Two datasets used to build the DEcancer pipelines from open access data. The tables in the cancerseek folder are uploaded for convenience and can also be found from the supplementary data of Early Cancer Detection from Multianalyte Blood Test Results paper. The nanoparticles dataset is processed with the Perseus software on the original data from Blume et al's Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. These are distinct datasets in that the nanoparticles is a cohort of patients from a high dimensional, low sample size proteomics dataset.
We present a direct comparison between two types of femtosecond 2 µm sources used for seeding of an ultrafast thulium-doped fiber amplifier based on all-normal dispersion supercontinuum and soliton self-frequency shift. Both nonlinear effects were generated in microstructured silica fibers, pumped with low-power femtosecond pulses at 1.56 µm originating from an erbium-doped fiber laser. We performed a full characterization of both nonlinear processes, including their shot-to-shot stability, phase coherence, and relative intensity noise. The results revealed that the solitons show comparable performance to supercontinuum in terms of relative intensity noise and shot-to-shot stability, despite the anomalous dispersion regime. Both sources can be successfully used as seeds for Tm-doped fiber amplifiers as an alternative to Tm-doped oscillators. The results show that the sign of chromatic dispersion of the fiber is not crucial for obtaining a stable, high-quality, and low-noise spectral conversion process when pumped with sub-50 fs laser pulses.
Two datasets used to build the DEcancer pipelines from open access data. The tables in the cancerseek folder are uploaded for convenience and can also be found from the supplementary data of Early Cancer Detection from Multianalyte Blood Test Results paper. The nanoparticles dataset is processed with the Perseus software on the original data from Blume et al's Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. These are distinct datasets in that the nanoparticles is a cohort of patients from a high dimensional, low sample size proteomics dataset.
The propagation of pulses in optical fibers is described by the generalized nonlinear Schrodinger equation (GNLSE), which takes into account the fiber losses, nonlinear effects, and higher-order chromatic dispersion. The GNLSE is a partial differential equation, whose order depends on the accounted nonlinear and dispersion effects. We present gnlse-python, a nonlinear optics modeling toolbox that contains a rich set of components and modules to solve the GNLSE using the split-step Fourier transform method (SSFM). The numerical solver is freely available, implemented in Python language, and includes a number of optical fiber analysis tools. Code and data are available at https://github.com/WUST-FOG/gnlse-python.
Numerical modeling of many optical-fiber-based devices' operation requires an exact knowledge of fiber's parameters like core diameter and dopant, usually GeO2, concentration. While diameters are typically specified in the fiber's data sheet, material composition, including dopant in the core, is rarely available. We present a procedure utilizing a reverse engineering approach to find GeO2 concentration in single-mode step-index optical fiber. Our method consists of several stages. First, we measured the numerical aperture NA for several commercially available fibers employing a onedimensional far-field scan. The far-filed mode intensity was acquired by a Ge detector placed on a rotation stage with a stepper motor for fiber end-face positioned on the motor's axis of rotation. We calculated NA for the angular detector position when the light intensity reached 13.5%, 5%, and 1% of its maximum value. Then, taking the corresponding values of core and cladding diameters and using the Sellmeier formula for pure (cladding) and GeO2 doped (core) silica glass, we found the concentration of GeO2 numerically matching calculated NA to the experimental data. We found that dopant concentration equals 9.0, 18.0, 34.0, 34.5, and 39.8 mol% for the fibers, respectively, 980-HP, UHNA1, UHNA3, UHNA4 and UHNA7 produced by Coherent. To verify the correctness of our method, we performed this procedure for several fibers with a known level of GeO2 concentration in the core fabricated by the Laboratory of Optical Fiber Technology, Maria Curie-Sklodowska University. The results of this simulation coincide with expectations with great accuracy.
The propagation of pulses in optical fibers is described by the generalized nonlinear Schrodinger equation (GNLSE), which takes into account the fiber losses, nonlinear effects, and higher-order chromatic dispersion. The GNLSE is a partial differential equation, whose order depends on the accounted nonlinear and dispersion effects. We present gnlse-python, a nonlinear optics modeling toolbox that contains a rich set of components and modules to solve the GNLSE using the split-step Fourier transform method (SSFM). The numerical solver is freely available, implemented in Python language, and includes a number of optical fiber analysis tools. Code and data are available at https://github.com/WUST-FOG/gnlse-python.
Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer's performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management.
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