Automatic 3D Reconstruction of Coronary Artery Centerlines from Monoplane X-ray Angiogram Images

We present a new method for the fully automatic 3D reconstruction of the coronary artery centerlines, using two X-ray angiogram projection images from a single rotating monoplane acquisition system. During the first stage, the input images are smoothed using curve evolution techniques. Next, a simple yet efficient multiscale method, based on the information of the Hessian matrix, for the enhancement of the vascular structure is introduced. Hysteresis thresholding using different image quantiles, is used to threshold the arteries. This stage is followed by a thinning procedure to extract the centerlines. The resulting skeleton image is then pruned using morphological and pattern recognition techniques to remove non-vessel like structures. Finally, edge-based stereo correspondence is solved using a parallel evolutionary optimization method based on f symbiosis. The detected 2D centerlines combined with disparity map information allow the reconstruction of the 3D vessel centerlines. The proposed method has been evaluated on patient data sets for evaluation purposes

The use of the Kalman filter in the automated segmentation of EIT lung images

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging

Hybrid hierarchical clustering: piecewise aggregate approximation, with applications

Piecewise Aggregate Approximation (PAA) provides a powerful yet computationally efficient tool for dimensionality reduction and feature extraction on large datasets compared to previously reported and well-used feature extraction techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insufficiency or over-segmentation, and because of this, additional relatively complex modifications have subsequently been reported, for instance, Adaptive Piecewise Constant Approximation (APCA). To recover some of the simplicity of the original PAA, whilst addressing the known problems, a distance-based Hierarchical Clustering (HC) technique is now proposed to adjust PAA segment frame sizes to focus segment density on information rich data regions. The efficacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies on non-time-series data viz. fault detection on industrial gas turbines, and ultrasonic biometric face identification. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional benefits with regard to both cluster separation and classification performance, compared to traditional PAA and the APCA alternative. The method is therefore demonstrated to provide a robust readily implemented algorithm for rapid feature extraction and identification for datasets

Inhibiting Phase Transfer of Protein Nanoparticles by Surface Camouflage-A Versatile and Efficient Protein Encapsulation Strategy

Engineering a system with a high mass fraction of active ingredients, especially water-soluble proteins, is still an ongoing challenge. In this work, we developed a versatile surface camouflage strategy that can engineer systems with an ultrahigh mass fraction of proteins. By formulating protein molecules into nanoparticles, the demand of molecular modification was transformed into a surface camouflage of protein nanoparticles. Thanks to electrostatic attractions and van der Waals interactions, we camouflaged the surface of protein nanoparticles through the adsorption of carrier materials. The adsorption of carrier materials successfully inhibited the phase transfer of insulin, albumin, β-lactoglobulin, and ovalbumin nanoparticles. As a result, the obtained microcomposites featured with a record of protein encapsulation efficiencies near 100% and a record of protein mass fraction of 77%. After the encapsulation in microcomposites, the insulin revealed a hypoglycemic effect for at least 14 d with one single injection, while that of insulin solution was only ∼4 h.Peer reviewe

Interface chemistry of contact metals and ferromagnets on the topological insulator Bi2Se3

The interface between the topological insulator Bi2Se3 and deposited metal films is investigated using x-ray photoelectron spectroscopy including conventional contact metals (Au, Pd, Cr, and Ir) and magnetic materials (Co, Fe, Ni, Co0.8Fe0.2, and Ni0.8Fe0.2). Au is the only metal to show little or no interaction with the Bi2Se3, with no interfacial layer between the metal and the surface of the TI. The other metals show a range of reaction behaviors with the relative strength of reaction (obtained from the amount of Bi2Se3 consumed during reaction) ordered as: Au < Pd < Ir < Co ≤ CoFe < Ni < Cr < NiFe < Fe, in approximate agreement with the behavior expected from the Gibbs free energies of formation for the alloys formed. Post metallization anneals at 300°C in vacuum were also performed for each interface. Several of the metal films were not stable upon anneal and desorbed from the surface (Au, Pd, Ni, and Ni0.8Fe0.2), while Cr, Fe, Co, and Co0.8Fe0.2 showed accelerated reactions with the underlying Bi2Se3, including inter-diffusion between the metal and Se. Ir was the only metal to remain stable following anneal, showing no significant increase in reaction with the Bi2Se3. This study reveals the nature of the metal-Bi2Se3 interface for a range of metals. The reactions observed must be considered when designing Bi2Se3 based devices

The London Classification: Improving Characterization and Classification of Anorectal Function with Anorectal Manometry.

PURPOSE OF REVIEW: Objective measurement of anorectal sensorimotor function is a requisite component in the clinical evaluation of patients with intractable symptoms of anorectal dysfunction. Regrettably, the utility of the most established and widely employed investigations for such measurement (anorectal manometry (ARM), rectal sensory testing and the balloon expulsion test) has been limited by wide variations in clinical practice. RECENT FINDINGS: This article summarizes the recently published International Anorectal Physiology Working Group (IAPWG) consensus and London Classification of anorectal disorders, together with relevant allied literature, to provide guidance on the indications for, equipment, protocol, measurement definitions and results interpretation for ARM, rectal sensory testing and the balloon expulsion test. The London Classification is a standardized method and nomenclature for description of alterations in anorectal motor and sensory function using office-based investigations, adoption of which should bring much needed harmonization of practice

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.Comment: To appear in Neural Network

JWST-TST DREAMS: Quartz Clouds in the Atmosphere of WASP-17b

Clouds are prevalent in many of the exoplanet atmospheres that have been observed to date. For transiting exoplanets, we know if clouds are present because they mute spectral features and cause wavelength-dependent scattering. While the exact composition of these clouds is largely unknown, this information is vital to understanding the chemistry and energy budget of planetary atmospheres. In this work, we observe one transit of the hot Jupiter WASP-17b with JWST's MIRI LRS and generate a transmission spectrum from 5-12 $\rm{\mu}$m. These wavelengths allow us to probe absorption due to the vibrational modes of various predicted cloud species. Our transmission spectrum shows additional opacity centered at 8.6 $\rm{\mu}$m, and detailed atmospheric modeling and retrievals identify this feature as SiO$_2$(s) (quartz) clouds. The SiO$_2$(s) clouds model is preferred at 3.5-4.2$\sigma$ versus a cloud-free model and at 2.6$\sigma$ versus a generic aerosol prescription. We find the SiO$_2$(s) clouds are comprised of small ${\sim}0.01$ $\rm{\mu}$m particles, which extend to high altitudes in the atmosphere. The atmosphere also shows a depletion of H$_2$O, a finding consistent with the formation of high-temperature aerosols from oxygen-rich species. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we will use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS).Comment: 19 pages, 7 figures, accepted for publication in ApJ

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License