Classification Scheme Research Articles

From Processed Foods to Ultraprocessed Foods: Evolution of an Industry Model and Impact on Dietary Quality, Health, and Society.

The impact of food technology and product development on the nutritional quality of foods is discussed in the context of food classification schemes, clinical research, and sociocultural studies. Food processing operations are analyzed in terms of their beneficial and detrimental consequences for the nutritional value of foods and ingredients. Several classification schemes are discussed, including dietary guidelines, nutrition information panels, and nutritional scores. The health impact of processed and ultraprocessed foods is discussed in connection with the processing-formulation scheme previously developed by the authors. The importance of product development as a driver for the food industry is highlighted, and formulation-based approaches to improve the healthfulness of industrially produced foods are discussed. Finally, the public perception of processed foods and its impact on the industry are discussed, and the need for a broad engagement among stakeholders to ensure the sustainability of our food system and healthy diets for individuals is emphasized.

Quantitative Structure-Activity Relationship Models to Predict Cardiac Adverse Effects.

Drug-induced cardiotoxicity represents one of the most common causes of attrition of drug candidates in preclinical and clinical development. For this reason, the evaluation of cardiac toxicity is essential during drug development and regulatory review. In the present study, drug-induced postmarket adverse event combinations from the FDA Adverse Event Reporting System were extracted for 2002 drugs using 243 cardiac toxicity-related preferred terms (PTs). These PTs were combined into 12 groups based on their clinical relevance to serve as training sets. The optimal classification scheme was determined using a combination of data sources that included drug labeling information, published literature, clinical study data, and postmarket surveillance data. Two commercial QSAR platforms were used to construct 12 models, including general cardiac toxicity, cardiac ischemia, heart failure, cardiac valve disease, myocardial disease, pericardial disease, structural heart disease, cardiac arrhythmia, Torsades de Pointes, long QT syndrome, atrial fibrillation and ventricular arrhythmia, and cardiac arrest. The cross-validated performance for the new models reached a sensitivity of up to 80% and negative predictivity of up to 80%. These new models covering a wide range of cardiac endpoints will provide fast, reliable, and comprehensive predictions of potential cardiotoxic compounds in drug discovery and regulatory safety assessment.

Review on classification of polypoidal choroidal vasculopathy.

Polypoidal choroidal vasculopathy (PCV) is a complex vascular disorder characterized by a branching neovascular network with polypoidal lesions at its termini. Despite extensive research, the etiology and pathogenesis of PCV remain inadequately understood, and the condition exhibits significant clinical and pathological heterogeneity. In recent years, numerous studies have identified potential subtypes within PCV and proposed various classification schemes. However, a universally accepted classification system for PCV has yet to be established. This review aims to provide a comprehensive overview of the different classification methods for PCV that have emerged alongside advancements in imaging technologies and a deeper understanding of PCV, highlighting their clinical relevance and prognostic implications. A systematic search of PubMed, Embase, Web of Science, and Cochrane databases was conducted. Literature management was facilitated using EndNote X9 software. By synthesizing existing classification approaches, this paper seeks to offer a clearer understanding of PCV subtypes, which may guide future research and inform treatment strategies.

Evaluation of ploidy and the DNA index by flow cytometry in central nervous system tumors: a review.

Research on central nervous system tumors (CNSTs) has a significant impact on the diagnosis and prognosis of patients. Currently, CNSTs are classified according to the schema proposed by the World Health Organization (WHO), which considers clinical, histopathological, and molecular characteristics, highlighting the importance of tumor biology for accurate diagnosis and optimal treatment approaches. Despite these advances, assessing DNA ploidy-a marker of tumor aggressiveness-remains complex in CNSTs. This review investigates the utility of DNA index (DNAi) and DNA ploidy analysis by flow cytometry in diagnosing CNSTs and prognosing their outcomes. We systematically reviewed studies in the PubMed database from 1990 to the present using the keywords "DNA Index", "Brain", "Flow cytometry", and "Ploidy". We identified 151 studies, 36 of which met our inclusion criteria. We found considerable variation in sample sizes and methodological variation across the studies. Discrepancies between the reported DNAi and ploidy values were observed. Aneuploidy is generally associated with more aggressive tumors, although exceptions exist. Higher DNAi levels correlate with increased malignancy, notably in glioblastomas, astrocytomas, and meningiomas, whereas diploid astrocytomas and oligodendrogliomas are associated with shorter survival rates. DNA ploidy assessment via flow cytometry could predict CNST behavior, yet methodological issues with tissue selection, adequate control samples, and technique variability remain. DNAi and ploidy assessments show promise as prognostic markers in CNSTs. However, the standardization of flow cytometry protocols and alignment with the current WHO classification schema are essential steps to integrate ploidy analysis in routine CNST assessment.

Wen, Literature, and Belles Lettres: Reconfiguring Chinese Literature in Nineteenth-Century English Sinology

ABSTRACT This article examines the fluid nature of literature as exemplified both semantically and conceptually in the English-language studies of Chinese literature conducted by Western Sinologists in the nineteenth century. If Chinese literary modernization of the early twentieth century is seen as shaped by the contact with foreign literary theories, Sinologists in the nineteenth century had already engaged actively with English literary concepts to reconfigure Chinese literature in their English-language writings. This article contends that, regardless of their impact on Chinese literary modernity, the Sinologists’ conceptions underlined the aesthetic and generic concerns that are fundamental to the modern understanding of literature and thereby fostered a renewed imagination of Chinese literature in the English-speaking world. Examining a wide range of primary materials, including dictionaries and Sinological writings, this article aims to unfold their transcultural naming and legitimizing process that gave rise to the knowledge category of Chinese literature through contingent English and Chinese literary terminology, narrative frameworks, and categorization schemes. This article traces the unnoticed history in comparative literary history to offer a genealogical understanding of the Western conception of Chinese literature. It also provides a new dimension for reviewing and analyzing the dialogue and interplay between Western and Chinese literary ideas within the Sinologists’ writings prior to the overwhelming influence of Western literary ideas during the period of Chinese literary modernity.

Crowd-sourced trait data can be used to delimit global biomes

Abstract. Terrestrial biomes and their biogeographic patterns have been derived from a large variety of variables including species distributions and bioclimate or remote sensing products. However, classifying the biosphere into biomes from a functional perspective using biophysical traits has rarely been tested. Such a trait-based biome classification has been limited by data availability. Here, we aimed to exploit crowd-sourced plant observations and trait databases to systematically assess which traits are most suitable for biome classification. We derived global patterns of 33 biophysical traits covering around 50 % of the land surface by combining crowd-sourced species distribution data from the Global Biodiversity Information Facility (GBIF) and trait observations from the TRY database. Using these trait maps as predictors for supervised cluster analyses, we tested to what extent we can reconstruct 31 published biome maps. A sensitivity analysis with randomly sampled combinations of traits was performed to identify the traits that are most appropriate for biome classification. Performance was quantified by comparing modeled biome maps and the respective observation-based biome maps. Finally, spatial gaps in the resulting biome maps were filled using species distribution models to obtain continuous global biome maps. We showed that traits can be used for biome classification and that the most appropriate traits are conduit density; rooting depth; height; and different leaf traits, including specific leaf area and leaf nitrogen content. The best performance of the biome classification was obtained for biome maps based on biogeographic zonation and species distributions, in contrast to biome maps derived from optical reflectance. The availability of crowd-sourced plant observations is heterogeneous, and, despite its exponential growth, large data gaps are prevalent. Nonetheless, it was possible to derive biome classification schemes from these data to predict global biome patterns with good agreement. Therefore, our analysis is a valuable approach towards understanding biome patterns based on biophysical traits and associated ecological strategies.

Renaissance Goo: Senses and Materials in Early Modern Apothecary Taxonomies and Soft Matter Science

This essay brings together research in the history of science and soft matter physics to consider how early modern Italian apothecaries organised and communicated their knowledge from the fourteenth to the seventeenth century through an “apothecary taxonomy.” This was based on a what we call a “hylocentric” classification scheme (from the Greek hyle = matter, material, stuff) founded on a tactile understanding of materials. We will investigate how the behaviour of medicines under deformation and flow – their “rheology” – is a previously underestimated organisational principle, and consider the specialist vocabulary these author-practitioners used to describe different liquid and liquid-like formulations. We will also suggest that the rheology of these formulations – which today falls under the domain of “soft matter science” – affected the material culture of apothecary shops, in the arrangement and selection of drug bottles and jars, which presented this knowledge visually to visitors and clients. That soft matter scientists organise the substances they study in similar ways to early modern apothecaries suggests the agency of materials in affecting human categorisations.

A Review of Applications of Deep Learning Techniques in Recommender Systems

Abstract. Nowadays, with tons of information brought by the development of the Internet, recommender systems are becoming more and more popular, and they are affecting many aspects of peoples daily lives. When people use the search engine, shop on e-commerce platforms or watch videos on video software, they are enjoying the content provided by recommender systems. Deep learning techniques also gained a rapid development recently due to their strengths in dealing with complex tasks. Deep learning techniques have triumphed in many areas. Also, there are more efforts contributed to the deep learning based recommender system. However, very few studies focused on giving a detailed review of deep -learning-based recommender system , and these reviews were usually conducted several years ago, which cannot reflect the latest research trends. Hence, the aim of this paper is to summary the deep learning techniques used in recommender systems. A classification scheme is given, and some possible future directions are pointed out.

Constructing a Classification Scheme - and its Consequences: A Field Study of Learning to Label Data for Computer Vision in a Hospital Intensive Care Unit

Research on data annotation for artificial intelligence (AI) has demonstrated that biases, power, and culture impact the ways that annotators apply labels to data and subsequently affect downstream AI systems. However, annotators can only apply labels that are available to them in the annotation classification scheme. Drawing on a 3-year ethnographic study of an R&D collaboration between medical and AI researchers, we argue that the construction of the classification schema itself -- decisions about what kinds of data can and cannot be collected, what activities can and cannot be detected in the data, what the possible annotation classes ought to be, and the rules by which an item ought to be classified into each class -- dramatically shape the annotation process, and through it, the AI. We draw on Bowker and Star's [9] classification theory to detail how the creation of a training data codebook for a computer vision algorithm in hospital intensive care units (ICUs) evolved from its original, clinically-driven goal of classifying complex clinical activities into a narrower goal of identifying physical objects and simpler activities in the ICU. This work reinforces how trade-offs and decisions made long before annotators begin labeling data are highly consequential to the resulting AI system.

Association between serum antibodies to oral microorganisms and nonalcoholic fatty liver disease in adults

BackgroundAlterations in the bacteria, such as the periodontal bacteria, might be considered potential risk factors for nonalcoholic fatty liver disease (NAFLD). Most studies analyzing this association have focused mainly on a specific periodontal bacteria (Porphyromonas gingivalis) and have involved relatively small study populations (tens or hundreds of individuals). To address this gap, a sizable, nationally representative adult population was utilized to investigate the association between the incidence of NAFLD and high serum IgG antibodies for 19 periodontal bacteria.MethodsTo explore this association, data from the Third National Health and Nutrition Examination Survey (NHANES III)—which provides a cross-sectional representation of the noninstitutionalized U.S. population, encompassing 33,994 individuals—were analyzed. Participants aged 40 years and above with data on NAFLD—determined by the gold standard of ultrasound examination (USON)—as well as comprehensive records of serum IgG antibodies against periodontal bacteria, were included, resulting in a final analysis subset of 6,330 individuals.ResultsUsing a cluster analysis based on the Socransky classification scheme for oral microorganisms, antibody titers for the 19 bacteria were grouped into four clusters—Red-Green, Orange-Blue, Yellow-Orange, and Orange-Red. When these clusters, as well as individual antibody relationships with NAFLD, were examined, the adjusted odds ratios (ORs) ranged from 0.958 [95% confidence interval (CI): 0.916, 1.003] to 1.021 [95% CI: 0.987, 1.055]. This indicated that no statistically significant associations were found (P > 0.05), underscoring the absence of a meaningful link.ConclusionsIn summary, it was discovered that there is currently no evidence to correlate serum antibodies to periodontal pathogens with NAFLD in the nationally representative NHANES III.Clinical trial numberNot applicable.

An Objective Classification Scheme for Solar-System Bodies Based on Surface Gravity

We introduce succinct and objective definitions of the various classes of objects in the solar system. Unlike the formal definitions adopted by the International Astronomical Union in 2006, group separation is obtained from measured physical properties of the objects. Thus, this classification scheme does not rely on orbital/environmental factors that are subject to debate—the physical parameters are intrinsic properties of the objects themselves. Surface gravity g is the property that single-handedly differentiates (a) planets from all other objects (and it leaves no room for questioning the demotion of Pluto), and (b) the six largest (g>1 m s−2) of the large satellites from dwarf planets. Large satellites are separated from small satellites by their sizes and masses/densities, which may serve as higher-order qualifiers for class membership. Size considerations are also sufficient for the classification of (i) main-belt asteroids (except possibly Ceres) as small solar-system bodies similar in physical properties to the small satellites; and (ii) a group of large Kuiper-belt objects as dwarf planets similar in physical properties to the large (but not the largest) satellites in our solar system. The selection criteria are simple and clear and reinforce the argument that body shape and environmental factors need not be considered in stipulating class membership of solar as well as extrasolar bodies.

Long‐Term Foehn Reconstruction Combining Unsupervised and Supervised Learning

ABSTRACTFoehn winds, characterised by abrupt temperature increases and wind speed changes, significantly impact regions on the leeward side of mountain ranges, e.g., by spreading wildfires. Understanding how foehn occurrences change under climate change is crucial. As foehn is a meteorological phenomenon, its prevalence has to be inferred from meteorological measurements employing suitable classification schemes. Hence, this approach is typically limited to specific periods for which the necessary data are available. We present a novel approach for reconstructing historical foehn occurrences using a combination of unsupervised and supervised probabilistic statistical learning methods. We utilise in situ measurements (available for recent decades) to train an unsupervised learner (finite mixture model) for automatic foehn classification. These labelled data are then linked to reanalysis data (covering longer periods) using a supervised learner (lasso or boosting). This allows us to reconstruct past foehn probabilities based solely on reanalysis data. Applying this method to ERA5 reanalysis data for six stations across Switzerland and Austria achieves accurate hourly reconstructions of north and south foehn occurrence, respectively, dating back to 1940. This paves the way for investigating how seasonal foehn patterns have evolved over the past 83 years, providing valuable insights into climate change impacts on these critical wind events.

Swift-XRT follow-up analysis of unidentified hard X-ray sources. Searching for soft X-ray counterparts of unidentified hard X-ray sources

It has been established that the sources contributing to the cosmic X-ray background (CXB) emission are mainly nearby active galactic nuclei (AGNs), in particular those that are obscured. Thus, it is important to fully identify the hard X-ray sky source population to accurately characterize the individual contribution of different AGNs to the overall CXB emission. We present a follow-up analysis of all 218 sources marked as unidentified in our previous revision of the third release of the Palermo Swift -BAT hard X-ray catalog (3PBC) based on our multifrequency classification scheme. These 218 sources were classified as unidentified in our previous analyses because they lacked an assigned low-energy counterpart. We searched for soft X-ray counterparts of these 218 3PBC sources in archival Swift -XRT observations obtained between January 1, 2005, and August 1, 2018. In particular, we found 1213 archival Swift -XRT observations for 192 of the 218 unidentified sources. We find 93 possible Swift -XRT counterparts within the Swift -BAT positional uncertainty regions. They correspond to 73 3PBC sources, 60 of which have only a single Swift -XRT detection; the rest have multiple detections. We present all the detected possible counterparts of the as-of-yet-unidentified hard X-ray sources to the community as a catalog for future spectroscopic follow-up targets, together with a short catalog of our classification of the ten sources for which there were available spectra.

A physical classification of Killing magnetic fields in Thurston geometries

In recent years, numerous studies have appeared that considered Killing vectors of three‐dimensional Riemannian manifolds as magnetic fields, since these vector fields are divergenceless by definition. The existence of adivergenceless vector field modeled as a magnetic field does not imply that it is physically realizable. In this study, we propose a physical classification scheme based on the divergences of the integral that defines the energy of a Killing magnetic field. We consider all the Killing magnetic fields of Thurston geometries studied in the literature and classify them as either physical or nonphysical.

Set Covering Routing Problems: A review and classification scheme

Set Covering Routing Problems: A review and classification scheme

A Novel Sea State Classification Scheme of the Global CFOSAT Wind and Wave Observations

AbstractOcean waves are essential elements across the air‐sea interface, regulating momentum and energy transfer. The mixture of wind sea and ocean swell coupled with surface winds results in diverse sea state conditions that modify the local air‐sea interaction. Previous classifications of wind waves and swells are mostly binary that are insufficient to represent the complexity of sea states. In this study, we utilize wind and wave measurements from the China‐France Oceanography Satellite (CFOSAT) to construct an observational wind‐wave ensemble. Four key parameters: wind speed, significant wave height, inverse wave age, and spectral width are selected out of six variables based on their correlations. Employing the unsupervised learning of k‐means clustering, global sea states are categorized into six distinct classes. These classes, characterized by unique centroids and separated in the feature space, represent specific wind regimes and degrees of wave development. Global occurrence highlights that each sea state is region‐specific, bridging the spatial gap of swell and wind sea dominated areas, respectively. This new grouping scheme complements the traditional wind sea and/or swell classification by resolving the diversity of wave regimes. The six‐class classification enables us to identify transitional states and hybrid conditions that may have been overlooked in the binary classification scheme, which shall help investigate the impact of ocean waves on the air‐sea interaction under varying sea states.

Generalizable classification methodology for quantification of atomized feedstock powder by 3D X-ray tomography data and machine learning

Laser powder bed fusion (LPBF) is currently being applied to manufacture engineering-crucial components. To maintain consistent part quality, accuracy and speed in the quality assurance of atomized metal feedstock powder is critical. 3D x-ray tomography (XRT), coupled with machine learning algorithms, provides a transformative route to powder characterization and classification. A recycled AA7050 feedstock powder was studied through XRT to demonstrate a scheme for classification of highly deformed particles which vary both in geometric morphology and degree of surface irregularity. Manual, unsupervised, and supervised classification algorithms were optimized to reproduce visual classification, demonstrating how different approaches to algorithm training may provide a balance between the amount of training data and acceptable final accuracy. The reported approach provides a robust methodology that links 3D measurements and powder classification as means to control powder-induced defects and improve mechanical performance in printed parts.

PIT-1/SF-1 co-expression in pituitary neuroendocrine tumors (PitNETs) with comprehensive review of the literature: How should we best characterize these neoplasms?

The nomenclature and classification of neuroendocrine tumors of the anterior pituitary have undergone significant change over the last few years. Despite the updated classification system as devised by the World Health Organization, some tumors do not fit neatly into the currently defined categories. The most common tumor type not defined by the updated guidelines is a pituitary neuroendocrine tumor with co-expression of SF-1 and PIT-1. In this manuscript, we provide our institutional experience with such unusual cases and combine our findings with those in the available literature to provide the largest, most comprehensive dataset regarding clinical and pathologic information for these unique tumors. Based on our findings, we also propose a classification scheme for pituitary neuroendocrine tumors to integrate lineage, hormonal expression, and clinical function as we believe this constellation of information will best help the clinical teams treating these patients.

Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering

ABSTRACT In the realm of medical diagnostic testing, diagnostic tests can assume either binary forms, distinguishing between diseased and non-diseased states, or ordinal forms, categorizing states from non-diseased to various stages (1 to K). Another significant classification scheme for multi-class scenarios is tree or umbrella ordering, which entails several unordered sub-classes (subtypes) within a biomarker. Within tree or umbrella ordering, the classifier assesses whether the marker measurement for one class surpasses or falls below those for the other classes. Although Receiver Operating Characteristic (ROC) curves and summary measures have been adapted to accommodate tree and umbrella ordering, these approaches often yield cut-off points that generate highly sensitive tests for certain disease subtypes while compromising specificity for others. This may not be ideal for all diseases. Hence, in this investigation, we explore diverse measures of diagnostic test accuracy and optimal cut-off point selection procedures under tree or umbrella ordering to foster more specific tests. We present numerical examples and simulation studies and demonstrate the approach using real data on lung cancer.

Shifting agricultural land use and their unintended water consumption in the North China Plain

Agricultural land use (ALU) critically influences food production and water resource allocation. This study examines the dynamics of ALU in the North China Plain (NCP), an area marked by intensive agriculture and severe groundwater over-exploitation, with a focus on the multidimensional drivers and their implications for water management. By employing an elaborate classification scheme based on satellite imagery and extensive first-hand field data, we identified significant shifts in crop patterns. From 2013 to 2017, there was a notable transition from double crops (primarily wheat-maize) to single crops (primarily maize), covering 4600 km2 and accounting for 42% of single crops in 2013. From 2017 to 2022, there was a shift from single crops to economic forests, encompassing 3600 km2 and 22% of economic forests in 2017, including orchards, timber trees, and shelter forest belts. These shifts resulted in an 11% decrease in grain acreage (6800 km2) and an 11% increase in crop water consumption (6.3 km3) during 2013–2022. Notably, water consumption by economic forests increased by 126% (9.4 km3) during this period. This study highlights the critical need to balance competing demands for food and water security, providing valuable insights for similar agricultural regions worldwide.