Two-Photon Processor (TPP) is a versatile, ready-to-use, and freely available software package in MATLAB to process data from in vivo two-photon calcium imaging. TPP includes routines to search for cell bodies in full-frame (Search for Neural Cells Accelerated; SeNeCA) and line-scan acquisition, routines for calcium signal calculations, filtering, spike-mining, and routines to construct parametric fields. Searching for somata in artificial in vivo data, our algorithm achieved better performance than human annotators. SeNeCA copes well with uneven background brightness and in-plane motion artifacts, the major problems in simple segmentation methods. In the fast mode, artificial in vivo images with a resolution of 256 × 256 pixels containing ≈ 100 neurons can be processed at a rate up to 175 frames per second (tested on Intel i7, 8 threads, magnetic hard disk drive). This speed of a segmentation algorithm could bring new possibilities into the field of in vivo optophysiology. With such a short latency (down to 5-6 ms on an ordinary personal computer) and using some contemporary optogenetic tools, it will allow experiments in which a control program can continuously evaluate the occurrence of a particular spatial pattern of activity (a possible correlate of memory or cognition) and subsequently inhibit/stimulate the entire area of the circuit or inhibit/stimulate a different part of the neuronal system. TPP will be freely available on our public web site. Similar all-in-one and freely available software has not yet been published.

• Klíčová slova
• algorithm, calcium imaging, processing, segmentation,
• MeSH
• algoritmy * MeSH
• aniliny analýza   MeSH
• fluoresceiny analýza   MeSH
• fluorescenční barviva MeSH
• mikroskopie fluorescenční multifotonová metody   MeSH
• mozková kůra chemie   MeSH
• myši inbrední C57BL MeSH
• myši MeSH
• software * MeSH
• vápník analýza   MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• aniliny MeSH
• fluoresceiny MeSH
• fluorescenční barviva MeSH
• Oregon green 488 BAPTA-1 MeSH Prohlížeč
• vápník MeSH

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

• Klíčová slova
• electrophysiology, epilepsy, machine learning, seizures,
• Publikační typ
• časopisecké články MeSH

The European Union (EU) General Data Protection Regulation (GDPR) imposes legal responsibilities concerning the collection and processing of personal information from individuals who live in the EU. It has particular implications for the remote monitoring of cardiac implantable electronic devices (CIEDs). This report from a joint Task Force of the European Heart Rhythm Association and the Regulatory Affairs Committee of the European Society of Cardiology (ESC) recommends a common legal interpretation of the GDPR. Manufacturers and hospitals should be designated as joint controllers of the data collected by remote monitoring (depending upon the system architecture) and they should have a mutual contract in place that defines their respective roles; a generic template is proposed. Alternatively, they may be two independent controllers. Self-employed cardiologists also are data controllers. Third-party providers of monitoring platforms may act as data processors. Manufacturers should always collect and process the minimum amount of identifiable data necessary, and wherever feasible have access only to pseudonymized data. Cybersecurity vulnerabilities have been reported concerning the security of transmission of data between a patient's device and the transceiver, so manufacturers should use secure communication protocols. Patients need to be informed how their remotely monitored data will be handled and used, and their informed consent should be sought before their device is implanted. Review of consent forms in current use revealed great variability in length and content, and sometimes very technical language; therefore, a standard information sheet and generic consent form are proposed. Cardiologists who care for patients with CIEDs that are remotely monitored should be aware of these issues.

• Klíčová slova
• Cardiac implantable electronic device, Cybersecurity, Data controller, Data processor, EHRA, ESC Regulatory Affairs Committee, General Data Protection Regulation, Informed consent, Informed consent form, Joint data controller, Remote monitoring,
• MeSH
• elektronika MeSH
• kardiologie * MeSH
• lidé MeSH
• monitorování fyziologických funkcí MeSH
• poradní výbory MeSH
• zabezpečení počítačových systémů MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests. The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT. The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.

• Klíčová slova
• DART, Hyperspectral data, LUT, Leaf traits, Machine learning model, Radiative transfer model, Synthetic spectral data,
• Publikační typ
• časopisecké články MeSH

BACKGROUND AND OBJECTIVES: The lack of medical facilities in isolated areas makes many patients remain aloof from quick and timely diagnosis of cardiovascular diseases, leading to high mortality rates. A deep learning based method for automatic diagnosis of multiple cardiac diseases from Phonocardiogram (PCG) signals is proposed in this paper. METHODS: The proposed system is a combination of deep learning based convolutional neural network (CNN) and power spectrogram Cardi-Net, which can extract deep discriminating features of PCG signals from the power spectrogram to identify the diseases. The choice of Power Spectral Density (PSD) makes the model extract highly discriminatory features significant for the multi-classification of four common cardiac disorders. RESULTS: Data augmentation techniques are applied to make the model robust, and the model undergoes 10-fold cross-validation to yield an overall accuracy of 98.879% on the test dataset to diagnose multi heart diseases from PCG signals. CONCLUSION: The proposed model is completely automatic, where signal pre-processing and feature engineering are not required. The conversion time of power spectrogram from PCG signals is very low range from 0.10 s to 0.11 s. This reduces the complexity of the model, making it highly reliable and robust for real-time applications. The proposed architecture can be deployed on cloud and a low cost processor, desktop, android app leading to proper access to the dispensaries in remote areas.

• Klíčová slova
• Cardiac disorders, Data augmentation, Deep learning, Phonocardiogram, Power spectrogram,
• MeSH
• kardiovaskulární nemoci * MeSH
• lidé MeSH
• nemoci srdce * diagnostické zobrazování   MeSH
• neuronové sítě MeSH
• počítačové zpracování signálu MeSH
• rostlinné extrakty MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• rostlinné extrakty MeSH

This paper presents a parallel implementation of a non-local transform-domain filter (BM4D). The effectiveness of the parallel implementation is demonstrated by denoising image series from computed tomography (CT) and magnetic resonance imaging (MRI). The basic idea of the filter is based on grouping and filtering similar data within the image. Due to the high level of similarity and data redundancy, the filter can provide even better denoising quality than current extensively used approaches based on deep learning (DL). In BM4D, cubes of voxels named patches are the essential image elements for filtering. Using voxels instead of pixels means that the area for searching similar patches is large. Because of this and the application of multi-dimensional transformations, the computation time of the filter is exceptionally long. The original implementation of BM4D is only single-threaded. We provide a parallel version of the filter that supports multi-core and many-core processors and scales on such versatile hardware resources, typical for high-performance computing clusters, even if they are concurrently used for the task. Our algorithm uses hybrid parallelisation that combines open multi-processing (OpenMP) and message passing interface (MPI) technologies and provides up to 283× speedup, which is a 99.65% reduction in processing time compared to the sequential version of the algorithm. In denoising quality, the method performs considerably better than recent DL methods on the data type that these methods have yet to be trained on.

• Klíčová slova
• high-performance computing, image denoising, medical imaging, parallel implementation, volumetric data,
• Publikační typ
• časopisecké články MeSH

The rapid evolution of Cloud-based services and the growing interest in deep learning (DL)-based applications is putting increasing pressure on hyperscalers and general purpose hardware designers to provide more efficient and scalable systems. Cloud-based infrastructures must consist of more energy efficient components. The evolution must take place from the core of the infrastructure (i.e., data centers (DCs)) to the edges (Edge computing) to adequately support new/future applications. Adaptability/elasticity is one of the features required to increase the performance-to-power ratios. Hardware-based mechanisms have been proposed to support system reconfiguration mostly at the processing elements level, while fewer studies have been carried out regarding scalable, modular interconnected sub-systems. In this paper, we propose a scalable Software Defined Network-on-Chip (SDNoC)-based architecture. Our solution can easily be adapted to support devices ranging from low-power computing nodes placed at the edge of the Cloud to high-performance many-core processors in the Cloud DCs, by leveraging on a modular design approach. The proposed design merges the benefits of hierarchical network-on-chip (NoC) topologies (via fusing the ring and the 2D-mesh topology), with those brought by dynamic reconfiguration (i.e., adaptation). Our proposed interconnect allows for creating different types of virtualised topologies aiming at serving different communication requirements and thus providing better resource partitioning (virtual tiles) for concurrent tasks. To further allow the software layer controlling and monitoring of the NoC subsystem, a few customised instructions supporting a data-driven program execution model (PXM) are added to the processing element's instruction set architecture (ISA). In general, the data-driven programming and execution models are suitable for supporting the DL applications. We also introduce a mechanism to map a high-level programming language embedding concurrent execution models into the basic functionalities offered by our SDNoC for easing the programming of the proposed system. In the reported experiments, we compared our lightweight reconfigurable architecture to a conventional flattened 2D-mesh interconnection subsystem. Results show that our design provides an increment of the data traffic throughput of 9.5% and a reduction of 2.2× of the average packet latency, compared to the flattened 2D-mesh topology connecting the same number of processing elements (PEs) (up to 1024 cores). Similarly, power and resource (on FPGA devices) consumption is also low, confirming good scalability of the proposed architecture.

• Klíčová slova
• data-driven, many-core, software-defined NoC,
• Publikační typ
• časopisecké články MeSH

BACKGROUND AND OBJECTIVES: Cardiovascular diseases are critical diseases and need to be diagnosed as early as possible. There is a lack of medical professionals in remote areas to diagnose these diseases. Artificial intelligence-based automatic diagnostic tools can help to diagnose cardiac diseases. This work presents an automatic classification method using machine learning to diagnose multiple cardiac diseases from phonocardiogram signals. METHODS: The proposed system involves a convolutional neural network (CNN) model because of its high accuracy and robustness to automatically diagnose the cardiac disorders from the heart sounds. To improve the accuracy in a noisy environment and make the method robust, the proposed method has used data augmentation techniques for training and multi-classification of multiple cardiac diseases. RESULTS: The model has been validated both heart sound data and augmented data using n-fold cross-validation. Results of all fold have been shown reported in this work. The model has achieved accuracy on the test set up to 98.60% to diagnose multiple cardiac diseases. CONCLUSIONS: The proposed model can be ported to any computing devices like computers, single board computing processors, android handheld devices etc. To make a stand-alone diagnostic tool that may be of help in remote primary health care centres. The proposed method is non-invasive, efficient, robust, and has low time complexity making it suitable for real-time applications.

• Klíčová slova
• Cardiac signals, Data augmentation, Deep neural networks, Multi-label classification, Phonocardiogram,
• MeSH
• lidé MeSH
• nemoci srdce * diagnostické zobrazování   MeSH
• neuronové sítě MeSH
• srdeční ozvy * MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The authors present results of serial quality and quantity microanalyses of bone patterns and dental tissue patterns in patient with desmoid fibromatosis. Methods of absorption spectroscopy, emission spectral analysis and X-ray diffraction analysis with follow-up to x-ray examination are tested. The above mentioned methods function in a on-line system by means of specially adjusted monitor unit which is controlled centrally by the computer processor system. The whole process of measurement is fully automated and the data obtained are recorded processed in the unit data structure classified into index sequence blocks of data. Serial microanalyses offer exact data for the study of structural changes of dental and bone tissues which manifest themselves in order of crystal grid shifts. They prove the fact that microanalyses give new possibilities in detection and interpretation of chemical and structural changes of apatite cell.

• MeSH
• difrakce rentgenového záření MeSH
• dospělí MeSH
• fibrom analýza   diagnostické zobrazování   MeSH
• lidé MeSH
• nádory hlavy a krku analýza   diagnostické zobrazování   MeSH
• radiografie MeSH
• spektrální analýza MeSH
• spektrofotometrie infračervená MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• kazuistiky MeSH

A low-cost Digital Signal Processor (DSP) unit for advanced Scanning Probe Microscopy measurements is presented. It is based on Red Pitaya board and custom built electronic boards with additional high bit depth AD and DA converters. By providing all the necessary information (position and time) with each data point collected it can be used for any scan path, using either existing libraries for scan path generation or creating adaptive scan paths using Lua scripting interface. The DSP is also capable of performing statistical calculations, that can be used for decision making during scan or for the scan path optimisation on the DSP level.

• Klíčová slova
• Adaptive sampling, Field programmable gate array, Scanning probe microscopy,
• Publikační typ
• časopisecké články MeSH