Complex Odor from Plants under Attack: Herbivore's Enemies React to the Whole, Not Its Parts

Background: Insect herbivory induces plant odors that attract herbivores ’ natural enemies. Assuming this attraction emerges from individual compounds, genetic control over odor emission of crops may provide a rationale for manipulating the distribution of predators used for pest control. However, studies on odor perception in vertebrates and invertebrates suggest that olfactory information processing of mixtures results in odor percepts that are a synthetic whole and not a set of components that could function as recognizable individual attractants. Here, we ask if predators respond to herbivoreinduced attractants in odor mixtures or to odor mixture as a whole. Methodology/Principal Findings: We studied a system consisting of Lima bean, the herbivorous mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis. We found that four herbivore-induced bean volatiles are not attractive in pure form while a fifth, methyl salicylate (MeSA), is. Several reduced mixtures deficient in one component compared to the full spider-mite induced blend were not attractive despite the presence of MeSA indicating that the predators cannot detect this component in these odor mixtures. A mixture of all five HIPV is most attractive, when offered together with the noninduced odor of Lima bean. Odors that elicit no response in their pure form were essential components of the attractive mixture. Conclusions/Significance: We conclude that the predatory mites perceive odors as a synthetic whole and that th

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Protein-Protein Interactions in Crystals of the Human Receptor-Type Protein Tyrosine Phosphatase ICA512 Ectodomain

ICA512 (or IA-2) is a transmembrane protein-tyrosine phosphatase located in secretory granules of neuroendocrine cells. Initially, it was identified as one of the main antigens of autoimmune diabetes. Later, it was found that during insulin secretion, the cytoplasmic domain of ICA512 is cleaved and relocated to the nucleus, where it stimulates the transcription of the insulin gene. The role of the other parts of the receptor in insulin secretion is yet to be unveiled. The structures of the intracellular pseudocatalytic and mature extracellular domains are known, but the transmembrane domain and several intracellular and extracellular parts of the receptor are poorly characterized. Moreover the overall structure of the receptor remains to be established. We started to address this issue studying by X-ray crystallography the structure of the mature ectodomain of ICA512 (ME ICA512) and variants thereof. The variants and crystallization conditions were chosen with the purpose of exploring putative association interfaces, metal binding sites and all other structural details that might help, in subsequent works, to build a model of the entire receptor. Several structural features were clarified and three main different association modes of ME ICA512 were identified. The results provide essential pieces of information for the design of new experiments aimed to assess the structure in vivo

Large tunable valley splitting in edge-free graphene quantum dots on boron nitride

Coherent manipulation of binary degrees of freedom is at the heart of modern quantum technologies. Graphene offers two binary degrees: the electron spin and the valley. Efficient spin control has been demonstrated in many solid state systems, while exploitation of the valley has only recently been started, yet without control on the single electron level. Here, we show that van-der Waals stacking of graphene onto hexagonal boron nitride offers a natural platform for valley control. We use a graphene quantum dot induced by the tip of a scanning tunneling microscope and demonstrate valley splitting that is tunable from -5 to +10 meV (including valley inversion) by sub-10-nm displacements of the quantum dot position. This boosts the range of controlled valley splitting by about one order of magnitude. The tunable inversion of spin and valley states should enable coherent superposition of these degrees of freedom as a first step towards graphene-based qubits

Looking ahead at the potential benefits of biotechnology-derived allergen therapeutics

While biotechnology-derived allergen therapeutics show promise in improving the safety of immunotherapy, they may prove to have additional benefits in comparison to conventional allergenic extracts that deserve commentary. These issues range from product stability and compatibility to medical practice issues, which will be the focus of this article

Distinguishing patterns in the dynamics of long-term medication use by Markov analysis: beyond persistence

<p>Abstract</p> <p>Background</p> <p>In order to accurately distinguish gaps of varying length in drug treatment for chronic conditions from discontinuation without resuming therapy, short-term observation does not suffice. Thus, the use of inhalation corticosteroids (ICS) in the long-term, during a ten-year period is investigated. To describe medication use as a continuum, taking into account the timeliness and consistency of refilling, a Markov model is proposed.</p> <p>Methods</p> <p>Patients, that filled at least one prescription in 1993, were selected from the PHARMO medical record linkage system (RLS) containing >95% prescription dispensings per patient originating from community pharmacy records of 6 medium-sized cities in the Netherlands.</p> <p>The probabilities of continuous use, the refilling of at least one ICS prescription in each year of follow-up, and medication free periods were assessed by Markov analysis. Stratified analysis according to new use was performed.</p> <p>Results</p> <p>The transition probabilities of the refilling of at least one ICS prescription in the subsequent year of follow-up, were assessed for each year of follow-up and for the total study period.</p> <p>The change of transition probabilities in time was evaluated, e.g. the probability of continuing ICS use of starters in the first two years (51%) of follow-up increased to more than 70% in the following years. The probabilities of different patterns of medication use were assessed: continuous use (7.7%), cumulative medication gaps (1–8 years 69.1%) and discontinuing (23.2%) during ten-year follow-up for new users. New users had lower probability of continuous use (7.7%) and more variability in ICS refill patterns than previous users (56%).</p> <p>Conclusion</p> <p>In addition to well-established methods in epidemiology to ascertain compliance and persistence, a Markov model could be useful to further specify the variety of possible patterns of medication use within the continuum of adherence. This Markov model describes variation in behaviour and patterns of ICS use and could also be useful to investigate continuous use of other drugs applied in chronic diseases.</p

Patient Care Teams in treatment of diabetes and chronic heart failure in primary care: an observational networks study

Contains fulltext : 97203.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: Patient care teams have an important role in providing medical care to patients with chronic disease, but insight into how to improve their performance is limited. Two potentially relevant determinants are the presence of a central care provider with a coordinating role and an active role of the patient in the network of care providers. In this study, we aimed to develop and test measures of these factors related to the network of care providers of an individual patient. METHODS: We performed an observational study in patients with type 2 diabetes or chronic heart failure, who were recruited from three primary care practices in The Netherlands. The study focused on medical treatment, advice on physical activity, and disease monitoring. We used patient questionnaires and chart review to measure connections between the patient and care providers, and a written survey among care providers to measure their connections. Data on clinical performance were extracted from the medical records. We used network analysis to compute degree centrality coefficients for the patient and to identify the most central health professional in each network. A range of other network characteristics were computed including network centralization, density, size, diversity of disciplines, and overlap among activity-specific networks. Differences across the two chronic conditions and associations with disease monitoring were explored. RESULTS: Approximately 50% of the invited patients participated. Participation rates of health professionals were close to 100%. We identified 63 networks of 25 patients: 22 for medical treatment, 16 for physical exercise advice, and 25 for disease monitoring. General practitioners (GPs) were the most central care providers for the three clinical activities in both chronic conditions. The GP's degree centrality coefficient varied substantially, and higher scores seemed to be associated with receiving more comprehensive disease monitoring. The degree centrality coefficient of patients also varied substantially but did not seem to be associated with disease monitoring. CONCLUSIONS: Our method can be used to measure connections between care providers of an individual patient, and to examine the association between specific network parameters and healthcare received. Further research is needed to refine the measurement method and to test the association of specific network parameters with quality and outcomes of healthcare

Shared communication processes within healthcare teams for rare diseases and their influence on healthcare professionals' innovative behavior and patient satisfaction

<p>Abstract</p> <p>Background</p> <p>A rare disease is a pattern of symptoms that afflicts less than five in 10,000 patients. However, as about 6,000 different rare disease patterns exist, they still have significant epidemiological relevance. We focus on rare diseases that affect multiple organs and thus demand that multidisciplinary healthcare professionals (HCPs) work together. In this context, standardized healthcare processes and concepts are mainly lacking, and a deficit of knowledge induces uncertainty and ambiguity. As such, individualized solutions for each patient are needed. This necessitates an intensive level of innovative individual behavior and thus, adequate idea generation. The final implementation of new healthcare concepts requires the integration of the expertise of all healthcare team members, including that of the patients. Therefore, knowledge sharing between HCPs and shared decision making between HCPs and patients are important. The objective of this study is to assess the contribution of shared communication and decision-making processes in patient-centered healthcare teams to the generation of innovative concepts and consequently to improvements in patient satisfaction.</p> <p>Methods</p> <p>A theoretical framework covering interaction processes and explorative outcomes, and using patient satisfaction as a measure for operational performance, was developed based on healthcare management, innovation, and social science literature. This theoretical framework forms the basis for a three-phase, mixed-method study. Exploratory phase I will first involve collecting qualitative data to detect central interaction barriers within healthcare teams. The results are related back to theory, and testable hypotheses will be derived. Phase II then comprises the testing of hypotheses through a quantitative survey of patients and their HCPs in six different rare disease patterns. For each of the six diseases, the sample should comprise an average of 30 patients with six HCP per patient-centered healthcare team. Finally, in phase III, qualitative data will be generated via semi-structured telephone interviews with patients to gain a deeper understanding of the communication processes and initiatives that generate innovative solutions.</p> <p>Discussion</p> <p>The findings of this proposed study will help to elucidate the necessity of individualized innovative solutions for patients with rare diseases. Therefore, this study will pinpoint the primary interaction and communication processes in multidisciplinary teams, as well as the required interplay between exploratory outcomes and operational performance. Hence, this study will provide healthcare institutions and HCPs with results and information essential for elaborating and implementing individual care solutions through the establishment of appropriate interaction and communication structures and processes within patient-centered healthcare teams.</p

A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions

Conservation agriculture involves reduced tillage, permanent soil cover and crop rotations to enhance soil fertility and to supply food from a dwindling land resource. Recently, conservation agriculture has been promoted in Southern Africa, mainly for maize-based farming systems. However, maize yields under rain-fed conditions are often variable. There is therefore a need to identify factors that influence crop yield under conservation agriculture and rain-fed conditions. Here, we studied maize grain yield data from experiments lasting 5 years and more under rain-fed conditions. We assessed the effect of long-term tillage and residue retention on maize grain yield under contrasting soil textures, nitrogen input and climate. Yield variability was measured by stability analysis. Our results show an increase in maize yield over time with conservation agriculture practices that include rotation and high input use in low rainfall areas. But we observed no difference in system stability under those conditions. We observed a strong relationship between maize grain yield and annual rainfall. Our meta-analysis gave the following findings: (1) 92% of the data show that mulch cover in high rainfall areas leads to lower yields due to waterlogging; (2) 85% of data show that soil texture is important in the temporal development of conservation agriculture effects, improved yields are likely on well-drained soils; (3) 73% of the data show that conservation agriculture practices require high inputs especially N for improved yield; (4) 63% of data show that increased yields are obtained with rotation but calculations often do not include the variations in rainfall within and between seasons; (5) 56% of the data show that reduced tillage with no mulch cover leads to lower yields in semi-arid areas; and (6) when adequate fertiliser is available, rainfall is the most important determinant of yield in southern Africa. It is clear from our results that conservation agriculture needs to be targeted and adapted to specific biophysical conditions for improved impact

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes