Mars Atmosphere Resource Verification INsitu (MARVIN) - In Situ Resource Demonstration for the Mars 2020 Mission

The making of oxygen from resources in the Martian atmosphere, known as In Situ Resource Utilization (ISRU), has the potential to provide substantial benefits for future robotic and human exploration. In particular, the ability to produce oxygen on Mars for use in propulsion, life support, and power systems can provide significant mission benefits such as a reducing launch mass, lander size, and mission and crew risk. To advance ISRU for possible incorporation into future human missions to Mars, NASA proposed including an ISRU instrument on the Mars 2020 rover mission, through an announcement of opportunity (AO). The purpose of the the Mars Atmosphere Resource Verification INsitu or (MARVIN) instrument is to provide the first demonstration on Mars of oxygen production from acquired and stored Martian atmospheric carbon dioxide, as well as take measurements of atmospheric pressure and temperature, and of suspended dust particle sizes and amounts entrained in collected atmosphere gases at different times of the Mars day and year. The hardware performance and environmental data obtained will be critical for future ISRU systems that will reduce the mass of propellants and other consumables launched from Earth for robotic and human exploration, for better understanding of Mars dust and mitigation techniques to improve crew safety, and to help further define Mars global circulation models and better understand the regional atmospheric dynamics on Mars. The technologies selected for MARVIN are also scalable for future robotic sample return and human missions to Mars using ISRU

Generator breast datamart\u2014the novel breast cancer data discovery system for research and monitoring: Preliminary results and future perspectives

Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer. Materials and methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs). Results: Data were classified into \u201cNot organized, not \u2018ontologized\u2019 data\u201d, \u201cOrganized, not \u2018ontologized\u2019 data\u201d, and \u201cOrganized and \u2018ontologized\u2019 data\u201d. Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available. Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system

Polysaccharides from Agaricus bisporus and Agaricus brasiliensis show similarities in their structures and their immunomodulatory effects on human monocytic THP-1 cells

<p>Abstract</p> <p>Background</p> <p>Mushroom polysaccharides have traditionally been used for the prevention and treatment of a multitude of disorders like infectious illnesses, cancers and various autoimmune diseases. Crude mushroom extracts have been tested without detailed chemical analyses of its polysaccharide content. For the present study we decided to chemically determine the carbohydrate composition of semi-purified extracts from 2 closely related and well known basidiomycete species, i.e. <it>Agaricus bisporus </it>and <it>A. brasiliensis </it>and to study their effects on the innate immune system, in particular on the <it>in vitro </it>induction of pro-inflammatory cytokines, using THP-1 cells.</p> <p>Methods</p> <p>Mushroom polysaccharide extracts were prepared by hot water extraction and precipitation with ethanol. Their composition was analyzed by GC-MS and NMR spectroscopy. PMA activated THP-1 cells were treated with the extracts under different conditions and the production of pro-inflammatory cytokines was evaluated by qPCR.</p> <p>Results</p> <p>Semi-purified polysaccharide extracts of <it>A. bisporus </it>and <it>A. brasiliensis </it>(= <it>blazei</it>) were found to contain (1→6),(1→4)-linked α-glucan, (1→6)-linked β-glucan, and mannogalactan. Their proportions were determined by integration of ¹H-NMR signs, and were considerably different for the two species. <it>A. brasiliensis </it>showed a higher content of β-glucan, while <it>A. bisporus </it>presented mannogalactan as its main polysaccharide. The extracts induced a comparable increase of transcription of the pro-inflammatory cytokine genes IL-1β and TNF-α as well as of COX-2 in PMA differentiated THP-1 cells. Pro-inflammatory effects of bacterial LPS in this assay could be reduced significantly by the simultaneous addition of <it>A. brasiliensis </it>extract.</p> <p>Conclusions</p> <p>The polysaccharide preparations from the closely related species <it>A. bisporus </it>and <it>A. brasiliensis </it>show major differences in composition: <it>A. bisporus </it>shows high mannogalactan content whereas <it>A. brasiliensis </it>has mostly β-glucan. Semi-purified polysaccharide extracts from both <it>Agaricus </it>species stimulated the production of pro-inflammatory cytokines and enzymes, while the polysaccharide extract of <it>A. brasiliensis </it>reduced synthesis of these cytokines induced by LPS, suggesting programmable immunomodulation.</p

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48&nbsp;h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48&nbsp;h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home

Stop & Go waves: a microscopic and a macroscopic description

In this paper we investigate a typical phenomenon of congested traffic: the stop-and-go waves. Since modelling properly this phenomenon is crucial for developing techniques aimed at reducing it, we present two different models: a microscopic and a macroscopic one, both of them able to reproduce stop-and-go waves. In the former, vehicles’ dynamics are described by a second-order microscopic Follow-the-Leader model, which is calibrated and validated by real measurements. Data are analysed and compared with the numerical solutions computed by the microscopic model. The latter provides a description of traffic dynamic via the macroscopic second-order CGARZ model. With the numerical implementation, by means of the 2CTM scheme, we test the ability of the model of capturing stop-and-go waves. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG