A micromanipulation setup for comparative tests of microgrippers

A micromanipulation setup allowing comparative tests of manipulation micro tools has been developed. Repeatability measurements of positioning as well as optimization of manipulation conditions can be run with parts of typically 5 to 50μm over a large set of parameters including environment conditions, substrate and tip specifications, and different strategies (robot trajectories at picking and releasing time). The workstation consists of a high precise parallel robot, the Delta3, to position the gripper, linear stages to place the parts in the field of view and two microscopes for the visual feedback and position measurement. The setup is placed in a chamber for controlling relative humidity and temperature. An interface was developed to integrate every kind of tool on the robot. Automated operations and measurement have been carried out based on localization and tracking of micro objects and gripper. Integration of micro tools was successfully accomplished and comparative tests were executed with micro tweezers. Sub micrometer position repeatability was achieved with a success rate of pick and pick operations of 95%

Micro-gripper à haute dynamique

Dans le monde microscopique, la force de gravité devient négligeable par rapport aux forces d’adhésion (capillarité, Van der Waals). Ce projet vise à utiliser ces forces pour la prise de bille d’une taille caractéristique de 50[μm]. La dépose quand à elle s’effectue de manière dynamique, en utilisant l’inertie de la bille soumise à une forte accélération. Le but de ce projet est de caractériser la prise (taux de succès) et la dépose (seuil d’accélération, taux de succès, précision et répétabilité). La prise dépend essentiellement du type de matériaux utilisés ; sa caractérisation a été faite pour un gripper en silicium et un gripper en verre dans l’air ambiant (20% humidité relative) et l’azote (3%). Pour fournir une accélération, un piézoélectrique est utilisé (cristal qui se déforme lorsqu’une tension est appliquée à ses bornes). L’avantage principal est que la déformation est bien contrôlable en intensité et direction. La dépose quand à elle a été caractérisée dans les mêmes conditions que la prise et en excitant le piézoélectrique avec des sinus continus ou en créant une seule impulsion Au final, il a été montré que cette technique est viable pour la micromanipulation. Toutefois des points clés nécessaires à un bon contrôle des opérations ont été identifiés, notamment au niveau de la rigidité du substrat pour éviter l’écrasement des objets et au niveau de la qualité des surfaces de contact

Characterization of an inertial micro gripper based on adhesion forces

Adhesive forces become predominant in the micro world comparing to the gravity effect implying the development of new micro manipulation strategies. This paper presents the design and conception of a gripper that use the inertial principle for the release (applying a high acceleration, in the order of 10’000g) and the adhesion for catching a micro part of 50μm with the goal of precisely control the position after release. Experiments were conducted and showed a positioning repeatability of 2μm to 6μm depending on the relative humidity with a success rate of more than 90%

The optimal use of vision as part of the manipulation of micron-sized objects

This project concerns the development and integration of a sub-millimeter objects manipulation setup and will take part in a CTI project named “Manipulating Microscale Objects with Nanoscale Precision”. On the way of manipulating microscale objects, we need to build a first setup adapted for sub-millimeter objects in order to be able to perform experiences and to validate some assumptions and choices. In the Laboratoire de Systèmes Robotiques (LSRO), ultra high precision parallel robots are developed and, in particular, the Delta3 a micromanipulator that presents three degrees of freedom (XYZ) and has a range of 4mm. This robot might be used within this project. The goal of this project is to develop an interface between vision system, robot and user that will allow measuring the position repeatability of different microscale objects during a manipulation task

Characterization of micro manipulation tasks operated with various controlled conditions by microtweezers

Micro manipulation tasks with micro tweezers were operated in different configurations. This paper discusses the main issues of pick and place operations with micro tweezers as geometric consideration, grasping force and quality of the contact surfaces. This study is based on positioning repeatability measurements and success rate of the tasks operated automatically on our micro manipulation setup. Results for a MEMS micro gripper show a high reliability of more than 90% of success rate and positioning repeatability under the micrometer

Transformer-based normative modelling for anomaly detection of early schizophrenia

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.Comment: 10 pages, 2 figures, 2 tables, presented at NeurIPS22@PAI4M

An automated machine learning approach to predict brain age from cortical anatomical measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE  =  2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE  =  6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE  =  6.0 cm), at 0.5 m resolution and over the lidar domain (750 m × 700 m)

The effect of posterior subtenon methylprednisolone acetate in the refractory diabetic macular edema: a prospective nonrandomized interventional case series

BACKGROUND: To investigate the efficacy of posterior subtenon methylprednisolone acetate injection in treatment of refractory diffuse clinically significant diabetic macular edema (CSME). METHODS: In a prospective, nonrandomized, interventional case series, 52 eyes were diagnosed with CSME and treated with at least two sessions of laser photocoagulation according to Early Treatment Diabetic Retinopathy Study guidelines. At least 3 months after laser therapy, eyes with a residual central macular thickness were offered posterior subtenon injection of 40 mg methylprednisolone acetate. Main outcome measures were visual acuity, macular thickness and intraocular pressure. Potential complications were monitored, including intraocular pressure response, cataract progression and scleral perforation. RESULTS: Mean baseline visual acuity (in logMAR) improved significantly (p = 0.003) from 0.8 ± 0.36 to 0.6 ± 0.41 at 3 months. Mean foveal thickness decreased from 388 ± 78 μm at baseline to 231 ± 40 μm after 3 months (p < 0.0001). Visual acuity improvement in eyes with CSME with extrafoveal hard exudates was significant (p = 0.0001), but not significant in eyes with CSME with subfoveal hard exudates (p = 0.32). Intraocular pressure increased from 14.7 ± 2.0 mmHg (range, 12–18 mmHg) to a maximum value of 15.9 ± 2.1 mmHg (range, 12–20 mmHg) during the follow-up period. Complications in two eyes developed focal conjunctival necrosis at the site of injection. CONCLUSION: Posterior subtenon methylprednisolone acetate may improve early visual outcome in diffuse diabetic macular edema that fails to respond to conventional laser photocoagulation. Visual acuity improvement in eyes with CSME with extrafoveal hard exudates was significant; and this improvement is depends on location of hard exudates. Further study is needed to assess the long-term efficacy, safety, and retreatment