Haptic interface control-design issues and experiments with a planar device

Describes the haptic rendering of a virtual environment by drawing upon concepts developed in the area of teleoperation. A four-channel teleoperation architecture is shown to be an effective means of coordinating the control of a 3-DOF haptic interface with the simulation of a virtual dynamic environmen

An Open-Source Research Kit for the da Vinci ® Surgical System

Abstract-We present a telerobotics research platform that provides complete access to all levels of control via opensource electronics and software. The electronics employs an FPGA to enable a centralized computation and distributed I/O architecture in which all control computations are implemented in a familiar development environment (Linux PC) and lowlatency I/O is performed over an IEEE-1394a (FireWire) bus at speeds up to 400 Mbits/sec. The mechanical components are obtained from retired first-generation da Vinci R Surgical Systems. This system is currently installed at 11 research institutions, with additional installations underway, thereby creating a research community around a common open-source hardware and software platform

Four small puzzles that Rosetta doesn't solve

A complete macromolecule modeling package must be able to solve the simplest structure prediction problems. Despite recent successes in high resolution structure modeling and design, the Rosetta software suite fares poorly on deceptively small protein and RNA puzzles, some as small as four residues. To illustrate these problems, this manuscript presents extensive Rosetta results for four well-defined test cases: the 20-residue mini-protein Trp cage, an even smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies, several lines of evidence indicate that conformational sampling is not the major bottleneck in modeling these small systems. Instead, approximations and omissions in the Rosetta all-atom energy function currently preclude discriminating experimentally observed conformations from de novo models at atomic resolution. These molecular "puzzles" should serve as useful model systems for developers wishing to make foundational improvements to this powerful modeling suite.Comment: Published in PLoS One as a manuscript for the RosettaCon 2010 Special Collectio

Distinguishing grade I meningioma from higher grade meningiomas without biopsy

BACKGROUND: Many meningiomas are identified by imaging and followed, with an assumption that they are WHO Grade I tumors. The purpose of our investigation is to find clinical or imaging predictors of WHO Grade II/III tumors to distinguish them from Grade I meningiomas. METHODS: Patients with a pathologic diagnosis of meningioma from 2002-2009 were included if they had pre-operative MRI studies and pathology for review. A Neuro-Pathologist reviewed and classified all tumors by WHO 2007. All Brain MRI imaging was reviewed by a Neuro-radiologist. Pathology and Radiology reviews were blinded from each other and clinical course. Recursive partitioning was used to create predictive models for identifying meningioma grades. RESULTS: Factors significantly correlating with a diagnosis of WHO Grade II-III tumors in univariate analysis: prior CVA (p = 0.005), CABG (p = 0.010), paresis (p = 0.008), vascularity index = 4/4: (p = 0.009), convexity vs other (p = 0.014), metabolic syndrome (p = 0.025), non-skull base (p = 0.041) and non-postmenopausal female (p = 0.045). Recursive partitioning analysis identified four categories: 1. prior CVA, 2. vascular index (vi) = 4 (no CVA), 3. premenopausal or male, vi \u3c 4, no CVA. 4. Postmenopausal, vi \u3c 4, no CVA with corresponding rates of 73, 54, 35 and 10% of being Grade II-III meningiomas. CONCLUSIONS: Meningioma patients with prior CVA and those grade 4/4 vascularity are the most likely to have WHO Grade II-III tumors while post-menopausal women without these features are the most likely to have Grade I meningiomas. Further study of the associations of clinical and imaging factors with grade and clinical behavior are needed to better predict behavior of these tumors without biopsy

A critical realist evaluation of a music therapy intervention in palliative care

BACKGROUND: Music therapy is increasingly used as an adjunct therapy to support symptom management in palliative care. However, studies to date have paid little attention to the processes that lead to changes in patient outcomes. To fill this gap, we examined the processes and experiences involved in the introduction of music therapy as an adjunct complementary therapy to palliative care in a hospice setting in the United Kingdom (UK). METHODS: Using a realistic evaluation approach, we conducted a qualitative study using a variety of approaches. These consisted of open text answers from patients (n = 16) on how music therapy helped meet their needs within one hospice in Northern Ireland, UK. We also conducted three focus groups with a range of palliative care practitioners (seven physicians, seven nursing staff, two social workers and three allied health professionals) to help understand their perspectives on music therapy's impact on their work setting, and what influences its successful implementation. This was supplemented with an interview with the music therapist delivering the intervention. RESULTS: Music therapy contains multiple mechanisms that can provide physical, psychological, emotional, expressive, existential and social support. There is also evidence that the hospice context, animated by a holistic approach to healthcare, is an important facilitator of the effects of music therapy. Examination of patients' responses helped identify specific benefits for different types of patients. CONCLUSIONS: There is a synergy between the therapeutic aims of music therapy and those of palliative care, which appealed to a significant proportion of participants, who perceived it as effective

Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density

The structures of a naturally empty cowpea mosaic virus particle and its genome-containing counterpart by cryo-electron microscopy

Cowpea mosaic virus (CPMV) is a picorna-like plant virus. As well as an intrinsic interest in CPMV as a plant pathogen, CPMV is of major interest in biotechnology applications such as nanotechnology. Here, we report high resolution cryo electron microscopy (cryo-EM) maps of wild type CPMV containing RNA-2, and of naturally-formed empty CPMV capsids. The resolution of these structures is sufficient to visualise large amino acids. We have refined an atomic model for each map and identified an essential amino acid involved in genome encapsidation. This work has furthered our knowledge of Picornavirales genome encapsidation and will assist further work in the development of CPMV as a biotechnological tool

Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy

Transpapillary drainage has no added benefit on treatment outcomes in patients undergoing EUS-guided transmural drainage of pancreatic pseudocysts: a large multicenter study

Background and Aims The need for transpapillary drainage (TPD) in patients undergoing transmural drainage (TMD) of pancreatic fluid collections (PFCs) remains unclear. The aims of this study were to compare treatment outcomes between patients with pancreatic pseudocysts undergoing TMD versus combined (TMD and TPD) drainage (CD) and to identify predictors of symptomatic and radiologic resolution. Methods This is a retrospective review of 375 consecutive patients with PFCs who underwent EUS-guided TMD from 2008 to 2014 at 15 academic centers in the United States. Main outcome measures included TMD and CD technical success, treatment outcomes (symptomatic and radiologic resolution) at follow-up, and predictors of treatment outcomes on logistic regression. Results A total of 375 patients underwent EUS-guided TMD of PFCs, of which 174 were pseudocysts. TMD alone was performed in 95 (55%) and CD in 79 (45%) pseudocysts. Technical success was as follows: TMD, 92 (97%) versus CD, 35 (44%) (P = .0001). There was no difference in adverse events between the TMD (15%) and CD (14%) cohorts (P = .23). Median long-term (LT) follow-up after transmural stent removal was 324 days (interquartile range, 72-493 days) for TMD and 201 days (interquartile range, 150-493 days) (P = .37). There was no difference in LT symptomatic resolution (TMD, 69% vs CD, 62%; P = .61) or LT radiologic resolution (TMD, 71% vs CD, 67%; P = .79). TPD attempt was negatively associated with LT radiologic resolution of pseudocyst (odds ratio, 0.11; 95% confidence interval, 0.02-0.8; P = .03). Conclusions TPD has no benefit on treatment outcomes in patients undergoing EUS-guided TMD of pancreatic pseudocysts and negatively affects LT resolution of PFCs