Human annexin A6 interacts with influenza a virus protein M2 and negatively modulates infection

Copyright © 2012, American Society for Microbiology. All Rights ReservedThe influenza A virus M2 ion channel protein has the longest cytoplasmic tail (CT) among the three viral envelope proteins and is well conserved between different viral strains. It is accessible to the host cellular machinery after fusion with the endosomal membrane and during the trafficking, assembly, and budding processes. We hypothesized that identification of host cellular interactants of M2 CT could help us to better understand the molecular mechanisms regulating the M2-dependent stages of the virus life cycle. Using yeast two-hybrid screening with M2 CT as bait, a novel interaction with the human annexin A6 (AnxA6) protein was identified, and their physical interaction was confirmed by coimmunoprecipitation assay and a colocalization study of virus-infected human cells. We found that small interfering RNA (siRNA)-mediated knockdown of AnxA6 expression significantly increased virus production, while its overexpression could reduce the titer of virus progeny, suggesting a negative regulatory role for AnxA6 during influenza A virus infection. Further characterization revealed that AnxA6 depletion or overexpression had no effect on the early stages of the virus life cycle or on viral RNA replication but impaired the release of progeny virus, as suggested by delayed or defective budding events observed at the plasma membrane of virus-infected cells by transmission electron microscopy. Collectively, this work identifies AnxA6 as a novel cellular regulator that targets and impairs the virus budding and release stages of the influenza A virus life cycle.This work was supported by the Research Fund for the Control of Infectious Disease (project 09080892) of the Hong Kong Government, the Area of Excellence Scheme of the University Grants Committee (grant AoE/M-12/-06 of the Hong Kong Special Administrative Region, China), the French Ministry of Health, the RESPARI Pasteur Network

Overexpression of the Steroidogenic Enzyme Cytochrome P450 Side Chain Cleavage in the Ventral Tegmental Area Increases 3 ,5 -THP and Reduces Long-Term Operant Ethanol Self-Administration

Neuroactive steroids are endogenous neuromodulators capable of altering neuronal activity and behavior. In rodents, systemic administration of endogenous or synthetic neuroactive steroids reduces ethanol self-administration. We hypothesized this effect arises from actions within mesolimbic brain regions that we targeted by viral gene delivery. Cytochrome P450 side chain cleavage (P450scc) converts cholesterol to pregnenolone, the rate-limiting enzymatic reaction in neurosteroidogenesis. Therefore, we constructed a recombinant adeno-associated serotype 2 viral vector (rAAV2), which drives P450scc expression and neuroactive steroid synthesis. The P450scc-expressing vector (rAAV2-P450scc) or control GFP-expressing vector (rAAV2-GFP) were injected bilaterally into the ventral tegmental area (VTA) or nucleus accumbens (NAc) of alcohol preferring (P) rats trained to self-administer ethanol. P450scc overexpression in the VTA significantly reduced ethanol self-administration by 20% over the 3 week test period. P450scc overexpression in the NAc, however, did not alter ethanol self-administration. Locomotor activity was unaltered by vector administration to either region. P450scc overexpression produced a 36% increase in (3α,5α)-3-hydroxypregnan-20-one (3α,5α-THP, allopregnanolone)-positive cells in the VTA, but did not increase 3α,5α-THP immunoreactivity in NAc. These results suggest that P450scc overexpression and the resultant increase of 3α,5α-THP-positive cells in the VTA reduces ethanol reinforcement. 3α,5α-THP is localized to neurons in the VTA, including tyrosine hydroxylase neurons, but not astrocytes. Overall, the results demonstrate that using gene delivery to modulate neuroactive steroids shows promise for examining the neuronal mechanisms of moderate ethanol drinking, which could be extended to other behavioral paradigms and neuropsychiatric patholog

A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios

[EN] Ecuador is worldwide considered as one of the main natural flower producers and exporters ¿being roses the most salient ones. Such a fact has naturally led the emergence of small and medium sized companies devoted to the production of quality roses in the Ecuadorian highlands, which intrinsically entails resource usage optimization. One of the first steps towards optimizing the use of resources is to forecast demand, since it enables a fair perspective of the future, in such a manner that the in-advance raw materials supply can be previewed against eventualities, resources usage can be properly planned, as well as the misuse can be avoided. Within this approach, the problem of forecasting the supply of roses was solved into two phases: the first phase consists of the macro-forecast of the total amount to be exported by the Ecuadorian flower sector by the year 2020, using multi-layer neural networks. In the second phase, the monthly demand for the main rose varieties offered by the study company was micro-forecasted by testing seven models. In addition, a Bayesian network model is designed, which takes into consideration macroeconomic aspects, the level of employability in Ecuador and weather-related aspects. This Bayesian network provided satisfactory results without the need for a large amount of historical data and at a low-computational cost.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS ¿Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems¿ (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015. In addition, the authors are greatly grateful by the support given by the SDAS Research Group (www.sdas-group.com)Herrera-Granda, ID.; Lorente-Leyva, LL.; Peluffo-Ordóñez, DH.; Alemany Díaz, MDM. (2021). A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios. Lecture Notes in Computer Science. 12566:245-258. https://doi.org/10.1007/978-3-030-64580-9_21S24525812566Asociación de Productores y Exportadores de Flores: Inicio – Expoflores. https://expoflores.com/Palacios, J., Rosero, D.: Análisis de las condiciones climáticas registradas en el Ecuador continental en el año 2013 y su impacto en el sector agrícola. Estud. e Investig. meteorológicas. Ina. Inst. Nac. Meteorol. e Hidrol. Ecuador, 28, p. (2014)Hidalgo-Proaño, M.: Variabilidad climática interanual sobre el Ecuador asociada a ENOS. CienciAmérica 6, 42–47 (2017)Ritchie, J.W., Abawi, G.Y., Dutta, S.C., Harris, T.R., Bange, M.: Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance. Aust. J. Agric. Resour. 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Syst. 106, 33–45 (2012). https://doi.org/10.1016/j.agsy.2011.10.005Scott, S.L., Varian, H.R.: Predicting the present with bayesian structural time series. Available SSRN 2304426 (2013)Prudhomme, C., Shaffrey, L., Woollings, T., Jackson, C., Fowler, H., Anderson, B.: IMPETUS: Improving predictions of drought for user decision-making. International Conference on Drought: Research and Science-Policy Interfacing, 2015. pp. 273–278. CRC Press/Balkema, Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom (2015)Wiles, P., Enke, D.: A hybrid neuro-fuzzy model to forecast the Soybean complex. International Annual Conference of the American Society for Engineering Management 2015, ASEM 2015. pp. 1–5. American Society for Engineering Management, Missouri University of Science and Technology, Engineering Management and Systems Engineering Department, United States (2015)Hansen, B.G., Li, Y.: An analysis of past world market prices of feed and milk and predictions for the future. Agribusiness 33, 175–193 (2017). https://doi.org/10.1002/agr.21474Johnson, M.D., Hsieh, W.W., Cannon, A.J., Davidson, A., Bedard, F.: Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agric. For. Meteorol. 218, 74–84 (2016). https://doi.org/10.1016/j.agrformet.2015.11.003Chen, J., Yang, J., Zhao, J., Xu, F., Shen, Z., Zhang, L.: Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method. Neurocomputing 174, 1087–1100 (2016). https://doi.org/10.1016/j.neucom.2015.09.105Fodor, N., et al.: Integrating plant science and crop modeling: assessment of the impact of climate change on soybean and maize production. Plant Cell Physiol. 58, 1833–1847 (2017). https://doi.org/10.1093/pcp/pcx141Chapman, R., et al.: Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: a proof of concept analysis. Comput. Electron. Agric. 151, 338–348 (2018). https://doi.org/10.1016/j.compag.2018.06.006Lara-Estrada, L., Rasche, L., Sucar, L.E., Schneider, U.A.: Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks. LAND. 7 (2018). https://doi.org/10.3390/land7010004Abdelaal, H.S.A., Thilmany, D.: Grains production prospects and long run food security in Egypt. Sustain. 11 (2019). https://doi.org/10.3390/su11164457Kusunose, Y., Ma, L., Van Sanford, D.: User responses to imperfect forecasts: findings from an experiment with Kentucky wheat farmers. Weather. Clim. Soc. 11, 791–808 (2019). https://doi.org/10.1175/wcas-d-18-0135.1Kadigi, I.L., et al.: Forecasting yields, prices and net returns for main cereal crops in Tanzania as probability distributions: a multivariate empirical (MVE) approach. Agric. Syst. 180 (2020). https://doi.org/10.1016/j.agsy.2019.102693McGrath, G., Rao, P.S.C., Mellander, P.-E., Kennedy, I., Rose, M., van Zwieten, L.: Real-time forecasting of pesticide concentrations in soil. Sci. Total Environ. 663, 709–717 (2019). https://doi.org/10.1016/j.scitotenv.2019.01.401Yang, B., Xie, L.: Bayesian network modelling for “direct farm” mode based agricultural supply chain risk. Ekoloji 28, 2361–2368 (2019)Zaporozhtseva, L.A., Sabetova, T. V, Yu Fedulova, I.: Assessment of the uncertainty factors in computer modelling of an agricultural company operation. International Conference on Information Technologies in Business and Industries, ITBI 2019. Institute of Physics Publishing, Voronezh State Agrarian University, Michurina Str. 30, Voronezh, 394087, Russian Federation (2019)Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time series analysis: forecasting and control. Wiley (2015)Hanke, J., Wichern, D.: Business forecast. 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The FARMSCAPE approach to farming systems research

Abstract From six years of participatory action research has emerged Farmers', Advisers' and Researchers' Monitoring, Simulation. Communication And Performance Evaluation (FARMSCAPE) as an approach for supporting farmers' management of dryland crop production. In contrast to the strategy of producing decision support software for farmers, FARMSCAPE features simulation-aided discussions about management among farmers, advisers, and (sometimes) researchers. The key is a capability to flexibly simulate the consequences of a wide range of crop and cropland management alternatives in a variable climate at a paddock scale using local soil and weather data. The high level of interest among farmers has led to a current focus on transfer of the technology to agricultural service providers. Keywords: Farming systems, on-farm, simulation, soil monitoring, action research The term "farming systems research" is most commonly used in Australia to mean "research on bio-physical sub-systems aimed at improving systems of farming". Research methodology tends to be a flexible and pragmatic use of formal experimental design and statistical analysis. Experiments are designed to represent aspects of farming sufficiently realistically for results to be meaningful to farmers and advisers but without unnecessarily or overly straining professional standards for methodology concerning making valid comparisons with adequate confidence. In the interest of the former, experiments are often located on commercial farms, and, increasingly, with farmers. A second established way of interpreting the term "farming systems research" is "systems research which is about farming". Here the emphasis is the application to farming of systems concepts and methodologies that have evolved over the past 50 years, mainly outside agriculture. This paradigm has been termed "systems agriculture" (1). Emphasis here is on approaches to learning/ research/ intervention when the system under study does not lend itself readily to scientific experimentation. Feasibility of the latter declines with increases in scale and/or, complexity and temporal variability. Two pools of methodological resources for addressing such systems are available—often termed "hard" and "soft" approaches. "Hard" systems approaches have, at their core, mathematical models of the systems of interest designed to represent the essential aspects of function in relation to environment. But the hard lesson in the main stream of the hard systems movement has been that the approach turns out to be appropriate only to those aspects of systems that are not complicated by people with purposes and freedom of choice (3). The fact that the specific nature of a farm system substantially reflects the design and management efforts of a farmer means that a "soft" systems approach, eg participative action research, should enhance the usefulness and impact of the research on real farming. McCown, RL; Carberry, PS; Foale, MA; Hochman, Z; Coutts, JA; Dalgliesh, NP (1998) The FARMSCAPE approach to farming systems research Proc. 9th Aust. Agron. Conf., Wagga Wagga (1998) 633-636

Farmers, advisers and researchers learning together better management of crops and croplands

Summary. Farmers in the northeastern sub-tropics of Australia must cope with very high climatic variability in order to succeed in crop production. Their capacity for innovation was tapped by means of an on-farm research project that brought farmers, advisers and researchers together on the Darling Downs and in central Queensland. The researchers added value to the farmers' own experiments on fertility and water use efficiency by soil and weather monitoring at specific sites and then using a simulation model of cropping systems to extend findings to a wider context of climate and soil. The advisers extended knowledge aquired from this experience via local farmer networks and have undertaken training in the use of simulation to support farmers' management decisions. The experience described opens up possibilities for developing new, cost-effective ways for devising and testing improved farm management

The contribution of metacognitions and attentional control to decisional procrastination

Earlier research has implicated metacognitions and attentional control in procrastination and self-regulatory failure. This study tested several hypotheses: (1) that metacognitions would be positively correlated with decisional procrastination; (2) that attentional control would be negatively correlated with decisional procrastination; (3) that metacognitions would be negatively correlated with attentional control; and (4) that metacognitions and attentional control would predict decisional procrastination when controlling for negative affect. One hundred and twenty-nine participants completed the Depression Anxiety Stress Scale 21, the Meta-Cognitions Questionnaire 30, the Attentional Control Scale, and the Decisional Procrastination Scale. Significant relationships were found between all three attentional control factors (focusing, shifting, and flexible control of thought) and two metacognitions factors (negative beliefs concerning thoughts about uncontrollability and danger, and cognitive confidence). Results also revealed that decisional procrastination was significantly associated with negative affect, all measured metacognitions factors, and all attentional control factors. In the final step of a hierarchical regression analysis only stress, cognitive confidence, and attention shifting were independent predictors of decisional procrastination. Overall these findings support the hypotheses and are consistent with the Self-Regulatory Executive Function model of psychological dysfunction. The implications of these findings are discussed

Gene expression profiles in rat mesenteric lymph nodes upon supplementation with Conjugated Linoleic Acid during gestation and suckling

Background Diet plays a role on the development of the immune system, and polyunsaturated fatty acids can modulate the expression of a variety of genes. Human milk contains conjugated linoleic acid (CLA), a fatty acid that seems to contribute to immune development. Indeed, recent studies carried out in our group in suckling animals have shown that the immune function is enhanced after feeding them with an 80:20 isomer mix composed of c9,t11 and t10,c12 CLA. However, little work has been done on the effects of CLA on gene expression, and even less regarding immune system development in early life. Results The expression profile of mesenteric lymph nodes from animals supplemented with CLA during gestation and suckling through dam's milk (Group A) or by oral gavage (Group B), supplemented just during suckling (Group C) and control animals (Group D) was determined with the aid of the specific GeneChip® Rat Genome 230 2.0 (Affymettrix). Bioinformatics analyses were performed using the GeneSpring GX software package v10.0.2 and lead to the identification of 89 genes differentially expressed in all three dietary approaches. Generation of a biological association network evidenced several genes, such as connective tissue growth factor (Ctgf), tissue inhibitor of metalloproteinase 1 (Timp1), galanin (Gal), synaptotagmin 1 (Syt1), growth factor receptor bound protein 2 (Grb2), actin gamma 2 (Actg2) and smooth muscle alpha actin (Acta2), as highly interconnected nodes of the resulting network. Gene underexpression was confirmed by Real-Time RT-PCR. Conclusions Ctgf, Timp1, Gal and Syt1, among others, are genes modulated by CLA supplementation that may have a role on mucosal immune responses in early life

Gene expression profiles in rat mesenteric lymph nodes upon supplementation with Conjugated Linoleic Acid during gestation and suckling

Background Diet plays a role on the development of the immune system, and polyunsaturated fatty acids can modulate the expression of a variety of genes. Human milk contains conjugated linoleic acid (CLA), a fatty acid that seems to contribute to immune development. Indeed, recent studies carried out in our group in suckling animals have shown that the immune function is enhanced after feeding them with an 80:20 isomer mix composed of c9,t11 and t10,c12 CLA. However, little work has been done on the effects of CLA on gene expression, and even less regarding immune system development in early life. Results The expression profile of mesenteric lymph nodes from animals supplemented with CLA during gestation and suckling through dam's milk (Group A) or by oral gavage (Group B), supplemented just during suckling (Group C) and control animals (Group D) was determined with the aid of the specific GeneChip® Rat Genome 230 2.0 (Affymettrix). Bioinformatics analyses were performed using the GeneSpring GX software package v10.0.2 and lead to the identification of 89 genes differentially expressed in all three dietary approaches. Generation of a biological association network evidenced several genes, such as connective tissue growth factor (Ctgf), tissue inhibitor of metalloproteinase 1 (Timp1), galanin (Gal), synaptotagmin 1 (Syt1), growth factor receptor bound protein 2 (Grb2), actin gamma 2 (Actg2) and smooth muscle alpha actin (Acta2), as highly interconnected nodes of the resulting network. Gene underexpression was confirmed by Real-Time RT-PCR. Conclusions Ctgf, Timp1, Gal and Syt1, among others, are genes modulated by CLA supplementation that may have a role on mucosal immune responses in early life

Nature reserves as catalysts for landscape change

Scientists have called repeatedly for a broader conservation agenda that emphasizes not only protected areas but also the landscapes in which those areas are embedded. We describe key advances in the science and practice of engaging private landowners in biodiversity conservation and propose a conceptual model for integrating conservation management on reserves and privately owned lands. The overall goal of our model is to blur the distinction between land management on reserves and the surrounding landscapes in a way that fosters widespread implementation of conservation practices. Reserves assume a new role as natural laboratories where alternative land-use practices, designed to achieve conservation objectives, can be explored. We articulate the details of the model using a case study from the North American tallgrass prairie ecoregion.Peer reviewedNatural Resource Ecology and Managemen