This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.

• MeSH
• algoritmy MeSH
• elektrokardiografie * metody   MeSH
• lidé MeSH
• metoda nejmenších čtverců MeSH
• monitorování plodu metody   MeSH
• plod fyziologie   MeSH
• počítačové zpracování signálu * MeSH
• těhotenství MeSH
• Check Tag
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.

• MeSH
• artefakty * MeSH
• dospělí MeSH
• elektroencefalografie metody   MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• mladý dospělý MeSH
• multimodální zobrazování metody   MeSH
• počítačové zpracování obrazu metody   MeSH
• zdraví dobrovolníci pro lékařské studie MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

• MeSH
• algoritmy * MeSH
• heuristika * MeSH
• počítačová simulace MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes.

• MeSH
• algoritmy MeSH
• deep learning * MeSH
• internet věcí * MeSH
• lidé MeSH
• technologie optických vláken MeSH
• telemedicína * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Pertuse je významnou příčinou nemocnosti a úmrtnosti u dětí. Acelulární vakcíny jsou považovány za bezpečné, ale přibývá důkazů, že acelulární vakcíny nejsou schopny onemocnění pertusí dostatečně kontrolovat. Je třeba stávající vakcíny zlepšit nebo vyvinout nové, účinnější vakcíny.

Pertussis is a significant cause of chilhood morbidity and mortality. Acellular pertussis vaccines are considered safer but there is growing evidence that the acellular vaccines are unable to optimal control of pertussis disease. It is necessary to improve current vaccines and to develop new, more effective vaccines.

• Klíčová slova
• celobuněčná vakcína, vyvanutí imunity, pertactin,
• MeSH
• acelulární vakcíny imunologie   MeSH
• adaptivní imunita imunologie   MeSH
• antigeny bakteriální imunologie   MeSH
• atenuované vakcíny imunologie   MeSH
• Bordetella pertussis * genetika   imunologie   patogenita   MeSH
• DNA bakterií genetika   MeSH
• faktory virulence rodu Bordetella genetika   imunologie   MeSH
• lidé MeSH
• molekulární evoluce MeSH
• pertuse * imunologie   prevence a kontrola   MeSH
• pertusová vakcína * imunologie   MeSH
• pertusový toxin imunologie   MeSH
• polymorfismus genetický MeSH
• proteiny vnější bakteriální membrány genetika   imunologie   MeSH
• vakcinace MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• přehledy MeSH

Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for distinguishing between malignant and healthy bone is essential. Bones that have cancer have a different texture than bones in unaffected areas. Diagnosing hematological illnesses relies on correct labeling and categorizing nucleated cells in the bone marrow. However, timely diagnosis and treatment are hampered by pathologists' need to identify specimens, which can be sensitive and time-consuming manually. Humanity's ability to evaluate and identify these more complicated illnesses has significantly been bolstered by the development of artificial intelligence, particularly machine, and deep learning. Conversely, much research and development is needed to enhance cancer cell identification-and lower false alarm rates. We built a deep learning model for morphological analysis to solve this problem. This paper introduces a novel deep convolutional neural network architecture in which hybrid multi-objective and category-based optimization algorithms are used to optimize the hyperparameters adaptively. Using the processed cell pictures as input, the proposed model is then trained with an optimized attention-based multi-scale convolutional neural network to identify the kind of cancer cells in the bone marrow. Extensive experiments are run on publicly available datasets, with the results being measured and evaluated using a wide range of performance indicators. In contrast to deep learning models that have already been trained, the total accuracy of 99.7% was determined to be superior.

• MeSH
• algoritmy MeSH
• deep learning * MeSH
• kostní dřeň diagnostické zobrazování   patologie   MeSH
• lidé MeSH
• nádory kostí patologie   diagnostické zobrazování   diagnóza   MeSH
• neuronové sítě * MeSH
• počítačové zpracování obrazu metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Therapeutic DNA vaccines against oncogenic infection with human papillomavirus type 16 (HPV16) are mostly targeted against viral oncoproteins E7 and E6. To adapt the E7 oncoprotein for DNA immunization, we have previously reduced its oncogenicity by modification of the Rb-binding site and enhanced immunogenicity of the modified E7GGG gene by the fusion with the 5'-terminus of the gene encoding E. coli beta-glucuronidase (GUS). In this study, we attempted to improve immunogenicity of the GUS-based anti-E7 vaccines by increasing the steady-state level of fusion proteins. We fused deletion mutants of E7GGG and codon-optimized E7GGG with the 5'-terminus of GUS and unaltered E7GGG with the 3'-terminus of GUS. Furthermore, we mutated the initiation codon of the GUS gene in the E7GGG.GUS construct, as GUS alone was produced from this fusion gene. We found that only the fusion of E7GGG with the 3'-terminus of GUS (GUS.E7GGG) and deletion mutants of E7GGG with the 5'-terminus of GUS increased the steady-state level of fusion proteins in transfected human 293T cells. Analysis of immune reactions induced in mice by vaccination via a gene gun showed that the increased steady-state level of fusion proteins resulted in augmented production of E7-specific antibodies, but did not enhance cell-mediated anti-tumor immunity. Finally, we joined the signal sequence of the adenoviral E3 protein with GUS.E7GGG. This modification led to the predominant localization of the fusion protein in the endoplasmic reticulum and enhancement of CD8+ T-cell response, while antibody production was reduced. In conclusion, we found modifications of the E7GGG.GUS fusion gene that augmented either humoral or cell-mediated immune responses.

• MeSH
• buňky NIH 3T3 MeSH
• cytotoxické T-lymfocyty imunologie   MeSH
• DNA vakcíny imunologie   MeSH
• financování organizované MeSH
• glukuronidasa genetika   imunologie   MeSH
• lidský papilomavirus 16 imunologie   MeSH
• myši inbrední C57BL MeSH
• myši MeSH
• onkogenní proteiny virové genetika   imunologie   MeSH
• Papillomavirus E7 - proteiny MeSH
• protilátky virové krev   MeSH
• rekombinantní fúzní proteiny imunologie   MeSH
• vakcíny proti papilomavirům imunologie   MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• ženské pohlaví MeSH
• zvířata MeSH

• MeSH
• matematika MeSH
• statistika jako téma MeSH

• Konspekt
• Statistika
• NLK Obory
• statistika, zdravotnická statistika

• MeSH
• lasery MeSH
• Publikační typ
• abstrakt z konference MeSH
• sborníky MeSH

• Konspekt
• Optika
• NLK Obory
• fyzika, biofyzika
• technika

Working with mitochondrial DNA from highly degraded samples is challenging. We present a whole mitogenome Illumina-based sequencing method suitable for highly degraded samples. The method makes use of double-stranded library preparation with hybridization-based target enrichment. The aim of the study was to implement a new user-friendly method for analysing many ancient DNA samples at low cost. The method combines the Swift 2S™ Turbo library preparation kit and xGen® panel for mitogenome enrichment. Swift allows to use low input of aDNA and own adapters and primers, handles inhibitors well, and has only two purification steps. xGen is straightforward to use and is able to leverage already pooled libraries. Given the ancient DNA is more challenging to work with, the protocol was developed with several improvements, especially multiplying DNA input in case of low concentration DNA extractions followed by AMPure® beads size selection and real-time pre-capture PCR monitoring in order to avoid cycle-optimization step. Nine out of eleven analysed samples successfully retrieved mitogenomes. Hence, our method provides an effective analysis of whole mtDNA, and has proven to be fast, cost-effective, straightforward, with utilisation in population-wide research of burial sites.

• MeSH
• analýza nákladů a výnosů MeSH
• genom mitochondriální * MeSH
• lidé MeSH
• mitochondriální DNA genetika   MeSH
• polymerázová řetězová reakce MeSH
• soudní genetika metody   MeSH
• starobylá DNA * MeSH
• vysoce účinné nukleotidové sekvenování metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH