Handling of large and heavy objects using a single mobile manipulator in combination with a roller board

This paper presents a method for autonomous loading, transportation, and unloading of large objects using a nonholonomic mobile manipulator. Here, the size of the transported object is considerably larger than the size of the mobile platform, which is made possible through the use of a roller board. In this way, the mobile manipulator can handle objects that exceed the manipulator's payload. The robot can load and unload the object onto its platform using the differential kinematics of the system for a null space motion to maintain the object's position in space. In order to localise the object, we apply 3D-perception using a depth-camera. While transporting the object to its destination, the robot is considered a tractor-trailer-wheeled system and can navigate using SLAM. Kinematic modelling and practical evaluation prove that the system can potentially take over arduous transportation tasks

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic

Eeloscope—Towards a Novel Endoscopic System Enabling Digital Aircraft Fuel Tank Maintenance

In this research article, a novel endoscopic system, which is suited to perform a digitalinspection of the aircraft wing fuel tanks, is introduced. The aim of this work is to specificallydesign and develop an assisting system, called Eeloscope, to allow accessing and diving throughan aircraft kerosene tank in a minimally invasive matter. Currently, mechanics often suffer fromthe harsh working environment and the arduous maintenance duties within the tank. To addresssuch challenges and derive a tailored solution, an adapted Design Thinking (DT) process is applied.The resulting system enables a fully digital inspection and generation of 3-dimensional structuralinspection data. Consequently, devices such as the Eeloscope will facilitate a more efficient andcontinuous inspection of fuel tanks to increase the transparency regarding the condition of hardlyaccessible aircraft structures and provide a work relief for mechanics at the same time

Eeloscope – Design eines neuartigen endoskopischen Systems für die Instandhaltung von Flugzeug-Treibstofftanks

Currently, mechanics must perform fuel tank inspection tasks manually inside the confined tank environment under physical, cognitive and psychological stress, while maintaining the highest safety and quality standards. In addition, there are issues such as the complex preparation and the lack of digital transparency regarding the tank condition. In order to address these problems and requirements, a suitable and specific solution needs to be identified. By applying an adapted Design Thinking process, this work presents a novel endoscopic system called ‘Eeloscope’, which allows to access and dive through an aircraft wing kerosene tank in a minimally invasive matter

Protein-specific, multicolor and 3D STED imaging in cells with DNA-labeled antibodies

Photobleaching is a major challenge in fluorescence microscopy, in particular if high excitation light intensities are used. Signal-to-noise and spatial resolution may be compromised, which limits the amount of information that can be extracted from an image. Photobleaching can be bypassed by using exchangeable labels, which transiently bind to and dissociate from a target, thereby replenishing the destroyed labels with intact ones from a reservoir. Here, we demonstrate confocal and STED microscopy with short, fluorophore-labeled oligonucleotides that transiently bind to complementary oligonucleotides attached to protein-specific antibodies. The constant exchange of fluorophore labels in DNA-based STED imaging bypasses photobleaching that occurs with covalent labels. We show that this concept is suitable for targeted, two-color STED imaging of whole cells

Serine-ubiquitination regulates Golgi morphology and the secretory pathway upon Legionella infection

SidE family of Legionella effectors catalyze non-canonical phosphoribosyl-linked ubiquitination (PR-ubiquitination) of host proteins during bacterial infection. SdeA localizes predominantly to ER and partially to the Golgi apparatus, and mediates serine ubiquitination of multiple ER and Golgi proteins. Here we show that SdeA causes disruption of Golgi integrity due to its ubiquitin ligase activity. The Golgi linking proteins GRASP55 and GRASP65 are PR-ubiquitinated on multiple serine residues, thus preventing their ability to cluster and form oligomeric structures. In addition, we found that the functional consequence of Golgi disruption is not linked to the recruitment of Golgi membranes to the growing Legionella-containing vacuoles. Instead, it affects the host secretory pathway. Taken together, our study sheds light on the Golgi manipulation strategy by which Legionella hijacks the secretory pathway and promotes bacterial infection

Linear ubiquitination of cytosolic Salmonella Typhimurium activates NF-κB and restricts bacterial proliferation

Ubiquitination of invading Salmonella Typhimurium triggers autophagy of cytosolic bacteria and restricts their spread in epithelial cells. Ubiquitin (Ub) chains recruit autophagy receptors such as p62/SQSTM1, NDP52/CALCOCO and optineurin (OPTN), which initiate the formation of double-membrane autophagosomal structures and lysosomal destruction in a process known as xenophagy. Besides this, the functional consequences and mechanistic regulation of differentially linked Ub chains at the host-Salmonella interface have remained unexplored. Here, we show, for the first time, that distinct Ub chains on cytosolic S. Typhimurium serve as a platform triggering further signalling cascades. By using single-molecule localization microscopy, we visualized the balance and nanoscale distribution pattern of linear (M1-linked) Ub chain formation at the surface of cytosolic S. Typhimurium. In addition, we identified the deubiquitinase OTULIN as central regulator of these M1-linked Ub chains on the bacterial coat. OTULIN depletion leads to enhanced formation of linear Ub chains, resulting in local recruitment of NEMO, activation of IKKα/IKKβ and ultimately NF-κB, which in turn promotes secretion of pro-inflammatory cytokines and restricts bacterial proliferation. Our results establish a role for the linear Ub coat around cytosolic S. Typhimurium as the local NF-κB signalling platform and provide insights into the function of OTULIN in NF-κB activation during bacterial pathogenesis

Democratising deep learning for microscopy with ZeroCostDL4Mic

International audienceDeep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction-fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes