Full Image Research Articles

A single stage knowledge distillation network for brain tumor segmentation on limited MR image modalities

Background and objectivePrecisely segmenting brain tumors using multimodal Magnetic Resonance Imaging (MRI) is an essential task for early diagnosis, disease monitoring, and surgical planning. Unfortunately, the complete four image modalities utilized in the well-known BraTS benchmark dataset: T1, T2, Fluid-Attenuated Inversion Recovery (FLAIR), and T1 Contrast-Enhanced (T1CE) are not regularly acquired in clinical practice due to the high cost and long acquisition time. Rather, it is common to utilize limited image modalities for brain tumor segmentation. MethodsIn this paper, we propose a single stage learning of knowledge distillation algorithm that derives information from the missing modalities for better segmentation of brain tumors. Unlike the previous works that adopted a two-stage framework to distill the knowledge from a pre-trained network into a student network, where the latter network is trained on limited image modality, we train both models simultaneously using a single-stage knowledge distillation algorithm. We transfer the information by reducing the redundancy from a teacher network trained on full image modalities to the student network using Barlow Twins loss on a latent-space level. To distill the knowledge on the pixel level, we further employ a deep supervision idea that trains the backbone networks of both teacher and student paths using Cross-Entropy loss. ResultsWe demonstrate that the proposed single-stage knowledge distillation approach enables improving the performance of the student network in each tumor category with overall dice scores of 91.11% for Tumor Core, 89.70% for Enhancing Tumor, and 92.20% for Whole Tumor in the case of only using the FLAIR and T1CE images, outperforming the state-of-the-art segmentation methods. ConclusionsThe outcomes of this work prove the feasibility of exploiting the knowledge distillation in segmenting brain tumors using limited image modalities and hence make it closer to clinical practices.

Asteroseismic age constraints on the open cluster NGC 2477 using oscillating stars identified with TESS FFI

Context. Asteroseismology is one of the few methods to derive ages of individual stars due to the high precision of their observations. Isochrone fitting is a powerful alternative method for deriving ages by studying clusters of stars. Pulsating stars in clusters should therefore allow for detailed studies of the stellar models. Aims. Our objectives are to exploit the NASA TESS data along with ESA Gaia data to search for and detect oscillations in cluster member stars. We analyse the intermediate-age open cluster NGC 2477, known to suffer from differential extinction, to explore if asteroseismology and cluster characteristics can help us understand the metallicity and extinction, as well as result in better age determinations than isochrone-fitting alone. Methods. We combined a multitude of recent observations from Gaia, high-resolution spectroscopy, and extinction maps to analyse the cluster and then search for and detect variability in the member stars using TESS full frame images (FFIs) data. To interpret all of these data, we used stellar structure, evolution and oscillation codes. Results. We conducted an in-depth analysis of the extinction and metallicity of NGC 2477, using the most recent spectroscopic, photometric, and extinction observations for the cluster. Analysis of dust and extinction maps confirmed that the differential extinction in the direction of the cluster is not due to the background. The cluster’s metallicity from high-resolution spectroscopy varies from 0.06 to 0.16 dex. We performed an isochrone fitting to the cluster using publically available isochrones (BASTI, MIST, and PARSEC), which provides a cluster age of between 0.6 to 1.1 Ga. Then using TESS FFI, we analysed the time dimension of the members of this cluster. We created optimised pixel light curves using the tessipack package which allows us to consider possible contamination by nearby stars. Using these light curves, we identified many interesting levels of variability of stars in this cluster, including binaries and oscillating stars. For the asteroseismic analysis, we selected a few uncontaminated A–F type oscillating stars and used the MESA and GYRE codes to interpret the frequency signals. By comparing the theoretical and the observed spectra, we identified frequency separations, Δν, for four stars. Then using the identified Δν and imposing that the best matched theoretical models have the same age, metallicity, and background extinction, we constrained the cluster’s age to 1.0 ± 0.1 Ga. Conclusions. We conclude that using the TESS FFI data, we can identify oscillating stars in clusters and constrain the age of the cluster using asteroseismology.

Beyond the disk: EUV coronagraphic observations of the Extreme Ultraviolet Imager on board Solar Orbiter

Context. Most observations of the solar corona beyond 2 R⊙ consist of broadband visible light imagery carried out with coronagraphs. The associated diagnostics mainly consist of kinematics and derivations of the electron number density. While the measurement of the properties of emission lines can provide crucial additional diagnostics of the coronal plasma (temperatures, velocities, abundances, etc.), these types of observations are comparatively rare. In visible wavelengths, observations at these heights are limited to total eclipses. In the ultraviolet (UV) to extreme UV (EUV) range, very few additional observations have been achieved since the pioneering results of the Ultraviolet Coronagraph Spectrometer (UVCS). Aims. One of the objectives of the Full Sun Imager (FSI) channel of the Extreme Ultraviolet Imager (EUI) on board the Solar Orbiter mission has been to provide very wide field-of-view EUV diagnostics of the morphology and dynamics of the solar atmosphere in temperature regimes that are typical of the lower transition region and of the corona. Methods. FSI carries out observations in two narrowbands of the EUV spectrum centered on 17.4 nm and 30.4 nm that are dominated, respectively, by lines of FeIX/X (formed in the corona around 1 MK) and by the resonance line of He II (formed around 80 kK in the lower transition region). Unlike previous EUV imagers, FSI includes a moveable occulting disk that can be inserted in the optical path to reduce the amount of instrumental stray light to a minimum. Results. FSI detects signals at 17.4 nm up to the edge of its field of view (7 R⊙), which is about twice further than was previously possible. Operation at 30.4 nm are for the moment compromised by an as-yet unidentified source of stray light. Comparisons with observations by the LASCO and Metis coronagraphs confirm the presence of morphological similarities and differences between the broadband visible light and EUV emissions, as documented on the basis of prior eclipse and space-based observations. Conclusions. The very-wide-field observations of FSI out to about 3 and 7 R⊙, without and with the occulting disk, respectively, are paving the way for future dedicated instruments.

Martin Luther's Great Surprise Translating the New Testament, 1522–2022

Martin Luther's Great Surprise Translating the New Testament, 1522–2022 Timothy J. Wengert An after-dinner speech, given at the celebration of the 500th anniversary of Luther's "September Testament" for the opening of the Kessler Collection exhibition on October 26, 2022, at the Pitts Theology Library of the Candler School of Theology, Emory University (Atlanta, Georgia). In November 1532, Martin Luther sat at table with his students and mused about his nearly completed translation of the entire Bible, ten years after his rendering of the New Testament in German had first appeared. He was working with Wittenberg's team of translators on the Apocrypha, which they considered an important but secondary part of the Bible. There is so much hard labor in translating Ecclesiasticus. When it is done, I will stop working altogether. Nowadays you have everything in Holy Scripture, which you can now use after my death. It certainly took enough hard work but is scarcely used by us. Our adversaries read it more than our own people. I believe that Duke George [of Saxony] reads it more diligently than our own nobility. For he confessed to having said, "If only that monk had completely translated the Bible and then gone back to where he belonged!" I have this testimony from Duke George and the entire Papal party, who now use our translation.1 Credit Katie Luther's beer for some after-dinner exaggeration by her spouse. But to appreciate fully just what an impact Luther's biblical translation work had on his contemporaries, there is no better place to begin than with Duke George of Saxony, sponsor of the Leipzig Debates between Luther and Johannes Eck in 1519 and Luther's implacable enemy ever after. [End Page 172] So, we begin with Duke George, cousin to Luther's prince, Elector Frederick the Wise. When the house of Wettin split into two parts in the fifteenth century, Frederick's side got Wittenberg and, hence, the claim to the electorship, the highest rank in the Holy Roman Empire of the German Nation, while George's side got Leipzig, a much better city, but they coveted the electoral dignity ever after until they were awarded it in 1547. According to one account, at the Leipzig Debates, when Luther said something positive about the martyred Czech reformer, Jan Hus, George was heard to gasp, "Egad, sir, that's the plague." With that, the lines were drawn, and, until his death in 1539, George fought Luther tooth and nail. It comes as no surprise, then, that when Wittenberg published Luther's translation of the New Testament in 1522, Duke George, far from welcoming it, started a campaign to rid his lands of all of Luther's writings, including that translation. Click for larger view View full resolution Image provided courtesy of the Richard C. Kessler Reformation Collection at Pitts Theology Library, Candler School of Theology, Emory University. [End Page 173] George's court theologian, Jerome Emser, even wrote a tract defending the banning of Luther's New Testament in ducal Saxony and pointing out ad nauseam every one of Luther's errors in that work. Luther responded by writing one of his most influential pamphlets, On Secular Authority: To What Extent It Should Be Obeyed.2 In it he counseled the ducal Saxon citizenry to a kind of passive resistance. When George's henchmen come a-calling, he wrote, let them look for the banned books but don't tell them where you hid them. Flee if you could, help others as you ought, accept punishment if you must, but in matters of the conscience before God, follow St. Peter's advice and say, "We must obey God rather than mortals." In addition to book-burning, Duke George also embarked on a different approach, one that will help us understand why anyone should celebrate Luther's translation 500 years later. On the one hand, on 21 September 1523 Emser finished that line-by-line refutation and correction of Luther's translation, commenting on the Reformer's prefaces, glosses, and individual points of translation, finishing with a wholesale attack on Luther's method and warning the reader not to buy Luther's...

An 82-year-old woman with retinal vascular sheathing and vitreous hemorrhage.

This report describes a case of hereditary transthyretin amyloidosis in an 82-year-old woman who presented with bilateral retinal arteriolar vascular sheathing and vitreous hemorrhage in one eye. The patient underwent a full history, physical examination, genetic workup, and cardiac imaging to establish the diagnosis. Fundus photographs, optical coherence tomography, fluorescein angiography, and indocyanine green angiography were performed throughout the patient's treatment course. Fundus examination demonstrated segmental sheathing of the retinal arterioles, telangiectasias, and intraretinal hemorrhages in both eyes without neovascularization. Fluorescein angiography revealed delayed filling of the nasal retinal arterioles in the right eye with severe temporal nonperfusion and areas hyperfluorescent segmental sheathing in both eyes. Ocular coherence tomography demonstrated retinal vessels with hyperreflective thickened walls. Cardiac amyloid nuclear scan was consistent with transthyretin-mediated amyloidosis amyloid heart disease, and genetic testing confirmed a heterozygous pathogenic transthyretin mutation: c.290 C>A (p.Ser97Tyr) also known as Ser77Tyr. Amyloidosis should be considered in the differential of vasculitis and vitreous hemorrhage with or without significant vitreous opacities. Ophthalmologists aware of this diagnosis should complete a full review of systems with specific focus on cardiac history and symptoms of peripheral neuropathy when amyloidosis is in the differential.

Thermospheric Temperature and ΣO/N2 Variations as Observed by GOLD and Compared to MSIS and WACCM‐X Simulations During 2019–2020 at Deep Solar Minimum

AbstractThe ultraviolet‐imaging spectrograph that comprises Global‐scale Observations of the Limb and Disk (GOLD) mission in geostationary orbit at 47.5°W longitude has taken full disk images at high cadence throughout the deep solar minimum period of 2019–2020. Synoptic (i.e., concurrent and spatially unified and resolved) observations of thermospheric temperature and composition at ∼150 km altitude are made for the first time, allowing GOLD to disambiguate temporal and spatial variations. Here we analyze the daytime effective temperature and column integrated O and N2 density ratio (ΣO/N2) data simultaneously observed by GOLD over 120°W–20°E longitude and 60°S–60°N latitude from 13 October 2019 to 12 October 2020. Daily zonal mean values are calculated for each latitude and compared with NRLMSIS 2.0 and simulations from the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM‐X). On average, the GOLD observations show higher temperatures than Mass Spectrometer Incoherent Scatter radar (MSIS) and WACCM‐X by ∼20–60 K (5%–10%) and 80–120 K (12%–18%), respectively. The ΣO/N2 ratios observed by GOLD are larger than the MSIS results by ∼0.4 (40%) but smaller than the WACCM‐X simulations by ∼0.3 (30%). The observed and modeled results are correlated at most latitudes (r = 0.4–0.8), and GOLD, MSIS, and WACCM‐X all display a similar seasonal variation and change with latitude. WACCM‐X simulates a larger annual variation in ΣO/N2, suggesting that the thermospheric circulation is overestimated and atmospheric waves and turbulence transport are not properly represented in the model.

4H-SiC Full Wafer Mapping Image of CMP-Finished Sub-Surface Damage by Laser Light Scattering

Developing an observation method for distributing sub-surface damage (SSD) on large-diameter 4H-SiC bulk wafers formed by mechanical processing can significantly improve the epitaxial and bulk growth processes. This study used a novel laser light scattering (LLS) technique to observe SSD distribution on a 6-inch 4H-SiC (0001) wafer. As a result, scattering intensity distributions similar to the grinding and lap-polishing traces and the shape of the jig used to hold the wafer during polishing were observed on the CMP-finished SiC wafer surface. Since the surface topography of the area was flat by a laser microscopy observation, it is assumed that this is the SSD. This result suggests that LLS can be a wafer inspection method that can observe SSD distribution. In addition, wafer inspection using LLS has demonstrated that it is possible to observe scratches, particles, and macrostep bunching. This method is anticipated to allow further optimization of the mechanical processing and thermal etching process prior to CVD epitaxial growth.

Keyframe-aided resolution enhancement network for dynamic super-resolution structured illumination microscopy.

Deep learning has been used to reconstruct super-resolution structured illumination microscopy (SR-SIM) images with wide-field or fewer raw images, effectively reducing photobleaching and phototoxicity. However, the dependability of new structures or sample observation is still questioned using these methods. Here, we propose a dynamic SIM imaging strategy: the full raw images are recorded at the beginning to reconstruct the SR image as a keyframe, then only wide-field images are recorded. A deep-learning-based reconstruction algorithm, named KFA-RET, is developed to reconstruct the rest of the SR images for the whole dynamic process. With the structure at the keyframe as a reference and the temporal continuity of biological structures, KFA-RET greatly enhances the quality of reconstructed SR images while reducing photobleaching and phototoxicity. Moreover, KFA-RET has a strong transfer capability for observing new structures that were not included during network training.

Transferable adversarial masked self-distillation for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to a related unlabeled target domain. Most existing works focus on minimizing the domain discrepancy to learn global domain-invariant representation using CNN-based architecture while ignoring both transferable and discriminative local representation, e.g, pixel-level and patch-level representation. In this paper, we propose the Transferable Adversarial Masked Self-distillation based on Vision Transformer architecture to enhance the transferability of UDA, named TAMS. Specifically, TAMS jointly optimizes three objectives to learn both task-specific class-level global representation and domain-specific local representation. First, we introduce adversarial masked self-distillation objective to distill representation from a full image to the representation predicted from a masked image, which aims to learn task-specific global class-level representation. Second, we introduce masked image modeling objectives to learn local pixel-level representation. Third, we introduce an adversarial weighted cross-domain adaptation objective to capture discriminative potentials of patch tokens, which aims to learn both transferable and discriminative domain-specific patch-level representation. Extensive studies on four benchmarks and the experimental results show that our proposed method can achieve remarkable improvements compared to previous state-of-the-art UDA methods.

Aeroengine Blade Surface Defect Detection System Based on Improved Faster RCNN

Aiming at the difficulty of automatic blade detection and the discontinuous defects on the full image, an aeroengine blade surface defect detection system based on improved faster RCNN is designed. Firstly, a dataset of blade surface defects is constructed. To solve the problem that the original faster RCNN is hard to detect tiny defects, RoI align is adopted to replace RoI pooling in the improved faster RCNN and the feature pyramid networks (FPN) combined with ResNet-50 are introduced for feature extraction. To address the issue of discontinuous defects on the full image, the nonmaximum suppression (NMS) algorithm is improved to ensure the continuity of defects. A four-degree-of-freedom (4-DOF) motion platform and an industrial camera are used to collect images of blade surfaces. The detection results generated by the improved faster RCNN are compared with the results of the unimproved method. The experimental results prove that the defect detection system based on the improved faster RCNN can realize automatic defect detection on the blade surface with high accuracy. It also solves the issues of tiny defect detection and discontinuous defects on the full result image of the blade.

The buccal corridor as a possible morphological risk indicator for sleep disordered breathing.

To explore a possible morphological relationship between buccal corridor, gingival display, transpalatal molar width, palatal height, and a cephalometric measurement (PV-A Line) as a risk indicator for sleep-disordered breathing. Thirty subjects were enrolled. Full face maximum smile images and CBCT scans were taken. A Pearson correlation coefficient was used to identify any relationships between the variables. No correlations were identified between any of the variables investigated in this study as they relate to risk factors for sleep-disordered breathing. The amount of buccal corridor space in relation to a patient's smile and the amount of gingival display does not appear to be a reliable metric in identifying certain morphological risk factors for sleep-disordered breathing. Assessing the amount of buccal corridor space in relation to a smile does not appear to be a reliable predictor of morphological risk for certain risk factors of sleep-disordered breathing. In addition, utilizing the amount of gingival display in a patient's maximum smile does not appear to relate directly to risks in sleep-disordered breathing. Other tests and discovery may be necessary to identify these types of patients.

Sex-, strain and lateral differences in brain cytoarchitecture across a large mouse population.

The mouse brain is by far the most intensively studied among mammalian brains, yet basic measures of its cytoarchitecture remain obscure. For example, quantifying cell numbers, and the interplay of sex-, strain-, and individual variability in cell density and volume is out of reach for many regions. The Allen Mouse Brain Connectivity project produces high-resolution full brain images of hundreds of brains. Although these were created for a different purpose, they reveal details of neuroanatomy and cytoarchitecture. Here, we used this population to systematically characterize cell density and volume for each anatomical unit in the mouse brain. We developed a deep neural network-based segmentation pipeline that uses the auto-fluorescence intensities of images to segment cell nuclei even within the densest regions, such as the dentate gyrus. We applied our pipeline to 507 brains of males and females from C57BL/6J and FVB.CD1 strains. Globally, we found that increased overall brain volume does not result in uniform expansion across all regions. Moreover, region-specific density changes are often negatively correlated with the volume of the region, therefore cell count does not scale linearly with volume. Many regions, including layer 2/3 across several cortical areas, showed distinct lateral bias. We identified strain-specific or sex-specific differences. For example, males tended to have more cells in extended amygdala and hypothalamic regions (MEA, BST, BLA, BMA, and LPO, AHN) while females had more cells in the orbital cortex (ORB). Yet, inter-individual variability was always greater than the effect size of a single qualifier. We provide the results of this analysis as an accessible resource for the community.

EESD special issue: AI and data‐driven methods in earthquake engineering – (Part 1)

EESD special issue: AI and data‐driven methods in earthquake engineering – (Part 1)

The MeerKAT Absorption Line Survey: Homogeneous continuum catalogues towards a measurement of the cosmic radio dipole

The number counts of homogeneous samples of radio sources are a tried and true method of probing the large-scale structure of the Universe, as most radio sources outside the Galactic plane are at cosmological distances. As such, they are expected to trace the cosmic radio dipole, an anisotropy analogous to the dipole seen in the cosmic microwave background (CMB). Results have shown that although the cosmic radio dipole matches the direction of the CMB dipole, it has a significantly larger amplitude. This unexplained result challenges our assumption of the Universe being isotropic, which can have large repercussions for the current cosmological paradigm. Though significant measurements have been made, sensitivity to the radio dipole is generally hampered by systematic effects that can cause large biases in the measurement. Here we assess these systematics with data from the MeerKAT Absorption Line Survey (MALS), a blind search for absorption lines with pointings centred on bright radio sources. With the sensitivity and field of view of MeerKAT, thousands of sources are observed in each pointing, allowing for the possibility of measuring the cosmic radio dipole given enough pointings. We present the analysis of ten MALS pointings, focusing on systematic effects that could lead to an inhomogeneous catalogue. We describe the calibration and creation of full band continuum images and catalogues, producing a combined catalogue containing 16 307 sources and covering 37.5 square degrees of sky down to a sensitivity of 10 μJy beam−1. We measure the completeness, purity, and flux recovery statistics for these catalogues using simulated data. We investigate different source populations in the catalogues by looking at flux densities and spectral indices and how they might influence source counts. Using the noise characteristics of the pointings, we find global measures that can be used to correct for the incompleteness of the catalogue, producing corrected number counts down to 100–200 μJy. We show that we can homogenise the catalogues and properly account for systematic effects. We determine that we can measure the dipole to 3σ significance with 100 MALS pointings.

Small Space Target Detection Based on a Convolutional Neural Network and Guidance Information

Although space targets have different shapes, sizes and intensities, their distributions share certain commonalities. However, it is difficult to summarize a generalized distribution function for space targets. Moreover, most of the existing methods based on deep learning are not suitable to use directly because of the size of targets and the cost of manual labeling for a full image. In this paper, we proposed a pattern for space target detection based on a convolutional neural network (CNN) to learn essential features of the targets from data. In the processing stage, the background is estimated and removed. Then, image techniques are used to search and process region proposals. Different sizes of region proposals are recognized by a discriminator, which is built upon a small CNN trained with the data of several specific targets. Finally, a non-maximum suppression (NMS) operation is used to remove redundant targets. In the network structure, to further enhance the influence of the effective area, the parameters calculated from the center region of the input are utilized as guidance information and added to the features before the full connection. Moreover, the bias loss is applied to increase the weights of unique features. The experimental results demonstrate the outstanding performance of the proposed method in terms of the number of detected targets, accuracy rate and false alarm rate compared with baseline methods. In particular, the proposed method has a simple network structure and a lower computational cost which can be further promoted and implemented in actual engineering.

Reconstructing the image scanning microscopy dataset: an inverse problem

Confocal laser-scanning microscopy (CLSM) is one of the most popular optical architectures for fluorescence imaging. In CLSM, a focused laser beam excites the fluorescence emission from a specific specimen position. Some actuators scan the probed region across the sample and a photodetector collects a single intensity value for each scan point, building a two-dimensional image pixel-by-pixel. Recently, new fast single-photon array detectors have allowed the recording of a full bi-dimensional image of the probed region for each scan point, transforming CLSM into image scanning microscopy (ISM). This latter offers significant improvements over traditional imaging but requires an optimal processing tool to extract a super-resolved image from the four-dimensional dataset. Here we describe the image formation process in ISM from a statistical point of view, and we use the Bayesian framework to formulate a multi-image deconvolution problem. Notably, the single-photon detector suffers exclusively from the photon shot noise, enabling the development of an effective likelihood model. We derive an iterative likelihood maximization algorithm and test it on experimental and simulated data. Furthermore, we demonstrate that the ISM dataset is redundant, enabling the possibility of obtaining reconstruction sampled at twice the scanning step. Our results prove that in ISM, under appropriate conditions, the Nyquist–Shannon sampling criterium is effectively relaxed. This finding can be exploited to speed up the acquisition process by a factor of four, further improving the versatility of ISM systems.

Thin wire segmentation and reconstruction based on a novel image overlap-partitioning and stitching algorithm in apple fruiting wall architecture for robotic picking

The layout of orchards usually requires the use of wires, to provide sturdy support. Such is the case of apple trees in fruiting wall architecture, where wires are conducive for mechanical harvesting and especially robotic picking. However, wires may cause damage to the robotic gripper, especially when direct picking occluded apple fruits. Hence, the importance of identifying the wires of a fruit wall architecture. In this study, a pixel-level segmentation network, BlendMask, was adopted to segment wires. The wires are thin and normally behind the branches or leaves, making difficult for their identification. Therefore, a novel data processing algorithm called image overlap-partitioning and stitching (IOS) is proposed for BlendMask to segment the wires. A total of 82 RGB (Red, Green, and Blue) images registered to create a raw dataset. The dataset was augmented and then overlap-partitioned into 12,736 images with a resolution of 800 × 1024 pixels and corresponding annotation files based on input size of BlendMask to make an overlap-partitioned dataset. Then BlendMask was trained with the overlap-partitioned dataset and tested on the other images, where additional stitching for the overlap-partitioned images was needed in the image testing dataset. Results showed that BlendMask with IOS obtained Intersection over Union (IoU) and pixel accuracy of 43.86 % and 61.01 %, respectively. It achieved a better average precision of 38.75 % with IoU of 0.5 on the overlap-partitioned dataset, which was 38.42 % higher than the full image dataset. Moreover, a reconstruction method based on feature point extraction and fitting was proposed to estimate wire skeletons, which achieved a reconstruction accuracy of 90.70 %. These results showed a promising potential using segmentation and reconstruction methods for identifying wires and thus providing a basis for robotic picking in modern orchards.

Using Laser Speckle Contrast Imaging to Quantify Perfusion Quality in Kidney and Pancreas Grafts on Vascular Reperfusion: A Proof-of-Principle Study.

This study was registered in ClinicalTrials.gov (NCT04202237). The Moor FLPI-2 blood flow imager was used in 4 patients (1 Simultaneous Pancreas and Kidney, 2 deceased, and 1 living donor kidney transplants) during reperfusion to capture reperfusion data. The following parameters were measured: flux (average speed × concentration of moving red blood cells in the sample volume), doppler centroid, total and valid pixels, valid rate, and total and valid area. Flux data were analyzed with Moor FLPI analysis software. The perfusion characteristics and flux images correlated with initial graft function. LSCI is a safe, noncontact imaging modality that provides real-time, accurate, high-resolution, full field blood flow images and a wide range of flux data to objectively quantify organ reperfusion intraoperatively in kidney/kidney-pancreas transplantation. This modality could be used to develop a robust numerical quantification system for the evaluation and reporting of intraoperative organ perfusion, and aid intraoperative decision-making. Perfusion data could be combined with biomarkers and immunological parameters to more accurately predict graft outcomes.

Deep learning techniques for imaging air Cherenkov telescopes

Very-high-energy (VHE) gamma rays and charged cosmic rays (CCRs) provide an observational window into the acceleration mechanisms of extreme astrophysical environments. One of the major challenges at imaging air Cherenkov telescopes (IACTs) designed to look for VHE gamma rays, is the separation of air showers initiated by CCRs which form a background to gamma-ray searches. Two other less well-studied problems at IACTs are (a) the classification of different primary nuclei among the CCR events, and (b) identification of anomalous events initiated by beyond-the-Standard-Model (BSM) particles that could give rise to shower signatures which differ from the standard images of either gamma rays or CCR showers. The problems of categorizing the primary particle that initiates a shower image, or the problem of tagging anomalous shower events in a model-independent way, are problems that are well suited to a machine learning approach. Traditional studies that have explored gamma-ray/CCR separation have used a multivariate analysis based on derived shower properties, which contains significantly reduced information about the shower. In our work, we address the problems outlined above by using machine learning architectures trained on full simulated shower images, as opposed to training on just a few derived shower properties. We illustrate the techniques of binary and multicategory classification using convolutional neural networks, and we also pioneer the use of autoencoders for anomaly detection at VHE gamma-ray experiments. The latter technique has been studied previously in the context of collider physics, to tag anomalous BSM candidates in a model-independent way. In this study, for the first time, we demonstrate the efficacy of these techniques in the domain of VHE gamma-ray experiments. As a case study, we apply our techniques to the High Energy Stereoscopic System experiment. However, the real strength of the techniques that we broach here in the context of VHE gamma-ray observatories, is that these methods can be applied broadly to any other IACT---such as the upcoming Cherenkov Telescope Array---or can even be suitably adapted to CCR experiments.

The Other Mrs. Clemm

The Other Mrs. Clemm