User-centered Development of a Clinical Decision Support System

Scientific progress is offering increasingly better ways to tailor a patient’s treatment to the patient’s needs, i.e., better support for optimal clinical decision-making can be offered. Choosing the appropriate treatment for a patient depends on numerous factors, including pathology results, tumor stage, genetic, and molecular characteristics. Bayesian networks are a type of probabilistic artificial intelligence, which in principle would be suitable to support complex clinical decision-making. However, most clinicians do not have experience with these networks. This paper describes an approach of developing a clinical decision support system based on Bayesian networks, that does not require insight knowledge about the underlying computational model for its use. It is developed as a therapy-oriented approach with a focus on usability and explainability. The approach features the computation and presentation of individualized treatment recommendations, comparison of treatments and patient cases, as well as explanations and visualizations providing additional information on the current patient case

Boundary Attention Mapping (BAM): Fine-grained saliency maps for segmentation of Burn Injuries

Burn injuries can result from mechanisms such as thermal, chemical, and electrical insults. A prompt and accurate assessment of burns is essential for deciding definitive clinical treatments. Currently, the primary approach for burn assessments, via visual and tactile observations, is approximately 60%-80% accurate. The gold standard is biopsy and a close second would be non-invasive methods like Laser Doppler Imaging (LDI) assessments, which have up to 97% accuracy in predicting burn severity and the required healing time. In this paper, we introduce a machine learning pipeline for assessing burn severities and segmenting the regions of skin that are affected by burn. Segmenting 2D colour images of burns allows for the injured versus non-injured skin to be delineated, clearly marking the extent and boundaries of the localized burn/region-of-interest, even during remote monitoring of a burn patient. We trained a convolutional neural network (CNN) to classify four severities of burns. We built a saliency mapping method, Boundary Attention Mapping (BAM), that utilises this trained CNN for the purpose of accurately localizing and segmenting the burn regions from skin burn images. We demonstrated the effectiveness of our proposed pipeline through extensive experiments and evaluations using two datasets; 1) A larger skin burn image dataset consisting of 1684 skin burn images of four burn severities, 2) An LDI dataset that consists of a total of 184 skin burn images with their associated LDI scans. The CNN trained using the first dataset achieved an average F1-Score of 78% and micro/macro- average ROC of 85% in classifying the four burn severities. Moreover, a comparison between the BAM results and LDI results for measuring injury boundary showed that the segmentations generated by our method achieved 91.60% accuracy, 78.17% sensitivity, and 93.37% specificity

Are the metabolomic responses to folivory of closely related plant species linked to macroevolutionary and plant-folivore coevolutionary processes?

Altres ajuts: MAGRAMA/OAPN-022/2008The debate whether the coevolution of plants and insects or macroevolutionary processes (phylogeny) is the main driver determining the arsenal of molecular defensive compounds of plants remains unresolved. Attacks by herbivorous insects affect not only the composition of defensive compounds in plants but also the entire metabolome. Metabolomes are the final products of genotypes and are constrained by macroevolutionary processes, so closely related species should have similar metabolomic compositions and may respond in similar ways to attacks by folivores. We analyzed the elemental compositions and metabolomes of needles from three closely related Pinus species with distant coevolutionary histories with the caterpillar of the processionary moth respond similarly to its attack. All pines had different metabolomes and metabolic responses to herbivorous attack. The metabolomic variation among the species and the responses to folivory reflected their macroevolutionary relationships, with P. pinaster having the most divergent metabolome. The concentrations of terpenes were in the attacked trees supporting the hypothesis that herbivores avoid plant individuals with higher concentrations. Our results suggest that macroevolutionary history plays important roles in the metabolomic responses of these pine species to folivory, but plant-insect coevolution probably constrains those responses. Combinations of different evolutionary factors and trade-offs are likely responsible for the different responses of each species to folivory, which is not necessarily exclusively linked to plant-insect coevolution

Atmo-ecometabolomics : a novel atmospheric particle chemical characterization methodology for ecological research

Aerosol particles play important roles in processes controlling the composition of the atmosphere and function of ecosystems. A better understanding of the composition of aerosol particles is beginning to be recognized as critical for ecological research to further comprehend the link between aerosols and ecosystems. While chemical characterization of aerosols has been practiced in the atmospheric science community, detailed methodology tailored to the needs of ecological research does not exist yet. In this study, we describe an efficient methodology (atmo-ecometabolomics), in step-by-step details, from the sampling to the data analyses, to characterize the chemical composition of aerosol particles, namely atmo-metabolome. This method employs mass spectrometry platforms such as liquid and gas chromatography mass spectrometries (MS) and Fourier transform ion cyclotron resonance MS (FT-ICR-MS). For methodology evaluation, we analyzed aerosol particles collected during two different seasons (spring and summer) in a low-biological-activity ecosystem. Additionally, to further validate our methodology, we analyzed aerosol particles collected in a more biologically active ecosystem during the pollination peaks of three different representative tree species. Our statistical results showed that our sampling and extraction methods are suitable for characterizing the atmo-ecometabolomes in these two distinct ecosystems with any of the analytical platforms. Datasets obtained from each mass spectrometry instrument showed overall significant differences of the atmo-ecometabolomes between spring and summer as well as between the three pollination peak periods. Furthermore, we have identified several metabolites that can be attributed to pollen and other plant-related aerosol particles. We additionally provide a basic guide of the potential use ecometabolomic techniques on different mass spectrometry platforms to accurately analyze the atmo-ecometabolomes for ecological studies. Our method represents an advanced novel approach for future studies in the impact of aerosol particle chemical compositions on ecosystem structure and function and biogeochemistry

A 5G Automated Guided Vehicle SME testbed for resilient future factories

Factory automation design engineers building the Smart Factory can use wireless 5G broadband networks for added design flexibility. 5G New Radio builds upon previous cellular communications standards to include technology for “massive machine-type communication” and “ultra-reliable and low-latency communication”. In this work, the authors augment an automated guided vehicle with 5G for additional capabilities (e.g., streaming high-resolution video and enabling long-distance teleoperation), increasing the mobile applications for industrial equipment. Such use cases will provide valuable knowledge to engineers examining 5G for novel smart manufacturing solutions. Our 5G private network testbed is a platform for wireless performance research in industrial locations and provides a development mule for flexible smart manufacturing systems. The rival wireless technology to 5G in industrial settings is Wi-Fi and it is included in the testbed. Furthermore, the authors noted challenges, often unconsidered, facing the move to digital manufacturing technologies. Therefore, the authors summarise the emerging challenges when implementing new digital factory systems, including challenges linked to societal concerns around sustainability and supply chain resilience. The new Smart Factory technologies, including 5G communications, will have their roles to play in alleviating these challenges and ensuring economies have resilient future factories

Rapid beam training at terahertz frequency with contextual multi-armed bandit learning

Terahertz (THz) frequency technology holds great promise for enabling high data rates and low latency, essential for manufacturing applications within Industry 4.0. To achieve these, beam training is necessary to enable MIMO communications without the need for explicit channel state information (CSI). In this context, the Multi-Armed Bandit (MAB) algorithms are able to facilitate online learning and decision-making in beam training, eliminating the necessity for extensive offline training and data collection. In this paper, we introduce three algorithms to investigate the applications of MAB in beam training at Terahertz frequency: UCB, Loc-LinUCB, and Probing-LinUCB. While UCB builds upon the well-established Upper Confidence Bound algorithm, Loc-LinUCB and Probing-LinUCB utilize the location of the user equipment (UE) and probing information to enhance decision-making, respectively. The beam training protocols for each algorithm are also detailed. We evaluate the performance of these algorithms using data generated by the DeepMIMO framework, which simulates abrupt changes and various challenging characteristics of wireless channels encountered in realistic scenarios as UEs move. The results illustrate that Loc-LinUCB and Probing-LinUCB outperform UCB, showing the potential of leveraging contextual MAB for beam training in Terahertz communications

Safe, High Power / Voltage Battery Design Challenges

NASA seeks safe, high performing battery module designs that can deliver 3C discharge rates continuously, achieve 160 Wh/kg, and is passively propagation resistant to a single cell thermal runaway event. One solution is presented that uses a patented oscillating heat pipe technology for thermal management. Combined with light weight packaging and a new commercially available gas permeable vent port, all 5 preliminary safety tests results performed to date are showing ample margins

Sub-6 GHz channel modelling and evaluation in indoor industrial environments

This paper presents sub-6 GHz channel measurements using a directional antenna at the transmitter and a directional or omnidirectional antenna at the receiver at 4.145 GHz in sparse and dense industrial environments for a line-of-sight scenario. Furthermore, the first measured over-the-air error vector magnitude (EVM) results depending on different 5G new radio modulation and coding schemes (MCSs of16 QAM, 64 QAM and 256 QAM) are provided. From the measurement campaigns, the path loss exponents (PLE) using a directional and an omnidirectional antenna at the receiver in the sparse and the dense environment are 1.24/1.39 and 1.35/1.5, respectively. PLE results are lower than the theoretical free space PLE of 2, indicating that indoor industrial environments have rich multipaths. The measured power delay profiles show the maximum root mean square (RMS) delay spreads of 11 ns with a directional antenna and 34 ns with an omnidirectional antenna at the receiver in a sparse industrial environment. However, in a dense industrial environment the maximum RMS delay spreads are significantly increased: maximum RMS delay spreads range from 226 to 282 ns for the omnidirectional and the directional antenna configuration. EVM measurements show that to increase coverage and enable higher MCS modes to be used for reliable data transmission, in both industrial environments using a directional antenna at the transmitter and the receiver is required. The large-scale path loss models, multipath time dispersion characteristics and EVM results provide insight into the deployments of 5G networks operating at sub-6 GHz frequency bands in different industrial environments

NLRX1 Sequesters STING to Negatively Regulate the Interferon Response, Thereby Facilitating the Replication of HIV-1 and DNA Viruses

SummaryUnderstanding the negative regulators of antiviral immune responses will be critical for advancing immune-modulated antiviral strategies. NLRX1, an NLR protein that negatively regulates innate immunity, was previously identified in an unbiased siRNA screen as required for HIV infection. We find that NLRX1 depletion results in impaired nuclear import of HIV-1 DNA in human monocytic cells. Additionally, NLRX1 was observed to reduce type-I interferon (IFN-I) and cytokines in response to HIV-1 reverse-transcribed DNA. NLRX1 sequesters the DNA-sensing adaptor STING from interaction with TANK-binding kinase 1 (TBK1), which is a requisite for IFN-1 induction in response to DNA. NLRX1-deficient cells generate an amplified STING-dependent host response to cytosolic DNA, c-di-GMP, cGAMP, HIV-1, and DNA viruses. Accordingly, Nlrx1−/− mice infected with DNA viruses exhibit enhanced innate immunity and reduced viral load. Thus, NLRX1 is a negative regulator of the host innate immune response to HIV-1 and DNA viruses