Using Application programming interface to integrate reverse engineering methodologies into solidworks

In this paper the authors present an application of Visual Basic Application Programming Interface (API) to develop numerical and procedural algorithm into CAD software. The paper focuses on Reverse Engineering embedded into Solidworks. In many RE applications there is the need to remodel the tessellated surface into an editable solid feature, to analyze it and to manipulate it. For this purpose they can be programmed numerical procedures which interact with native geometrical entities in order to improve the modelling capability using automation protocols. The presented example of API and Solidworks interaction is about the acquisition and processing of surfaces acquired by 3d laser scanner. The problem is to acquire the tessellated geometry, build up a parametric editable feature, perform topological analysis and manipulate more fragments to reconstruct an unique entity. The proposed methodology is based on the integration between native geometrical entities in Solidworks and advanced mathematics algorithms about nonlinear optimization. Both of them can be accessed and manipulated by the user using simple graphic windows. In the paper the authors describe how to implement the interaction among these entities, discussing the role of API focusing on limits and capabilities and presenting the proposed algorithms underling the critical points

A Data-Driven Approach to Estimate the Power Loss and Thermal Behaviour of Cylindrical Gearboxes under Transient Operating Conditions

This paper proposes an innovative methodology to estimate the thermal behaviour of the cylindrical gearbox system, considering, as a thermal source, the power loss calculated under transient operating conditions. The power loss of the system in transient conditions is computed through several approaches: a partial elasto-hydrodynamic lubrication model (EHL) is adopted to estimate the friction coefficients of the gears, while analytical and semiempirical models are used to compute other power loss sources. Furthermore, considering a limited set of operating condition points as a training set, a reduced-order model for the evaluation of the power loss based on a neural network is developed. Using this method, it is possible to simulate thermal behaviour with high accuracy through a thermal network approach in all steady-state and transient operating conditions, reducing computational time. The results obtained by means of the proposed method have been compared and validated with the experimental results available in the literature. This methodology has been tested with the FZG rig test gearbox but can be extended to any transmission layout to predict the overall efficiency and component temperatures with a low computational burden

naked eye fingerprinting of single nucleotide polymorphisms on psoriasis patients

We report a low-cost test, based on gold nanoparticles, for the colorimetric (naked-eye) fingerprinting of a panel of single nucleotide polymorphisms (SNPs), relevant for the personalized therapy of psoriasis. Such pharmacogenomic tests are not routinely performed on psoriasis patients, due to the high cost of standard technologies. We demonstrated high sensitivity and specificity of our colorimetric test by validating it on a cohort of 30 patients, through a double-blind comparison with two state-of-the-art instrumental techniques, namely reverse dot blotting and sequencing, finding 100% agreement. This test offers high parallelization capabilities and can be easily generalized to other SNPs of clinical relevance, finding broad utility in diagnostics and pharmacogenomics

SlingDrone: Mixed Reality System for Pointing and Interaction Using a Single Drone

We propose SlingDrone, a novel Mixed Reality interaction paradigm that utilizes a micro-quadrotor as both pointing controller and interactive robot with a slingshot motion type. The drone attempts to hover at a given position while the human pulls it in desired direction using a hand grip and a leash. Based on the displacement, a virtual trajectory is defined. To allow for intuitive and simple control, we use virtual reality (VR) technology to trace the path of the drone based on the displacement input. The user receives force feedback propagated through the leash. Force feedback from SlingDrone coupled with visualized trajectory in VR creates an intuitive and user friendly pointing device. When the drone is released, it follows the trajectory that was shown in VR. Onboard payload (e.g. magnetic gripper) can perform various scenarios for real interaction with the surroundings, e.g. manipulation or sensing. Unlike HTC Vive controller, SlingDrone does not require handheld devices, thus it can be used as a standalone pointing technology in VR.Comment: ACM VRST 2019 conferenc

Gold-Nanoparticle-Based Colorimetric Discrimination of Cancer-Related Point Mutations with Picomolar Sensitivity

Point mutations in the Kirsten rat sarcoma viral oncogene homologue (KRAS) gene are being increasingly recognized as important diagnostic and prognostic markers in cancer. In this work, we describe a rapid and low-cost method for the naked-eye detection of cancer-related point mutations in KRAS based on gold nanoparticles. This simple colorimetric assay is sensitive (limit of detection in the low picomolar range), instrument-free, and employs nonstringent room temperature conditions due to a combination of DNA-conjugated gold nanoparticles, a probe design which exploits cooperative hybridization for increased binding affinity, and signal enhancement on the surface of magnetic beads. Additionally, the scheme is suitable for point-of-care applications, as it combines naked-eye detection, small sample volumes, and isothermal (PCR-free) amplification

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p