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Abstract Biofilms that form on implanted medical devices cause recalcitrant infections. The early events enabling contaminating bacteria to evade immune clearance, before a mature biofilm is established, are poorly understood. Live imaging in vitro demonstrated that Staphylococcus aureus sparsely inoculated on an abiotic surface can go undiscovered by human neutrophils , grow, and form aggregates. Small (~50 μm 2) aggregates of attached bacteria resisted killing by human neutrophils, resulting in neutrophil lysis and bacterial persistence. In vivo, neutrophil recruitment to a peritoneal implant was spatially heterogenous, with some bacterial aggregates remaining undiscovered by neutrophils after 24 h. Intravital imaging in mouse skin revealed that attached S. aureus aggregates grew and remained undiscovered by neutrophils for up to 3 h. These results suggest a model in which delayed recruitment of neutrophils to an abiotic implant presents a critical window in which bacteria establish a nascent biofilm and acquire tolerance to neutrophil killing.

With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity. Although modern network science opens new frontiers of analyzing connectivity patterns in such datasets, we still lack data-driven methods for extracting an integral connectional fingerprint of a multi-view graph population, let alone disentangling the typical from the atypical variations across the population samples. We present the multi-view graph normalizer network (MGN-Net2), a graph neural network based method to normalize and integrate a set of multi-view biological networks into a single connectional template that is centered, representative, and topologically sound. We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer’s disease and Autism spectrum disorder patients. Additionally, by comparing the learned templates of healthy and disordered populations, we show that MGN-Net significantly outperforms conventional network integration methods across extensive experiments in terms of producing the most centered templates, recapitulating unique traits of populations, and preserving the complex topology of biological networks. Our evaluations showed that MGN-Net is powerfully generic and easily adaptable in design to different graph-based problems such as identification of relevant connections, normalization and integration.

Funder: Azrieli Centre for Autism Research Funder: Canadian Institutes of Health Research; Id: http://dx.doi.org/10.13039/501100000024 Funder: Fonds de Recherche du Québec Santé Background: Since the 1990s, increasing research has been devoted to the identification of biomarkers for autism to help attain more objective diagnosis; enable early prediction of prognosis; and guide individualized intervention options. Early studies focused on the identification of genetic variants associated with autism, but more recently, research has expanded to investigate neurodevelopmental markers. While ethicists have extensively discussed issues around advances in autism genomics, much less ethical scrutiny has focused on research on early neurodevelopment and on the interventions being developed as a result. Objectives: We summarize the current state of the science on the identification of early markers for autism and its potential clinical applications, before providing an overview of the ethical issues arising from increasing understanding of children's neurodevelopment in very early life. Results: Advances in the understanding of brain and behavioral trajectories preceding later autism diagnosis raise ethical concerns around three themes: (a) New models for understanding autism; (b) Risks and benefits of early identification and intervention; and (c) Communication of early concerns to families. These ethical issues should be further investigated in research conducted in partnership with autistic people and their families. Conclusions: This paper highlights the need for ethical scrutiny of early neurodevelopmental research in autism. Scrutiny requires expertise and methods from the basic sciences and bioethics, as well as constructive collaborations among autistic people, their parents, and autism researchers to anticipate early interventions that serve the community's interests and accommodate the varied experiences and preferences of people on the spectrum and their families.

Abstract: Background: Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients. Methods: We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP < 0.15). Results: Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E−08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E−07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E−08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E−08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy. Conclusions: We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited. Funder: FP7 Ideas: European Research Council; doi: http://dx.doi.org/10.13039/100011199; Grant(s): HEALTH-F2-2009-223175

1) Sample registration data. Stitch2p is a function that registeres 2p and 1p images of retina. INPUT: PATH: is the string path where the recordings are saved. RANGE is a 1x2 matrix that defines the range of movies to include in the analysis this works because each recording is assigned a number matching their order(movie_n). eg: calling [0 3] will analyze movie_0 to movie_3 in the specified path. CENTER is a path containing the blood vessel pattern acquired during the recording session. OUTPUT: ROIS = struct with all the 2p recording movies and bloodvessels used in the stiching STITCHED: Array with raw stitched image TESTIMAGE: converted 8bit image with color multiplier for visualization to use with provided sample images open matlab and set the path and center variables. For example: path = "C:\Downloads\Remapping process files\sample 2p recordings" center = "C:\Downloads\Remapping process files\sample 2p recordings\movie_10" Stich2p(path,[0 3],center); Should display a stitched image and save it, along with a matfile, to the path defined in 'center' Example images: any folder in the "sample 2p recordings" folder will contain example files of remapped images along with original recording data. Remapped images are based on the image in the "confocal ROIs" folder. File naming definitions: RoiSet: mask containing the cells of interest. bloodvessels: mask containing the bloodvessels for marker intensity calculations stim_x: individual presented stimuli along with relevant information expression_23_06_20_mx: CSV file containing assigned markers remap: the remapped image from which the markers where assigned remap_points: land mark points used to make the Remapped image landmark points are listed as the confocal and 2p image. 2) Visual response data: Matfile (*.mat) containing a single struct called 'compiled'. Struct field definitions: mb: struct containing rawTrace: Response averaged from 2 presentations of a bar moving in 8 different directions. Bar velocity = 1000um/sec. 8-bar sequence was preceded by a brief (.5s) flash. stimTrace: vector showing stimulus timing allignedTrace: Time x bar array with RGC responses corrected for position mbAngleOrder: bar direction vcctor rawTime: time vector for rawTrace allignedTime: time vector for allignedTrace ff: struct containing rawTrace: response averaged from 3 presentations of a full field flash. rawtime: time vector for rawTrace stimTrace: vector showing stimulus timing mbs: struct ordered the same way as mb. Contains data averaged from 2 presentations of a bar moving in 8 different directions. Bar velocity = 200um/sec. 8-bar sequence was preceded by a brief (.5s) flash. ROI: struct containing: mask - binary mask that defines RGC. xy: Roi centroid position. ost, Brn3c, nr2, calb, gfp: 8bit intensity of the indicated marker within RGC ROI defined by mask. size: Roi area. mrk: Marker classification. theta: angular preference computed from moving bar stimulus and set relative to retinal orientation. dsi: direction selective index computed from moving bar stimulus. osi: orientation selective index computed from moving bar stimulus. Nearly 50 different mouse retinal ganglion cell (RGC) types sample the visual scene for distinct features. RGC feature selectivity arises from its synapses with a specific subset of amacrine (AC) and bipolar cell (BC) types, but how RGC dendrites arborize and collect input from these specific subsets remains poorly understood. Here we examine the hypothesis that RGCs employ molecular recognition systems to meet this challenge. By combining calcium imaging and type-specific histological stains we define a family of circuits that express the recognition molecule Sidekick 1 (Sdk1) which include a novel RGC type (S1-RGC) that responds to local edges. Genetic and physiological studies revealed that Sdk1 loss selectively disrupts S1-RGC visual responses which result from a loss of excitatory and inhibitory inputs and selective dendritic deficits on this neuron. We conclude that Sdk1 shapes dendrite growth and wiring to help S1-RGCs become feature selective. Two Datasets are provided. 1) Sample registration data - contains images, code, and ROIs to register two-photon imaged fields of retinae containing GCaMP6f+ RGCs with the same retinae following staining with antibodies to marker genes for Sdk1 RGC types. 2) Visual response data - contains responses of Sdk1 RGCs to a full-field flash and moving bar, grouped according to expression of Ost, Brn3c, Nr2f2, and Calbindin.

This project publishes the code used in the following publication: Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer’s disease, NeuroImage: Clinical, 2021 Link: https://doi.org/10.1016/j.nicl.2021.102712, arxiv.org/2012.08769 Starting point: Overview.ipynb {"references": ["Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer's disease, NeuroImage: Clinical, 2021"]} https://gitlab.com/radiology/neuro/bron-cross-cohort/-/tree/v1.0

Background: Growing evidence suggests that exposure to green space is associated with improved childhood health and development, but the influence of different green space types remains relatively unexplored. In the present study, we investigated the association between early-life residential exposure to vegetation and early childhood development and evaluated whether associations differed according to land cover types, including paved land. Methods: Early childhood development was assessed via kindergarten teacher-ratings on the Early Development Instrument (EDI) in a large population-based birth cohort (n = 27,539) in Metro Vancouver, Canada. The residential surrounding environment was characterized using a high spatial resolution land cover map that was linked to children by six-digit residential postal codes. Early-life residential exposure (from birth to time of EDI assessment, mean age = 5.6 years) was calculated as the mean of annual percentage values of different land cover classes (i.e., total vegetation, tree cover, grass cover, paved surfaces) within a 250 m buffer zone of postal code centroids. Multilevel models were used to analyze associations between respective land cover classes and early childhood development. Results: In adjusted models, one interquartile range increase in total vegetation percentage was associated with a 0.33 increase in total EDI score (95% CI: 0.21, 0.45). Similar positive associations were observed for tree cover (β-coefficient: 0.26, 95% CI: 0.15, 0.37) and grass cover (β-coefficient: 0.12, 95% CI: 0.02, 0.22), while negative associations were observed for paved surfaces (β-coefficient: -0.35, 95% CI: -0.47, -0.23). Conclusions: Our findings indicate that increased early-life residential exposure to vegetation is positively associated with early childhood developmental outcomes, and that associations may be stronger for residential exposure to tree cover relative to grass cover. Our results further indicate that childhood development may be negatively associated with residential exposure to paved surfaces. These findings can inform urban planning to support early childhood developmental health. This work was supported by the Canadian Institutes of Health Research (grant numbers 156152 and 165754) and by the European Union's Horizon 2020 research and innovation programme under a Marie Skłodowska-Curie grant agreement No 891538. We acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. The land cover map was provided by Metro Vancouver. NDVI metrics and the Canadian Marginalization Index, all indexed to DMTI Spatial Inc. postal codes, were provided by CANUE (Canadian Urban Environmental Research Consortium). The Canadian Marginalization Index, used by CANUE, were developed by Flora Matheson, Jim Dunn, Katherine Smith, Rahim Moineddin, and Richard Glazier, using Statistics Canada Census data. We are grateful for the support of the Integrated Remote Sensing Studio at UBC and Nicholas Coops for the development of the spectrally unmixed Landsat greenness time series. The data that support the findings of this study are available from the Medical Services Plan of BC, BC Ministry of Health, BC Vital Statistics Agency, Perinatal Services BC, and the Human Early Learning Partnership, but restrictions apply to the availability of the data, which were used under agreement for research in the current study and are not openly available. Data are, however, accessible via Population Data BC and the relevant Data Stewards following approval of a Data Access Request and within the terms of Population Data BC data access. All inferences, opinions, and conclusions drawn in this manuscript are those of the authors, and do not reflect the opinions or policies of the Data Steward(s).

Familial hypercholesterolemia, a common genetic metabolic disorder characterized by high cholesterol levels, is involved in the development of atherosclerosis and other preventable diseases. Familial hypercholesterolemia can also cause tendinous abnormalities, such as thickening and xanthoma (tendon lipid accumulation) in the Achilles, which may impede tendon biomechanics. The objective of this study was to investigate the effect of cholesterol accumulation on the biomechanical performance of Achilles tendons, in vivo. 16 participants (10 men, 6 women; 37±6 years) with familial hypercholesterolemia, diagnosed with tendon xanthoma, and 16 controls (10 men, 6 women; 36±7 years) underwent Achilles biomechanical assessment. Achilles biomechanical data was obtained during preferred pace, shod, walking by analysis of lower limb kinematics and kinetics utilizing 3D motion capture and an instrumented treadmill. Gastrocnemius medialis muscle-tendon junction displacement was imaged using ultrasonography. Achilles stiffness, hysteresis, strain and force were calculated from displacement-force data acquired during loading cycles, and tested for statistical differences using one-way ANOVA. Statistical parametric mapping was used to examine group differences in temporal data. Participants with familial hypercholesterolemia displayed lower Achilles stiffness compared to the control group (familial hypercholesterolemia group: 87±20 N/mm; controls: 111±18 N/mm; p = 0.001), which appeared to be linked to Achilles loading rate rather than an increased strain (FH: 5.27±1.2%; controls: 4.95±0.9%; p = 0.413). We found different Achilles loading patterns in the familial hypercholesterolemia group, which were traced to differences in the centre of pressure progression that affected ankle moment. This finding may indicate that individuals with familial hypercholesterolemia use different Achilles loading strategies. Participants with familial hypercholesterolemia also demonstrated significantly greater Achilles hysteresis than the control group (familial hypercholesterolemia: 57.5±7.3%; controls: 43.8±10%; p<0.001), suggesting that walking may require a greater metabolic cost. Our results indicate that cholesterol accumulation could contribute to reduced Achilles function, while potentially increasing the chance of injury.

Fertilization is defined as the union of two gametes. During fertilization, sperm and egg fuse to form a diploid zygote to initiate prenatal development. In mammals, fertilization involves multiple ordered steps, including the acrosome reaction, zona pellucida penetration, sperm–egg attachment, and membrane fusion. Given the success of in vitro fertilization, one would think that the mechanisms of fertilization are understood; however, the precise details for many of the steps in fertilization remain a mystery. Recent studies using genetic knockout mouse models and structural biology are providing valuable insight into the molecular basis of sperm–egg attachment and fusion. Here, we review the cell biology of fertilization, specifically summarizing data from recent structural and functional studies that provide insights into the interactions involved in human gamete attachment and fusion.

Rare coding variants of the microglial triggering receptor expressed on myeloid cells 2 (TREM2) confer an increased risk for Alzheimer's disease (AD) characterized by the progressive accumulation of aggregated forms of amyloid �� peptides (A��). A�� peptides are generated by proteolytic processing of the amyloid precursor protein (APP). Heterogeneity in proteolytic cleavages and additional post-translational modifications result in the production of several distinct A�� variants that could differ in their aggregation behavior and toxic properties. Here, we sought to assess whether post-translational modifications of A�� affect the interaction with TREM2. Biophysical and biochemical methods revealed that TREM2 preferentially interacts with oligomeric A��, and that phosphorylation of A�� increases this interaction. Phosphorylation of A�� also affected the TREM2 dependent interaction and phagocytosis by primary microglia and in APP transgenic mouse models. Thus, TREM2 function is important for sensing phosphorylated A�� variants in distinct aggregation states and reduces the accumulation and deposition of these toxic A�� species in preclinical models of Alzheimer's disease. Funder: Canadian Institutes of Health Research; Id: http://dx.doi.org/10.13039/501100000024 Funder: Wellcome Trust Collaborative Award in Science Funder: Alzheimer's Association (Zenith Award) Funder: UK Alzheimer Society and ARUK