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Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving data is investigated. The core challenge is framed as protecting previously acquired representations from being catastrophically forgotten. However, comparison of individual methods is nevertheless performed in isolation from the real world by monitoring accumulated benchmark test set performance. The closed world assumption remains predominant, i.e. models are evaluated on data that is guaranteed to originate from the same distribution as used for training. This poses a massive challenge as neural networks are well known to provide overconfident false predictions on unknown and corrupted instances. In this work we critically survey the literature and argue that notable lessons from open set recognition, identifying unknown examples outside of the observed set, and the adjacent field of active learning, querying data to maximize the expected performance gain, are frequently overlooked in the deep learning era. Hence, we propose a consolidated view to bridge continual learning, active learning and open set recognition in deep neural networks. Finally, the established synergies are supported empirically, showing joint improvement in alleviating catastrophic forgetting, querying data, selecting task orders, while exhibiting robust open world application.
The fundamental structure of cortical networks arises early in development prior to the onset of sensory experience. However, how endogenously generated networks respond to the onset of sensory experience, and how they form mature sensory representations with experience remains unclear. Here we examine this "nature-nurture transform" using in vivo calcium imaging in ferret visual cortex. At eye-opening, visual stimulation evokes robust patterns of cortical activity that are highly variable within and across trials, severely limiting stimulus discriminability. Initial evoked responses are distinct from spontaneous activity of the endogenous network. Visual experience drives the development of low-dimensional, reliable representations aligned with spontaneous activity. A computational model shows that alignment of novel visual inputs and recurrent cortical networks can account for the emergence of reliable visual representations.
The fundamental structure of cortical networks arises early in development prior to the onset of sensory experience. However, how endogenously generated networks respond to the onset of sensory experience, and how they form mature sensory representations with experience remains unclear. Here we examine this ‘nature-nurture transform’ using in vivo calcium imaging in ferret visual cortex. At eye-opening, visual stimulation evokes robust patterns of cortical activity that are highly variable within and across trials, severely limiting stimulus discriminability. Initial evoked responses are distinct from spontaneous activity of the endogenous network. Visual experience drives the development of low-dimensional, reliable representations aligned with spontaneous activity. A computational model shows that alignment of novel visual inputs and recurrent cortical networks can account for the emergence of reliable visual representations.
Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data.
Results We investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes.
Conclusions Our approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP.
Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to image simultaneously large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data. The core of our pipeline is a deep convolutional neural network, which was pretrained on a general-purpose image library, and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labelled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection and reaches a near human-level detection performance. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.
The first measurement of the e+e− pair production at low lepton pair transverse momentum (pT,ee) and low invariant mass (mee) in non-central Pb−Pb collisions at sNN−−−√=5.02 TeV at the LHC is presented. The dielectron production is studied with the ALICE detector at midrapidity (|ηe|<0.8) as a function of invariant mass (0.4≤mee<2.7 GeV/c2) in the 50−70% and 70−90% centrality classes for pT,ee<0.1 GeV/c, and as a function of pT,ee in three mee intervals in the most peripheral Pb−Pb collisions. Below a pT,ee of 0.1 GeV/c, a clear excess of e+e− pairs is found compared to the expectations from known hadronic sources and predictions of thermal radiation from the medium. The mee excess spectra are reproduced, within uncertainties, by different predictions of the photon−photon production of dielectrons, where the photons originate from the extremely strong electromagnetic fields generated by the highly Lorentz-contracted Pb nuclei. Lowest-order quantum electrodynamic (QED) calculations, as well as a model that takes into account the impact-parameter dependence of the average transverse momentum of the photons, also provide a good description of the pT,ee spectra. The measured ⟨p2T,ee⟩−−−−−√ of the excess pT,ee spectrum in peripheral Pb−Pb collisions is found to be comparable to the values observed previously at RHIC in a similar phase-space region.
A central concern in genetics is to identify mechanisms of transcriptional regulation. The aim is to unravel the mapping between the DNA sequence and gene expression. However, it turned out that this is extremely complex. Gene regulation is highly cell type-specific and even moderate changes in gene ex- pression can have functional consequences.
Important contributors to gene regulation are transcription factors (TFs), that are able to directly interact with the DNA. Often, a first step in understanding the effect of a TF on the gene’s regulation is to identify the genomic regions a TF binds to. Therefore, one needs to be aware of the TF’s binding preferences, which are commonly summarized in TF binding motifs. Although for many TFs the binding motif is experimentally validated, there is still a large number of TFs where no binding motif is known. There exist many tools that link TF binding motifs to TFs. We developed the method Massif that improves the performance of such tools by incorporating a domain score that uses the DNA binding domain of the studied TF as additional information.
TF binding sites are often enriched in regulatory elements (REMs) such as promoters or enhancers, where the latter can be located megabases away from its target gene. However, to understand the regulation of a gene it is crucial to know where the REMs of a gene are located. We introduced the EpiRegio webserver that holds REMs associated to target genes predicted across many cell types and tissues using STITCHIT, a previously established method. Our publicly available webserver enables to query for REMs associated to genes (gene query) and REMs overlapping genomic regions (region query). We illus- trated the usefulness of EpiRegio by pointing to a TF that occurs enriched in the REMs of differential expressed genes in circPLOD2 depleted pericytes. Further, we highlighted genes, which are affected by CRISPR-Cas induced mutations in non-coding genomic regions using EpiRegio’s region query. Non-coding genetic variants within REMs may alter gene expression by modifying TF binding sites, which can lead to various kinds of traits or diseases. To understand the underlying molecular mechanisms, one aims to evaluate the effect of such genetic variations on TF binding sites. We developed an accurate and fast statistical approach, that can assess whether a single nucleotide polymorphism (SNP) is regulatory. Further, we combined this approach with epigenetic data and additional analyses in our Sneep workflow. For instance, it enables to identify TFs whose binding preferences are affected by the analyzed SNPs, which is illustrated on eQTL datasets for different cell types. Additionally, we used our Sneep workflow to highlight cardiovascular disease genes using regulatory SNPs and REM-gene interactions.
Overall, the described results allow a better understanding of REM-gene interactions and their interplay with TFs on gene regulation.
Measurements of the production cross sections of prompt D0, D+, D∗+, D+s, Λ+c, and Ξ+c charm hadrons at midrapidity in proton−proton collisions at s√=13 TeV with the ALICE detector are presented. The D-meson cross sections as a function of transverse momentum (pT) are provided with improved precision and granularity. The ratios of pT-differential meson production cross sections based on this publication and on measurements at different rapidity and collision energy provide a constraint on gluon parton distribution functions at low values of Bjorken-x (10−5−10−4). The measurements of Λ+c (Ξ+c) baryon production extend the measured pT intervals down to pT=0(3)~GeV/c. These measurements are used to determine the charm-quark fragmentation fractions and the cc¯¯ production cross section at midrapidity (|y|<0.5) based on the sum of the cross sections of the weakly-decaying ground-state charm hadrons D0, D+, D+s, Λ+c, Ξ0c and, for the first time, Ξ+c, and of the strongly-decaying J/psi mesons. The first measurements of Ξ+c and Σ0,++c fragmentation fractions at midrapidity are also reported. A significantly larger fraction of charm quarks hadronising to baryons is found compared to e+e− and ep collisions. The cc¯¯ production cross section at midrapidity is found to be at the upper bound of state-of-the-art perturbative QCD calculations.
A newly developed observable for correlations between symmetry planes, which characterize the direction of the anisotropic emission of produced particles, is measured in Pb-Pb collisions at sNN−−−√=2.76 TeV with ALICE. This so-called Gaussian Estimator allows for the first time the study of these quantities without the influence of correlations between different flow amplitudes. The centrality dependence of various correlations between two, three and four symmetry planes is presented. The ordering of magnitude between these symmetry plane correlations is discussed and the results of the Gaussian Estimator are compared with measurements of previously used estimators. The results utilizing the new estimator lead to significantly smaller correlations than reported by studies using the Scalar Product method. Furthermore, the obtained symmetry plane correlations are compared to state-of-the-art hydrodynamic model calculations for the evolution of heavy-ion collisions. While the model predictions provide a qualitative description of the data, quantitative agreement is not always observed, particularly for correlators with significant non-linear response of the medium to initial state anisotropies of the collision system. As these results provide unique and independent information, their usage in future Bayesian analysis can further constrain our knowledge on the properties of the QCD matter produced in ultrarelativistic heavy-ion collisions.
The inclusive production of the charm-strange baryon Ω0c is measured for the first time via its hadronic decay into Ω−π+ at midrapidity (|y|<0.5) in proton-proton (pp) collisions at the centre-of-mass energy s√=13 TeV with the ALICE detector at the LHC. The transverse momentum (pT) differential cross section multiplied by the branching ratio is presented in the interval 2<pT<12 GeV/c. The pT dependence of the Ω0c-baryon production relative to the prompt D0-meson and to the prompt Ξ0c-baryon production is compared to various models that take different hadronisation mechanisms into consideration. In the measured pT interval, the ratio of the pT-integrated cross sections of Ω0c and prompt Λ+c baryons multiplied by the Ω−π+ branching ratio is found to be larger by a factor of about 20 with a significance of about 4σ when compared to e+e− collisions.