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Simple Summary: With recent progress in radiation therapy, patients with bone metastases can be treated curatively, provided precise delineation of metastatic lesions is adequately identified. Tumor segmentation is a highly active area of research, but only limited studies have been on bone metastasis. This review aims to investigate methods for differentiating benign from malignant bone lesions and characterizing malignant bone lesions specifically in the context of bone metastases. While computer vision techniques have opened new opportunities for quantifying cancer growth with minimal expert supervision, fully automatic segmentation algorithms still require improvement. This is partly due to limited contrast between tumors and surrounding tissue and the lack of a widely agreed upon "gold standard" for defining these boundaries. Additionally, many studies do not provide evidence that their proposed methods are suitable for use in clinical practice. Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. Conclusion: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations. [ABSTRACT FROM AUTHOR]

Background: Imaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion of high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis of the resulting images is essential to effectively extract biological information from this data. CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements. Results: Herein we describe CellProfiler 4, a new version of this software with expanded functionality. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimizations to reduce the time and cost associated with running common large-scale analysis pipelines. Conclusions: CellProfiler 4 provides significantly improved performance in complex workflows compared to previous versions. This release will ensure that researchers will have continued access to CellProfiler's powerful computational tools in the coming years. [ABSTRACT FROM AUTHOR]

Summary Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualization tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses. Availability CellProfiler Analyst binaries for Windows and MacOS are freely available for download at https://cellprofileranalyst.org/. Source code is implemented in Python 3 and is available at https://github.com/CellProfiler/CellProfiler-Analyst/. A sample dataset is available at https://cellprofileranalyst.org/examples , based on images freely available from the Broad Bioimage Benchmark Collection. [ABSTRACT FROM AUTHOR]

Drug resistance is a challenge in anticancer therapy. In many cases, cancers can be resistant to the drug prior to exposure, that is, possess intrinsic drug resistance. However, we lack target-independent methods to anticipate resistance in cancer cell lines or characterize intrinsic drug resistance without a priori knowledge of its cause. We hypothesized that cell morphology could provide an unbiased readout of drug resistance. To test this hypothesis, we used HCT116 cells, a mismatch repair-deficient cancer cell line, to isolate clones that were resistant or sensitive to bortezomib, a well-characterized proteasome inhibitor and anticancer drug to which many cancer cells possess intrinsic resistance. We then expanded these clones and measured high-dimensional single-cell morphology profiles using Cell Painting, a high-content microscopy assay. Our imagingand computation-based profiling pipeline identified morphological features that differed between resistant and sensitive cells. We used these features to generate a morphological signature of bortezomib resistance. We then employed this morphological signature to analyze a set of HCT116 clones (five resistant and five sensitive) that had not been included in the signature training dataset, and correctly predicted sensitivity to bortezomib in seven cases, in the absence of drug treatment. This signature predicted bortezomib resistance better than resistance to other drugs targeting the ubiquitin-proteasome system, indicating specificity for mechanisms of resistance to bortezomib. Our results establish a proof-of-concept framework for the unbiased analysis of drug resistance using high-content microscopy of cancer cells, in the absence of drug treatment. [ABSTRACT FROM AUTHOR]

Ryanodine receptors (RyRs) mediate calcium release from calcium stores and have been implicated in axonal degeneration. Here, we use an intravital imaging approach to determine axonal fate after spinal cord injury (SCI) in real-time and assess the efficacy of ryanodine receptor inhibition as a potential therapeutic approach to prevent intra-axonal calcium-mediated axonal degeneration. Adult 6-8 week old Thy1YFP transgenic mice that express YFP in axons, as well as triple transgenic Avil-Cre:Ai9:Ai95 mice that express the genetically-encoded calcium indicator GCaMP6f in tdTomato positive axons, were used to visualize axons and calcium changes in axons, respectively. Mice received a mild SCI at the T12 level of the spinal cord. Ryanodine, a RyR antagonist, was given at a concentration of 50 μM intrathecally within 15 min of SCI or delayed 3 h after injury and compared with vehicle-treated mice. RyR inhibition within 15 min of SCI significantly reduced axonal spheroid formation from 1 h to 24 h after SCI and increased axonal survival compared with vehicle controls. Delayed ryanodine treatment increased axonal survival and reduced intra-axonal calcium levels at 24 h after SCI but had no effect on axonal spheroid formation. Together, our results support a role for RyR in secondary axonal degeneration. [ABSTRACT FROM AUTHOR]

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler‐plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency‐heavy. Here, we present an upgraded CellProfiler‐plugins repository, an example of accessing containerised tools, improved documentation and added citation/reference tools to facilitate the use and contribution of the community. [ABSTRACT FROM AUTHOR]

Falls in older adults represent the most common cause of injuries and a major cause of mortality in this vulnerable population. The morbidity and mortality rate of falls among older people makes balance analysis in older adults very important. Therefore, this study aims to explore different metrics that can potentially be used to identify early indications of balance loss and fall risk. To that end, the motion strategies and chest and pelvis coordination of a group of younger, a group of older non-faller and a group of older faller participants while conducting the functional reach test were investigated. To analyse the motion strategies of the different participant groups, four metrics of maximum angular rotation of chest, maximum angular rotation of pelvis, time warped chest and pelvis angular rotation difference, and the mean continuous relative phase of the chest and pelvis were assessed. In this study younger participants are found to have larger maximum chest rotation, maximum pelvis rotation, and time warped chest and pelvis angular rotation difference compared to older participants. However, these metrics were not significantly different in older non-fallers compared to older fallers. Meanwhile, the mean continuous relative phase of the chest and pelvis was the only metric found to be significantly different among all three participant groups. This metric is indicative of the chest and pelvis coordination which is associated with the ability to construct proper coordination and maintain balance. The mean continuous relative phase yielded the sensitivity of 92.3% and specificity of 73.7% in recognizing older fallers from older non-fallers. The results suggest that this metric might be useful in identifying the risk of falling in older population, thus, it should be further studied in a prospective study. [ABSTRACT FROM AUTHOR]

The blue skate (Dipturus batis) has a patchy distribution across the North‐East Atlantic Ocean, largely restricted to occidental seas around the British Isles following fisheries‐induced population declines and extirpations. The viability of remnant populations remains uncertain and could be impacted by continued fishing and by‐catch pressure, and the projected impacts of climate change. We genotyped 503 samples of D. batis, obtained opportunistically from the widest available geographic range, across 6 350 single nucleotide polymorphisms (SNPs) using a reduced‐representation sequencing approach. Genotypes were used to assess the species' contemporary population structure, estimate effective population sizes and identify putative signals of selection in relation to environmental variables using a seascape genomics approach. We identified genetic discontinuities between inshore (British Isles) and offshore (Rockall and Faroe Island) populations, with differentiation most pronounced across the deep waters of the Rockall Trough. Effective population sizes were largest in the Celtic Sea and Rockall, but low enough to be of potential conservation concern among Scottish and Faroese sites. Among the 21 candidate SNPs under positive selection was one significantly correlated with environmental variables predicted to be affected by climate change, including bottom temperature, salinity and pH. The paucity of well‐annotated elasmobranch genomes precluded us from identifying a putative function for this SNP. Nevertheless, our findings suggest that climate change could inflict a strong selective force upon remnant populations of D. batis, further constraining its already‐restricted habitat. Furthermore, the results provide fundamental insights on the distribution, behaviour and evolutionary biology of D. batis in the North‐East Atlantic that will be useful for the establishment of conservation actions for this and other critically endangered elasmobranchs. [ABSTRACT FROM AUTHOR]

Vulnerable marine ecosystems (VMEs) are particularly susceptible to bottom-fishing activity as they are easily disturbed and slow to recover. A data-driven approach was developed to provide management options for the protection of VMEs under the European Union "deep-sea access regulations." A total of two options within two scenarios were developed. The first scenario defined VME closure areas without consideration of fishing activity. Option 1 proposed closures for the protection of VME habitats and likely habitat, while Option 2 also included areas where four types of VME geophysical elements were present. The second scenario additionally considered fishing. This scenario used VME biomass—fishing intensity relationships to identify a threshold where effort of mobile bottom-contact gears was low and unlikely to have caused significant adverse impacts. Achieving a high level of VME protection requires the creation of many closures (> 100), made up of many small (∼50 km2) and fewer larger closures (> 1000 km2). The greatest protection of VMEs will affect approximately 9% of the mobile fleet fishing effort, while closure scenarios that avoid highly fished areas reduce this to around 4–6%. The framework allows managers to choose the level of risk-aversion they wish to apply in protecting VMEs by comparing alternative strategies. [ABSTRACT FROM AUTHOR]

• Driver cognitive fatigue patterns also reflect in postural behaviour. • Body posture variations of alert drivers are minimal in long monotonous freeways. • In pre-fatigue state, driver's yawning behaviour has trivial sternum variations. • Fatigued posture has frequent occurrences of nodding and shaking of the head. • Driver's postural variations are validated against brain signals technique. Driver cognitive fatigue can significantly affect driving and may lead to fatal accidents. In this regard, automatic detection of underload driver cognitive fatigue based on upper body posture dynamics is studied in this paper, where a semi-supervised approach is developed to identify the cognitive fatigue patterns of driver posture. Initially, an unsupervised Gaussian Mixture Model (GMM) clustering is applied to the acceleration data representing the driver's head, neck, and sternum obtained in a simulated driving through a motion capture suit. This provides the optimum clusters of the most-similar and correlated time-series data of driver upper posture. Then, an automatic labelling algorithm is developed that mines the maximal value and the standard deviation of each GMM cluster and assigns a symbol according to the discrepancy in postural behaviour. Finally, novel machine learning supervised classifiers, including Gaussian Support Vector Machines, and Bootstrap-Aggregating based Ensemble Classifiers, are trained on the GMM-labelled upper body posture dataset, as real-time algorithms, to detect the driver fatigue. The proposed method was validated against cognitive fatigue measured through a neurophysiological method based on an electroencephalogram. The results show that the proposed semi-supervised approach outperforms the existing state-of-art systems in accurately detecting the cognitive fatigue patterns. It successfully recognizes different driving postures with accuracies of 93% and 90% for two test subjects. The shortcomings of the proposed work and directions for potential expansion of current work are discussed. [ABSTRACT FROM AUTHOR]