Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling.

Assessing the impact of SARS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms of viral replication. We combine label-free holotomographic microscopy with Artificial Intelligence to visualize and quantify the subcellular changes triggered by SARS-CoV-2 infection. We study the dynamics of shape, position and dry mass of nucleoli, nuclei, lipid droplets and mitochondria within hundreds of single cells from early infection to syncytia formation and death.

Global change alters coastal plankton food webs by promoting the microbial loop: An inverse modelling and network analysis approach on a mesocosm experiment.

Marine organisms are currently, and will continue to be, exposed to the simultaneous effects of multiple environmental changes. Plankton organisms form the base of pelagic marine food webs and are particularly sensitive to ecosystem changes. Thus, warming, acidification, and changes in dissolved nutrient concentrations have the potential to alter these assemblages, with consequences for the entire ecosystem.

Evolutionary Analysis of Cnidaria Small Cysteine-Rich Proteins (SCRiPs), an Enigmatic Neurotoxin Family from Stony Corals and Sea Anemones (Anthozoa: Hexacorallia).

Cnidarians (corals, sea anemones, and jellyfish) produce toxins that play central roles in key ecological processes, including predation, defense, and competition, being the oldest extant venomous animal lineage. Cnidaria small cysteine-rich proteins (SCRiPs) were the first family of neurotoxins detected in stony corals, one of the ocean's most crucial foundation species. Yet, their molecular evolution remains poorly understood.

An integrated multiple driver mesocosm experiment reveals the effect of global change on planktonic food web structure.

Global change puts coastal marine systems under pressure, affecting community structure and functioning. Here, we conducted a mesocosm experiment with an integrated multiple driver design to assess the impact of future global change scenarios on plankton, a key component of marine food webs. The experimental treatments were based on the RCP 6.

Removal of smoke effects in laparoscopic surgery via adversarial neural network and the dark channel prior.

Background: In laparoscopic surgery, image quality can be severely degraded by surgical smoke caused by the use of tissue dissection tools that reduce the visibility of the observed organs and tissues.

Objective: Improve visibility in laparoscopic surgery by combining image processing techniques based on classical methods and artificial intelligence.

Method: Development of a hybrid approach to eliminating the effects of surgical smoke, based on the combination of the dark channel prior (DCP) method and a pixel-to-pixel neural network architecture known as a generative adversarial network (GAN).

Afrocolombian Struggles for Food, Land, and Culture: The Case of El Tiple.

El Tiple is one of many marginalized Afrodescendant communities confined within a green desert located in the southwest region of Colombia. This green desert is most widely known as the second-largest sugarcane monoculture field in the Americas. Herein, we describe a transdisciplinary and participatory effort to understand agroindustrial expansion in the region through the lens of the El Tiple community.

Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception.

A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research.

On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System.

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor.

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes.