Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder

Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular biology, where an important goal is to determine functionally-relevant forms/structures that a protein molecule employs to interact with molecular partners in the living cell. This goal is typically pursued under the umbrella of stochastic optimization with algorithms that optimize a scoring function. Research repeatedly shows that current scoring function, though steadily improving, correlate weakly with molecular activity. Inspired by recent momentum in generative deep learning, this paper proposes and evaluates an alternative approach to generating functionally-relevant three-dimensional structures of a protein. Though typically deep generative models struggle with highly-structured data, the work presented here circumvents this challenge via graph-generative models. A comprehensive evaluation of several deep architectures shows the promise of generative models in directly revealing the latent space for sampling novel tertiary structures, as well as in highlighting axes/factors that carry structural meaning and open the black box often associated with deep models. The work presented here is a first step towards interpretative, deep generative models becoming viable and informative complementary approaches to protein structure prediction.
An introduction to computational complexity in Markov Chain Monte Carlo methods

The aim of this work is to give an introduction to the theoretical background and computational complexity of Markov chain Monte Carlo methods. Most of the mathematical results related to the convergence are not found in most of the statistical references, and computational complexity is still an open question for most of the MCMC methods. In this work, we provide a general overview, references, and discussion about all these theoretical subjects.
Relative Positioning of Autonomous Systems using Signals of Opportunity

For reliable operation, next generation autonomous agents will need enhanced situational perception as well as precise navigation capabilities. The global navigation satellite system (GNSS) signals that are utilized by practically all modern positioning systems cannot satisfy this requirement for heighten autonomy levels and positioning is becoming a decisive factor for their proliferation. This work investigates how relative positioning can be achieved using signals that are already accessible in the environment, and derives an online procedure for the exploitation of these signals for localization in GNSS-challenged areas. The proposed relative positioning system (RPS) explores the signal properties over a large spectrum of frequency bands, and derives a vehicle tracking algorithm to accurately estimate the vehicle’s trajectory in space and time using an arbitrary set of unknown reference positions. Experimental results demonstrate the applicability of RPS and investigate its performance over the different parameter values.
Fever worsens outcomes in animal models of ischaemic stroke: A systematic review and meta-analysis

Background Subfebrile temperatures and fever in the first days after stroke are associated with a greater risk of a poor outcome. If this relation is causal, prevention of hyperthermia may improve outcome. Causality can be tested in animal models. We therefore assessed the effects of hyperthermia on outcomes in animal models of ischaemic stroke and explored under which conditions prevention of hyperthermia could be most effective. Methods We performed a systematic review and meta-analysis of data from animal experiments testing the effect of spontaneous or induced hyperthermia on outcome after focal cerebral ischaemia. Our primary outcome measure was infarct size. Normalised mean differences were combined using the random effects model and stratified meta-analysis was used to explore the impact of study characteristics. Results We included 19 publications, reporting on 49 comparisons involving 603 animals. Overall, hyperthermia increased infarct size by 43.4% (95% confidence interval, 29.8–56.9%) and worsened neurobehavioral outcomes by 48.5% (17.2–79.8%). The increase in infarct size was larger with higher temperatures. Hyperthermia was most harmful if present for more than 2 h and when started at the time of artery occlusion rather than later. Conclusion Hyperthermia substantially increased infarct size in animal models of ischaemic stroke, suggesting that the relation between fever and poor outcome observed in patients is at least in part causal. These data provide support to trials testing the effect of the prevention of fever with antipyretic drugs in patients with acute stroke.
Improved SAR Imaging via Cross-Learning From Camera Images

In this article, we propose a novel concept of cross-learning in order to improve synthetic aperture radar (SAR) images by learning from the camera images, in the manifold domain. We present multilevel abstraction approaches to materialize knowledge transfer between these two very different modalities (i.e., the radar and the camera), namely, a canonical correlation analysis-based approach and a manifold alignment-based approach. We provide experimental results on real data, along with qualitative as well as quantitative analyses, to validate the proposed methodologies.
Experiments and long-term high-performance computations on amplitude modulations of strato-rotational flows

The present paper describes a combined experimental and high-performance computing study of new specific behaviours of the Strato-Rotational Instability (SRI). The SRI is a purely hydrodynamical instability that consists of a classical Taylor–Couette (TC) system under stable axial density stratification. The density stratification causes a change on the marginal instability transition when compared to classical non-stratified TC systems, making the flow unstable in regions where – without stratification – it would be stable. This characteristic makes the SRI a relevant phenomenon in planetary and astrophysical applications, particularly in accretion disk theory. In spite of many advances in the understanding of strato-rotational flows, the confrontation of experimental data with non-linear numerical simulations remains relevant, since involved linear aspects and non-linear interactions of SRI modes still need to be better understood. These comparisons also reveal new non-linear phenomena and patterns not yet observed in the SRI that can contribute for our understanding of geophysical flows. The experiment designed to investigate these SRI-related phenomena consists of two cylinders that can rotate independently, with the space between these two vertical cylinders filled with a silicon oil. For obtaining a stable density stratification along the cylinder axis, the bottom lid of the setup is cooled, and its top part is heated, with temperature differences varying between , establishing an axial linear gradient, leading to Froude numbers between 1.5<Fr<4.5, where is the inner cylinder rotation and N is the buoyancy frequency. The flow field resulting from the cylinders rotation interacting with the stable density stratification is measured using low frequency Particle Image Velocimetry (PIV). In the present investigation, we focus on cases of moderate Reynolds numbers ( , based on the inner cylinder radius and angular velocities), varying between and , and rotation ratio between outer and inner cylinders fixed at , a value slightly smaller than the Keplerian velocity profile, but beyond the Rayleigh limit. The same experimental configuration is also investigated by performing several Direct Numerical Simulations using a parallel high-order compact schemes incompressible code that solves the Boussinesq equations combining a 2d-pencil decomposition and the reduced Parallel Diagonal Dominant for an efficient parallelization. Both simulations and experiments reveal, in agreement with recent linear stability analyses, the occurrence of a return to stable flows with respect to the SRI when the Reynolds numbers increase. Low frequency velocity amplitude modulations related to two competing spiral wave modes, not yet reported, are observed both numerically and experimentally.
Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery

Building extraction from aerial images has several applications in problems such as urban planning, change detection, and disaster management. With the increasing availability of data, Convolutional Neural Networks (CNNs) for semantic segmentation of remote sensing imagery has improved significantly in recent years. However, convolutions operate in local neighborhoods and fail to capture non-local features that are essential in semantic understanding of aerial images. In this work, we propose to improve building segmentation of different sizes by capturing long-range dependencies using contextual pyramid attention (CPA). The pathways process the input at multiple scales efficiently and combine them in a weighted manner, similar to an ensemble model. The proposed method obtains state-of-the-art performance on the Inria Aerial Image Labelling Dataset with minimal computation costs. Our method improves 1.8 points over current state-of-the-art methods and 12.6 points higher than existing baselines on the Intersection over Union (IoU) metric without any post-processing. Code and models will be made publicly available.
Backflow for open quantum systems of two identical particles

In this work, quantum backflow for open quantum systems of two identical spinless particles is addressed within the Caldeira-Leggett (CL) approach under the presence of an opposing force and by using single Gaussian and non-Gaussian wave packets. Backflow is shown to be reduced for small opposing forces, relaxation rate, temperature and a certain degree of non-Gaussianity, but never suppressed. This effect seems to be persistent at long times when considering only one wave packet, implying that interference plays no role. This remarkable behavior is attributed to the time dependence of the width of the probability density expressed in terms of relaxation rate and temperature through the diffusion coefficient. For identical spinless particles, the so-called single-particle probability density and simultaneous/joint detection probability are evaluated. Backflow is found to be occurred only for the dissipative case, being again persistent in time only without an opposing force. Our calculations show that identical spinless fermions display a higher amount of backflow with respect to identical spinless bosons and distinguishable particles. With damping and temperature, the decoherence process, loss of being indistinguishable, is settled gradually with time. At decoherence, the nature of the particles is no longer relevant.
Line Art Correlation Matching Network for Automatic Animation Colorization

Automatic animation line art colorization is a challenging computer vision problem, since the information of the line art is highly sparse and abstracted and there exists a strict requirement for the color and style consistency between frames. Recently, a lot of Generative Adversarial Network (GAN) based image-to-image translation methods for single line art colorization have emerged. They can generate perceptually appealing results conditioned on line art images. However, these methods can not be adopted for the purpose of animation colorization because there is a lack of consideration of the in-between frame consistency. Existing methods simply input the previous colored frame as a reference to color the next line art, which will mislead the colorization due to the spatial misalignment of the previous colored frame and the next line art especially at positions where apparent changes happen. To address these challenges, we design a kind of correlation matching model (called CM) to align the colored reference in a learnable way and integrate the model into an U-Net structure generator in a coarse-to-fine manner. This enables the generator to reconstruct the layer-wise synchronized features from the deep semantic code to the content progressively. Extension evaluations show that CM model can effectively improve the in-between consistency and the quality of colored frames especially when the motion is intense and diverse.
Circular Polarization from Inhomogeneous Synchrotron Sources

Inhomogeneities can influence the polarization emerging from a synchrotron source. However, it is shown that the frequency distribution of circular polarization is only marginally affected, although its magnitude may change substantially. This is used to argue that the observed properties of compact radio sources imply a radiating plasma in which the characteristic waves are nearly circular. As a result, restrictions can be put on the low-energy part of the energy distribution of the relativistic electrons as well as the presence of electron–positron pairs. It is emphasized that this constrains theoretical modeling of the acceleration process for the relativistic electrons; for example, some of the currently popular scenarios seem to need modifications to become consistent with observations.
On Variant Discovery in Genomes of Fungal Plant Pathogens

Comparative genome analyses of eukaryotic pathogens including fungi and oomycetes have revealed extensive variability in genome composition and structure. The genomes of individuals from the same population can exhibit different numbers of chromosomes and different organization of chromosomal segments, defining so-called accessory compartments that have been shown to be crucial to pathogenicity in plant-infecting fungi. This high level of structural variation confers a methodological challenge for population genomic analyses. Variant discovery from population sequencing data is typically achieved using established pipelines based on the mapping of short reads to a reference genome. These pipelines have been developed, and extensively used, for eukaryote genomes of both plants and animals, to retrieve single nucleotide polymorphisms and short insertions and deletions. However, they do not permit the inference of large-scale genomic structural variation, as this task typically requires the alignment of complete genome sequences. Here, we compare traditional variant discovery approaches to a pipeline based on de novo genome assembly of short read data followed by whole genome alignment, using simulated data sets with properties mimicking that of fungal pathogen genomes. We show that the latter approach exhibits levels of performance comparable to that of read-mapping based methodologies, when used on sequence data with sufficient coverage. We argue that this approach further allows additional types of genomic diversity to be explored, in particular as long-read third-generation sequencing technologies are becoming increasingly available to generate population genomic data.
Walking With Ears: Altered Auditory Feedback Impacts Gait Step Length in Older Adults

Auditory feedback may provide the nervous system with valuable temporal (e. g., footstep sounds) and spatial (e.g., external reference sounds) information that can assist in the control of upright walking. As such, hearing loss may directly contribute to declines in mobility among older adults. Our purpose was to examine the impact of auditory feedback on the control of walking in older adults. Twenty older adults (65–86 years) with no diagnosed hearing loss walked on a treadmill for three sound conditions: Baseline, Ear Plugs, and White Noise. We hypothesized that in response to reduced temporal auditory feedback during the Ear Plugs and White Noise conditions, participants would adapt shorter and faster steps that are traditionally believed to increase mechanical stability. This hypothesis was not supported. Interestingly, we observed increases in step length (p = 0.047) and step time (p = 0.026) during the Ear Plugs condition vs. Baseline. Taking longer steps during the Ear Plugs condition may have increased ground reaction forces, thus allowing participants to sense footsteps via an occlusion effect. As a follow-up, we performed a Pearson's correlation relating the step length increase during the Ear Plugs condition to participants' scores on a clinical walking balance test, the Functional Gait Assessment. We found a moderate negative relationship (rho = −0.44, p = 0.055), indicating that participants with worse balance made the greatest increases in step length during the Ear Plugs condition. This trend suggests that participants may have actively sought auditory feedback with longer steps, sacrificing a more mechanically stable stepping pattern. We also hypothesized that reduced spatial localization feedback during the Ear Plugs and White Noise conditions would decrease control of center of mass (COM) dynamics, resulting in an increase in lateral COM excursion, lateral margin of stability, and maximum Lyapunov exponent. However, we found no main effects of auditory feedback on these metrics (p = 0.580, p = 0.896, and p = 0.056, respectively). Overall, these results suggest that during a steady-state walking task, healthy older adults can maintain walking control without auditory feedback. However, increases in step length observed during the Ear Plugs condition suggest that temporal auditory cues provide locomotor feedback that becomes increasingly valuable as balance deteriorates with age.
Integrative Analysis for Elucidating Transcriptomics Landscapes of Glucocorticoid-Induced Osteoporosis

Osteoporosis is the most common bone metabolic disease, characterized by bone mass loss and bone microstructure changes due to unbalanced bone conversion, which increases bone fragility and fracture risk. Glucocorticoids are clinically used to treat a variety of diseases, including inflammation, cancer and autoimmune diseases. However, excess glucocorticoids can cause osteoporosis. Herein we performed an integrated analysis of two glucocorticoid-related microarray datasets. The WGCNA analysis identified 3 and 4 glucocorticoid-related gene modules, respectively. Differential expression analysis revealed 1047 and 844 differentially expressed genes in the two datasets. After integrating differentially expressed glucocorticoid-related genes, we found that most of the robust differentially expressed genes were up-regulated. Through protein-protein interaction analysis, we obtained 158 glucocorticoid-related candidate genes. Enrichment analysis showed that these genes are significantly enriched in the osteoporosis related pathways. Our results provided new insights into glucocorticoid-induced osteoporosis and potential candidate markers of osteoporosis.
Identifying Shared Risk Genes for Asthma, Hay Fever, and Eczema by Multi-Trait and Multiomic Association Analyses

Asthma, hay fever and eczema are three comorbid diseases with high prevalence and heritability. Their common genetic architectures have not been well-elucidated. In this study, we first conducted a linkage disequilibrium score regression analysis to confirm the strong genetic correlations between asthma, hay fever and eczema. We then integrated three distinct association analyses (metaCCA multi-trait association analysis, MAGMA genome-wide and MetaXcan transcriptome-wide gene-based tests) to identify shared risk genes based on the large-scale GWAS results in the GeneATLAS database. MetaCCA can detect pleiotropic genes associated with these three diseases jointly. MAGMA and MetaXcan were performed separately to identify candidate risk genes for each of the three diseases. We finally identified 150 shared risk genes, in which 60 genes are novel. Functional enrichment analysis revealed that the shared risk genes are enriched in inflammatory bowel disease, T cells differentiation and other related biological pathways. Our work may provide help on treatment of asthma, hay fever and eczema in clinical applications.
Assessing Urinary Metabolomics in Giant Pandas Using Chromatography/Mass Spectrometry: Pregnancy-Related Changes in the Metabolome

Giant pandas represent one of the most endangered species worldwide, and their reproductive capacity is extremely low. They have a relatively long gestational period, mainly because embryo implantation is delayed. Giant panda cubs comprise only a small proportion of the mother's body weight, making it difficult to determine whether a giant panda is pregnant. Timely determination of pregnancy contributes to the efficient breeding and management of giant pandas. Meanwhile, metabolomics studies the metabolic composition of biological samples, which can reflect metabolic functions in cells, tissues, and organisms. This work explored the urinary metabolites of giant pandas during pregnancy. A sample of 8 female pandas was selected. Differences in metabolite levels in giant panda urine samples were analyzed via ultra-high-performance liquid chromatography/mass spectrometry comparing pregnancy to anoestrus. Pattern recognition techniques, including partial least squares-discriminant analysis and orthogonal partial least squares-discriminant analysis, were used to analyze multiple parameters of the data. Compared with the results during anoestrus, multivariate statistical analysis of results obtained from the same pandas being pregnant identified 16 differential metabolites in the positive-ion mode and 43 differential metabolites in the negative-ion mode. The levels of tryptophan, choline, kynurenic acid, uric acid, indole-3-acetaldehyde, taurine, and betaine were higher in samples during pregnancy, whereas those of xanthurenic acid and S-adenosylhomocysteine were lower. Amino acid metabolism, lipid metabolism, and organic acid production differed significantly between anoestrus and pregnancy. Our results provide new insights into metabolic changes in the urine of giant pandas during pregnancy, and the differential levels of metabolites in urine provide a basis for determining pregnancy in giant pandas. Understanding these metabolic changes could be helpful for managing pregnant pandas to provide proper nutrients to their fetuses.
Organoid Models of Glioblastoma to Study Brain Tumor Stem Cells

Glioblastoma represents an aggressive form of brain cancer characterized by poor prognosis and a 5-year survival rate of only 3–7%. Despite remarkable advances in brain tumor research in the past decades, very little has changed for patients, due in part to the recurrent nature of the disease and to the lack of suitable models to perform genotype-phenotype association studies and personalized drug screening. In vitro culture of cancer cells derived from patient biopsies has been fundamental in understanding tumor biology and for testing the effect of various drugs. These cultures emphasize the role of in vitro cancer stem cells (CSCs), which fuel tumor growth and are thought to be the cause of relapse after treatment. However, it has become clear over the years that a 2D monolayer culture of these CSCs has certain disadvantages, including the lack of heterogeneous cell-cell and cell-environment interactions, which can now be partially overcome by the introduction of 3D organoid cultures. This is a novel and expanding field of research and in this review, I describe the emerging 3D models of glioblastoma. I also discuss their potential to advance our knowledge of tumor biology and CSC heterogeneity, while debating their current limitations.