Notes

2 fresh species of Hirsutella (Ophiocordycipitaceae, Sordariomycetes) that are parasitic about lepidopteran pests coming from Cina.
The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.Skin biopsy histopathological analysis is one of the primary methods used for pathologists to assess the presence and deterioration of melanoma in clinical. A comprehensive and reliable pathological analysis is the result of correctly segmented melanoma and its interaction with benign tissues, and therefore providing accurate therapy. In this study, we applied the deep convolution network on the hyperspectral pathology images to perform the segmentation of melanoma. To make the best use of spectral properties of three dimensional hyperspectral data, we proposed a 3D fully convolutional network named Hyper-net to segment melanoma from hyperspectral pathology images. In order to enhance the sensitivity of the model, we made a specific modification to the loss function with caution of false negative in diagnosis. The performance of Hyper-net surpassed the 2D model with the accuracy over 92%. The false negative rate decreased by nearly 66% using Hyper-net with the modified loss function. These findings demonstrated the ability of the Hyper-net for assisting pathologists in diagnosis of melanoma based on hyperspectral pathology images.We present the design and performance of a new compact preclinical system combining positron emission tomography (PET) and magnetic resonance imaging (MRI) for simultaneous scans. The PET contains sixteen SiPM-based detector heads arranged in two octagons and covers an axial field of view (FOV) of 102.5 mm. Depth of interaction effects and detector's temperature variations are compensated by the system. The PET is integrated in a dry magnet operating at 7 T. PET and MRI characteristics were assessed complying with international standards and interferences between both subsystems during simultaneous scans were addressed. For the rat size phantom, the peak noise equivalent count rates (NECR) were 96.4 kcps at 30.2 MBq and 132.3 kcps at 28.4 MBq respectively with and without RF coil. CHR2797 For mouse, the peak NECR was 300.0 kcps at 34.5 MBq and 426.9 kcps at 34.3 MBq respectively with and without coil. At the axial centre of the FOV, spatial resolutions expressed as full width at half maximum / full width at tenth maximum (FWHM/FWTM) ranged from 1.69/3.19 mm to 2.39/4.87 mm. The peak absolute sensitivity obtained with a 250-750 keV energy window was 7.5% with coil and 7.9% without coil. Spill over ratios of the NEMA NU4-2008 image quality (NEMA-IQ) phantom ranged from 0.25 to 0.96 and the percentage of non-uniformity was 5.7%. The image count versus activity was linear up to 40 MBq. The principal magnetic field variation was 0.03 ppm/mm over 40 mm. The qualitative and quantitative aspects of data were preserved during simultaneous scans.In this pictorial, we present the design and making process of Data Badges as they were deployed during a one-week academic seminar. Data Badges are customizable physical conference badges that invite participants to make their own independent and personalized expressions of their academic profile by choosing and assembling a collection of predefined physical tokens on a flat wearable canvas. As our modular and intuitive design approach allows the construction to occur as a shared, collective activity, Data Badges take advantage of the creative, affective, and social values that underlie physicalization and its construction to engage participants in reflecting on personal data. Among other unexpected phenomena, we noticed how the freedom of assembly and interpretation encouraged a variety of appropriations, which expanded its intended representational space from fully representative to more resistive and provocative forms of data expression.The value of a data representation is traditionally judged based on aspects like effectiveness and efficiency that are important in utilitarian or work-related contexts. Most multisensory data representations, however, are employed in casual contexts where creativity, affective, physical, intellectual, and social engagement might be of greater value. We introduce Move&Find, a multisensory data representation in which people pedalled on a bicycle to exert the energy required to power a search query on Google's servers. To evaluate Move&Find, we operationalized a framework suitable to evaluate the value of data representations in casual contexts and experimentally compared Move&Find to a corresponding visualization. With Move&Find, participants achieved a higher understanding of the data. Move&Find was judged to be more creative and encouraged more physical and social engagement-components of value that would have been missed using more traditional evaluation frameworks.In response to participant preferences and new ethics guidelines, researchers are increasingly sharing data with health study participants, including data on their own household chemical exposures. Data physicalization may be a useful tool for these communications, because it is thought to be accessible to a general audience and emotionally engaged. However, there are limited studies of data physicalization in the wild with diverse communities. Our application of this method in the Green Housing Study is an early example of using data physicalization in environmental health report-back. We gathered feedback through community meetings, prototype testing, and semistructured interviews, leading to the development of data t-shirts and other garments and person-sized bar charts. We found that participants were enthusiastic about data physicalizations, it connected them to their previous experience, and they had varying desires to share their data. Our findings suggest that researchers can enhance environmental communications by further developing the human experience of physicalizations and engaging diverse communities.In the last years, crowdsourcing is transforming the way classification sets are obtained. Instead of relying on a single expert, crowdsourcing shares the effort among a large number of collaborators. This is being applied in the laureate Laser Interferometer Gravitational Waves Observatory (LIGO) in order to detect glitches which might hinder the identification of gravitational-waves. Probabilistic methods, such as Gaussian Processes (GP), have proven successful in crowdsourcing. However, GPs do not scale well to large sets (such as LIGO), which hampers their broad adoption. This has led to the recent introduction of deep learning based crowdsourcing methods, which have become the state-of-the-art. However, the accurate uncertainty quantification of GPs has been sacrificed. In this work, we first leverage a standard sparse GP approximation (SVGP) to develop a GP-based crowdsourcing method that factorizes into mini-batches. This makes it able to cope with previously-prohibitive sets. This first approach, Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR), brings back GP-based methods to the state-of-the-art, and excels at uncertainty quantification. SVGPCR outperforms deep learning methods and previous probabilistic ones on LIGO data. Its behavior is analyzed in a controlled experiment on MNIST. Moreover, recent GP inference techniques are also adapted to crowdsourcing and evaluated experimentally.This paper presents a new approach for dimension reduction of data observed on spherical surfaces. link2 Several dimension reduction techniques have been developed in recent years for non-Euclidean data analysis. As a pioneer work, Hauberg (2016) attempted to implement principal curves on Riemannian manifolds. However, this approach uses approximations to process data on Riemannian manifolds, resulting in distorted results. link3 This study proposes a new approach to project data onto a continuous curve to construct principal curves on spherical surfaces. Our approach lies in the same line of Hastie and Stuetzle (1989) that proposed principal curves for data on Euclidean space. We further investigate the stationarity of the proposed principal curves that satisfy the self-consistency on spherical surfaces. The results on the real data analysis and simulation examples show promising empirical characteristics of the proposed approach.Visual Grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. In real-world VG applications, however, we usually have to deal with ambiguous queries and images with complicated scene structures. Identifying the target based on highly redundant and correlated information can be very challenging, leading to unsatisfactory performance. To tackle this, in this paper, we exploit an attention module for each kind of information to reduce the internal redundancies. We then propose the Accumulated Attention mechanism to reason among all the attention modules jointly, thus the correlations among different kinds of information can be explicitly captured. Moreover, to improve the performance and robustness of our VG models, we introduce some noises into the training procedure to bridge the distribution gap between the human-labeled training data and the real-world poor quality data. With this ``noised'' training strategy, we further learn a bounding box regressor, which can be used to refine the bounding box of the target object. We evaluate the proposed methods on four benchmark datasets. The experimental results show that our methods significantly outperform all previous works on every dataset in terms of both speed and accuracy.Numerous anti-cancer drugs perturb thymidylate biosynthesis and lead to genomic uracil incorporation contributing to their antiproliferative effect. Still, it is not yet characterized if uracil incorporations have any positional preference. Here, we aimed to uncover genome-wide alterations in uracil pattern upon drug treatments in human cancer cell line models derived from HCT116. We developed a straightforward U-DNA sequencing method (U-DNA-Seq) that was combined with in situ super-resolution imaging. Using a novel robust analysis pipeline, we found broad regions with elevated probability of uracil occurrence both in treated and non-treated cells. Correlation with chromatin markers and other genomic features shows that non-treated cells possess uracil in the late replicating constitutive heterochromatic regions, while drug treatment induced a shift of incorporated uracil towards segments that are normally more active/functional. Data were corroborated by colocalization studies via dSTORM microscopy. This approach can be applied to study the dynamic spatio-temporal nature of genomic uracil.Coronavirus disease 2019 (COVID-19) is a new, rapidly spreading pandemic that can lead to a life-threatening disease. Accurate and transparent COVID-19 case reports provide systematic clinical observations supporting researchers designing clinical trials and clinicians delivering health care. The checklist described here is designed to systematically and accurately capture data from case reports and case series for documentation on COVID-19. It is aligned with the CARE guidelines, available from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network.
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