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

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

Land-use and land-cover mapping of the Brazilian cerrado based mainly on Landsat-8 satellite images.

The Brazilian Cerrado is one of the world´s biodiversity hotspot and hosts some of the most intensive agricultural activities for food production in the world. The objective of this study was to produce a land-use and land-cover (LULC) map of the Cerrado based on Landsat-8 Operational Land Imager (OLI) images. A set of 121 scenes from 2013 was processed using the image segmentation technique. The segments were exported in the shapefile format and interpreted visually in a geographical information system software using RGB/564 color composites. The following LULC classes were considered: annual croplands, perennial croplands, cultivated pasturelands, reforestation, mosaic of occupation, urban areas, mining areas, bare soil, forestlands, non-forestlands, water bodies, and non-identified (clouds and burned areas). The overall accuracy was estimated by an independent scientist with large experience in Cerrado´s image interpretation. The results showed that 43.4% of the study area (88.5 million hectares) were already converted into agricultural, urban and mining areas, 54.6% (111 million hectares) were still natural areas, and 1.9% (3.9 million hectares) was classified as non-identified. Cultivated pasturelands were the most representative land-use type (29.5%), followed by annual croplands (8.5%) and perennial croplands (3.1%). The overall accuracy of the final map was 80.2%.Título em português: Mapeamento de uso e cobertura de terras do cerrado com base principalmente em imagens do satélite Landsat-8

Land-use and land-cover mapping of the Brazilian cerrado based mainly on Landsat-8 satellite images.

The Brazilian Cerrado is one of the world´s biodiversity hotspot and hosts some of the most intensive agricultural activities for food production in the world. The objective of this study was to produce a land-use and land-cover (LULC) map of the Cerrado based on Landsat-8 Operational Land Imager (OLI) images. A set of 121 scenes from 2013 was processed using the image segmentation technique. The segments were exported in the shapefile format and interpreted visually in a geographical information system software using RGB/564 color composites. The following LULC classes were considered: annual croplands, perennial croplands, cultivated pasturelands, reforestation, mosaic of occupation, urban areas, mining areas, bare soil, forestlands, non-forestlands, water bodies, and non-identified (clouds and burned areas). The overall accuracy was estimated by an independent scientist with large experience in Cerrado´s image interpretation. The results showed that 43.4% of the study area (88.5 million hectares) were already converted into agricultural, urban and mining areas, 54.6% (111 million hectares) were still natural areas, and 1.9% (3.9 million hectares) was classified as non-identified. Cultivated pasturelands were the most representative land-use type (29.5%), followed by annual croplands (8.5%) and perennial croplands (3.1%). The overall accuracy of the final map was 80.2%.Título em português: Mapeamento de uso e cobertura de terras do cerrado com base principalmente em imagens do satélite Landsat-8

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

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

Analysis of the effects of depression associated polymorphisms on the activity of the BICC1 promoter in amygdala neurones

ACKNOWLEDGMENTS This work was funded by The BBSRC (BB/D004659/1) the Wellcome Trust (080980/Z/06/Z) and the Medical Research Council (G0701003).Peer reviewedPublisher PD

Land use dynamics in the Brazilian Cerrado in the period from 2002 to 2013.

The objective of this work was to analyze land use dynamics in the Brazilian Cerrado region from 2002 to 2013. This analysis was based on the interpretation of Landsat satellite images carried out by the projects Projeto de Conservação e Utilização Sustentável da Diversidade Biológica Brasileira (Probio) and TerraClass Cerrado 2013, both coordinated by Ministério do Meio Ambiente. In 2002, 38.9% of the Cerrado was covered by some type of anthropic activity. In 2013, this percentage increased to 43.4%. One of the main highlights is the emergence of a new agricultural frontier in the northern region of the study area, known as Matopiba.Título em português: Dinâmica do uso das terras no Cerrado no período de 2002 a 2013

A confiabilidade do PRODES: estimativa da acurácia do mapeamento do desmatamento no estado Mato Grosso.

PRODES is completing almost thirty years of uninterrupted monitoring of clear-cut deforestation over the Brazilian Amazon. Until now, no estimate of its mapping accuracy has been made. In this sense, this article brings a first approximation of mapping accuracy estimation of PRODES deforested areas, taking as example the state of Mato Grosso for the year 2014. For this, a random sampling panel was constructed, stratified with two strata, the deforestation of 2014 and the remaining forest. The sample size was calculated using the binomial function. In addition, a web platform was built to evaluate the points drawn by three independent evaluators. The global accuracy of the mapping of deforestation for the state of Mato Grosso, for the year 2014 was 94.5%, and may vary between 92.4% and 96.5%, in the evaluated scenario there was no class discordance to be found. Regarding the Forest class, the user accuracy was 90.5% and the producer's accuracy was 88.4%, this imbalance between user accuracy and producer accuracy indicates that there is a tendency for the forest class area to be underestimated for this mapping, in this year

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14$^{+}$ monocytes, CD16$^{+}$ neutrophils, and naive CD4$^{+}$ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of $\textit{cis}$-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.This work was predominantly funded by the EU FP7 High Impact Project BLUEPRINT (HEALTH-F5-2011-282510) and the Canadian Institutes of Health Research (CIHR EP1-120608). The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 282510 (BLUEPRINT), the European Molecular Biology Laboratory, the Max Planck society, the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013-2017’, SEV-2012-0208 and Spanish National Bioinformatics Institute (INB-ISCIII) PT13/0001/0021 co-funded by FEDER "“Una Manera de hacer Europa”. D.G. is supported by a “la Caixa”-Severo Ochoa pre-doctoral fellowship, M.F. was supported by the BHF Cambridge Centre of Excellence [RE/13/6/30180], K.D. is funded as a HSST trainee by NHS Health Education England, S.E. is supported by a fellowship from La Caixa, V.P. is supported by a FEBS long-term fellowship and N.S.'s research is supported by the Wellcome Trust (Grant Codes WT098051 and WT091310), the EU FP7 (EPIGENESYS Grant Code 257082 and BLUEPRINT Grant Code HEALTH-F5-2011-282510) and the NIHR BRC. The Blood and Transplant Unit (BTRU) in Donor Health and Genomics is part of and funded by the National Institute for Health Research (NIHR) and is a partnership between the University of Cambridge and NHS Blood and Transplant (NHSBT) in collaboration with the University of Oxford and the Wellcome Trust Sanger Institute. The T-cell data was produced by the McGill Epigenomics Mapping Centre (EMC McGill). It is funded under the Canadian Epigenetics, Environment, and Health Research Consortium (CEEHRC) by the Canadian Institutes of Health Research and by Genome Quebec (CIHR EP1-120608), with additional support from Genome Canada and FRSQ. T.P. holds a Canada Research Chair