An intelligent real time 3D vision system for robotic welding tasks

MARWIN is a top-level robot control system that has been designed for automatic robot welding tasks. It extracts welding parameters and calculates robot trajectories directly from CAD models which are then verified by real-time 3D scanning and registration. MARWIN's 3D computer vision provides a user-centred robot environment in which a task is specified by the user by simply confirming and/or adjusting suggested parameters and welding sequences. The focus of this paper is on describing a mathematical formulation for fast 3D reconstruction using structured light together with the mechanical design and testing of the 3D vision system and show how such technologies can be exploited in robot welding tasks

Robot trajectory planning using OLP and structured light 3D machine vision

This paper proposes a new methodology for robotic offline programming (OLP) addressing the issue of automatic program generation directly from 3D CAD models and verification through online 3D reconstruction. Limitations of current OLP include manufacturing tolerances between CAD and workpieces and inaccuracies in workpiece placement and modelled work cell. These issues are addressed and demonstrated through surface scanning, registration, and global and local error estimation. The method allows the robot to adjust the welding path designed from the CAD model to the actual workpiece. Alternatively, for non-repetitive tasks and where a CAD model is not available, it is possible to interactively define the path online over the scanned surface

Discovery and Characterisation of Dual Inhibitors of Tryptophan 2,3-Dioxygenase (TDO2) and Indoleamine 2,3-Dioxygenase 1 (IDO1) Using Virtual Screening

Cancers express tryptophan catabolising enzymes indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO2) to produce immunosuppressive tryptophan metabolites that undermine patients' immune systems, leading to poor disease outcomes. Both enzymes are validated targets for cancer immunotherapy but there is a paucity of potent TDO2 and dual IDO1/TDO2 inhibitors. To identify novel dual IDO1/TDO2 scaffolds, 3D shape similarity and pharmacophore in silico screening was conducted using TDO2 as a model for both systems. The obtained hits were tested in cancer cell lines expressing mainly IDO1 (SKOV3-ovarian), predominantly TDO2 (A172-brain), and both IDO1 and TDO2 (BT549-breast). Three virtual screening hits were confirmed as inhibitors (TD12, TD18 and TD34). Dose response experiments showed that TD34 is the most potent inhibitor capable of blocking both IDO1 and TDO2 activity, with the IC50 value for BT549 at 3.42 µM. This work identified new scaffolds able to inhibit both IDO1 and TDO2, thus enriching the collection of dual IDO1/TDO2 inhibitors and providing chemical matter for potential development into future anticancer drugs

Co-transcriptional R-loops are the main cause of estrogen-induced DNA damage.

The hormone estrogen (E2) binds the estrogen receptor to promote transcription of E2-responsive genes in the breast and other tissues. E2 also has links to genomic instability, and elevated E2 levels are tied to breast cancer. Here, we show that E2 stimulation causes a rapid, global increase in the formation of R-loops, co-transcriptional RNA-DNA products, which in some instances have been linked to DNA damage. We show that E2-dependent R-loop formation and breast cancer rearrangements are highly enriched at E2-responsive genomic loci and that E2 induces DNA replication-dependent double-strand breaks (DSBs). Strikingly, many DSBs that accumulate in response to E2 are R-loop dependent. Thus, R-loops resulting from the E2 transcriptional response are a significant source of DNA damage. This work reveals a novel mechanism by which E2 stimulation leads to genomic instability and highlights how transcriptional programs play an important role in shaping the genomic landscape of DNA damage susceptibility

Burnout and Adverse Outcomes in Athletic Training Students: Why All Healthcare Educators Should Be Concerned

Background: Burnout is linked to various adverse outcomes (i.e., thoughts of dropout, depression, unprofessional behaviors) in healthcare students (i.e., nursing students, medical students). However, potential adverse outcomes associated with burnout in athletic training students, a subset of healthcare students, have yet to be identified. Objective: To adapt a previously tested theoretical model to explore relationships between student workload, burnout, and potential adverse outcomes in a sample of graduate athletic training students. Methods: An online survey assessing the variables of interest and study information was sent to program directors of graduate-level athletic training programs at their publicly accessible email addresses with a request to forward the opportunity to their students. This was a nationwide sample of graduate athletic training students with 320 graduate athletic training students completing the survey. Descriptive statistics and structural equation modeling was used in our analysis. Results: Structural equation modeling confirmed that our hypothesized model successfully described relationships between academic workload, burnout, and adverse outcomes in athletic training students. Specifically, academic workload predicted burnout, and burnout in turn predicted various adverse outcomes (i.e., thoughts of dropout, depression, unprofessional behaviors) in athletic training students. Educators should be aware of the potential adverse outcomes identified in this sample of athletic training students that have also been reported in other healthcare students. Conclusions: Methods to combat symptoms of burnout to enhance student well-being and avoid potential adverse outcomes should be identified. Future research should use the adapted theoretical model discussed in this article within other healthcare students\u27 samples to understand further the complex network of relationships between academic workload, burnout, and adverse outcomes in the educational environment

Preceding rule induction with instance reduction methods

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of the training set, prior to inducing a set of rules using Clark and Boswell's modification of CN2. A hybrid instance reduction algorithm (comprised of AllKnn and DROP5) is also tested. For most of the datasets, pruning the training set using ENN, AllKnn or the hybrid significantly reduces the number of rules generated by CN2, without adversely affecting the predictive performance. The hybrid achieves the highest average predictive accuracy