Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40 × : 95.50%, and BreakHis 100 × : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net's aptness for agricultural growth as well as human cancer classification.

• Klíčová slova
• Agriculture, Cancer classification, Convolutional neural network, Graph convolutional network, Nutrition deficiency, Plant disease, Spatial pyramid pooling,
• MeSH
• deep learning * MeSH
• lidé MeSH
• listy rostlin MeSH
• nemoci rostlin * MeSH
• neuronové sítě * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Computer assisted image acquisition techniques, including confocal microscopy, require efficient tools for an automatic sorting of vast amount of generated image data. The complexity of the classification process, absence of adequate tools, and insufficient amount of reference data has made the automated processing of images challenging. Mastering of this issue would allow implementation of statistical analysis in research areas such as in research on formation of t-tubules in cardiac myocytes. We developed a system aimed at automatic assessment of cardiomyocyte development stages (SAACS). The system classifies confocal images of cardiomyocytes with fluorescent dye stained sarcolemma. We based SAACS on a densely connected convolutional network (DenseNet) topology. We created a set of labelled source images, proposed an appropriate data augmentation technique and designed a class probability graph. We showed that the DenseNet topology, in combination with the augmentation technique is suitable for the given task, and that high-resolution images are instrumental for image categorization. SAACS, in combination with the automatic high-throughput confocal imaging, will allow application of statistical analysis in the research of the tubular system development or remodelling and loss.

• MeSH
• buněčná diferenciace MeSH
• fluorescenční barviva MeSH
• kardiomyocyty cytologie   ultrastruktura   MeSH
• konfokální mikroskopie metody   MeSH
• krysa rodu Rattus MeSH
• modely kardiovaskulární MeSH
• neuronové sítě MeSH
• počítačové zpracování obrazu metody   MeSH
• sarkolema ultrastruktura   MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• zvířata MeSH
• Check Tag
• krysa rodu Rattus MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• fluorescenční barviva MeSH

Deepfake (DF) is a kind of forged image or video that is developed to spread misinformation and facilitate vulnerabilities to privacy hacking and truth masking with advanced technologies, including deep learning and artificial intelligence with trained algorithms. This kind of multimedia manipulation, such as changing facial expressions or speech, can be used for a variety of purposes to spread misinformation or exploitation. This kind of multimedia manipulation, such as changing facial expressions or speech, can be used for a variety of purposes to spread misinformation or exploitation. With the recent advancement of generative adversarial networks (GANs) in deep learning models, DF has become an essential part of social media. To detect forged video and images, numerous methods have been developed, and those methods are focused on a particular domain and obsolete in the case of new attacks/threats. Hence, a novel method needs to be developed to tackle new attacks. The method introduced in this article can detect various types of spoofs of images and videos that are computationally generated using deep learning models, such as variants of long short-term memory and convolutional neural networks. The first phase of this proposed work extracts the feature frames from the forged video/image using a sparse autoencoder with a graph long short-term memory (SAE-GLSTM) method at training time. The first phase of this proposed work extracts the feature frames from the forged video/image using a sparse autoencoder with a graph long short-term memory (SAE-GLSTM) method at training time. The proposed DF detection model is tested using the FFHQ database, 100K-Faces, Celeb-DF (V2) and WildDeepfake. The evaluated results show the effectiveness of the proposed method.

• Klíčová slova
• Capsule convolution neural network, Deep learning, DeepFake, Generative adversarial networks, Graph LSTM, Long short term memory (LSTM),
• Publikační typ
• časopisecké články MeSH

Interpretation of QSAR models is useful to understand the complex nature of biological or physicochemical processes, guide structural optimization or perform knowledge-based validation of QSAR models. Highly predictive models are usually complex and their interpretation is non-trivial. This is particularly true for modern neural networks. Various approaches to interpretation of these models exist. However, it is difficult to evaluate and compare performance and applicability of these ever-emerging methods. Herein, we developed several benchmark data sets with end-points determined by pre-defined patterns. These data sets are purposed for evaluation of the ability of interpretation approaches to retrieve these patterns. They represent tasks with different complexity levels: from simple atom-based additive properties to pharmacophore hypothesis. We proposed several quantitative metrics of interpretation performance. Applicability of benchmarks and metrics was demonstrated on a set of conventional models and end-to-end graph convolutional neural networks, interpreted by the previously suggested universal ML-agnostic approach for structural interpretation. We anticipate these benchmarks to be useful in evaluation of new interpretation approaches and investigation of decision making of complex "black box" models.

• Klíčová slova
• Atom contributions, Benchmark data set, Graph convolutional neural networks, Interpretability metrics, QSAR model interpretation, Synthetic data set,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Cognitive impairment in Parkinson's disease (PD) is associated with altered connectivity of the resting state networks (RSNs). Longitudinal studies in well cognitively characterized PD subgroups are missing. OBJECTIVES: To assess changes of the whole-brain connectivity and between-network connectivity (BNC) of large-scale functional networks related to cognition in well characterized PD patients using a longitudinal study design and various analytical methods. METHODS: We explored the whole-brain connectivity and BNC of the frontoparietal control network (FPCN) and the default mode, dorsal attention, and visual networks in PD with normal cognition (PD-NC, n = 17) and mild cognitive impairment (PD-MCI, n = 22) as compared to 51 healthy controls (HC). We applied regions of interest-based, partial least squares, and graph theory based network analyses. The differences among groups were analyzed at baseline and at the one-year follow-up visit (37 HC, 23 PD all). RESULTS: The BNC of the FPCN and other RSNs was reduced, and the whole-brain analysis revealed increased characteristic path length and decreased average node strength, clustering coefficient, and global efficiency in PD-NC compared to HC. Values of all measures in PD-MCI were between that of HC and PD-NC. After one year, the BNC was further increased in the PD-all group; no changes were detected in HC. No cognitive domain z-scores deteriorated in either group. CONCLUSION: As compared to HC, PD-NC patients display a less efficient transfer of information globally and reduced BNC of the visual and frontoparietal control network. The BNC increases with time and MCI status, reflecting compensatory efforts.

• Klíčová slova
• Between-network connectivity, Parkinson’s disease, cognitive resting state brain networks, functional MRI, graph measures, longitudinal, mild cognitive impairment, partial least squares analysis,
• MeSH
• dospělí MeSH
• kognitivní dysfunkce etiologie   patologie   psychologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• longitudinální studie MeSH
• magnetická rezonanční tomografie MeSH
• mozek diagnostické zobrazování   patologie   MeSH
• nervová síť diagnostické zobrazování   patologie   MeSH
• neurozobrazování MeSH
• Parkinsonova nemoc komplikace   patologie   psychologie   MeSH
• prefrontální mozková kůra patologie   MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• studie případů a kontrol MeSH
• temenní lalok patologie   MeSH
• testy pro posouzení mentálních funkcí a demence MeSH
• zrakové korové centrum patologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH