AMR Dependency Parsing with a Typed Semantic Algebra

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see https://acl2018.org/programme/papers/

Sex influences clinical phenotype in frontotemporal dementia

INTRODUCTION: Frontotemporal dementia (FTD) encompasses a wide spectrum of genetic, clinical, and histological findings. Sex is emerging as a potential biological variable influencing FTD heterogeneity; however, only a few studies explored this issue with nonconclusive results. OBJECTIVE: To estimate the role of sex in a single-center large cohort of FTD patients. METHODS: Five hundred thirty-one FTD patients were consecutively enrolled. Demographic, clinical, and neuropsychological features, survival rate, and serum neurofilament light (NfL) concentration were determined and compared between sex. RESULTS: The behavioral variant of FTD was more common in men, whereas primary progressive aphasia was overrepresented in women (p < 0.001). While global cognitive impairment was comparable, females had a more severe cognitive impairment, namely in Trail Making Test parts A and B (p = 0.003), semantic fluency (p = 0.03), Short Story Recall Test (p = 0.003), and the copy of Rey Complex Figure (p = 0.005). On the other hand, men exhibited more personality/behavioral symptoms (Frontal Behavior Inventory [FBI] AB, p = 0.003), displaying higher scores in positive FBI subscales (FBI B, p < 0.001). In particular, apathy (p = 0.02), irritability (p = 0.006), poor judgment (p = 0.033), aggressivity (p = 0.008), and hypersexuality (p = 0.006) were more common in men, after correction for disease severity. NfL concentration and survival were not statistically different between men and women (p = 0.167 and p = 0.645, respectively). DISCUSSION: The present study demonstrated that sex is a potential factor in determining FTD phenotype, while it does not influence survival. Although the pathophysiological contribution of sex in neurodegeneration is not well characterized yet, our findings highlight its role as deserving biological variable in FTD

Graph theory applied to neuroimaging data reveals key functional connectivity alterations in brain of behavioral variant Frontotemporal Dementia subjects

Brain functional architecture and anatomical structure have been intensively studied to generate efficient models of its complex mechanisms. Functional alterations and cognitive impairments are the most investigated aspects in the recent clinical research as distinctive traits of neurodegeneration. Although specific behaviours are clearly associated to neurodegeneration, information flow breakdown within the brain functional network, responsible to deeply affect cognitive skills, remains not completely understood. Behavioural variant Frontotemporal Dementia (bvFTD) is the most common type of Frontotemporal degeneration, marked by behavioural disturbances, social instabilities and impairment of executive functions. Mathematical modelling offers effective tools to inspect deviations from physiological cognitive functions and connectivity alterations. As a popular recent methodology, graph theoretical approaches applied to imaging data expanded our knowledge of neurodegenerative disorders, although the need for unbiased metrics is still an open issue. In this thesis, we propose an integrated analysis of functional features among brain areas in bvFTD patients, to assess global connectivity and topological network alterations respect to the healthy condition, using a minimum spanning tree (MST) based-model to resting state functional MRI (rs-fMRI) data. Contrary to several graph theoretical approaches, dependent to arbitrary criteria (e.g., correlation thresholds, network density or a priori distribution), MST represents an unambiguous modelling solution, ensuring full reproducibility and robustness in different conditions. Our MSTs were obtained from wavelet correlation matrices derived from mean time series intensities, extracted from 116 regions of interest (ROIs) of 41 bvFTD patients and 39 healthy controls (HC), which underwent rs-fMRI. The resulting graphs were tested for global connectivity and topological differences between the two groups, by applying a Wilcoxon rank sum test with a significance level at 0.05 (nonparametric median difference estimates with 95% confidence interval). The same test was applied for methodological comparison between MST and other common graph theory methods. After methodological comparisons, our MST model achieved the best bvFTD/HC separation performances, without a priori assumptions. Direct MST comparison between bvFTD and healty controls revealed key brain functional architecture differences. Diseased subjects showed a linear-shape network configuration tendency, with high distance between nodes, low centrality parameter values, and a low exchange information capacity (i.e., low network integration) in MST parameters. Moreover, edge-level and node-level features (i.e., superhighways, and node degree and betweenness centrality) indicated a more complex scenario, showing some of the key bvFTD dysfunctions observed in large scale resting-state functional networks (default-mode (DMN), salience (SN), and executive (EN) networks), suggesting an underlying involvement of the limbic system in the observed functional deterioration. Functional isolation has been observed as a generalized process affecting the entire bvFTD network, showing brain macro-regions isolation, with homogeneous functional distribution of brain areas, longer distances between hubs, and longer within-lobe superhighways. Conversely, the HC network showed marked functional integration, where superhighways serve as shortcuts to connect areas from different brain macro-regions. The combination of this theoretical model with rs-fMRI data constitutes an effective method to generate a clear picture of the functional divergence between bvFTD and HCs, providing possible insights on the effects of frontotemporal neurodegeneration and compensatory mechanisms underlying characteristic bvFTD cognitive, social, and executive impairments

Assessment of Pre-Trained Models Across Languages and Grammars

We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures. We aim to recover constituent and dependency structures by casting parsing as sequence labeling. To do so, we select a few LLMs and study them on 13 diverse UD treebanks for dependency parsing and 10 treebanks for constituent parsing. Our results show that: (i) the framework is consistent across encodings, (ii) pre-trained word vectors do not favor constituency representations of syntax over dependencies, (iii) sub-word tokenization is needed to represent syntax, in contrast to character-based models, and (iv) occurrence of a language in the pretraining data is more important than the amount of task data when recovering syntax from the word vectors.Comment: Accepted at IJCNLP-AACL 202

Primary Progressive Aphasia: Toward a Pathophysiological Synthesis

PURPOSE OF REVIEW: The term primary progressive aphasia (PPA) refers to a diverse group of dementias that present with prominent and early problems with speech and language. They present considerable challenges to clinicians and researchers. RECENT FINDINGS: Here, we review critical issues around diagnosis of the three major PPA variants (semantic variant PPA, nonfluent/agrammatic variant PPA, logopenic variant PPA), as well as considering 'fragmentary' syndromes. We next consider issues around assessing disease stage, before discussing physiological phenotyping of proteinopathies across the PPA spectrum. We also review evidence for core central auditory impairments in PPA, outline critical challenges associated with treatment, discuss pathophysiological features of each major PPA variant, and conclude with thoughts on key challenges that remain to be addressed. New findings elucidating the pathophysiology of PPA represent a major step forward in our understanding of these diseases, with implications for diagnosis, care, management, and therapies

Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction

Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets predominantly feature zero values, indicating no incidents, with sporadic high-risk values for severe incidents. Notably, a majority of current models, especially deep learning methods, focus solely on estimating risk values, overlooking the uncertainties arising from the inherently unpredictable nature of incidents. To tackle this challenge, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks (STZITD-GNNs). Our model merges the reliability of traditional statistical models with the flexibility of graph neural networks, aiming to precisely quantify uncertainties associated with road-level traffic incident risks. This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity. Furthermore, a zero-inflated component helps to identify the non-incident risk scenarios. As a result, the STZITD-GNNs effectively capture the dataset's skewed distribution, placing emphasis on infrequent but impactful severe incidents. Empirical tests using real-world traffic data from London, UK, demonstrate that our model excels beyond current benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also in its adeptness at curtailing uncertainties, delivering robust predictions over short (7 days) and extended (14 days) timeframes

The Behavioural Phenotype of pThr175-Tau Expression in the Hippocampus of Female Adult Sprague Dawley Rats

Amyotrophic lateral sclerosis with cognitive impairment (ALSci) can be characterized by pathological inclusions of microtubule associated protein tau (tau) uniquely phosphorylated at Thr175 (pThr175-tau). The purpose of this study was to characterize the behavioural consequences of expressing a pseudophosphorylated tau mimic of pThr175-tau (Thr175Asp-tau) in rat hippocampus. Expression was hypothesized to lead to pathological tau fibril formation resulting in cognitive and behavioural deficits. Expression was accomplished in female Sprague Dawley rats through stereotactic inoculations of recombinant adeno-associated virus (rAAV9) vector with human tau gene. Pathological tau fibrillary structures were identified, but behavioural testing up to 12 months post-surgery revealed no deficits in Thr175Asp-tau group when compared with the controls. Control inoculums included: wt-human tau, phosphorylation inhibition (Thr175Ala-tau), and green fluorescent protein. There were age-related behavioural changes across testing time points. This study serves an important step towards the development of an animal model for ALS with cognitive syndromes, which is essential for understanding disease progression

Anomalies in language as a biomarker for schizophrenia

PURPOSE OF REVIEW: After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established. This article reviews current advances in evaluating the use of language as a diagnostic or prognostic tool in schizophrenia. RECENT FINDINGS: The development of computational linguistic tools to quantify language disturbances is rapidly gaining ground in the field of schizophrenia research. Current applications are the use of semantic space models and acoustic analyses focused on phonetic markers. These features are used in machine learning models to distinguish patients with schizophrenia from healthy controls or to predict conversion to psychosis in high-risk groups, reaching accuracy scores (generally ranging from 80 to 90%) that exceed clinical raters. Other potential applications for a language biomarker in schizophrenia are monitoring of side effects, differential diagnostics and relapse prevention. SUMMARY: Language disturbances are a key feature of schizophrenia. Although in its early stages, the emerging field of research focused on computational linguistics suggests an important role for language analyses in the diagnosis and prognosis of schizophrenia. Spoken language as a biomarker for schizophrenia has important advantages because it can be objectively and reproducibly quantified. Furthermore, language analyses are low-cost, time efficient and noninvasive in nature