A systematic review of criteria used to report complications in soft tissue and oncologic surgical clinical research studies in dogs and cats.

ObjectiveTo evaluate reporting of surgical complications and other adverse events in clinical research articles describing soft tissue and oncologic surgery in dogs and cats.Study designSystematic literature review.SampleEnglish-language articles describing soft tissue and oncologic surgeries in client-owned dogs and cats published in peer-reviewed journals from 2013 to 2016.MethodsCAB, AGRICOLA, and MEDLINE databases were searched for eligible articles. Article characteristics relevant to complications were abstracted and summarized, including reported events, definitions, criteria used to classify events according to severity and time frame, and relevant citations.ResultsOne hundred fifty-one articles involving 10 522 animals were included. Canine retrospective case series of dogs predominated. Ninety-two percent of articles mentioned complications in study results, but only 7.3% defined the term complication. Articles commonly described complications according to time frame and severity, but terminology and classification criteria were highly variable, conflicting between studies, or not provided. Most (58%) reported complications could have been graded with a published veterinary adverse event classification scheme, although common intraoperative complications were notable exceptions.ConclusionDefinitions and criteria used to classify and report soft tissue and oncologic surgical complications are often absent, incomplete, or contradictory among studies.Clinical significanceLack of consistent terminology contributes to inadequate communication of important information about surgical complications. Standardization of terminology and consistency in severity scoring will improve comparative evaluation of clinical research results

A robust ransac-based planet radius estimation for onboard visual based navigation

Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images

CloudScout: A deep neural network for on-board cloud detection on hyperspectral images

The increasing demand for high-resolution hyperspectral images from nano and microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A possible approach to mitigate this problem consists in reducing the amount of data to transmit to ground through on-board processing of hyperspectral images. In this paper, we propose a custom Convolutional Neural Network (CNN) deployed for a nanosatellite payload to select images eligible for transmission to ground, called CloudScout. The latter is installed on the Hyperscout-2, in the frame of the Phisat-1 ESA mission, which exploits a hyperspectral camera to classify cloud-covered images and clear ones. The images transmitted to ground are those that present less than 70% of cloudiness in a frame. We train and test the network against an extracted dataset from the Sentinel-2 mission, which was appropriately pre-processed to emulate the Hyperscout-2 hyperspectral sensor. On the test set we achieve 92% of accuracy with 1% of False Positives (FP). The Phisat-1 mission will start in 2020 and will operate for about 6 months. It represents the first in-orbit demonstration of Deep Neural Network (DNN) for data processing on the edge. The innovation aspect of our work concerns not only cloud detection but in general low power, low latency, and embedded applications. Our work should enable a new era of edge applications and enhance remote sensing applications directly on-board satellite

Donkey milk fermentation by lactococcus lactis subsp. Cremoris and lactobacillus rhamnosus affects the antiviral and antibacterial milk properties

Background: Milk is considered an important source of bioactive peptides, which can be produced by endogenous or starter bacteria, such as lactic acid bacteria, that are considered effective and safe producers of food-grade bioactive peptides. Among the various types of milk, donkey milk has been gaining more and more attention for its nutraceutical properties. Methods: Lactobacillus rhamnosus 17D10 and Lactococcus lactis subsp. cremoris 40FEL3 were selected for their ability to produce peptides from donkey milk. The endogenous peptides and those obtained after bacterial fermentation were assayed for their antioxidant, antibacterial, and antiviral activities. The peptide mixtures were characterized by means of LC-MS/MS and then analyzed in silico using the Milk Bioactive Peptide DataBase. Results: The peptides produced by the two selected bacteria enhanced the antioxidant activity and reduced E. coli growth. Only the peptides produced by L. rhamnosus 17D10 were able to reduce S. aureus growth. All the peptide mixtures were able to inhibit the replication of HSV-1 by more than 50%. Seventeen peptides were found to have 60% sequence similarity with already known bioactive peptides. Conclusions: A lactic acid bacterium fermentation process is able to enhance the value of donkey milk through bioactivities that are important for human health

Cancer stem cell biomarkers predictive of radiotherapy response in rectal cancer: A systematic review

Background: Rectal cancer (RC) is one of the most commonly diagnosed and particularly challenging tumours to treat due to its location in the pelvis and close proximity to critical genitouri-nary organs. Radiotherapy (RT) is recognised as a key component of therapeutic strategy to treat RC, promoting the downsizing and downstaging of large RCs in neoadjuvant settings, although its therapeutic effect is limited due to radioresistance. Evidence from experimental and clinical studies indicates that the likelihood of achieving local tumour control by RT depends on the complete eradica-tion of cancer stem cells (CSC), a minority subset of tumour cells with stemness properties. Methods: A systematic literature review was conducted by querying two scientific databases (Pubmed and Scopus). The search was restricted to papers published from 2009 to 2021. Results: After assessing the quality and the risk of bias, a total of 11 studies were selected as they mainly focused on biomarkers predictive of RT-response in CSCs isolated from patients affected by RC. Specifically these studies showed that elevated levels of CD133, CD44, ALDH1, Lgr5 and G9a are associated with RT-resistance and poor prognosis. Conclusions: This review aimed to provide an overview of the current scenario of in vitro and in vivo studies evaluating the biomarkers predictive of RT-response in CSCs derived from RC patients

Avalanche boron fusion by laser picosecond block ignition with magnetic trapping for clean and economic reactor

After the very long consideration of the ideal energy source by fusion of the protons of light hydrogen with the boron isotope 11 (boron fusion HB11) the very first two independent measurements of very high reaction gains by lasers basically opens a fundamental breakthrough. The non-thermal plasma block ignition with extremely high power laser pulses above petawatt of picosecond duration in combination with up to ten kilotesla magnetic fields for trapping has to be combined to use the measured high gains as proof of an avalanche reaction for an environmentally clean, low cost and lasting energy source as potential option against global warming. The unique HB11 avalanche reaction is are now based on elastic collisions of helium nuclei (alpha particles) limited only to a reactor for controlled fusion energy during a very short time within a very small volume.Comment: 11 pages, 6 figures, Submitted to Proceedings 2nd Symposium High Power Laser Science and Engineering, 14-18 MARCH 2016, Suzhou/Chin