Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

• Klíčová slova
• Biocatalysis, Enzyme engineering, Machine learning, Predictive models, Protein dynamics, Protein representations, Representation learning,
• MeSH
• biokatalýza * MeSH
• enzymy metabolismus   chemie   genetika   MeSH
• neuronové sítě MeSH
• proteiny chemie   metabolismus   MeSH
• strojové učení * MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• enzymy MeSH
• proteiny MeSH

Hippocampal activity is thought to encode spatial representations in a distributed associative network. This idea predicts that partial hippocampal lesions would spare acquisition and impair retrieval of a place response as long as enough connections remained intact to encode associations. Water maze experiments supported the predictions, but the prediction of impaired retrieval was not supported when tetrodotoxin (TTX) was injected into one hippocampus and rats were tested in a place avoidance task on a rotating arena with shallow water. The rotation dissociated relevant distal stimuli from irrelevant self-motion stimuli. To explain the discrepancy, we hypothesized that the segregation of relevant and irrelevant stimuli and stimuli association into representations are distinct hippocampus-dependent operations, and whereas associative representation is more sensitive to disruption during retrieval than learning, stimulus segregation is more sensitive to disruption during learning than during retrieval. The following predictions were tested: (1) the TTX injection would spare learning but (2) impair retrieval of a place response in the water maze, which has a high associative representational demand but a low demand for segregation; (3) the injection would impair learning but (4) spare retrieval of place avoidance in the rotating arena filled with water, which has a high demand for stimulus segregation but a low associative representational demand. All four predictions were confirmed. The hypothesis also explains the pattern of sparing and impairment after the TTX injection in other place avoidance task variants, leading us to conclude that stimulus separation and association representation are dissociable functions of the hippocampus.

• MeSH
• analýza rozptylu MeSH
• anestetika lokální toxicita   MeSH
• bludiště - učení účinky léků   fyziologie   MeSH
• časové faktory MeSH
• chování zvířat MeSH
• hipokampus účinky léků   zranění   fyziologie   MeSH
• krysa rodu Rattus MeSH
• poruchy paměti chemicky indukované   patofyziologie   MeSH
• potkani Long-Evans MeSH
• rozpomínání účinky léků   fyziologie   MeSH
• tetrodotoxin toxicita   MeSH
• učení vyhýbat se účinky léků   fyziologie   MeSH
• úniková reakce účinky léků   fyziologie   MeSH
• vnímání prostoru účinky léků   fyziologie   MeSH
• zvířata MeSH
• Check Tag
• krysa rodu Rattus MeSH
• mužské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• srovnávací studie MeSH
• Názvy látek
• anestetika lokální MeSH
• tetrodotoxin MeSH

TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions throughout Individual Case Safety Report (ICSR) processing, especially with rule-based automations such as robotic process automation, lookups, and workflows, moving from planning to piloting to implementation over the 3 survey years. Companies remain highly interested in other technologies such as machine learning (ML) and artificial intelligence, which can deliver a human-like interpretation of data and decision making rather than just automating tasks. Intelligent automation solutions are usually used in combination with more than one technology being used simultaneously for the same ICSR process step. Challenges to implementing intelligent automation solutions include finding/having appropriate training data for ML models and the need for harmonized regulatory guidance.

• MeSH
• automatizace MeSH
• farmakovigilance * MeSH
• lidé MeSH
• strojové učení MeSH
• technologie MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Research has shown a link between depression risk and how gamers form relationships with their in-game figure of representation, called avatar. This is reinforced by literature supporting that a gamer's connection to their avatar may provide broader insight into their mental health. Therefore, it has been argued that if properly examined, the bond between a person and their avatar may reveal information about their current or potential struggles with depression offline. To examine whether the connection with an individuals' avatars may reveal their risk for depression, longitudinal data from 565 adults/adolescents (Mage = 29.3 years, SD = 10.6) were evaluated twice (six months apart). Participants completed the User-Avatar-Bond [UAB] scale and Depression Anxiety Stress Scale to measure avatar bond and depression risk. A series of tuned and untuned artificial intelligence [AI] classifiers analyzed their responses concurrently and prospectively. This allowed the examination of whether user-avatar bond can provide cross-sectional and predictive information about depression risk. Findings revealed that AI models can learn to accurately and automatically identify depression risk cases, based on gamers' reported UAB, age, and length of gaming involvement, both at present and six months later. In particular, random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor. Study outcomes demonstrate that UAB can be translated into accurate, concurrent, and future, depression risk predictions via trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these results.

• Klíčová slova
• Artificial intelligence, Avatar, Depression, Internet gaming, Machine learning,
• MeSH
• avatar * MeSH
• deprese * MeSH
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• průřezové studie MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations. In this paper we attempt to follow a big picture view while also providing a particular theory and its implementation to present a novel, purposely simple, and interpretable hierarchical architecture. This architecture incorporates the unsupervised learning of a model of the environment, learning the influence of one's own actions, model-based reinforcement learning, hierarchical planning, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations which are increasingly more abstract, but can retain details when needed. We demonstrate the universality of the architecture by testing it on a series of diverse environments ranging from audio/visual compression to discrete and continuous action spaces, to learning disentangled representations.

• MeSH
• algoritmy MeSH
• lidé MeSH
• neuronové sítě MeSH
• posilování (psychologie) MeSH
• strojové učení bez učitele MeSH
• učení fyziologie   MeSH
• umělá inteligence * MeSH
• životní prostředí * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria. METHOD: The objective was to determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-the-curve. RESULTS: The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-the-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant. CONCLUSION: Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding multiple sclerosis diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.

• Klíčová slova
• Biomedical, Dysarthria, Machine learning, Multiple sclerosis, Phonetics, Speech acoustics, Technology assessment,
• MeSH
• lidé MeSH
• pilotní projekty MeSH
• řeč * MeSH
• roztroušená skleróza * MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.

• Klíčová slova
• Artificial intelligence, Artificial neural networks, Brain–computer interfaces, Fuzzy logic, Machine learning, Nature-inspired optimization techniques,
• MeSH
• algoritmy MeSH
• kvalita života MeSH
• lidé MeSH
• mozek MeSH
• počítače MeSH
• rozhraní mozek-počítač * MeSH
• strojové učení MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

Recent research has demonstrated the significance of incorporating invariance into neural networks. However, existing methods require direct sampling over the entire transformation set, notably computationally taxing for large groups like the affine group. In this study, we propose a more efficient approach by addressing the invariances of the subgroups within a larger group. For tackling affine invariance, we split it into the Euclidean group E(n) and uni-axial scaling group US(n), handling invariance individually. We employ an E(n)-invariant model for E(n)-invariance and average model outputs over data augmented from a US(n) distribution for US(n)-invariance. Our method maintains a favorable computational complexity of O(N2) in 2D and O(N4) in 3D scenarios, in contrast to the O(N6) (2D) and O(N12) (3D) complexities of averaged models. Crucially, the scale range for augmentation adapts during training to avoid excessive scale invariance. This is the first time nearly exact affine invariance is incorporated into neural networks without directly sampling the entire group. Extensive experiments unequivocally confirm its superiority, achieving new state-of-the-art results in affNIST and SIM2MNIST classifications while consuming less than 15% of inference time and fewer computational resources and model parameters compared to averaged models.

• Klíčová slova
• Affine invariance, Data augmentation, Representation learning, Self-supervised method,
• MeSH
• neuronové sítě * MeSH
• učení * MeSH
• Publikační typ
• časopisecké články MeSH

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

• Klíčová slova
• dielectric spectroscopy, fibrosis, machine learning, microwave, non-alcoholic steatohepatitis, relative permittivity,
• MeSH
• jaterní cirhóza MeSH
• játra patologie   MeSH
• myši inbrední C57BL MeSH
• myši MeSH
• nealkoholová steatóza jater * diagnóza   patologie   MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• fyziologie * MeSH
• lidé MeSH
• počítačem řízená výuka * MeSH
• umělá inteligence MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH