Investigating the role of Copy Number Variants in Specific Language Impairment and identification of new candidate genes

Specific language impairment (SLI) is a complex neurodevelopmental disorder defined as an unexpected failure to develop normal language abilities for no obvious reason. Copy number variants (CNVs) are an important source of variation in the susceptibility to neuropsychiatric disorders. Therefore, a CNV study within SLI families was performed to investigate the role of structural variants in SLI. Among the identified CNVs, we focused on CNVs on chromosome 15q11-q13, recurrently observed in neuropsychiatric conditions, and a homozygous exonic microdeletion in ZNF277. Since this microdeletion falls within the AUTS1 locus, a region linked to autism spectrum disorders (ASD), we investigated a potential role of ZNF277 in SLI and ASD. Frequency data and expression analysis of the ZNF277 microdeletion suggested that this variant may contribute to the risk of language impairments in a complex manner, that is independent of the autism risk previously described in this region. Moreover, we identified an affected individual with a dihydropyrimidine dehydrogenase (DPD) deficiency, caused by compound heterozygosity of two deleterious variants in the gene DPYD. Since DPYD represents a good candidate gene for both SLI and ASD, we investigated its involvement in the susceptibility to these two disorders, focusing on the splicing variant rs3918290, the most common mutation in the DPD deficiency. We observed a higher frequency of rs3918290 in SLI cases (1.2%), compared to controls (~0.6%), while no difference was observed in a large ASD cohort. DPYD mutation screening in 4 SLI and 7 ASD families carrying the splicing variant identified six known missense changes and a novel variant in the promoter region. These data suggest that the combined effect of the mutations identified in affected individuals may lead to an altered DPD activity and that rare variants in DPYD might contribute to a minority of cases, in conjunction with other genetic or non-genetic factors.

Language, action, and sensoriality: a multimodal analysis of interactions in inclusive sport climbing with visually impaired athletes

In sport climbing, athletes with vision impairments are constantly accompanied by their guides – usually trainers – both during the preparatory inspection of the routes and whilst climbing. Trainers are, so to speak, the climbers’ eyes, in the sense that they systematically put their vision in the service of the climbers’ mobility and sporting performance. The synergy between trainers and athletes is based on peculiar, strictly multimodal interactive practices that are focused on the body and on its constantly evolving sensory engagement with the materiality of routes. In this context, sensory perception and embodied actions required to plan and execute the climb are configured as genuinely interactive accomplishments. Drawing on the theoretical framework of Embodied and Situated Cognition and on the methodology of Conversation Analysis, this thesis engages in the multimodal analysis of trainer-athlete interactions in paraclimbing. The analysis is based on a corpus of video recorded climbing sessions. The major findings of the study can be summarized as follows. 1) Intercorporeality is key to interactions between trainers and athletes with visual impairments. The participants orient to perceiving the climbing space and acting in it as a ‘We’. 2) The grammar, lexicon, prosody, and timing of the trainers’ instructions are finely tuned to the ongoing corporeal experience of the climbers. 3) Climbers with visual impairments build their actions by using sensory resources that are provided by their trainers. This result is of particular importance as it shows that resources and constraints for action are in a fundamental way constituted in interaction with Others and with specific socio-material ecologies, rather than being defined a priori by the organs and functions of individuals’ body and mind. Individual capabilities are thus enhanced and extended in interaction, which encourages a more ecological view of (dis)ability.

Language and Embodiment: sensory-motor and linguistic-social experience. Evidence on sentence comprehension

In this work I address the study of language comprehension in an “embodied” framework. Firstly I show behavioral evidence supporting the idea that language modulates the motor system in a specific way, both at a proximal level (sensibility to the effectors) and at the distal level (sensibility to the goal of the action in which the single motor acts are inserted). I will present two studies in which the method is basically the same: we manipulated the linguistic stimuli (the kind of sentence: hand action vs. foot action vs. mouth action) and the effector by which participants had to respond (hand vs. foot vs. mouth; dominant hand vs. non-dominant hand). Response times analyses showed a specific modulation depending on the kind of sentence: participants were facilitated in the task execution (sentence sensibility judgment) when the effector they had to use to respond was the same to which the sentences referred. Namely, during language comprehension a pre-activation of the motor system seems to take place. This activation is analogous (even if less intense) to the one detectable when we practically execute the action described by the sentence. Beyond this effector specific modulation, we also found an effect of the goal suggested by the sentence. That is, the hand effector was pre-activated not only by hand-action-related sentences, but also by sentences describing mouth actions, consistently with the fact that to execute an action on an object with the mouth we firstly have to bring it to the mouth with the hand. After reviewing the evidence on simulation specificity directly referring to the body (for instance, the kind of the effector activated by the language), I focus on the specific properties of the object to which the words refer, particularly on the weight. In this case the hypothesis to test was if both lifting movement perception and lifting movement execution are modulated by language comprehension. We used behavioral and kinematics methods, and we manipulated the linguistic stimuli (the kind of sentence: the lifting of heavy objects vs. the lifting of light objects). To study the movement perception we measured the correlations between the weight of the objects lifted by an actor (heavy objects vs. light objects) and the esteems provided by the participants. To study the movement execution we measured kinematics parameters variance (velocity, acceleration, time to the first peak of velocity) during the actual lifting of objects (heavy objects vs. light objects). Both kinds of measures revealed that language had a specific effect on the motor system, both at a perceptive and at a motoric level. Finally, I address the issue of the abstract words. Different studies in the “embodied” framework tried to explain the meaning of abstract words The limit of these works is that they account only for subsets of phenomena, so results are difficult to generalize. We tried to circumvent this problem by contrasting transitive verbs (abstract and concrete) and nouns (abstract and concrete) in different combinations. The behavioral study was conducted both with German and Italian participants, as the two languages are syntactically different. We found that response times were faster for both the compatible pairs (concrete verb + concrete noun; abstract verb + abstract noun) than for the mixed ones. Interestingly, for the mixed combinations analyses showed a modulation due to the specific language (German vs. Italian): when the concrete word precedes the abstract one responses were faster, regardless of the word grammatical class. Results are discussed in the framework of current views on abstract words. They highlight the important role of developmental and social aspects of language use, and confirm theories assigning a crucial role to both sensorimotor and linguistic experience for abstract words.

Advanced neuroimaging methodologies to improve connectivity detection in normal and abnormal language brain networks

The language connectome was in-vivo investigated using multimodal non-invasive quantitative MRI. In PPA patients (n=18) recruited by the IRCCS ISNB, Bologna, cortical thickness measures showed a predominant reduction on the left hemisphere (p<0.005) with respect to matched healthy controls (HC) (n=18), and an accuracy of 86.1% in discrimination from Alzheimer’s disease patients (n=18). The left temporal and para-hippocampal gyri significantly correlated (p<0.01) with language fluency. In PPA patients (n=31) recruited by the Northwestern University Chicago, DTI measures were longitudinally evaluated (2-years follow-up) under the supervision of Prof. M. Catani, King’s College London. Significant differences with matched HC (n=27) were found, tract-localized at baseline and widespread in the follow-up. Language assessment scores correlated with arcuate (AF) and uncinate (UF) fasciculi DTI measures. In left-ischemic stroke patients (n=16) recruited by the NatBrainLab, King’s College London, language recovery was longitudinally evaluated (6-months follow-up). Using arterial spin labelling imaging a significant correlation (p<0.01) between language recovery and cerebral blood flow asymmetry, was found in the middle cerebral artery perfusion, towards the right. In HC (n=29) recruited by the DIBINEM Functional MR Unit, University of Bologna, an along-tract algorithm was developed suitable for different tractography methods, using the Laplacian operator. A higher left superior temporal gyrus and precentral operculum AF connectivity was found (Talozzi L et al., 2018), and lateralized UF projections towards the left dorsal orbital cortex. In HC (n=50) recruited in the Human Connectome Project, a new tractography-driven approach was developed for left association fibres, using a principal component analysis. The first component discriminated cortical areas typically connected by the AF, suggesting a good discrimination of cortical areas sharing a similar connectivity pattern. The evaluation of morphological, microstructural and metabolic measures could be used as in-vivo biomarkers to monitor language impairment related to neurodegeneration or as surrogate of cognitive rehabilitation/interventional treatment efficacy.

The language of action. How language translates the dynamics of our actions.

The general aim of the thesis was to investigate how and to what extent the characteristics of action organization are reflected in language, and how they influence language processing and understanding. Even though a huge amount of research has been devoted to the study of the motor effects of language, this issue is very debated in literature. Namely, the majority of the studies have focused on low-level motor effects such as effector-relatedness of action, whereas only a few studies have started to systematically investigate how specific aspects of action organization are encoded and reflected in language. After a review of previous studies on the relationship between language comprehension and action (chapter 1) and a critical discussion of some of them (chapter 2), the thesis is composed by three experimental chapters, each devoted to a specific aspect of action organization. Chapter 3 presents a study designed with the aim to disentangle the effective time course of the involvement of the motor system during language processing. Three kinematics experiments were designed in order to determine whether and, at which stage of motor planning and execution effector-related action verbs influence actions executed with either the same or a different effector. Results demonstrate that the goal of an action can be linguistically re-activated, producing a modulation of the motor response. In chapter 4, a second study investigates the interplay between the role of motor perspective (agent) and the organization of action in motor chains. More specifically, this kinematics study aims at deepening how goal can be translated in language, using as stimuli simple sentences composed by a pronoun (I, You, He/She) and a verb. Results showed that the perspective activated by the pronoun You reflects the motor pattern of the “agent” combined with the chain structure of the verb. These data confirm an early involvement of the motor system in language processing, suggesting that it is specifically modulated by the activation of the agent’s perspective. In chapter 5, the issue of perspective is specifically investigated, focusing on its role in language comprehension. In particular, this study aimed at determining how a specific perspective (induced for example by a personal pronoun) modulates motor behaviour during and after language processing. A classical compatibility effect (the Action-sentence compatibility effect) has been used to this aim. In three behavioural experiments the authors investigated how the ACE is modulated by taking first or third person perspective. Results from these experiments showed that the ACE effect occurs only when a first-person perspective is activated by the sentences used as stimuli. Overall, the data from this thesis contributed to disentangle several aspects of how action organization is translated in language, and then reactivated during language processing. This constitutes a new contribution to the field, adding lacking information on how specific aspects such as goal and perspective are linguistically described. In addition, these studies offer a new point of view to understand the functional implications of the involvement of the motor system during language comprehension, specifically from the point of view of our social interactions.

Analysis of the Slow Food movement impact on the farmers and rural areas’ sustainable development

The evaluation of the farmers’ communities’ approach to the Slow Food vision, their perception of the Slow Food role in supporting their activity and their appreciation and expectations from participating in the event of Mother Earth were studied. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was adopted in an agro-food sector context. A survey was conducted, 120 questionnaires from farmers attending the Mother Earth in Turin in 2010 were collected. The descriptive statistical analysis showed that both Slow Food membership and participation to Mother Earth Meeting were much appreciated for the support provided to their business and the contribution to a more sustainable and fair development. A positive social, environmental and psychological impact on farmers also resulted. Results showed also an interesting perspective on the possible universality of the Slow Food and Mother Earth values. Farmers declared that Slow Food is supporting them by preserving the biodiversity and orienting them to the use of local resources and reducing the chemical inputs. Many farmers mentioned the language/culture and administration/bureaucratic issues as an obstacle to be a member in the movement and to participate to the event. Participation to Mother Earth gives an opportunity to exchange information with other farmers’ communities and to participate to seminars and debates, helpful for their business development. The absolute majority of positive answers associated to the farmers’ willingness to relate to Slow Food and participate to the next Mother Earth editions negatively influenced the UTAUT model results. A factor analysis showed that the variables associated to the UTAUT model constructs Performance Expectancy and Effort Expectancy were consistent, able to explain the construct variability, and their measurement reliable. Their inclusion in a simplest Technology Acceptance Model could be considered in future researches.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Performing inclusion and exclusion in the peer group. Children's social organization and peer socializing practices in the classroom

This PhD thesis investigates children’s peer practices in two primary schools in Italy, focusing on the ordinary and the Italian L2 classroom. The study is informed by the paradigm of language socialization and considers peer interactions as a ‘double opportunity space’, allowing both children’s co-construction of their social organization and children’s sociolinguistic development. These two foci of attention are explored on the basis of children’s social interaction and of the verbal, embodied, and material resources that children agentively deploy during their mundane activities in the peer group. The study is based on a video ethnography that lasted nine months. Approximately 30 hours of classroom interactions were video-recorded, transcribed, and analyzed with an approach that combines the micro-analytic instruments of Conversation Analysis and the use of ethnographic information. Three main social phenomena were selected for analysis: (a) children’s enactment of the role of the teacher, (b) children’s reproduction of must-formatted rules, and (c) children’s argumentative strategies during peer conflict. The analysis highlights the centrality of the institutional frame for children’s peer interactions in the classroom. Moreover, the study illustrates that children socialize their classmates to the linguistic, social, and moral expectations of the context in and through various practices. Notably, these practices are also germane to the local negotiation of children’s social organization and hierarchy. Therefore, the thesis underlines that children’s peer interactions are both a resource for children’s sociolinguistic development and a potentially problematic locus where social exclusion is constructed and brought to bear. These insights are relevant for teachers’ professional practice. Children’s peer interactions are a resource that can be integrated in everyday didactics. Nevertheless, the role of the teacher in supervising and steering children’s peer practices appears crucial: an acritical view of children’s autonomous work, often implied in teaching methods such as peer tutoring, needs to be problematized.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.