Forward and Backward Dynamics in implicitly defined Overlapping Generations Models

In dynamic economic models derived from optimization principles, the forward equilibrium dynamics may not be uniquely defined, while the backward dynamics is well defined. We derive properties of the global forward equilibrium paths based on properties of the backward dynamics. We propose the framework of iterated function systems (IFS) to describe the set of forward equilibria, and apply the IFS framework to a one- and a two-dimensional version of the overlapping generations (OLG)-model. We show that, if the backward dynamics is chaotic and has a homoclinic orbit (a “snap-back repellerâ€) the set of forward equilibrium paths converges to a fractal attractor. Forward equilibria may be interpreted as sunspot equilibria, where a random sunspot sequence determines equilibrium selection at each date.

The Emergence ofBull and BearDynamics in a Nonlinear Model of Interacting Markets

We develop a three-dimensional nonlinear dynamic model in which the stock markets of two countries are linked through the foreign exchange market. Connections are due to the trading activity of heterogeneous speculators. Using analytical and numerical tools, we seek to explore how the coupling of the markets may affect the emergence ofbull and bearmarket dynamics. The dimension of the model can be reduced by restricting investors' trading activity, which enables the dynamic analysis to be performed stepwise, from low-dimensional cases up to the full three-dimensional model. In our paper we focus mainly on the dynamics of the one- and two- dimensional cases, with numerical experiments and some analytical results, and also show that the main features persist in the three-dimensional model

Single Nucleotide Polymorphisms in collagen genes and association with skin quality trait

AbstractLivestock skin is largely employed in the manufacturing of clothing and shoes, sector in which Italy is a world leader. To sustain Italian products against foreign competition in the globalization era particular attention is to be focus on product quality. Here we investigate the association of SNP mutations in genes coding for collagen proteins present in animal skin with a number of phisico-chemical parameters influencing skin quality for the tanning industry.Skin and blood were sampled from 73 Italian Friesian and Italian Brown bovines and from 43 Bergamasca and Sarda ovines, classified by sex and age. Skins were characterised for a set of chemico-physical parameters (thickness, density, humidity, protein content, ashes, lipid content, hydrossi-proline and DNA content).Regions of the collagen type I, III and IV were screened for SNP discovery in the two species by sequencing a set of reference animals. In bovine 15 polymorphisms were identified: (2 in collagen type I, 9 in collagen type III alp..

Association between OLR1 K167N SNP and intima media thickness of the common carotid artery in the general population

Background and Purpose: The lectin-like oxidised LDL receptor-1 (OLR1) gene encodes a scavenger receptor implicated in the pathogenesis of atherosclerosis. Although functional roles have been suggested for two variants, epidemiological studies on OLR1 have been inconsistent. Methods - We tested the association between the non-synonymous substitution K167N (rs11053646) and intima media thickness of the common carotid artery (CCA-IMT) in 2,141 samples from the Progression of Lesions in the Intima of the Carotid (PLIC) study (a prospective population-based study). Results: Significantly increased IMT was observed in male carriers of the minor C (N) allele compared to GC and GG (KN and KK) genotype. Functional analysis on macrophages suggested a decreased association to Ox-LDL in NN carriers compared to KN and KK carriers which is also associated with a reduced OLR1 mRNA expression. Macrophages from NN carriers present also a specific inflammatory gene expression pattern compared to cells from KN and KK carriers. Conclusions: These data suggest that the 167N variant of LOX-1 receptor affects the atherogenic process in the carotid artery prior to evidence of disease through an inflammatory process. © 2012 Predazzi et al

Internal controls for quantitative polymerase chain reaction of swine mammary glands during pregnancy and lactation.

High-throughput microarray analysis is an efficient means of obtaining a genome-wide view of transcript profiles across physiological states. However, quantitative PCR (qPCR) remains the chosen method for high-precision mRNA abundance analysis. Essential for reliability of qPCR data is normalization using appropriate internal control genes (ICG), which is now, more than ever before, a fundamental step for accurate gene expression profiling. We mined mammary tissue microarray data on >13,000 genes at -34, -14, 0, 7, 14, 21, and 28 d relative to parturition in 27 crossbred primiparous gilts to identify suitable ICG. Initial analysis revealed TBK1, PCSK2, PTBP1, API5, VAPB, QTRT1, TRIM41, TMEM24, PPP2R5B, and AP1S1 as the most stable genes (sample/reference = 1 +/- 0.2). We also included 9 genes previously identified as ICG in bovine mammary tissue. Gene network analysis of the 19 genes identified AP1S1, API5, MTG1, VAPB, TRIM41, MRPL39, and RPS15A as having no known co-regulation. In addition, UXT and ACTB were added to this list, and mRNA abundance of these 9 genes was measured by qPCR. Expression of all 9 of these genes was decreased markedly during lactation. In a previous study with bovine mammary tissue, mRNA of stably expressed genes decreased during lactation due to a dilution effect brought about by large increases in expression of highly abundant genes. To verify this effect, highly abundant mammary genes such as CSN1S2, SCD, FABP3, and LTF were evaluated by qPCR. The tested ICG had a negative correlation with these genes, demonstrating a dilution effect in the porcine mammary tissue. Gene stability analysis identified API5, VABP, and MRPL39 as the most stable ICG in porcine mammary tissue and indicated that the use of those 3 genes was most appropriate for calculating a normalization factor. Overall, results underscore the importance of proper validation of internal controls for qPCR and highlight the limitations of using absence of time effects as the criteria for selection of appropriate ICG. Further, we showed that use of the same ICG from one organism might not be suitable for qPCR normalization in other species

Evidence of rehabilitative impact of progressive resistance training (PRT) programs in Parkinson disease: an umbrella review

Parkinson disease (PD) is a chronic neurodegenerative condition that leads to progressive disability. PD-related reductions in muscle strength have been reported to be associated with lower functional performance and balance confidence with an increased risk of falls. Progressive resistance training (PRT) improves strength, balance, and functional abilities. This umbrella review examines the efficacy of PRT regarding muscular strength in PD patients. The PubMed, PEDro, Scopus, and Cochrane Library databases were searched from January 2009 to August 2019 for systematic reviews and meta-analyses conducted in English. The populations included had diagnoses of PD and consisted of males and females aged >18 years old. Outcomes measured were muscle strength and enhanced physical function. Eight papers (six systematic reviews and meta-analyses and two systematic reviews) were considered relevant for qualitative analysis. In six of the eight studies, the reported severity of PD was mild to moderate. Each study analyzed how PRT elicited positive effects on muscle strength in PD patients, suggesting 10 weeks on average of progressive resistance exercises for the upper and lower limbs two to three times per week. However, none of the studies considered the postworkout follow-up, and there was no detailed evidence about the value of PRT in preventing falls. The possibility of PRT exercises being effective for increasing muscle strength in patients with PD, but without comorbidities or severe disability, is discussed. Overall, this review suggests that PRT should be included in rehabilitation programs for PD patients, in combination with balance training for postural control and other types of exercise, in order to preserve cardiorespiratory fitness and improve endurance in daily life activities

Pain associated with COVID-19 vaccination is unrelated to skin biopsy abnormalities

Previous clinical observations raised the possibility that COVID-19 vaccination might trigger a small-fibre neuropathy.Objectives:In this uncontrolled observational study, we aimed to identify small fibre damage in patients complaining of generalized sensory symptoms and pain after COVID-19 vaccination.Methods:We collected clinical data, including a questionnaire for assessing autonomic symptoms (Composite Autonomic Symptom Score-31), and investigated quantitative sensory testing (QST) and skin biopsy in 15 prospectively enrolled patients with generalized sensory symptoms and pain after COVID-19 vaccination. Nine patients complaining of orthostatic intolerance also underwent cardiovascular autonomic tests.Results:We found that all patients experienced widespread pain, and most of them (11 of 15) had a fibromyalgia syndrome. All patients had normal skin biopsy findings, and in the 9 patients with orthostatic intolerance, cardiovascular autonomic tests showed normal findings. Nevertheless, 5 patients had cold and warm detection abnormalities at the QST investigation.Conclusions:In our study, most patients complaining of generalized sensory symptoms and pain after COVID-19 vaccination had clinical and diagnostic test findings compatible with a fibromyalgia syndrome. Although the abnormal QST findings we found in 5 patients might be compatible with a small-fibre neuropathy, they should be cautiously interpreted given the psychophysical characteristics of this diagnostic test. Further larger controlled studies are needed to define precisely the association between small fibre damage and COVID-19 vaccination

Variation-based design of an AM demodulator in a printed complementary organic technology

n/

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) grants nos. NNX12AP74G, NNX10AG01A, and NNX11AO08A. M. Altaf Arain thanks the support of Natural Sciences and Engineering Research Council (NSREC) of Canada. Penelope Serrano Ortiz was partially supported by the GEISpain project (CGL2014-52838-C2-1-R) funded by the Spanish Ministry of Economy and Competitiveness and the European Union ERDF funds. Sebastian Wolf acknowledges support from a Marie Curie International Outgoing Fellowship (European Commission, grant 300083). The FLUXCOM initiative is coordinated by Martin Jung, Max Planck Institute for Biogeochemistry (Jena, Germany). This work used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia and the US Department of Energy, and the databasing and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California - Berkeley, and the University of Virginia.Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2  0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.European Union (EU) GA 283080 283080 640176European Research Council (ERC) 647423Ministry of the Environment, Japan 2-1401JAXA Global Change Observation Mission (GCOM) project 115National Aeronautics & Space Administration (NASA) NNX12AP74G NNX10AG01A NNX11AO08ANatural Sciences and Engineering Research Council of CanadaGEISpain project - Spanish Ministry of Economy and Competitiveness CGL2014-52838-C2-1-REuropean Commission Joint Research Centre 300083United States Department of Energy (DOE) DE-FG02-04ER63917 DE-FG02-04ER63911FAO-GTOS-TCOiLEAPSMax Planck Institute for BiogeochemistryNational Science Foundation (NSF)University of Tusci

Understanding the Distributions of Benthic Foraminifera in the Adriatic Sea with Gradient Forest and Structural Equation Models

Abstract: In the last three decades, benthic foraminiferal ecology has been intensively investigated to improve the potential application of these marine organisms as proxies of the effects of climate change and other global change phenomena. It is still challenging to define the most important factors affecting foraminiferal communities and derived faunistic parameters. In this study, we examined the abiotic-biotic relationships of foraminiferal communities in the central-southern area of the Adriatic Sea using modern machine learning techniques. We combined gradient forest (Gf) and structural equation modeling (SEM) to test hypotheses about determinants of benthic foraminiferal assemblages. These approaches helped determine the relative effect of sizes of different environmental variables responsible for shaping living foraminiferal distributions. Four major faunal turnovers (at 13–28 m, 29–58 m, 59–215 m, and &gt;215 m) were identified along a large bathymetric gradient (13–703 m water depth) that reflected the classical bathymetric distribution of benthic communities. Sand and organic matter (OM) contents were identified as the most relevant factors influencing the distribution of foraminifera either along the entire depth gradient or at selected bathymetric ranges. The SEM supported causal hypotheses that focused the factors that shaped assemblages at each bathymetric range, and the most notable causal relationships were direct effects of depth and indirect effects of the Gf-identified environmental parameters (i.e., sand, pollution load Index–PLI, organic matter–OM and total nitrogen–N) on foraminifera infauna and diversity. These results are relevant to understanding the basic ecology and conservation of foraminiferal communitie