Understanding the dynamic of tropical agriculture for remote sensing applications: a case study of Southeastern Brazil.

Abstract: The agricultural activity can greatly benefit from remote sensing technology (RS). Optical passive RS has been vastly explored for agricultural mapping and monitoring, in despite of cloud cover issue. This is observed even in the tropics, where frequency of clouds is very high. However, more studies are needed to better understand the high dynamism of tropical agriculture and its impact on the use of passive RS. In tropical countries, such as in Brazil, the use of current agricultural technologies, associated with favourable climate, allow the planting period to be wide and to have plants of varying phenological cycles. In this context, the main objective of the current study is to better understand the dynamics of a selected area in Southeast of São Paulo state, and its impact on the use of orbital passive RS. For that purpose, data (from field and satellite) from 55 agricultural fields, including annual, semi-perennial and perennial crops and silviculture, were acquired between July 2014 and December 2016. Field campaigns were conducted in a monthly base to gather information about the condition of the crops along their development (data available in a website). Field data corresponding to the 2014-2015 crop year were associated with a time series of Landsat-8/OLI RGB false-colour compositions images and MODIS/Terra NDVI profiles. The type of information that can be extracted (such as specie identification, crop management practices adopted, date of harvest, type o production system used etc) by combining passive remote sensing data with field data is discussed in the paper

Identification of gaps in sugarcane plantations using UAV images.

The objective of this study is to present a methodology for the detection and quantification of gaps formed during planting or growing of sugarcane crops. The use of UAV images for precision agriculture is relevant because it brings new possibilities for improving crop's productivity by feeding the producer with highly accurate data about the crop status

Lem benchmark database for tropical agricultural remote sensing application.

Abstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data

Análise da variabilidade espectro-temporal intraespecífica do milho.

Esse estudo visa melhor compreender a variabilidade intraespecífica do comportamento espectral da cultura do milho, ao longo do seu desenvolvimento e averiguar o potencial e as dificuldades de se identificar espectralmente essa cultura por meio de dados obtidos por sensoriamento remoto

Análise da variabilidade espectro-temporal intraespecífica do milho.

Remote sensing data has been widely used worldwide to estimate crop field?s parameters such as area. For that purpose, we use automatic classification algorithms to identify different land uses and land covers (e.g. agricultural and native vegetation), groups of crops (e.g. annual and perennial crops) or crops species (e.g. maize, sugarcane or soybean). For agricultural applications, the ultimate goal is to be able to use remote sensing technology to map crops in the specie level, and then to monitor them. One essential input data used in the classifications algorithms is the spectral information of the ground targets (e.g. reflectance and vegetation indices). Therefore, it is important to know the spectral behavior of all targets. However, the ability of one classifier to distinguish between plant species is probably dependent on the amount of intraspecific variability. In other words, if a crop specie has high intraspecific spectral variation, it will be difficult to classify this specie among others. Thus, the aim of this work is to analyze the intraspecific spectral temporal variability of maize crop. To accomplish that, spectral data (OLI/Landsat-8) were acquired from first and second harvest maize plots, cultivated over distinct management systems (irrigated and non-irrigated), along two agricultural crop years, (2014/2015 and 2015/2016). We concluded that maize fields harvested in different years, sowed in different seasons, irrigated or not, have a high temporal spectral variation, which cannot be associated with these known characteristics

CSF parvalbumin levels reflect interneuron loss linked with cortical pathology in multiple sclerosis

INTRODUCTION AND METHODS: In order to verify whether parvalbumin (PVALB), a protein specifically expressed by GABAergic interneurons, could be a MS-specific marker of grey matter neurodegeneration, we performed neuropathology/molecular analysis of PVALB expression in motor cortex of 40 post-mortem progressive MS cases, with/without meningeal inflammation, and 10 control cases, in combination with cerebrospinal fluid (CSF) assessment. Analysis of CSF PVALB and neurofilaments (Nf-L) levels combined with physical/cognitive/3TMRI assessment was performed in 110 naïve MS patients and in 32 controls at time of diagnosis. RESULTS: PVALB gene expression was downregulated in MS (fold change = 3.7 ± 1.2, P < 0.001 compared to controls) reflecting the significant reduction of PVALB+ cell density in cortical lesions, to a greater extent in MS patients with high meningeal inflammation (51.8, P < 0.001). Likewise, post-mortem CSF-PVALB levels were higher in MS compared to controls (fold change = 196 ± 36, P < 0.001) and correlated with decreased PVALB+ cell density (r = -0.64, P < 0.001) and increased MHC-II+ microglia density (r = 0.74, P < 0.01), as well as with early age of onset (r = -0.69, P < 0.05), shorter time to wheelchair (r = -0.49, P < 0.05) and early age of death (r = -0.65, P < 0.01). Increased CSF-PVALB levels were detected in MS patients at diagnosis compared to controls (P = 0.002). Significant correlation was found between CSF-PVALB levels and cortical lesion number on MRI (R = 0.28, P = 0.006) and global cortical thickness (R = -0.46, P < 0.001), better than Nf-L levels. CSF-PVALB levels increased in MS patients with severe cognitive impairment (mean ± SEM:25.2 ± 7.5 ng/mL) compared to both cognitively normal (10.9 ± 2.4, P = 0.049) and mild cognitive impaired (10.1 ± 2.9, P = 0.024) patients. CONCLUSIONS: CSF-PVALB levels reflect loss of cortical interneurons in MS patients with more severe disease course and might represent an early, new MS-specific biomarker of cortical neurodegeneration, atrophy, and cognitive decline

Behavioral challenges in policy analysis with conflicting objectives

Public policy problems are rife with conflicting objectives: efficiency versus fairness, technical criteria versus political goals, costs versus multiple benefits. Multi-Criteria Decision Analysis provides robust methodologies to support policy makers in making tough choices and in designing better policy options when considering these conflicting objectives. However, important behavioral challenges exist in developing these models: the use of expert judgments, whenever evidence is not available; the elicitation of preferences and priorities from policy makers and communities; and the effective management of group decision processes. The extensive developments in behavioral decision research, social psychology, facilitated decision modeling, and incomplete preference models shed light on how decision analysts should address these issues, so we can provide better decision support and develop high quality decision models. In this tutorial I discuss the main findings of these extensive, but rather fragmented, literatures providing a coherent and practical framework for managing behavioral issues, minimizing behavioral biases, and optimizing the quality of human judgments in policy analysis models with conflicting objectives. I illustrate these guidelines with policy analysis interventions that we have conducted over the last decade for several organizations, such as the World Health Organization (WHO), the Food and Agriculture Organization of the United Nations (FAO), the UK Department of Environment Food and Rural Affairs (DEFRA), the Malaria Consortium/USAID, the UK National Audit Office, among others

Endoplasmic reticulum stress signalling – from basic mechanisms to clinical applications

The endoplasmic reticulum (ER) is a membranous intracellular organelle and the first compartment of the secretory pathway. As such, the ER contributes to the production and folding of approximately one‐third of cellular proteins, and is thus inextricably linked to the maintenance of cellular homeostasis and the fine balance between health and disease. Specific ER stress signalling pathways, collectively known as the unfolded protein response (UPR), are required for maintaining ER homeostasis. The UPR is triggered when ER protein folding capacity is overwhelmed by cellular demand and the UPR initially aims to restore ER homeostasis and normal cellular functions. However, if this fails, then the UPR triggers cell death. In this review, we provide a UPR signalling‐centric view of ER functions, from the ER's discovery to the latest advancements in the understanding of ER and UPR biology. Our review provides a synthesis of intracellular ER signalling revolving around proteostasis and the UPR, its impact on other organelles and cellular behaviour, its multifaceted and dynamic response to stress and its role in physiology, before finally exploring the potential exploitation of this knowledge to tackle unresolved biological questions and address unmet biomedical needs. Thus, we provide an integrated and global view of existing literature on ER signalling pathways and their use for therapeutic purposes

LEM BENCHMARK DATABASE FOR TROPICAL AGRICULTURAL REMOTE SENSING APPLICATION

The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data