julilili42/papers huggingface dataset:
Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases
2021-06-01

Abstract For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient’s data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together &gt;300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of &gt;19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe.
Lichen planus – a clinical guide
2021-06-01

Summary Lichen planus (LP) is a chronic lichenoid inflammatory disorder of the skin, mucosa and of the appendages. LP is classically characterized by the presence of a rich infiltration of inflammatory T cells, which migrate in the upper part of the dermis, arranged in a band‐like pattern. Different sub types of the disease have been so far described. Albeit LP is clinically well defined, the disease still represents a therapeutic enigma. Especially with regard to mucosal or scalp affecting LP types, which often present a recalcitrant and treatment unresponsive course, efficacious therapeutic options are still lacking. Thus, LP represents a disease with a high psychosocial burden. Yet, development in the deciphering of LP pathogenesis reveals possible new druggable targets, thus paving the way for future therapeutic options. In this clinical guide, we summarize the current clinical knowledge and therapeutic standards and discuss the future perspective for the management of LP.
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning
2021-06-01

Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to transfer knowledge across heterogeneous modalities, even though these data modalities may not be semantically correlated. Rather than directly aligning the representations of different modalities, we compose audio, image, and video representations across modalities to uncover richer multi-modal knowledge. Our main idea is to learn a compositional embedding that closes the cross-modal semantic gap and captures the task-relevant semantics, which facilitates pulling together representations across modalities by compositional contrastive learning. We establish a new, comprehensive multi-modal distillation benchmark on three video datasets: UCF101, ActivityNet, and VGGSound. Moreover, we demonstrate that our model significantly outperforms a variety of existing knowledge distillation methods in transferring audio-visual knowledge to improve video representation learning. Code is released here: https://github.com/yanbeic/CCL.
Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration
2021-06-01

Registering point clouds of dressed humans to parametric human models is a challenging task in computer vision. Traditional approaches often rely on heavily engineered pipelines that require accurate manual initialization of human poses and tedious post-processing. More recently, learning-based methods are proposed in hope to automate this process. We observe that pose initialization is key to accurate registration but existing methods often fail to provide accurate pose initialization. One major obstacle is that, despite recent effort on rotation representation learning in neural networks, regressing joint rotations from point clouds or images of humans is still very challenging. To this end, we propose novel piecewise trans-formation fields (PTF), a set of functions that learn 3D translation vectors to map any query point in posed space to its correspond position in rest-pose space. We com-bine PTF with multi-class occupancy networks, obtaining a novel learning-based framework that learns to simultaneously predict shape and per-point correspondences between the posed space and the canonical space for clothed human. Our key insight is that the translation vector for each query point can be effectively estimated using the point-aligned local features; consequently, rigid per bone trans-formations and joint rotations can be obtained efficiently via a least-square fitting given the estimated point correspondences, circumventing the challenging task of directly regressing joint rotations from neural networks. Further-more, the proposed PTF facilitate canonicalized occupancy estimation, which greatly improves generalization capability and results in more accurate surface reconstruction with only half of the parameters compared with the state-of-the-art. Both qualitative and quantitative studies show that fit-ting parametric models with poses initialized by our net-work results in much better registration quality, especially for extreme poses.
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
2021-06-01

Machine Learning as a Service (MLaaS) is a popular and convenient way to access a trained machine learning (ML) model trough an API. However, if the user's input is sensitive, sending it to the server is not an option. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. As a solution, we propose MLCapsule, a guarded offline deployment of MLaaS. MLCapsule executes the machine learning model locally on the user's client and therefore the data never leaves the client. Meanwhile, we show that MLCapsule is able to offer the service provider the same level of control and security of its model as the commonly used server-side execution. Beyond protecting against direct model access, we demonstrate that MLCapsule allows for implementing defenses against advanced attacks on machine learning models such as model stealing, reverse engineering and membership inference.
Cutaneous melanoma in children and adolescents: The EXPeRT/PARTNER diagnostic and therapeutic recommendations
2021-06-01

Abstract Cutaneous melanoma is rare in children and, like other very rare pediatric tumors, it suffers from a shortage of knowledge and clinical expertise. The clinical management of pediatric melanoma is often challenging. Its clinical and pathological diagnosis may be difficult, and there is no standard treatment. In the absence of specific treatment guidelines, young patients are generally treated following the same principle as for adults, but concern remains about their access to clinical trials and new drugs, which have been shown to dramatically change the natural history of advanced melanoma. This paper presents the internationally recognized recommendations for the diagnosis and treatment of children and adolescents with cutaneous melanoma, established by the European Cooperative Study Group for Pediatric Rare Tumors (EXPeRT) within the EU‐funded project called PARTNER (Paediatric Rare Tumours Network ‐ European Registry). Main recommendations for melanoma are to discuss pediatric patients in multidisciplinary teams that include both pediatric oncologists and specialists in adult melanoma; to enroll patients in prospective trials, if available; to collect data in national–international databases; and to develop an effective international collaboration between pediatric and adult melanoma groups in order to facilitate the transfer of potentially effective new agents from the adult to the pediatric setting.
D-NeRF: Neural Radiance Fields for Dynamic Scenes
2021-06-01

Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF), which trains a deep network to map 5D input coordinates (representing spatial location and viewing direction) into a volume density and view-dependent emitted radiance. However, despite achieving an unprecedented level of photorealism on the generated images, NeRF is only applicable to static scenes, where the same spatial location can be queried from different images. In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions from a \\emph{single} camera moving around the scene. For this purpose we consider time as an additional input to the system, and split the learning process in two main stages: one that encodes the scene into a canonical space and another that maps this canonical representation into the deformed scene at a particular time. Both mappings are simultaneously learned using fully-connected networks. Once the networks are trained, D-NeRF can render novel images, controlling both the camera view and the time variable, and thus, the object movement. We demonstrate the effectiveness of our approach on scenes with objects under rigid, articulated and non-rigid motions. Code, model weights and the dynamic scenes dataset will be released.
Exploratory analysis of the effect of helminth infection on the immunogenicity and efficacy of the asexual blood-stage malaria vaccine candidate GMZ2
2021-06-01

Background Helminths can modulate the host immune response to Plasmodium falciparum and can therefore affect the risk of clinical malaria. We assessed here the effect of helminth infections on both the immunogenicity and efficacy of the GMZ2 malaria vaccine candidate, a recombinant protein consisting of conserved domains of GLURP and MSP3, two asexual blood-stage antigens of P . falciparum . Controlled human malaria infection (CHMI) was used to assess the efficacy of the vaccine. Methodology In a randomized, double-blind Phase I clinical trial, fifty, healthy, lifelong malaria-exposed adult volunteers received three doses of GMZ2 adjuvanted with either Cationic Adjuvant Formulation (CAF) 01 or Alhydrogel, or a control vaccine (Rabies) on days (D) 0, D28 and D56, followed by direct venous inoculation (DVI) of 3,200 P . falciparum sporozoites (PfSPZ Challenge) approximately 13 weeks after last vaccination to assess vaccine efficacy. Participants were followed-up on a daily basis with clinical examinations and thick blood smears to monitor P . falciparum parasitemia for 35 days. Malaria was defined as the presence of P . falciparum parasites in the blood associated with at least one symptom that can be associated to malaria over 35 days following DVI of PfSPZ Challenge. Soil-transmitted helminth (STH) infection was assessed by microscopy and by polymerase chain reaction (PCR) on stool, and Schistosoma infection was assessed by microscopy on urine. Participants were considered as infected if positive for any helminth either by PCR and/or microscopy at D0 and/or at D84 (Helm+) and were classified as mono-infection or co-infection. Total vaccine-specific IgG concentrations assessed on D84 were analysed as immunogenicity outcome. Main findings The helminth in mono-infection, particularly Schistosoma haematobium and STH were significantly associated with earlier malaria episodes following CHMI, while no association was found in case of coinfection. In further analyses, the anti-GMZ2 IgG concentration on D84 was significantly higher in the S . haematobium -infected and significantly lower in the Strongyloides stercoralis- infected groups, compared to helminth-negative volunteers. Interesting, in the absence of helminth infection, a high anti-GMZ2 IgG concentration on D84 was significantly associated with protection against malaria. Conclusions Our results suggest that helminth infection may reduce naturally acquired and vaccine-induced protection against malaria. Vaccine-specific antibody concentrations on D84 may be associated with protection in participants with no helminth infection. These results suggest that helminth infection affect malaria vaccine immunogenicity and efficacy in helminth endemic countries.
Learning Graph Embeddings for Compositional Zero-shot Learning
2021-06-01

In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) in the training set. This is challenging because the same state can for example alter the visual appearance of a dog drastically differently from a car. As a solution, we propose a novel graph formulation called Compositional Graph Embedding (CGE) that learns image features, compositional classifiers, and latent representations of visual primitives in an end-to-end manner. The key to our approach is exploiting the dependency between states, objects, and their compositions within a graph structure to enforce the relevant knowledge transfer from seen to unseen compositions. By learning a joint compatibility that encodes semantics between concepts, our model allows for generalization to unseen compositions without relying on an external knowledge base like WordNet. We show that in the challenging generalized compositional zero-shot setting our CGE significantly outperforms the state of the art on MIT-States and UT-Zappos. We also propose a new benchmark for this task based on the recent GQA dataset. Code is available at: https://github.com/ExplainableML/czsl
The SOX2 Status of Disseminated Tumor Cells in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy
2021-06-01

SOX2-positive DTCs can be detected in breast cancer patients after NAT, even in patients with SOX2-negative primary tumors. This suggests that these populations may have evolved independently of each other.
Impact of the Disk Thickness on X-Ray Reflection Spectroscopy Measurements
2021-06-01

Abstract In a previous paper, we presented an extension of our reflection model relxill _ nk to include the finite thickness of the accretion disk following the prescription in Taylor &amp; Reynolds. In this paper, we apply our model to fit the 2013 simultaneous observations by the Nuclear Spectroscopic Telescope Array (NuSTAR) and XMM-Newton of the supermassive black hole in MCG-06-30-15 and the 2019 NuSTAR observation of the Galactic black hole in EXO 1846-031. The high-quality data of these spectra had previously led to precise black hole spin measurements and very stringent constraints on possible deviations from the Kerr metric. We find that the disk thickness does not change previous spin results found with a model employing an infinitesimally thin disk, which confirms the robustness of spin measurements in high radiative efficiency disks, where the impact of disk thickness is minimal. Similar analysis on lower accretion rate systems will be an important test for measuring the effect of disk thickness on black hole spin measurements.
Global AM fungi are dominating mycorrhizal communities in a tropical premontane dry forest in Laipuna, South Ecuador
2021-06-01

Abstract Tropical dry forests are an intricate ecosystem with special adaptations to periods of drought. Arbuscular mycorrhizal fungi (AMF) are essential for plant survival in all terrestrial ecosystems but might be of even greater importance in dry forests as plant growth is limited due to nutrient and water deficiency during the dry season. Tropical dry forests in Ecuador are highly endangered, but studies about AMF communities are scarce. We investigated the AMF community of a premontane semi-deciduous dry forest in South Ecuador during the dry season. We estimated AMF diversity, distribution, and composition of the study site based on operational taxonomic units (OTUs) and compared the results to those from the tropical montane rainforest and páramo in South Ecuador. OTU delimitation was based on part of the small ribosomal subunit obtained by cloning and Sanger sequencing. Nearly all OTUs were Glomeraceae. The four frequent OTUs were Glomus , and comparison with the MaarjAM database revealed these to be globally distributed with a wide range of ecological adaptations. Several OTUs are shared with virtual taxa from dry forests in Africa. Ordination analysis of AMF communities from the tropical dry and montane rainforests in South Ecuador revealed a unique AMF community in the dry forest with only few overlapping OTUs. Most OTUs that were found in both dry and rainforests and on the two continents were globally distributed Glomus .
A meta‐analysis on allergen‐specific immunotherapy using MCT® (MicroCrystalline Tyrosine)‐adsorbed allergoids in pollen allergic patients suffering from allergic rhinoconjunctivitis
2021-06-01

Abstract Background The World Allergy Organization and the European Academy of Allergy and Clinical Immunology recommend to perform product‐specific meta‐analyses for allergen‐specific immunotherapies because of the high degree of heterogeneity between individual products. This meta‐analysis evaluates the efficacy and safety of Glutaraldehyde‐modified and MCT ® (MicroCrystalline Tyrosine)‐adsorbed allergoids (MATA). Methods The databases MEDLINE, LILACS, embase, LIVIVO, Web of Science and Google (Scholar) were searched for publications on MATA up to June 2019. Primary endpoint was the combined symptom and medication score (CSMS). Secondary endpoints were single scores, immunogenicity and improvement of allergic condition. Secondary safety endpoints were the occurrence of side effects. A random effects model was applied with (standardized) mean differences ([S]MDs) including confidence intervals (CI). Heterogeneity was analyzed using the I 2 index and publication bias using Egger's test and Funnel plots. Subgroups were analyzed regarding age and asthma status. Results Eight randomized double‐blind placebo‐controlled trials were selected for efficacy and 43 publications for safety analysis. In total, 4531 patients were included in this analysis including eight studies containing data on children and adolescents. AIT with MATA significantly reduced allergic symptoms and medication use with a SMD for CSMS of −0.8 (CI: −1.24, −0.36) in comparison to placebo. Heterogeneity was moderate between the studies. The total symptom score (−1.2 [CI: −2.11, −0.29]) and the total medication score (−2.2 [CI: −3.65, −0.74]) were also significantly reduced after MATA treatment. Patient's condition improved significantly after treatment with MATA, with an odds ratio of 3.05 (CI: 1.90, 4.90) when compared to placebo. The proportion of patients, who developed side effects was 38% (CI: 19%, 57%). No serious side effects occurred. Safety in the subgroups of asthmatic patients, children and adolescents did not differ from the overall patient population. Conclusions This meta‐analysis reveals a large body of evidence from publications investigating MATA. MATA significantly improved allergic symptoms and reduced the use of anti‐allergic medication in comparison to placebo, with an excellent safety profile. Especially for children and asthmatic patients, the use of MATAs can be considered as safe, because the safety profiles in these groups did not differ from the total patient population.
Patterns of extreme temperature-related catastrophic events in Europe including the Russian Federation: a cross-sectional analysis of the Emergency Events Database
2021-06-01

Objective To investigate reported extreme temperature-related catastrophic events and associated mortality on the European continent including the Russian Federation. Design Cross-sectional respecting Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) criteria. Settings Data source: Emergency Events Database (EM-DAT). Participants Search criteria: location—European continent including Russian Federation, time—years 1988 until 2019 (close of database 12 July 2019), catastrophic events—extreme temperatures. Primary outcome measures Numbers of heat waves, cold waves, severe winter conditions and associated number of deaths, overall, and per country and year, respecting STROBE criteria. Results The most frequent type of the 243 events recorded in EM-DAT were cold waves (54.7%). However, cold waves and severe winter conditions only accounted for 6460 deaths (4.5%), while heat waves were associated with 137 533 deaths (95.5%). The five most severe heat waves in 2003, 2006, 2010, 2013 and 2015 were associated with a total of 135 089 deaths. The most severe heat waves were geographically distributed over the Russian Federation (2010), as well as France, Italy, Spain and Germany, each in 2003. Conclusion Although cold waves are more frequently reported in EM-DAT, heat waves are the major cause for temperature-related deaths. In order to better protect the public, it is important to address resiliency and vulnerability of populations at risk and age groups.
Disentangling the Protection Suit: Images, Artefacts, and the Making of the Health-Security Nexus
2021-06-01

The protection suit is the icon of infectious disease outbreaks. I argue that the protection suit has performative power not only by shaping the daily tasks of health workers and their interaction with patients, but also as a visual artefact and as a two-dimensional image determining how health crises and their security implications are understood and dealt with. The article proceeds in three steps: it firstly highlights the current absence of visuality in the academic literature on health security and makes the case for including it in the debate. Secondly, with recourse to Actor-Network Theory (ANT) it theoretically locates visual representations, as images and artefacts, as actors that act and enact each other in the health-security nexus. The third part of the article follows the protection suit during the Ebola epidemic in West Africa and maps the visual network it creates. In doing so this article demonstrates how the suit as artefact and image constitutes a network that visually and sensually links the bodies of patients, health workers and distant viewers in complex and at times competing makings of health security.
Open World Compositional Zero-Shot Learning
2021-06-01

Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions some of which might be unfeasible. In this setting, we start from the cosine similarity between visual features and compositional embeddings. After estimating the feasibility score of each composition, we use these scores to either directly mask the output space or as a margin for the cosine similarity between visual features and compositional embeddings during training. Our experiments on two standard CZSL benchmarks show that all the methods suffer severe performance degradation when applied in the open world setting. While our simple CZSL model achieves state-of-the-art performances in the closed world scenario, our feasibility scores boost the performance of our approach in the open world setting, clearly outperforming the previous state of the art.