julilili42/papers huggingface dataset:
High-resolution structures of the bound effectors avadomide (CC-122) and iberdomide (CC-220) highlight advantages and limitations of the MsCI4 soaking system
2022-02-18

Cereblon (CRBN) is the substrate receptor of the CRL4 CRBN E3 ubiquitin ligase and is a central player in targeted protein degradation. It is the target of the thalidomide-derived immunomodulatory drugs (IMiDs) and is one of the most widely employed receptors for proteolysis-targeting chimeras (PROTACs), both of which induce the ubiquitination and subsequent proteasomal degradation of target proteins. Structural studies of ligand binding to CRBN are crucial to elucidate the mechanisms of action and for mediation of side effects, ultimately aiding the development of next-generation IMiDs and PROTACs. With this aim, a crystal-soaking system based on the single-domain bacterial homologue MsCI4 has previously been established and used to delineate the binding modes of several classes of small molecules, including FDA-approved drugs, at the molecular level. Here, this system was used to characterize the binding of the next-generation IMiDs avadomide (CC-122) and iberdomide (CC-220) at high resolution, highlighting the advantages and limitations of the MsCI4 system and its implications for the development of future cereblon effectors.
Molecular Charge Transfer Effects on Perylene Diimide Acceptor and Dinaphthothienothiophene Donor Systems
2022-02-18

The interactions between organic donor and acceptor molecules and the related charge transfer (CT) effects are of great interest in organic optoelectronics. Here, we present a comprehensive investigation of cocrystal formation and charge transfer effects in weakly interacting organic semiconductor mixtures. As a model system, we choose dinaphthothienothiophene (DNTT) as a donor molecule and two different perylene diimide derivatives (PTCDI-C8-CN2 and PDIF-CN2) as acceptors, which differ in the fluorination of the side chains in the imide position. Experimentally, both systems show a small ground-state CT governed by hybridized HOMO–1 and LUMO+1 levels. In contrast, the respective HOMO and LUMO levels of the complex are localized on the acceptor and donor molecule. This leads to the observation of a nearly pure charge transfer excitation from the acceptor to the donor in the absorption spectra. We discuss the general impact of localized HOMO and LUMO levels on the optoelectronic properties in CT complexes dependent on comparison with first-principles calculations based on density functional theory and many-body perturbation theory.
The Relation of Attitude Toward Technology and Mastery Experience After an App-Guided Physical Exercise Intervention: Randomized Crossover Trial
2022-02-18

Background Physical exercise has been found to assert a positive impact on many muscular conditions. Exercise under face-to-face supervision is the gold standard, but access to it is limited, for instance, for economic reasons. App-guided therapy is an intervention that is more affordable and easily accessible. However, attitude toward technology is a key predictor for media adoption and is therefore expected to shape user experience during app-guided therapy. This might be of particular importance for mastery experience, which is crucial for promoting exercise-related self-efficacy and perceived usefulness of the interaction. Both should empower patients to continuously exercise. Objective This study sought to test whether attitudes toward technology predict mastery experience and perceived usefulness of the interaction after an app- versus a physiotherapist-guided treatment. We expect that attitudes toward technology positively predict both outcomes in case of the app-guided but not in case of the physiotherapist-guided treatment. Methods Patients (n=54) with clinically diagnosed hip osteoarthritis participated in 2 training sessions with the same exercise intervention, once guided by an app on a tablet computer and once guided by a physiotherapist in a German university hospital. The order of the sessions was randomized. Attitude toward technology was assessed as predictor before the first session, while mastery experience and the global perceived usefulness of interaction as self-reported outcomes after each session. Results In line with our hypotheses, attitude toward technology predicted mastery experience (b=0.16, standard error=0.07, P=.02) and usefulness of interaction (b=0.17, standard error=0.06, P=.01) after the app-based training but not after the training delivered by a physiotherapist (P&gt;.3 in all cases). Mastery experience was lower for the app-based training but reached a very similar level as the physiotherapist-guided training for those holding a very positive attitude toward technology. Conclusions The attitude toward technology predicts the extent of mastery experience after app-guided exercise therapy. As mastery experience is highly important for self-efficacy and future exercise behavior, attitudes toward technology should be considered when delivering app-guided exercise treatments. Trial Registration German Clinical Trials Register DRKS00015759; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&amp;TRIAL_ID=DRKS00015759
Axial perturbations of hairy Gauss-Bonnet black holes with a massive self-interacting scalar field
2022-02-18

We study the axial quasinormal modes of hairy black holes in Gauss-Bonnet gravity with massive self-interacting scalar field. Two coupling functions of the scalar field to the Gauss-Bonnet invariant are adopted with one of them leading to black hole scalarization. The axial perturbations are studied via time evolution of the perturbation equation, and the effect of the scalar field mass and the self-interaction constant on the oscillation frequency and damping time is examined. We study as well the effect of nonzero scalar field potential on the critical point at which the perturbation equation loses hyperbolicity in the case of black hole scalarization. The results show that the non-zero scalar field potential extends the range of parameters where such loss of hyperbolicity is observed thus shrinking the region of stable black hole existence. This will have an important effect on the nonlinear dynamical simulation studies in massive scalar Gauss-Bonnet gravity.
Segmentation and classification of total hip endoprosthesis in x-ray images
2022-02-18

Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained. Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified. Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks. Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class.
Report on 2112.05078v2
2022-02-18

We consider Markovian open quantum systems subject to stochastic resetting, which means that the dissipative time evolution is reset at randomly distributed times to the initial state.We show that the ensuing dynamics is non-Markovian and has the form of a generalized Lindblad equation.Interestingly, the statistics of quantum-jumps can be exactly derived.This is achieved by combining techniques from the thermodynamics of quantum-jump trajectories with the renewal structure of the resetting dynamics.We consider as an application of our analysis a driven two-level and an intermittent three-level system.Our findings show that stochastic resetting may be exploited as a tool to tailor the statistics of the quantum-jump trajectories and the dynamical phases of open quantum systems.
DNA Replication proteins in primary microcephaly syndromes
2022-02-19

Abstract Improper expansion of neural stem and progenitor cells during brain development manifests in primary microcephaly. This disease is characterized by a reduced head circumference, which correlates with a reduction in brain size. This often corresponds to a general underdevelopment of the brain and entails cognitive, behavioral and motoric retardation. In the past decade significant research efforts have been undertaken to identify genes and the molecular mechanisms underlying microcephaly. One such gene set encompasses factors required for DNA replication. Intriguingly, a growing body of evidence indicates that a substantial number of these genes mediate faithful centrosome and cilium function in addition to their canonical function in genome duplication. Here, we summarize, which DNA replication factors are associated with microcephaly syndromes and to which extent they impact on centrosomes and cilia.
Human Attention in Fine-grained Classification
2022-02-19

The way humans attend to, process and classify a given image has the potential to vastly benefit the performance of deep learning models. Exploiting where humans are focusing can rectify models when they are deviating from essential features for correct decisions. To validate that human attention contains valuable information for decision-making processes such as fine-grained classification, we compare human attention and model explanations in discovering important features. Towards this goal, we collect human gaze data for the fine-grained classification dataset CUB and build a dataset named CUB-GHA (Gaze-based Human Attention). Furthermore, we propose the Gaze Augmentation Training (GAT) and Knowledge Fusion Network (KFN) to integrate human gaze knowledge into classification models. We implement our proposals in CUB-GHA and the recently released medical dataset CXR-Eye of chest X-ray images, which includes gaze data collected from a radiologist. Our result reveals that integrating human attention knowledge benefits classification effectively, e.g. improving the baseline by 4.38% on CXR. Hence, our work provides not only valuable insights into understanding human attention in fine-grained classification, but also contributes to future research in integrating human gaze with computer vision tasks. CUB-GHA and code are available at https://github.com/yaorong0921/CUB-GHA.
Two languages, one treebank: building a Turkish–German code-switching treebank and its challenges
2022-02-19

Abstract This paper presents the SAGT Turkish–German code-switching treebank, and observations and annotation challenges we encountered during its development. The treebank consists of transcriptions of bilingual conversations annotated with several layers: language IDs, lemmas, POS tags, morphological features, and dependency relations. The annotations follow the Universal Dependencies annotation scheme and the conventions used in monolingual treebanks as much as possible. We present and discuss a number of issues that arise because of the need for consistent multilingual annotation within a single treebank, as well as the informal language, which is where code-switching is observed most. Besides proposing solutions to these issues, we present some observations about code-switching phenomena that are only possible to observe in a data set with rich linguistic annotation. The treebank was annotated with a focus on quality of annotations through an iterative process of detecting and correcting annotation errors. We also present quantitative measures for indication of annotation quality. The code-switching treebank created in this study is released to the public through Universal Dependencies repositories.
The role of ventral stream areas for viewpoint-invariant object recognition
2022-02-19

Object constancy is one of the most crucial mechanisms of the human visual system enabling viewpoint invariant object recognition. However, the neuronal foundations of object constancy are widely unknown. Research has shown that the ventral visual stream is involved in processing of various kinds of object stimuli and that several regions along the ventral stream are possibly sensitive to the orientation of an object in space. To systematically address the question of viewpoint sensitive object perception, we conducted a study with stroke patients as well as an fMRI experiment with healthy participants applying object stimuli in several spatial orientations, for example in typical and atypical viewing conditions. In the fMRI experiment, we found stronger BOLD signals and above-chance classification accuracies for objects presented in atypical viewing conditions in fusiform face sensitive and lateral occipito-temporal object preferring areas. In the behavioral patient study, we observed that lesions of the right fusiform gyrus were associated with lower performance in object recognition for atypical views. The complementary results from both experiments emphasize the contributions of fusiform and lateral-occipital areas to visual object constancy and indicate that visual object constancy is particularly enabled through increased neuronal activity and specific activation patterns for objects in demanding viewing conditions.
Reporting and interpreting non-significant results in animal cognition research
2022-02-19

How negative results are reported and interpreted following null hypothesis significance testing is often criticised. With small sample sizes and often low number of test trials, studies in animal cognition are prone to producing non-significant p-values, irrespective of whether this is a false negative or true negative result. Thus, we assessed how negative results are reported and interpreted across published articles in animal cognition and related fields. In this study, we manually extracted and classified how researchers report and interpret non-significant p-values, and examined the p-value distribution of these non-significant results. We found a large amount of heterogeneity in how researchers report non-significant p-values in the result sections of articles, and how they interpret them in the titles and abstracts. “No Effect” interpretations were common in the titles (84%), abstracts (64%), and results sections (41%) of papers, whereas “Non-Significant” interpretations were less common in the titles (0%) and abstracts (26%), but were present in the results (52%). Discussions of effect sizes were rare (&amp;lt;5% of articles). A p-value distribution analysis was consistent with research being performed with low power research to detect effect sizes of interest.
Transkingdom mechanism of MAMP generation by chitotriosidase (CHIT1) feeds oligomeric chitin from fungal pathogens and allergens into TLR2-mediated innate immune sensing
2022-02-19

Abstract Chitin is a highly abundant polysaccharide in nature and linked to immune recognition of fungal infections and asthma in humans. Ubiquitous in fungi and insects, chitin is absent in mammals and plants and, thus, represents a microbe-associated molecular pattern (MAMP). However, the highly polymeric chitin is insoluble, which potentially hampers recognition by host immune sensors. In plants, secreted chitinases degrade polymeric chitin into diffusible oligomers, which are ‘fed to’ innate immune receptors and co-receptors. In human and murine immune cells, a similar enzymatic activity was shown for human chitotriosidase (CHIT1) and oligomeric chitin is sensed via an innate immune receptor, Toll-like receptor (TLR) 2. However, a complete system of generating MAMPs from chitin and feeding them into a specific receptor/co-receptor-aided sensing mechanism has remained unknown in mammals. Here, we show that the secreted chitinolytic host enzyme, CHIT1, converts inert polymeric chitin into diffusible oligomers that can be sensed by TLR1-TLR2 co-receptor/receptor heterodimers, a process promoted by the lipopolysaccharide binding protein (LBP) and CD14. Furthermore, we observed that Chit1 is induced via the β-glucan receptor Dectin-1 upon direct contact of immortalized human macrophages to the fungal pathogen Candida albicans , whereas the defined fungal secreted aspartyl proteases, Sap2 and Sap6, from C. albicans were able to degrade CHIT1 in vitro. Our study shows the existence of an inducible system of MAMP generation in the human host that enables contact-independent immune activation by diffusible MAMP ligands with striking similarity to the plant kingdom. Moreover, this study highlights CHIT1 as a potential therapeutic target for TLR2-mediated inflammatory processes that are fueled by oligomeric chitin.
Total transfemoral access in a new off-the-shelf thoracoabdominal inner-branched endograft
2022-02-19

Abstract Branched endovascular aneurysm repair (BEVAR) is increasingly being performed with the off-the-shelf devices. We evaluated the use of the wire-assisted sheath stabilization technique for the new inner branch-based (iBEVAR), off-the-shelf E-nside endograft in 2 patients. The cannulation of the 8 reno-visceral target vessels and the BSG implantation were successfully performed using the total transfemoral approach. Complications were not observed during the 3-month follow-up. The wire-assisted sheath stabilization technique was shown feasible in an off-the-shelf iBEVAR.
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
2022-02-19

In recent years, several results in the supervised learning setting suggested that classical statistical learning-theoretic measures, such as VC dimension, do not adequately explain the performance of deep learning models which prompted a slew of work in the infinite-width and iteration regimes. However, there is little theoretical explanation for the success of neural networks beyond the supervised setting. In this paper we argue that, under some distributional assumptions, classical learning-theoretic measures can sufficiently explain generalization for graph neural networks in the transductive setting. In particular, we provide a rigorous analysis of the performance of neural networks in the context of transductive inference, specifically by analysing the generalisation properties of graph convolutional networks for the problem of node classification. While VC Dimension does result in trivial generalisation error bounds in this setting as well, we show that transductive Rademacher complexity can explain the generalisation properties of graph convolutional networks for stochastic block models. We further use the generalisation error bounds based on transductive Rademacher complexity to demonstrate the role of graph convolutions and network architectures in achieving smaller generalisation error and provide insights into when the graph structure can help in learning. The findings of this paper could re-new the interest in studying generalisation in neural networks in terms of learning-theoretic measures, albeit in specific problems.
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
2022-02-19

Deep neural networks are prone to overconfident predictions on outliers. Bayesian neural networks and deep ensembles have both been shown to mitigate this problem to some extent. In this work, we aim to combine the benefits of the two approaches by proposing to predict with a Gaussian mixture model posterior that consists of a weighted sum of Laplace approximations of independently trained deep neural networks. The method can be used post hoc with any set of pre-trained networks and only requires a small computational and memory overhead compared to regular ensembles. We theoretically validate that our approach mitigates overconfidence "far away" from the training data and empirically compare against state-of-the-art baselines on standard uncertainty quantification benchmarks.
Convolutional Motif Kernel Networks
2022-02-19

Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring the decision process and preventing scientist to fully conceptualize predicted outcomes. Furthermore, domain experts like healthcare providers need explainable predictions to assess whether a predicted outcome can be trusted in high stakes scenarios and to help them integrating a model into their own routine. Therefore, interpretable models play a crucial role for the incorporation of machine learning into high stakes scenarios like healthcare. In this paper we introduce Convolutional Motif Kernel Networks, a neural network architecture that involves learning a feature representation within a subspace of the reproducing kernel Hilbert space of the position-aware motif kernel function. The resulting model enables to directly interpret and evaluate prediction outcomes by providing a biologically and medically meaningful explanation without the need for additional post-hoc analysis. We show that our model is able to robustly learn on small datasets and reaches state-of-the-art performance on relevant healthcare prediction tasks. Our proposed method can be utilized on DNA and protein sequences. Furthermore, we show that the proposed method learns biologically meaningful concepts directly from data using an end-to-end learning scheme.
Projected GANs Converge Faster
2022-02-19

Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-parameter sweeps. We make significant headway on these issues by projecting generated and real samples into a fixed, pretrained feature space. Motivated by the finding that the discriminator cannot fully exploit features from deeper layers of the pretrained model, we propose a more effective strategy that mixes features across channels and resolutions. Our Projected GAN improves image quality, sample efficiency, and convergence speed. It is further compatible with resolutions of up to one Megapixel and advances the state-of-the-art Fréchet Inception Distance (FID) on twenty-two benchmark datasets. Importantly, Projected GANs match the previously lowest FIDs up to 40 times faster, cutting the wall-clock time from 5 days to less than 3 hours given the same computational resources.