Juha Hirvonen

Pneumatic cell stretching system for cardiac differentiation and culture

This paper introduces a compact mechanical stimulation device suitable for applications to study cellular mechanobiology. The pneumatically controlled device provides equiaxial strain for cells on a coated polydimethylsiloxane (PDMS) membrane and enables real time observation of cells with an inverted microscope. This study presents the implementation and operation principles of the device and characterizes membrane stretching. Different coating materials are also analyzed on an unstretched membrane to optimize the cell attachment on PDMS. As a result, gelatin coating was selected for further experiments to demonstrate the function of the device and evaluate the effect of long-term cyclic equiaxial stretching on human pluripotent stem cells (hPSCs). Cardiac differentiation was induced with mouse visceral endoderm-like (END-2) cells, either on an unstretched membrane or with mechanical stretching. In conclusion, hPSCs grew well on the stretching platform and cardiac differentiation was induced. Thus, the platform provides a new possibility to study the effect of stretching on cellular properties including differentiation and stress induced cardiac diseases.

Towards fully automated pick and place operations of individual natural fibers

This paper reports automated image-based pick and place procedures for manipulation of individual natural fibers. The developed procedures are part of an effort to develop a fully automated microrobotic-based platform for fiber characterization. The presented procedures are divided into unit operations, which can reused in multiple tasks that the platform must perform. Two different demonstrations: pick and place, and coordinated fiber lifting are presented. In addition, a component-based software that promotes reusability of the developed unit operations is presented.

Design and implementation of an illumination system for microrobotic paper fiber studies

This paper discusses design and implementation of an illumination system for microrobotic manipulation of natural fibrous materials, such as paper fibers. Three different illumination types potentially suitable for this imaging task are discussed and prototypes are built for further testing. The final illumination system is implemented based on the test results. It uses polarized light and it is integrated to the sample holder of a microrobotic platform. The final system provides an excellent contrast between the fibers and the background.

Microcutting of living tissue slices and stem cell colonies by using mechanical tool and liquid jet

There is a growing need for methods to cut living tissues in vitro and cell cultures in microscale in biological and medical research. This paper presents two different microrobotic methods for cutting: mechanical microdissection using a sharp needle and liquid jet cutting utilizing a pressured liquid jet. Test devices for both the methods were built and the experiments were conducted with thin tissue slices and stem cell colonies. The devices built as well as the structure of the experiments and the results gained are discussed in this paper and the methods are compared with each other.

Estimation of Electrical Cell–Capillary Admittance During Injection with Frequency Response Method

Abstract This paper describes electrical equivalent circuit models of cell–capillary admittance during injection of a living cell and presents a measurement system to estimate corresponding frequency responses during microinjection tests. Since the admittance estimate is calculated from data collected during injection, the amount of data is limited. To overcome this constraint, the approach proposed in this paper takes advantage of properties of periodic pseudo random binary sequence (PRBS) excitation signal and avoids end effect anomalies of correlation calculation. The fast and accurate estimation is used to detect the degree of contact during cell injection and to detect breakage and clogging of capillary during a sequence of multiple operations.

Method for 3D fibre reconstruction on a microrobotic platform.

Automated handling of a natural fibrous object requires a method for acquiring the three‐dimensional geometry of the object, because its dimensions cannot be known beforehand. This paper presents a method for calculating the three‐dimensional reconstruction of a paper fibre on a microrobotic platform that contains two microscope cameras. The method is based on detecting curvature changes in the fibre centreline, and using them as the corresponding points between the different views of the images.

Scale and Rotation Invariant Two View Microgripper Detection that Uses a Planar Pattern

Abstract In automated grasping of microparts or objects with unknown dimensions and orientations, at least two cameras have to be used to acquire the depth information. In addition to recognition and reconstruction of the real-world coordinates of the target objects, the system has to be able to detect also the real-world coordinates of the microgrippers from the images. This paper presents a scale and rotation invariant microgripper detection method that uses a planar pattern. The method is suitable especially for prototyping systems, whose composition might vary between the experiments. The gripper detection is shown to be accurate enough for challenging micromanipulation tasks of small electronic components and individual paper fibers.

Automated estimation of contact angle on hydrophobic fibers using a microrobotic platform

This paper presents an automated computer vision algorithm for estimating contact angles that a droplet of probe liquid forms on hydrophobic fibers. A specially designed microrobotic platform is utilized in manipulating the microscopic fibers, shooting droplets in the scale of tens of nanoliters on the fibers and capturing images of the experiments. The images are then processed with the automated computer vision algorithm. The algorithm is proven to be reliable and repeatable with totally 29 experiments on five different bio-based fiber samples.

Automated microrobotic manipulation of paper fiber bonds

This paper presents a novel method for automated manipulation of individual paper fiber bonds using a microrobotic platform, a computer vision algorithm and a robotic software framework. This is a challenging task due to the three-dimensional, heterogeneous and complex morphology of the fiber bonds. The goal is to automatically grasp the fiber bond, and break it by pulling apart the fibers it consists of. We present the components of the microrobotic platform, and the different rules utilized in detecting suitable grasp points from a 3D reconstruction of the bond generated from an image pair. We demonstrate the functionality of the approach with bond breaking experiments of seven fiber bonds. The time required for grasping and breaking of a bond is 10 - 15 seconds making the approach much faster than the current state-of-the-art testing, which is based on manual manipulation. The success rate of the tests is as high as 80 %.

Automatic image-based detection and inspection of paper fibres for grasping

An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 µm. Also, the choices made by the algorithm are much more consistent than the human choices.