Phenotyping spontaneous locomotor activity in inbred and outbred mouse strains by using Digital Ventilated Cages

Mouse strains differ markedly in all behaviors, independently of their genetic background. We undertook this study to disentangle the diurnal activity and feature key aspects of three non-genetically altered mouse strains widely used in research, C57BL/6NCrl (inbred), BALB/cAnNCrl (inbred) and CRL:CD1(ICR) (outbred). With this aim, we conducted a longitudinal analysis of the spontaneous locomotor activity of the mice during a 24-h period for 2 months, in two different periods of the year to reduce the seasonality effect. Mice (males and females) were group-housed in Digital Ventilated Cages (Tecniplast), mimicking standard housing conditions in research settings and avoiding the potential bias provided in terms of locomotor activity by single housing. The recorded locomotor activity was analyzed by relying on different and commonly used circadian metrics (i.e., day and night activity, diurnal activity, responses to lights-on and lights-off phases, acrophase and activity onset and regularity disruption index) to capture key behavioral responses for each strain. Our results clearly demonstrate significant differences in the circadian activity of the three selected strains, when comparing inbred versus outbred as well as inbred strains (C57BL/6NCrl versus BALB/cAnNCrl). Conversely, males and females of the same strain displayed similar motor phenotypes; significant differences were recorded only for C57BL/6NCrl and CRL:CD1(ICR) females, which displayed higher average locomotor activity from prepuberty to adulthood. All strain-specific differences were further confirmed by an unsupervised machine learning approach. Altogether, our data corroborate the concept that each strain behaves under characteristic patterns, which needs to be taken into consideration in the study design to ensure experimental reproducibility and comply with essential animal welfare principles

Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest

Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features

Attachment-Related Representations and Suicidal Ideations in Nonsuicidal Self-Injury Adolescents with and without Suicide Attempts: A Pilot Study

Objectives: Consistent with the debate surrounding the association between nonsuicidal self-injury (NSSI), suicidal intent, and suicidal behavior, and between NSSI and dysregulation processes, we attempted to analyze suicide intent and emotion dysregulation in NSSI adolescents, in the framework of the attachment representations and exploring these clues of emotion dysregulation characteristics of insecure attachment. Furthermore, we intended to focus on these attachment-related segregated systems regarding death and suicidal ideations, to explore how differently they would characterize self-injuring adolescents with and without suicide attempts. Methods: Thirty-four NSSI inpatient adolescents, 17 with suicide attempts, 17 without suicide attempts, and 17 healthy controls (age 11–17) were assessed using Adult Attachment Projective, which allows for the classification of attachment status and related emotion dysregulation and segregated systems. Results: The majority of the NSSI group with and without suicide attempts showed unresolved (disorganized) attachment-related representations and clues of damaged reflective functions, whereas only the NSSI with suicide attempts showed clues of impaired interpersonal relationships. The two clinical groups used words expressing suicidal intent, whereas the healthy group did not. Conclusions: Therapists are encouraged not to underestimate suicidal ideation in NSSI regardless of whether or not they have already attempted suicide

MLOps: A Taxonomy and a Methodology

Over the past few decades, the substantial growth in enterprise-data availability and the advancements in Artificial Intelligence (AI) have allowed companies to solve real-world problems using Machine Learning (ML). ML Operations (MLOps) represents an effective strategy for bringing ML models from academic resources to useful tools for solving problems in the corporate world. The current literature on MLOps is still mostly disconnected and sporadic. In this work, we review the existing scientific literature and we propose a taxonomy for clustering research papers on MLOps. In addition, we present methodologies and operations aimed at defining an ML pipeline to simplify the release of ML applications in the industry. The pipeline is based on ten steps: business problem understanding, data acquisition, ML methodology, ML training & testing, continuous integration, continuous delivery, continuous training, continuous monitoring, explainability, and sustainability. The scientific and business interest and the impact of MLOps have grown significantly over the past years: the definition of a clear and standardized methodology for conducting MLOps projects is the main contribution of this paper

Age-related changes of cortical excitability and connectivity in healthy humans: non-invasive evaluation of sensorimotor network by means of TMS-EEG

The sensorimotor cortical system undergoes structural and functional changes across its lifespan. Some of these changes are physiological and parallel the normal aging process, while others might represent pathophysiological mechanisms underlying neurodegenerative disorders. In the last years, the study of possible age-related modifications in brain sensorimotor functional characteristics has been the focus of several research projects. Here we have used the transcranial magnetic stimulation (TMS)-electroencephalography (EEG) navigated co-registration to investigate the influence of physiological aging on the excitability and connectivity of the human sensorimotor cortical system. To this end, we compared the TMS-evoked EEG potentials (TEPs) collected after stimulating the dominant primary motor cortex (M1) in healthy young subjects (mean age 24.5years) with those collected in healthy older adults (mean age 67.6years). We have shown that, after stimulation of the left motor cortex, TEPs are significantly affected by physiological aging. This phenomenon has a clear spatio-temporal specificity and we speculate that normal aging per se leads to some changes in the excitability of specific cortical neural assemblies whereas other alterations could reflect compensatory mechanisms to such changes

Age related differences in functional synchronization of EEG activity as evaluated by means of TMS-EEG coregistrations

It was recently demonstrated that the characteristics of EEG rhythms preceding a transcranial magnetic stimulation (TMS) of the motor cortex (M1) influence the motor-evoked potential (MEP) amplitude with a peculiar pattern, thus reflecting the M1 functional state. As physiological aging is related to a decrease in motor performance and changes in excitability and connectivity strength within cerebral sensorimotor circuits, we aimed to explore whether aging affects EEG-MEP interactions. Using MRI-navigated TMS and multichannel EEG, we compared the EEG-MEP interactions observed in healthy aged subjects with those observed in young volunteers. We divided the MEPs amplitude into two different subgroups consisting of "high" and "low" MEPs, based on the 50th percentile of their amplitude distribution. Then we analysed the characteristics of the pre-stimulus EEG from M1 and correlated areas separately for the "high" and "low" MEPs, comparing the two conditions. In both young and old subjects, significantly larger MEPs were evoked when the stimulated M1 was coupled in the beta-2 band with the homolateral prefrontal cortex. Conversely, only in young participants was the MEP size modulated when the M1 and homolateral parieto-occipital cortices were coupled in the delta band. The elderly didn't show this kind of pattern. Importantly, this coupling was significantly higher in elderly brains than in young brains, both for high and low MEPs. Our results suggest an age-related significant influence of time-varying coupling of spatially patterned EEG rhythms on motor cortex excitability in response to TMS

A multimodal ensemble driven by multiobjective optimisation to predict overall survival in non-small-cell lung cancer

Lung cancer accounts for more deaths worldwide than any other cancer disease. In order to provide patients with the most effective treatment for these aggressive tumours, multimodal learning is emerging as a new and promising field of research that aims to extract complementary information from the data of different modalities for prognostic and predictive purposes. This knowledge could be used to optimise current treatments and maximise their effectiveness. To predict overall survival, in this work, we investigate the use of multimodal learning on the CLARO dataset, which includes CT images and clinical data collected from a cohort of non-small-cell lung cancer patients. Our method allows the identification of the optimal set of classifiers to be included in the ensemble in a late fusion approach. Specifically, after training unimodal models on each modality, it selects the best ensemble by solving a multiobjective optimisation problem that maximises both the recognition performance and the diversity of the predictions. In the ensemble, the labels of each sample are assigned using the majority voting rule. As further validation, we show that the proposed ensemble outperforms the models learning a single modality, obtaining state-of-the-art results on the task at hand