Els incendis forestals des d'una perspectiva antròpica

Cercar relacions entre l'evolució socioeconòmica, cultural i política d'un territori i els incendis forestals que l'hagin afectat en els darrers decennis, ens pot ajudar a comprendre part de les arrels d'aquesta problemàtica socioambiental. Saber d'on venim, pot esdevenir, també, una eina per decidir cap on volem anar; o com a mínim per a focalitzar certes qüestions, reptes de futur, sobre els boscos i el seu paper en la nostra societat.Los incendios forestales desde una perspectiva antrópica. Establecer nexos entre la evolución socio-económica, cultural i política de un territorio y los incendios forestales que le hayan podido afectar en los últimos decenios, puede ser de utilidad para comprender parte de las raíces de esta problemática socioambiental. Saber de dónde venimos puede ser, también, una herramienta para decidir hacia dónde pretendemos ir o como mínimo para centrarnos en ciertas cuestiones y retos de futuro, sobre los bosques y su papel en nuestra sociedad.Forest fire from anthropic perspective. To find the relationships between the evolution of social-economics, culture and politics of a territory, and a forest damaged by fire in the last decades, this can then help us to understand the roots of this socio-environmental problem. To know where we come from is essential to decide where we want to go or at least, can give us some perspectives and future goals about the forests and their role in our society

Acústica de la veu i els sons musicals : seqüència didàctica per a l'estudi del so

Recull de materials de les pràctiques dutes a terme durant els tallers REVIR, impartits pel CRECIM (Centre de Recerca per a l'Educació Científica i Matemàtica). La seqüència pretén que l'alumnat de 2n de Batxillerat assoleixi una major comprensió del so mitjançant la seva caracterització (intensitat, to i timbre), la caracterització de les ones sonores i les seves magnituds associades (període, freqüència, amplitud i longitud d'ona), així com el reconeixement de la transferència d'energia sense transferència de matèria, els sons fonamentals i harmònics i les diferències entre so i soroll

Combining H-FABP and GFAP increases the capacity to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

Mild traumatic brain injury (mTBI) patients may have trauma-induced brain lesions detectable using CT scans. However, most patients will be CT-negative. There is thus a need for an additional tool to detect patients at risk. Single blood biomarkers, such as S100B and GFAP, have been widely studied in mTBI patients, but to date, none seems to perform well enough. In many different diseases, combining several biomarkers into panels has become increasingly interesting for diagnoses and to enhance classification performance. The present study evaluated 13 proteins individually—H-FABP, MMP-1, MMP-3, MMP-9, VCAM, ICAM, SAA, CRP, GSTP, NKDA, PRDX1, DJ-1 and IL-10—for their capacity to differentiate between patients with and without a brain lesion according to CT results. The best performing proteins were then compared and combined with the S100B and GFAP proteins into a CT-scan triage panel. Patients diagnosed with mTBI, with a Glasgow Coma Scale score of 15 and one additional clinical symptom were enrolled at three different European sites. A blood sample was collected at hospital admission, and a CT scan was performed. Patients were divided into two two-centre cohorts and further dichotomised into CT-positive and CT-negative groups for statistical analysis. Single markers and panels were evaluated using Cohort 1. Four proteins—H-FABP, IL-10, S100B and GFAP—showed significantly higher levels in CT-positive patients. The best-performing biomarker was H-FABP, with a specificity of 32% (95% CI 23–40) and sensitivity reaching 100%. The best-performing two-marker panel for Cohort 1, subsequently validated in Cohort 2, was a combination of H-FABP and GFAP, enhancing specificity to 46% (95% CI 36–55). When adding IL-10 to this panel, specificity reached 52% (95% CI 43–61) with 100% sensitivity. These results showed that proteins combined into panels could be used to efficiently classify CT-positive and CT-negative mTBI patients