A NEW APPROACH TO THE SPATIO-TEMPORAL PATTERN IDENTIFICATION IN NEURONAL MULTI-ELECTRODE REGISTRATIONS

A lot of methods were created in last decade for the spatio-temporal analysis of multi-electrode array (MEA) neuronal data sets. All these methods were implemented starting from a channel to channel analysis, with a great computational effort and onerous spatial pattern recognition task. Our idea is to approach the MEA data collection from a different point of view, i.e. considering all channels simultaneously. We transform the 2D plus time MEA signal in a mono-dimensional plus time signal and elaborate it as a normal 1D signal, using the Space-Amplitude Transform method. This geometrical transformation is completely invertible and allows to employ very fast processing algorithms

A new approach to the spatio-temporal pattern identification in neuronal multi-electrode registrations

A lot of methods were created in last decade for the spatio-temporal analysis of multi-electrode array (MEA) neuronal data sets. All these methods were implemented starting from a channel to channel analysis, with a great computational effort and onerous spatial pattern recognition task. 
Our idea is to approach the MEA data collection from a different point of view, i.e. considering all channels simultaneously. We transform the 2D plus time MEA signal in a mono-dimensional plus time signal and elaborate it as a normal 1D signal, using the Space-Amplitude Transform method. 
This geometrical transformation is completely invertible and allows to employ very fast processing algorithms. 
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Rapid mapping of visual receptive fields by filtered back-projection: application to multi-neuronal electrophysiology and imaging

Neurons in the visual system vary widely in the spatiotemporal properties of their receptive fields (RFs), and understanding these variations is key to elucidating how visual information is processed. We present a new approach for mapping RFs based on the filtered back projection (FBP), an algorithm used for tomographic reconstructions. To estimate RFs, a series of bars were flashed across the retina at pseudo‐random positions and at a minimum of five orientations. We apply this method to retinal neurons and show that it can accurately recover the spatial RF and impulse response of ganglion cells recorded on a multi‐electrode array. We also demonstrate its utility for in vivo imaging by mapping the RFs of an array of bipolar cell synapses expressing a genetically encoded Ca2+ indicator. We find that FBP offers several advantages over the commonly used spike‐triggered average (STA): (i) ON and OFF components of a RF can be separated; (ii) the impulse response can be reconstructed at sample rates of 125 Hz, rather than the refresh rate of a monitor; (iii) FBP reveals the response properties of neurons that are not evident using STA, including those that display orientation selectivity, or fire at low mean spike rates; and (iv) the FBP method is fast, allowing the RFs of all the bipolar cell synaptic terminals in a field of view to be reconstructed in under 4 min. Use of the FBP will benefit investigations of the visual system that employ electrophysiology or optical reporters to measure activity across populations of neurons

Archaeological Survey of Sīnīya Island, Umm al-Quwain

The preliminary results of a comprehensive survey of Sīnīya Island in the Khawr al-Bayḍāʾ of Umm al-Quwain are presented here. The onset of human occupation remains to be confirmed, with scarce evidence for limited activity in the late pre-Islamic period (LPI, c. 300 BC – AD 300). The first major phase of occupation dates to the seventh and eighth centuries (early Islamic period) when a monastery and settlement were established in the north-east of the island. Probably the peak occupation falls between the fourteenth and fifteenth centuries, when the stone-town of Old Umm al-Quwain 1 was built, followed by the eighteenth to early nineteenth century when the settlement moved to neighbouring Old Umm al-Quwain 2. The town was destroyed by the British in 1820 and moved to the facing tidal island, where Old Umm al-Quwain 3 (the modern city of the same name) developed. This resulted in an emptying of the landscape, and Sīnīya Island was little visited in the nineteenth and twentieth centuries, except for the estate of the ruling Āl Muʿallā represented by the Mallāh Towers.The preliminary results of a comprehensive survey of Sīnīya Island in the Khawr al-Bayḍāʾ of Umm al-Quwain are presented here. The onset of human occupation remains to be confirmed, with scarce evidence for limited activity in the late pre-Islamic period (LPI, c. 300 BC – AD 300). The first major phase of occupation dates to the seventh and eighth centuries (early Islamic period) when a monastery and settlement were established in the north-east of the island. Probably the peak occupation falls between the fourteenth and fifteenth centuries, when the stone-town of Old Umm al-Quwain 1 was built, followed by the eighteenth to early nineteenth century when the settlement moved to neighbouring Old Umm al-Quwain 2. The town was destroyed by the British in 1820 and moved to the facing tidal island, where Old Umm al-Quwain 3 (the modern city of the same name) developed. This resulted in an emptying of the landscape, and Sīnīya Island was little visited in the nineteenth and twentieth centuries, except for the estate of the ruling Āl Muʿallā represented by the Mallāh Towers

A system for automatic on-line time detection and classification of neural spikes based on a digital signal processor and a FPGA controller

Definition of single spikes from multiunit spike trains plays a critical role in neurophysiology and in neuroengineering as it does the question of how much information is encoded by single neurons in a neuronal network. Moreover, the possibility to develop a bidirectional communication between electronic devices and neuronal networks provides great perspectives in neuroengineering. Traditionally, the functional properties of neurons and neuronal networks have been investigated using conventional electrodes, such as glass micropipettes, thus allowing neurophysiologists to disclose a detailed picture about the single cell properties. Thirty years ago Micro-Electrode Array devices (MEAs) have been developed as tools providing distributed information about learning, memory and information processing in a cultured neuronal network. Recent applications of this technologies have the problem of the recording and storage of the huge amount of data processed. Here we describe a system based on a FPGA controller coupled to a Digital Signal Processor for the automatic single spike detection, sorting and classification. The first step involves FPGA: its inputs are 60 neuronal signals caming from the 60 channels of MEA and its outputs are time stamps for single electrode and templates of the spikes for single channel. At this level, an adaptative threshold method is used for spike detection. The second step involves the DSP: the principal components of previously recorded templates are computed. Spikes are classified using information about their shape, characterized by different features; the principal component analysis is one method for choosing these features automatically. The challenge is to accurately and reliably separate the spikes from a single neuron from spikes from other neurons and classify them. The on-line application of this method provides an efficient system to reduce the computation time and the space on the storage unit. Our aim is to estimate the number of neurons that are naturally interconnected in complex networks and to discriminate single template’s shape of individual neuronal cell

Exosomes from Plasma of Neuroblastoma Patients Contain Doublestranded DNA Reflecting the Mutational Status of Parental Tumor Cells

Neuroblastoma (NB) is an aggressive infancy tumor, leading cause of death among preschool age diseases. Here we focused on characterization of exosomal DNA (exo-DNA) isolated from plasma cell-derived exosomes of neuroblastoma patients, and its potential use for detection of somatic mutations present in the parental tumor cells. Exosomes are small extracellular membrane vesicles secreted by most cells, playing an important role in intercellular communications. Using an enzymatic method, we provided evidence for the presence of double-stranded DNA in the NB exosomes. Moreover, by whole exome sequencing, we demonstrated that NB exo-DNA represents the entire exome and that it carries tumor-specific genetic mutations, including those occurring on known oncogenes and tumor suppressor genes in neuroblastoma (ALK, CHD5, SHANK2, PHOX2B, TERT, FGFR1, and BRAF). NB exo-DNA can be useful to identify variants responsible for acquired resistance, such as mutations of ALK, TP53, and RAS/MAPK genes that appear in relapsed patients. The possibility to isolate and to enrich NB derived exosomes from plasma using surface markers, and the quick and easy extraction of exo-DNA, gives this methodology a translational potential in the clinic. Exo-DNA can be an attractive non-invasive biomarker for NB molecular diagnostic, especially when tissue biopsy cannot be easily available

Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy

Nitrogen concentration in plants is normally determined by expensive and time consuming chemical analyses. As an alternative, chlorophyll meter readings and N-NO3 concentration determination in petiole sap were proposed, but these assays are not always satisfactory. Spectral reflectance values of tomato leaves obtained by visible-near infrared spectrophotometry are reported to be a powerful tool for the diagnosis of plant nutritional status. The aim of the study was to evaluate the possibility and the accuracy of the estimation of tomato leaf nitrogen concentration performed through a rapid, portable and non-destructive system, in comparison with chemical standard analyses, chlorophyll meter readings and N-NO3 concentration in petiole sap. Mean reflectance leaf values were compared to each reference chemical value by partial least squares chemometric multivariate methods. The correlation between predicted values from spectral reflectance analysis and the observed chemical values showed in the independent test highly significant correlation coefficient (r = 0.94). The utilization of the proposed system, increasing efficiency, allows better knowledge of nutritional status of tomato plants, with more detailed and sharp information and on wider areas. More detailed information both in space and time is an essential tool to increase and stabilize crop quality levels and to optimize the nutrient use efficiency

The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase

The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018¿2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023¿2026).This work has been funded by the European Union Horizon 2020 research and innovation program under the ChEESE project, Grant Agreement No. 823844, by the European High Performance Computing Joint Undertaking (JU), Grant Agreement No 101093038, and by 4 different Partnership for Advanced Computing in Europe (PRACE) projects from calls 20 and 21 for granting ChEESE activities with a total of 170M core hours on different machines: VOHA (ID 2019215114), TSUCAST (ID 2019215169), SEISVIEW (ID 2019215212) and TsuHazAP (ID 2020225386)

Optimization of a GCaMP calcium indicator for neural activity imaging

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Neuroscience 32 (2012): 13819-13840, doi:10.1523/JNEUROSCI.2601-12.2012.Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials in short bursts in several systems in vivo. Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery of in vitro assays, we have increased the dynamic range of GCaMP3 by severalfold, creating a family of “GCaMP5” sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, and in vivo in Caenorhabditis chemosensory neurons, Drosophila larval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2- to 3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combining in vivo imaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3. GCaMP5 allows more sensitive detection of neural activity in vivo and may find widespread applications for cellular imaging in general.A.F. has been supported by a European Molecular Biology Organization long-term fellowship. Work in H.B.’s laboratory was funded by the National Institutes of Health (NIH) Nanomedicine Development Center “Optical Control of Biological Function,” and work in S.S.-H.W.’s laboratory was funded by NIH R01 NS045193