Confinement energy landscape classification reveals membrane receptor nano-organization mechanisms.

The cell membrane organization has an essential functional role through the control of membrane receptor confinement in micro- or nanodomains. Several mechanisms have been proposed to account for these properties, though some features have remained controversial, notably the nature, size, and stability of cholesterol- and sphingolipid-rich domains- or lipid rafts. Here, we probed the effective energy landscape actin on single nanoparticle-labeled membrane receptors confined in raft nanodomains - Epidermal Growth Factor, C. perfringens ε-toxin and C. Septicum α-toxin receptors (EGFR , CPεTR and CSαTR,) - and compared it with hop-diffusing transferrin receptors (TfR). By establishing a new analysis pipeline combining Bayesian inference, decision trees and clustering approaches, we systematically classified single protein trajectories according to the type of effective confining energy landscape. This revealed the existence of only two distinct organization modalities: (A) confinement in a quadratic energy landscape for EGF, CPεT and CSαT receptors and (B) free diffusion in confinement domains resulting from the steric hindrance due to F-actin barriers for TfR. The further characterization of effective confinement energy landscapes by Bayesian inference revealed the role of interactions with the domain environment in cholesterol- and sphingolipid-rich domains with (EGFR) or without (CPεTR and CSαTR) interactions with F-actin, to regulate the confinement energy depth. These two distinct mechanisms result in the same organization type (A). We revealed that the apparent domain sizes for these receptor trajectories resulted from Brownian exploration of the energy landscape in a steady-state like regime at a common effective temperature, independently of the underlying molecular mechanisms. These results highlight that confinement domains may be adequately described as interaction hotspots rather than rafts with abrupt domain boundaries. Altogether, these results support a new model for functional receptor confinement in membrane nanodomains and pave the way to the constitution of an atlas of membrane protein organization.

Motion of VAPB molecules reveals ER-mitochondria contact site subdomains.

To coordinate cellular physiology, eukaryotic cells rely on the rapid exchange of molecules at specialized organelle-organelle contact sites1,2. Endoplasmic reticulum-mitochondrial contact sites (ERMCSs) are particularly vital communication hubs, playing key roles in the exchange of signalling molecules, lipids and metabolites3,4. ERMCSs are maintained by interactions between complementary tethering molecules on the surface of each organelle5,6. However, due to the extreme sensitivity of these membrane interfaces to experimental perturbation7,8, a clear understanding of their nanoscale organization and regulation is still lacking. Here we combine three-dimensional electron microscopy with high-speed molecular tracking of a model organelle tether, Vesicle-associated membrane protein (VAMP)-associated protein B (VAPB), to map the structure and diffusion landscape of ERMCSs. We uncovered dynamic subdomains within VAPB contact sites that correlate with ER membrane curvature and undergo rapid remodelling. We show that VAPB molecules enter and leave ERMCSs within seconds, despite the contact site itself remaining stable over much longer time scales. This metastability allows ERMCSs to remodel with changes in the physiological environment to accommodate metabolic needs of the cell. An amyotrophic lateral sclerosis-associated mutation in VAPB perturbs these subdomains, likely impairing their remodelling capacity and resulting in impaired interorganelle communication. These results establish high-speed single-molecule imaging as a new tool for mapping the structure of contact site interfaces and reveal that the diffusion landscape of VAPB at contact sites is a crucial component of ERMCS homeostasis.

A single-centre experience of 29 total ankle replacement in haemophiliac patients: Therapeutic management, factor consumption and cost.

INTRODUCTION: In patients with haemophilia, repeated bleeding in large joints leads to chronic haemophilic arthropathy, a rare disease that can be managed surgically with ankle arthrodesis or with total ankle replacement (TAR). TAR has been reported to provide good surgical results in the medium/long-term and allow preservation of joint mobility but the medical therapeutic management of the patients has not been described. AIM: To describe the medical therapeutic management of TAR. METHODS: All patients with haemophilia A/B, with haemophilic ankle arthropathy, and who underwent TAR between April 2006 and October 2019 were retrospectively included. Factor consumption, perioperative and early complications, volume of blood lost, and orthopaedic data were collected. RESULTS: A total of 25 patients underwent 29 TAR (mean age was 44.7 years [range: 26-65]). In the 17 patients with HA without history of anti-FVIII inhibitor, the mean ± SD consumption the day of surgery was 116 ± 16 UI/kg when clotting factors were administered by continuous infusion, 106 ± 13 UI/kg when SHL factors were administered by bolus infusion, and 75 ± 22 UI/kg when EHL factors were administered by bolus infusion. During hospitalisation, the mean factor cost was €38,073 (83.7% of the total cost of surgery). Mean blood loss was significantly lower in patients treated with tranexamic acid (164 mL, range: 40-300) than in those not (300 mL, range: 70-800; p = .01). Six patients had haematoma. The 10-year survival free of any prosthesis removal/arthrodesis was estimated to be 92.2% (95% CI [83; 100]). CONCLUSION: The medical therapeutic management of TAR is complex, carried out by a multidisciplinary team but effective in avoiding the occurrence of complications.

Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context.

The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, it is essential to use tools that can monitor occupant habits without altering them. Invasive methods such as body sensors or cameras could potentially disrupt the natural habits of the occupants. In our study, we primarily focus on occupancy states as a representation of occupant habits. We have created a model based on artificial neural networks (ANNs) to ascertain the occupancy state of a building using environmental data such as CO2 concentration and noise level. These data are collected through non-intrusive sensors. Our approach involves rule-based a priori labeling and the use of a long short-term memory (LSTM) network for predictive purposes. The model is designed to predict four distinct states in a residential building. Although we lack data on actual occupancy states, the model has shown promising results with an overall prediction accuracy ranging between 78% and 92%.

Transverse subtrochanteric shortening osteotomy with double tension-band fixation during THA for Crowe III-IV developmental dysplasia: 12-year outcomes.

BACKGROUND: When performing total hip arthroplasty in patients with severe developmental dysplasia, shortening the femur facilitates reduction while also preventing sciatic or crural nerve injury and excessive length of the operated limb. No consensus exists about the optimal type of implant and best internal fixation procedure, two parameters that directly govern the risk of the most common intraoperative and postoperative complications (diaphyseal fractures, dislocation, non-union). To minimise these risks, we developed a technique combining a transverse subtrochanteric shortening osteotomy, a long ovoid-profiled, cementless stem anchored in the metaphysis, and double tension-band wiring for internal fixation. The primary objective of this study was to evaluate the outcomes of this technique with emphasis on (i) complications and femoral implant survival, (ii) clinical outcomes (functional scores and lower-limb length inequality [LLLI]), and time to healing. HYPOTHESIS: Our technique is associated with low rates of intraoperative and postoperative complications. MATERIAL AND METHODS: This single-centre retrospective cohort study included patients who underwent THA with a transverse subtrochanteric shortening osteotomy and fixation using double tension-band wiring to treat severe (Crowe III or IV) developmental hip dysplasia. The femoral implant was a long, ovoid, cementless stem fully coated with hydroxyapatite. We collected the intraoperative and postoperative complications, survival, LLLI, functional scores (Harris Hip Score [HHS] and Forgotten Joint Score [FJS]), patient satisfaction, and radiographic outcomes. RESULTS: The study included 31 hips in 25 patients. Two patients (2/31 hips, 6.5%) were lost to follow-up, leaving 29 hips for the analysis of postoperative outcomes. Mean follow-up was 13.7±4.2 years (range, 5.8-18.3 years). The four intraoperative complications (4/31, 12.9%) consisted fracture of the diaphysis (2/31, 6.5%), fracture of the greater trochanter (1/31, 3.2%), and sciatic nerve injury followed by a full recovery (1/29, 3.4%). The 8 (8/29, 27.5%) postoperative complications consisted of dislocation (2/29, 6.9%), stem subsidence (2/29, 6.9%), and non-union (4/29, 13.8%). Femoral implant survival at last follow-up was 87.1% (95% CI, 76.1-99.7). The mean HHS increased from 39.6±12.0 (range, 14-61) before surgery to 81.7±13.2 (range, 48-100) at last follow-up (p<0.01). The FJS at last follow-up indicated that the joint was forgotten in 14/29 (48.2%) cases and caused only acceptable symptoms in 9/29 (31.0%) of cases. Clinically significant (≥ 1cm) LLLI was present in 8/29 (27.6%) patients postoperatively compared to 19/29 (65.5%) preoperatively. The mean LLLI decreased from 20.8±19.7mm (range, 0-60mm) to 5.0±7.3mm (range, 0-30mm). Mean time to healing was 4.3±2.4 months (range, 2-11 months). CONCLUSION: Regarding these complex procedures, this technique was associated with low rates of intraoperative fractures and early postoperative complications. However, femoral stem survival was shorter than in earlier studies and the non-union rate was high, despite satisfactory functional and clinical outcomes.

Fast-track virtual reality software to facilitate 3-dimensional reconstruction in congenital heart disease.

OBJECTIVES: Two limitations of the clinical use of 3-dimensional (3D) reconstruction and virtual reality systems are the relatively high cost and the amount of experience required to use hardware and software to effectively explore medical images. We have tried to simplify the process and validate a new tool developed for this purpose with a novel software package. METHODS: Five patients with right partial anomalous pulmonary venous return with adequate preoperative images acquired with magnetic resonance imaging were enrolled. Five volunteers with no previous experience in the field of 3D reconstruction were instructed to use the software after viewing a short video tutorial. Users were then asked to create a 3D model of each patient's heart using DIVA software. Their results were compared quantitatively and qualitatively with a benchmark reconstruction performed by an experienced user. RESULTS: All our participants recreated 3D models in a relatively short time, maintaining a good overall quality (average quality score ≥ 3 on a scale of 1-5). The overall trend of all the parameters analysed showed a statistical improvement between case 1 and case 5, as users became more and more experienced. CONCLUSIONS: DIVA is a simple software program that allows accurate 3D reconstruction in a relatively short time ("fast-track" virtual reality). In this study, we demonstrated the potential use of DIVA by inexperienced users, with a significant improvement in quality and time after a few cases were performed. Further studies are needed to confirm the potential application of this technology on a larger scale.

Survival and complications at a minimum 5years' follow-up of the modular Mark-2 Extreme™ cementless femoral stem: Does the reduced modularity resolve the mechanical issues of the Mark-I stem?

INTRODUCTION: The Extreme™ modular stem was developed for implant revision with metaphyseal-diaphyseal defect. Due to the high breakage rate, a new "reduced modularity" design has been introduced, but without reported results. We therefore conducted a retrospective assessment of (1) overall stem survival, (2) functional results, (3) osseointegration, and (4) the rate of complications, and notably of mechanical failure. HYPOTHESIS: Reduced modularity reduces the risk of revision surgery for mechanical failure. MATERIAL AND METHODS: Forty-five prostheses were implanted between January 2007 and December 2010 in 42 patients with severe bone defect (Paprosky≥III) or periprosthetic shaft fracture. Mean age was 69.6years (range: 44-91years). Minimum follow-up was 5years, for a mean 115.4months (range: 60-156months). The main study endpoint was femoral stem survival, counting all-cause explantation as event. Functional assessment comprised subjective rating of satisfaction, Postel Merle d'Aubigné (PMA) and Harris Hip scores, and Forgotten Joint Score (FJS). Whether the revision assembly was carried out in situ, in the patient's hip, or outside, on the operating table, was not known in 2 cases; in the other 43, assembly was in situ in 15 cases (35%) and on the operating table in 28 (65%). RESULTS: Five-year stem survival was 75.7% (95% CI: 61.9-89.5%), taking all causes of change together. Seventeen patients (45.9%) had complications, 13 (35.1%) requiring revision surgery, including 10 (27.0%) for stem replacement. Five patients (13.5%) had steam breakage at the junction between the metaphysis and the diaphyseal stem, 4 of which occurred within 2 years of implantation or of fixation of a periprosthetic fracture. Mean preoperative Harris score was 48.4 [IQR (25-75% interquartile range): 37-58] and PMA score 11.1 (IQR: 10-12), compared to respectively 74 (IQR: 67-89) and 13.6 (IQR: 12.5-16) at follow-up. Mean FJS at follow-up was 71.5 (IQR: 61-94.5). In the 15 in situ assemblies, there were 3 breakages (20%), compared to 2 (7.1%) in the 28 table assemblies (p=0.21). DISCUSSION: The stem breakage rate was high despite the reduced modularity, which concentrated all stress on a single junction but without reducing the risk of mechanical failure. Surgical technique was faulty in some cases, with in situ assembly of the metaphysis after implanting the diaphyseal stem, which does not respect the manufacturer's recommendations. LEVEL OF EVIDENCE: IV; retrospective study.

Simulation-based inference for non-parametric statistical comparison of biomolecule dynamics.

Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment, out-of-equilibrium fluxes and conformal changes of the tracked molecules. Recorded trajectories are often too short to reliably discern such multi-scale dynamics, which precludes unambiguous assessment of the type of random walk and its parameters. Furthermore, the motion of biomolecules may not be well described by a single, canonical random walk model. Here, we develop a two-step statistical testing scheme for comparing biomolecule dynamics observed in different experimental conditions without having to identify or make strong prior assumptions about the model generating the recorded random walks. We first train a graph neural network to perform simulation-based inference and thus learn a rich summary statistics vector describing individual trajectories. We then compare trajectories obtained in different biological conditions using a non-parametric maximum mean discrepancy (MMD) statistical test on their so-obtained summary statistics. This procedure allows us to characterise sets of random walks regardless of their generating models, without resorting to model-specific physical quantities or estimators. We first validate the relevance of our approach on numerically simulated trajectories. This demonstrates both the statistical power of the MMD test and the descriptive power of the learnt summary statistics compared to estimates of physical quantities. We then illustrate the ability of our framework to detect changes in α-synuclein dynamics at synapses in cultured cortical neurons, in response to membrane depolarisation, and show that detected differences are largely driven by increased protein mobility in the depolarised state, in agreement with previous findings. The method provides a means of interpreting the differences it detects in terms of single trajectory characteristics. Finally, we emphasise the interest of performing various comparisons to probe the heterogeneity of experimentally acquired datasets at different levels of granularity (e.g., biological replicates, fields of view, and organelles).

Variational inference of fractional Brownian motion with linear computational complexity.

We introduce a simulation-based, amortized Bayesian inference scheme to infer the parameters of random walks. Our approach learns the posterior distribution of the walks' parameters with a likelihood-free method. In the first step a graph neural network is trained on simulated data to learn optimized low-dimensional summary statistics of the random walk. In the second step an invertible neural network generates the posterior distribution of the parameters from the learned summary statistics using variational inference. We apply our method to infer the parameters of the fractional Brownian motion model from single trajectories. The computational complexity of the amortized inference procedure scales linearly with trajectory length, and its precision scales similarly to the Cramér-Rao bound over a wide range of lengths. The approach is robust to positional noise, and generalizes to trajectories longer than those seen during training. Finally, we adapt this scheme to show that a finite decorrelation time in the environment can furthermore be inferred from individual trajectories.

Challenges of intracellular visualization using virtual and augmented reality.

Microscopy image observation is commonly performed on 2D screens, which limits human capacities to grasp volumetric, complex, and discrete biological dynamics. With the massive production of multidimensional images (3D + time, multi-channels) and derived images (e.g., restored images, segmentation maps, and object tracks), scientists need appropriate visualization and navigation methods to better apprehend the amount of information in their content. New modes of visualization have emerged, including virtual reality (VR)/augmented reality (AR) approaches which should allow more accurate analysis and exploration of large time series of volumetric images, such as those produced by the latest 3D + time fluorescence microscopy. They include integrated algorithms that allow researchers to interactively explore complex spatiotemporal objects at the scale of single cells or multicellular systems, almost in a real time manner. In practice, however, immersion of the user within 3D + time microscopy data represents both a paradigm shift in human-image interaction and an acculturation challenge, for the concerned community. To promote a broader adoption of these approaches by biologists, further dialogue is needed between the bioimaging community and the VR&AR developers.

High-throughput automated methods for classical and operant conditioning of Drosophila larvae.

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i.e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i.e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.

TRamWAy: mapping physical properties of individual biomolecule random motion in large-scale single-particle tracking experiments.

MOTIVATION: Single-molecule localization microscopy allows studying the dynamics of biomolecules in cells and resolving the biophysical properties of the molecules and their environment underlying cellular function. With the continuously growing amount of data produced by individual experiments, the computational cost of quantifying these properties is increasingly becoming the bottleneck of single-molecule analysis. Mining these data requires an integrated and efficient analysis toolbox. RESULTS: We introduce TRamWAy, a modular Python library that features: (i) a conservative tracking procedure for localization data, (ii) a range of sampling techniques for meshing the spatio-temporal support of the data, (iii) computationally efficient solvers for inverse models, with the option of plugging in user-defined functions and (iv) a collection of analysis tools and a simple web-based interface. AVAILABILITY AND IMPLEMENTATION: TRamWAy is a Python library and can be installed with pip and conda. The source code is available at https://github.com/DecBayComp/TRamWAy.

Hematopoietic stem cell transplantation chemotherapy causes microglia senescence and peripheral macrophage engraftment in the brain.

Hematopoietic stem cell transplantation (HSCT) is a therapy used for multiple malignant and nonmalignant diseases, with chemotherapy used for pretransplantation myeloablation. The post-HSCT brain contains peripheral engrafted parenchymal macrophages, despite their absence in the normal brain, with the engraftment mechanism still undefined. Here we show that HSCT chemotherapy broadly disrupts mouse brain regenerative populations, including a permanent loss of adult neurogenesis. Microglial density was halved, causing microglial process expansion, coinciding with indicators of broad senescence. Although microglia expressed cell proliferation markers, they underwent cell cycle arrest in S phase with a majority expressing the senescence and antiapoptotic marker p21. In vivo single-cell tracking of microglia after recovery from chemical depletion showed loss of their regenerative capacity, subsequently replaced with donor macrophages. We propose that HSCT chemotherapy causes microglial senescence with a gradual decrease to a critical microglial density, providing a permissive niche for peripheral macrophage engraftment of the brain.

Breast Magnetic Resonance Image Analysis for Surgeons Using Virtual Reality: A Comparative Study.

PURPOSE: The treatment of breast cancer, the leading cause of cancer and cancer mortality among women worldwide, is mainly on the basis of surgery. In this study, we describe the use of a medical image visualization tool on the basis of virtual reality (VR), entitled DIVA, in the context of breast cancer tumor localization among surgeons. The aim of this study was to evaluate the speed and accuracy of surgeons using DIVA for medical image analysis of breast magnetic resonance image (MRI) scans relative to standard image slice-based visualization tools. MATERIALS AND METHODS: In our study, residents and practicing surgeons used two breast MRI reading modalities: the common slice-based radiology interface and the DIVA system in its VR mode. Metrics measured were compared in relation to postoperative anatomical-pathologic reports. RESULTS: Eighteen breast surgeons from the Institut Curie performed all the analysis presented. The MRI analysis time was significantly lower with the DIVA system than with the slice-based visualization for residents, practitioners, and subsequently the entire group (P < .001). The accuracy of determination of which breast contained the lesion significantly increased with DIVA for residents (P = .003) and practitioners (P = .04). There was little difference between the DIVA and slice-based visualization for the determination of the number of lesions. The accuracy of quadrant determination was significantly improved by DIVA for practicing surgeons (P = .01) but not significantly for residents (P = .49). CONCLUSION: This study indicates that the VR visualization of medical images systematically improves surgeons' analysis of preoperative breast MRI scans across several different metrics irrespective of surgeon seniority.

Objective comparison of methods to decode anomalous diffusion.

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Dynamic spatiotemporal coordination of neural stem cell fate decisions occurs through local feedback in the adult vertebrate brain.

Neural stem cell (NSC) populations persist in the adult vertebrate brain over a lifetime, and their homeostasis is controlled at the population level through unknown mechanisms. Here, we combine dynamic imaging of entire NSC populations in their in vivo niche over several weeks with pharmacological manipulations, mathematical modeling, and spatial statistics and demonstrate that NSCs use spatiotemporally resolved local feedback signals to coordinate their decision to divide in adult zebrafish brains. These involve Notch-mediated short-range inhibition from transient neural progenitors and a dispersion effect from the dividing NSCs themselves exerted with a delay of 9-12 days. Simulations from a stochastic NSC lattice model capturing these interactions demonstrate that these signals are linked by lineage progression and control the spatiotemporal distribution of output neurons. These results highlight how local and temporally delayed interactions occurring between brain germinal cells generate self-propagating dynamics that maintain NSC population homeostasis and coordinate specific spatiotemporal correlations.

Fast-track virtual reality for cardiac imaging in congenital heart disease.

BACKGROUND AND AIM OF THE STUDY: We sought to evaluate the appropriateness of cardiac anatomy renderings by a new virtual reality (VR) technology, entitled DIVA, directly applicable to raw magnetic resonance imaging (MRI) data without intermediate segmentation steps in comparison to standard three-dimensional (3D) rendering techniques (3D PDF and 3D printing). Differences in post-processing times were also evaluated. METHODS: We reconstructed 3D (STL, 3D-PDF, and 3D printed ones) and VR models of three patients with different types of complex congenital heart disease (CHD). We then asked a senior pediatric heart surgeon to compare and grade the results obtained. RESULTS: All anatomical structures were well visualized in both VR and 3D PDF/printed models. Ventricular-arterial connections and their relationship with the great vessels were better visualized with the VR model (Case 2); aortic arch anatomy and details were also better visualized by the VR model (Case 3). The median post-processing time to get VR models using DIVA was 5 min in comparison to 8 h (range 8-12 h including printing time) for 3D models (PDF/printed). CONCLUSIONS: VR directly applied to non-segmented 3D-MRI data set is a promising technique for 3D advanced modeling in CHD. It is systematically more consistent and faster when compared to standard 3D-modeling techniques.

Partial breast resection for multifocal lower quadrant breast tumour using virtual reality.

Oncoplastic surgery allows an increase in the number of indications for conservative breast cancer treatments. However, uncertainty as to whether it can be performed still exists in certain situations such as with multicentric or multifocal lesions, even when the breast volume can accommodate it. With the aid of a virtual reality software, DIVA, allowing the precise visualisation of tumours and breast volumes based entirely on the patient's MRI, we report the ability to rapidly confirm and secure an indication for partial surgery of multiple lesions in a 31-year-old patient. With the described approach, the patient did not have to suffer significant disfigurement from cancerous breast surgery without compromising safety.

Towards Human in the Loop Analysis of Complex Point Clouds: Advanced Visualizations, Quantifications, and Communication Features in Virtual Reality.

Multiple fields in biological and medical research produce large amounts of point cloud data with high dimensionality and complexity. In addition, a large set of experiments generate point clouds, including segmented medical data or single-molecule localization microscopy. In the latter, individual molecules are observed within their natural cellular environment. Analyzing this type of experimental data is a complex task and presents unique challenges, where providing extra physical dimensions for visualization and analysis could be beneficial. Furthermore, whether highly noisy data comes from single-molecule recordings or segmented medical data, the necessity to guide analysis with user intervention creates both an ergonomic challenge to facilitate this interaction and a computational challenge to provide fluid interactions as information is being processed. Several applications, including our software DIVA for image stack and our platform Genuage for point clouds, have leveraged Virtual Reality (VR) to visualize and interact with data in 3D. While the visualization aspects can be made compatible with different types of data, quantifications, on the other hand, are far from being standard. In addition, complex analysis can require significant computational resources, making the real-time VR experience uncomfortable. Moreover, visualization software is mainly designed to represent a set of data points but lacks flexibility in manipulating and analyzing the data. This paper introduces new libraries to enhance the interaction and human-in-the-loop analysis of point cloud data in virtual reality and integrate them into the open-source platform Genuage. We first detail a new toolbox of communication tools that enhance user experience and improve flexibility. Then, we introduce a mapping toolbox allowing the representation of physical properties in space overlaid on a 3D mesh while maintaining a point cloud dedicated shader. We introduce later a new and programmable video capture tool in VR and desktop modes for intuitive data dissemination. Finally, we highlight the protocols that allow simultaneous analysis and fluid manipulation of data with a high refresh rate. We illustrate this principle by performing real-time inference of random walk properties of recorded trajectories with a pre-trained Graph Neural Network running in Python.