Early CSF Biomarkers and Late Functional Outcomes in Spinal Cord Injury. A Pilot Study

Although, biomarkers are regarded as an important tool for monitoring injury severity and treatment efficacy, and for predicting clinical evolution in many neurological diseases and disorders including spinal cord injury, there is still a lack of reliable biomarkers for the assessment of clinical course and patient outcome. In this study, a biological dataset of 60 cytokines/chemokines, growth factorsm and intracellular and extracellular matrix proteins, analyzed in CSF within 24 h of injury, was used for correlation analysis with the clinical dataset of the same patients. A heat map was generated of positive and negative correlations between biomarkers and clinical rating scale scores at discharge, and between biomarkers and changes in clinical scores during the observation period. Using very stringent statistical criteria, we found 10 molecules which correlated with clinical scores at discharge, and five molecules, which correlated with changes in clinical scores. The proposed methodology may be useful for generating hypotheses regarding “predictive” and “treatment effectiveness” biomarkers, thereby suggesting potential candidates for disease-modifying therapies using a “bed-to-bench” approach.     

Optimization of the silane treatment of cellulosic fibers from eucalyptus wood using response surface methodology

Viscose cellulosic fibers from eucalyptus wood were treated with organosilanes to introduce specific functionalities on the fibers and enhance their wettability and adhesion with phenolic matrices in composites. Modeling procedures were employed to optimize the conditions of the treatments of the fibers with the silanes (3‐aminopropyl) trimethoxysilane (APS) and 3‐(2‐aminoethylamino) propyltrimethoxysilane (AAPS). The analyzed responses were relative intensities of the bands 1565/897 and 1120/897 cm⁻¹, measured by Fourier transform infrared spectroscopy, and the silicon amount incorporated into the cellulosic fibers, which was determined by energy dispersive X‐ray analysis. In addition, surface morphology of the silane treated fibers was observed using scanning electron microscopy. The treatments of the cellulosic fibers with 2.2% APS for 120 min and 1.5% AAPS for 100 min were selected as optimums. According to contact angle measurements, both treatments enhanced the wettability between the fibers and a resol‐type phenolic resin, revealing the possible use of the silane treated fibers as reinforcement in phenolic composites. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42157.     

An econometric analysis of the effects of the penalty points system driver's license in Spain

This article seeks to quantify the effects of the penalty points system driver's license during the 18-month period following its coming into force. This is achieved by means of univariate and multivariate unobserved component models set up in a state space framework estimated using maximum likelihood. A detailed intervention analysis is carried out in order to test for the effects and their duration of the introduction of the penalty points system driver's license in Spain. Other variables, mainly indicators of the level of economic activity in Spain, are also considered. Among the main effects, we can mention an average reduction of almost 12.6% in the number of deaths in highway accidents. It would take at least 2 years for that effect to disappear. For the rest of the safety indicator variables (vehicle occupants injured in highway accidents and vehicle occupants injured in accidents built-up areas) the effects disappeared 1 year after the law coming into force.     

Rural electrification systems based on renewable energy: The social dimensions of an innovative technology

Many areas in rural non-Western regions are installing electrification systems based on renewable energy. Although these projects are usually welcome, they sometimes fail. Explanations for failures often cite technical reasons. In this research, partly based on the results of the SOPRA_RE project, we focus on the users, studying the relationships between technology and society. By analysing potential sources of failures, we identify the root of the problem as how a new technology is received by the local society. In this paper we introduce dimensions that we consider to be especially important in the process of technology integration. We defend the importance of combining the technical outlook with the sociological one, based on the idea that the latter is often indispensable as a complementary element of technical explanations of system failures, which in turn provides a better basis for solving them. We use specific examples to bolster our insistence on the need to achieve convergence between the technical and the sociological outlooks.     

Study of nanofiber scaffolds of PAA,PAA/CS,and PAA/ALG for its potential use in biotechnological applications

In recent times, electrospun nanofibers have been widely studied from several biotechnological approaches; in this work, poly(acrylic acid) (PAA) solutions mixed with chitosan and alginate were electrospun and characterized to determine the behavior of these fibers when used in combination with bacteria, different samples were incubated with the bacterial strains: Streptomyces spp., Micromonospora spp., and Escherichia coli and a OD600 test was performed. The formation of nanofibers via electrospinning and the physicochemical properties of the obtained fibers were evaluated. Results showed that the presence of chitosan enhanced the thermal stability of PAA, since PAA/alginate fibers lost 5% of their mass at 41°C, whereas PAA/chitosan lost this amount at around 125°C. The fibers demonstrated suitable characteristics to be used as a bacteria bioreactor.     

Worst-case estimation for econometric models with unobservable components

A worst-case estimator for econometric models containing unobservable components, based on minimax principles for optimal selection of parameters, is proposed. Worst-case estimators are robust against the averse effects of unobservables. Computing worst-case estimators involves solving a minimax continuous problem, which is quite a challenging task. Large sample theory is considered, and a Monte Carlo study of finite-sample properties is conducted. A financial application is considered.     

Estimating forage quantity and quality under different stress and senescent biomass conditions via spectral reflectance

Assessing forage quantity and quality through remote sensing can facilitate grassland and pasture management. However, the high spatial and temporal variability of canopy conditions may limit the predictive accuracy of models based on reflectance measurements. The objective of this work was to develop this type of models, and to challenge their capacity to predict plant properties under a wide range of environmental conditions. We manipulated Paspalum dilatatum canopies through different stress treatments (flooding, drought, nutrient availability, and control) and by artificially varying the amount of senescent biomass. We measured canopy reflectance and constructed simple models, based on either normalized vegetation indices or a few selected wavebands, to estimate biomass and two variables related to forage quality: proportion of photosynthetic vegetation and biomass C:N ratio. General models satisfactorily predicted plant properties for the whole set of environmental conditions, but failed under specific conditions such as drought (for estimates of plant biomass), fertilization (for estimates of C:N ratio), and different levels of senescent tillers (for estimates of the proportion of photosynthetic vegetation). Where general models failed, specific models, based on different bands, achieved satisfactory accuracy. The general models performed better when based on a few selected bands than when based on two-band vegetation indices, having better accuracy (higher R²) and parsimony (lower BIC). However specific models performed similarly for both approaches (similar R² and BIC). These results indicate that these plant properties can be predicted from reflectance information under a broad range of conditions, but not for some particular conditions, where ancillary data or more complex models are probably needed to increase predictive accuracy.