Learning Compliant Box-in-Box Insertion through Haptic-Based Robotic Teleoperation.

In modern logistics, the box-in-box insertion task is representative of a wide range of packaging applications, and automating compliant object insertion is difficult due to challenges in modelling the object deformation during insertion. Using Learning from Demonstration (LfD) paradigms, which are frequently used in robotics to facilitate skill transfer from humans to robots, can be one solution for complex tasks that are difficult to mathematically model. In order to automate the box-in-box insertion task for packaging applications, this study makes use of LfD techniques. The proposed framework has three phases. Firstly, a master-slave teleoperated robot system is used in the initial phase to haptically demonstrate the insertion task. Then, the learning phase involves identifying trends in the demonstrated trajectories using probabilistic methods, in this case, Gaussian Mixture Regression. In the third phase, the insertion task is generalised, and the robot adjusts to any object position using barycentric interpolation. This method is novel because it tackles tight insertion by taking advantage of the boxes' natural compliance, making it possible to complete the task even with a position-controlled robot. To determine whether the strategy is generalisable and repeatable, experimental validation was carried out.

Robotization of Industrial Processes: Motivational Differences between Companies with and without Existing Robotic Processes.

OCCUPATIONAL APPLICATIONSOur survey of 100 manufacturing facilities revealed statistically significant differences among company types in their perceptions of cost savings, productivity gains, and safety improvements as benefits of robotic implementation. Regardless of company type or size, indications of presence of cumulative, incidental, and static postural hazards were identified as primary perceived factors for injury potential. More than half of the surveyed companies reported being unaware of general safety standards utilized within their facilities, and most (70%) robotic companies were unaware of any robotic-specific standards utilized at their company. Our results indicate the importance of accounting for varying perspectives between company types regarding motivation for and safety impacts of robotics. With the advancement of technology and robotization of the manufacturing industry, there are also pressing needs for advancing robotic safety standards, implementing training programs, and continuous promotion of the general safety awareness.

Background: Introducing robotics to industrial processes is generally thought to lead to increased productivity, decreased costs, and improved occupational health and safety. These expected benefits are assumed to drive the motivation of companies to robotize their manufacturing processes.Purpose: Robotics presumably pose different hazards than manual labor, illustrating their potential to positively alter company safety outcomes. However, our literature review identified minimal research showcasing driving factors for why companies choose to utilize industrial robotics. Moreover, the question of how motivational factors differ between companies based on their type or size, has not been fully explored.Methods: We investigated the differentiation in motivation for robotization with an emphasis on safety related issues for companies that already have robotic processes (robotic companies) and companies without current robotic installations (non-robotic). For the latter, a finer distinction was made between those planning on obtaining robots in the future and those not planning to do so. A custom designed and validated survey was distributed, and data were collected from 100 companies in the general manufacturing sector (52 of which currently utilize robotics).Results: We found statistically significant differences in motivational factors among robotic companies, companies planning to obtain robotics, and companies not planning on obtaining robotics. Economic aspects were the biggest justification factor for all company types. Perceived safety hazards and differences in injury types between robotic and non-robotic companies were identified. Robotics showcase the potential to reduce certain hazard types, but also to pose harm to employees if not properly implemented.Conclusions: Regardless of their size or geographic location, companies with and without existing robotic processes differ in their perspectives on motivation and safety impacts of robotization of industrial processes.

HTS discovery of PARP1-HPF1 complex inhibitors in cancer.

PARP1/2 inhibitors (PARPi) are effective clinically used drugs for the treatment of cancers with BRCA deficiencies. PARPi have had limited success and applicability beyond BRCA deficient cancers, and their effect is diminished by resistance mechanisms. The recent discovery of Histone PARylation Factor (HPF1) and the role it plays in the PARylation reaction by forming a shared active site with PARP1 raises the possibility that novel inhibitors that target the PARP1-HPF1 complex can be identified. Herein we describe a simple and cost-effective high-throughput screening (HTS) method aimed at discovering inhibitors of the PARP1-HPF1 complex. Upon HTS validation, we first applied this method to screen a small PARP-focused library of compounds and then scale up our approach using robotic automation to conduct a pilot screen of 10,000 compounds and validating >100 hits. This work demonstrates for the first time the capacity to discover potent inhibitors of the PARP1-HPF1 complex, which may have utility as probes to better understand the DNA damage response and as therapeutics for cancer.

Adaptable Method for Dynamic Planning of 3D Spatial Wireframes Extrusion with Neural Networks and Robotic Automation.

3D extrusion of spatial wireframe provides an alternate method for additive manufacture, which holds a significant advantage over the typical layering printing method in terms of material and time efficiency. However, the complexity of the structure and unsupported printing process are major challenges for path planning of extrusion process. This article presents a method to plan and control the path for extruding 3D spatial wireframes by synthesizing dynamic material behaviors such as heat deformation, plasticity, and bending, during the curing process as active contributors to the printed form. With this method, a novel system of printing spatial wireframe model is developed that disassociate the extruding path and extruded form by dimensional, that is, printing a 3D spatial wireframe with a 2D printing path. The workflow of the research combines robotic/mechanical automation with machine vision and artificial intelligence, to generate material models without any preexisting knowledge of the material. Based on the feedback loop between machine vision and printing control, this system is capable of automatically conducting material experiments at a large scale, observe the results, and learn to generate an end-to-end solution that directly bridges the design intention to the fabrication of spatial wireframes based on nonstandard material behaviors. The development of the method allows designers to design and fabricate dynamic spatial frames, without the support of existing material and structural models.

Evaluation of a septumless mini-cartridge for automated solid-phase extraction cleanup in gas chromatographic analysis of >250 pesticides and environmental contaminants in fatty and nonfatty foods.

The QuEChERSER mega-method has recently been introduced to quantify and identify a wide range of chemical residues (pesticides, veterinary drugs, environmental contaminants, among others) in nearly all types of foods. The approach calls for taking a small amount of the initial extract to cover analytes amenable to liquid chromatography, and the remainder is salted out for analysis by gas chromatography (GC), both with mass spectrometry (MS) based detection. In the case of GC-MS(/MS), the extract undergoes automated robotic mini-cartridge solid-phase extraction (SPE) cleanup in a technique known as µSPE or instrument-top sample preparation (ITSP). In 2022, a septumless mini-cartridge for µSPE was introduced to improve upon the ITSP design. The new design houses a bed of 20 mg anhydrous MgSO4, 12 mg each of C18 and primary secondary amine sorbents, and 1 mg of graphitized carbon black, the latter substituting for CarbonX used in the ITSP product. The septumless µSPE mini-cartridge employs a different gripping mechanism with the syringe needle that allows leak-free operation at higher flow rates (e.g. 10 µL/s), whereas the ITSP design is limited to 2 µL/s. Based on cleanup and analyte elution profiles, the extract load volume and flow rate was increased in µSPE for QuEChERSER from 300 µL at 2 µL/s to 500 µL at 5 µL/s, which improved accuracy of results, sped the cleanup step, and obviated the need for micro-vial inserts in the receiving vials. The new design also reduced both the amount and consistency of dead (void) volumes in the mini-cartridges from 83 ± 14 µL to 52 ± 7 µL for 200-600 µL load volumes. Normalization of peak areas to internal standards led to recoveries between 80 and 120% with typical RSDs <5% in low-pressure GC-MS/MS of 227-242 out of 252 pesticides, polychlorinated biphenyls, polybrominated diphenyl ethers, and polycyclic aromatic hydrocarbons in hemp powder, spinach, whole milk, egg, avocado, and lamb meat.

Progress in automating patch clamp cellular physiology.

Patch clamp electrophysiology has transformed research in the life sciences over the last few decades. Since their inception, automatic patch clamp platforms have evolved considerably, demonstrating the capability to address both voltage- and ligand-gated channels, and showing the potential to play a pivotal role in drug discovery and biomedical research. Unfortunately, the cell suspension assays to which early systems were limited cannot recreate biologically relevant cellular environments, or capture higher order aspects of synaptic physiology and network dynamics. In vivo patch clamp electrophysiology has the potential to yield more biologically complex information and be especially useful in reverse engineering the molecular and cellular mechanisms of single-cell and network neuronal computation, while capturing important aspects of human disease mechanisms and possible therapeutic strategies. Unfortunately, it is a difficult procedure with a steep learning curve, which has restricted dissemination of the technique. Luckily, in vivo patch clamp electrophysiology seems particularly amenable to robotic automation. In this review, we document the development of automated patch clamp technology, from early systems based on multi-well plates through to automated planar-array platforms, and modern robotic platforms capable of performing two-photon targeted whole-cell electrophysiological recordings in vivo.

Robotic Automation of In Vivo Two-Photon Targeted Whole-Cell Patch-Clamp Electrophysiology.

Whole-cell patch-clamp electrophysiological recording is a powerful technique for studying cellular function. While in vivo patch-clamp recording has recently benefited from automation, it is normally performed "blind," meaning that throughput for sampling some genetically or morphologically defined cell types is unacceptably low. One solution to this problem is to use two-photon microscopy to target fluorescently labeled neurons. Combining this with robotic automation is difficult, however, as micropipette penetration induces tissue deformation, moving target cells from their initial location. Here we describe a platform for automated two-photon targeted patch-clamp recording, which solves this problem by making use of a closed loop visual servo algorithm. Our system keeps the target cell in focus while iteratively adjusting the pipette approach trajectory to compensate for tissue motion. We demonstrate platform validation with patch-clamp recordings from a variety of cells in the mouse neocortex and cerebellum.

Application of a high-throughput relative chemical stability assay to screen therapeutic protein formulations by assessment of conformational stability and correlation to aggregation propensity.

In this study, an automated high-throughput relative chemical stability (RCS) assay was developed in which various therapeutic proteins were assessed to determine stability based on the resistance to denaturation post introduction to a chaotrope titration. Detection mechanisms of both intrinsic fluorescence and near UV circular dichroism (near-UV CD) are demonstrated. Assay robustness was investigated by comparing multiple independent assays and achieving r(2) values >0.95 for curve overlays. The complete reversibility of the assay was demonstrated by intrinsic fluorescence, near-UV CD, and biologic potency. To highlight the method utility, we compared the RCS assay with differential scanning calorimetry and dynamic scanning fluorimetry methodologies. Utilizing C1/2 values obtained from the RCS assay, formulation rank-ordering of 12 different mAb formulations was performed. The prediction of long-term stability on protein aggregation is obtained by demonstrating a good correlation with an r(2) of 0.83 between RCS and empirical aggregation propensity data. RCS promises to be an extremely useful tool to aid in candidate formulation development efforts based on the complete reversibility of the method to allow for multiple assessments without protein loss and the strong correlation between the C1/2 data obtained and accelerated stability under stressed conditions.