Sex-related differences in amyotrophic lateral sclerosis: A 2-[F]FDG-PET study.

Purpose: We investigated sex-related brain metabolic differences in Amyotrophic Lateral Sclerosis (ALS) and healthy controls (HC).

Methods: We collected two equal-sized groups of male (m-ALS) and female ALS (f-ALS) patients (n = 130 each), who underwent 2-[F]FDG-PET at diagnosis, matched for site of onset, cognitive status and King's stage. We included 168 age-matched healthy controls, half female (f-HC) and half male (m-HC).

Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.

Objective: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB).

Methods: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered.

Brain metabolic differences between pure bulbar and pure spinal ALS: a 2-[F]FDG-PET study.

Background: MRI studies reported that ALS patients with bulbar and spinal onset showed focal cortical changes in corresponding regions of the motor homunculus. We evaluated the capability of brain 2-[F]FDG-PET to disclose the metabolic features characterizing patients with pure bulbar or spinal motor impairment.

Methods: We classified as pure bulbar (PB) patients with bulbar onset and a normal score in the spinal items of the ALSFRS-R, and as pure spinal (PS) patients with spinal onset and a normal score in the bulbar items at the time of PET.

Role of brain 2-[F]fluoro-2-deoxy-D-glucose-positron-emission tomography as survival predictor in amyotrophic lateral sclerosis.

Purpose: The identification of prognostic tools in amyotrophic lateral sclerosis (ALS) would improve the design of clinical trials, the management of patients, and life planning. We aimed to evaluate the accuracy of brain 2-[F]fluoro-2-deoxy-D-glucose-positron-emission tomography (2-[F]FDG-PET) as an independent predictor of survival in ALS.

Methods: A prospective cohort study enrolled 418 ALS patients, who underwent brain 2-[F]FDG-PET at diagnosis and whose survival time was available.

On component-wise dissimilarity measures and metric properties in pattern recognition.

In many real-world applications concerning pattern recognition techniques, it is of utmost importance the automatic learning of the most appropriate dissimilarity measure to be used in object comparison. Real-world objects are often complex entities and need a specific representation grounded on a composition of different heterogeneous features, leading to a non-metric starting space where Machine Learning algorithms operate. However, in the so-called unconventional spaces a family of dissimilarity measures can be still exploited, that is, the set of component-wise dissimilarity measures, in which each component is treated with a specific sub-dissimilarity that depends on the nature of the data at hand.

(Hyper)graph Kernels over Simplicial Complexes.

Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered.

Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations.

Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces.

An Infoveillance System for Detecting and Tracking Relevant Topics From Italian Tweets During the COVID-19 Event.

The year 2020 opened with a dramatic epidemic caused by a new species of coronavirus that soon has been declared a pandemic by the WHO due to the high number of deaths and the critical mass of worldwide hospitalized patients, of order of millions. The COVID-19 pandemic has forced the governments of hundreds of countries to apply several heavy restrictions in the citizens' socio-economic life. Italy was one of the most affected countries with long-term restrictions, impacting the socio-economic tissue.

Metabolic networks classification and knowledge discovery by information granulation.

Graphs are powerful structures able to capture topological and semantic information from data, hence suitable for modelling a plethora of real-world (complex) systems. For this reason, graph-based pattern recognition gained a lot of attention in recent years. In this paper, a general-purpose classification system in the graphs domain is presented.