Neuro-immune signatures in chronic low back pain subtypes

We recently showed that patients with different chronic pain conditions (such as chronic low back pain, fibromyalgia, migraine, and Gulf War Illness) demonstrated elevated brain and/or spinal cord levels of the glial marker 18 kDa translocator protein, which suggests that neuroinflammation might be a pervasive phenomenon observable across multiple etiologically heterogeneous pain disorders. Interestingly, the spatial distribution of this neuroinflammatory signal appears to exhibit a degree of disease specificity (e.g. with respect to the involvement of the primary somatosensory cortex), suggesting that different pain conditions may exhibit distinct “neuroinflammatory signatures”. To further explore this hypothesis, we tested whether neuroinflammatory signal can characterize putative etiological subtypes of chronic low back pain patients based on clinical presentation. Specifically, we explored neuroinflammation in patients whose chronic low back pain either did or did not radiate to the leg (i.e. “radicular” vs. “axial” back pain). Fifty-four chronic low back pain patients, twenty-six with axial back pain (43.7 ± 16.6 y.o. [mean±SD]) and twenty-eight with radicular back pain (48.3 ± 13.2 y.o.), underwent PET/MRI with [11C]PBR28, a second-generation radioligand for the 18 kDa translocator protein. [11C]PBR28 signal was quantified using standardized uptake values ratio (validated against volume of distribution ratio; n = 23). Functional MRI data were collected simultaneously to the [11C]PBR28 data 1) to functionally localize the primary somatosensory cortex back and leg subregions and 2) to perform functional connectivity analyses (in order to investigate possible neurophysiological correlations of the neuroinflammatory signal). PET and functional MRI measures were compared across groups, cross-correlated with one another and with the severity of “fibromyalgianess” (i.e. the degree of pain centralization, or “nociplastic pain”). Furthermore, statistical mediation models were employed to explore possible causal relationships between these three variables. For the primary somatosensory cortex representation of back/leg, [11C]PBR28 PET signal and functional connectivity to the thalamus were: 1) higher in radicular compared to axial back pain patients, 2) positively correlated with each other and 3) positively correlated with fibromyalgianess scores, across groups. Finally, 4) fibromyalgianess mediated the association between [11C]PBR28 PET signal and primary somatosensory cortex-thalamus connectivity across groups. Our findings support the existence of “neuroinflammatory signatures” that are accompanied by neurophysiological changes, and correlate with clinical presentation (in particular, with the degree of nociplastic pain) in chronic pain patients. These signatures may contribute to the subtyping of distinct pain syndromes and also provide information about inter-individual variability in neuro-immune brain signals, within diagnostic groups, that could eventually serve as targets for mechanism-based precision medicine approaches

Nonlinear system identification using a recurrent network in a Bayesian framework

Modern deep neural networks are being widelyexploited to solve challenging learning tasks, including nonlinearsystem identification. Bayesian system identification intrinsicallyencapsulate uncertainty in model parameters and provides fore-casting distribution enabling enhanced analysis, simulation andcontrol system design. Nevertheless, the application of the full Bayesian approach to articulated models as deep neural networksresults quite challenging in practice. In this work we propose anidentification technique for nonlinear dynamic systems exploitinga deep recurrent neural network with Long-Short Term Memory(LSTM) units retaining a Bayesian framework. To such an aim,we stacked the recurrent neural network with a probabilisticlayer, decomposing the nonlinear dynamic model into a combi-nation of flexible functions. Hence, deterministic and stochasticlayers are trained jointly, forcing the learning algorithm totransform the input data sequences into a deterministic featurespace encoded by the LSTM, useful for predictions. Besides, wedeployed a scalable technique based on Variational Inference todeal with the exact inference intractability. We show the effec-tiveness of the proposed approach by the application to a widelyexploited open benchmark for nonlinear system identificatio

Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices

The availability of accurate day-ahead energy prices forecasts is crucial to achieve a successful participation to liberalized electricity markets. Moreover, forecasting systems providing prediction intervals and densities (i.e. probabilistic forecasting) are fundamental to enable enhanced bidding and planning strategies considering uncertainty explicitly. Nonetheless, the vast majority of available approaches focus on point forecast. Therefore, we propose a novel methodology for probabilistic energy price forecast based on Bayesian deep learning techniques. A specific training method has been deployed to guarantee scalability to complex network architectures. Moreover, we developed a model originally supporting heteroscedasticity, thus avoiding the common homoscedastic assumption with related preprocessing effort. Experiments have been performed on two day ahead markets characterized by different behaviors. Then, we demonstrated the capability of the proposed method to achieve robust performances in out-of-sample conditions while providing forecast uncertainty indications

Flexible measuring machine based on a double interferometer

In this work a prototype of a flexible electro-optical measuring machine suitable for dimensional quality control of inner and outer diameters of mechanical workpieces, is presented. Two measuring probes are displaced by means of motorized slides over a range of ± 100 mm enabling the measurement of diameters from 0 up to 200 mm. The position of the probes is gauged by a double interferometer designed for the application. The characterization of the prototype has shown that repeatability and accuracy are better than ± 0.16 μm