Identifying and explaining the farming system composition of agricultural landscapes: The role of socioeconomic drivers under strong biophysical gradients

In mountain landscapes, agricultural abandonment is taking place in the most vulnerable areas, while intensification increases in the most productive lands. These contrasting processes, which have different impacts on biodiversity and ecosystem services (BES), are related to changes in the farming system component of these landscapes. Farming systems are identified based on farmer’s decisions on, for example, type of crop and level of fertilizers, which represent the descriptors of farming systems and can be grouped into several dimensions (e.g. land use and intensity). Since obtaining this data at farm-level is often difficult, an alternative is to study the spatial combinations of farming systems at parish-level, i.e., Farming System Mixes (FSM), relying on agricultural census data. Other biophysical (e.g. climate, soil) and socioeconomic (e.g. labour, farmer’s age) variables, independent of farmers' decisions, represent the exogenous drivers of these decisions. The separation between descriptors and drivers is important to improve knowledge about what drives farmers' decisions regarding farming system choice, as these choices are often the focus of policies aiming the support of BES. In this study, we explored the underlying drivers of FSM and assessed the role of socioeconomic drivers, main target for policy makers, in a context of strong biophysical gradients. Biophysical drivers emerge as those that primarily discriminate between the FSM located in different topographic positions (valleys, mountains and plateau). In the situations where there is a greater range of productive choices available for farmers, such as in valleys, socioeconomic drivers assume a preponderant role on farming system choiceinfo:eu-repo/semantics/publishedVersio

Assessment of plasma chitotriosidase activity, CCL18/PARC concentration and NP-C suspicion index in the diagnosis of Niemann-Pick disease type C: A prospective observational study

Background: Niemann-Pick disease type C (NP-C) is a rare, autosomal recessive neurodegenerative disease caused by mutations in either the NPC1 or NPC2 genes. The diagnosis of NP-C remains challenging due to the non-specific, heterogeneous nature of signs/symptoms. This study assessed the utility of plasma chitotriosidase (ChT) and Chemokine (C-C motif) ligand 18 (CCL18)/pulmonary and activation-regulated chemokine (PARC) in conjunction with the NP-C suspicion index (NP-C SI) for guiding confirmatory laboratory testing in patients with suspected NP-C. Methods: In a prospective observational cohort study, incorporating a retrospective determination of NP-C SI scores, two different diagnostic approaches were applied in two separate groups of unrelated patients from 51 Spanish medical centers (n = 118 in both groups). From Jan 2010 to Apr 2012 (Period 1), patients with =2 clinical signs/symptoms of NP-C were considered ''suspected NP-C'' cases, and NPC1/NPC2 sequencing, plasma chitotriosidase (ChT), CCL18/PARC and sphingomyelinase levels were assessed. Based on findings in Period 1, plasma ChT and CCL18/PARC, and NP-C SI prediction scores were determined in a second group of patients between May 2012 and Apr 2014 (Period 2), and NPC1 and NPC2 were sequenced only in those with elevated ChT and/or elevated CCL18/PARC and/or NP-C SI =70. Filipin staining and 7-ketocholesterol (7-KC) measurements were performed in all patients with NP-C gene mutations, where possible. Results: In total across Periods 1 and 2, 10/236 (4%) patients had a confirmed diagnosis o NP-C based on gene sequencing (5/118 4.2%] in each Period): all of these patients had two causal NPC1 mutations. Single mutant NPC1 alleles were detected in 8/236 (3%) patients, overall. Positive filipin staining results comprised three classical and five variant biochemical phenotypes. No NPC2 mutations were detected. All patients with NPC1 mutations had high ChT activity, high CCL18/PARC concentrations and/or NP-C SI scores =70. Plasma 7-KC was higher than control cut-off values in all patients with two NPC1 mutations, and in the majority of patients with single mutations. Family studies identified three further NP-C patients. Conclusion: This approach may be very useful for laboratories that do not have mass spectrometry facilities and therefore, they cannot use other NP-C biomarkers for diagnosis