653 research outputs found
Teleoperation Methods for High-Risk, High-Latency Environments
In-Space Servicing, Assembly, and Manufacturing (ISAM) can enable larger-scale and longer-lived infrastructure projects in space, with interest ranging from commercial entities to the US government. Servicing, in particular, has the potential to vastly increase the usable lifetimes of satellites. However, the vast majority of spacecraft on low Earth orbit today were not designed to be serviced on-orbit. As such, several of the manipulations during servicing cannot easily be automated and instead require ground-based teleoperation.
Ground-based teleoperation of on-orbit robots brings its own challenges of high latency communications, with telemetry delays of several seconds, and difficulties in visualizing the remote environment due to limited camera views. We explore teleoperation methods to alleviate these difficulties, increase task success, and reduce operator load.
First, we investigate a model-based teleoperation interface intended to provide the benefits of direct teleoperation even in the presence of time delay. We evaluate the model-based teleoperation method using professional robot operators, then use feedback from that study to inform the design of a visual planning tool for this task, Interactive Planning and Supervised Execution (IPSE). We describe and evaluate the IPSE system and two interfaces, one 2D using a traditional mouse and keyboard and one 3D using an Intuitive Surgical da Vinci master console. We then describe and evaluate an alternative 3D interface using a Meta Quest head-mounted display. Finally, we describe an extension of IPSE to allow human-in-the-loop planning for a redundant robot. Overall, we find that IPSE improves task success rate and decreases operator workload compared to a conventional teleoperation interface
4D FLOW CMR in congenital heart disease
This thesis showed that the use of a cloud-based reconstruction applicationwith advanced eddy currents correction, integrated with interactiveimaging evaluation tools allowed for remote visualization and interpretationof 4D flow data and that was sufficient for gross visualizationof aortic valve regurgitation. Further, this thesis demonstrated that bulkflow and pulmonary regurgitation can be accurately quantified using 4Dflow imaging analyzed. Peak systolic velocity over the pulmonary valvemay be underestimated. However, the measurement of peak systolicvelocity can be optimized if measured at the level of highest velocity inthe pulmonary artery. Also correlated against invasive measurements (inan animal model), this thesis shows that aorta flow and pulmonary flowcan be accurately and simultaneously measured by 4D flow MRI.When applied in clinical practice, 4D flow has extra advantages, of beingable to visualize flow pattern, vorticity and to predict aortic growth. InASD patients it can measure shunt volume directly following the septumframe by frame. In Fontan patients in can visualize better than standardMRI the Fontan circuit and it can measure flow at multiple points alongthe Fontan circuit. We observed in our Fontan population that shunt lesionswere very common, most of the time via veno-venous collaterals.Further using advanced computations, we showed that WSS angle wasthe only independent predictor of aortic growth in BAV patients. We alsoshowed the feasibility of GLS analysis on 4D flow MRI and presented anintegrative approach in which flow and functional data are acquired inone sequence.From the technical point of view, 4D flow MRI has proved to complementthe traditional components of the standard cardiac MR exams, enablingin-depth insights into hemodynamics. At this moment it proved its addedvalue, but in most of the cases it is not able yet to replace the standardexam. This is still due to long scanning times and relatively longpost-processing times.<br/
Lifelong Learning in the Clinical Open World
Despite mounting evidence that data drift causes deep learning models to deteriorate over time, the majority of medical imaging research is developed for - and evaluated on - static close-world environments. There have been exciting advances in the automatic detection and segmentation of diagnostically-relevant findings. Yet the few studies that attempt to validate their performance in actual clinics are met with disappointing results and little utility as perceived by healthcare professionals. This is largely due to the many factors that introduce shifts in medical image data distribution, from changes in the acquisition practices to naturally occurring variations in the patient population and disease manifestation. If we truly wish to leverage deep learning technologies to alleviate the workload of clinicians and drive forward the democratization of health care, we must move away from close-world assumptions and start designing systems for the dynamic open world.
This entails, first, the establishment of reliable quality assurance mechanisms with methods from the fields of uncertainty estimation, out-of-distribution detection, and domain-aware prediction appraisal. Part I of the thesis summarizes my contributions to this area. I first propose two approaches that identify outliers by monitoring a self-supervised objective or by quantifying the distance to training samples in a low-dimensional latent space. I then explore how to maximize the diversity among members of a deep ensemble for improved calibration and robustness; and present a lightweight method to detect low-quality lung lesion segmentation masks using domain knowledge.
Of course, detecting failures is only the first step. We ideally want to train models that are reliable in the open world for a large portion of the data. Out-of-distribution generalization and domain adaptation may increase robustness, but only to a certain extent. As time goes on, models can only maintain acceptable performance if they continue learning with newly acquired cases that reflect changes in the data distribution. The goal of continual learning is to adapt to changes in the environment without forgetting previous knowledge. One practical strategy to approach this is expansion, whereby multiple parametrizations of the model are trained and the most appropriate one is selected during inference. In the second part of the thesis, I present two expansion-based methods that do not rely on information regarding when or how the data distribution changes.
Even when appropriate mechanisms are in place to fail safely and accumulate knowledge over time, this will only translate to clinical usage insofar as the regulatory framework allows it. Current regulations in the USA and European Union only authorize locked systems that do not learn post-deployment. Fortunately, regulatory bodies are noting the need for a modern lifecycle regulatory approach. I review these efforts, along with other practical aspects of developing systems that learn through their lifecycle, in the third part of the thesis.
We are finally at a stage where healthcare professionals and regulators are embracing deep learning. The number of commercially available diagnostic radiology systems is also quickly rising. This opens up our chance - and responsibility - to show that these systems can be safe and effective throughout their lifespan
The fungal skin microbiota of healty and dermatoses-affected areas in HIV-positive and HIV-negative children
The purpose of the study is to investigate the Candida, Malassezia, Saccharomyces, and Debaryomyces species composition in dermatoses-affected and healthy skin samples from HIV-positive and HIV-negative children.Цель исследования – изучить видовой состав грибов рода Candida, Malassezia, Saccharomyces и Debaryomyces в соскобах с участков кожи, пораженных дерматозом, и неизмененных у ВИЧ-положительных и ВИЧ-отрицательных детей
Recent Advances in Health Biotechnology During Pandemic
The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which
emerged in 2019, cut the epoch that will make profound fluctuates in the history of the world
in social, economic, and scientific fields. Urgent needs in public health have brought with
them innovative approaches, including diagnosis, prevention, and treatment. To exceed the
coronavirus disease 2019 (COVID-19) pandemic, various scientific authorities in the world
have procreated advances in real time polymerase chain reaction (RT-PCR) based diagnostic
tests, rapid diagnostic kits, the development of vaccines for immunization, and the purposing
pharmaceuticals for treatment. Diagnosis, treatment, and immunization approaches put for-
ward by scientific communities are cross-fed from the accrued knowledge of multidisciplinary
sciences in health biotechnology. So much so that the pandemic, urgently prioritized in the
world, is not only viral infections but also has been the pulsion in the development of novel
approaches in many fields such as diagnosis, treatment, translational medicine, virology, mi-
crobiology, immunology, functional nano- and bio-materials, bioinformatics, molecular biol-
ogy, genetics, tissue engineering, biomedical devices, and artificial intelligence technologies.
In this review, the effects of the COVID-19 pandemic on the development of various scientific
areas of health biotechnology are discussed
Industrial, Collaborative and Mobile Robotics in Latin America: Review of Mechatronic Technologies for Advanced Automation
Mechatronics and Robotics (MaR) have recently gained importance in product development and manufacturing settings and applications. Therefore, the Center for Space Emerging Technologies (C-SET) has managed an international multi-disciplinary study to present, historically, the first Latin American general review of industrial, collaborative, and mobile robotics, with the support of North American and European researchers and institutions. The methodology is developed by considering literature extracted from Scopus, Web of Science, and Aerospace Research Central and adding reports written by companies and government organizations. This describes the state-of-the-art of MaR until the year 2023 in the 3 Sub-Regions: North America, Central America, and South America, having achieved important results related to the academy, industry, government, and entrepreneurship; thus, the statistics shown in this manuscript are unique. Also, this article explores the potential for further work and advantages described by robotic companies such as ABB, KUKA, and Mecademic and the use of the Robot Operating System (ROS) in order to promote research, development, and innovation. In addition, the integration with industry 4.0 and digital manufacturing, architecture and construction, aerospace, smart agriculture, artificial intelligence, and computational social science (human-robot interaction) is analyzed to show the promising features of these growing tech areas, considering the improvements to increase production, manufacturing, and education in the Region. Finally, regarding the information presented, Latin America is considered an important location for investments to increase production and product development, taking into account the further proposal for the creation of the LATAM Consortium for Advanced Robotics and Mechatronics, which could support and work on roboethics and education/R+D+I law and regulations in the Region. Doi: 10.28991/ESJ-2023-07-04-025 Full Text: PD
Medical devices with embedded electronics: design and development methodology for start-ups
358 p.El sector de la biotecnología demanda innovación constante para hacer frente a los retos del sector sanitario. Hechos como la reciente pandemia COVID-19, el envejecimiento de la población, el aumento de las tasas de dependencia o la necesidad de promover la asistencia sanitaria personalizada tanto en entorno hospitalario como domiciliario, ponen de manifiesto la necesidad de desarrollar dispositivos médicos de monitorización y diagnostico cada vez más sofisticados, fiables y conectados de forma rápida y eficaz. En este escenario, los sistemas embebidos se han convertido en tecnología clave para el diseño de soluciones innovadoras de bajo coste y de forma rápida. Conscientes de la oportunidad que existe en el sector, cada vez son más las denominadas "biotech start-ups" las que se embarcan en el negocio de los dispositivos médicos. Pese a tener grandes ideas y soluciones técnicas, muchas terminan fracasando por desconocimiento del sector sanitario y de los requisitos regulatorios que se deben cumplir. La gran cantidad de requisitos técnicos y regulatorios hace que sea necesario disponer de una metodología procedimental para ejecutar dichos desarrollos. Por ello, esta tesis define y valida una metodología para el diseño y desarrollo de dispositivos médicos embebidos
The role of ATRX in neuroblastoma: Uncovering novel targets for medicinal interventions
Cancer is worldwide the number one cause of death among young children. A frequently occurring type of paediatric cancer is neuroblastoma, which arises in parasympathetic ganglia. A specific subgroup of neuroblastoma patients with high-risk disease is characterised by genetic aberrations in the chromatin remodeller ATRX. ATRX is responsible for the incorporation of the histone variant H3.3 at repetitive DNA. It is assumed that this function is important for genome stability. In neuroblastoma distinct mutations occur within the ATRX gene. However, an overview of all occurring mutations, their frequencies and their associations with patient and tumour characteristics was lacking. Therefore, I first focussed on generating an overview of the ATRX mutational landscape in neuroblastoma and all other types of paediatric cancer. To accomplish this, we determined the frequency of ATRX nonsense mutations, missense mutations, and multi-exon deletions (MEDs). This led us to the discovery that the latter occurred almost exclusively in neuroblastoma and that 70% of the ATRX aberrations in neuroblastoma are MEDs. Furthermore, we predicted that 75% of these MEDs are likely in-frame and result in the production of shorter protein products. Additionally, we discovered a slightly better overall survival for patients with ATRX missense mutations compared to the other mutation types. We also found that 11q deletions, which are independently associated with poor survival, co-occur more frequently with ATRX MEDs compared to the other ATRX mutation types or wild-type. Altogether, this suggests the existence of different ATRX sub-types within neuroblastoma. To investigate whether such different ATRX subtypes exist we generated multiple isogenic cell line models with distinct ATRX aberrations, namely knock-out, exon 2-10 MED and exon 2-13 MED models. On these models we conducted total RNA-sequencing and for our analysis we also included the RNA-sequencing data of eight neuroblastoma cell lines, including three with an ATRX MED, and data of nine neuroblastoma tumours, including two with an exon 2-10 MED. By comparing the gene expression against the ATRX wild-type cells we discovered in all ATRX mutant changes in the expression of genes involved in ribosome biogenesis and metabolism. We found reduced gene expression related to these processes in the ATRX exon 2-10 MEDs models, but in sharp contrast we observed increased expression in ATRX KO and ATRX exon 2-13 models. In this manner we confirmed the existence of distinct ATRX sub-types. Subsequently, we conducted drug screens and genome-wide CRISPR-Cas9 synthetic lethality screens on ATRX aberrant cells to discover fast implementable drugs or identify novel drug targets. By performing drug screens we discovered three drugs that showed effectivity against all neuroblastoma cell lines tested independent of the ATRX mutational status. Additionally, we discovered 541 and 376 potential synthetic lethal interactions for ATRX KO and ATRX MED, respectively, compared to ATRX-wildtype cells. These synthetic lethal interactions give hope for the future as they could potentially be used as novel therapeutic targets. Finally, I discuss the implications of our findings in relation to one another and to the current knowledge regarding ATRX, additionally I mention potential directions for future research
Automated cache optimisations of stencil computations for partial differential equations
This thesis focuses on numerical methods that solve partial differential equations.
Our focal point is the finite difference method, which solves partial
differential equations by approximating derivatives with explicit finite differences.
These partial differential equation solvers consist of stencil computations on structured grids.
Stencils for computing real-world practical applications are patterns often
characterised by many memory accesses and non-trivial arithmetic expressions
that lead to high computational costs compared to simple stencils used in much prior
proof-of-concept work.
In addition, the loop nests to express stencils on structured grids may often be complicated.
This work is highly motivated by a specific domain of stencil computations where one of the challenges is non-aligned to the structured grid ("off-the-grid") operations.
These operations update neighbouring grid points through scatter and gather operations via non-affine memory accesses, such as {A[B[i]]}.
In addition to this challenge, these practical stencils often include many computation fields (need to store multiple grid copies), complex data dependencies and imperfect loop nests.
In this work, we aim to increase the performance of stencil kernel execution.
We study automated cache-memory-dependent optimisations for stencil computations.
This work consists of two core parts with their respective contributions.The first part of our work tries to reduce the data movement in stencil computations of practical interest.
Data movement is a dominant factor affecting the performance of high-performance computing applications.
It has long been a target of optimisations due to its impact on execution time and energy consumption.
This thesis tries to relieve this cost by applying temporal blocking optimisations, also known as time-tiling, to stencil computations.
Temporal blocking is a well-known technique to enhance data reuse in stencil computations.
However, it is rarely used in practical applications but rather in theoretical examples to prove its efficacy.
Applying temporal blocking to scientific simulations is more complex.
More specifically, in this work, we focus on the application context of seismic and medical imaging.
In this area, we often encounter scatter and gather operations due to signal sources and receivers at arbitrary locations in the computational domain.
These operations make the application of temporal blocking challenging.
We present an approach to overcome this challenge and successfully apply temporal blocking.In the second part of our work, we extend the first part as an automated approach targeting a wide range of simulations modelled with partial differential equations.
Since temporal blocking is error-prone, tedious to apply by hand and highly complex to assimilate theoretically and practically, we are motivated to automate its application and automatically generate code that benefits from it.
We discuss algorithmic approaches and present a generalised compiler pipeline to automate the application of temporal blocking.
These passes are written in the Devito compiler. They are used to accelerate the computation of stencil kernels in areas such as seismic and medical imaging, computational fluid dynamics and machine learning.
\href{www.devitoproject.org}{Devito} is a Python package to implement optimised stencil computation (e.g., finite differences, image processing, machine learning) from high-level symbolic problem definitions.
Devito builds on \href{www.sympy.org}{SymPy} and employs automated code generation and just-in-time compilation to execute optimised computational kernels on several computer platforms, including CPUs, GPUs, and clusters thereof.
We show how we automate temporal blocking code generation without user intervention and often achieve better time-to-solution.
We enable domain-specific optimisation through compiler passes and offer temporal blocking gains from a high-level symbolic abstraction.
These automated optimisations benefit various computational kernels for solving real-world application problems.Open Acces
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