Fold cracking of coated papers: investigation on automated computer-aided visual assessment method

In this paper, white pixel percentage (WPP) value, as an overall measure of fold crack damages, has been analysed with respect to selected parameters of sample preparation and digitalization process, as well as the results of residual tensile strength. The WPP values were derived by an automated image processing algorithm, developed earlier, based on extensive comparative analysis of the existing computer-aided methods. Results indicate that WPP values correlate well with the extent of fold cracks on the coated samples, as far as the used parameters of sample preparation and digitalization are concerned. In the case of correlation with residual tensile strength, results for samples folded in cross direction revealed that the extent of the visually registered fold cracks agree well with the actual damage, while for samples folded in machine direction, the overall strength losses weren’t alarming, although the fold cracks were detected correctly. In addition, results pointed out that the simplest sample placement position (inner angle of 180°) is not applicable for realistic sample representation. Furthermore, scanners could provide a superior image quality in lab conditions, but for industry application, a camera-based solution would be more purposeful, while micrographs are more suitable for traditional visual analysis.

Construction of individual ddRAD libraries v2

This protocol describes a double digested restriction-site associated DNA (ddRADseq) procedure, that is a variation on the original RAD sequencing method (Davey & Blaxter 2011), which is used for de novo SNP discovery and genotyping. This protocol differs from the original ddRADseq protocol (Peterson et al 2012), in which the samples are pooled just after the ligation to adaptors (i.e. before size selection and PCR). The present ddRAD protocol as been slightly adapted from Alan Brelsford's protocol published in the supplementary material of this paper: Brelsford, A., Dufresnes, C. & Perrin, N. 2016. High-density sex-specific linkage maps of a European tree frog (Hyla arborea) identify the sex chromosome without information on offspring sex. Heredity 116, 177–181 (2016). https://doi.org/10.1038/hdy.2015.83 In the present protocol, all samples are treated separately, in a microplate, until final PCR amplification performed before pooling. Despite being slightly more costly and time-consuming in the lab, it allows for fine adjustement of each sample representation in the final library pool, ensuring similar number of sequencing reads per sample in the final dataset. Briefly, genomic DNA from the samples are individually digested with 2 restriction enzymes (one rare-cutter and one more frequent cutter) then ligated to a barcoded adaptor (among 24 available) at one side, and a single adaptor at the other side, purified with magnetic beads, and PCR-amplified allowing the addition of a Illumina index (among 12 available) for multiplexing a maximum of 288 sample per library. Samples are then pooled in equimolar conditions after visualisation on an agarose gel. Purification and size selection is then performed before final quality control of the library and sequencing.

Identifying gaps in the coverage of survivorship care services.

6583 Background: Despite advancements in reimbursement, anecdotal evidence suggests patients are not able to access guideline concordant survivorship care services due to a lack of coverage by payers. We present the results of a mixed methods study aimed to determine the practice-reported rates and sources of delay/denial on evidence-based, guideline concordant survivorship care services. Methods: A quantitative survey was developed by ASCO’s Cancer Survivorship Committee (CSC) to assess which services are being denied by payers for coverage/reimbursement. Questions were limited to disease sites for which practice guidelines exist. 533 ASCO members who provide survivorship care were surveyed, with a focus on obtaining representation from rural/urban, academic/private practice, pediatric/adult, and geographic location across the U.S. Semi-structured telephone interviews were conducted in October and November 2020 with geographic sub sample representation to further explore the nature of and extent to which coverage barriers are experienced for guideline-concordant care, specific to the provider or clinic’s primary disease site or specialty. Results: 120 responses from 50 states were included. Respondents were primarily clinicians (88%) with the majority treating patients with Medicare/Medicaid/CHIP (60%), followed by private/employer insurance (38%). There was little issue with coverage of hormone therapies. One-third reported issues some of the time with maintenance chemotherapy (38%) and immunotherapy (35%). Coverage denials for screening for recurrence for breast cancer (MRI, 63.5%), Hodgkin Lymphoma (PET/CT 47%; Breast MRI, 44.4%), and lung cancer (Low-dose CT 37.4%) were common. Half of the survey respondents reported denials for supportive care/symptom management services (Table). Private or employer-based insurance denials were most often the source of barriers (57.7%). Through interviews, denials were found to be the same across sites and not unique to a single payer or region. Most had a process to appeal denials for evidence-based services. Conclusions: Denial for survivorship care, particularly supportive care services, is common. There is a need for better advocacy with payers, improved policy, and support for providers/practices to implement protocols to obtain coverage for services, particularly in the face of burnout.[Table: see text]

Contrasting Perspectives on the Comfort and Safety of Pedestrians Interacting with Other Road Users

Assessments of interactions between road users are crucial to understanding comfort and safety. However, observers may vary in their perceptions and ratings of road user interactions. The objective of this paper is to examine how perceptions of yielding, comfort, and safety for pedestrian interactions vary among observers, ranging from members of the public to road safety experts. Video clips of pedestrian interactions with motor vehicles and bicycles were collected from 11 crosswalks and shown to three groups of participants (traffic safety experts, an engaged citizen advisory group, and members of the general public) along with questions about yielding, comfort, and risk of injury. Experts had similar views of yielding and comfort to the other two groups, but a consistently lower assessment of injury risk for pedestrians in the study. Respondent socio-demographics did not relate to perceptions of yielding, comfort, or risk, but self-reported travel habits did. Respondents who reported walking more frequently rated pedestrian comfort as lower, and respondents who reported cycling more frequently rated risk as lower for pedestrian interactions with both motor vehicles and bicycles. Findings suggest small groups of engaged citizens can provide useful information about public perspectives on safety that likely diverge from expert assessments of risk, and that sample representation should be assessed in relation to travel habits rather than socio-demographics.

A neighborhood prior constrained collaborative representation for classification

With the development of machine learning and computer vision, classification technology is becoming increasingly important. Due to the advantage in efficiency and effectiveness, collaborative representation-based classifiers (CRC) have been applied to many practical cognitive fields. In this paper, we propose a new neighborhood prior constrained collaborative representation model for pattern classification. Compared with the naive CRC models which approximate the test sample with all the training data globally, our proposed methods emphasize the guidance of the neighborhood priors in the coding process. Two different kinds of neighbor priors and the models’ weighted extensions are explored from the view of sample representation ability and relationships between the samples. Consequently, the contributions of different samples can be distinguished adaptively and the obtained representations can be more discriminative for the recognition. Experimental results on several popular databases can verify the effectiveness of our proposed methods in comparison with other state-of-the-art classifiers.

On the comparability of adults with low literacy across LEO, PIAAC, and NEPS. Methodological considerations and empirical evidence

Background In Germany, three large-scale surveys–the Level One Study (LEO), the Programme for the International Assessment of Adult Competencies (PIAAC), and the National Educational Panel Study (NEPS)–provide complementary data on adults’ literacy skills that can be harnessed to study adults with low literacy. To ensure that research on low-literate adults using these surveys arrives at valid and robust conclusions, it is imperative to ascertain the comparability of the three surveys’ low-literacy samples. Towards that end, in the present study, we comprehensively assess the comparability of adults with low literacy across these surveys with regard to their sociodemographic and socioeconomic characteristics. Methods We used data from LEO, PIAAC, and NEPS. We identified features of the sample representation and measurement of (low) literacy as potential causes for variations in the low-literacy samples across the surveys. We then compared the low-literacy samples with regard to their sociodemographic and socioeconomic characteristics and performed logistic regressions to compare the relative importance of these characteristics as correlates of low literacy. Results The key insight our study provides is that–despite different sample representations and measurement approaches–the low-literacy samples in the three surveys are largely comparable in terms of their socioeconomic and sociodemographic characteristics. Although there were small differences between the surveys with regard to the distribution of gender, educational attainment, and the proportion of non-native speakers within the group of low-literate adults, results revealed that both the prevalence of low literacy and its correlates were largely robust across LEO, PIAAC, and NEPS. Across all three surveys, lower educational attainment emerged as the most significant correlate of low literacy, followed by a non-German language background, unemployment and low occupational status. Conclusions Our study provides evidence that all three surveys can be used for investigating adults with low literacy. The small differences between the low-literacy samples across the three surveys appear to be associated with sample representation and certain assessment features that should be kept in mind when using the surveys for research and policy purposes. Nevertheless, our study showed that we do not compare apples with oranges when dealing with low-literate adults across different large-scale surveys.

Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition

Motivation The recent development of sequencing technologies revolutionized our understanding of the inner workings of the cell as well as the way disease is treated. A single RNA sequencing (RNA-Seq) experiment, however, measures tens of thousands of parameters simultaneously. While the results are information rich, data analysis provides a challenge. Dimensionality reduction methods help with this task by extracting patterns from the data by compressing it into compact vector representations. Results We present the factorized embeddings (FE) model, a self-supervised deep learning algorithm that learns simultaneously, by tensor factorization, gene and sample representation spaces. We ran the model on RNA-Seq data from two large-scale cohorts and observed that the sample representation captures information on single gene and global gene expression patterns. Moreover, we found that the gene representation space was organized such that tissue-specific genes, highly correlated genes as well as genes participating in the same GO terms were grouped. Finally, we compared the vector representation of samples learned by the FE model to other similar models on 49 regression tasks. We report that the representations trained with FE rank first or second in all of the tasks, surpassing, sometimes by a considerable margin, other representations. Availability and implementation A toy example in the form of a Jupyter Notebook as well as the code and trained embeddings for this project can be found at: https://github.com/TrofimovAssya/FactorizedEmbeddings. Supplementary information Supplementary data are available at Bioinformatics online.