Miracle-dz/UniFAR-FTUs huggingface dataset:
UDante: First Steps Towards the Universal Dependencies Treebank of Dante's Latin Works

English. This paper1 presents the early stages of the development of a new treebank containing all of Dante Alighieri’s Latin works. In particular, it describes the conversion of the original TEI-XML files to CoNLL-U, the creation of a gold standard, the process of training four annotators and the evaluation of the syntactic annotation in terms of inter-annotator agreement and LA, UAS and LAS. The aim is to release a new resource, in view of the celebrations for the 700th anniversary of Dante’s death, which can support the development of the Vocabolario Dantesco.
The CREENDER Tool for Creating Multimodal Datasets of Images and Comments

English. While text-only datasets are widely produced and used for research purposes, limitations set by image-based social media platforms like Instagram make it difficult for researchers to experiment with multimodal data. We therefore developed CREENDER, an annotation tool to create multimodal datasets with images associated with semantic tags and comments, which we make freely available under Apache 2.0 license. The software has been extensively tested with school classes, allowing us to improve the tool and add useful features not planned in the first development phase.1 Italiano. Mentre i dataset testuali sono ampiamenti creati e usati per scopi di ricerca, le limitazioni imposte dai social media basati sulle immagini (come Instagram) rendono difficile per i ricercatori sperimentare con dati multimodali. Abbiamo quindi sviluppato CREENDER, un tool di annotazione per la creazione di dataset multimodali in cui immagini vengono associate a etichette semantiche e commenti, e che abbiamo reso disponibile gratuitamente con la licenza Apache 2.0. Il software è stato testato in un laboratorio con alcune classi scolastiche, permettendoci di ottimizzare alcune procedure e di aggiungere feature non previste nella
A Deep Learning Model for the Analysis of Medical Reports in ICD-10 Clinical Coding Task

English. The practice of assigning a uniquely identifiable and easily traceable code to pathology from medical diagnoses is an added value to the current modality of archiving health data collected to build the clinical history of each of us. Unfortunately, the enormous amount of possible pathologies and medical conditions has led to the realization of extremely wide international codifications that are difficult to consult even for a human being. This difficulty makes the practice of annotation of diagnoses with ICD-10 codes very cumbersome and rarely performed. In order to support this operation, a classification model was proposed, able to analyze medical diagnoses written in natural language and automatically assign one or more international reference codes. The model has been evaluated on a dataset released in the Spanish language for the eHealth challenge (CodiEsp) of the international conference CLEF 2020, but it could be extended to any language with latin characters. We proposed a model based on a two-step classification process based on BERT and BiLSTM. Although still far from an accuracy sufficient to do without a licensed physician opinion, the results obtained show the feasibility of the task and are a starting point for future studies in this direction. Copyright c © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Italian. La pratica di assegnare un codice univocamente identificabile e facilmente riconducibile ad una patologia a partire da diagnosi mediche e un valore aggiunto alla attuale modalità di archiviazione dei dati sanitari raccolti per costruire la storia clinica di ciascuno di noi. Purtroppo però, lenorme numero di possibili patologie e condizioni mediche ha portato alla realizzazione di codifiche internazionali estremamente ampie e di difficile consultazione anche per un essere umano. Tale difficolt rende la pratica di annotazione delle diagnosi con i codici ICD-10 molto complessa e raramente svolta. Col fine di supportare tale operazione si è proposto un modello di classificazione, in grado di analizzare le diagnosi mediche scritte in linguaggio naturale ed assegnarle automaticamente uno o più codici internazionali di riferimento. Il modello è stato valutato su un dataset rilasciato in lingua Spagnola per la challenge (CodiEsp) di eHealth della conferenza internazionale CLEF 2020 ma è di semplice estensione su qualsiasi lingua con caratteri latini. Abbiamo proposto un modello basato su due passi di classificazione e basati sullutilizzo di BERT e delle BiLSTM. I risultati ottenuti, seppur ancora lontani da una accuratezza sufficiente per far a meno di un parere di un medico esperto, mostrano la fattibilità del task e si pongono come punto di partenza per futuri studi in tale direzione.
Plasticity of fibroblast transcriptional response to physical and biochemical cues revealed by dynamic network analysis

Data on human skin fibroblast transcriptional responses to external cues were used to reconstruct dynamic gene regulatory networks. The goal of the reconstruction was to determine dynamic network interactions (quantitative predictive relationships of mutual regulatory influences of and on transcription factor expression) from time course data on 56 transcript expression levels obtained following different external cues. The inherently under-determined nature of this problem was addressed in part by excluding putative regulatory motifs that did not appear to be functional in multiple independent experiments from different independent external perturbations. Data were obtained from a previously published experiment in which the 56 transcripts were assayed by bioluminescence in live cells cultured on substrates of varying levels of stiffness and exposed to different levels of arginylglycylaspartic acid (RGD) peptide. The inferred dynamical networks were validated via comparison of predictions to a priori known interactions from gene databases. We discovered that exposures to different substrate stiffnesses and to RGD stimulate responses that are mediated through GATA4, SMAD3/4, ETS-1, and STAT5 and other genes, which can initiate hypertrophic, fibrotic, and inflammatory responses. The developed dynamical system identification method for discovering new mechanotransduction pathways is applicable to the identification of gene regulatory networks in numerous emerging applications where time-series data on multiple state variables and from multiple external perturbations are available.
TAG-it @ EVALITA2020: Overview of the Topic, Age, and Gender Prediction Task for Italian

The Topic, Age, and Gender (TAG-it) prediction task in Italian was organised in the context of EVALITA 2020, using forum posts as textual evidence for profiling their authors. The task was articulated in two separate subtasks: one where all three dimensions (topic, gender, age) were to be predicted at once; the other where training and test sets were drawn from different forum topics and gender or age had to be predicted separately. Teams tackled the problems both with classical machine learning methods as well as neural models. Using the training-data to fine-tuning a BERT-based monolingual model for Italian proved eventually as the most successful strategy in both subtasks. We observe that topic and gender are easier to predict than age. The higher results for gender obtained in this shared task with respect to a comparable challenge at EVALITA 2018 might be due to the larger evidence per author provided at this edition, as well as to the availability of pre-trained large models for fine-tuning, which have shown improvement on very many NLP tasks.
UNIMIB @ DIACR-Ita: Aligning Distributional Embeddings with a Compass for Semantic Change Detection in the Italian Language (short paper)

In this paper, we present our results related to the EVALITA 2020 challenge, DIACR-Ita, for semantic change detection for the Italian language. Our approach is based on measuring the semantic distance across time-specific word vectors generated with Compass-aligned Distributional Embeddings (CADE). We first generate temporal embeddings with CADE, a strategy to align word embeddings that are specific for each time period; the quality of this alignment is the main asset of our proposal. We then measure the semantic shift of each word, combining two different semantic shift measures. Eventually, we classify a word meaning as changed or not changed by defining a threshold over the semantic distance across time.
Natural Language Generation in Dialogue Systems for Customer Care

English. In this paper we discuss the role of natural language generation (NLG) in modern dialogue systems (DSs). In particular, we will study the role that a linguistically sound NLG architecture can have in a DS. Using real examples from a new corpus of dialogue in customer-care domain, we will study how the non-linguistic contextual data can be exploited by using NLG.
Stiffness of vascular smooth muscle cells from monkeys studied using atomic force microscopy

Vascular smooth muscle cells (VSMC) are the main cellular components of blood vessel walls and bear external mechanical forces caused by blood flow and pressure. In this report, we have verified the following hypothesis through experiments: The increase in VSMC stiffness may be mainly due to changes in vascular stiffness due to aging. Although aging enhances the stiffness and adhesion of VSMC, there is no significant difference in apparent elastic modulus and adhesion between the VSMC obtained by male and female. The effect of aging through the ECM-integrin-cytoskeleton axis is related to increased VSMC stiffness and matrix adhesion rather than gender.
Paucity of adaptive selection in PmrAB two-component system may resist emergence of colistin resistance in Acinetobacter baumannii

Investigations on the selection pressure acting on point mutations in PmrAB two-component system may provide insights into the future of colistin therapy in Acinetobacter baumannii, since mutations in pmrAB are implicated in colistin resistance. We performed adaptive selection analysis of pmrAB and compared with the available data on colistin resistant strains. We analysed PmrAB sequences in 3113 draft genomes of A. baumannii obtained from RefSeq database. Adaptive selection analysis was performed by two widely used programs namely, HyPhy and PAML. In addition, to examine the reliability of the approach, the same analysis was performed on gyrA of Escherichia coli and Salmonella enterica, since adaptive mutations on gyrA confer quinolone resistance. Mutations that had caused colistin resistance were found to be neither adaptive nor polymorphic, rather they occur at sites that are either under neutral or purifying selection. Strong negative evolutionary selection pressure is also observed at sites throughout both PmrA and PmrB. Sites with high levels of polymorphisms in PmrAB were found to be under neutral selection. Notably, there was no sign of positive selection. Some of them are rather deleterious. These conditions might be maintaining the incidence of colistin resistance in A. baumannii under check. Therefore, in the context of colistin resistance, natural selection plays only a minor role and we assert that in future, A. baumannii may not be able to sustain and successfully disseminate colistin resistance. Therefore, at present the concerns raised about continuing the usage of colistin for the treatment against A. baumannii infections appears to be unnecessary.
Loss of p32 triggers energy deficiency and impairs goblet cell differentiation in ulcerative colitis

Cell differentiation in the colonic crypt is driven by a metabolic switch from glycolysis to mitochondrial oxidation. Mitochondrial and goblet cell (GC) dysfunction have been attributed to the pathology of ulcerative colitis (UC). We hypothesized that p32/gC1qR/HABP1, which critically maintains oxidative phosphorylation, is involved in GC differentiation and hence in the pathogenesis of UC. In UC patients in remission, colonic GC differentiation was significantly decreased compared to controls in a p32-dependent manner. Plasma/serum lactate and colonic pAMPK level were increased, pointing at high glycolytic activity and energy deficiency. Consistently, p32 silencing in mucus-secreting HT29-MTX cells abolished butyrate-induced differentiation and induced a shift towards glycolysis. In mitochondrial respiratory chain complex V-deficient mice, colonic p32 expression correlated with loss of differentiated GCs, resulting in a thinner mucus layer. Conversely, feeding mice an isocaloric glucose-free, high-protein diet increased mucosal energy supply that promoted colonic p32 level, GC differentiation and mucus production. We here describe a new molecular mechanism linking mucosal energy deficiency in UC to impaired p32-dependent GC differentiation that may be therapeutically prevented by nutritional intervention.
SSNCSE-NLP @ EVALITA2020: Textual and Contextual Stance Detection from Tweets Using Machine Learning Approach (short paper)

Opinions expressed via online social media platforms can be used to analyse the stand taken by the public about any event or topic. Recognizing the stand taken is the stance detection, in this paper an automatic stance detection approach is proposed that uses both deep learning based feature extraction and hand crafted feature extraction. BERT is used as a feature extraction scheme along with stylistic, structural, contextual and community based features extracted from tweets to build a machine learning based model. This work has used multilayer perceptron to detect the stances as favour, against and neutral tweets. The dataset used is provided by SardiStance task with tweets in Italian about Sardines movement. Several variants of models were built with different feature combinations and are compared against the baseline model provided by the task organisers. The models with BERT and the same combined with other contextual features proven to be the best per-forming models that outperform the baseline model performance.
Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model

We propose a new Bayesian parametric model for integrative, unsupervised clustering across data sources. Such approaches are important in disease subtyping. In our method, we cluster samples in relation to each data source, distinguishing it from methods like Cluster of Cluster Assignments and iCluster, but cluster labels have across-dataset meaning, allowing cluster information to be shared between data sources. We propose a two-way latent structure of cluster assignment variables, within each dataset and across datasets (per sample), which allows borrowing strength across data sources. Importantly, a common scaling across data sources is not required. Inference is obtained by a Gibbs Sampler, which we improve to better cope with sparsity of unoccupied clusters and speed of convergence, and ﬁt models with Gaussian and more general densities which inﬂuences across-dataset cluster label sharing. Uniquely, our formulation makes no nestedness assumptions of samples across data sources. We apply our model to a Norwegian breast cancer cohort of ductal carcinoma in-situ and invasive tumors, comprised of somatic copy-number alteration, methylation and expression datasets. We ﬁnd enrichment in the Her2 subtype and ductal carcinoma among those observations exhibiting greater cluster correspondence across expression and CNA data.
Topological Insights into Mutant Huntingtin Exon 1 and PolyQ Aggregates by Cryo-Electron Tomography

Huntington disease (HD) is a neurodegenerative trinucleotide repeat disorder caused by an expanded poly-glutamine (polyQ) tract in the mutant huntingtin (mHTT) protein. The formation and topology of filamentous mHTT inclusions in the brain (hallmarks of HD implicated in neurotoxicity) remain elusive. Using cryo-electron tomography and subtomogram averaging, here we show that mHTT exon 1 and polyQ-only aggregates in vitro are structurally heterogenous and filamentous, similar to prior observations with other methods. Yet, we observed some filaments in both types of aggregates under ∼2 nm in width, thinner than previously reported, while other regions form large sheets. In addition, our data show a prevalent subpopulation of filaments exhibiting a lumpy, slab-shaped morphology in both aggregates, supportive of the “polyQ core” model. This provides a basis for future cryoET studies of various aggregated mHTT and polyQ constructs to improve their structure-based modeling and their identification in cells without fusion tags.
A Quantitative Measurement to Describe the Relative Proximity of Fluorescent Biomarkers

Determining the relationship between biomarkers via fluorescence microscopy is a key step in the characterisation of cellular phenotypes. We define a simple distance-based measurement termed a perimeter distance mean (PDmean) which quantifies the relative proximity of objects in one fluorescent channel to objects in a second fluorescent channel in 2D or 3D microscopy datasets. PDmean measurements were able to accurately identify known changes in colocalisation in computer-generated and real-world microscopy datasets. We argue that this approach provides substantial advantages over currently used distance-based colocalisation analysis methods. We also introduce PyBioProx, an extensible open-source Python module and graphical user interface that produces PDmean measurements.
Assessment of coral restoration’s contribution to reef recovery using machine learning

Coral restoration emerged globally as a form of life support for coral reefs, awaiting urgent mitigation of anthropogenic pressure. Yet its efficiency is difficult to assess, as ambitious transplantation programs handle hundreds of thousands of fragments, with survival rates inherently time-intensive to monitor. Due to limited available data, the influence of most environmental and methodological factors is still unknown. We therefore propose a new method which leverages machine learning to track each colony’s individual health and growth on a large sample size. This is the first time artificial intelligence techniques were used to monitor coral at a colony scale, providing an unprecedented amount of data on coral health and growth. Here we show the influence of genus, depth and initial fragment size, alongside providing an outlook on coral restoration’s efficiency. We show that among 77,574 fragments, individual survival rate was 31% after 2 years (21% after 4 years), which is much lower than most reported results. In the absence of significant anthropogenic pressure, we showed that there was a depth limit below which Pocillopora fragments outperformed Acropora fragments, while the opposite was true past this threshold. During the mid-2019 heatwave, our research indicates that Pocillopora fragments were 37% more likely to survive than Acropora fragments. Overall, the total amount of live coral steadily increased over time, by more than 3,700 liters a year, as growth compensated for mortality. This supports the use of targeted coral restoration to accelerate reef recovery after mass bleaching events.
Patterns, design claims, and assertions

This paper compares three structured formats for capturing and sharing design knowledge that have evolved in different disciplines: patterns, design claims, and assertions. It identifies the elements of each format, maps between similar elements, and examines differences between the formats. The primary audience of this paper include people interested in the representation of design knowledge and pattern authors who want to learn about other formats.
Molecular and physiological responses predict acclimation limits in juvenile brook trout (Salvelinus fontinalis)

Brook trout (Salvelinus fontinalis) populations are at risk of exposure to high water temperatures in the species’ native range in eastern North America. We quantified the physiological and molecular responses of juvenile brook trout to six acclimation temperatures that span the thermal distribution of the species (5, 10, 15, 20, 23, and 25°C). Using quantitative PCR (qPCR), we measured the mRNA transcript abundance of temperature-induced cellular stress genes to identify a potential sub-lethal temperature threshold for brook trout between 20–23°C. Brook trout exhibited an upregulation of stress-related genes (heat shock protein 90-beta; heat shock cognate 71 kDa protein; glutathione peroxidase 1) and a downregulation of transcription factors and osmoregulation-related genes (Na+/K+/2Cl− co-transporter-1-a; nuclear protein 1) at temperatures ≥20°C. We also used respirometry to assess the effects of the acclimation temperatures on oxygen consumption. Standard metabolic rate results indicated that energy expenditure was higher at temperatures ≥20°C. We then examined the effects of acclimation temperature on metabolic rate and blood plasma parameters in fish exposed to an acute exhaustive exercise and air exposure stress. Fish acclimated to temperatures ≥20°C exhibited elevated levels of plasma cortisol, muscle lactate, and plasma glucose after exposure to the acute stressors. After 24 h of recovery, fish showed longer metabolic recovery times at 15 and 20°C and cortisol levels remaining elevated at temperatures ≥20°C. Our findings suggest that brook trout may have a limited ability to acclimate to temperatures >20°C and increases in temperatures beyond 20°C may impact brook trout populations.