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Michael J Rempe,1,2 William C Clegern,2 Jonathan P Wisor2 1Mathematics and Computer Science, Whitworth University, Spokane, WA, USA; 2College of Medical Sciences and Sleep and Performance Research Center, Washington State University, Spokane, WA, USAIntroduction: Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2–10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.Methods: We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.Results: More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.Conclusions: Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.Keywords: EEG, automated scoring, principal component analysis, Bayes classification

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing., Methods: We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing., Results: By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing., Conclusions: MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://medshapenet.ikim.nrw/., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F 1 -detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Selma Ugurel reports a relationship with Bristol Myers Squibb that includes: consulting or advisory, funding grants, speaking and lecture fees, and travel reimbursement. Selma Ugurel reports a relationship with Merck Serono that includes: consulting or advisory, funding grants, and speaking and lecture fees. Selma Ugurel reports a relationship with Merck Sharp & Dohme that includes: consulting or advisory, speaking and lecture fees, and travel reimbursement. Selma Ugurel reports a relationship with Novartis that includes: consulting or advisory, speaking and lecture fees, and travel reimbursement. Selma Ugurel reports a relationship with Almirall that includes: travel reimbursement. Selma Ugurel reports a relationship with IGEA Clinical Biophysics that includes: travel reimbursement. Selma Ugurel reports a relationship with Pierre Fabre that includes: travel reimbursement. Selma Ugurel reports a relationship with Sun Pharma that includes: travel reimbursement. Jens T. Siveke reports a relationship with AstraZeneca that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Bayer AG that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Boehringer Ingelheim that includes: consulting or advisory, funding grants, and speaking and lecture fees. Jens T. Siveke reports a relationship with Bristol-Myers Squibb that includes: consulting or advisory, funding grants, and speaking and lecture fees. Jens T. Siveke reports a relationship with Immunocore that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with MSD Sharp Dohme that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Novartis that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Roche Genentech that includes: consulting or advisory, funding grants, and speaking and lecture fees. Jens T. Siveke reports a relationship with Servier that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Abalos Therapeutics that includes: funding grants. Jens T. Siveke reports a relationship with Celgene that includes: funding grants. Jens T. Siveke reports a relationship with Eisbach Bio that includes: funding grants. Jens T. Siveke reports a relationship with Pharma15 that includes: board membership and equity or stocks., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background and Objective: We present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich complex valued k-space., Methods: Using four datasets from different institutions with a total of around 200,000 MRI slices, we show that our network can perform skull-stripping on the raw data of MRIs while preserving the phase information which no other skull stripping algorithm is able to work with. For two of the datasets, skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain is used as the ground truth, whereas the third and fourth dataset comes with per-hand annotated brain segmentations., Results: All four datasets were very similar to the ground truth (DICE scores of 92 %-99 % and Hausdorff distances of under 5.5 pixel). Results on slices above the eye-region reach DICE scores of up to 99 %, whereas the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-Strip often has smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold., Conclusion: With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Besides preserving valuable information for further diagnostics, this approach makes an immediate anonymization of patient data possible, already before being transformed into the image domain. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2023. Published by Elsevier B.V.)

Sleep is required for the full expression of plasticity during the visual critical period (CP). However, the precise role of rapid-eye-movement (REM) sleep in this process is undetermined. Previous studies in rodents indicate that REM sleep weakens cortical circuits following MD, but this has been explored in only one class of cortical neuron (layer 5 apical dendrites). We investigated the role of REM sleep in ocular dominance plasticity (ODP) in layer 2/3 neurons using 2-photon calcium imaging in awake CP mice. In contrast to findings in layer 5 neurons, we find that REM sleep promotes changes consistent with synaptic strengthening and weakening. This supports recent suggestions that the effects of sleep on plasticity are highly dependent upon the type of circuit and preceding waking experience., Competing Interests: The authors have no conflicts of interest., (© 2022 The Authors.)

During earthquake propagation, geologic faults lose their strength, then strengthen as slip slows and stops. Many slip-weakening mechanisms are active in the upper-mid crust, but healing is not always well-explained. Here we show that the distinct structure and rate-dependent properties of amorphous nanopowder (not silica gel) formed by grinding of quartz can cause extreme strength loss at high slip rates. We propose a weakening and related strengthening mechanism that may act throughout the quartz-bearing continental crust. The action of two slip rate-dependent mechanisms offers a plausible explanation for the observed weakening: thermally-enhanced plasticity, and particulate flow aided by hydrodynamic lubrication. Rapid cooling of the particles causes rapid strengthening, and inter-particle bonds form at longer timescales. The timescales of these two processes correspond to the timescales of post-seismic healing observed in earthquakes. In natural faults, this nanopowder crystallizes to quartz over 10s-100s years, leaving veins which may be indistinguishable from common quartz veins.

Displaced starburst amacrine cells (SACs) are retinal interneurons that exhibit GABA( A ) receptor-mediated and Cl (-) cotransporter-mediated, directionally selective (DS) light responses in the rabbit retina. They depolarize to stimuli that move centrifugally through the receptive field surround and hyperpolarize to stimuli that move centripetally through the surround (Gavrikov et al, PNAS 100(26):16047-16052, 2003, PNAS 103(49):18793-18798, 2006). They also play a key role in the activity of DS ganglion cells (DS GC; Amthor et al, Vis Neurosci 19:495-509 2002; Euler et al, Nature 418:845-852, 2002; Fried et al, Nature 420:411- 414, 2002; Gavrikov et al, PNAS 100(26):16047-16052, 2003, PNAS 103(49):18793-18798, 2006; Lee and Zhou, Neuron 51:787-799 2006; Yoshida et al, Neuron 30:771-780, 2001). In this paper we present a model of strong DS behavior of SACs which relies on the GABA-mediated communication within a tightly interconnected network of these cells and on the glutamate signal that the SACs receive from bipolar cells (a presynaptic cell that receives input from cones). We describe how a moving light stimulus can produce a large, sustained depolarization of the SAC dendritic tips that point in the direction that the stimulus moves (i.e., centrifugal motion), but produce a minimal depolarization of the dendritic tips that point in the opposite direction (i.e., centripetal motion). This DS behavior, which is quantified based on the relative size and duration of the depolarizations evoked by stimulus motion at dendritic tips pointing in opposite directions, is robust to changes of many different parameter values and consistent with experimental data. In addition, the DS behavior is strengthened under the assumptions that the Cl(-) cotransporters Na( + )-K( + )-Cl( -) and K( + )-Cl( -) are located in different regions of the SAC dendritic tree (Gavrikov et al, PNAS 103(49):18793-18798, 2006) and that GABA evokes a long-lasting response (Gavrikov et al, PNAS 100(26):16047-16052, 2003, PNAS 103(49):18793-18798, 2006; Lee and Zhou, Neuron 51:787-799, 2006). A possible mechanism is discussed based on the generation of waves of local glutamate and GABA secretion, and their postsynaptic interplay as the waves travel between cell compartments.