The morpho-kinematics of the circumstellar envelope around the AGB star EP Aqr

ALMA observations of CO(1-0) and CO(2-1) emissions of the circumstellar envelope of EP Aqr, an oxygen-rich AGB star, are reported. A thorough analysis of their properties is presented using an original method based on the separation of the data-cube into a low velocity component associated with an equatorial outflow and a faster component associated with a bipolar outflow. A number of important and new results are obtained concerning the distribution in space of the effective emissivity, the temperature, the density and the flux of matter. A mass loss rate of (1.6$\pm$0.4)10$^{-7}$ solar masses per year is measured. The main parameters defining the morphology and kinematics of the envelope are evaluated and uncertainties inherent to de-projection are critically discussed. Detailed properties of the equatorial region of the envelope are presented including a measurement of the line width and a precise description of the observed inhomogeneity of both morphology and kinematics. In particular, in addition to the presence of a previously observed spiral enhancement of the morphology at very small Doppler velocities, a similarly significant but uncorrelated circular enhancement of the expansion velocity is revealed, both close to the limit of sensitivity. The results of the analysis place significant constraints on the parameters of models proposing descriptions of the mass loss mechanism, but cannot choose among them with confidence.Comment: 26 pages, 31 figures, accepted for publication in MNRA

Observation of narrow polar jets in the nascent wind of oxygen-rich AGB star EP Aqr

Using ALMA observations of $^{12}$CO(2-1), $^{28}$SiO(5-4) and $^{32}$SO$_2$(16$_{6,10}$-17$_{5,13}$) emissions of the circumstellar envelope of AGB star EP Aqr, we describe the morpho-kinematics governing the nascent wind. Main results are: 1) Two narrow polar structures, referred to as jets, launched from less than 25 au away from the star, build up between $\sim$ 20 au and $\sim$ 100 au to a velocity of $\sim$ 20 \kms. They fade away at larger distances and are barely visible in CO data. 2) SO$_2$, SiO and CO emissions explore radial ranges reaching respectively $\sim$30 au, 250 au and 1000 au from the star, preventing the jets to be detected in SO$_2$ data. 3) Close to the star photosphere, rotation (undetected in SiO and CO data) and isotropic radial expansion combine with probable turbulence to produce a broad SO$_2$ line profile ($\sim$ 7.5 \kms\ FWHM). 4) A same axis serves as axis of rotation close to the star, as jet axis and as axi-symmetry axis at large distances. 5) A radial wind builds up at distances up to $\sim$ 300 au from the star, with larger velocity near polar than equatorial latitudes. 6) A sharp depletion of SiO and CO emissions, starting near the star, rapidly broadens to cover the whole blue-western quadrant, introducing important asymmetry in the CO and particularly SiO observations. 7) The $^{12}$C/$^{13}$C abundance ratio is measured as 9$\pm$2. 8) Plausible interpretations are discussed, in particular assuming the presence of a companion.Comment: 18 pages, 16 figures, MNRAS accepte

QuTIE: Quantum optimization for Target Identification by Enzymes

Target Identification by Enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is an NP-complete problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this paper, we consider the TIE problem for identifying drug targets in metabolic networks. We develop the first quantum optimization solution, called QuTIE (Quantum optimization for Target Identification by Enzymes), to this NP-complete problem. We do that by developing an equivalent formulation of the TIE problem in Quadratic Unconstrained Binary Optimization (QUBO) form, then mapping it to a logical graph, which is then embedded on a hardware graph on a quantum computer. Our experimental results on 27 metabolic networks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIE yields solutions which are optimal or almost optimal. Our experiments also demonstrate that QuTIE can successfully identify enzyme targets already verified in wet-lab experiments for 14 major disease classes

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education

Targeted subendothelial matrix oxidation by myeloperoxidase triggers myosin II-dependent de-adhesion and alters signaling in endothelial cells

During inflammation, myeloperoxidase (MPO) released by circulating leukocytes accumulates within the subendothelial matrix by binding to and transcytosing the endothelium. Oxidative reactions catalyzed by subendothelial-localized MPO are implicated as a key cause of endothelial dysfunction in inflammatory vascular diseases. Whilst the subendothelial matrix is a reactive target for MPO-derived oxidants in disease, the functional implications of oxidative matrix modification for the endothelium are largely unknown. Here we show that hypochlorous acid (HOCl) produced by endothelial-transcytosed MPO oxidizes the subendothelial matrix, involving covalent crosslinking of the adhesive matrix protein fibronectin. Real-time biosensor and live cell imaging studies showed that HOCl-mediated matrix oxidation triggers rapid membrane retraction from the substratum and adjacent cells (de-adhesion). This de-adhesion was linked with the alteration of Tyr-118 phosphorylation of paxillin, a key focal adhesion-dependent signaling process, as well as Rho kinase-dependent myosin light chain-2 phosphorylation. De-adhesion dynamics were dependent on the contractile state of cells, with myosin II inhibition with blebbistatin markedly attenuating the rate of membrane retraction. Rho kinase inhibition with Y-27632 also conferred protection, but not during the initial phase of membrane retraction, which was driven by pre-existing actomyosin tensile stress. Notably, diversion of MPO from HOCl production by thiocyanate and nitrite attenuated de-adhesion and associated signaling responses, despite the latter substrate supporting MPO-catalyzed fibronectin nitration. This study indicates that HOCl-mediated matrix oxidation by subendothelial MPO deposits may play an important and previously unrecognized role in altering endothelial adhesion, signaling and integrity during inflammatory vascular disorders

A Thermodynamics Analysis for Improvement of Carbon Dioxide Removal Technologies for Space

The carbon dioxide removal assembly (CDRA) has been used for the past two decades to continually remove carbon dioxide (CO2) as part of the air revitalization system onboard the international space station (ISS). The CDRA is an adsorption-based system that relies on sorbent materials that require a significant energy input to be thermally regenerated. Additionally, the system faces challenges in reliability and size/weight, so it is being re-evaluated for viability beyond-lowearth-orbit missions. The CDRA removes CO2 from the cabin air through a cyclical adsorption-desorption process that uses four molecular sieve beds. The main components include two desiccant beds to remove H2O, two CO2 zeolite sorbent beds, an air blower, two resistive heaters, and a cooling heat exchanger. Past studies on the CDRA primarily focus on predictive physics-based modeling of the sorbent beds to understand reliability, performance, and sorbent kinetics, with very few performing a thermodynamic analysis of the entire system. This study aims to improve the understanding of component-level losses of the CDRA using exergy destruction analysis and to quantify the losses. We developed a thermodynamics black-box model using a first and second law balances over each individual component over one operational cycle. The results indicate that the molecular sieve sorbent beds are major contributors to lost work within the CDRA. However, the total exergy destruction in the desiccant beds is greater than the sorbent beds. This indicates that the desiccant beds are the largest contributor of losses. Removing water prior to the removal of CO2 from the flow stream is a necessary step because the zeolite sorbent will preferentially adsorb water. Our findings motivate the use of alternative components that may offer direct separation of water at higher efficiencies

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering

In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.Comment: ImageCLEF2023 published version: https://ceur-ws.org/Vol-3497/paper-129.pd