Virtual and Augmented Reality in Basic and Advanced Life Support Training

The use of augmented reality (AR) and virtual reality (VR) for life support training is increasing. These technologies provide an immersive experience that supports learning in a safe and controlled environment. This review focuses on the use of AR and VR for emergency care training for health care providers, medical students, and nonprofessionals. In particular, we analyzed (1) serious games, nonimmersive games, both single-player and multiplayer; (2) VR tools ranging from semi-immersive to immersive virtual and mixed reality; and (3) AR applications. All the toolkits have been investigated in terms of application goals (training, assessment, or both), simulated procedures, and skills. The main goal of this work is to summarize and organize the findings of studies coming from multiple research areas in order to make them accessible to all the professionals involved in medical simulation. The analysis of the state-of-the-art technologies reveals that tools and studies related to the multiplayer experience, haptic feedback, and evaluation of user’s manual skills in the foregoing health care-related environments are still limited and require further investigation. Also, there is an additional need to conduct studies aimed at assessing whether AR/VR-based systems are superior or, at the minimum, comparable to traditional training methods

Property-driven Training: All You (N)Ever Wanted to Know About

Neural networks are known for their ability to detect general patterns in noisy data. This makes them a popular tool for perception components in complex AI systems. Paradoxically, they are also known for being vulnerable to adversarial attacks. In response, various methods such as adversarial training, data-augmentation and Lipschitz robustness training have been proposed as means of improving their robustness. However, as this paper explores, these training methods each optimise for a different definition of robustness. We perform an in-depth comparison of these different definitions, including their relationship, assumptions, interpretability and verifiability after training. We also look at constraint-driven training, a general approach designed to encode arbitrary constraints, and show that not all of these definitions are directly encodable. Finally we perform experiments to compare the applicability and efficacy of the training methods at ensuring the network obeys these different definitions. These results highlight that even the encoding of such a simple piece of knowledge such as robustness in neural network training is fraught with difficult choices and pitfalls.Comment: 10 pages, under revie

Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure

<p>Abstract</p> <p>Background</p> <p>The present knowledge of protein structures at atomic level derives from some 60,000 molecules. Yet the exponential ever growing set of hypothetical protein sequences comprises some 10 million chains and this makes the problem of protein structure prediction one of the challenging goals of bioinformatics. In this context, the protein representation with contact maps is an intermediate step of fold recognition and constitutes the input of contact map predictors. However contact map representations require fast and reliable methods to reconstruct the specific folding of the protein backbone.</p> <p>Methods</p> <p>In this paper, by adopting a GRID technology, our algorithm for 3D reconstruction FT-COMAR is benchmarked on a huge set of non redundant proteins (1716) taking random noise into consideration and this makes our computation the largest ever performed for the task at hand.</p> <p>Results</p> <p>We can observe the effects of introducing random noise on 3D reconstruction and derive some considerations useful for future implementations. The dimension of the protein set allows also statistical considerations after grouping per SCOP structural classes.</p> <p>Conclusions</p> <p>All together our data indicate that the quality of 3D reconstruction is unaffected by deleting up to an average 75% of the real contacts while only few percentage of randomly generated contacts in place of non-contacts are sufficient to hamper 3D reconstruction.</p

Malignant epithelioid neoplasm of the ileum with ACTB-GLI1 fusion mimicking an adnexal mass

Background: Malignant epithelioid neoplasm with ACTB-GLI1 fusion are considered different from the more common pericytic lesions, such myopericytoma, because they have a spectrum of different genetic abnormalities. They appear to pursue a benign clinical course in young adults, although in sporadic cases lymph node metastasis were described. The categorization of this new type of tumor may also lead to new therapeutic strategies, because they might be sensitive to SHH pathway inhibitors. Case presentation: The case involves a 72-years-old multiparous woman who accessed our department after an incidental finding of a right adnexal mass of 43&nbsp;mm with contrast-enhancement on a control computed tomography scan made for suspected diverticulitis. Our intervention was a detailed ultrasound description of the suspected neoplasm; a diagnostic laparoscopy and the contextual laparotomic removal of abdominal mass; its histological and immunohistochemical analysis. Our main outcome measure is the definition and future recognition of new pathologic entity called malignant epithelioid neoplasm with ACTB-GLI1 fusion. Conclusions: We described for the first time the ultrasound characteristic of this type of lesion using standardized terminology and we believe that it may be the first step to improve the acknowledgement of this novel pathologic entity defined as malignant epithelioid neoplasm with GLI-1 fusions

ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification

Verification of machine learning models used in Natural Language Processing (NLP) is known to be a hard problem. In particular, many known neural network verification methods that work for computer vision and other numeric datasets do not work for NLP. Here, we study technical reasons that underlie this problem. Based on this analysis, we propose practical methods and heuristics for preparing NLP datasets and models in a way that renders them amenable to known verification methods based on abstract interpretation. We implement these methods as a Python library called ANTONIO that links to the neural network verifiers ERAN and Marabou. We perform evaluation of the tool using an NLP dataset R-U-A-Robot suggested as a benchmark for verifying legally critical NLP applications. We hope that, thanks to its general applicability, this work will open novel possibilities for including NLP verification problems into neural network verification competitions, and will popularise NLP problems within this community.Comment: To appear in proceedings of 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (Affiliated with CAV 2023

Case report of a familial triple: a syndrome and review of the literature

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EX-HOM (EXome HOMozygosity): A Proof of Principle

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Uncertainties in limits on TeV-gravity from neutrino-induced showers

In models with TeV-scale gravity, ultrahigh energy cosmic rays can generate microscopic black holes in the collision with atmospheric and terrestrial nuclei. It has been proposed that stringent bounds on TeV-scale gravity can be obtained from the absence of neutrino cosmic ray showers mediated by black holes. However, uncertainties in the cross section of black hole formation and, most importantly, large uncertainties in the neutrino flux affects these bounds. As long as the cosmic neutrino flux remains unknown, the non-observation of neutrino induced showers implies less stringent limits than present collider limits.Comment: Changes to match published versio

A variational approach to the local character of G-closure: the convex case

This article is devoted to characterize all possible effective behaviors of composite materials by means of periodic homogenization. This is known as a $G$-closure problem. Under convexity and $p$-growth conditions ($p>1$), it is proved that all such possible effective energy densities obtained by a $\Gamma$-convergence analysis, can be locally recovered by the pointwise limit of a sequence of periodic homogenized energy densities with prescribed volume fractions. A weaker locality result is also provided without any kind of convexity assumption and the zero level set of effective energy densities is characterized in terms of Young measures. A similar result is given for cell integrands which enables to propose new counter-examples to the validity of the cell formula in the nonconvex case and to the continuity of the determinant with respect to the two-scale convergence.Comment: 24 pages, 1 figur

Mutant MYO1F alters the mitochondrial network and induces tumor proliferation in thyroid cancer

Familial aggregation is a significant risk factor for the development of thyroid cancer and Familial Non-Medullary Thyroid Cancer (FNMTC) accounts for 5-7% of all NMTC. Whole Exome Sequencing analysis in the family affected by FNMTC with oncocytic features where our group previously identified a predisposing locus on chromosome 19p13.2, revealed a novel heterozygous mutation (c.400G>A, NM_012335; p.Gly134Ser) in exon 5 of MYO1F, mapping to the linkage locus. In the thyroid FRTL-5 cell model stably expressing the mutant MYO1F p.Gly134Ser protein we observed an altered mitochondrial network, with increased mitochondrial mass and a significant increase of both intracellular and extracellular Reactive Oxygen Species, compared to cells expressing the wild-type protein or carrying the empty vector. The mutation conferred a significant advantage in colony formation, invasion and anchorage independent growth. These data were corroborated by in vivo studies in zebrafish, since we demonstrated that the mutant MYO1F p.Gly134Ser, when overexpressed, can induce proliferation in whole vertebrate embryos, compared to the wild-type one. MYO1F screening in additional 192 FNMTC families identified another variant in exon 7, which leads to exon skipping, and is predicted to alter the ATP-binding domain in MYO1F. Our study identified for the first time a role for MYO1F in NMTC. This article is protected by copyright. All rights reserved