Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

BACKGROUND: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. OBJECTIVE: The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. METHODS: The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. RESULTS: The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). CONCLUSIONS: The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.

Industrial Case-Study-Based Computational Fluid Dynamic (CFD) Modeling of Stirred and Aerated Bioreactors.

Industrial bioreactors featuring inadequate geometry and operating conditions may depress the effectiveness and the efficiency of the hosted bioprocess. Computational fluid dynamics (CFD) can be used to find a suitable operating match between the target bioprocess and the available bioreactor. The aim of this work is to investigate the feasibility of addressing bioreactor improvement problems in the bioprocess industry with the aid of such mainstream tools as industry-standard CFD. This study illustrates how to effectively simulate both the impeller rotation and air supply and discusses the way toward model validation at the 4.1 m3 capacity scale. Referring to experimentally measured process values, the developed full-scale model successfully predicted the power draw, liquid phase level, and mixing time with errors lower than 4.6, 1.1, and 6.7%, respectively, thus suggesting the illustrated approach as a best practice design method for the bioprocess industry. The validated model was employed to improve performance by reducing the power draw in aerated conditions with a minimal operational derating.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging.

Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

The structures and functions of correlations in neural population codes.

The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.

Correlations enhance the behavioral readout of neural population activity in association cortex.

Noise correlations (that is, trial-to-trial covariations in neural activity for a given stimulus) limit the stimulus information encoded by neural populations, leading to the widely held prediction that they impair perceptual discrimination behaviors. However, this prediction neglects the effects of correlations on information readout. We studied how correlations affect both encoding and readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations reduced the encoded stimulus information, but, seemingly paradoxically, were higher when mice made correct rather than incorrect choices. Single-trial behavioral choices depended not only on the stimulus information encoded by the whole population, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, they enhanced the conversion of sensory information into behavioral choices, overcoming their detrimental information-limiting effects. Thus, correlations in association cortex can benefit task performance even if they decrease sensory information.

Extended field-of-view ultrathin microendoscopes for high-resolution two-photon imaging with minimal invasiveness.

Imaging neuronal activity with high and homogeneous spatial resolution across the field-of-view (FOV) and limited invasiveness in deep brain regions is fundamental for the progress of neuroscience, yet is a major technical challenge. We achieved this goal by correcting optical aberrations in gradient index lens-based ultrathin (≤500 µm) microendoscopes using aspheric microlenses generated through 3D-microprinting. Corrected microendoscopes had extended FOV (eFOV) with homogeneous spatial resolution for two-photon fluorescence imaging and required no modification of the optical set-up. Synthetic calcium imaging data showed that, compared to uncorrected endoscopes, eFOV-microendoscopes led to improved signal-to-noise ratio and more precise evaluation of correlated neuronal activity. We experimentally validated these predictions in awake head-fixed mice. Moreover, using eFOV-microendoscopes we demonstrated cell-specific encoding of behavioral state-dependent information in distributed functional subnetworks in a primary somatosensory thalamic nucleus. eFOV-microendoscopes are, therefore, small-cross-section ready-to-use tools for deep two-photon functional imaging with unprecedentedly high and homogeneous spatial resolution.

Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception.

How does neural activity generate perception? Finding the combinations of spatial or temporal activity features (such as neuron identity or latency) that are consequential for perception remains challenging. We trained mice to recognize synthetic odors constructed from parametrically defined patterns of optogenetic activation, then measured perceptual changes during extensive and controlled perturbations across spatiotemporal dimensions. We modeled recognition as the matching of patterns to learned templates. The templates that best predicted recognition were sequences of spatially identified units, ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individual units contributed additively, with larger contributions from earlier-activated units. Our synthetic approach reveals the fundamental logic of the olfactory code and provides a general framework for testing links between sensory activity and perception.

High-Accuracy Detection of Neuronal Ensemble Activity in Two-Photon Functional Microscopy Using Smart Line Scanning.

Two-photon functional imaging using genetically encoded calcium indicators (GECIs) is one prominent tool to map neural activity. Under optimized experimental conditions, GECIs detect single action potentials in individual cells with high accuracy. However, using current approaches, these optimized conditions are never met when imaging large ensembles of neurons. Here, we developed a method that substantially increases the signal-to-noise ratio (SNR) of population imaging of GECIs by using galvanometric mirrors and fast smart line scan (SLS) trajectories. We validated our approach in anesthetized and awake mice on deep and dense GCaMP6 staining in the mouse barrel cortex during spontaneous and sensory-evoked activity. Compared to raster population imaging, SLS led to increased SNR, higher probability of detecting calcium events, and more precise identification of functional neuronal ensembles. SLS provides a cheap and easily implementable tool for high-accuracy population imaging of neural GCaMP6 signals by using galvanometric-based two-photon microscopes.

Mechano-sensitization of mammalian neuronal networks through expression of the bacterial large-conductance mechanosensitive ion channel.

Development of remote stimulation techniques for neuronal tissues represents a challenging goal. Among the potential methods, mechanical stimuli are the most promising vectors to convey information non-invasively into intact brain tissue. In this context, selective mechano-sensitization of neuronal circuits would pave the way to develop a new cell-type-specific stimulation approach. We report here, for the first time, the development and characterization of mechano-sensitized neuronal networks through the heterologous expression of an engineered bacterial large-conductance mechanosensitive ion channel (MscL). The neuronal functional expression of the MscL was validated through patch-clamp recordings upon application of calibrated suction pressures. Moreover, we verified the effective development of in-vitro neuronal networks expressing the engineered MscL in terms of cell survival, number of synaptic puncta and spontaneous network activity. The pure mechanosensitivity of the engineered MscL, with its wide genetic modification library, may represent a versatile tool to further develop a mechano-genetic approach.This article has an associated First Person interview with the first author of the paper.

Hydraulic separation of plastic wastes: Analysis of liquid-solid interaction.

The separation of plastic wastes in mechanical recycling plants is the process that ensures high-quality secondary raw materials. An innovative device employing a wet technology for particle separation is presented in this work. Due to the combination of the characteristic flow pattern developing within the apparatus and density, shape and size differences among two or more polymers, it allows their separation into two products, one collected within the instrument and the other one expelled through its outlet ducts. The kinematic investigation of the fluid flowing within the apparatus seeded with a passive tracer was conducted via image analysis for different hydraulic configurations. The two-dimensional turbulent kinetic energy results strictly connected to the apparatus separation efficacy. Image analysis was also employed to study the behaviour of mixtures of passive tracer and plastic particles with different physical characteristics in order to understand the coupling regime between fluid and solid phases. The two-dimensional turbulent kinetic energy analysis turned out to be fundamental to this aim. For the tested operating conditions, two-way coupling takes place, i.e., the fluid exerts an influence on the plastic particle and the opposite occurs too. Image analysis confirms the outcomes from the investigation of the two-phase flow via non-dimensional numbers (particle Reynolds number, Stokes number and solid phase volume fraction).

Hyperspectral Monitoring of Green Roof Vegetation Health State in Sub-Mediterranean Climate: Preliminary Results.

In urban and industrial environments, the constant increase of impermeable surfaces has produced drastic changes in the natural hydrological cycle. Decreasing green areas not only produce negative effects from a hydrological-hydraulic perspective, but also from an energy point of view, modifying the urban microclimate and generating, as shown in the literature, heat islands in our cities. In this context, green infrastructures may represent an environmental compensation action that can be used to re-equilibrate the hydrological and energy balance and reduce the impact of pollutant load on receiving water bodies. To ensure that a green infrastructure will work properly, vegetated areas have to be continuously monitored to verify their health state. This paper presents a ground spectroscopy monitoring survey of a green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. This study is part of a larger research project financed by European Structural funds aimed at understanding the influence of green roofs on rainwater management and energy consumption for air conditioning in the Mediterranean area. Reflectance values were acquired with a field-portable spectroradiometer that operates in the range of wavelengths 350-2500 nm. The survey was carried out during the time period November 2014-June 2015 and data were acquired weekly. Climatic, thermo-physical, hydrological and hydraulic quantities were acquired as well and related to spectral data. Broadband and narrowband spectral indices, related to chlorophyll content and to chlorophyll-carotenoid ratio, were computed. The two narrowband indices NDVI705 and SIPI turned out to be the most representative indices to detect the plant health status.

Investigation on an innovative technology for wet separation of plastic wastes.

This paper presents an original device for the separation of plastic polymers from mixtures. Due to the combination of a characteristic flow pattern developing within the apparatus and density, shape and size differences among two or more polymers, this device allows their separation into two products, one collected within the instrument and the other one expelled through its outlet ducts. Experimental tests have been conducted to investigate the effectiveness of the apparatus, using two geometric arrangements, nine hydraulic configurations and three selections of polymers at three stages of a material life cycle. Tests with samples composed of a single typology of polymer have been used to understand the interaction between the particles and the carrying fluid within the apparatus in different hydraulic configurations and geometric arrangements. Multi-material tests are essential to simulate the real conditions in an industrial recycling plant. The separation results have been evaluated in terms of grade and recovery of a useful material. Under the proper hydraulic configurations, the experimentation showed that it is possible to produce an almost pure concentrate of Polyethylene Terephthalate (PET) from a mixture of 85% PET and 15% Polycarbonate (PC) (concentrate grade and recovery equal to 99.5% and 95.1%) and a mixture of 85% PET and 15% Polyvinyl Chloride (PVC) (concentrate grade and recovery equal to 97.9% and 100.0%). It is further demonstrated that almost pure concentrates of PVC and PC can be produced from a mixture of 85% PVC and 15% PC (PVC grade and recovery equal to 99.9% and 99.7%) and a mixture of 85% PC and 15% PVC (PC grade and recovery equal to 99.0% and 99.5%).

PET and PVC separation with hyperspectral imagery.

Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers--polyethylene terephthalate (PET) and polyvinyl chloride (PVC)--in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900-1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

Experimental investigation of a local recirculation photobioreactor for mass cultures of photosynthetic microorganisms.

The present work deals with the experimental fluid mechanics analysis of a wavy-bottomed cascade photobioreactor, to characterize the extent and period of recirculatory and straight-flowing streams establishing therein as a function of reactor inclination and liquid flow rate. The substream characterization via Feature Tracking (FT) showed that a local recirculation zone establishes in each vane only at inclinations ≤6° and that its location changes from the lower (≤3°) to the upper part of each vane (6°). A straight-flowing stream flows opposite (above or below) the local recirculation stream. The recirculation time ranges from 0.86 s to 0.23 s, corresponding, respectively, to the minimum flow rate at the minimum inclination and to the maximum flow rate at the maximum inclination where recirculation was observed. The increase of photosynthetic activity, resulting from the entailed "flash effect", was estimated to range between 102 and 113% with respect to equivalent tubular and bubble column photobioreactors.

Hyperspectral proximal sensing of Salix Alba trees in the Sacco river valley (Latium, Italy).

Recent developments in hardware and software have increased the possibilities and reduced the costs of hyperspectral proximal sensing. Through the analysis of high resolution spectroscopic measurements at the laboratory or field scales, this monitoring technique is suitable for quantitative estimates of biochemical and biophysical variables related to the physiological state of vegetation. Two systems for hyperspectral imaging have been designed and developed at DICEA-Sapienza University of Rome, one based on the use of spectrometers, the other on tunable interference filters. Both systems provide a high spectral and spatial resolution with low weight, power consumption and cost. This paper describes the set-up of the tunable filter platform and its application to the investigation of the environmental status of the region crossed by the Sacco river (Latium, Italy). This was achieved by analyzing the spectral response given by tree samples, with roots partly or wholly submerged in the river, located upstream and downstream of an industrial area affected by contamination. Data acquired is represented as reflectance indices as well as reflectance values. Broadband and narrowband indices based on pigment content and carotenoids vs. chlorophyll content suggest tree samples located upstream of the contaminated area are 'healthier' than those downstream.

Mosaicing of hyperspectral images: the application of a spectrograph imaging device.

Hyperspectral monitoring of large areas (more than 10 km(2)) can be achieved via the use of a system employing spectrometers and CMOS cameras. A robust and efficient algorithm for automatically combining multiple, overlapping images of a scene to form a single composition (i.e., for the estimation of the point-to-point mapping between views), which uses only the information contained within the images themselves is described here. The algorithm, together with the 2D fast Fourier transform, provides an estimate of the displacement between pairs of images by accounting for rotations and changes of scale. The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system. This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification. The performances of the algorithm were evaluated using sample images and image sequences acquired during a proximal sensing field campaign conducted in San Teodoro (Olbia-Tempio-Sardinia). The hyperspectral cube closely corresponds to the mosaic. Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements.

Separation of plastic waste via the hydraulic separator Multidune under different geometric configurations.

The recovery of high-quality plastic materials is becoming an increasingly challenging issue for the recycling sector. Technologies for plastic recycling have to guarantee high-quality secondary raw material, complying with specific standards, for use in industrial applications. The variability in waste plastics does not always correspond to evident differences in physical characteristics, making traditional methodologies ineffective for plastic separation. The Multidune separator is a hydraulic channel allowing the sorting of solid particles on the basis of differential transport mechanisms by generating particular fluid dynamic conditions due to its geometric configuration and operational settings. In this paper, the fluid dynamic conditions were investigated by an image analysis technique, allowing the reconstruction of velocity fields generated inside the Multidune, considering two different geometric configurations of the device, Configuration A and Configuration B. Furthermore, tests on mono- and bi-material samples were completed with varying operational conditions under both configurations. In both series of experiments, the bi-material samples were composed of differing proportions (85% vs. 15%) to simulate real conditions in an industrial plant for the purifying of a useful fraction from a contaminating fraction. The separation results were evaluated in terms of grade and recovery of the useful fraction.

The hydraulic separator Multidune: preliminary tests on fluid-dynamic features and plastic separation feasibility.

Recycling of plastic materials is a rapidly developing discipline because of environmental awareness, the need to conserve materials and energy, and the growing demand to increase the production economy. The main problem in plastics recovery and recycling is related to the variety of plastic wastes, even if selective collection occurs. Therefore, plastic materials can be recycled either as mixtures or as single types, separating the different typologies by their physical (size, specific mass, etc.) and/or chemical properties. However, separation of plastics in single typologies by traditional processes and devices is difficult due to their typical low variability in properties. This paper presents a new research development for recycling industry: the Multidune separator. This is a device constructed from a sequence of parallel semi-cylindrical tubes of transparent plastic welded together in a plane. The lower half is shifted laterally and then fixed relative to the upper half. Flow is then induced in the lateral direction normal to the axis of the tubes, creating a main flow channel and two recirculation zones. This apparatus creates a differential transport of particles of low specific mass, near to 1g/cm3, allowing their separation. The flow field in the Multidune separator is studied via Particle Tracking Velocimetry (PTV). Eulerian analysis of the data is performed to gather information about the fluid-dynamics features established by different hydraulic heads at the inlet of the Multidune. Preliminary tests on monomaterial samples have been performed, varying several operative parameters to determine the best set of values. Therefore, separation tests have been executed on composite samples, obtaining satisfactory results in terms of plastic separation feasibility.

Application of the finite-size Lyapunov exponent to particle tracking velocimetry in fluid mechanics experiments.

A finite-size (or scale) Lyapunov exponent (FSLE), lambdaa(x), is presented in a statistical mechanical framework and employed to characterize mixing in a variety of laboratory and computational fluid mechanics experiments. The FSLE is the exponential rate at which two particles separate from a distance x to ax. Laboratory particle tracking experiments are used to study penetrative convection and flow in porous media while computational experiments are used to study Lévy processes and deterministic diffusion. The apparent scaling relation lambaa(x) approximately Cax(-beta(a)) of the FSLE holds over intermediate initial separations where the laboratory experiment data is most accurate and asymptotically for the computational experiments. The dependence of the exponent beta on a decreases with increasing a. In the matched index porous system, Ca is also a function of mean fluid velocity. The exponent beta is alpha when the Lévy process is alpha-stable and in this case beta is independent of a.