila melanogaster gene encoding for the ATP synthase subunit c, ATPsynC, in order to create a fruit fly model for investigating defects in mitochondrial bioenergetics and to better understand the comprehensive pathological spectrum associated with mitochondrial ATP synthase dysfunctions. Using P-element and EMS mutagenesis, we isolated a set of mutations showing a wide range of effects, from larval lethality to complex pleiotropic phenotypes encompassing developmental delay, early adult lethality, hypoactivity, sterility, hypofertility, aberrant male courtship behavior, locomotor defects and aberrant gonadogenesis. ATPsynC mutations impair ATP synthesis and mitochondrial morphology, and represent a powerful toolkit for the screening of genetic modifiers that can lead to potential therapeutic solutions. Furthermore, the molecular characterization of ATPsynC mutations allowed us to better understand the genetics of the ATPsynC locus and to define three broad pathological consequences of mutations affecting the mitochondrial ATP synthase functionality in Drosophila: i) pre-adult lethality; ii) multi-trait pathology accompanied by early adult lethality; iii) multi-trait adult pathology. We finally predict plausible parallelisms with genetic defects of mitochondrial ATP synthase in humans., This work was supported by grants from Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) to C.C. and University of Bari D.R. n. 12939 to D.P.

This paper describes the role of ambient pressure in self-heating torrefaction of livestock manure. We explored the initiating temperatures required to cause self-heating of wet dairy cattle manure at different ambient pressures (0.1, 0.4, 0.7, and 1.0 MPa). Then, we conducted proximate, elemental, and calorific analyses of biochar torrefied at 210, 250, and 290 degrees C. The results showed that self-heating was induced at 155 degrees C or higher for 0.1 MPa and at 115 degrees C or lower for 0.4 MPa or higher. The decrease of the initiating temperature at elevated pressure was due not only to more oxygen, but also to the retention of moisture that can promote chemical oxidation of manure. Biochar yields decreased with increasing torrefaction temperature and pressure, and the yield difference at 0.1 and 1.0 MPa was more substantial at lower temperatures: a 29.8, 16.4, and 9.4% difference at 210, 250, and 290 degrees C, respectively. Proximate and elemental analyses showed that elevated pressure promotes devolatilization, deoxygenation, and coalification compared to atmospheric pressure; its impact, however, was less at higher temperatures as the torrefaction temperature became more dominant. Calorific analysis revealed that elevated pressure can increase the higher heating value (HHV) on a dry and ash-free basis at 210 degrees C because of the increase in carbon content, but its impact is limited at 250 and 290 degrees C. Meanwhile, the HHV on a dry basis exhibited the opposite trend due primarily to an enlargement of ash content. The present study revealed that ambient pressure considerably affects the initiating temperature of self-heating and the chemical properties of biochar at a low torrefaction temperature.

Neural mechanisms which bind items into sequences have been investigated in a large body of research in animal neurophysiology and human neuroimaging. However, a major problem in interpreting this data arises from a fact that several unrelated processes, such as memory load, sensory adaptation, and reward expectation, also change in a consistent manner as the sequence unfolds. In this paper we use computational simulations and data from two fMRI experiments to show that a host of unrelated neural processes can masquerade as sequence representations. We show that dissociating such unrelated processes from a dedicated sequence representation is an especially difficult problem for fMRI data, which is almost exclusively the modality used in human experiments. We suggest that such fMRI results must be treated with caution and in many cases the assumed neural representation might actually reflect unrelated processes.

Smartphones and other mobile devices have fundamentally changed patterns of Internet use in everyday life by making online access constantly available. The present paper offers a theoretical explication and empirical assessment of the concept of online vigilance, referring to users' permanent cognitive orientation towards online content and communication as well as their disposition to exploit these options constantly. Based on four studies, a validated and reliable self-report measure of online vigilance was developed. In combination, the results suggest that the Online Vigilance Scale (OVS) shows a stable factor structure in various contexts and user populations and provides future work in communication, psychology, and other social sciences with a new measure of the individual cognitive orientation towards ubiquitous online communication.

By analyzing a unique dataset of more than 270,000 scientists, we discovered substantial gender differences in scientific collaborations. While men are more likely to collaborate with other men, women are more egalitarian. This is consistently observed over all fields and regardless of the number of collaborators a scientist has. The only exception is observed in the field of engineering, where this gender bias disappears with increasing number of collaborators. We also found that the distribution of the number of collaborators follows a truncated power law with a cut-off that is gender dependent and related to the gender differences in the number of published papers. Considering interdisciplinary research, our analysis shows that men and women behave similarly across fields, except in the case of natural sciences, where women with many collaborators are more likely to have collaborators from other fields., PLoS ONE, 12 (5), ISSN:1932-6203

We compare the use of multi-criteria decision analysis (MCDA)–or more precisely, models used in multi-attribute value theory (MAVT)–to integrated assessment (IA) models for supporting long-term water supply planning in a small town case study in Switzerland. They are used to evaluate thirteen system scale water supply alternatives in four future scenarios regarding forty-four objectives, covering technical, social, environmental, and economic aspects. The alternatives encompass both conventional and unconventional solutions and differ regarding technical, spatial and organizational characteristics. This paper focuses on the impact assessment and final evaluation step of the structured MCDA decision support process. We analyze the performance of the alternatives for ten stakeholders. We demonstrate the implications of model assumptions by comparing two IA and three MAVT evaluation model layouts of different complexity. For this comparison, we focus on the validity (ranking stability), desirability (value), and distinguishability (value range) of the alternatives given the five model layouts. These layouts exclude or include stakeholder preferences and uncertainties. Even though all five led us to identify the same best alternatives, they did not produce identical rankings. We found that the MAVT-type models provide higher distinguishability and a more robust basis for discussion than the IA-type models. The needed complexity of the model, however, should be determined based on the intended use of the model within the decision support process. The best-performing alternatives had consistently strong performance for all stakeholders and future scenarios, whereas the current water supply system was outperformed in all evaluation layouts. The best-performing alternatives comprise proactive pipe rehabilitation, adapted firefighting provisions, and decentralized water storage and/or treatment. We present recommendations for possible ways of improving water supply planning in the case study and beyond. ISSN:1932-6203

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstrallike features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. This work was supported by the Spanish Government grant TEC2014-53390-P.

Target agents are peculiar oncological drugs which differ from the traditional therapies in their ability of recognizing specific molecules expressed by tumor cells and microenvironment. Thus, their toxicity is generally lower than that associated to chemotherapy, and they represent nowadays a new standard of care in a number of tumors. This paper deals with the relationship between economic costs and toxicity of target agents. At this aim, a cluster analysis-based exploration of the main features of a large collection of them is carried out, with a specific focus on the variables leading to the identification of their toxicity and related costs. The analysis of the toxicity is based on the Severe Adverse Events (SAE) and Discontinuation (D) rates of each target agent considering data published on PubMed from 1965 to 2016 in the phase II and III studies that have led to the approval of these drugs for cancer patients by US Food and Drug Administration. The construction of the dataset represents a key step of the research, and is grounded on the critical analysis of a wide set of clinical studies. In order to capture different evaluation strategies of the toxicity, clustering is performed according to three different criteria (including Voronoi tessellation). Our procedure allows us to identify 5 different groups of target agents pooled by similar SAE and D rates and, at the same time, 3 groups based on target agents' costs for 1 month and for the median whole duration of therapy. Results highlight several specific regularities for toxicity and costs. This study present several limitations, being realized starting from clinical trials and not from individual patients' data. However, a macroscopic perspective suggests that costs are rather heterogeneous, and they do not clearly follow the clustering based on SAE and D rates.

Angiogenesis modelling is an important tool to understand the underlying mechanisms yielding tumour growth. Nevertheless, there is usually a gap between models and experimental data. We propose a model based on the intrinsic microscopic reactions defining the angiogenesis process to link experimental data with previous macroscopic models. The microscopic characterisation can describe the macroscopic behaviour of the tumour, which stability analysis reveals a set of predicted tumour states involving different morphologies. Additionally, the microscopic description also gives a framework to study the intrinsic stochasticity of the reactive system through the resulting Langevin equation. To follow the goal of the paper, we use available experimental information on the Lewis lung carcinoma to infer meaningful parameters for the model that are able to describe the different stages of the tumour growth. Finally we explore the predictive capabilities of the fitted model by showing that fluctuations are determinant for the survival of the tumour during the first week and that available treatments can give raise to new stable tumour dormant states with a reduced vascular network. Work of M.J.P. and M.B. was supported by the Ministry of Science and Higher Education Republic of Poland, within the Iuventus Plus Grant: “Mathematical modelling of neoplastic processes” grant No. IP2011 041971. P.G. and R.P.-C. thank the Wellcome Trust for financial support under grant 098325.

Media, and particularly TV media, have a great impact on the general public. In recent years, spatial patterns of information and the relevance of intangible geographies have become increasingly important. Gatekeeping plays a critical role in the selection of information that is transformed into media. Therefore, gatekeeping, through national media, also co-forms the generation of mental maps. In this paper, correspondence analysis (a statistical method) combined with cloud lines (a new visual analytics technique) is used to analyze how individual major regional events in one of the post-communist countries, the Czech Republic, penetrate into the media on a national scale. Although national news should minimize distortions about regions, this assumption has not been verified by our research. Impressions presented by the media of selected regions that were markedly influenced by one or several events in those regions demonstrate that gatekeepers, especially news reporters, functioned as a filter by selecting only a few specific, and in many cases, unusual events for dissemination. Web of Science 11 11 art. no. e0165527

Next-generation sequencing (NGS) technologies have led to a huge amount of genomic data that need to be analyzed and interpreted. This fact has a huge impact on the DNA sequence alignment process, which nowadays requires the mapping of billions of small DNA sequences onto a reference genome. In this way, sequence alignment remains the most time-consuming stage in the sequence analysis workflow. To deal with this issue, state of the art aligners take advantage of parallelization strategies. However, the existent solutions show limited scalability and have a complex implementation. In this work we introduce SparkBWA, a new tool that exploits the capabilities of a big data technology as Spark to boost the performance of one of the most widely adopted aligner, the Burrows-Wheeler Aligner (BWA). The design of SparkBWA uses two independent software layers in such a way that no modifications to the original BWA source code are required, which assures its compatibility with any BWA version (future or legacy). SparkBWA is evaluated in different scenarios showing noticeable results in terms of performance and scalability. A comparison to other parallel BWA-based aligners validates the benefits of our approach. Finally, an intuitive and flexible API is provided to NGS professionals in order to facilitate the acceptance and adoption of the new tool. The source code of the software described in this paper is publicly available at https://github.com/citiususc/SparkBWA, with a GPL3 license This work was supported by Ministerio de Economía y Competitividad (Spain) (http://www.mineco.gob.es) grants TIN2013-41129-P and TIN2014-54565-JIN. There was no additional external funding received for this study SI

In this paper, we present a three-dimensional (3D) digitization technique for natural objects, such as insects and plants. The key idea is to combine X-ray computed tomography (CT) and photographs to obtain both complicated 3D shapes and surface textures of target specimens. We measure a specimen by using an X-ray CT device and a digital camera to obtain a CT volumetric image (volume) and multiple photographs. We then reconstruct a 3D model by segmenting the CT volume and generate a texture by projecting the photographs onto the model. To achieve this reconstruction, we introduce a technique for estimating a camera position for each photograph. We also present techniques for merging multiple textures generated from multiple photographs and recovering missing texture areas caused by occlusion. We illustrate the feasibility of our 3D digitization technique by digitizing 3D textured models of insects and flowers. The combination of X-ray CT and a digital camera makes it possible to successfully digitize specimens with complicated 3D structures accurately and allows us to browse both surface colors and internal structures.

In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover's Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.

The valuation of ecosystem services is a complex process as it includes several dimensions (ecological, socio-cultural and economic) and not all of these can be quantified in monetary units. The aim of this paper is to conduct an ecosystem services valuation study for mangroves ecosystems, the results of which can be used to inform governance and management of mangroves. We used an expert-based participatory approach (the Delphi technique) to identify, categorize and rank the various ecosystem services provided by mangrove ecosystems at a global scale. Subsequently we looked for evidence in the existing ecosystem services literature for monetary valuations of these ecosystem service categories throughout the biogeographic distribution of mangroves. We then compared the relative ranking of ecosystem service categories between the monetary valuations and the expert based analysis. The experts identified 16 ecosystem service categories, six of which are not adequately represented in the literature. There was no significant correlation between the expert based valuation (the Delphi technique) and the economic valuation, indicating that the scope of valuation of ecosystem services needs to be broadened. Acknowledging this diversity in different valuation approaches, and developing methodological frameworks that foster the pluralism of values in ecosystem services research, are crucial for maintaining the credibility of ecosystem services valuation. To conclude, we use the findings of our dual approach to valuation to make recommendations on how to assess and manage the ecosystem services provided by mangrove ecosystems., SCOPUS: ar.j, info:eu-repo/semantics/published