Designing Deep Learning Frameworks for Plant Biology

In recent years the parallel progress in high-throughput microscopy and deep learning drastically widened the landscape of possible research avenues in life sciences. In particular, combining high-resolution microscopic images and automated imaging pipelines powered by deep learning dramatically reduced the manual annotation work required for quantitative analysis. In this work, we will present two deep learning frameworks tailored to the needs of life scientists in the context of plant biology. First, we will introduce PlantSeg, a software for 2D and 3D instance segmentation. The PlantSeg pipeline contains several pre-trained models for different microscopy modalities and multiple popular graph-based instance segmentation algorithms. In the second part, we will present CellTypeGraph, a benchmark for quantitatively evaluating graph neural networks. The benchmark is designed to test the ability of machine learning methods to classify the types of cells in an \textit{Arabidopsis thaliana} ovules. CellTypeGraph's prime aim is to give a valuable tool to the geometric learning community, but at the same time it also offers a framework for plant biologists to perform fast and accurate cell type inference on new data

TLR4 expression in ex-Lichenoid lesions—oral squamous cell carcinomas and its surrounding epithelium: the role of tumor inflammatory microenvironment

Abstract: Toll-like receptors (TLRs) regulate innate and adaptive immune responses. Moreover, TLRs can induce a pro-survival and pro-proliferation response in tumor cells. This study aims to investigate the expression of TLR4 in the epithelium surrounding oral squamous cell carcinomas (OSCC) in relation to its inflammatory microenvironment. This study included 150 human samples: 30 normal oral control (NOC), 38 non-lichenoid epithelium surrounding OSCC (NLE-OSCC), 28 lichenoid epithelium surrounding OSCC (LE-OSCC), 30 OSCC ex-non oral lichenoid lesion (OSCC Ex-NOLL), and 24 OSCC ex-oral lichenoid lesion (OSCC Ex-OLL). TLR4 expression was investigated by immuno histochemistry and the percentage of positive cells was quantified. In addition, a semiquantitative analysis of staining intensity was performed. Immunohistochemical analysis revealed that TLR4 is strongly upregulated in LE-OSCC as compared to normal control epithelium and NLE-OSCC. TLR4 expression was associated with the inflammatory environment, since the percentage of positive cells increases from NOC and NLE-OSCC to LE-OSCC, reaching the highest value in OSCC Ex–OLL. TLR4 was detected in the basal third of the epithelium in NLE-OSCC, while in LE-OSCC, TLR4 expression reached the intermediate layer. These results demonstrated that an inflammatory microenvironment can upregulate TLR4, which may boost tumor development

Accurate and versatile 3D segmentation of plant tissues at cellular resolution

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Nephrogenic Diabetes Insipidus in Childhood: Assessment of Volume Status and Appropriate Fluid Replenishment

Patients affected by nephrogenic diabetes insipidus (NDI) can present with hypernatremic dehydration, and first-line rehydration schemes are completely different from those largely applied in usual conditions determining a mild to severe hypovolemic dehydration/shock. In reporting the case of a patient affected by NDI and presenting with severe dehydration triggered by acute pharyngotonsillitis and vomiting, we want to underline the difficulties in managing this condition. Restoring the free-water plasma amount in patients affected by NDI may not be easy, but some key points can help in the first line management of these patients: (1) hypernatremic dehydration should always be suspected; (2) even in presence of severe dehydration, skin turgor may be normal and therefore the skinfold recoll should not be considered in the dehydration assessment; (3) decreased thirst is an important red flag for dehydration; (4) if an incontinent patient with NDI appears to be dehydrated, it is important to place the urethral catheter to accurately measure urine output and to be guided in parenteral fluid administration; (5) if the intravenous route is necessary, the more appropriate fluid replenishment is 5% dextrose in water with an infusion rate that should slightly exceed the urine output; (6) the 0.9% NaCl solution (10 mL/kg) should only be used to restore the volemia in a shocked NDI patient; and (7) it could be useful to stop indomethacin administration until complete restoration of hydration status to avoid a possible worsening of a potential prerenal acute renal failure

Role of imaging in rare COVID-19 vaccine multiorgan complications

As of September 18th, 2021, global casualties due to COVID-19 infections approach 200 million, several COVID-19 vaccines have been authorized to prevent COVID-19 infection and help mitigate the spread of the virus. Despite the vast majority having safely received vaccination against SARS-COV-2, the rare complications following COVID-19 vaccination have often been life-threatening or fatal. The mechanisms underlying (multi) organ complications are associated with COVID-19, either through direct viral damage or from host immune response (i.e., cytokine storm). The purpose of this manuscript is to review the role of imaging in identifying and elucidating multiorgan complications following SARS-COV-2 vaccination-making clear that, in any case, they represent a minute fraction of those in the general population who have been vaccinated. The authors are both staunch supporters of COVID-19 vaccination and vaccinated themselves as well