The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. In this paper we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached the highest F1 score up to 0.874 in the infected bee detection and up to 0.714 in the detection of the Varroa destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for the Varroa destructor mite detection on a honey bee. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies in real time.

• Klíčová slova
• Apis mellifera, SSD, Varroa destructor, YOLO, bee health monitoring, deep learning, object detection, western honey bee,
• MeSH
• paraziti * MeSH
• Varroidae * MeSH
• včely MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

Time-lapse imaging is a rich data source offering potential kinetic information of cellular activity and behavior. Tracking and extracting measurements of objects from time-lapse datasets are challenges that result from the complexity and dynamics of each object's motion and intensity or the appearance of new objects in the field of view. A wide range of strategies for proper data sampling, object detection, image analysis, and post-analysis interpretation are available. Theory and methods for single-particle tracking, spot detection, and object linking are discussed in this unit, as well as examples with step-by-step procedures for utilizing semi-automated software and visualization tools for achieving tracking results and interpreting this output.

• Klíčová slova
• digital imaging, image analysis, image segmentation methods, object linking, object tracking, sampling frequency, single-particle tracking,
• MeSH
• časosběrné zobrazování MeSH
• Chlamydomonas cytologie   MeSH
• dánio pruhované MeSH
• fluorescence MeSH
• krevní buňky cytologie   MeSH
• malá interferující RNA metabolismus   MeSH
• regionální krevní průtok MeSH
• rozpoznávání automatizované metody   MeSH
• zobrazování trojrozměrné * MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• malá interferující RNA MeSH

Infertility has recently emerged as a severe medical problem. The essential elements in male infertility are sperm morphology, sperm motility, and sperm density. In order to analyze sperm motility, density, and morphology, laboratory experts do a semen analysis. However, it is simple to err when using a subjective interpretation based on laboratory observation. In this work, a computer-aided sperm count estimation approach is suggested to lessen the impact of experts in semen analysis. Object detection techniques concentrating on sperm motility estimate the number of active sperm in the semen. This study provides an overview of other techniques that we can compare. The Visem dataset from the Association for Computing Machinery was used to test the proposed strategy. We created a labelled dataset to prove that our network can detect sperms in images. The best not-super tuned result is mAP 72.15.

• Klíčová slova
• computer-aided sperm analysis, small-object detection, sperm-cell detection, yolo,
• MeSH
• analýza spermatu MeSH
• lidé MeSH
• motilita spermií MeSH
• mužská infertilita * diagnóza   MeSH
• sperma * MeSH
• spermie MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity-hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated.

• Klíčová slova
• background, dynamic background, motion detection, object detection, person detection, static background,
• MeSH
• algoritmy * MeSH
• lidé MeSH
• nelékařská veřejná a soukromá zařízení * MeSH
• počítačové zpracování obrazu * MeSH
• pohyb * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

OBJECTIVES: Until recently, routine toxicological analysis of some hallucinogens in biological material posed problems which were only resolved after the introduction of modern analytic systems into toxicological laboratories. The most frequent hallucinogens in clinical and forensic toxicology can be grouped as: cannabinoids, tropane alkaloids, N,N-dimethyltryptamine derivatives and synthetic or semisynthetic hallucinogens. METHODS & RESULTS: There are several methods currently used for their analysis. Immunoassay analysis of abused hallucinogens is limited to the cannabinoids. Thin layer chromatography (TLC) is able to detect higher concentrations of 1-nor-delta- 9-tetrahydrocannabinol-9-carboxylic acid (THC-COOH), tropane alkaloids (atropine and scopolamine) and ketamine (synthetic hallucinogen) in urine but for lower concentrations and for some other substances it lacks sensitivity. A reliable solution to the demand for specific and sensitive analysis of hallucinogens in biological material is gas chromatography - mass spectrometry (GC-MS). Thus, at present, analysis of cannabinoids, tropane alkaloids, ketamine as well as psilocin (N,N-dimethyltryptamine derivative) is well-managed. CONCLUSIONS: The introduction of GC-MS systems appears to be indispensable for satisfactory qualitative and quantitative analysis of drugs of abuse, particularly hallucinogens in biological material.

• MeSH
• biologické modely MeSH
• chromatografie na tenké vrstvě MeSH
• halucinogeny krev   moč   MeSH
• lidé MeSH
• odhalování abúzu drog metody   MeSH
• plynová chromatografie s hmotnostně spektrometrickou detekcí MeSH
• soudní toxikologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• Názvy látek
• halucinogeny MeSH

Chest X-ray (CXR) is considered to be the most widely used modality for detecting and monitoring various thoracic findings, including lung carcinoma and other pulmonary lesions. However, X-ray imaging shows particular limitations when detecting primary and secondary tumors and is prone to reading errors due to limited resolution and disagreement between radiologists. To address these issues, we developed a deep-learning-based automatic detection algorithm (DLAD) to automatically detect and localize suspicious lesions on CXRs. Five radiologists were invited to retrospectively evaluate 300 CXR images from a specialized oncology center, and the performance of individual radiologists was subsequently compared with that of DLAD. The proposed DLAD achieved significantly higher sensitivity (0.910 (0.854-0.966)) than that of all assessed radiologists (RAD 10.290 (0.201-0.379), p < 0.001, RAD 20.450 (0.352-0.548), p < 0.001, RAD 30.670 (0.578-0.762), p < 0.001, RAD 40.810 (0.733-0.887), p = 0.025, RAD 50.700 (0.610-0.790), p < 0.001). The DLAD specificity (0.775 (0.717-0.833)) was significantly lower than for all assessed radiologists (RAD 11.000 (0.984-1.000), p < 0.001, RAD 20.970 (0.946-1.000), p < 0.001, RAD 30.980 (0.961-1.000), p < 0.001, RAD 40.975 (0.953-0.997), p < 0.001, RAD 50.995 (0.985-1.000), p < 0.001). The study results demonstrate that the proposed DLAD could be utilized as a decision-support system to reduce radiologists' false negative rate.

• Klíčová slova
• YOLO, computer-aided diagnosis, convolutional neural network, deep learning, lung cancer, object detection, pulmonary lesion,
• Publikační typ
• časopisecké články MeSH

Human perception and cognition are based predominantly on visual information processing. Much of the information regarding neuronal correlates of visual processing has been derived from functional imaging studies, which have identified a variety of brain areas contributing to visual analysis, recognition, and processing of objects and scenes. However, only two of these areas, namely the parahippocampal place area (PPA) and the lateral occipital complex (LOC), were verified and further characterized by intracranial electroencephalogram (iEEG). iEEG is a unique measurement technique that samples a local neuronal population with high temporal and anatomical resolution. In the present study, we aimed to expand on previous reports and examine brain activity for selectivity of scenes and objects in the broadband high-gamma frequency range (50-150 Hz). We collected iEEG data from 27 epileptic patients while they watched a series of images, containing objects and scenes, and we identified 375 bipolar channels responding to at least one of these two categories. Using K-means clustering, we delineated their brain localization. In addition to the two areas described previously, we detected significant responses in two other scene-selective areas, not yet reported by any electrophysiological studies; namely the occipital place area (OPA) and the retrosplenial complex. Moreover, using iEEG we revealed a much broader network underlying visual processing than that described to date, using specialized functional imaging experimental designs. Here, we report the selective brain areas for scene processing include the posterior collateral sulcus and the anterior temporal region, which were already shown to be related to scene novelty and landmark naming. The object-selective responses appeared in the parietal, frontal, and temporal regions connected with tool use and object recognition. The temporal analyses specified the time course of the category selectivity through the dorsal and ventral visual streams. The receiver operating characteristic analyses identified the PPA and the fusiform portion of the LOC as being the most selective for scenes and objects, respectively. Our findings represent a valuable overview of visual processing selectivity for scenes and objects based on iEEG analyses and thus, contribute to a better understanding of visual processing in the human brain.

• Klíčová slova
• high-frequency gamma activity, human brain, lateral occipital complex, objects, parahippocampal place area, scenes, stereoencephalography, visual processing,
• Publikační typ
• časopisecké články MeSH

Bloodborne pathogens (BBPs) pose formidable challenges in the realm of infectious diseases, representing significant risks to both human and animal health worldwide. The review paper provides a thorough examination of bloodborne pathogens, highlighting the serious worldwide threat they pose and the effects they have on animal and human health. It addresses the potential dangers of exposure that healthcare workers confront, which have affected 3 million people annually, and investigates the many pathways by which these viruses can spread. The limitations of traditional detection techniques like PCR and ELISA have been criticized, which has led to the investigation of new detection methods driven by advances in sensor technology. The objective is to increase the amount of knowledge that is available regarding bloodborne infections as well as effective strategies for their management and detection. This review provides a thorough overview of common bloodborne infections, including their patterns of transmission, and detection techniques.

• Klíčová slova
• biosensors, bloodborne pathogens, diagnosis, rapid detection techniques, transmission,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

BACKGROUND AND OBJECTIVE: While prostate cancer (PCa) incidence and mortality rates continue to rise, early detection of PCa remains highly controversial, and the research landscape is rapidly evolving. Existing systematic reviews (SRs) and meta-analyses (MAs) provide valuable insights, but often focus on single aspects of early detection, hindering a comprehensive understanding of the topic. We aim to fill this gap by providing a comprehensive SR of contemporary SRs covering different aspects of early detection of PCa in the European Union (EU) and the UK. METHODS: On June 1, 2023, we searched four databases (Medline ALL via Ovid, Embase, Web of Science, and Cochrane Central Register of Controlled Trials) and Google Scholar. To avoid repetition of previous studies, only SRs (qualitative, quantitative, and/or MAs) were considered eligible. In the data, common themes were identified to present the evidence systematically. KEY FINDINGS AND LIMITATIONS: We identified 1358 citations, resulting in 26 SRs eligible for inclusion. Six themes were identified: (1) invitation: men at general risk should be invited at >50 yr of age, and testing should be discontinued at >70 yr or with <10 yr of life expectancy; (2) decision-making: most health authorities discourage population-based screening and instead recommend a shared decision-making (SDM) approach, but implementation of SDM in clinical practice varies widely; decision aids help men make more informed and value-consistent screening decisions and decrease men's intention to attempt screening, but these do not affect screening uptake; (3) acceptance: facilitators for men considering screening include social prompting by partners and clinician recommendations, while barriers include a lack of knowledge, low-risk perception, and masculinity attributes; (4) screening test and algorithm: prostate-specific antigen-based screening reduces PCa-specific mortality and metastatic disease in men aged 55-69 yr at randomisation if screened at least twice; (5) harms and benefits: these benefits come at the cost of unnecessary biopsies, overdiagnosis, and subsequent overtreatment; and (6) future of screening: risk-adapted screening including (prebiopsy) risk calculators, magnetic resonance imaging, and blood- and urine-based biomarkers could reduce these harms. To enable a comprehensive overview, we focused on SRs. These do not include the most recent prospective studies, which were therefore incorporated in the discussion. CONCLUSIONS AND CLINICAL IMPLICATIONS: By identifying consistent and conflicting evidence, this review highlights the evidence-based foundations that can be built upon, as well as areas requiring further research and improvement to reduce the burden of PCa in the EU and UK. PATIENT SUMMARY: This review of 26 reviews covers various aspects of prostate cancer screening such as invitation, decision-making, screening tests, harms, and benefits. This review provides insights into existing evidence, highlighting the areas of consensus and discrepancies, to guide future research and improve prostate cancer screening strategies in Europe.

• Klíčová slova
• Benefits, Early detection, Harms, Mortality, Opportunistic testing, Prostate cancer, Prostate-specific antigen, Screening, Screening behaviour,
• MeSH
• časná detekce nádoru * MeSH
• Evropská unie MeSH
• lidé MeSH
• nádory prostaty * diagnóza   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• systematický přehled MeSH
• Geografické názvy
• Spojené království MeSH

BACKGROUND: Early diagnosis of cancer is essential for its effective treatment. Currently, established screening tests are cancer-specific and require screening for each type of cancer separately. The primary objective of cancer research is to develop methods that can detect multiple types of tumors from a single body fluid sample. Multicancer early detection tests aim to detect fragments of circulating tumor DNA, cell-free DNA, circulating microRNAs, or proteins released by cancer cells in the patient's body fluids. However, these tests are not suitable for routine cancer prevention due to their high cost. Therefore, in recent years, cancer screening tests have been developed to detect volatile organic compounds in urine using living organisms, such as nematodes, Caenorhabditis elegans. Measuring only 1 mm in length, C. elegans has the potential to offer a new, efficient, cost-effective, quick, and painless method to detect the presence of tumor. PURPOSE: The purpose of this review is to present an overview of the literature on the development and validation of C. elegans-based cancer detection methods. The potential benefits of these assays are significant, as they could become a valuable tool for the early identification and diagnosis of cancer, even though this research is still in its initial stages of development.

• Klíčová slova
• Caenorhabditis elegans, cancer, cancer diagnosis, detection methods,
• MeSH
• Caenorhabditis elegans * MeSH
• časná detekce nádoru * metody   MeSH
• lidé MeSH
• nádorové biomarkery MeSH
• nádory * diagnóza   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• nádorové biomarkery MeSH