Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging

Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest.

NMCMDA: neural multicategory MiRNA–disease association prediction

Motivation There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA–disease associations are time-saving and cost-effective that are highly desired for us. Results We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA–disease association prediction (NMCMDA) for predicting multiple-category miRNA–disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA–disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA–disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA–disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.

Why Social Cognitive Development Research Needs Intersectionality

Children develop rich concepts of social categories based on gender, race, and other social dimensions throughout early and middle childhood. However, less is known about the development of representations at the intersection of multiple categories. This is a critical issue because overlooking how children integrate information about multiple category identities causes a major gap in our understanding of the development of social cognition. To address this issue, we suggest researchers adopt an intersectional framework. By intersectional framework, we mean consideration of both how power structures contribute to systems of inequality as well as variability in how group-based bias is expressed towards people with one vs. multiple minoritized identities. Using research on children’s use of race and gender, we describe how our current understanding of social categorization is incomplete, and how an intersectional framework can advance both equity and theory.

A Secure and Effective Construction Scheme for Blockchain Networks

Blockchain technology has emerged as a novel distributed ledger technology, facilitating data sharing and system management securely and efficiently without interventions from a central authority. However, blockchain technology alone is not suitable for enterprise-class applications, mainly due to the limitations in capacity expansion and verification speed of blockchain systems. This paper proposes a secure and effective construction scheme for blockchain networks to improve performance and address the effective management concerns of blockchain data based on transaction categories. We designed a network link protocol to construct a directed acyclic graph (DAG) blockchain network and used a sharding protocol to divide the DAG blockchain into multiple category shards to process transactions in parallel. We then extensively evaluated our proposed design on local clusters. The experimental results show that our link and shard protocols achieved high throughput and the category-based sharded DAG blockchain demonstrated high scalability.

Cilioretinal Arteries in Highly Myopic Eyes: A Photographic Classification System and Its Association With Myopic Macular Degeneration

Purpose: To develop a photographic classification for cilioretinal arteries and to investigate its association with myopic macular degeneration (MMD).Methods: One thousand six hundred ninety-two highly myopic eyes of 1,692 patients were included. The presence of a cilioretinal artery was determined by fundus photographs, and a photographic classification was proposed. MMD was classified according to the International META-PM Classification. Associations of the cilioretinal artery and its classifications with MMD and visual acuity were analyzed.Results: Of the eyes tested, 245 (14.5%) had a cilioretinal artery. The cilioretinal arteries were classified into four categories (temporal “cake-fork,” 35.92%; temporal “ribbon,” 53.47%; “multiple,” 6.53%; “nasal,” 4.08%) and 3 distributions based on whether its visible branches reached the central foveal area. Eyes with cilioretinal arteries had significantly less MMD of grade ≥3 and better visual acuity than those without (P < 0.01). Multiple linear regression analysis showed that younger age, male sex, shorter axial length, and the presence of a cilioretinal artery were associated with better visual acuity in highly myopic eyes (all P < 0.05). The “nasal” category presented more MMD with grade ≥3 and worse visual acuity than the other categories (P < 0.05), whereas the “multiple” category contained no eyes with MMD grade ≥3. The cilioretinal arteries reaching the central foveal area showed less MMD of grade ≥3 and better visual acuity than those not (P < 0.05).Conclusions: We propose a photographic classification for cilioretinal arteries that has good clinical relevance to visual functions. The cilioretinal artery may potentially afford protection against MMD.

Turning 75: Why Age Matters for Victims of Assaultive Violence Reported to Police

Americans are able to age in more active and engaged ways than previous generations. These changes bring many positive opportunities, but also might affect the risk of criminal victimization for older adults. In considering these risks, an initial question is how best to identify older adults. One common default is to use age 65 and older, which suggests older individuals are part of a homogenous group. The Census Bureau’s multiple category approach illustrates another option, which captures variations as Americans age. This study explores the risk and characteristics of non-fatal assaultive violence using a multiple-category age definition and uses police data from the Federal Bureau of Investigation’s 2016 National Incident-Based Reporting System (NIBRS). These data were collected from 34 states and cover a range of crimes including aggravated and simple assaults. NIBRS data are well suited for this study as they collect details about the crime including victim and offender demographics and incident details such as victim-offender relationship, weapons, location and arrest. Preliminary results indicate that 34,689 assault victims were adults over the age of 65. Using a generic measure of older adult (age 65 and above) masks important variations in these assaults. Distinct patterns are observed between those aged 65 to 74, 75 to 84 and 85 and above. Within these age categories, differences also occur across racial and sex groups. The patterns observed can provide more nuanced guidance to challenge traditional assumptions about older adult crime victims and inform policies tailored to support these victims.

O corpo é a camuflagem: construções ficcionais de si na produção artística de mulheres nos anos 1970

A crítica feminista elaborou a questão da representação na arte de diferentes maneiras. Nessa perspectiva crítica, um dos problemas são as imagens das mulheres feitas por um olhar masculino ao longo das narrativas tradicionais da história da arte. Respondendo a esse problema, algumas artistas realizaram ações para as câmeras de vídeo e fotografia. Nestas imagens, elas utilizaram o próprio corpo para demonstrar as construções ficcionais dos gêneros. Nesse artigo, analiso esses trabalhos pelas seguintes leituras: a crítica aos rituais de feminilidade, o feminino monstruoso e a identidade como categoria múltipla, tendo como marco teórico as contribuições de Janet Wolff e Jayne Wark. Também me apoio no discurso das artistas sobre seus métodos de trabalho, a partir de entrevistas inéditas. Palavras-chave: Representação. Corpo. Crítica feminista. Vídeo. Fotografia. AbstractFeminist criticism raised the issue of representation in art in different ways. In this critical perspective, one of the problems is the images of women made by the male gaze throughout the traditional narratives of art history. Responding to this problem, some artists performed actions for video and photography. In these images, they used their own bodies to demonstrate the fictional constructions of gender. In this article, I analyze these works through the following readings: the criticism of femininity rituals, the monstrous feminine and identity as a multiple category, having as a theoretical framework the contributions of Janet Wolff and Jayne Wark. I also rely on the artists' discourse about their work methods, based on unpublished interviews.Keywords: Representation. Body. Feminist criticism. Video. Photography.

Automated Coding Using Machine Learning and Remapping the U.S. Nonprofit Sector: A Guide and Benchmark

This research developed a machine learning classifier that reliably automates the coding process using the National Taxonomy of Exempt Entities as a schema and remapped the U.S. nonprofit sector. I achieved 90% overall accuracy for classifying the nonprofits into nine broad categories and 88% for classifying them into 25 major groups. The intercoder reliabilities between algorithms and human coders measured by kappa statistics are in the “almost perfect” range of .80 to 1.00. The results suggest that a state-of-the-art machine learning algorithm can approximate human coders and substantially improve researchers’ productivity. I also reassigned multiple category codes to more than 439,000 nonprofits and discovered a considerable amount of organizational activities that were previously ignored. The classifier is an essential methodological prerequisite for large-N and Big Data analyses, and the remapped U.S. nonprofit sector can serve as an important instrument for asking or reexamining fundamental questions of nonprofit studies. The working directory with all data sets, source codes, and historical versions are available on GitHub ( https://github.com/ma-ji/npo_classifier ).