32 research outputs found
New developments in Stimulated Raman Scattering and applications to plastic particle detection in the environment and human tissue
Get PDFThis thesis deals with an advanced laser-based microscopy technique to detect micrometer-size objects with molecular specificity. Applications are shown from the aquatic environment and from the medical world. The final chapters describe an option to increase the penetration depth through scattering samples and simulation software to help optimize the measurement settings. One of the most prominent materials in modern life is plastic, but this also results in the large-scale production of plastic waste. A portion of this waste reaches the environment and is fragmented into small pieces, called microplastics. Microplastics pollution affects the environment and potentially our health in ways we are only beginning to understand. To study it, we need to have a solid measurement and monitoring platform, based on reliable microplastics detection. Detection of microplastics is difficult due to their small size and heterogeneity and they can be found in different types of matrices in the environment and even in the human body. A label-free microscopy imaging technique, called Stimulated Raman Scattering (SRS) microscopy, is able to create images of small particles, like microplastics, based on their molecular structure. SRS makes use of two synchronized pulsed lasers of different colors, of which the energy difference matches a specific vibration of the target molecule. In this thesis, we used SRS for identifying five polymer types. First, we tested the approach on an artificial mixture of plastic particles, and we identified polyethylene terephthalate particles extracted from nail polish, demonstrating also the thousand‐fold higher speed of mapping compared with conventional Raman. Furthermore, we found 12,000 plastic particles per kilogram dry weight in a Rhine estuary sediment sample. SRS was the fastest microplastics detection method at the time of publication. We concluded that SRS can be an efficient method for monitoring microplastics in the environment and potentially many other matrices of interest. Another application area that was studied with SRS is breast tissue from explanted breast implants. Implant failure occurs in approximately a tenth of patients within 10 years, and even without a major rupture silicone can still leak. We showed how SRS can detect silicone material in breast tissue slices, without additional sample treatment. SRS images revealed the distribution and quantity of silicone material. Twenty-two donor-matched capsules from eleven patients experiencing unilateral capsular contraction complaints were included in a clinical study after bilateral explantation surgery. This method showed the correlation between silicone presence and capsular contraction. Depth penetration of the light into the sample is an issue with any light based technique. We showed the use of a long wavelength SRS microscope system capable of greater depth imaging compared with the more common configuration with shorter wavelengths. It showed an improved depth penetration in polyethylene plastic material, in a silicone test sample with embedded polyethylene microbeads, and into subcutaneous fat tissue. In SRS imaging we have to consider multiple parameters that influence the imaging speed, image quality and the spatial resolution. In order to find the optimized imaging setup, we developed two simulation programs for SRS imaging systems with lock-in amplifier. One simulation program was used to find parameters optimized for either image quality or acquisition time. With the second program we evaluated SRS imaging; the simulations agreed very well with experimental SRS images. The same software was used to simulate multiplexed SRS imaging. of six channels, including the inter-channel crosstalk. These programs will be useful for operating an SRS imaging setup, as well as for designing novel setups
Black Plastic Identification for Sorting and Recycling With Deep-UV Raman Spectroscopy
No full textWe present a deep-ultraviolet (DUV) Raman spectrometer for plastic identification for sorting purposes in a recycling facility. Plastic recycling entails processing large volumes of inhomogeneous plastic waste of different colors, sizes, and polymer types. Currently, hyperspectral imaging in the near-infrared is one of the leading techniques for plastic identification for sorting purposes, but it falls short for dark or black plastics due to strong absorption. Alternatively, Raman spectroscopy could be used; however, conventional Raman technology at visible wavelengths often suffers from a strong fluorescence background that overwhelms the Raman signal, hampering the identification of black plastics in particular. In contrast, Raman spectroscopy in the DUV wavelength range is suitable for black plastics because the fluorescence emission that occurs over the near-UV and visible range will not overlap with the Raman spectrum. Here, we present a dedicated DUV setup, based on a 248.6-nm NeCu laser and designed and tested for stand-off detection of black plastic waste objects on a conveyor belt. We characterized key parameters such as focal depth and detection at different conveyor speeds, emphasizing real-world applicability under recycling factory conditions. In this work, we focus on polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), as these are considered commercially interesting. Analysis of the deep-UV spectra led to promising results for identification, for both stationary and moving conditions. We also demonstrate successful identification of multilayer materials (metalized or black coated) that are challenging for hyperspectral NIR techniques. We discuss advantages of this DUV Raman modality, options to overcome its current shortcomings, and its future potential for polyolefin sorting in a recycling factory.</p
Artificial intelligence in head and neck cancer diagnosis
Get PDFIntroduction: Artificial intelligence (AI) is currently being used to augment histopathological diagnostics in pathology. This systematic review aims to evaluate the evolution of these AI-based diagnostic techniques for diagnosing head and neck neoplasms. Materials and methods: Articles regarding the use of AI for head and neck pathology published from 1982 until March 2022 were evaluated based on a search strategy determined by a multidisciplinary team of pathologists and otolaryngologists. Data from eligible articles were summarized according to author, year of publication, country, study population, tumor details, study results, and limitations. Results: Thirteen articles were included according to inclusion criteria. The selected studies were published between 2012 and March 1, 2022. Most of these studies concern the diagnosis of oral cancer; in particular, 6 are related to the oral cavity, 2 to the larynx, 1 to the salivary glands, and 4 to head and neck squamous cell carcinoma not otherwise specified (NOS). As for the type of diagnostics considered, 12 concerned histopathology and 1 cytology. Discussion: Starting from the pathological examination, artificial intelligence tools are an excellent solution for implementing diagnosis capability. Nevertheless, today the unavailability of large training datasets is a main issue that needs to be overcome to realize the true potential
Stimulated Raman scattering simulation for imaging optimization
No full textAbstract Two simulation programs of a stimulated Raman scattering microscopy (SRS) imaging system with lock-in amplifier (LIA) detection were developed. SRS is an imaging technique based on the vibrational Raman cross-section as the contrast mechanism and enables fast, label-free imaging. Most SRS implementations are based on LIA detection of a modulated signal. However, building and operating such SRS set-ups still poses a challenge when selecting the LIA parameter settings for optimized acquisition speed or image quality. Moreover, the type of sample, e.g. a sparse sample vs. a densely packed sample, the required resolution as well as the Raman cross-section and the laser powers affect the parameter choice. A simulation program was used to find these optimal parameters. The focal spot diameters of the individual lasers (pump and Stokes) were used to estimate the effective SRS signal focal spot and the (optical) spatial resolution. By calibrating the signal and noise propagation through an SRS system for a known molecule, we estimated the signal and noise input to the LIA. We used a low pass filter model to simulate the LIA behavior in order to find the optimal parameters (i.e. filter order and time constant). Optimization was done for either image quality (expressed as contrast to noise ratio) or acquisition time. The targeted object size was first determined as a measure for the required resolution. The simulation output consisted of the LIA parameters, pixel dwell time and contrast to noise ratio. In a second simulation we evaluated SRS imaging based on the same principles as the optimal setting simulation, i.e. the signals were propagated through an imaging system and LIA detection. The simulated images were compared to experimental SRS images of polystyrene beads. Finally, the same software was used to simulate multiplexed SRS imaging. In this study we modeled a six-channel frequency-encoded multiplexed SRS system demodulated with six LIA channels. We evaluated the inter-channel crosstalk as a function of chosen LIA parameters, which in multiplex SRS imaging also needs to be considered. These programs to optimize the contrast to noise ratio, acquisition speed, resolution and crosstalk will be useful for operating stimulated Raman scattering imaging setup, as well as for designing novel setups
Stimulated Raman scattering microscopy with long wavelengths for improved imaging depth
No full textStimulated Raman scattering (SRS) imaging is a fast, label-free, and sensitive technique to map the distribution of a vibrational species in a microscopy setting. It has great potential for applications in many fields, such as lipid imaging in biomedicine. However, depth penetration of the light into the sample is an issue with any light-based technique, especially with multiphoton techniques such as SRS. Using longer wavelengths allows deeper penetration into densely scattering materials, but applying wavelengths above 1,500 nm is challenging technically. We have built a flexible SRS microscope system capable of imaging with a combination of 1,064 nm and wavelengths over 1,500 nm, using the idler output of an optical parametric oscillator (OPO). For comparison, the same system was also operated in the more common configuration, using 1,064 nm in combination with the OPO signal output around 800 nm. With the long-wavelength settings, we show improved depth penetration in polyethylene plastic material and in a silicone phantom with embedded polymer microbeads, and we report images of lipid structure in biological tissue. These results demonstrate the technical feasibility of using these long wavelengths for SRS imaging. Disadvantages such as poorer spatial resolution and lower signal strength are also discussed. The application of this new approach to SRS microscopy can allow greater insight into deep-lying structures in a non-invasive way
Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review
Get PDFBackground: Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy. Methods: A systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms "kidney", "biopsy", "transplantation" and "artificial intelligence" and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study. Results: Of 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising. Conclusion: All studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidney biopsy clinical practice
Schurian-finiteness of blocks of type Hecke algebras
Get PDFFor any algebra over an algebraically closed field , we say
that an -module is Schurian if . We
say that is Schurian-finite if there are only finitely many isomorphism
classes of Schurian -modules, and Schurian-infinite otherwise. By work of
Demonet, Iyama and Jasso it is known that Schurian-finiteness is equivalent to
-tilting-finiteness, so that we may draw on a wealth of known results in
the subject. We prove that for the type Hecke algebras with quantum
characteristic , all blocks of weight at least are
Schurian-infinite in any characteristic. Weight and blocks are known by
results of Erdmann and Nakano to be representation finite, and are therefore
Schurian-finite. This means that blocks of type Hecke algebras (when ) are Schurian-infinite if and only if they have wild representation type if
and only if the module category has finitely many wide subcategories. Along the
way, we also prove a graded version of the Scopes equivalence, which is likely
to be of independent interest.Comment: 40 pages. v4 combines the paper with its sequel arXiv:2208.05711 by
Lyle and Speyer, adding Lyle as an author. v5 is the final version, to appear
in The Journal of the London Mathematical Societ
Multi-Parameter Analysis of Nanoplastics in Flow:Taking Advantage of High Sensitivity and Time Resolution Enabled by Stimulated Raman Scattering
No full textHere, we demonstrate the detection of nanoplastics (NPLs) in flow with stimulated Raman scattering (SRS) for the first time. NPLs (plastic particles <1000 nm) have recently been detected in different environmental samples and personal care products. However, their characterization is still an analytical challenge. Multiple parameters, including size, chemical composition, and concentration (particle number and mass), need to be determined. In an earlier paper, online field flow fractionation (FFF)-Raman analysis with optical trapping was shown to be a promising tool for the detection of particles in this size range. SRS, which is based on the enhancement of a vibrational transition by the matching energy difference of two laser beams, would allow for much more sensitive detection and, hence, much shorter acquisition times compared to spontaneous Raman microspectroscopy (RM). Here, we show the applicability of SRS for the flow-based analysis of individual, untrapped NPLs. It was possible to detect polyethylene (PE), polystyrene (PS), and poly(methyl methacrylate) (PMMA) beads with diameters of 100-5000 nm. The high time resolution of 60.5 μs allows us to detect individual signals per particle and to correlate the number of detected particles to the injected mass concentration. Furthermore, due to the high time resolution, optically trapped beads could be distinguished from untrapped beads by their peak shapes. The SRS wavenumber settings add chemical selectivity to the measurement. Whereas optical trapping is necessary for the flow-based detection of particles by spontaneous RM, the current study demonstrates that SRS can detect particles in a flow without trapping. Additionally, the mean particle size could be estimated using the mean width (duration) and intensity of the SRS signals.</p
Decomposable Specht modules indexed by bihooks II
Get PDFPreviously, the last two authors found large families of decomposable Specht
modules labelled by bihooks, over the Iwahori--Hecke algebra of type . In
most cases we conjectured that these were the only decomposable Specht modules
labelled by bihooks, proving it in some instances. Inspired by a recent
semisimplicity result of Bowman, Bessenrodt and the third author, we look back
at our decomposable Specht modules and show that they are often either
semisimple, or very close to being so. We obtain their exact structure and
composition factors in these cases. In the process, we determine the graded
decomposition numbers for almost all of the decomposable Specht modules indexed
by bihooks.Comment: 33 pages, comments are welcome. v2 is the final version, to appear in
Algebras and Representation Theor
