A Quantum-Classical Model of Brain Dynamics

The study of the human psyche has elucidated a bipartite structure of cognition reflecting the quantum-classical nature of any process that generates knowledge and learning governed by brain activity. Acknowledging the importance of such a finding for modelization, we posit an approach to study brain by means of the quantum-classical dynamics of a Mixed Weyl symbol. The Mixed Weyl symbol is used to describe brain processes at the microscopic level and provides a link to the results of measurements made at the mesoscopic scale. Within this approach, quantum variables (such as,for example, nuclear and electron spins, dipole momenta of particles or molecules, tunneling degrees of freedom, etc may be represented by spinors while the electromagnetic fields and phonon modes involved in the processes are treated either classically or semi-classically, by also considering quantum zero-point fluctuations. Zero-point quantum effects can be incorporated into numerical simulations by controlling the temperature of each field mode via coupling to a dedicated Nos\`e-Hoover chain thermostat. The temperature of each thermostat is chosen in order to reproduce quantum statistics in the canonical ensemble. In this first paper, we introduce a quantum-classical model of brain dynamics, clarifying its mathematical strucure and focusing the discussion on its predictive value. Analytical consequences of the model are not reported in this paper, since they are left for future work. Our treatment incorporates compatible features of three well-known quantum approaches to brain dynamics - namely the electromagnetic field theory approach, the orchestrated objective reduction theory, and the dissipative quantum model of the brain - and hints at convincing arguments that sustain the existence of quantum-classical processes in the brain activity. All three models are reviewed.Comment: Submitted to Entropy [MDPI], Special Issue "Quantum Processes in Living Systems

Image-based biomechanical models of the musculoskeletal system

Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described

Deep Learning Algorithm for Advanced Level-3 Inverse-Modeling of Silicon-Carbide Power MOSFET Devices

Inverse modelling with deep learning algorithms involves training deep architecture to predict device's parameters from its static behaviour. Inverse device modelling is suitable to reconstruct drifted physical parameters of devices temporally degraded or to retrieve physical configuration. There are many variables that can influence the performance of an inverse modelling method. In this work the authors propose a deep learning method trained for retrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET (SiC Power MOS). The SiC devices are used in applications where classical silicon devices failed due to high-temperature or high switching capability. The key application of SiC power devices is in the automotive field (i.e. in the field of electrical vehicles). Due to physiological degradation or high-stressing environment, SiC Power MOS shows a significant drift of physical parameters which can be monitored by using inverse modelling. The aim of this work is to provide a possible deep learning-based solution for retrieving physical parameters of the SiC Power MOSFET. Preliminary results based on the retrieving of channel length of the device are reported. Channel length of power MOSFET is a key parameter involved in the static and dynamic behaviour of the device. The experimental results reported in this work confirmed the effectiveness of a multi-layer perceptron designed to retrieve this parameter.Comment: 13 pages, 8 figures, to be published on Journal of Physics: Conference Serie

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability

Determinants of bone damage: An ex-vivo study on porcine vertebrae

Bone\u2019s resistance to fracture depends on several factors, such as bone mass, microarchitecture, and tissue material properties. The clinical assessment of bone strength is generally performed by Dual-X Ray Photon Absorptiometry (DXA), measuring bone mineral density (BMD) and trabecular bone score (TBS). Although it is considered the major predictor of bone strength, BMD only accounts for about 70% of fragility fractures, while the remaining 30% could be described by bone \u201cquality\u201d impairment parameters, mainly related to tissue microarchitecture. The assessment of bone microarchitecture generally requires more invasive techniques, which are not applicable in routine clinical practice, or X-Ray based imaging techniques, requiring a longer post-processing. Another important aspect is the presence of local damage in the bony tissue that may also affect the prediction of bone strength and fracture risk. To provide a more comprehensive analysis of bone quality and quantity, and to assess the effect of damage, here we adopt a framework that includes clinical, morphological, and mechanical analyses, carried out by means of DXA, \u3bcCT and mechanical compressive testing, respectively. This study has been carried out on trabecular bones, taken from porcine trabecular vertebrae, for the similarity with human lumbar spine. This study confirms that no single method can provide a complete characterization of bone tissue, and the combination of complementary characterization techniques is required for an accurate and exhaustive description of bone status. BMD and TBS have shown to be complementary parameters to assess bone strength, the former assessing the bone quantity and resistance to damage, and the latter the bone quality and the presence of damage accumulation without being able to predict the risk of fracture

Replacing Maize Grain with Ancient Wheat Lines By-Products in Organic Laying Hens' Diet Affects Intestinal Morphology and Enzymatic Activity

The effects of replacement of maize grain with ancient wheat by-products on intestinal morphometry and enzymatic activity in laying hens was studied. Eighty hens were divided into two groups (40 each, 8 replicates, 5 hens/replicate) fed two isoproteic and isoenergetic diets. In the treated group, part of the maize was replaced by a mix of ancient grains (AGs) middling, in a 50:50 ratio of Triticum aestivum L. var. spelta (spelt) and Triticum durum dicoccum L. (emmer wheat). The AG diet affected the weight of all the large intestine tracts, decreasing the weight of caeca (p < 0.01) and increasing those of colon (p < 0.01), rectum and cloaca (p < 0.05). Villus height in the AG group was higher (p < 0.01) than the control for the duodenum and jejunum, while for the ileum, the control group showed the highest values (p < 0.01). The submucosa thickness was higher (p < 0.01) in the control group for the duodenum and ileum, while the jejunum for the AG group showed the highest (p < 0.05) submucosa thickness. The crypts depth was higher (p < 0.01) in the control group for the duodenum and ileum. Enzyme activity was enhanced by AGs (p < 0.01) in the duodenum. Regarding the jejunum, sucrase-isomaltase and alkaline phosphatase had higher activity (p < 0.05 and p < 0.01, respectively) in the AG group. In the ileum, sucrase-isomaltase showed higher activity (p < 0.01) in the control group, while alkaline phosphatase showed the highest values (p < 0.05) in the AG group. Overall, results suggested that the dietary inclusion of AGs exerted positive effects in hens, showing an improved intestinal function

Replacing Maize Grain with Ancient Wheat Lines By-Products in Organic Laying Hens’ Diet Affects Intestinal Morphology and Enzymatic Activity

The effects of replacement of maize grain with ancient wheat by-products on intestinal morphometry and enzymatic activity in laying hens was studied. Eighty hens were divided into two groups (40 each, 8 replicates, 5 hens/replicate) fed two isoproteic and isoenergetic diets. In the treated group, part of the maize was replaced by a mix of ancient grains (AGs) middling, in a 50:50 ratio of Triticum aestivum L. var. spelta (spelt) and Triticum durum dicoccum L. (emmer wheat). The AG diet affected the weight of all the large intestine tracts, decreasing the weight of caeca (p < 0.01) and increasing those of colon (p < 0.01), rectum and cloaca (p < 0.05). Villus height in the AG group was higher (p < 0.01) than the control for the duodenum and jejunum, while for the ileum, the control group showed the highest values (p < 0.01). The submucosa thickness was higher (p < 0.01) in the control group for the duodenum and ileum, while the jejunum for the AG group showed the highest (p < 0.05) submucosa thickness. The crypts depth was higher (p < 0.01) in the control group for the duodenum and ileum. Enzyme activity was enhanced by AGs (p < 0.01) in the duodenum. Regarding the jejunum, sucrase-isomaltase and alkaline phosphatase had higher activity (p < 0.05 and p < 0.01, respectively) in the AG group. In the ileum, sucrase-isomaltase showed higher activity (p < 0.01) in the control group, while alkaline phosphatase showed the highest values (p < 0.05) in the AG group. Overall, results suggested that the dietary inclusion of AGs exerted positive effects in hens, showing an improved intestinal function

The role of rehabilitation in the management of late-onset Pompe disease: a narrative review of the level of evidence

Late-onset Pompe disease (LOPD) is characterized by progressive muscle weakness, respiratory muscle dysfunction, and minor cardiac involvement. Although in LOPD, as in other neuromuscular diseases, controlled low impact sub-maximal aerobic exercise and functional ability exercise can improve general functioning and quality of life, as well as respiratory rehabilitation, the bulk of evidence on that is weak and guidelines are lacking. To date, there is no specific focus on rehabilitation issues in clinical recommendations for the care of patients with Pompe disease, and standard practice predominantly follows general recommendation guidelines for neuromuscular diseases. The Italian Association of Myology, the Italian Association of Pulmonologists, the Italian Society of Neurorehabilitation, and the Italian Society of Physical Medicine and Rehabilitation, have endorsed a project to formulate recommendations on practical, technical, and, whenever possible, disease-specific guidance on rehabilitation procedures in LOPD, with specific reference to the Italian scenario. In this first paper, we review available evidence on the role of rehabilitation in LOPD patients, particularly addressing the unmet needs in the management of motor and respiratory function for these patients

VizieR Online Data Catalog: Differential rotation in solar-like stars (Distefano+, 2016)

The average rotation period, the parameters ωmin ωmax, ∆Ωphot and alphaphot are reported for 111 late-type stars belonging to loose young stellar associations. For each target, the main physical parameters are also reported. The Spectral types, the photometric data and the distances are taken by previous works. The masses, the effective temperatures and the convective turn-over time-scales have been inferred by comparing absolute magnitudes with different sets of theoretical isochrones

Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance

Purpose To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI). Material and methods This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consisted of stability-based (ICC &gt; 0.75) and significance-based selections (ranking features by decreasing Mann-Whitney p-value). Class balancing was performed to oversample the minority (i.e., benign) class. Selected features were used to train and test a support vector machine (SVM) to discriminate benign from malignant spine tumors using tenfold cross-validation. Results A total of 76.4% radiomic features were stable. The quality metrics for the SVM were evaluated as a function of the number of selected features. The radiomic model with the best performance and the lowest number of features for classifying tumor types included 8 features. The metrics were 78% sensitivity, 68% specificity, 76% accuracy and AUC 0.78. Conclusion SVM classifiers based on radiomic features extracted from T2- and diffusion-weighted imaging with ADC map are promising for classification of spine bone tumors. Radiomic features of spine bone tumors show good reproducibility rates