Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra

Diagnóstico diferencial no melanoma primário e metastático por espectroscopia FT-Raman

PURPOSE: To qualify the FT-Raman spectral data of primary and metastatic cutaneous melanoma in order to obtain a differential diagnosis. METHODS: Ten normal human skin samples without any clinical or histopathological alterations, ten cutaneous melanoma fragments, and nine lymph node metastasis samples were used; 105, 140 and 126 spectra were obtained respectively. Each sample was divided into 2 or 3 fragments of approximately 2 mm³ and positioned in the Raman spectrometer sample holder in order to obtain the spectra; a monochrome laser light Nd:YAG at 1064 nm was used to excite the inelastic effect. RESULTS: To differentiate the three histopathological groups according to their characteristics extracted from the spectra, data discriminative analysis was undertaken. Phenylalanine, DNA, and Amide-I spectral variables stood out in the differentiation of the three groups. The percentages of correctly classified groups based on Phenylalanine, DNA, and Amide-I spectral features was 93.1%. CONCLUSION: FT-Raman spectroscopy is capable of differentiating melanoma from its metastasis, as well as from normal skin.OBJETIVO: Qualificar os dados espectrais FT-Raman do melanoma cutâneo primário e metastático e assim realizar o diagnóstico diferencial. MÉTODOS: Foram utilizadas amostras de 10 fragmentos de pele sem alterações clínicas ou histopatológicas, 10 de melanomas cutâneos e 9 de metástases linfonodais; 105, 140 and 126 espectros foram obtidos respectivamente. Cada amostra foi dividida em 2 ou 3 frações de 2 mm³ e posicionada no porta amostras do espectrômetro Raman para obtenção dos espectros, por meio da excitação do espalhamento inelástico pelo laser de Nd:YAG em 1064 nm incididos na amostra. RESULTADOS: Para diferenciar os três grupos formados de acordo com as características fornecidas pelos espectros, realizamos a análise discriminante dos dados. As variáveis espectrais Fenilalanina, DNA e Amida-I se destacaram na capacidade de diferenciação dos três grupos histológicos. A porcentagem de classificação correta utilizando estes critérios foi de 93,1%; o que mostra a eficiência da análise realizada. CONCLUSÃO: A espectroscopia FT-Raman é capaz de diferenciar o melanoma de sua metástase, assim como da pele normal.UNIFESPUniversidade Federal de São Paulo (UNIFESP) Department of SurgeryPathology DepartmentPathology Department Federal University of ABC Head of Center for Human and Natural Sciences (CCNH)UNIVAP Institute of Research and Development Head of Biomedical Vibrational Spectroscopy LaboratoryUniversidade Federal de São Paulo (UNIFESP) Department of Surgery Head of Division of Plastic SurgeryUNIFESP, Department of SurgeryUNIFESP, Department of Surgery Head of Division of Plastic SurgerySciEL

Where is the Machine Looking? : Locating Discriminative Light-Scattering Features by Class-Activation Mapping

We explore a technique called class-activation mapping (CAM) to investigate how a Machine Learning (ML) architecture learns to classify particles based on their light-scattering signals. We release our code, and also find that different regions of the light-scattering signals play different roles in ML classification. These regions depend on the type of particles being classified and on the nature of the data obtained and trained. For instance, the Mueller-matrix elements S-11*, S-1(2)* and S-21* had the greatest classification activation in the diffraction region. Linear polarization elements S-1(2)* and S-21* were most accurate in the backscattering region for clusters of spheres and spores, and was most accurate in the diffraction region for other particle classes. The CAM technique was able to highlight light-scattering angles that maximize the potential for discrimination of similar particle classes. Such information is useful for designing detector systems to classify particles where limited space or resources are available, including flow cytometry and satellite remote sensing. (C) 2020 The Authors. Published by Elsevier Ltd.Peer reviewe

Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters

Many materials have distinct spectral profiles. This facilitates estimation of the material composition of a scene at each pixel by first acquiring its hyperspectral image, and subsequently filtering it using a bank of spectral profiles. This process is inherently wasteful since only a set of linear projections of the acquired measurements contribute to the classification task. We propose a novel programmable camera that is capable of producing images of a scene with an arbitrary spectral filter. We use this camera to optically implement the spectral filtering of the scene's hyperspectral image with the bank of spectral profiles needed to perform per-pixel material classification. This provides gains both in terms of acquisition speed --- since only the relevant measurements are acquired --- and in signal-to-noise ratio --- since we invariably avoid narrowband filters that are light inefficient. Given training data, we use a range of classical and modern techniques including SVMs and neural networks to identify the bank of spectral profiles that facilitate material classification. We verify the method in simulations on standard datasets as well as real data using a lab prototype of the camera

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer

Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis-linear discriminant analysis (PCA-LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA-LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated

Proof of enantioselectivity in a multilayer with a strong exciton polariton coupling and through asymmetric polarization

The plasmonic investigation involves activating a multilayer consisting of silver, platinum, silica, and silicon. Hybrid waveguide and surface plasmon polariton modes define the device plasmonic activity. The presence of micrometric features on the multilayer surface facilitates the formation of the waveguide mode. Moreover, they produce a transmitted signal that is associated with localized surface plasmon resonance. A red dye adsorbed onto a multilayer surface causes a strong coupling between excitons and polaritons. This coupling increases the radiation force, and the dye-induced enhancement of radiation force supports the use of passive chirality spectroscopy to measure the optical forces acting on enantiomers. In conclusion, when a Kretschmann scheme is combined with the de-polarization, a built-in asymmetry results in a different optical flux of spectrum photons, resulting in distinct, enantioselective, and solely polarization-dependent spectral contrast, and the enantioselectivity is demonstrated for the D and L penicillamine.Comment: 14 pages, 6 Figure