Ulva as potential stimulant and attractant for a valuable sea urchin species: a chemosensory study

The green seaweed Ulva is close to becoming popular due to its suitability as potential feedstock production and for food items. However, there is a general lack of studies on the aversion or acceptability of this alga by marine organisms, particularly on its role as a chemoattractant and/or phagostimulant activity. Here we tested the effect of Ulva compressa and other biochemicals as potential chemostimulating compounds for a valuable sea urchin species, Paracentrotus lividus, selected as model species for our tests. Sea urchins’ chemical sensitivity was estimated by analysing movements of spines, pedicellariae, tube feet, and individual locomotion using an innovative bioassay. Our results showed that all forms of Ulva (fresh, defrosted, and fragmented) resulted in an effective stimulus, evoking in sea urchins strong responses with robust activation of spines and tube feet, where the defrosted one was the most stimulating. Among the amino acids tested, glycine, alanine, and glutamine produced a significant response, highlighting for the latter a concentration–response relationship. Sea urchins responded to glucose, not to fructose and sucrose. Spirulina resulted as the most effective stimulus, acting in a dose-dependent manner. Major results indicate the role of Ulva as a chemostimulant and strongly attractant for such herbivore species. From an applied point of view, the presence of potential Ulva’s feed-related compounds, acting as chemoattractants (to reduce food searching time) and/or feeding stimulants (to stimulate ingestion), would improve the several applications of Ulva in the formulation of the feeds for sustainable aquacultur

Supervised cnn strategies for optical image segmentation and classification in interventional medicine

The analysis of interventional images is a topic of high interest for the medical-image analysis community. Such an analysis may provide interventional-medicine professionals with both decision support and context awareness, with the final goal of improving patient safety. The aim of this chapter is to give an overview of some of the most recent approaches (up to 2018) in the field, with a focus on Convolutional Neural Networks (CNNs) for both segmentation and classification tasks. For each approach, summary tables are presented reporting the used dataset, involved anatomical region and achieved performance. Benefits and disadvantages of each approach are highlighted and discussed. Available datasets for algorithm training and testing and commonly used performance metrics are summarized to offer a source of information for researchers that are approaching the field of interventional-image analysis. The advancements in deep learning for medical-image analysis are involving more and more the interventional-medicine field. However, these advancements are undeniably slower than in other fields (e.g. preoperative-image analysis) and considerable work still needs to be done in order to provide clinicians with all possible support during interventional-medicine procedures

Learned and handcrafted features for early-stage laryngeal SCC diagnosis

Squamous cell carcinoma (SCC) is the most common and malignant laryngeal cancer. An early-stage diagnosis is of crucial importance to lower patient mortality and preserve both the laryngeal anatomy and vocal-fold function. However, this may be challenging as the initial larynx modifications, mainly concerning the mucosa vascular tree and the epithelium texture and color, are small and can pass unnoticed to the human eye. The primary goal of this paper was to investigate a learning-based approach to early-stage SCC diagnosis, and compare the use of (i) texture-based global descriptors, such as local binary patterns, and (ii) deep-learning-based descriptors. These features, extracted from endoscopic narrow-band images of the larynx, were classified with support vector machines as to discriminate healthy, precancerous, and early-stage SCC tissues. When tested on a benchmark dataset, a median classification recall of 98% was obtained with the best feature combination, outperforming the state of the art (recall = 95%). Despite further investigation is needed (e.g., testing on a larger dataset), the achieved results support the use of the developed methodology in the actual clinical practice to provide accurate early-stage SCC diagnosis. [Figure not available: see fulltext.]

The babyPose dataset

none5noThe database here described contains data relevant to preterm infants' movement acquired in neonatal intensive care units (NICUs). The data consists of 16 depth videos recorded during the actual clinical practice. Each video consists of 1000 frames (i.e., 100s). The dataset was acquired at the NICU of the Salesi Hospital, Ancona (Italy). Each frame was annotated with the limb-joint location. Twelve joints were annotated, i.e., left and right shoul- der, elbow, wrist, hip, knee and ankle. The database is freely accessible at http://doi.org/10.5281/zenodo.3891404. This dataset represents a unique resource for artificial intelligence researchers that want to develop algorithms to provide healthcare professionals working in NICUs with decision support. Hence, the babyPose dataset is the first annotated dataset of depth images relevant to preterm infants' movement analysis.openMigliorelli L.; Moccia S.; Pietrini R.; Carnielli V.P.; Frontoni E.Migliorelli, L.; Moccia, S.; Pietrini, R.; Carnielli, V. P.; Frontoni, E

Outcomes of feeding activity of the sea cucumber Holothuria tubulosa on quantity, biochemical composition, and nutritional quality of sedimentary organic matter

Introduction: Holothuria tubulosa is one of the most common sea cucumbers in the Mediterranean Sea, generally associated with organically enriched coastal sediments and seagrass beds. As a deposit-feeder, it is responsible for strong bioturbation processes and plays a putative key role in sedimentary carbon cycling and benthic trophodynamics. With the aim of exploring the potential use of holothuroids as a tool for remediating eutrophicated sediments, we investigated the effects of H. tubulosa on sedimentary organic matter quantity, biochemical composition, and nutritional quality. Methods: Holothuroids and associated samples of ambient sediments were collected in two sites located in the Central-Western Mediterranean Sea (Sardinia, Italy) and characterized by different trophic status backgrounds: the site of Oristano characterized by sandy-muddy sediments and the presence of mariculture plants (ranked as meso-eutrophic) and the site of Teulada characterized by sandy sediments and Posidonia oceanica meadows (ranked as oligo-mesotrophic). We compared the biochemical composition (proteins, carbohydrates, lipids) of ambient sediment vs sea cucumbers feces and the sedimentary protein content vs protein content in the sediments retrieved in different gut sections (esophagus, mid gut, end gut) of the holothuroid. Results: Our results reveal that holothuroids feeding on meso-eutrophic sediments can increase protein (1.5 times) and lipid (1.3 times) content through their defecation, thus making these substrates a more labile food source for other benthic organisms. We report here that H. tubulosa feeding on meso-eutrophic sediment is most likely able to actively select particles rich in labile organic matter with buccal tentacles, as revealed by the protein content in the esophagus that is up to 2-folds higher than that in the source sediment. According to the inverse relationship between assimilation rates and availability of organic substrates and the optimal foraging theory, H. tubulosa feeding on oligo-mesotrophic sediments showed potential assimilation of proteins ca. 25% higher than that of specimens feeding on meso-eutrophic sediments. Discussion: Our results reveal that H. tubulosa feeding on meso-eutrophic sediments can profoundly influence the benthic trophic status, specifically modifying the biochemical composition and nutritional quality of organic matter, thus paving the way to its possible use in bioremediation actions of eutrophicated sediments and in Integrated Multi-Trophic Aquaculture systems

Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks

Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools for data-driven human-in-the-loop control and monitoring. However, present solutions focus on extracting few specific metrics from dedicated sensors with no unified full-body approach. We investigate a general, real-time, full-body pose estimation framework based on two RGB+D camera streams with non-overlapping views mounted on a smart walker equipment used in rehabilitation. Human keypoint estimation is performed using a two-stage neural network framework. The 2D-Stage implements a detection module that locates body keypoints in the 2D image frames. The 3D-Stage implements a regression module that lifts and relates the detected keypoints in both cameras to the 3D space relative to the walker. Model predictions are low-pass filtered to improve temporal consistency. A custom acquisition method was used to obtain a dataset, with 14 healthy subjects, used for training and evaluating the proposed framework offline, which was then deployed on the real walker equipment. An overall keypoint detection error of 3.73 pixels for the 2D-Stage and 44.05mm for the 3D-Stage were reported, with an inference time of 26.6ms when deployed on the constrained hardware of the walker. We present a novel approach to patient monitoring and data-driven human-in-the-loop control in the context of smart walkers. It is able to extract a complete and compact body representation in real-time and from inexpensive sensors, serving as a common base for downstream metrics extraction solutions, and Human-Robot interaction applications. Despite promising results, more data should be collected on users with impairments, to assess its performance as a rehabilitation tool in real-world scenarios.Comment: Accepted for publication in Expert Systems with Application

Therapeutic potential of endothelial colony‐forming cells in ischemic disease: Strategies to improve their regenerative efficacy

Cardiovascular disease (CVD) comprises a range of major clinical cardiac and circulatory diseases, which produce immense health and economic burdens worldwide. Currently, vascular regenerative surgery represents the most employed therapeutic option to treat ischemic disorders, even though not all the patients are amenable to surgical revascularization. Therefore, more efficient therapeutic approaches are urgently required to promote neovascularization. Therapeutic angiogenesis represents an emerging strategy that aims at reconstructing the damaged vascular network by stimulating local angiogenesis and/or promoting de novo blood vessel formation according to a process known as vasculogenesis. In turn, circulating endothelial colony‐forming cells (ECFCs) represent truly endothelial precursors, which display high clonogenic potential and have the documented ability to originate de novo blood vessels in vivo. Therefore, ECFCs are regarded as the most promising cellular candidate to promote therapeutic angiogenesis in patients suffering from CVD. The current briefly summarizes the available information about the origin and characterization of ECFCs and then widely illustrates the preclinical studies that assessed their regenerative efficacy in a variety of ischemic disorders, including acute myocardial infarction, peripheral artery disease, ischemic brain disease, and retinopathy. Then, we describe the most common pharmacological, genetic, and epigenetic strategies employed to enhance the vasoreparative potential of autologous ECFCs by manipulating crucial pro‐angiogenic signaling pathways, e.g., extracellular‐signal regulated kinase/Akt, phosphoinositide 3‐kinase, and Ca2+ signaling. We conclude by discussing the possibility of targeting circulating ECFCs to rescue their dysfunctional phenotype and promote neovascularization in the presence of CVD

A cloud-based healthcare infrastructure for neonatal intensive-care units

Intensive medical attention of preterm babies is crucial to avoid short-term and long- term complications. Within neonatal intensive care units (NICUs), cribs are equipped with electronic devices aimed at: monitoring, administering drugs and supporting clinician in making diagnosis and offer treatments. To manage this huge data flux, a cloud-based healthcare infrastructure that allows data collection from different devices (i.e., patient monitors, bilirubinometers, and transcutaneous bilirubinometers), storage, processing and transferring will be presented. Communication protocols were designed to enable the communication and data transfer between the three different devices and a unique database and an easy to use graphical user interface (GUI) was implemented. The infrastructure is currently used in the “Women’s and Children’s Hospital G.Salesi” in Ancona (Italy), supporting clinicians and health opertators in their daily activities

Effects of Field Simulated Marine Heatwaves on Sedimentary Organic Matter Quantity, Biochemical Composition, and Degradation Rates

Since rising temperature (T) will enhance biochemical reactions and coastal marine sediments are hotspots of carbon cycling, marine heatwaves’ (MHWs’) intensification caused by climate change will affect coastal biogeochemistry. We investigated the effects of MHWs on sediment organic matter (OM) in a nearshore locality (NW Sardinia, Mediterranean Sea) receiving an artificial warm water plume generating T anomalies of 1.5–5.0 °C. Sediments were collected before and after 3 and 11 weeks from the initial plume release. Both MHWs influenced sedimentary OM quantity, composition, and degradation rates, with major effects associated with the highest T anomaly after 3 weeks. Both MHWs enhanced sedimentary OM contents, with larger effects associated with the highest T anomaly. Phytopigment contents increased in the short term but dropped to initial levels after 11 weeks, suggesting the occurrence of thermal adaptation or stress of microphytobenthos. In the longer term we observed a decrease in the nutritional quality of OM and a slowdown of its turnover mediated by extracellular enzymes, suggestive of a decreased ecosystem functioning. We anticipate that intensification of MHWs will affect benthic communities not only through direct effects on species tolerance but also by altering benthic biogeochemistry and the efficiency of energy transfer towards higher trophic levels