Atypical Samples Research Articles

Nontargeted Metabolomics to Understand the Impact of Modified Atmospheric Packaging on Metabolite Profiles of Cooked Normal-pH and Atypical Dark-Cutting Beef.

Background: Limited knowledge is currently available on the effects of modified atmospheric packaging (MAP) on the metabolite profiles of cooked beef. The objective was to evaluate the impact of packaging on the cooked color and cooked metabolite profile of normal-pH (normal bright-red color) and atypical-dark-cutting beef (inherently slightly dark-colored) longissimus lumborum muscle. Methods: Normal-pH (pH 5.56) and atypical dark-cutting (pH 5.63) loins (n = 6) were procured from a commercial meat processor. Steaks were randomly assigned to one of three different packaging methods: vacuum packaging, carbon monoxide (CO-MAP), and high oxygen (HiOx-MAP). Following 5 d of retail display, steaks were cooked to 71 °C on a clamshell-style grill, and samples were collected for untargeted metabolites using gas-chromatography mass spectrometry. Results: Raw atypical dark-cutting steaks were less red (p < 0.05) than raw normal-pH steaks. However, there were no differences in internal cooked color between normal-pH and atypical dark-cutting steaks. Steaks packaged in HiOx-MAP steaks had a lower (p < 0.05) cooked redness than vacuum and CO-MAP steaks. A total of 129 metabolite features were identified in the study. Serine and tryptophan were over-abundant in cooked atypical dark-cutting beef compared to raw atypical samples. Citric acid levels were greater in HiOx-MAP packaged beef compared with VP both in normal and atypical dark-cutting beef after cooking, while no differentially abundant metabolites were shared between vacuum and CO-MAP steaks after cooking. Discussion: A slight increase in pH did not influence metabolite profiles in different packaging. However, there were packaging effects within normal and atypical dark-cutting beef. Conclusions: This study suggests that packaging conditions change metabolite profiles, which can influence cooked metabolites. Therefore, the metabolomics approach can be used to better understand cooked color defects such as premature browning.

Typicality- and instance-dependent label noise-combating: a novel framework for simulating and combating real-world noisy labels for endoscopic polyp classification

Learning with noisy labels aims to train neural networks with noisy labels. Current models handle instance-independent label noise (IIN) well; however, they fall short with real-world noise. In medical image classification, atypical samples frequently receive incorrect labels, rendering instance-dependent label noise (IDN) an accurate representation of real-world scenarios. However, the current IDN approaches fail to consider the typicality of samples, which hampers their ability to address real-world label noise effectively. To alleviate the issues, we introduce typicality- and instance-dependent label noise (TIDN) to simulate real-world noise and establish a TIDN-combating framework to combat label noise. Specifically, we use the sample’s distance to decision boundaries in the feature space to represent typicality. The TIDN is then generated according to typicality. We establish a TIDN-attention module to combat label noise and learn the transition matrix from latent ground truth to the observed noisy labels. A recursive algorithm that enables the network to make correct predictions with corrections from the learned transition matrix is proposed. Our experiments demonstrate that the TIDN simulates real-world noise more closely than the existing IIN and IDN. Furthermore, the TIDN-combating framework demonstrates superior classification performance when training with simulated TIDN and actual real-world noise.

Transductive zero-shot learning with generative model-driven structure alignment

Zero-shot learning (ZSL) facilitates the transfer of knowledge from seen to unseen categories through high-dimensional vectors that capture both known and unknown class names. However it encounters challenges with domain shift arising from a lack of sufficient labeled data. Although transductive zero-shot learning (TZSL) addresses this bias by including samples from unseen classes, it still faces obstacles in enhancing TZSL performance. In this study, We introduce the Structure Alignment Variational Autoencoder Generative Adversarial Network (SA-VAEGAN), a novel approach that enhances the alignment between visual and auxiliary spaces. We delved into the underlying causes of domain shift and introduced a structural alignment (SA) strategy to tackle these challenges. The SA model thoroughly accounts for both inter-class and intra-class dynamics, designed to leverage the model’s comprehension of high-level semantic relations to disambiguate confusion among similar classes and mitigate intra-class confusion by penalizing atypical visual samples within classes. Assessed across four benchmark datasets, SA-VAEGAN has established a new performance standard, underscoring its efficiency in addressing the domain shift challenge within TZSL tasks, and achieving high accuracy.

Interobserver variability in cytopathology: How much do we agree?

Interobserver variability remains a major challenge for cytopathologists despite the development of standardized reporting and classification systems. Indeed, whereas moderate-to-good interobserver agreement is generally achievable when the differential diagnosis between benign and malignant entities is straightforward, high levels of variability make the diagnostic interpretation of atypical and suspicious samples not consistent. This review explores the landscape of interobserver agreement in cytopathology across different anatomical sites.

Abstract 1073: AI-assisted high-dimensional cytological single cell analysis distinguishes normal and malignant urine samples using cell morphology images with high accuracy at the single cell level

Abstract Background: Urothelial cancer (UCC) is the 4th most common malignancy in men. The current clinical problem in UCC management is the high interobserver variability of tissue UCC grading and the low sensitivity of urine cytology for early detection of recurrences and progression. We hypothesize that assessing urine samples and UCC tissues with detailed quantitative image analysis based on artificial intelligence has strong potential in both urine-based early detection of tumor recurrence and progression as well as tissue-based pathological grading. Methods: Here, we utilized the Deepcell® Human Foundation Model (HFM), a self-supervised deep learning model, to assess a cohort of urine samples from 54 normal healthy cases, 19 atypical cases, and 4 UCC cases. High resolution images of label-free single cells were captured and analyzed using the HFM to identify distinct morphological characteristics of cells from different urine origins. The urine samples were loaded onto a single-use microfluidic cartridge as part of the Deepcell® REM-I platform and brightfield images of single cells were captured in real-time. For data analysis, images of all cells run through the REM-I instrument are automatically sent to the Deepcell® Axon Suite, which provides an analysis platform to visualize cell images from specific runs, generate UMAPs of the embedding space, and run additional deep neural network models on samples in silico. Results: High-dimensional analysis of 115 deep learning and morphometric features reveals 12 morphotypes among the pooled urine samples, as represented by morphology UMAP plots. Morphology analysis reveals distinct morphotypes in normal compared to pathological urine samples. Confusion matrix showed deep learning model performance in classifying atypical versus UCC with 89.1% (Atypical (n = 8642), UCC (n = 12637)). This indicates high model performance accuracy for AI prediction of atypical versus UCC samples at the single cell level. Conclusions: The Deepcell® REM-I platform performs well with urine samples and identified numerous cell clusters among normal and pathological urines. AI-assisted high-dimensional morphology analysis distinguishes atypical urine samples from UCC cases with 89.1% accuracy at the single cell level. Future applications of this study include early detection or monitoring of UCC cases using non-invasive patient samples such as urine. Citation Format: Thierry P. van den Bosch, Geert J.L.H. van Leenders, Siamala Sinnadurai, Zhouyang Lian, Ryan Carelli, Matthew Nakaki, Vivian Lu, Manisha Ray, Janifer Cruz, Kelly R. Miller, Mahyar Salek, Maddison Masaeli, Peter J. van der Spek. AI-assisted high-dimensional cytological single cell analysis distinguishes normal and malignant urine samples using cell morphology images with high accuracy at the single cell level [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1073.

Phylogenetic and Recombination Analysis of Clinical Vitreous Humor-Derived Adenovirus Isolates Reveals Discordance Between Serotype and Phylogeny.

To sequence, identify, and perform phylogenetic and recombination analysis on three clinical adenovirus samples taken from the vitreous humor at the Bascom Palmer Eye Institute. The PacBio Sequel II was used to sequence the genomes of the three clinical adenovirus isolates. To identify the isolates, a full genome-based multiple sequence alignment (MSA) of 722 mastadenoviruses was generated using multiple alignment using fast Fourier transform (MAFFT). MAFFT was also used to generate genome-based human adenovirus B (HAdV-B) MSAs, as well as HAdV-B fiber, hexon, and penton protein-based MSAs. To examine recombination within HAdV-B, RF-Net 2 and Bootscan software programs were used. In the course of classifying three new atypical ocular adenovirus samples, taken from the vitreous humor, we found that all three isolates were HAdV-B species. The three Bascom Palmer HAdV-B genomes were then combined with over 300 HAdV-B genome sequences, including nine ocular HAdV-B genome sequences. Attempts to categorize the penton, hexon, and fiber serotypes using phylogeny of the three Bascom Palmer samples were inconclusive due to incongruence between serotype and phylogeny in the dataset. Recombination analysis using a subset of HAdV-B strains to generate a hybridization network detected recombination between nonhuman primate and human-derived strains, recombination between one HAdV-B strain and the HAdV-E outgroup, and limited recombination between the B1 and B2 clades. The discordance between serotype and phylogeny detected in this study suggests that the current classification system does not accurately describe the natural history and phylogenetic relationships among adenoviruses.

Decoding meningioma heterogeneity and neoplastic cell—macrophage interaction through single-cell transcriptome profiling across pathological grades

BackgroundAnalyzing meningioma of distinct pathological types at the single-cell level can provide new and valuable insights into the specific biological mechanisms of each cellular subpopulation, as well as their vital interplay within the tumor microenvironment.MethodsWe recruited patients diagnosed with four distinct types of meningioma and performed single-cell RNA sequencing on their tumor samples, concurrently analyzing a publicly available dataset for comparison. Next, we separated the cells into discrete clusters and identified their unique identities. Using pseudotime analysis, we demonstrated cellular differentiation and dynamics. To investigate biological function, we employed weighted gene co-expression network analysis, gene regulatory network, and gene set enrichment analysis. Additionally, we conducted cell–cell communication analyses to characterize interactions among different clusters and validated a crucial interaction using multiple immunofluorescence staining.ResultsThe single-cell transcriptomic profiles for five meningioma of different pathological types demonstrated that neoplastic cells exhibited high inter-sample heterogeneity and diverse biological functions featured by metabolic regulation. A small cluster of neoplastic cells (N5 cluster, < 3%) was most proliferative, indicated by high expression of MKI67 and TOP2A. They were primarily observed in our atypical and transitional meningioma samples and located at the beginning of the pseudotime differentiation branch for neoplastic cells. Macrophages, the most abundant immune cells present, showed two distinct developmental trajectories, one promoting and the other suppressing meningioma growth, with the MIF-CD74 interaction serving as the primary signaling pathway for MIF signals in the tumor environment. Unexpectedly, despite its small cluster size, the N5 cluster demonstrated a significant contribution in this interaction. By staining pathological sections of more samples, we found that this interaction was widely present in different types of meningiomas.ConclusionsMeningioma neoplastic cells' diverse types cause inter-sample heterogeneity and a wide range of functions. Some proliferative neoplastic cell may educate macrophages, which promotes tumorigenesis possibly through the MIF-CD74 interaction. It provides novel clues for future potential therapeutic avenues.

Gene Rearrangement and Expression of PRKACA and PRKACB Govern Morphobiology of Pancreatobiliary Oncocytic Neoplasms

Intraductal oncocytic papillary neoplasms (IOPNs) are distinct from intraductal papillary mucinous neoplasms based on characteristic morphologic and genetic features represented by fusion genes involving PRKACA or PRKACB (PRKACA/B). However, pancreatic and biliary tumors with partial oncocytic features are often encountered clinically, and their molecular features are yet to be clarified. This study included 80 intraductal papillary neoplasms: 32 tumors with mature IOPN morphology (typical), 28 with partial or subclonal oncocytic features (atypical), and 20 without oncocytic features (control). We analyzed PRKACA/B fusion genes, including ATP1B1::PRKACA, DNAJB1::PRKACA, and ATP1B1::PRKACB, by reverse-transcription PCR; mRNA expression of fusion genes and nonrearranged PRKACA/B genes by quantitative reverse-transcription PCR; mutations in KRAS, BRAF, and GNAS by targeted sequencing or droplet digital PCR; and the expression of cyclic adenosine monophosphate (cAMP)–dependent protein kinase catalytic subunits α (PRKACA) and β (PRKACB), phosphorylated cAMP response element-binding protein, and aberrations of p16, p53, SMAD4, STK11, and β-catenin by immunohistochemistry. PRKACA/B fusion genes were detected in 100% (32/32) of typical, 46% (13/28) of atypical, and 0% (0/20) of control (P < .05). Expression of PRKACA, PRKACB, and phosphorylated cAMP response element-binding protein was upregulated in neoplasms with PRKACA/B fusion genes (P < .05). mRNA expression of the PRKACA/B fusion genes and protein expression of PRKACA or PRKACB tended to be higher in typical than in atypical cases (mRNA, P = .002; protein expression, P = .054). In some atypical neoplasms with mixed subtypes, PRKACA/B fusion genes were superimposed exclusively on oncocytic components. Typical IOPNs harbored fewer KRAS and GNAS mutations than control samples and fewer alterations in p53 and STK11 than atypical samples (P < .05). In conclusion, PRKACA/B fusion genes not only are the characteristic drivers of IOPNs but also play a crucial role in the development of subclonal oncocytic neoplasms. Moreover, oncocytic morphology is strongly associated with upregulation of PRKACA/B, which may provide clues for potential therapeutic options.

2S-BUSGAN: A Novel Generative Adversarial Network for Realistic Breast Ultrasound Image with Corresponding Tumor Contour Based on Small Datasets.

Deep learning (DL) models in breast ultrasound (BUS) image analysis face challenges with data imbalance and limited atypical tumor samples. Generative Adversarial Networks (GAN) address these challenges by providing efficient data augmentation for small datasets. However, current GAN approaches fail to capture the structural features of BUS and generated images lack structural legitimacy and are unrealistic. Furthermore, generated images require manual annotation for different downstream tasks before they can be used. Therefore, we propose a two-stage GAN framework, 2s-BUSGAN, for generating annotated BUS images. It consists of the Mask Generation Stage (MGS) and the Image Generation Stage (IGS), generating benign and malignant BUS images using corresponding tumor contours. Moreover, we employ a Feature-Matching Loss (FML) to enhance the quality of generated images and utilize a Differential Augmentation Module (DAM) to improve GAN performance on small datasets. We conduct experiments on two datasets, BUSI and Collected. Moreover, results indicate that the quality of generated images is improved compared with traditional GAN methods. Additionally, our generated images underwent evaluation by ultrasound experts, demonstrating the possibility of deceiving doctors. A comparative evaluation showed that our method also outperforms traditional GAN methods when applied to training segmentation and classification models. Our method achieved a classification accuracy of 69% and 85.7% on two datasets, respectively, which is about 3% and 2% higher than that of the traditional augmentation model. The segmentation model trained using the 2s-BUSGAN augmented datasets achieved DICE scores of 75% and 73% on the two datasets, respectively, which were higher than the traditional augmentation methods. Our research tackles imbalanced and limited BUS image data challenges. Our 2s-BUSGAN augmentation method holds potential for enhancing deep learning model performance in the field.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer.

Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets. In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort. Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties. Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2). The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry and machine learning allow the rapid authentication of ground black pepper and dried oregano: A proof-of-concept study.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry (TD-DART-HRMS) approaches have gained popularity for fast screening of a variety of samples. With rapid volatilization of the sample at increasing temperatures outside the mass spectrometer, this technique can provide a direct readout of the sample content with no sample preparation. In this study, TD-DART-HRMS's utility for establishing spice authenticity was examined. To this aim, we directly analyzed authentic (typical) and adulterated (atypical) samples of ground black pepper and dried oregano in positive and negative ion modes. We analyzed a set of authentic ground black pepper samples (n = 14) originating from Brazil, Sri Lanka, Madagascar, Ecuador, Vietnam, Costa Rica, Indonesia, Cambodia, and adulterated samples (n = 25) consisting of mixtures of ground black pepper with this spice's nonfunctional by-products (pinheads or spent) or with different exogenous materials (olive kernel, green lentils, black mustard seeds, red beans, gypsum plaster, garlic, papaya seeds, chili, green aniseed, or coriander seeds). TD-DART-HRMS facilitated the capture of informative fingerprinting of authentic dried oregano (n = 12) originating from Albania, Turkey, and Italy and those spiked (n = 12) with increasing percentages of olive leaves, sumac, strawberry tree leaves, myrtle, and rock rose. A predictive LASSO classifier was built, after merging by low-level data fusion, the positive and negative datasets for ground black pepper. Fusing multimodal data allowed retrieval of more comprehensive information from both datasets. The resultant classifier achieved on the withheld test set accuracy, sensitivity, and specificity of 100%, 75%, and 90%, respectively. On the contrary, the sole TD-(+)DART-HRMS spectra of the oregano samples allowed construction of a LASSO classifier that predicted the adulteration of the oregano with excellent statistical indicators. This classifier achieved, on the withheld test set, 100% each for accuracy, sensitivity, and specificity.

Memorization Weights for Instance Reweighting in Adversarial Training

Adversarial training is an effective way to defend deep neural networks (DNN) against adversarial examples. However, there are atypical samples that are rare and hard to learn, or even hurt DNNs' generalization performance on test data. In this paper, we propose a novel algorithm to reweight the training samples based on self-supervised techniques to mitigate the negative effects of the atypical samples. Specifically, a memory bank is built to record the popular samples as prototypes and calculate the memorization weight for each sample, evaluating the "typicalness" of a sample. All the training samples are reweigthed based on the proposed memorization weights to reduce the negative effects of atypical samples. Experimental results show the proposed method is flexible to boost state-of-the-art adversarial training methods, improving both robustness and standard accuracy of DNNs.

Efficacy of categorization of urothelial malignancy in atypical urine samples by the Paris system for reporting urinary cytology

Background: Urine cytology is an important screening tool of patients for urothelial carcinoma and follow-up of patients. Ease of procurement, cost-effectiveness, and lower turnaround time are the advantages. Aims and Objectives: The aim of the study was to identify the accuracy of TPS criteria in diagnosing HGUC, considering biopsy as the gold standard and to describe the cytomorphological features in atypical urine specimens and categorization as per reference to TPS of reporting urine cytology. Definition – Atypical urine sample: Urine samples of patients with macroscopic hematuria and other unexplained urinary symptoms, suspicious of urothelial malignancy. Materials and Methods: The study was performed on patients attending urology OPD with macroscopic hematuria and other unexplained urinary symptoms, which were suspicious of urothelial malignancy and whose urine cytology and subsequent biopsy specimens were submitted to the Department of Pathology. Continuous sampling of 56 patients was done. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to compare TPS with histopathology. SPSS was used to assess correlation, done by diagnostic test evaluation. Results: Age group of study population ranges from 40 to 84 years with mean age of population as 62 years. Sensitivity, specificity, PPV, and NPV of TPS for reporting urinary cytology in the investigation were 85%, 75%, 65.38%, and 90%. Combination of elevated nuclear-to-cytoplasmic ratio, irregular nuclear border, and hyperchromasia was predictive of malignancy. Conclusion: The criteria outlined by TPS facilitated the standardization of urine cytology reporting and significantly increased the sensitivity of diagnosing HGUC.

Computational flow cytometry provides accurate assessment of measurable residual disease in chronic lymphocytic leukaemia.

Undetectable measurable residual disease (MRD) is associated with favourable clinical outcomes in chronic lymphocytic leukaemia (CLL). While assessment is commonly performed using multiparameter flow cytometry (MFC), this approach is associated with limitations including user bias and expertise that may not be widely available. Implementation of unsupervised clustering algorithms in the laboratory can address these limitations and have not been previously reported in a systematic quantitative manner. We developed a computational pipeline to assess CLL MRD using FlowSOM. In the training step, a self-organising map was generated with nodes representing the full breadth of normal immature and mature B cells along with disease immunophenotypes. This map was used to detect MRD in multiple validation cohorts containing a total of 456 samples. This included an evaluation of atypical CLL cases and samples collected from two different laboratories. Computational MRD showed high correlation with expert analysis (Pearson's r > 0.99 for typical CLL). Binary classification of typical CLL samples as either MRD positive or negative demonstrated high concordance (>98%). Interestingly, computational MRD detected disease in a small number of atypical CLL cases in which MRD was not detected by expert analysis. These results demonstrate the feasibility and value of automated MFC analysis in a diagnostic laboratory.

Euarchontans from Fantasia, an upland middle Eocene locality at the western margin of the Bighorn Basin

The fossil record of North American Eocene mammals is best known from relatively low-elevation ‘basin center’ fossil localities in intermontane depositional basins of the Western Interior. This sampling bias, largely drawn from preservational bias, has limited our understanding of fauna from higher elevation Eocene fossil localities. Here we describe new specimens of crown primates and microsyopid plesiadapiforms from a middle Eocene (Bridgerian) locality (‘Fantasia’) from the western margin of the Bighorn Basin in Wyoming. Fantasia has been considered a ‘basin-margin’ site and geological evidence suggests that it was already at a high elevation relative to the basin center at the time of deposition. New specimens were described and identified using comparisons across museum collections and published faunal descriptions. Linear measurements were used to characterize the patterns of variation in dental size. Contrary to expectations derived from other Eocene basin-margin sites in the Rocky Mountains, Fantasia has low anaptomorphine omomyid diversity and lacks evidence for the co-occurrence of ancestor-descendant pairs. Fantasia also differs from other Bridgerian sites in having low abundance of Omomys and unusual body sizes of several euarchontan taxa. Some specimens of Anaptomorphus and cf. Omomys are larger than those found in coeval sites, while specimens of Notharctus and Microsyops are intermediate in size between middle and late Bridgerian samples of these genera from basin-center sites. These findings suggest that high elevation fossil localities like Fantasia may record atypical faunal samples that should be more thoroughly explored to understand faunal dynamics during the periods of significant regional uplift like that represented by the middle Eocene record of the Rocky Mountains. Furthermore, modern faunal data indicate that species body mass may be influenced by elevation, which may further complicate the use of body mass to determine species identity in the fossil record in the regions of high topographic relief.

Graphic Aspects of Textual Interpretation of Dostoevsky’s Handwritten Text

The article discusses the issues of textual interpretation of Dostoevsky’s handwritten text using the writer’s graphics as research materials. It presents the results of the analysis of Dostoevsky’s 1868–1869 workbook (“The Idiot”) and later entries. It became clear that without reference to handwritten sources, it is impossible to solve certain tasks that include the correction of research errors in reading Dostoevsky’s manuscripts and determination of the author’s original text. Methodologically significant conditions of textual work with autographs are a combination of linguistic (that is grammatical, spelling, punctuation) analysis of Dostoevsky’s drafts and the study of letter and non-letter graphics. Previously, researchers had to depend on isolated, accidentally discovered examples of similar spellings and, in general, on their accumulated experience of reading Dostoevsky’s handwriting. This method did not ensure the representativeness of the result, because there was no large-scale continuous sampling of each letter’s handwriting in different positions or summary tables or databases. Non-automated, “manual” analysis did not always allow to quickly find an unambiguous and reliably articulated solution. Today a graphics database on the “Archive of F. M. Dostoevsky” portal has become an important tool for the textual research of the writer’s manuscripts, which significantly increases work efficiency. This is a digital archive of images of the writer’s notebooks and their interpretation in authentic and modern language versions. Based on this archive, a replenishable “Library of graphic samples” is also being created. It contains samples of different types of the writer’s handwriting and nonverbal graphics (drawings, figures, author’s signs, editing marks). The electronic catalog of graphic samples allows to systematize the writing, find typical and atypical samples among them. This is an additional important criterion for the argumentation of certain research readings when interpreting Dostoevsky’s handwritten text. Technical tools have been developed specifically for the database, that is: marker fields that describe the individual parameters of each lexeme and allow conducting an advanced search in the “Library of Graphic Samples.” With its help, it is possible to quickly find all patterns of the same type for their further analysis. Thus, this resource will become an important tool in the complex graphic and linguotextological analysis of the writer’s handwritten material.

Improved Generalization in Signal Identification With Unsupervised Spiking Neuron Networks for Fiber-Optic Distributed Acoustic Sensor

More and more machine learning techniques are involved in fiber-optic distributed acoustic sensor (DAS) to improve its intelligent recognition ability. However, the supervised methods need sufficient and comprehensive labeled data, or they may present poor generalization when the test and the training data are inconsistent. Thus, in this paper a novel unsupervised Spiking Neural Network (SNN) is introduced into DAS for the first time to improve its generalization in different scenes and time-varying backgrounds. The theoretical mechanism, construction and optimization of the new network are first discussed in details; then its effectiveness is validated through three groups of field tests, using small dataset A with balanced, typical and consistent data, bad datasets B and C full of inconstant and atypical data, and full imbalanced dataset D respectively. It shows that the proposed SNN using the unsupervised learning mechanism, spiking-timing dependent plasiticity (STDP), is more stable than the mainstream supervised CNN when trained with small number of samples or imbalanced dataset, what often happens in practice. Especially in the most challenging case full of atypical or inconsistent samples, its generalization and anti-overfitting abilities are proved to be much better, where the average accuracy in the random test using bad datasets B and C has been increased by 18.0% and 14.4%. In the field test with full dataset, its recognition perform is also stably better than the compared CNN but with a longer time.

PFAS Experts Symposium 2: An update on advances in chemical analysis of PFAS

AbstractAs quickly as our understanding of the nature and extent of per‐ and polyfluoroalkyl substances (PFAS) in the environment has developed, the analytical chemistry tools we use to study and quantify them have evolved. The last 5 years have seen commercial laboratories scramble to analyze atypical and unusual samples submitted by concerned citizens and governmental agencies. The unusual matrices have pushed innovative sample extraction techniques, as the regulated community and the laboratories await a final multilaboratory validated method to provide direction for the analysis of PFAS in matrices beyond drinking water. The large number of PFAS compounds has also sparked interest in the development of methods to screen for all PFAS without quantifying the individual constituents. Measurement of total PFAS is also useful in consumer product testing. New methods to determine total PFAS are in varying stages of development and are described in detail herein. The fact that there are more PFAS chemicals than can be quantified by commercial laboratories has also promoted the development of nontarget analysis (NTA) using sophisticated instrumentation. This article provides the basis of NTA and examples of its novel applications in forensics and compound identification. The pace of change in PFAS analysis is breathtaking and challenging for all those studying environmental impacts of PFAS. Updates on key topics as can be found in this article serve an important need for the remediation community.

Atypical Urine Samples with Polyoma Virus Cytopathic Effect: Role of SV40 Immunostaining

Atypical Urine Samples with Polyoma Virus Cytopathic Effect: Role of SV40 Immunostaining

Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences

We present an approach to discriminate SARS-CoV-2 virus types based on their RNA sequence descriptions avoiding a sequence alignment. For that purpose, sequences are preprocessed by feature extraction and the resulting feature vectors are analyzed by prototype-based classification to remain interpretable. In particular, we propose to use variants of learning vector quantization (LVQ) based on dissimilarity measures for RNA sequence data. The respective matrix LVQ provides additional knowledge about the classification decisions like discriminant feature correlations and, additionally, can be equipped with easy to realize reject options for uncertain data. Those options provide self-controlled evidence, i.e., the model refuses to make a classification decision if the model evidence for the presented data is not sufficient. This model is first trained using a GISAID dataset with given virus types detected according to the molecular differences in coronavirus populations by phylogenetic tree clustering. In a second step, we apply the trained model to another but unlabeled SARS-CoV-2 virus dataset. For these data, we can either assign a virus type to the sequences or reject atypical samples. Those rejected sequences allow to speculate about new virus types with respect to nucleotide base mutations in the viral sequences. Moreover, this rejection analysis improves model robustness. Last but not least, the presented approach has lower computational complexity compared to methods based on (multiple) sequence alignment.