DeepSpatial: Intelligent Spatial Sensor to Perception of Things

This paper discusses a spatial sensor to identify and track objects in the environment. The sensor is composed of an RGB-D camera that provides point cloud and RGB images and an egomotion sensor able to identify its displacement in the environment. The proposed sensor also incorporates a data processing strategy developed by the authors to conferring to the sensor different skills. The adopted approach is based on four analysis steps: egomotive, lexical, syntax, and prediction analysis. As a result, the proposed sensor can identify objects in the environment, track these objects, calculate their direction, speed, and acceleration, and also predict their future positions. The on-line detector YOLO is used as a tool to identify objects, and its output is combined with the point cloud information to obtain the spatial location of each identified object. The sensor can operate with higher precision and a lower update rate, using YOLOv2, or with a higher update rate, and a smaller accuracy using YOLOv3-tiny. The object tracking, egomotion, and collision prediction skills are tested and validated using a mobile robot having a precise speed control. The presented results show that the proposed sensor (hardware + software) achieves a satisfactory accuracy and usage rate, powering its use to mobile robotic. This paper's contribution is developing an algorithm for identifying, tracking, and predicting the future position of objects embedded in a compact hardware. Thus, the contribution of this paper is to convert raw data from traditional sensors into useful information.info:eu-repo/semantics/publishedVersio

Dental pulp tissue engineering

Dental pulp is a highly specialized mesenchymal tissue, which have a restrict regeneration capacity due to anatomical arrangement and post-mitotic nature of odontoblastic cells. Entire pulp amputation followed by pulp-space disinfection and filling with an artificial material cause loss of a significant amount of dentin leaving as life-lasting sequelae a non-vital and weakened tooth. However, regenerative endodontics is an emerging field of modern tissue engineering that demonstrated promising results using stem cells associated with scaffolds and responsive molecules. Thereby, this article will review the most recent endeavors to regenerate pulp tissue based on tissue engineering principles and providing insightful information to readers about the different aspects enrolled in tissue engineering. Here, we speculate that the search for the ideal combination of cells, scaffolds, and morphogenic factors for dental pulp tissue engineering may be extended over future years and result in significant advances in other areas of dental and craniofacial research. The finds collected in our review showed that we are now at a stage in which engineering a complex tissue, such as the dental pulp, is no longer an unachievable and the next decade will certainly be an exciting time for dental and craniofacial research

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost