SurfaceSlide: A Multitouch Digital Pathology Platform

Background: Digital pathology provides a digital environment for the management and interpretation of pathological images and associated data. It is becoming increasing popular to use modern computer based tools and applications in pathological education, tissue based research and clinical diagnosis. Uptake of this new technology is stymied by its single user orientation and its prerequisite and cumbersome combination of mouse and keyboard for navigation and annotation.

Methodology: In this study we developed SurfaceSlide, a dedicated viewing platform which enables the navigation and annotation of gigapixel digitised pathological images using fingertip touch. SurfaceSlide was developed using the Microsoft Surface, a 30 inch multitouch tabletop computing platform. SurfaceSlide users can perform direct panning and zooming operations on digitised slide images. These images are downloaded onto the Microsoft Surface platform from a remote server on-demand. Users can also draw annotations and key in texts using an on-screen virtual keyboard. We also developed a smart caching protocol which caches the surrounding regions of a field of view in multi-resolutions thus providing a smooth and vivid user experience and reducing the delay for image downloading from the internet. We compared the usability of SurfaceSlide against Aperio ImageScope and PathXL online viewer.

Conclusion: SurfaceSlide is intuitive, fast and easy to use. SurfaceSlide represents the most direct, effective and intimate human–digital slide interaction experience. It is expected that SurfaceSlide will significantly enhance digital pathology tools and applications in education and clinical practice.

Virtual state scattering with cold electrons: para-xylene and para-difluorobenzene

The scattering of electrons with kinetic energies down to a few meV by para-xylene and para-difluorobenzene has been observed experimentally with an electron beam energy resolution of 0.95 to 1.5 meV (full width half maximum). At low electron energies the collisions can be considered as cold scattering events because the de Broglie wavelength of the electron is considerably larger than the target dimensions. The scattering cross sections measured rise rapidly at low energy due to virtual state scattering. The nature of this scattering process is discussed using s- and p-wave phase shifts derived from the experimental data. Scattering lengths are derived of, respectively, -9.5+/-0.5 and -8.0+/-0.5 a.u. for para-xylene and para-difluorobenzene. The virtual state effect is interpreted in terms of nuclear diabatic and partially adiabatic models, involving the electronic and vibronic symmetries of the unoccupied orbitals in the target species. The concept of direct and indirect virtual state scattering is introduced, through which the present species, in common with carbon dioxide and benzene, scatter through an indirect virtual state process, whereas other species, such as perfluorobenzene, scatter through a direct process. (C) 2005 American Institute of Physics.

Fluorescence (FISH) and chromogenic (CISH) in situ hybridisation in prostate carcinoma cell lines: comparison and use of virtual microscopy

Chromogenic in situ hybridisation (CISH) has become an attractive alternative to fluorescence in situ hybridisation (FISH) due to its permanent stain which is more familiar to pathologists and because it can be viewed using light microscopy, The aim of the present study is to examine reproducibility in the assessment of abnormal chromosome number by CISH in comparison to FISH. Using three prostate cell lines - PNTIA (derived from normal epithelium), LNCAP and DU145 (derived from prostatic carcinoma), chromosomes 7 and 8 were counted in 40 nuclei in FISH preparations (x100 oil immersion) and 100 nuclei in CISH preparations (x40) by two independent observers. The CISH slides were examined using standard fight microscopy and virtual microscopy. Reproducibitity was examined using paired Student's t-test (P

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes

Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable part of the production line. The outputs of the processed system may be used for process monitoring and control purposes. A second contribution of this work is the introduction of Elastic Nets, a regularization and variable selection technique for the modelling of highly-correlated datasets, as a technique for the development of VM models. Elastic Nets and the proposed VM system are illustrated using real data from a multi-stage etch process used in the fabrication of disk drive read/write heads. © 2013 IEEE.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

Effectiveness of Community versus Hospital Eye Service follow-up for patients with neovascular age-related macular degeneration with quiescent disease (ECHoES): a virtual non-inferiority trial

OBJECTIVES: To compare the ability of ophthalmologists versus optometrists to correctly classify retinal lesions due to neovascular age-related macular degeneration (nAMD).

DESIGN: Randomised balanced incomplete block trial. Optometrists in the community and ophthalmologists in the Hospital Eye Service classified lesions from vignettes comprising clinical information, colour fundus photographs and optical coherence tomographic images. Participants' classifications were validated against experts' classifications (reference standard).

SETTING: Internet-based application.

PARTICIPANTS: Ophthalmologists with experience in the age-related macular degeneration service; fully qualified optometrists not participating in nAMD shared care.

INTERVENTIONS: The trial emulated a conventional trial comparing optometrists' and ophthalmologists' decision-making, but vignettes, not patients, were assessed. Therefore, there were no interventions and the trial was virtual. Participants received training before assessing vignettes.

MAIN OUTCOME MEASURES: Primary outcome-correct classification of the activity status of a lesion based on a vignette, compared with a reference standard. Secondary outcomes-potentially sight-threatening errors, judgements about specific lesion components and participants' confidence in their decisions.

RESULTS: In total, 155 participants registered for the trial; 96 (48 in each group) completed all assessments and formed the analysis population. Optometrists and ophthalmologists achieved 1702/2016 (84.4%) and 1722/2016 (85.4%) correct classifications, respectively (OR 0.91, 95% CI 0.66 to 1.25; p=0.543). Optometrists' decision-making was non-inferior to ophthalmologists' with respect to the prespecified limit of 10% absolute difference (0.298 on the odds scale). Optometrists and ophthalmologists made similar numbers of sight-threatening errors (57/994 (5.7%) vs 62/994 (6.2%), OR 0.93, 95% CI 0.55 to 1.57; p=0.789). Ophthalmologists assessed lesion components as present less often than optometrists and were more confident about their classifications than optometrists.

CONCLUSIONS: Optometrists' ability to make nAMD retreatment decisions from vignettes is not inferior to ophthalmologists' ability. Shared care with optometrists monitoring quiescent nAMD lesions has the potential to reduce workload in hospitals.

TRIAL REGISTRATION NUMBER: ISRCTN07479761; pre-results registration.

N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

Drivetrain Effects on Small Engine Performance

Presented is a study that expands the body of knowledge on the effect of in-cycle speed fluctuations on performance of small engines. It uses the methods developed previously by Callahan, et al. (1) to examine a variety of two-stroke engines and one four-stroke engine. The two-stroke engines were: a high performance single-cylinder, a low performance single-cylinder, a high performance multi-cylinder, and a medium performance multi-cylinder. The four-stroke engine was a high performance single-cylinder unit. Each engine was modeled in Virtual Engines, which is a fully detailed one-dimensional thermodynamic engine simulator. Measured or predicted in-cycle speed data were input into the engine models. Predicted performance changes due to drivetrain effects are shown in each case, and conclusions are drawn from those results. The simulations for the high performance single-cylinder two-stroke engine predicted significant in-cycle crankshaft speed fluctuation amplitudes and significant changes in performance when the fluctuations were input into the engine model. This was validated experimentally on a firing test engine based on a Yamaha YZ250. The four-stroke engine showed significant changes in predicted performance compared to the prediction with zero speed fluctuation assumed in the model. Measured speed fluctuations from a firing Yamaha YZ400F engine were applied to the simulation in addition to data from a simple free mass model. Both methods predicted similar fluctuation profiles and changes in performance. It is shown that the gear reduction between the crankshaft and clutch allowed for this similar behavior. The multi-cylinder, high performance two-stroke engine also showed significant changes in performance, in this case depending on the firing configuration. The low output two-stroke engine simulation showed only a negligible change in performance in spite of high amplitude speed fluctuations. This was due to its flat torque versus speed characteristic. The medium performance multi-cylinder two-stroke engine also showed only a negligible change in performance, in this case due to a relatively high inertia rotating assembly and multiple cylinder firing events within the revolution. These smoothed the net torque pulsations and reduced the amplitude of the speed fluctuation itself.