Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation

Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation. Existing deep network models require a large amount of labeled data. However, obtaining accurate pixel-wise labeling in X-ray images relies heavily on skilled clinicians due to the large overlaps of anatomy and the complex texture patterns. On the other hand, organs in 3D CT scans preserve clearer structures as well as sharper boundaries and thus can be easily delineated. In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans. Specifically, a Dense Image-to-Image network (DI2I) for multi-organ segmentation is first trained on X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT volumes. Then we introduce a Task Driven Generative Adversarial Network (TD-GAN) architecture to achieve simultaneous style transfer and parsing for unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure for pixel-to-pixel translation between DRRs and X-ray images and an added module leveraging the pre-trained DI2I to enforce segmentation consistency. The TD-GAN framework is general and can be easily adapted to other learning tasks. In the numerical experiments, we validate the proposed model on 815 DRRs and 153 topograms. While the vanilla DI2I without any adaptation fails completely on segmenting the topograms, the proposed model does not require any topogram labels and is able to provide a promising average dice of 85% which achieves the same level accuracy of supervised training (88%)

Developing a novel rabbit model of atherosclerotic plaque rupture and thrombosis by cold-induced endothelial injury

<p>Abstract</p> <p>Background</p> <p>It is widely believed that atherosclerotic plaque rupture and subsequent thrombosis leads to acute coronary events and stroke. However, study of the mechanism and treatment of human plaque rupture is hampered by lack of a suitable animal model. Our aim was to develop a novel animal model of atherosclerotic plaque rupture to facilitate the study of human plaque disruption and thrombosis.</p> <p>Methods</p> <p>28 healthy male New Zealand white rabbits were randomly divided into two groups: rabbits in group A (n = 12) were only fed a high-fat diet for eight weeks; rabbits in group B (n = 16) underwent cold-induced endothelial injury with liquid nitrogen, then were given a high-fat diet for eight weeks. After completion of the preparatory regimen, triggering of plaque rupture was attempted by local injection of liquid nitrogen in both groups.</p> <p>Results</p> <p>All rabbits in group B had disrupted plaques or rupture-driven occlusive thrombus formation, but none in group A showed any effects. More importantly, the cold-induced plaques in our model were reminiscent of human atherosclerotic plaques in terms of architecture, cellular composition, growth characteristics, and patterns of lipid accumulation.</p> <p>Conclusion</p> <p>We successfully developed a novel rabbit model of atherosclerotic plaque rupture and thrombosis, which is simple, fast, inexpensive, and reproducible, and has a low mortality and a high yield of triggering. This model will allow us to better understand the mechanism of human plaque rupture and also to develop plaque-stabilizing therapies.</p

Contrasting responses of soil microbial biomass and extracellular enzyme activity along an elevation gradient on the eastern Qinghai-Tibetan Plateau

Soil microbial community composition and extracellular enzyme activity are two main drivers of biogeochemical cycling. Knowledge about their elevational patterns is of great importance for predicting ecosystem functioning in response to climate change. Nevertheless, there is no consensus on how soil microbial community composition and extracellular enzyme activity vary with elevation, and little is known about their elevational variations on the eastern Qinghai-Tibetan Plateau, a region sensitive to global change. We therefore investigated the soil microbial community composition using phospholipid fatty acids (PLFAs) analysis, and enzyme activities at 2,820 m (coniferous and broadleaved mixed forest), 3,160 m (dark coniferous forest), 3,420 m (alpine dwarf forest), and 4,280 m (alpine shrubland) above sea level. Our results showed that soil microbial community composition and extracellular enzyme activities changed significantly along the elevational gradient. Biomass of total microbes, bacteria, and arbuscular mycorrhizal fungi at the highest elevation were the significantly lowest among the four elevations. In contrast, extracellular enzyme activities involved in carbon (C)-, nitrogen (N)-, and phosphorus (P)- acquiring exhibited the maximum values at the highest elevation. Total nutrients and available nutrients, especially P availability jointly explained the elevational pattern of soil microbial community, while the elevational variation of extracellular enzyme activities was dependent on total nutrients. Microbial metabolism was mainly C- and P-limited with an increasing C limitation but a decreasing P limitation along the elevational gradient, which was related significantly to mean annual temperature and total P. These results indicated a vital role of soil P in driving the elevational patterns of soil microbial community and metabolism. Overall, the study highlighted the contrasting responses of soil microbial biomass and extracellular enzyme activities to elevation, possibly suggesting the differences in adaption strategy between population growth and resource acquisition responding to elevation. The results provide essential information for understanding and predicting the response of belowground community and function to climate change on the eastern Qinghai-Tibetan Plateau

Superconducting Diode Effect and Large Magnetochiral Anisotropy in Td_d-MoTe2_2 Thin Film

In the absence of time-reversal invariance, metals without inversion symmetry may exhibit nonreciprocal charge transport -- a magnetochiral anisotropy that manifests as unequal electrical resistance for opposite current flow directions. If superconductivity also sets in, the charge transmission may become dissipationless in one direction while remaining dissipative in the opposite, thereby realizing a superconducting diode. Through both direct-current and alternating-current measurements, we study the nonreciprocal effects in thin films of the noncentrosymmetric superconductor T$_d$-MoTe\textsubscript{2} with disorders. We observe nonreciprocal superconducting critical currents with a diode efficiency close to 20\%~, and a large magnetochiral anisotropy coefficient up to \SI{5.9e8}{\per\tesla\per\ampere}, under weak out-of-plane magnetic field in the millitesla range. The great enhancement of rectification efficiency under out-of-plane magnetic field is likely abscribed to the vortex ratchet effect, which naturally appears in the noncentrosymmetric superconductor with disorders. Intriguingly, unlike the finding in Rashba systems, the strongest in-plane nonreciprocal effect does not occur when the field is perpendicular to the current flow direction. We develop a phenomenological theory to demonstrate that this peculiar behavior can be attributed to the asymmetric structure of spin-orbit coupling in T$_d$-MoTe\textsubscript{2}. Our study highlights how the crystallographic symmetry critically impacts the nonreciprocal transport, and would further advance the research for designing the superconducting diode with the best performance.Comment: 7 pages, 5figure

Robust non-rigid registration through agent-based action learning

International audienceRobust image registration in medical imaging is essential for comparison or fusion of images, acquired from various perspectives, modalities or at different times. Typically, an objective function needs to be minimized assuming specific a priori deformation models and pre-defined or learned similarity measures. However, these approaches have difficulties to cope with large deformations or a large variability in appearance. Using modern deep learning (DL) methods with automated feature design, these limitations could be resolved by learning the intrinsic mapping solely from experience. We investigate in this paper how DL could help organ-specific (ROI-specific) deformable registration, to solve motion compensation or atlas-based segmentation problems for instance in prostate diagnosis. An artificial agent is trained to solve the task of non-rigid registration by exploring the parametric space of a statistical deformation model built from training data. Since it is difficult to extract trustworthy ground-truth deformation fields, we present a training scheme with a large number of synthetically deformed image pairs requiring only a small number of real inter-subject pairs. Our approach was tested on inter-subject registration of prostate MR data and reached a median DICE score of .88 in 2-D and .76 in 3-D, therefore showing improved results compared to state-of-the-art registration algorithms

A proposed disease classification system for duck viral hepatitis

The nomenclature of duck viral hepatitis (DVH) was historically not a problem. However, 14 hepatotropic viruses among 10 different genera are associated with the same disease name, DVH. Therefore, the disease name increasingly lacks clarity and may no longer fit the scientific description of the disease. Because one disease should not be attributed to 10 genera of viruses, this almost certainly causes misunderstanding regarding the disease-virus relationship. Herein, we revisited the problem and proposed an update to DVH disease classification. This classification is based on the nomenclature of human viral hepatitis and the key principle of Koch's postulates (“one microbe and one disease”). In total, 10 types of disease names have been proposed. These names were literately matched with hepatitis-related viruses. We envision that this intuitive nomenclature system will facilitate scientific communication and consistent interpretation in this field, especially in the Asian veterinary community, where these diseases are most commonly reported

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve