Els Factors competitius de les pimes a Catalunya

El principal objectiu d'aquest estudi és reflectir quins són els principals factors competitius per a les pymes a Catalunya. Per això, es va escollir una mostra de 1000 empreses de diferents sectors i amb els límits que marca la Unió Europea per a definir una pyme. A partir d'aquí, es va analitzar la informació financera. Amb aquesta font d'informació, es va poder arribar a la conclusió que tots aquells aspectes relacionats amb el personal i reflectits a través de ràtios, eren els més significatius estadísticament per a poder explicar la rendibilitat en les empreses. També es va enviar un qüestionari a la mostra esmentada i es va preguntar el punt de vista del gestor o empresari. Només 50 empreses van contestar, i una vegada més, el factor humà es va revelar com el més important. S'ha d'assenyalar que altres elements tals com la inversió en tecnologia, que s'esperava fossin significatius, no van donar els resultats esperats en el sentit que cap altre fos considerat tan rellevant com els recursos humans.

Rainfall erosivity and rainfall return period in the experimental watershed of Aracruz, in the coastal plain of Espirito Santo, Brazil

Knowledge on the factors influencing water erosion is fundamental for the choice of the best land use practices. Rainfall, expressed by rainfall erosivity, is one of the most important factors of water erosion. The objective of this study was to determine rainfall erosivity and the return period of rainfall in the Coastal Plains region, near Aracruz, a town in the state of Espírito Santo, Brazil, based on available data. Rainfall erosivity was calculated based on historic rainfall data, collected from January 1998 to July 2004 at 5 min intervals, by automatic weather stations of the Aracruz Cellulose S.A company. A linear regression with individual rainfall and erosivity data was fit to obtain an equation that allowed data extrapolation to calculate individual erosivity for a 30-year period. Based on this data the annual average rainfall erosivity in Aracruz was 8,536 MJ mm ha-1 h-1 yr-1. Of the total annual rainfall erosivity 85 % was observed in the most critical period October to March. Annual erosive rains accounted for 38 % of the events causing erosion, although the runoff volume represented 88 % of the total. The annual average rainfall erosivity return period was estimated to be 3.4 years.

There and back again? Combining habitat suitability modelling and connectivity analyses to assess a potential return of the otter to Switzerland

After a steady decline in the early 20th century, several terrestrial carnivore species have recently recovered in Western Europe, either through reintroductions or natural recolonization. Because of the large space requirements of these species and potential conflicts with human activities, ensuring their recovery requires the implementation of conservation and management measures that address the environmental, landscape and social dimensions of the problem. Few examples exist of such integrated management. Taking the case of the otter (Lutra lutra) in Switzerland, we propose a multi-step approach that allows to (1) identify areas with potentially suitable habitat, (2) evaluate their connectivity, (3) verify the potentiality of the species recolonization from populations in neighbouring countries. We showed that even though suitable habitat is available for the species and the level of structural connectivity within Switzerland is satisfactory, the level of connectivity with neighbouring populations is crucial to prioritize strategies that favour the species recovery in the field. This research is the first example integrating habitat suitability and connectivity assessment at different scales with other factors in a multi-step assessment for species recovery.

Predicting potential distribution of the jaguar (Panthera onca) in Mexico: identification of priority areas for conservation

Aim The jaguar, Panthera onca, is a species of global conservation concern. In Mexico, the northernmost part of its distribution range, its conservation status, is particularly critical, while its potential and actual distribution is poorly known. We propose an ensemble model (EM) of the potential distribution for the jaguar in Mexico and identify the priority areas for conservation.Location Mexico.Methods We generated our EM based on three presence-only methods (Ecological Niche Factor Analysis, Mahalanobis distance, Maxent) and considering environmental, biological and anthropogenic factors. We used this model to evaluate the efficacy of the existing Mexican protected areas (PAs), to evaluate the adequacy of the jaguar conservation units (JCUs) and to propose new areas that should be considered for conservation and management of the species in Mexico.Results Our results outline that 16% of Mexico (c. 312,000 km2) can be considered as suitable for the presence of the jaguar. Furthermore, 13% of the suitable areas are included in existing PAs and 14% are included in JCUs (Sanderson et al., 2002).Main conclusions Clearly much more should be carried out to establish a proactive conservation strategy. Based on our results, we propose here new jaguar conservation and management areas that are important for a nationwide conservation blueprint.

The impact of fasting on the interpretation of triglyceride levels for predicting myocardial infarction risk in HIV-positive individuals: the D:A:D study.

We assessed whether fasting modifies the prognostic value of these measurements for the risk of myocardial infarction (MI). Analyses used mixed effect models and Poisson regression. After confounders were controlled for, fasting triglyceride levels were, on average, 0.122 mmol/L lower than nonfasting levels. Each 2-fold increase in the latest triglyceride level was associated with a 38% increase in MI risk (relative rate, 1.38; 95% confidence interval, 1.26-1.51); fasting status did not modify this association. Our results suggest that it may not be necessary to restrict analyses to fasting measurements when considering MI risk.

Predicting current and future spatial community patterns of plant functional traits

Community-level patterns of functional traits relate to community assembly and ecosystem functioning. By modelling the changes of different indices describing such patterns - trait means, extremes and diversity in communities - as a function of abiotic gradients, we could understand their drivers and build projections of the impact of global change on the functional components of biodiversity. We used five plant functional traits (vegetative height, specific leaf area, leaf dry matter content, leaf nitrogen content and seed mass) and non-woody vegetation plots to model several indices depicting community-level patterns of functional traits from a set of abiotic environmental variables (topographic, climatic and edaphic) over contrasting environmental conditions in a mountainous landscape. We performed a variation partitioning analysis to assess the relative importance of these variables for predicting patterns of functional traits in communities, and projected the best models under several climate change scenarios to examine future potential changes in vegetation functional properties. Not all indices of trait patterns within communities could be modelled with the same level of accuracy: the models for mean and extreme values of functional traits provided substantially better predictive accuracy than the models calibrated for diversity indices. Topographic and climatic factors were more important predictors of functional trait patterns within communities than edaphic predictors. Overall, model projections forecast an increase in mean vegetation height and in mean specific leaf area following climate warming. This trend was important at mid elevation particularly between 1000 and 2000 m asl. With this study we showed that topographic, climatic and edaphic variables can successfully model descriptors of community-level patterns of plant functional traits such as mean and extreme trait values. However, which factors determine the diversity of functional traits in plant communities remains unclear and requires more investigations.

Factors de risc de conducta violenta intrainstitucional en interns penitenciaris

Existeixen un gran nombre d’investigacions sobre els indicadors de risc de la conducta violenta intrainstitucional en l’àmbit penitenciari, especialment en els EUA. Malgrat això, poc se sap sobre aquesta qüestió a Espanya, tot i que aquest és el país que presenta un dels índex de població reclusa més elevats d’Europa Occidental. Aquest estudi té per objecte la determinació de la prevalença i els correlats associats a les infraccions disciplinàries violentes i potencialment violentes d’11 centres penitenciaris de Catalunya. Per assolir aquest objectiu el present estudi prospectiu, basat en dades oficials, examina la relació entre variables sociodemogràfiques, criminals, penitenciàries i psicològiques d’una mostra de 7517 interns/es durant un període de seguiment de 2 anys. Els resultats obtinguts indiquen que ser més jove, estar en situació de presó preventiva en el cas de les dones, haver presentat conducta violenta o antisocial anteriorment, tant en la comunitat com en l’àmbit penitenciari, respondre d’una manera poc exitosa als tractaments recents o mostrar problemes en el consum d’alcohol o drogues i les actituds procriminals són factors de gran potència en la predicció d’infraccions disciplinàries. Per concloure, cal esmentar que investigacions addicionals en altres àrees geogràfiques i la consideració de variables situacionals són necessàries per corroborar els resultats aquí obtinguts. També es discuteixen algunes implicacions per a polítiques penitenciàries i per a futures línies d’investigació.

Density, climate and varying return points: an analysis of long-term population fluctuations in the threatened European tree frog.

Experimental research has identified many putative agents of amphibian decline, yet the population-level consequences of these agents remain unknown, owing to lack of information on compensatory density dependence in natural populations. Here, we investigate the relative importance of intrinsic (density-dependent) and extrinsic (climatic) factors impacting the dynamics of a tree frog (Hyla arborea) population over 22 years. A combination of log-linear density dependence and rainfall (with a 2-year time lag corresponding to development time) explain 75% of the variance in the rate of increase. Such fluctuations around a variable return point might be responsible for the seemingly erratic demography and disequilibrium dynamics of many amphibian populations.

Predicting the occurrence of alate aphids in Brassicaceae

The objective of this work was to predict the occurrence of alates of Brevicoryne brassicae, Lipaphis erysimi, and Myzus persicae (Hemiptera, Aphididae) in Brassicaceae. The alate aphids were collected in yellow water traps from July 1997 to August 2005. Aphid population peaks were predicted using a degree‑day model. The meteorological factors, temperature, air relative humidity, rainfall, and sunshine hours, were used to provide precision indexes to evaluate the best predictor for the date of the first capture of alate aphids by the traps. The degree‑day model indicated that the peak population of the evaluated aphid species can be predicted using one of the following biofix dates: January 1st, June 1st, and the date of the first capture of the alate aphid species by the yellow water traps. The best predictor of B. brassicae occurrence is the number of days with minimum temperature >15°C, and of L. erysimi and M. persicae, the number of days with rainfall occurrence.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0.13 Mg ha-1 on May 24, 2009 (dry season) to 62.0 Mg ha-1 on March 11, 2007 (rainy season). In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha-1 , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha-1 , but the central area, with a loss of nearly 300.0 Mg ha-1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.

Transcranial Doppler for predicting delayed cerebral ischemia after subarachnoid hemorrhage.

OBJECTIVE: Transcranial Doppler (TCD) is widely used to monitor the temporal course of vasospasm after subarachnoid hemorrhage (SAH), but its ability to predict clinical deterioration or infarction from delayed cerebral ischemia (DCI) remains controversial. We sought to determine the prognostic utility of serial TCD examination after SAH. METHODS: We analyzed 1877 TCD examinations in 441 aneurysmal SAH patients within 14 days of onset. The highest mean blood flow velocity (mBFV) value in any vessel before DCI onset was recorded. DCI was defined as clinical deterioration or computed tomographic evidence of infarction caused by vasospasm, with adjudication by consensus of the study team. Logistic regression was used to calculate adjusted odds ratios for DCI risk after controlling for other risk factors. RESULTS: DCI occurred in 21% of patients (n = 92). Multivariate predictors of DCI included modified Fisher computed tomographic score (P = 0.001), poor clinical grade (P = 0.04), and female sex (P = 0.008). After controlling for these variables, all TCD mBFV thresholds between 120 and 180 cm/s added a modest degree of incremental predictive value for DCI at nearly all time points, with maximal sensitivity by SAH day 8. However, the sensitivity of any mBFV more than 120 cm/s for subsequent DCI was only 63%, with a positive predictive value of 22% among patients with Hunt and Hess grades I to III and 36% in patients with Hunt and Hess grades IV and V. Positive predictive value was only slightly higher if mBFV exceeded 180 cm/s. CONCLUSION: Increased TCD flow velocities imply only a mild incremental risk of DCI after SAH, with maximal sensitivity by day 8. Nearly 40% of patients with DCI never attained an mBFV more than 120 cm/s during the course of monitoring. Given the poor overall sensitivity of TCD, improved methods for identifying patients at high risk for DCI after SAH are needed.

Empirical models for predicting the production of wild mushrooms in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees

Mushroom picking has become a widespread autumn recreational activity in the Central Pyrenees and other regions of Spain. Predictive models that relate mushroom production or fungal species richness with forest stand and site characteristics are not available. This study used mushroom production data from 24 Scots pine plots over 3 years to develop a predictive model that could facilitate forest management decisions when comparing silvicultural options in terms of mushroom production. Mixed modelling was used to model the dependence of mushroom production on stand and site factors. The results showed that productions were greatest when stand basal area was approximately 20 m2 ha-1. Increasing elevation and northern aspect increased total mushroom production as well as the production of edible and marketed mushrooms. Increasing slope decreased productions. Marketed Lactarius spp., the most important group collected in the region, showed similar relationships. The annual variation in mushroom production correlated with autumn rainfall. Mushroom species richness was highest when the total production was highest.

Cerebral oximetry and return of spontaneous circulation after cardiac arrest: A systematic review and meta-analysis.

AIM: The prediction of return of spontaneous circulation (ROSC) during resuscitation of patients suffering of cardiac arrest (CA) is particularly challenging. Regional cerebral oxygen saturation (rSO2) monitoring through near-infrared spectrometry is feasible during CA and could provide guidance during resuscitation. METHODS: We conducted a systematic review and meta-analysis on the value of rSO2 in predicting ROSC both after in-hospital (IH) or out-of-hospital (OH) CA. Our search included MEDLINE (PubMed) and EMBASE, from inception until April 4th, 2015. We included studies reporting values of rSO2 at the beginning of and/or during resuscitation, according to the achievement of ROSC. RESULTS: A total of nine studies with 315 patients (119 achieving ROSC, 37.7%) were included in the meta-analysis. The majority of those patients had an OHCA (n=225, 71.5%; IHCA: n=90, 28.5%). There was a significant association between higher values of rSO2 and ROSC, both in the overall calculation (standardized mean difference, SMD -1.03; 95%CI -1.39,-0.67; p<0.001), and in the subgroups analyses (rSO2 at the beginning of resuscitation: SMD -0.79; 95%CI -1.29,-0.30; p=0.002; averaged rSO2 value during resuscitation: SMD -1.28; 95%CI -1.74,-0.83; p<0.001). CONCLUSIONS: Higher initial and average regional cerebral oxygen saturation values are both associated with greater chances of achieving ROSC in patients suffering of CA. A note of caution should be made in interpreting these results due to the small number of patients and the heterogeneity in study design: larger studies are needed to clinically validate cut-offs for guiding cardiopulmonary resuscitation.

Risk-Return Trade-Off for European Stock Markets

This paper adopts dynamic factor models with macro-finance predictors to test the intertemporal risk-return relation for 13 European stock markets. We identify country specific, euro area, and global macro-finance factors to determine the conditional risk and return. Empirically, the risk- return trade-off is generally negative. However, a Markov switching model documents that there is time-variation in this trade-off that is linked to the state of the economy. Keywords: Risk-return trade-off; Dynamic factor model; Macro-finance predictors; European stock markets; Markov switching model JEL Classifications: C22; G11; G12; G17