Математическое моделирование плавления массивных ледяных структур применительно к роботизированным аппаратам глубинного бурения

Математическое моделирование процессов тепло- и массопереноса, протекающих совместно, в условиях плавления больших массивов льда при движении криобота под действием силы тяжести. В работе варьировались параметры криобота, а также температура льда. По результатам численного моделирования было установлено значительное охлаждение поверхности криобота. Последнее связано с высоким эндотермическим эффектом таяния льда.The mathematical modeling results of the simultaneous processes of heat and mass transfer under the conditions of intense phase changes (melting of ice) during the movement of cryobot have been given. The cryobot parameters and ice temperature varied in the work. According to the results of the numerical simulation, considerable cooling of the cryobot sheath has been established. The latter is due to the high endothermic effect of melting ic

Multimodal microscopy for automated histologic analysis of prostate cancer

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is the single most prevalent cancer in US men whose gold standard of diagnosis is histologic assessment of biopsies. Manual assessment of stained tissue of all biopsies limits speed and accuracy in clinical practice and research of prostate cancer diagnosis. We sought to develop a fully-automated multimodal microscopy method to distinguish cancerous from non-cancerous tissue samples.</p> <p>Methods</p> <p>We recorded chemical data from an unstained tissue microarray (TMA) using Fourier transform infrared (FT-IR) spectroscopic imaging. Using pattern recognition, we identified epithelial cells without user input. We fused the cell type information with the corresponding stained images commonly used in clinical practice. Extracted morphological features, optimized by two-stage feature selection method using a minimum-redundancy-maximal-relevance (mRMR) criterion and sequential floating forward selection (SFFS), were applied to classify tissue samples as cancer or non-cancer.</p> <p>Results</p> <p>We achieved high accuracy (area under ROC curve (AUC) >0.97) in cross-validations on each of two data sets that were stained under different conditions. When the classifier was trained on one data set and tested on the other data set, an AUC value of ~0.95 was observed. In the absence of IR data, the performance of the same classification system dropped for both data sets and between data sets.</p> <p>Conclusions</p> <p>We were able to achieve very effective fusion of the information from two different images that provide very different types of data with different characteristics. The method is entirely transparent to a user and does not involve any adjustment or decision-making based on spectral data. By combining the IR and optical data, we achieved high accurate classification.</p

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study

This is the final version. Available on open access from the American Chemical Society via the DOI in this recordThe variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.COST (European Cooperation in Science and Technology)Portuguese Foundation for Science and TechnologyNational Research Fund of Luxembourg (FNR)China Scholarship Council (CSC)BOKU Core Facilities Multiscale ImagingDeutsche Forschungsgemeinschaft (DFG, German Research Foundation

Chemical imaging of articular cartilage sections with Raman mapping, employing uni- and multi-variate methods for data analysis

Raman mapping in combination with uni- and multi-variate methods of data analysis is applied to articular cartilage samples. Main differences in biochemical composition and collagen fibers orientation between superficial, middle and deep zone of the tissue are readily observed in the samples. Collagen, non-collagenous proteins, proteoglycans and nucleic acids can be distinguished on the basis of their different spectral characteristics, and their relative abundance can be mapped in the label-free tissue samples, at so high a resolution as to permit the analysis at the level of single cells. Differences between territorial and inter-territorial matrix, as well as inhomogeneities in the inter-territorial matrix, are properly identified. Multivariate methods of data analysis prove to be complementary to the univariate approach. In particular, our partial least squares regression model gives a semiquantitative mapping of the biochemical constituents in agreement with average composition found in the literature. The combination of hierarchical and fuzzy cluster analysis succeeds in detecting variations between different regions of the extra-cellular matrix. Because of its characteristics as an imaging technique, Raman mapping could be a promising tool for studying biochemical changes in cartilage occurring during aging or osteoarthritis

Extensive Remineralization of Peatland‐Derived Dissolved Organic Carbon and Ocean Acidification in the Sunda Shelf Sea, Southeast Asia

Southeast Asia is a hotspot of riverine export of terrigenous organic carbon to the ocean, accounting for ∼10% of the global land-to-ocean riverine flux of terrigenous dissolved organic carbon (tDOC). While anthropogenic disturbance is thought to have increased the tDOC loss from peatlands in Southeast Asia, the fate of this tDOC in the marine environment and the potential impacts of its remineralization on coastal ecosystems remain poorly understood. We collected a multi-year biogeochemical time series in the central Sunda Shelf (Singapore Strait), where the seasonal reversal of ocean currents delivers water masses from the South China Sea first before (during Northeast Monsoon) and then after (during Southwest Monsoon) they have mixed with run-off from peatlands on Sumatra. The concentration and stable isotope composition of DOC, and colored dissolved organic matter spectra, reveal a large input of tDOC to our site during Southwest Monsoon. Using isotope mass balance calculations, we show that 60%–70% of the original tDOC input is remineralized in the coastal waters of the Sunda Shelf, causing seasonal acidification. The persistent CO2 oversaturation drives a CO2 efflux of 2.4–4.9 mol m−2 yr−1 from the Singapore Strait, suggesting that a large proportion of the remineralized peatland tDOC is ultimately emitted to the atmosphere. However, incubation experiments show that the remaining 30%–40% tDOC exhibits surprisingly low lability to microbial and photochemical degradation, suggesting that up to 20%–30% of peatland tDOC might be relatively refractory and exported to the open ocean