Distributed opto-mechanical analysis of liquids outside standard fibers coated with polyimide

The analysis of surrounding media has been a long-standing challenge of optical fiber sensors. Measurements are difficult due to the confinement of light to the inner core of standard fibers. Over the last two years, new sensor concepts have enabled the analysis of liquids outside the cladding boundary, where light does not reach. Sensing is based on opto-mechanical, forward stimulated Brillouin scattering interactions between guided light and sound waves. In most previous works, however, the protective polymer coating of the fiber had to be removed first. In this work, we report the opto-mechanical analysis of liquids outside commercially available, standard single-mode fibers with polyimide coating. The polyimide layer provides mechanical protection but can also transmit acoustic waves from the fiber cladding towards outside media. Comprehensive analysis of opto-mechanical coupling in coated fibers that are immersed in liquid is provided. The model shows that forward stimulated Brillouin scattering spectra in coated fibers are more complex than those of bare fibers, and strongly depend on the exact coating diameter and the choice of acoustic mode. Nevertheless, sensing outside coated fibers is demonstrated experimentally. Integrated measurements over 100 meters of fiber clearly distinguish between air, ethanol and water outside polyimide coating. Measured spectra are in close quantitative agreement with the analytic predictions. Further, distributed opto-mechanical time-domain reflectometry mapping of water and ethanol outside coated fiber is reported, with a spatial resolution of 100 meters. The results represent a large step towards practical opto-mechanical fiber sensors

DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph

This paper describes DeepKey, an end-to-end deep neural architecture capable of taking a digital RGB image of an 'everyday' scene containing a pin tumbler key (e.g. lying on a table or carpet) and fully automatically inferring a printable 3D key model. We report on the key detection performance and describe how candidates can be transformed into physical prints. We show an example opening a real-world lock. Our system is described in detail, providing a breakdown of all components including key detection, pose normalisation, bitting segmentation and 3D model inference. We provide an in-depth evaluation and conclude by reflecting on limitations, applications, potential security risks and societal impact. We contribute the DeepKey Datasets of 5, 300+ images covering a few test keys with bounding boxes, pose and unaligned mask data.Comment: 14 pages, 12 figure

Urinary chitinase 3-like protein 1 for early diagnosis of acute kidney injury : a prospective cohort study in adult critically ill patients

Background: Acute kidney injury (AKI) occurs frequently and adversely affects patient and kidney outcomes, especially when its severity increases from stage 1 to stages 2 or 3. Early interventions may counteract such deterioration, but this requires early detection. Our aim was to evaluate whether the novel renal damage biomarker urinary chitinase 3-like protein 1 (UCHI3L1) can detect AKI stage >= 2 more early than serum creatinine and urine output, using the respective Kidney Disease vertical bar Improving Global Outcomes (KDIGO) criteria for definition and classification of AKI, and compare this to urinary neutrophil gelatinase-associated lipocalin (UNGAL). Methods: This was a translational single-center, prospective cohort study at the 22-bed surgical and 14-bed medical intensive care units (ICU) of Ghent University Hospital. We enrolled 181 severely ill adult patients who did not yet have AKI stage >= 2 based on the KDIGO criteria at time of enrollment. The concentration of creatinine (serum, urine) and CHI3L1 (serum, urine) was measured at least daily, and urine output hourly, in the period from enrollment till ICU discharge with a maximum of 7 ICU-days. The concentration of UNGAL was measured at enrollment. The primary endpoint was the development of AKI stage >= 2 within 12 h after enrollment. Results: After enrollment, 21 (12 %) patients developed AKI stage >= 2 within the next 7 days, with 6 (3 %) of them reaching this condition within the first 12 h. The enrollment concentration of UCHI3L1 predicted the occurrence of AKI stage >= 2 within the next 12 h with a good AUC-ROC of 0.792 (95 % CI: 0.726-0.849). This performance was similar to that of UNGAL (AUC-ROC of 0.748 (95 % CI: 0.678-0.810)). Also, the samples collected in the 24-h time frame preceding diagnosis of the 1st episode of AKI stage >= 2 had a 2.0 times higher (95 % CI: 1.3-3.1) estimated marginal mean of UCHI3L1 than controls. We further found that increasing UCHI3L1 concentrations were associated with increasing AKI severity. Conclusions: In this pilot study we found that UCHI3L1 was a good biomarker for prediction of AKI stage >= 2 in adult ICU patients

Identifying the presence of prostate cancer in individuals with PSA levels <20 ng ml−1 using computational data extraction analysis of high dimensional peripheral blood flow cytometric phenotyping data

Determining whether an asymptomatic individual with Prostate-Specific Antigen (PSA) levels below 20 ng ml−1 has prostate cancer in the absence of definitive, biopsy-based evidence continues to present a significant challenge to clinicians who must decide whether such individuals with low PSA values have prostate cancer. Herein, we present an advanced computational data extraction approach which can identify the presence of prostate cancer in men with PSA levels <20 ng ml−1 on the basis of peripheral blood immune cell profiles that have been generated using multi-parameter flow cytometry. Statistical analysis of immune phenotyping datasets relating to the presence and prevalence of key leukocyte populations in the peripheral blood, as generated from individuals undergoing routine tests for prostate cancer (including tissue biopsy) using multi-parametric flow cytometric analysis, was unable to identify significant relationships between leukocyte population profiles and the presence of benign disease (no prostate cancer) or prostate cancer. By contrast, a Genetic Algorithm computational approach identified a subset of five flow cytometry features (CD8+CD45RA−CD27−CD28− (CD8+ Effector Memory cells); CD4+CD45RA−CD27−CD28− (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA); CD3−CD19+ (B cells); CD3+CD56+CD8+CD4+ (NKT cells)) from a set of twenty features, which could potentially discriminate between benign disease and prostate cancer. These features were used to construct a prostate cancer prediction model using the k-Nearest-Neighbor classification algorithm. The proposed model, which takes as input the set of flow cytometry features, outperformed the predictive model which takes PSA values as input. Specifically, the flow cytometry-based model achieved Accuracy = 83.33%, AUC = 83.40%, and optimal ROC points of FPR = 16.13%, TPR = 82.93%, whereas the PSA-based model achieved Accuracy = 77.78%, AUC = 76.95%, and optimal ROC points of FPR = 29.03%, TPR = 82.93%. Combining PSA and flow cytometry predictors achieved Accuracy = 79.17%, AUC = 78.17% and optimal ROC points of FPR = 29.03%, TPR = 85.37%. The results demonstrate the value of computational intelligence-based approaches for interrogating immunophenotyping datasets and that combining peripheral blood phenotypic profiling with PSA levels improves diagnostic accuracy compared to using PSA test alone. These studies also demonstrate that the presence of cancer is reflected in changes in the peripheral blood immune phenotype profile which can be identified using computational analysis and interpretation of complex flow cytometry datasets

Anticipation and Risk – From the inverse problem to reverse computation

Abstract. Risk assessment is relevant only if it has predictive relevance. In this sense, the anticipatory perspective has yet to contribute to more adequate predictions. For purely physics-based phenomena, predictions are as good as the science describing such phenomena. For the dynamics of the living, the physics of the matter making up the living is only a partial description of their change over time. The space of possibilities is the missing component, complementary to physics and its associated predictions based on probabilistic methods. The inverse modeling problem, and moreover the reverse computation model guide anticipatory-based predictive methodologies. An experimental setting for the quantification of anticipation is advanced and structural measurement is suggested as a possible mathematics for anticipation-based risk assessment

Lay-up characterization and elastic property determination in composite laminates

This dissertation focuses on two important nondestructive evaluation and materials characterization problems related to composite laminates: ply lay-up characterization and elastic property determination. For ply lay-up characterization, we have developed a shear wave transmission technique to effectively detect ply lay-up errors in composite laminates. The effects of fiber orientation on normal-incident shear waves propagating through a composite laminate have been investigated both theoretically and experimentally. To facilitate rotation, EMATs (electromagnetic acoustic transducers) were used to generate and receive the shear waves. It was found that the transmitted shear waves when the EMAT transmitter and receiver were perpendicular to each other had a great sensitivity to ply lay-up errors. This technique has been successfully demonstrated on both cured and uncured composite laminates. For elastic property determination, we have first applied the simultaneous velocity and thickness imaging technique to map out small changes in ultrasonic velocity (hence elastic constant) when the material thickness was unknown or varied spatially. Applications to several industrial materials have demonstrated the usefulness of this technique for both materials characterization and flaw detection in metals and composite laminates. We have also extended this technique to generate images of sample surface contours and cross-sectional profiles when the velocity was unknown. Next, we have extended the synthetic aperture scanning method using planar transducers in an immersion leaky wave reflection or transmission measurement to allow the use of focused transducers. The complex transducer point approach has been used to model the receiver output voltage and to analyze the transducer beam effects on the result of a synthetic aperture scan. It was found that the large angular beam spread of focused transducers can be used for rapid mapping of the reflection or transmission coefficient and the associated dispersion spectrum. A novel stepwise, targeted procedure has also been developed to allow efficient reconstruction of material elastic property with only minimal use of the highly redundant dispersion spectrum data. Experiments on both isotropic and anisotropic plates showed that this method can be used for rapid evaluation of the elastic behavior of composite laminates and other plate materials with a reasonably good accuracy