Satellite time series analysis for land use/cover change detection

Currently, Brazilian land use data comes from the national agricultural census and land cover data comes from global data sets with sparse temporal coverage. This no longer meets the needs of the earth system modeling community. Long-term satellite image datasets with high temporal frequency yield a sequence of data points in a time series that can be used to detect and monitor land use and land cover changes. The vegetation phenological cycles are reflected in the satellite time series, allowing the classification of land cover types in time segments. This research aims at developing an automatic methodology to yield information about land use and land cover trajectories. To construct land use/cover trajectories maps, Dynamic Time Warping (DTW) is used to extract information from the MODIS 2-band Enhanced Vegetation Index (EVI2) time series. Validation tests were made in the areas of Mato Grosso state, Brazil. The preliminary results for the proposed methods are promising when compared with the official TerraClass land use maps in the Amazon Biome, finding 78.2% and 85.0% global accuracy for 2008 and 2010, respectively. Exploratory DTW results show significant potential to detect land use and cover changes

Production of docosahexaenoic acid by Aurantiochytrium sp. ATCC PRA-276

The high costs and environmental concerns associated with using marine resources as sources of oils rich in polyunsaturated fatty acids have prompted searches for alternative sources of such oils. Some microorganisms, among them members of the genus Aurantiochytrium, can synthesize large amounts of these biocompounds. However, various parameters that affect the polyunsaturated fatty acids production of these organisms, such as the carbon and nitrogen sources supplied during their cultivation, require further elucidation. The objective of this investigation was to study the effect of different concentrations of carbon and total nitrogen on the production of polyunsaturated fatty acids, particularly docosahexaenoic acid, by Aurantiochytrium sp. ATCC PRA-276. We performed batch system experiments using an initial glucose concentration of 30 g/L and three different concentrations of total nitrogen, including 3.0, 0.44, and 0.22 g/L, and fed-batch system experiments in which 0.14 g/L of glucose and 0.0014 g/L of total nitrogen were supplied hourly. To assess the effects of these different treatments, we determined the biomass, glucose, total nitrogen and polyunsaturated fatty acids concentration. The maximum cell concentration (23.9 g/L) was obtained after 96 h of cultivation in the batch system using initial concentrations of 0.22 g/L total nitrogen and 30 g/L glucose. Under these conditions, we observed the highest level of polyunsaturated fatty acids production (3.6 g/L), with docosahexaenoic acid and docosapentaenoic acid ω6 concentrations reaching 2.54 and 0.80 g/L, respectively

dtwSat: An R Package for Land Cover Classification Using Satellite Image Time Series

Open access to satellite data has boosted the development of new approaches to quantify and understand Earth's changes. The large spatiotemporal availability of satellite imagery, for example, has improved our capability to map and monitor land use and land cover changes over vast areas. Given the open availability of large image data sets, the Earth Observation community would get much benefit from methods that are openly available, reproducible and comparable. This paper presents the R package dtwSat, which provides an implementation of the Time-Weighted Dynamic Time Warping (TWDTW) method for land cover mapping using sequences of multi-band satellite images. Methods based on Dynamic Time Warping (DTW) are suitable to handle irregularly sampled and out-of-phase time series, which is frequently the case of those from remote sensing. TWDTW algorithm has achieved significant results using MODIS, Landsat, and Sentinel-2 time series to classify natural vegetation and crop types in different regions. Using existing R packages as building blocks dtwSat supports the full cycle of land cover classification using satellite time series, ranging from selecting temporal patterns to visualizing and assessing the results. To handle the satellite images, dtwSat uses well-known data structures from the R package raster, which offers the option to work with large raster data sets stored on disk instead of loading into memory (RAM) at once. The current version of the dtwSat package provides pixel-based time series classification, i.e., each time series is processed independently from each other, and therefore, the code is easily parallelizable. dtwSat is open source and distributed under a GNU General Public License GPL (≥ 2). A binary version is available from the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/dtwSat) and the development version from GitHub (https://github.com/vwmaus/dtwSat). Future versions of the package envisage new features to reduce border effects, increase spatial homogeneity (i.e., reduce the called 'salt and pepper effect') and improve the temporal consistency of land cover transitions. dtwSat makes it straightforward to apply and compare the TWDTW approach with other methods, contributing to rapid advance automated and semi-automated methods to analyze satellite time series

DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity

Nowadays, events usually burst and are propagated online through multiple modern media like social networks and search engines. There exists various research discussing the event dissemination trends on individual medium, while few studies focus on event popularity analysis from a cross-platform perspective. Challenges come from the vast diversity of events and media, limited access to aligned datasets across different media and a great deal of noise in the datasets. In this paper, we design DancingLines, an innovative scheme that captures and quantitatively analyzes event popularity between pairwise text media. It contains two models: TF-SW, a semantic-aware popularity quantification model, based on an integrated weight coefficient leveraging Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series alignment model matching different event phases adapted from Dynamic Time Warping. We also propose three metrics to interpret event popularity trends between pairwise social platforms. Experimental results on eighteen real-world event datasets from an influential social network and a popular search engine validate the effectiveness and applicability of our scheme. DancingLines is demonstrated to possess broad application potentials for discovering the knowledge of various aspects related to events and different media

CAR-Based Approaches to Cutaneous T-Cell Lymphoma

Cutaneous T cell lymphomas (CTCL) are a heterogeneous group of malignancies characterized by the expansion of a malignant T cell clone. Chimeric Antigen Receptor (CAR) T cell therapy has shown impressive results for the treatment of B-cell tumors, but several challenges have prevented this approach in the context of T cell lymphoma. These challenges include the possibilities of fratricide due to shared T-cell antigens, T cell immunodeficiency, and CAR transduction of malignant cells if CAR T are manufactured in the autologous setting. In this review, we discuss these and other challenges in detail and summarize the approaches currently in development to overcome these challenges and offer cellular targeting of T cell lymphomas

The potential of Landsat time series to characterize historical dynamic and monitor future disturbances in human-modified rainforests of Indonesia

In this study we demonstrated for the first time the potential of using full time series from high spatial resolution (30 m) Landsat satellites, covering a period from 1987-2017, for characterizing historical dynamics in Indonesian humid tropical rainforests. Our special focus was on mapping forest disturbance and post-disturbance regrowth, which in turn can potentially be used to map primary (undisturbed) forests, secondary (disturbed/degraded) forests, and forest land converted to oil palm plantation. We applied the Breaks For Additive Season and Trend (BFAST) Monitor framework for continuous change detection; BFAST is a generic and transparent method, which can be used for near-real-time monitoring. To verify our approach, a preliminary spatial accuracy assessment was carried out for disturbance detection using 418 sample pixels interpreted from very high spatial resolution images acquired through Digital Globe viewing service. Besides, we identified the sources of detection errors and approaches to overcome them. Implementation of the potential map product in existing international and national policies will be discusse

Current approaches to increase CAR T cell potency in solid tumors: targeting the tumor microenvironment

Chimeric antigen receptor (CAR) T-cell therapy represents a revolutionary treatment for haematological malignancies (i.e. B-ALL). However, the success of this type of treatment has not yet been achieved in solid tumors. One hypothesis is that the immunosuppressive nature of the tumor microenvironment (TME) influences and affects the efficacy of adoptive immunotherapy. Understanding the role of the TME and its interaction with CAR T-cells is crucial to improve the potency of adoptive immunotherapy. In this review, we discuss the strategies and potential combinatorial approaches recently developed in mouse models to enhance the efficacy of CAR T-cells, with particular emphasis on the translational potential of these approaches

Making CAR T Cells a Solid Option for Solid Tumors

Adoptive cell therapy with chimeric antigen receptor (CAR) T cells aims to redirect the patient's own immune system to selectively attack cancer cells. To do so, CAR T cells are endowed with specific antigen recognition moieties fused to signaling and costimulatory domains. While this approach has shown great success for the treatment of B cell malignancies, response rates among patients with solid cancers are less favorable. The major challenges for CAR T cell immunotherapy in solid cancers are the identification of unique tumor target antigens, as well as improving CAR T cell trafficking to and expansion at the tumor site. This review focuses on combinatorial antigen targeting, regional delivery and approaches to improve CAR T cell persistence in the face of a hostile tumor microenvironment

STILF - A spatiotemporal interval logic formalism for reasoning about events in remote sensing data

Although several studies perform time series analysis using remote sensing data provided by Earth observation satellites, few have been explored concerning the reasoning about land use change using these data. Besides, exists the challenge of make the best use of big Earth observation data sets to represent change. In this context, this work presents a new formalism - STILF (Spatiotemporal Interval Logic Formalism), and shows how to use it for reasoning about land use change using big Earth observation data. Extending the ideas from Allen’s interval temporal logic, we introduce predicates holds(o, p, t) and occur(o, p, Te) to build a general framework to reason about events. Events can be defined as complete entities on their respective time intervals and their lifetime is limited while objects persist in time, with a defined begin and end. Since events are intrinsically related to the objects they modify, a geospatial event formalism should specify not only what happens, but also which objects are affected by such changes. The formalism proposed and predicates extended from Allen''''''''s ideas can model and capture changes using big Earth observation data, and also allows reasoning about land use trajectories in regional or global areas. Examples for tropical forest area application is presented to better understand our proposal using STILF. For the future, the proposed formalism will be include other temporal analysis tools to thinking about events related the land use and cover change

Mapping global extraction of abiotic and biotic raw materials

Reducing global environmental and social impacts related to final consumption is a significant societal as well as scientific challenge, especially as production and consumption are increasingly geographically disconnected via complex supply chains. Tracing the interlinkages between consumption and production as well as related impacts in a spatially explicit way can contribute to overcoming this challenge. Currently, the spatial resolution of global models of raw material extraction, trade and consumption is limited to the national level. Thus, they fail to link specific supply chains to the actual geographical location of production and related impacts. Detailed global spatiotemporal datasets would allow tracing the heterogeneity of environmental and social conditions within producing countries. In this contribution, we present our preliminary results mapping global biotic and abiotic raw materials extraction in 5-arc-minutes (around 10 km x 10 km at the equator) grid cell level, starting from the year 2000. Our datasets will include around 60 different raw materials, covering crops, fishery, fossil energy resources, metal ores and non-metallic minerals. In the future, our database will also include spatially explicit data on environmental and social impacts related to the extraction of these raw materials. The new database, methods, and algorithms will be openly available to the research community and the wider public, supporting open and reproducible science. Our novel database will allow developing new methods to assess the interlinkages between consumption and various environmental and social impacts related to extraction on a grid cell level. It can boost the spatially explicit assessments of supply chains and consumption patterns in both developed and developing countries, which is crucial for the design of international policy instruments to achieve sustainable production and consumption patterns