PRELIMINARY CONCERNS ABOUT AGRONOMIC INTERPRETATION OF NDVI TIME SERIES FROM SENTINEL-2 DATA: PHENOLOGY AND THERMAL EFFICIENCY OF WINTER WHEAT IN PIEMONTE (NW ITALY)

Abstract. TELECER project is supported through Rural Development Programme regional action of EU CAP and is aimed at providing Precision Agriculture–devoted services for cereals monitoring in the Piemonte Region (NW-Italy) context. In this work authors explored some general and preliminary issues mainly aimed at demonstrating and formalizing those evident relationships existing between NDVI image time series and the main ordinary agronomic parameters, with special focus on phenology and thermal efficiency of crops as related to Growing Degrees Day (GDD). Winter wheat was investigated and relationships calibrated at field level, making possible to spatially characterise environmental and management effects. Two different analysis were achieved: (i) one aimed at mapping crop phenological metrics, as derivable from NDVI S2 time series; (ii) one aimed at locally modelling relationship linking GDD and NDVI to somehow test the thermal efficiency of crops in the different parts of the study area. The first analysis showed that the end of season appears to be the most constant phenological metric in the study area possibly demonstrating a time concentration of harvest operations in the area. Differently, the peak of season and the start of season metrics showed to be largely varying in the study, thus suggesting to be stronger predictors: (i) of crop development; (ii) of the effects induced by local agronomical practices. Several base temperatures were used to compute correspondent GDD. These were tested against NDVI and modelled by a parabolic model at field level. Model coefficients distribution were analysed and mapped the correspondent agronomic interpretation suggested

Effect of Operating and Sampling Conditions on the Exhaust Gas Composition of Small-Scale Power Generators

Small stationary diesel engines, like in generator sets, have limited emission control measures and are therefore responsible for 44% of the particulate matter (PM) emissions in the United States. The diesel exhaust composition depends on operating conditions of the combustion engine. Furthermore, the measurements are influenced by the used sampling method. This study examines the effect of engine loading and exhaust gas dilution on the composition of small-scale power generators. These generators are used in different operating conditions than road-transport vehicles, resulting in different emission characteristics. Experimental data were obtained for gaseous volatile organic compounds (VOC) and PM mass concentration, elemental composition and nitrate content. The exhaust composition depends on load condition because of its effect on fuel consumption, engine wear and combustion temperature. Higher load conditions result in lower PM concentration and sharper edged particles with larger aerodynamic diameters. A positive correlation with load condition was found for K, Ca, Sr, Mn, Cu, Zn and Pb adsorbed on PM, elements that originate from lubricating oil or engine corrosion. The nitrate concentration decreases at higher load conditions, due to enhanced nitrate dissociation to gaseous NO at higher engine temperatures. Dilution on the other hand decreases PM and nitrate concentration and increases gaseous VOC and adsorbed metal content. In conclusion, these data show that operating and sampling conditions have a major effect on the exhaust gas composition of small-scale diesel generators. Therefore, care must be taken when designing new experiments or comparing literature results

Connectivity characterization of the mouse basolateral amygdalar complex.

The basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks

Connectivity characterization of the mouse basolateral amygdalar complex.

The basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks