Acquiring verb classes through bottom-up semantic verb clustering

In this paper, we present the first analysis of bottom-up manual semantic clustering of verbs in three languages, English, Polish and Croatian. Verb classes including syntactic and semantic information have been shown to support many NLP tasks by allowing abstraction from individual words and thereby alleviating data sparseness. The availability of such classifications is however still non-existent or limited in most languages. While a range of automatic verb classification approaches have been proposed, high-quality resources and gold standards are needed for evaluation and to improve the performance of NLP systems. We investigate whether semantic verb classes in three different languages can be reliably obtained from native speakers without linguistics training. The analysis of inter-annotator agreement shows an encouraging degree of overlap in the classifications produced for each language individually, as well as across all three languages. Comparative examination of the resultant classifications provides interesting insights into cross-linguistic semantic commonalities and patterns of ambiguity

Semantic data set construction from human clustering and spatial arrangement

Abstract Research into representation learning models of lexical semantics usually utilizes some form of intrinsic evaluation to ensure that the learned representations reflect human semantic judgments. Lexical semantic similarity estimation is a widely used evaluation method, but efforts have typically focused on pairwise judgments of words in isolation, or are limited to specific contexts and lexical stimuli. There are limitations with these approaches that either do not provide any context for judgments, and thereby ignore ambiguity, or provide very specific sentential contexts that cannot then be used to generate a larger lexical resource. Furthermore, similarity between more than two items is not considered. We provide a full description and analysis of our recently proposed methodology for large-scale data set construction that produces a semantic classification of a large sample of verbs in the first phase, as well as multi-way similarity judgments made within the resultant semantic classes in the second phase. The methodology uses a spatial multi-arrangement approach proposed in the field of cognitive neuroscience for capturing multi-way similarity judgments of visual stimuli. We have adapted this method to handle polysemous linguistic stimuli and much larger samples than previous work. We specifically target verbs, but the method can equally be applied to other parts of speech. We perform cluster analysis on the data from the first phase and demonstrate how this might be useful in the construction of a comprehensive verb resource. We also analyze the semantic information captured by the second phase and discuss the potential of the spatially induced similarity judgments to better reflect human notions of word similarity. We demonstrate how the resultant data set can be used for fine-grained analyses and evaluation of representation learning models on the intrinsic tasks of semantic clustering and semantic similarity. In particular, we find that stronger static word embedding methods still outperform lexical representations emerging from more recent pre-training methods, both on word-level similarity and clustering. Moreover, thanks to the data set’s vast coverage, we are able to compare the benefits of specializing vector representations for a particular type of external knowledge by evaluating FrameNet- and VerbNet-retrofitted models on specific semantic domains such as “Heat” or “Motion.”</jats:p

Investigating the cross-lingual translatability of VerbNet-style classification

VerbNet—the most extensive online verb lexicon currently available for English—has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper. © 2017 The Author(s)</p

Denoising Two-Photon Calcium Imaging Data

Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models.National Institutes of Health (U.S.) (DP1 OD003646-01)National Institutes of Health (U.S.) (R01EB006385-01)National Institutes of Health (U.S.) (EY07023)National Institutes of Health (U.S.) (EY017098

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Multisensory body representation in autoimmune diseases

Body representation has been linked to the processing and integration of multisensory signals. An outstanding example of the pivotal role played by multisensory mechanisms in body representation is the Rubber Hand Illusion (RHI). In this paradigm, multisensory stimulation induces a sense of ownership over a fake limb. Previous work has shown high interindividual differences in the susceptibility to the RHI. The origin of this variability remains largely unknown. Given the tight and bidirectional communication between the brain and the immune system, we predicted that the origin of this variability could be traced, in part, to the immune system's functioning, which is altered by several clinical conditions, including Coeliac Disease (CD). Consistent with this prediction, we found that the Rubber Hand Illusion is stronger in CD patients as compared to healthy controls. We propose a biochemical mechanism accounting for the dependency of multisensory body representation upon the Immune system. Our finding has direct implications for a range of neurological, psychiatric and immunological conditions where alterations of multisensory integration, body representation and dysfunction of the immune system co-exist

Disconnection between the default mode network and medial temporal lobes in post-traumatic amnesia

Post-traumatic amnesia is very common immediately after traumatic brain injury. It is characterised by a confused, agitated state and a pronounced inability to encode new memories and sustain attention. Clinically, post-traumatic amnesia is an important predictor of functional outcome. However, despite its prevalence and functional importance, the pathophysiology of post-traumatic amnesia is not understood. Memory processing relies on limbic structures such as the hippocampus, parahippocampus and parts of the cingulate cortex. These structures are connected within an intrinsic connectivity network, the Default Mode Network. Interactions within the Default Mode Network can be assessed using resting state functional magnetic resonance imaging, which can be acquired in confused patients unable to perform tasks in the scanner. Here we used this approach to test the hypothesis that the mnemonic symptoms of post-traumatic amnesia are caused by functional disconnection within the Default Mode Network. We assessed whether the hippocampus and parahippocampus showed evidence of transient disconnection from cortical brain regions involved in memory processing. 19 traumatic brain injury patients were classified into post-traumatic amnesia and traumatic brain injury control groups, based on their performance on a paired associates learning task. Cognitive function was also assessed with a detailed neuropsychological test battery. Functional interactions between brain regions were investigated using resting-state functional magnetic resonance imaging. Together with impairments in associative memory patients in post-traumatic amnesia demonstrated impairments in information processing speed and spatial working memory. Patients in post-traumatic amnesia showed abnormal functional connectivity between the parahippocampal gyrus and posterior cingulate cortex. The strength of this functional connection correlated with both associative memory and information processing speed and normalised when these functions improved. We have previously shown abnormally high posterior cingulate cortex connectivity in the chronic phase after traumatic brain injury, and this abnormality was also observed in patients with post-traumatic amnesia. Patients in post-traumatic amnesia showed evidence of widespread traumatic axonal injury measured using diffusion magnetic resonance imaging. This change was more marked within the cingulum bundle, the tract connecting the parahippocampal gyrus to the posterior cingulate cortex. These findings provide novel insights into the pathophysiology of post-traumatic amnesia and evidence that memory impairment acutely after traumatic brain injury results from altered parahippocampal functional connectivity, perhaps secondary to the effects of axonal injury on white matter tracts connecting limbic structures involved in memory processing

Calmodulin Activation by Calcium Transients in the Postsynaptic Density of Dendritic Spines

The entry of calcium into dendritic spines can trigger a sequence of biochemical reactions that begins with the activation of calmodulin (CaM) and ends with long-term changes to synaptic strengths. The degree of activation of CaM can depend on highly local elevations in the concentration of calcium and the duration of transient increases in calcium concentration. Accurate measurement of these local changes in calcium is difficult because the spaces are so small and the numbers of molecules are so low. We have therefore developed a Monte Carlo model of intracellular calcium dynamics within the spine that included calcium binding proteins, calcium transporters and ion channels activated by voltage and glutamate binding. The model reproduced optical recordings using calcium indicator dyes and showed that without the dye the free intracellular calcium concentration transient was much higher than predicted from the fluorescent signal. Excitatory postsynaptic potentials induced large, long-lasting calcium gradients across the postsynaptic density, which activated CaM. When glutamate was released at the synapse 10 ms before an action potential occurred, simulating activity patterns that strengthen hippocampal synapses, the calcium gradient and activation of CaM in the postsynaptic density were much greater than when the order was reversed, a condition that decreases synaptic strengths, suggesting a possible mechanism underlying the induction of long-term changes in synaptic strength. The spatial and temporal mechanisms for selectivity in CaM activation demonstrated here could be used in other signaling pathways

Cortical Layer 1 and Layer 2/3 Astrocytes Exhibit Distinct Calcium Dynamics In Vivo

Cumulative evidence supports bidirectional interactions between astrocytes and neurons, suggesting glial involvement of neuronal information processing in the brain. Cytosolic calcium (Ca2+) concentration is important for astrocytes as Ca2+ surges co-occur with gliotransmission and neurotransmitter reception. Cerebral cortex is organized in layers which are characterized by distinct cytoarchitecture. We asked if astrocyte-dominant layer 1 (L1) of the somatosensory cortex was different from layer 2/3 (L2/3) in spontaneous astrocytic Ca2+ activity and if it was influenced by background neural activity. Using a two-photon laser scanning microscope, we compared spontaneous Ca2+ activity of astrocytic somata and processes in L1 and L2/3 of anesthetized mature rat somatosensory cortex. We also assessed the contribution of background neural activity to the spontaneous astrocytic Ca2+ dynamics by investigating two distinct EEG states (“synchronized” vs. “de-synchronized” states). We found that astrocytes in L1 had nearly twice higher Ca2+ activity than L2/3. Furthermore, Ca2+ fluctuations of processes within an astrocyte were independent in L1 while those in L2/3 were synchronous. Pharmacological blockades of metabotropic receptors for glutamate, ATP, and acetylcholine, as well as suppression of action potentials did not have a significant effect on the spontaneous somatic Ca2+ activity. These results suggest that spontaneous astrocytic Ca2+ surges occurred in large part intrinsically, rather than neural activity-driven. Our findings propose a new functional segregation of layer 1 and 2/3 that is defined by autonomous astrocytic activity

mRNA accumulation in the Cajal bodies of the diplotene larch microsporocyte

In microsporocytes of the European larch, we demonstrated the presence of several mRNAs in spherical nuclear bodies. In the nuclei of microsporocytes, we observed up to 12 bodies, ranging from 0.5 to 6 μm in diameter, during the prophase of the first meiotic division. Our previous studies revealed the presence of polyadenylated RNA (poly(A) RNA) in these bodies, but did not confirm the presence of nascent transcripts or splicing factors of the SR family. The lack of these molecules precludes the bodies from being the sites of synthesis and early maturation of primary transcripts (Kołowerzo et al., Protoplasma 236:13–19, 2009). However, the bodies serve as sites for the accumulation of splicing machinery, including the Sm proteins and small nuclear RNAs. Characteristic ultrastructures and the molecular composition of the nuclear bodies, which contain poly(A) RNA, are indicative of Cajal bodies (CBs). Here, we demonstrated the presence of several housekeeping gene transcripts—α-tubulin, pectin methylesterase, peroxidase and catalase, ATPase, and inositol-3-phosphate synthase—in CBs. Additionally, we observed transcripts of the RNA polymerase II subunits RPB2 and RPB10 RNA pol II and the core spliceosome proteins mRNA SmD1, SmD2, and SmE. The co-localization of nascent transcripts and mRNAs indicates that mRNA accumulation/storage, particularly in CBs, occurs in the nucleus of microsporocytes