Sexual dimorphism in resource acquisition and deployment: both size and timing matter.

Background and Aims The males and females of many dioecious plant species differ from one another in important life-history traits, such as their size. If male and female reproductive functions draw on different resources, for example, one should expect males and females to display different allocation strategies as they grow. Importantly, these strategies may differ not only between the two sexes, but also between plants of different age and therefore size. Results are presented from an experiment that asks whether males and females of Mercurialis annua, an annual plant with indeterminate growth, differ over time in their allocation of two potentially limiting resources (carbon and nitrogen) to vegetative (below-and above-ground) and reproductive tissues.Methods Comparisons were made of the temporal patterns of biomass allocation to shoots, roots and reproduction and the nitrogen content in the leaves between the sexes of M. annua by harvesting plants of each sex after growth over different periods of time.Key Results and Conclusions Males and females differed in their temporal patterns of allocation. Males allocated more to reproduction than females at early stages, but this trend was reversed at later stages. Importantly, males allocated proportionally more of their biomass towards roots at later stages, but the roots of females were larger in absolute terms. The study points to the important role played by both the timing of resource deployment and the relative versus absolute sizes of the sinks and sources in sexual dimorphism of an annual plant.

Peroxisomal polyhydroxyalkanoate biosynthesis is a promising strategy for bioplastic production in high biomass crops.

Polyhydroxyalkanoates (PHAs) are bacterial carbon storage polymers with diverse plastic-like properties. PHA biosynthesis in transgenic plants is being developed as a way to reduce the cost and increase the sustainability of industrial PHA production. The homopolymer polyhydroxybutyrate (PHB) is the simplest form of these biodegradable polyesters. Plant peroxisomes contain the substrate molecules and necessary reducing power for PHB biosynthesis, but peroxisomal PHB production has not been explored in whole soil-grown transgenic plants to date. We generated transgenic sugarcane (Saccharum sp.) with the three-enzyme Ralstonia eutropha PHA biosynthetic pathway targeted to peroxisomes. We also introduced the pathway into Arabidopsis thaliana, as a model system for studying and manipulating peroxisomal PHB production. PHB, at levels up to 1.6%-1.8% dry weight, accumulated in sugarcane leaves and A. thaliana seedlings, respectively. In sugarcane, PHB accumulated throughout most leaf cell types in both peroxisomes and vacuoles. A small percentage of total polymer was also identified as the copolymer poly (3-hydroxybutyrate-co-3-hydroxyvalerate) in both plant species. No obvious deleterious effect was observed on plant growth because of peroxisomal PHA biosynthesis at these levels. This study highlights how using peroxisomal metabolism for PHA biosynthesis could significantly contribute to reaching commercial production levels of PHAs in crop plants.

Stress Perception in Public Organizations: Expanding the Job Demands - Job Resources Model by Including Public Service Motivation

Although stress has been a longstanding issue in organizations and management studies, it has never been studied in relation to Public Service Motivation. This article therefore aims to integrate PSM into the job demands-job resources model of stress in order to determine whether PSM might contribute to stress in public organizations. Drawing upon original data from a questionnaire in a Swiss municipality, this study unsurprisingly shows that "red tape" is an antecedent of stress perception, whereas satisfaction with organizational support, positive feedback, and recognition significantly decrease the level of perceived stress. Astonishingly, the empirical results show that PSM is positively and significantly related to stress perception. By increasing individuals' expectations towards their jobs, PSM might thus contribute to increased pressure on public agents. Ultimately, this article investigates the "dark side" of PSM, which has been neglected by the literature thus far.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Do resources, justice administration practices and federalism have an impact on registered and sentenced crime prevalence ?

This contribution, based on a statistical approach, undertakes to link data on resources (personnel and financial means) and the working of the administration of penal justice (prosecution, sentencing) taking into account the nationality of those prosecuted. In order to be able to distinguish prosecution and sentencing practices of judicial authorities and possible processes of discrimination, diverse sources have been used such as data from court administrations, public finances and police forces, collected by the Swiss Federal Statistical Office and the Swiss Federal administration of finances. The authors discuss discrimination in prosecution and sentencing between Swiss residents and foreigners taking into account localization and resources regarding personnel and public finances.

Comparison of HIV-infected patients' characteristics, healthcare resources use and cost between native and migrant patients

OBJECTIVES: To assess whether patients' characteristics and healthcare resources consumption and costs were different between native and migrant populations in Switzerland. METHODS: All adult patients followed-up in the Swiss HIV-cohort study in our institution during 2000-2003 were considered. Patients' characteristics were retrieved from the cohort database. Hospital and outpatient resource use were extracted from individual charts and valued with 2002 tariffs. RESULTS: The 66 migrants were younger (29 +/- 8 years versus 37 +/- 11, p < 0.001), less often of male gender (38 % versus 70 %, p < 0.001), predominantly infected via heterosexual contact (87 % versus 52 %, p < 0.01), with lower mean CD4 level at enrollment (326 +/- 235 versus 437 +/- 305, p = 0.002) than their 200 native counterparts. Migrants had fewer hospitalizations, more frequent outpatient visits, laboratory tests, and lower total cost of care per year of follow-up (<euro> 2'215 +/- 4'206 versus 4'155 +/- 12'304, p = 0.037). Resource use and costs were significantly higher in people with < 200 CD4 cell counts in both groups. CONCLUSIONS: Migrant population had more advanced disease, more outpatient visits but less hospitalizations, resulting in lower costs of care when compared with native population.

Light receptor action is critical for maintaining plant biomass at warm ambient temperatures.

The ability to withstand environmental temperature variation is essential for plant survival. Former studies in Arabidopsis revealed that light signalling pathways had a potentially unique role in shielding plant growth and development from seasonal and daily fluctuations in temperature. In this paper we describe the molecular circuitry through which the light receptors cry1 and phyB buffer the impact of warm ambient temperatures. We show that the light signalling component HFR1 acts to minimise the potentially devastating effects of elevated temperature on plant physiology. Light is known to stabilise levels of HFR1 protein by suppressing proteasome-mediated destruction of HFR1. We demonstrate that light-dependent accumulation and activity of HFR1 are highly temperature dependent. The increased potency of HFR1 at warmer temperatures provides an important restraint on PIF4 that drives elongation growth. We show that warm ambient temperatures promote the accumulation of phosphorylated PIF4. However, repression of PIF4 activity by phyB and cry1 (via HFR1) is critical for controlling growth and maintaining physiology as temperatures rise. Loss of this light-mediated restraint has severe consequences for adult plants which have greatly reduced biomass.

Database resources for the tuberculosis community.

Access to online repositories for genomic and associated "-omics" datasets is now an essential part of everyday research activity. It is important therefore that the Tuberculosis community is aware of the databases and tools available to them online, as well as for the database hosts to know what the needs of the research community are. One of the goals of the Tuberculosis Annotation Jamboree, held in Washington DC on March 7th-8th 2012, was therefore to provide an overview of the current status of three key Tuberculosis resources, TubercuList (tuberculist.epfl.ch), TB Database (www.tbdb.org), and Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org). Here we summarize some key updates and upcoming features in TubercuList, and provide an overview of the PATRIC site and its online tools for pathogen RNA-Seq analysis.

Forest fires cluster detection with space-time scan statistics

Forest fires are defined as uncontrolled fires often occurring in wildland areas, but that can also affect houses or agricultural resources. Causes are both natural (e.g.,lightning phenomena) and anthropogenic (human negligence or arsons).Major environmental factors influencing the fire ignition and propagation are climate and vegetation. Wildfires are most common and severe during drought period and on windy days. Moreover, under water-stress conditions, which occur after a long hot and dry period, the vegetation is more vulnerable to fire. These conditions are common in the United State and Canada, where forest fires represent a big problem. We focused our analysis on the state of Florida, for which a big dataset on forest fires detection is readily available. USDA Forest Service Remote Sensing Application Center, in collaboration with NASA-Goddard Space Flight Center and the University of Maryland, has compiled daily MODIS Thermal Anomalies (fires and biomass burning images) produced by NASA using a contextual algorithm that exploits the strong emission of mid-infrared radiation from fires. Fire classes were converted in GIS format: daily MODIS fire detections are provided as the centroids of the 1 kilometer pixels and compiled into daily Arc/INFO point coverage.

Different arbuscular mycorrhizal fungi alter coexistence and resource distribution between co-occurring plants

It is often thought that the coexistence of plants and plant diversity is determined by resource heterogeneity of the abiotic environment. However, the presence and heterogeneity of biotic plant resources, such as arbuscular mycorrhizal fungi (AMF), could also affect plant species coexistence. In this study, Brachypodium pinnatum and Prunella vulgaris were grown together in pots and biotic resource heterogeneity was simulated by inoculating these pots with one of three different AMF taxa, with a mixture of these three taxa, or pots remained uninoculated. The AMF acted as biotic plant resources since the biomass of plants in pots inoculated with AMF was on average 11.8 times higher than uninoculated pots. The way in which the two plant species coexisted, and the distribution of phosphorus and nitrogen between the plant species, varied strongly depending on which AMF were present. The results showed that the composition of AMF communities determines how plant species coexist and to which plant species nutrients are allocated. Biotic plant resources such as AMF should therefore be considered as one of the factors that determine how plant species coexist and how soil resources are distributed among co-occurring plant species.