Fractal Fluctuations and Quantum-Like Chaos in the Brain by Analysis of Variability of Brain Waves: A New Method Based on a Fractal Variance Function and Random Matrix Theory

We developed a new method for analysis of fundamental brain waves as recorded by EEG. To this purpose we introduce a Fractal Variance Function that is based on the calculation of the variogram. The method is completed by using Random Matrix Theory. Some examples are given

MATNet: Multi-Level Fusion and Self-Attention Transformer-Based Model for Multivariate Multi-Step Day-Ahead PV Generation Forecasting

The integration of renewable energy sources (RES) into modern power systems has become increasingly important due to climate change and macroeconomic and geopolitical instability. Among the RES, photovoltaic (PV) energy is rapidly emerging as one of the world's most promising. However, its widespread adoption poses challenges related to its inherently uncertain nature that can lead to imbalances in the electrical system. Therefore, accurate forecasting of PV production can help resolve these uncertainties and facilitate the integration of PV into modern power systems. Currently, PV forecasting methods can be divided into two main categories: physics-based and data-based strategies, with AI-based models providing state-of-the-art performance in PV power forecasting. However, while these AI-based models can capture complex patterns and relationships in the data, they ignore the underlying physical prior knowledge of the phenomenon. Therefore, we propose MATNet, a novel self-attention transformer-based architecture for multivariate multi-step day-ahead PV power generation forecasting. It consists of a hybrid approach that combines the AI paradigm with the prior physical knowledge of PV power generation of physics-based methods. The model is fed with historical PV data and historical and forecast weather data through a multi-level joint fusion approach. The effectiveness of the proposed model is evaluated using the Ausgrid benchmark dataset with different regression performance metrics. The results show that our proposed architecture significantly outperforms the current state-of-the-art methods with an RMSE equal to 0.0460. These findings demonstrate the potential of MATNet in improving forecasting accuracy and suggest that it could be a promising solution to facilitate the integration of PV energy into the power grid

Voluntary movement takes shape. the link between movement focusing and sensory input gating

The aim of the study was to investigate the relationship between motor surround inhibition (mSI) and the modulation of somatosensory temporal discrimination threshold (STDT) induced by voluntary movement. Seventeen healthy volunteers participated in the study. To assess mSI, we delivered transcranial magnetic stimulation (TMS) single pulses to record motor evoked potentials (MEPs) from the right abductor digiti minimi (ADM; “surround muscle”) during brief right little finger flexion. mSI was expressed as the ratio of ADM MEP amplitude during movement to MEP amplitude at rest. We preliminarily measured STDT values by assessing the shortest interval at which subjects were able to recognize a pair of electric stimuli, delivered over the volar surface of the right little finger, as separate in time. We then evaluated the STDT by using the same motor task used for mSI. mSI and STDT modulation were evaluated at the same time points during movement. mSI and STDT modulation displayed similar time-dependent changes during index finger movement. In both cases, the modulation was maximally present at the onset of the movement and gradually vanished over about 200 ms. Our study provides the first neurophysiological evidence about the relationship between mSI and tactile-motor integration during movement execution

Cognitive behavioral group therapy versus psychoeducational intervention in Parkinson's disease

Objective: The aim of the current study was to evaluate whether cognitive behavioral group therapy has a positive impact on psychiatric, and motor and non-motor symptoms in Parkinson’s disease (PD). Methods: We assigned 20 PD patients with a diagnosis of psychiatric disorder to either a 12-week cognitive behavioral therapy (CBT) group or a psychoeducational protocol. For the neurological examination, we administered the Unified Parkinson’s Disease Rating Scale and the non-motor symptoms scale. The severity of psychiatric symptoms was assessed by means of the Hamilton Depression Rating Scale, the Hamilton Anxiety Rating Scale, the Brief Psychiatric Rating Scale, and the Clinical Global Impressions. Results: Cognitive behavioral group therapy was effective in treating depression and anxiety symptoms as well as reducing the severity of non-motor symptoms in PD patients; whereas, no changes were observed in PD patients treated with the psychoeducational protocol. Conclusion: CBT offered in a group format should be considered in addition to standard drug therapy in PD patient

Abnormal temporal coupling of tactile perception and motor action in Parkinson's disease

Evidence shows altered somatosensory temporal discrimination threshold (STDT) in Parkinson's disease in comparison to normal subjects. In healthy subjects, movement execution modulates STDT values through mechanisms of sensory gating. We investigated whether STDT modulation during movement execution in patients with Parkinson's disease differs from that in healthy subjects. In 24 patients with Parkinson's disease and 20 healthy subjects, we tested STDT at baseline and during index finger abductions (at movement onset "0", 100, and 200 ms thereafter). We also recorded kinematic features of index finger abductions. Fifteen out of the 24 patients were also tested ON medication. In healthy subjects, STDT increased significantly at 0, 100, and 200 ms after movement onset, whereas in patients with Parkinson's disease in OFF therapy, it increased significantly at 0 and 100 ms but returned to baseline values at 200 ms. When patients were tested ON therapy, STDT during index finger abductions increased significantly, with a time course similar to that of healthy subjects. Differently from healthy subjects, in patients with Parkinson's disease, the mean velocity of the finger abductions decreased according to the time lapse between movement onset and the delivery of the paired electrical stimuli for testing somatosensory temporal discrimination. In conclusion, patients with Parkinson's disease show abnormalities in the temporal coupling between tactile information and motor outflow. Our study provides first evidence that altered temporal processing of sensory information play a role in the pathophysiology of motor symptoms in Parkinson's disease

Metabolomics investigation of post-mortem human pericardial fluid

Introduction Due to its peculiar anatomy and physiology, the pericardial fluid is a biological matrix of particular interest in the forensic field. Despite this, the available literature has mainly focused on post-mortem biochemistry and forensic toxicology, while to the best of authors' knowledge post-mortem metabolomics has never been applied. Similarly, estimation of the time since death or post-mortem interval based on pericardial fluids has still rarely been attempted.ObjectivesWe applied a metabolomic approach based on H-1 nuclear magnetic resonance spectroscopy to ascertain the feasibility of monitoring post-mortem metabolite changes on human pericardial fluids with the aim of building a multivariate regression model for post-mortem interval estimation.MethodsPericardial fluid samples were collected in 24 consecutive judicial autopsies, in a time frame ranging from 16 to 170 h after death. The only exclusion criterion was the quantitative and/or qualitative alteration of the sample. Two different extraction protocols were applied for low molecular weight metabolites selection, namely ultrafiltration and liquid-liquid extraction. Our metabolomic approach was based on the use of H-1 nuclear magnetic resonance and multivariate statistical data analysis.ResultsThe pericardial fluid samples treated with the two experimental protocols did not show significant differences in the distribution of the metabolites detected. A post-mortem interval estimation model based on 18 pericardial fluid samples was validated with an independent set of 6 samples, giving a prediction error of 33-34 h depending on the experimental protocol used. By narrowing the window to post-mortem intervals below 100 h, the prediction power of the model was significantly improved with an error of 13-15 h depending on the extraction protocol. Choline, glycine, ethanolamine, and hypoxanthine were the most relevant metabolites in the prediction model.ConclusionThe present study, although preliminary, shows that PF samples collected from a real forensic scenario represent a biofluid of interest for post-mortem metabolomics, with particular regard to the estimation of the time since death