The effect of a hiding space on the behaviour and heart rate variability of dairy calves during temporary separation from the dam.

In natural settings, newborn calves hide for several days before joining the herd. It is unclear whether dairy calves housed indoors would show similar hiding behaviour. This study aimed to describe the use of an artificial hide provided to calves during temporary separation from the dam and assess the effect it has on lying and sleep-like behaviour, as well as heart rate variability (HRV). Twenty-eight cow-calf pairs were randomly assigned to having a hide (n = 14), or no hide (n = 14). Hide use (n = 14), as well as lying and sleep-like behaviour (n = 28), were recorded continuously via video camera during the first hour after the dam was removed for morning milking on day three to seven. Heart rate and R-R intervals were recorded using Polar equine monitors for a subsample of 12 calves (n = 6 per treatment) on day six. Descriptive statistics were calculated for hide use. Wilcoxon Signed Rank tests were used to evaluate whether having a hide affected lying and sleep-like behaviours as well as HRV. Hide use decreased over days and was highly variable between calves. Lying behaviour did not differ between treatments. Duration of sleep-like behaviour was higher for calves without a hide compared to those with a hide. Calves with a hide tended to show signs of higher HRV and parasympathetic activity compared to calves without a hide. Results suggest that providing a hiding space to young calves may be beneficial during periods when the cow is removed from the pen for milking.

Reconstructing disease dynamics for mechanistic insights and clinical benefit.

Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data. We demonstrate the utility of TimeAx by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identify a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiates between early and late tumors within the same tumor subtype, uncovering molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics-providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction.

Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps.

Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease.

The impact of providing hiding spaces to farmed animals: A scoping review.

Many wild animals perform hiding behaviours for a variety of reasons, such as evading predators or other conspecifics. Unlike their wild counterparts, farmed animals often live in relatively barren environments without the opportunity to hide. Researchers have begun to study the impact of access to hiding spaces ("hides") in farmed animals, including possible effects on animal welfare. The aims of this scoping review were to: 1) identify the farmed species that have been most used in research investigating the provision of hides, 2) describe the context in which hides have been provided to farmed animals, and 3) describe the impact (positive, negative or neutral/inconclusive) that hides have on animals, including indicators of animal welfare. Three online databases (CAB Abstracts, Web of Science, and PubMed) were used to search for a target population of farmed animals with access to hiding spaces. From this search, 4,631 citations were screened and 151 were included in the review. Fourteen animal types were represented, most commonly chickens (48% of papers), cattle (9%), foxes (8%), and fish (7%). Relatively few papers were found on other species including deer, quail, ducks, lobsters, turkeys, and goats. Hides were used in four contexts: at parturition or oviposition (56%), for general enrichment (43%), for neonatal animals (4%), or for sick or injured animals (1%). A total of 218 outcomes relevant to our objectives were found including 7 categories: hide use, motivation, and/or preference (47% of outcomes), behavioural indicators of affective state (17%), health, injuries, and/or production (16%), agonistic behaviour (8%), abnormal repetitive behaviours (6%), physiological indicators of stress (5%), and affiliative behaviours (1%). Hiding places resulted in 162 positive (74%), 14 negative (6%), and 42 neutral/inconclusive (19%) outcomes. Hides had a generally positive impact on the animals included in this review; more research is encouraged for under-represented species.

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Squidpy: a scalable framework for spatial omics analysis.

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.

Image-based profiling: a powerful and challenging new data type.

Software has provided cell biologists the power to quantify specific cellular features in cell images at scale. Before long, these biologists also recognized the potential to extract much more biological information from the same images. From here, the field of image-based profiling, the process of extracting unbiased representations that capture morphological cell state, was born. We are still in the early days of image-based profiling, and it is clear that the many opportunities to interrogate biological systems come with significant challenges. These challenges include building expressive and biologically-relevant representations, adjusting for technical noise, writing generalizable software infrastructure, continuing to foster a culture of open science, and promoting FAIR (findable, accessible, interoperable, and reusable) data. We present a workshop at the Pacific Symposium on Biocomputing 2022 to introduce the field of image-based profiling to the broader computational biology community. In the following document, we introduce image-based profiling, discuss current state-of-the-art methods and limitations, and provide rationale for why now is the perfect time for the field to expand. We also introduce our invited speakers and agenda, which together provide an introduction to the field complemented by in-depth application areas in industry and academia. We also include five lightning talks to complement the invited speakers on various methodological and discovery advances.

Convolutional neural networks for cytoarchitectonic brain mapping at large scale.

Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional differences in the arrangement and composition of neuronal cells are indicators of changes in connectivity and function. Automated scanning procedures and observer-independent methods are prerequisites to reliably identify cytoarchitectonic areas, and to achieve reproducible models of brain segregation. Time becomes a key factor when moving from the analysis of single regions of interest towards high-throughput scanning of large series of whole-brain sections. Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains. It is based on a Deep Convolutional Neural Network (CNN), which is trained on a pair of section images with annotations, with a large number of un-annotated sections in between. The model learns to create all missing annotations in between with high accuracy, and faster than our previous workflow based on observer-independent mapping. The new workflow does not require preceding 3D-reconstruction of sections, and is robust against histological artefacts. It processes large data sets with sizes in the order of multiple Terabytes efficiently. The workflow was integrated into a web interface, to allow access without expertise in deep learning and batch computing. Applying deep neural networks for cytoarchitectonic mapping opens new perspectives to enable high-resolution models of brain areas, introducing CNNs to identify borders of brain areas.

Diagnosis and management of pharyngoesophageal stenosis: A comprehensive approach to prophylactic, endoscopic, and reconstructive treatment options.

BACKGROUND: Pharyngoesophageal stenosis (PES) is a serious complication that substantially impacts functional outcomes and quality of life (QOL) for up to a third of head and neck cancer patients who undergo radiotherapy. Dysphagia is often multifactorial in nature and is a devastating complication of treatment that impacts patients' QOL, general health and overall wellbeing. The authors detail the clinical presentation, risk factors, imaging characteristics, preventive measures, and multimodality treatment options for PES. METHODS: The authors present a comprehensive management algorithm for PES, including treatment by dilation, stenting, spray cryotherapy and dilation, and reconstructive treatment options utilizing different pedicled and free flaps. RESULTS: The authors advocate for a thorough assessment of the extent and degree of pharyngoesophageal involvement of PES to determine the optimal management strategy. CONCLUSIONS: The development of post treatment dysphagia requires appropriate imaging and biopsy, when indicated, to rule out the presence of persistent/recurrent cancer. Multidisciplinary management by a team of physicians well-versed in the range of diagnostic and therapeutic interventions available for PES is critical to its successful management.

Primary Hyperparathyroidism During Pregnancy Treated With Parathyroidectomy: A Systematic Review.

OBJECTIVES/HYPOTHESIS: The primary objective of this study was to assess the safety of parathyroidectomy during pregnancy as treatment for hyperparathyroidism (HPTH) in comparison to nonsurgical management plans. Secondary outcomes involved analyzing the safety of surgery in the third trimester and the benefit of operating on asymptomatic pregnant patients. STUDY DESIGN: Systematic review. METHODS: PRISMA-guided systematic review of all cases of primary hyperparathyroidism during pregnancy published in peer-reviewed English journals on PubMed/MEDLINE, EMBASE, and SCOPUS from 1980 to 2020. RESULTS: A total of 75 manuscripts were included in this review describing 382 cases of gestational hyperparathyroidism. The median maternal age was 31 years. Overall, 108 cases (28.3%) underwent parathyroidectomy during pregnancy while 274 cases (71.7%) were treated nonsurgically. The majority of surgeries took place during the second trimester (67.6%). Complications and/or deaths were less likely to occur after surgery in the second trimester (4.48%) as compared to surgery in the third trimester (21.1%). Nine surgically treated cases resulted in infant complications and/or death; however, none of these nine cases had any surgical complications. Despite these complications, the overall infant complication rate for patients who underwent surgical treatment remained lower than that of patients treated with conservative therapy (9.1% vs. 38.9%). CONCLUSIONS: This review suggests that for all pregnant patients with diagnosed HPTH, parathyroidectomy should be considered regardless of symptomatology. Our data suggest that parathyroidectomy is associated with fewer risks than more conservative treatments and results in better fetal outcomes. Surgery during the third trimester is feasible and surgery should be considered in both symptomatic and asymptomatic patients. Laryngoscope, 131:1915-1921, 2021.

Deep learning networks reflect cytoarchitectonic features used in brain mapping.

The distribution of neurons in the cortex (cytoarchitecture) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations of currently used cytoarchitectonic mapping methods, but typically lack insight as to what extent they follow cytoarchitectonic principles. We therefore investigated in how far the internal structure of deep convolutional neural networks trained for cytoarchitectonic brain mapping reflect traditional cytoarchitectonic features, and compared them to features of the current grey level index (GLI) profile approach. The networks consisted of a 10-block deep convolutional architecture trained to segment the primary and secondary visual cortex. Filter activations of the networks served to analyse resemblances to traditional cytoarchitectonic features and comparisons to the GLI profile approach. Our analysis revealed resemblances to cellular, laminar- as well as cortical area related cytoarchitectonic features. The networks learned filter activations that reflect the distinct cytoarchitecture of the segmented cortical areas with special regard to their laminar organization and compared well to statistical criteria of the GLI profile approach. These results confirm an incorporation of relevant cytoarchitectonic features in the deep convolutional neural networks and mark them as a valid support for high-throughput cytoarchitectonic mapping workflows.

BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices.

Histological atlases of the cerebral cortex, such as those made famous by Brodmann and von Economo, are invaluable for understanding human brain microstructure and its relationship with functional organization in the brain. However, these existing atlases are limited to small numbers of manually annotated samples from a single cerebral hemisphere, measured from 2D histological sections. We present the first whole-brain quantitative 3D laminar atlas of the human cerebral cortex. It was derived from a 3D histological atlas of the human brain at 20-micrometer isotropic resolution (BigBrain), using a convolutional neural network to segment, automatically, the cortical layers in both hemispheres. Our approach overcomes many of the historical challenges with measurement of histological thickness in 2D, and the resultant laminar atlas provides an unprecedented level of precision and detail. We utilized this BigBrain cortical atlas to test whether previously reported thickness gradients, as measured by MRI in sensory and motor processing cortices, were present in a histological atlas of cortical thickness and which cortical layers were contributing to these gradients. Cortical thickness increased across sensory processing hierarchies, primarily driven by layers III, V, and VI. In contrast, motor-frontal cortices showed the opposite pattern, with decreases in total and pyramidal layer thickness from motor to frontal association cortices. These findings illustrate how this laminar atlas will provide a link between single-neuron morphology, mesoscale cortical layering, macroscopic cortical thickness, and, ultimately, functional neuroanatomy.

The use of negative pressure wound therapy in the primary setting for high-risk head and neck surgery.

BACKGROUND: In head and neck surgery, dead space is typically managed by transferring a secondary pedicled flap or harvesting a larger composite flap with a muscular component. We demonstrate the novel use of prophylactic negative pressure wound therapy (NPWT) to obliterate dead space and reduce possible communication between the upper aerodigestive tract and the contents of the neck. METHODS: We present a single-institutional case series of five patients with high-risk head and neck cancer treated with NPWT after ablative and reconstructive surgery to eliminate dead space following surgical resection. RESULTS: All patients achieved successful wound closure following NPWT, which was applied in the secondary setting to combat infection in one patient and the primary setting to prophylactically eliminate dead space in four patients. CONCLUSION: NPWT can be used to treat unfilled dead space in the primary setting of head and neck ablative and reconstructive surgery and help to avoid wound healing problems as well as the need for secondary flap transfers.