Development and validation of the Normalized Organoid Growth Rate (NOGR) metric in brightfield imaging-based assays.

This study focuses on refining growth-rate-based drug response metrics for patient-derived tumor organoid screening using brightfield live-cell imaging. Traditional metrics like Normalized Growth Rate Inhibition (GR) and Normalized Drug Response (NDR) have been used to assess organoid responses to anticancer treatments but face limitations in accurately quantifying cytostatic and cytotoxic effects across varying growth rates. Here, we introduce the Normalized Organoid Growth Rate (NOGR) metric, specifically developed for brightfield imaging-based assays.

A dataset of drone-captured, segmented images for oil spill detection in port environments.

The high incidence of oil spills in port areas poses a serious threat to the environment, prompting the need for efficient detection mechanisms. Utilizing automated drones for this purpose can significantly improve the speed and accuracy of oil spill detection. Such advancements not only expedite cleanup operations, reducing environmental harm but also enhance polluter accountability, potentially deterring future incidents.

High-resolution compound-specific mapping in works of art via data fusion of MA-XRPD with hyperspectral data (part 1: Method evaluation).

Background: Hyperspectral imaging techniques have emerged as powerful tools for non-invasive investigation of artworks. This paper employs either reflectance imaging spectroscopy (RIS) or macroscopic X-ray fluorescence (MA-XRF) imaging in combination with macroscopic X-ray powder diffraction (MA-XRPD) for state-of-the-art chemical imaging of painted cultural heritage artefacts. While RIS can provide molecular information and MA-XRF can offer elemental distribution maps of paintings of high lateral resolution, the unique advantage of MA-XRPD lies in its ability to visualize the distributions of specific pigments and estimate in a quantitative manner the relative concentrations of the crystalline phases at the surface of artworks.

Automated thermographic detection of blood vessels for DIEP flap reconstructive surgery.

Purpose: Inadequate perfusion is the most common cause of partial flap loss in tissue transfer for post-mastectomy breast reconstruction. The current state-of-the-art uses computed tomography angiography (CTA) to locate the best perforators. Unfortunately, these techniques are expensive and time-consuming and not performed during surgery.

Exploring the Use of Cold Atmospheric Plasma for Sound and Vibration Generation.

In this study, we investigate the potential of cold atmospheric plasma (CAP) as a non-contact excitation device, comparing its performance with an ultrasound transmitter. Utilizing a scanning Laser Doppler Vibrometer (LDV), we visualize the acoustic wavefront generated by a CAP probe and an ultrasound sensor within a designated 50 mm × 50 mm area in front of each probe. Our focus lies in assessing the applicability of a CAP probe for exciting a small polymethyl methacrylate (PMMA) sample.

Auranofin repurposing for lung and pancreatic cancer: low CA12 expression as a marker of sensitivity in patient-derived organoids, with potentiated efficacy by AKT inhibition.

Background: This study explores the repurposing of Auranofin (AF), an anti-rheumatic drug, for treating non-small cell lung cancer (NSCLC) adenocarcinoma and pancreatic ductal adenocarcinoma (PDAC). Drug repurposing in oncology offers a cost-effective and time-efficient approach to developing new cancer therapies. Our research focuses on evaluating AF's selective cytotoxicity against cancer cells, identifying RNAseq-based biomarkers to predict AF response, and finding the most effective co-therapeutic agents for combination with AF.

Single-organoid analysis reveals clinically relevant treatment-resistant and invasive subclones in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal diseases, characterized by a treatment-resistant and invasive nature. In line with these inherent aggressive characteristics, only a subset of patients shows a clinical response to the standard of care therapies, thereby highlighting the need for a more personalized treatment approach. In this study, we comprehensively unraveled the intra-patient response heterogeneity and intrinsic aggressive nature of PDAC on bulk and single-organoid resolution.

Seaweed Growth Monitoring with a Low-Cost Vision-Based System.

In this paper, we introduce a method for automated seaweed growth monitoring by combining a low-cost RGB and stereo vision camera. While current vision-based seaweed growth monitoring techniques focus on laboratory measurements or above-ground seaweed, we investigate the feasibility of the underwater imaging of a vertical seaweed farm. We use deep learning-based image segmentation (DeeplabV3+) to determine the size of the seaweed in pixels from recorded RGB images.

On the Angular Control of Rotating Lasers by Means of Line Calculus on Hyperboloids.

We propose a new paradigm for modelling and calibrating laser scanners with rotation symmetry, as is the case for lidars or for galvanometric laser systems with one or two rotating mirrors. Instead of bothering about the intrinsic parameters of a physical model, we use the geometric properties of the device to model it as a specific configuration of lines, which can be recovered by a line-data-driven procedure. Compared to universal data-driven methods that train general line models, our algebraic-geometric approach only requires a few measurements.

Design and Metrological Analysis of a Backlit Vision System for Surface Roughness Measurements of Turned Parts.

The focus of this study is to design a backlit vision instrument capable of measuring surface roughness and to discuss its metrological performance compared to traditional measurement instruments. The instrument is a non-contact high-magnification imaging system characterized by short inspection time which opens the perspective of in-line implementation. We combined the use of the modulation transfer function to evaluate the imaging conditions of an electrically tunable lens to obtain an optimally focused image.

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis.

Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex vivo drug screenings. However, current adenosine triphosphate (ATP)-based drug screening assays do not capture the complexity of a drug response (cytostatic or cytotoxic) and intratumor heterogeneity that has been shown to be retained in PDTOs due to a bulk readout. Live-cell imaging is a powerful tool to overcome this issue and visualize drug responses more in-depth.

A Continuous Motion Shape-from-Focus Method for Geometry Measurement during 3D Printing.

In 3D printing, as in other manufacturing processes, there is a push for zero-defect manufacturing, mainly to avoid waste. To evaluate the quality of the printed parts during the printing process, an accurate 3D measurement method is required. By scanning the part during the buildup, potential nonconformities to tolerances can be detected early on and the printing process could be adjusted to avoid scrapping the part.

OrBITS: label-free and time-lapse monitoring of patient derived organoids for advanced drug screening.

Background: Patient-derived organoids are invaluable for fundamental and translational cancer research and holds great promise for personalized medicine. However, the shortage of available analysis methods, which are often single-time point, severely impede the potential and routine use of organoids for basic research, clinical practise, and pharmaceutical and industrial applications.

Methods: Here, we developed a high-throughput compatible and automated live-cell image analysis software that allows for kinetic monitoring of organoids, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), by combining computer vision with a convolutional network machine learning approach.

The effect of local non-thermal plasma therapy on the cancer-immunity cycle in a melanoma mouse model.

Melanoma remains a deadly cancer despite significant advances in immune checkpoint blockade and targeted therapies. The incidence of melanoma is also growing worldwide, which highlights the need for novel treatment options and strategic combination of therapies. Here, we investigate non-thermal plasma (NTP), an ionized gas, as a promising, therapeutic option.

Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements.

To automatically evaluate the ergonomics of workers, 3D skeletons are needed. Most ergonomic assessment methods, like REBA, are based on the different 3D joint angles. Thanks to the huge amount of training data, 2D skeleton detectors have become very accurate.

Cold Atmospheric Plasma Does Not Affect Stellate Cells Phenotype in Pancreatic Cancer Tissue in Ovo.

Pancreatic ductal adenocarcinoma (PDAC) is a challenging neoplastic disease, mainly due to the development of resistance to radio- and chemotherapy. Cold atmospheric plasma (CAP) is an alternative technology that can eliminate cancer cells through oxidative damage, as shown in vitro, in ovo, and in vivo. However, how CAP affects the pancreatic stellate cells (PSCs), key players in the invasion and metastasis of PDAC, is poorly understood.

Identification of Corrosion Minerals Using Shortwave Infrared Hyperspectral Imaging.

In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900-1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra.

Gaze Gesture Recognition by Graph Convolutional Networks.

Gaze gestures are extensively used in the interactions with agents/computers/robots. Either remote eye tracking devices or head-mounted devices (HMDs) have the advantage of hands-free during the interaction. Previous studies have demonstrated the success of applying machine learning techniques for gaze gesture recognition.

A Bayesian Deep Neural Network for Safe Visual Servoing in Human-Robot Interaction.

Safety is an important issue in human-robot interaction (HRI) applications. Various research works have focused on different levels of safety in HRI. If a human/obstacle is detected, a repulsive action can be taken to avoid the collision.

Qualitative Comparison of 2D and 3D Atmospheric Corrosion Detection Methods.

In this article, we report the use of a Confocal Laser Scanning Microscope (CLSM) to apply a qualitative assessment of atmospheric corrosion on steel samples. From the CLSM, we obtain high-resolution images, together with a 3D heightmap. The performance of four different segmentation algorithms that use the high-resolution images as input is qualitatively assessed and discussed.

A Fast Shape-from-Focus-Based Surface Topography Measurement Method.

Shape from focus is an accurate, but relatively time-consuming, 3D profilometry technique (compared to e.g., laser triangulation or fringe projection).

Extrinsic Camera Calibration with Line-Laser Projection.

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ.

Characterizing the Complex Modulus of Asphalt Concrete Using a Scanning Laser Doppler Vibrometer.

Asphalt mixtures are the most common types of pavement material used in the world. Characterizing the mechanical behavior of these complex materials is essential in durable, cost-effective, and sustainable pavement design. One of the important properties of asphalt mixtures is the complex modulus of elasticity.

Interactive Camera Network Design Using a Virtual Reality Interface.

The traditional literature on camera network design focuses on constructing automated algorithms. These require problem-specific input from experts in order to produce their output. The nature of the required input is highly unintuitive, leading to an impractical workflow for human operators.