Classification Scheme Research Articles

Typical earthworm assemblages of European ecosystem types

AbstractFor nature conservation and planning, terrestrial ecosystems are commonly classified based on their plant communities. Although soils are fundamental to ecosystem functioning, ecosystem classifications based on soil organisms are rare, and it is poorly understood whether their assemblage compositions follow existing classification schemes. We examined whether commonly used ecosystem types capture variation in earthworm (Lumbricidae) assemblages—a crucial biotic component of soil ecosystems. To this end, we created four ecosystem classifications by combining large‐scale climatic classifications (Biogeographic Regions [BGR] and Holdridge Life Zones [HLZ]) with small‐scale land cover classifications (CORINE Land Cover [CLC] and European Nature Information System [EUNIS]). European earthworm assemblage data from the sWORM and Edaphobase databases were analysed for variation in composition within and among ecosystem types, using Permutational Analysis of Variance and Analysis of Similarities. Additionally, we used Typical Species Analysis to establish typical earthworm assemblages (TAs) for each ecosystem type. Ecosystem classifications using the BGR explained more variance than HLZ, but HLZ showed a higher separation of assemblages between ecosystem types. The differentiation between Atlantic and Continental climates in the BGR could explain the superiority over the HLZ, which had only one category for the cool temperate zone of our study region. The typical assemblages contained on average six species, with some habitat generalists present in most. This study shows that combinations of ecosystem properties from different spatial scales can be used to distinguish between earthworm assemblages at the European level. However, earthworm assemblages across Europe were highly similar due to low species richness and the dominance of a few widespread species. This limits the possibility of applying TAs on large spatial scales, for example, for environmental monitoring. We suggest that future studies should explore the use of more species‐rich groups of soil organisms to characterize ecosystem types.

P20-09 Evaluating the consistency of judgments derived through both in silico and expert application of the Cramer Classification Scheme

P20-09 Evaluating the consistency of judgments derived through both in silico and expert application of the Cramer Classification Scheme

Types of high-riding vertebral artery: a classification system for preoperative planning of C2 instrumentation based on 908 potential screw insertion sites

BACKGROUND CONTEXTOur recent studies indicated that a high-riding vertebral artery (HRVA) is a common variant posing a risk of injuring the vessel during C2 instrumentation. However, several different types fit in the current definition of HRVA, which may require a different strategy for C2 screw placement. PURPOSETo classify HRVA and provide a clinical aid for preoperative planning of C2 instrumentation. A secondary goal was to estimate coexistence of each HRVA type with the ipsilateral narrow C2 pedicle (NP). STUDY DESIGNA retrospective observational study involving radiologic measurements of the estimated number of anonymized cervical computed tomography (CT) scans. STROBE checklist was applied. PATIENT SAMPLEA total of 908 potential screw insertion sites (PSIS) of 454 consecutive cervical CT scans were analyzed. The sample size was estimated using ScalaR SP function in RStudio. OUTCOME MEASURESThree types of HRVA based on a series of C2 vertebral morphological parameters including the C2 isthmus height (C2IsH) and C2 internal height (C2InH). Also, the prevalences of each HRVA type and coexisting NP based on the C2 pedicle width (C2PW). METHODSHRVA was defined as C2IsH of ≤5 mm and/or C2InH of ≤2 mm measured 3 mm lateral to the lateral border of the spinal canal. A narrow pedicle was defined as C2PW of ≤4 mm. Measurements were done using Syn.govia software. Interobserver, intraobserver, and inter-software agreement coefficients for C2IsH, C2InH, and C2PW parameters were adopted from our previous study. K-means cluster analysis was applied. RESULTSPrevalence of at least 1 HRVA was 24.9% (n=113 subjects) and 16.2% of PSIS (n=147 sites). Based on the measurements and K-means clustering, the following 3 types of HRVA have been distinguished: type 1—isthmic with only C2IsH being reduced and normal C2InH; type 2—internal with only C2InH being reduced and C2IsH within normal limits; type 3—isthmo-internal with both C2IsH and C2InH being reduced. Kruskal-Wallis test followed by unadjusted and Bonferroni-adjusted post-hoc multiple comparison analysis detected significant differences across the types. The prevalences of the newly identified types were as follows: 78.2%, 8.8%, and 12.9% for type 1, type 2, and type 3, respectively. 73.9% of type 1 HRVA, 53.8% of type 2 HRVA, and 100% of type 3 HRVA had a concomitant ipsilateral NP. Prediction of the HRVA types by the K-means clustering has been evaluated. Screw placement techniques for each type are proposed and discussed. CONCLUSIONWe present the first classification system for the high-riding vertebral artery distinguishing 3 types based on the large homogenous cohort, which may serve as an adjunct to preoperative planning of C2 instrumentation. External validation of this classification scheme shall determine its further clinical utility.

Toward mainstreaming care activities in transportation: a time use and mobility segmentation approach

ABSTRACT This paper assesses the importance of incorporating care dimensions into activity-travel segmentation to understand daily life mobility strategies. The data came from six neighbourhoods in Concepción, Chile, and included detailed information on activity-travel time use and interaction and classification schemes to identify care purposes. The study uses self-organizing maps to build incremental behavioural segments from weekly mobility and time use variables, adding care activities to assess their role in this segmentation. The results identify groups with a higher burden on care than others, emphasizing the role of transport mode and time use patterns. The result remarks caregiving activities hidden within other categories, identifying groups of caregivers, including domestic workers and women who work and have intense accompanying activities with children. The results highlight the differences between mobility patterns between different segments to make more invisible care and other related activities disproportionately performed by groups of women.

Peatland vegetation of the Syra Pogonia massif of the Rivneskyi Nature Reserve (Polissia, Ukraine): ecological characteristics

Background. Peat bog complexes are self-sufficient ecosystems in which unique microclimatic conditions leading to a significant diversity of valuable plant communities are formed. Such communities are sensitive to the effects of climate change or any anthropogenic intervention. The lack of reliable information on their distribution on the territory of the Syra Pogonia peat-bog massif of the Rivnenskyi Nature Reserve necessitates a detailed study of the bog vegetation. Therefore, the purpose of the work was to classify the community of peatland vegetation of the Syra Pogonia massif and to determine the features of their syntaxonomic and ecological differentiation for further development of environmental management strategies. Materials and Methods. The study of the peculiarities of peatland vegetation was conducted on transects that represent the variety of local conditions of the complex system. A total of 141 relevés were analysed using the Braun–Blanquet method. The material was analysed using TURBOVEG 2.79 and JUICE 7.0.83 software. Vegetation units were separated using the method of two-factor indicator species analysis (TWINSPAN). Diagnostic species of syntaxa were determined by the fidelity coefficient phi, the fidelity threshold for which was > 25 %. The statistical significance of the phi coefficient was determined with the Fisher’s test at P < 0.001. The DCA-ordination method was used to identify an ecological differentiation of units. Results. Peatland vegetation of the Syra Pogonia massif of the Rivnenskyi Nature Reserve was analysed. We identified 7 associations belonging to 6 alliances, 6 orders and 4 classes and compiled a vegetation syntaxonomic scheme. The leading factor of ecological differentiation of community is humidity of the area. The greatest diversity is inherent in mesotrophic areas, rare species are found and grouped according to diffe­rent ecological conditions. The species composition of 7 associations includes 79 plant species, of which 65 are vascular and 14 are bryophytes. Using methods of phytosociological analysis, we established that the distribution of community in multi-dimensional space of ecological factors occurs under the conditions of their complex action. At the same time, changes in the humidity regime are of crucial importance for the selected syntaxa (Andromedo polifoliae-Sphagnetum magellanicі). The results of the phytoindicative analysis proved that the associations identified on the territory of the Syra Pogonia peat-bog complex are acidophilic in terms of acidity, and oligotrophic in terms of the requirements for the content of nutriens. Conclusions. As orders, and result of the analysis of relevés, 7 associations, which belong to 6 alliances, 6 orders and 4 classes were selected. Based on the results of the dataset analysis, a vegetation classification scheme was compiled. As a result of the cluster analysis of 7 associations, a significant difference between communities of watered and wet habitats was revealed. We established that the differentiation of the vegetation on the territory of the peat massif of Syra Pogonia mainly depends on the change in the moisture regime.

RadioGraphics Update: The Ninth Edition TNM Staging System for Thymic Epithelial Tumors.

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

A new sponge from the Marjum Formation of Utah documents the Cambrian origin of the hexactinellid body plan.

Modern poriferans are classified into four classes-Calcarea, Demospongiae, Hexactinellida and Homoscleromorpha-the recognition of which in fossil specimens almost exclusively relies on spicule morphology and arrangement. Early fossil representatives of the phylum Porifera are morphologically diverse, and many of them problematically display characteristics that are incompatible with the classification scheme developed for modern taxa. Critically, hexactine spicules-a diagnostic feature of hexactinellids among modern taxa-are found in various Cambrian and Ordovician taxa that cannot be accommodated within the hexactinellid body plan. Here we describe a new poriferan from the Drumian Marjum Formation of Utah, Polygoniella turrelli gen. et sp. nov., which exhibits a unique combination of complex anatomical features for a Cambrian form, including a syconoid-like organization, a thick body wall, and a multi-layered hexactin-based skeleton. The hexactinellid-like body wall architecture of this new species supports a Cambrian origin of the hexactinellid body plan and provides valuable insights into character evolution in early glass sponges.

GCDkit.Mineral: A customizable, platform-independent R-language environment for recalculation, plotting, and classification of electron probe microanalyses of common rock-forming minerals

Abstract GCDkit.Mineral is a platform-independent (Windows/Mac/Linux) freeware for recalculation, plotting, and statistical treatment of mineral data obtained by microbeam techniques, typically an electron microprobe. It is written in R, a language providing a feature-rich environment for statistics and data visualization. This new program imports compositional data in various commonly used file formats or retrieves them from the clipboard. Routines are available for data management, i.e., grouping, searching, and generation of subsets, using regular expressions and Boolean logic. Raw compositional data (wt%) are recalculated to atoms per formula unit (apfu) based on a required number of O equivalents, atoms, or charges, with or without FeII/FeIII estimation by various methods. Analyses may then be recast to structural formulae, i.e., the atoms are distributed into appropriate crystallographic sites. For minerals forming solid solutions, the molar percentages of end-members are computed. All the data may be treated statistically, either by built-in functions for descriptive and multivariate statistics or using the wealth of tools provided by the wide R community. Raw and recalculated mineral data may be plotted on assorted binary and ternary plots and boxplots. Most are defined as internal templates that provide a means to make later changes to the plot (zooming and scaling, adding comments or legend, identifying data points, altering the size or color of the plotting symbols, etc.). The publication-ready graphics may be saved into several vector-(PostScript, PDF, and WMF) and bitmap-based (e.g., PNG, TIF, and JPG) formats, ready to be imported into a professional graphical, presentation, or desktop publishing software. Importantly, the graphical templates are used as a basis for classification. The general classification routine looks for the name of the polygon within the diagram (= graphical template), into which the analysis falls according to its x–y coordinates. The outcome may be either the name of a mineral or a link to another diagram in the case of more complex classification schemes. Following the rules of the International Mineralogical Association (IMA), in some cases, the classification is not done graphically but using prescribed algorithms. The class mechanism in R provides an elegant solution to the computational problems presented by the differing requirements of each mineral group. By assigning each mineral species to a particular class, all algorithms may be implemented as mutually independent but mineral group-specific methods. The default recalculation options for each mineral class are stored externally in a small and simple text file. The program is designed to cater to three potential user groups. For users with no familiarity with R, the program is fully menu-driven and contains embedded default recalculation options for many common rock-forming minerals. More experienced users may easily tweak these parameters, as they are saved in a logically structured plain text file. Seasoned R users may invoke GCDkit.Mineral in command line mode, use batch scripts or Python-driven notebooks (e.g., of project Jupyter), or modify and develop new recalculations or plugins. The lucid, open, and modular design thus makes GCDkit.Mineral a versatile workbench for everyday use, as well as a promising platform for community-driven development. The GCDkit family of R tools, including GCDkit. Mineral, is distributed through the WWW. The current version may be downloaded from http://mineral.gcdkit.org.

The Phenomenon of Ethnocultural Enclaves of Russophone Koreans “Koryŏin Maŭl” in South Korea

This article details the findings of a study conducted from October 2022 to December 2023 on the ethnocultural enclaves of Russophone Koreans in South Korea. The research delves into the origins of the term “Koryŏin Maŭl” and proposes a refined definition. It identifies key characteristics shared by these enclaves and introduces a preliminary classification scheme. Distinguished by a strong ethnic presence and a somewhat insular community formed around a common language (Russian) and shared cultural practices, the “Koryŏin Maŭl” can be defined as ethnocultural enclaves. These areas are self-determined and universally recognized among their residents.Through my research, I have concluded that the Koryŏin Maŭls in South Korea share a number of common characteristics. They all have distinct boundaries, formed by both terrain features and patterns of residential development. These neighborhoods also house a significant number of ethnic businesses, whose success largely hinges on the customer base formed within the ethnic enclave itself.

Chrono-EEG dynamics influencing hand gesture decoding: a 10-hour study

Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG dynamics over time within the context of movement. Twenty-two healthy individuals were measured six times from 2 p.m. to 12 a.m. with intervals of 2 h while performing four right-hand gestures. Analysis of movement-related cortical potentials (MRCPs) revealed a reduction in amplitude for the motor and post-motor potential during later hours of the day. Evaluation in source space displayed an increase in the activity of M1 of the contralateral hemisphere and the SMA of both hemispheres until 8 p.m. followed by a decline until midnight. Furthermore, we investigated how changes over time in MRCP dynamics affect the ability to decode motor information. This was achieved by developing classification schemes to assess performance across different scenarios. The observed variations in classification accuracies over time strongly indicate the need for adaptive decoders. Such adaptive decoders would be instrumental in delivering robust results, essential for the practical application of BCIs during day and nighttime usage.

Pathologic Changes in and Immunophenotyping of Polymyositis in the Dutch Kooiker Dog.

Earlier, we described a breed-specific inflammatory myopathy in Dutch Kooiker dogs (Het Nederlandse Kooikerhondje), one of the nine Dutch breeds. The disease commonly manifests itself with clinical signs of difficulty walking, muscle weakness, exercise intolerance, and/or dysphagia. In nearly all dogs' creatine kinase (CK) activity was elevated. Histopathology reveals the infiltration of inflammatory cells within the skeletal muscles. The objective of this study was to further investigate and characterize the histopathological changes in muscle tissue and immunophenotype the inflammatory infiltrates. FFPE fixed-muscle biopsies from 39 purebred Kooiker dogs were included and evaluated histopathologically according to a tailored classification scheme for skeletal muscle inflammation. As in other breed-related inflammatory myopathies, multifocal, mixed, and predominantly mononuclear cell infiltration was present, with an initial invasion of viable muscle fibres and the surrounding stroma leading to inflammation, necrosis, and tissue damage. Immunophenotyping primarily revealed lymphohistiocytic infiltrates, with CD3+ T-cells being the predominant inflammatory cell type, accompanied by CD8+ cytotoxic T-cells. The concurrent expression of MHC-II class molecules on myofibres suggests their involvement in initiating and maintaining inflammation. Additionally, CD20+ B-cells were identified, though in lower numbers compared to T-cells, and IBA-1-positive macrophages were frequently seen. These findings suggest a breed-specific subtype of polymyositis in Kooiker dogs, akin to other breeds. This study sheds light on the immune response activation, combining adaptive and innate mechanisms, contributing to our understanding of polymyositis in this breed.

Limited Utility of Dynamic Contrast Enhancement Imaging Sequences Within the PI-RADS v2.1 Classification Scheme: A Retrospective Cross-Sectional Study of MRI Reports.

Background/Objective: We sought to characterize the proportion of peripheral zone lesions "upgraded" within the PI-RADS v2.1 protocol using DCE imaging sequences in a large patient population undergoing multiparametric prostate MRI. Methods: A retrospective review of radiologist reports for 2742 prostate MRI exams at 2 large Alberta teaching hospitals between January 2017 and January 2022 was conducted. Prostate specific antigen (PSA), prostate volume, sequence specific and overall PI-RADS scores, and lesion positivity for DCE were collected if present in the accompanying radiology report. Further, pathology reports of biopsies of the upgraded lesions within upgraded patients were reviewed to see if upgraded lesions were deemed clinically significant by gleason score/grade group. Results: The median age was 63 years, with a median PSA and PSA density of 7.5 ng/mL and 0.13 ng/mL2 respectively. A total of 1809 lesions were reported, with 69.4% of all lesions being DCE positive. Of the lesions within the peripheral zone, 548 were overall PI-RADS 4. A total of 87/2742 (3.2%) of patients were upgraded to a PI-RADS 4 by DCE imaging. Within these patients, 65 had pathology reports available, of which 18 had a clinically significant lesion at the upgrade site. Conclusion: Contrast enhancement is only beneficial for a very small portion of patients undergoing prostate MRI. Given the invasive nature of contrast enhanced studies, potential contrast induced side effects, added imaging time, and the cost of contrast agent, routine use of contrast for prostate MRI is questioned. Further studies are necessary to determine if it should be part of routine prostate MRI imaging protocols.

Steam Reforming of Tar Impurities from Biomass Gasification with Ni-Co/Mg(Al)O Catalysts—Operating Parameter Effects

The elimination of tar impurities from biomass gasification by catalytic steam reforming can provide clean syngas for downstream biofuel synthesis (Fischer–Tropsch). The effects of key operating parameters in CH4/tar steam reforming were investigated. Ni-Co/Mg(Al)O catalyst performance was tested at model conditions (10/35/25/25/5 wt% CH4/H2/CO/CO2/N2), changing the temperature (650–800 °C), steam-to-carbon ratio (2–5), tar loading (10–30 g/Nm3), and tar composition (toluene, 1-methylenaphthalene, and phenol). Complete tar elimination was achieved under all conditions, at the expense of catalyst deactivation by coke formation. Post-operation coke characterization was obtained with TPO-MS, Raman spectroscopy, and STEM analysis, providing vital insight into coke morphology and location. Critical low-temperature and high-tar loading limits were identified, where rapid deactivation was accompanied by increasing amounts of hard coke species. A coke classification scheme is proposed, including strongly adsorbed surface carbon species (soft coke A), initial scattered carbon filaments (hard coke B1.1), filament clusters and fused filaments (B2), and strongly deactivating bulk encapsulating coke (B3), formed through progressive filament cluster graphitization. High-molecular-weight tar was found to enhance the formation of strongly deactivating metal-particle-encapsulating coke (B1.2). The results contribute to the understanding of coke formation in the presence of biomass gasification tar impurities.

Phylogenetic synthesis of morphological and molecular data reveals insights on the classification of diogenid hermit crabs (Crustacea: Decapoda: Anomura).

The family Diogenidae Ortmann, 1892 is a diverse and abundance group of hermit crabs, but their systematics and phylogenetic relationships are highly complex and unresolved. Herein, we gathered nucleotide sequence data from two mitochondrial (16S rRNA and COI) and two nuclear (NaK and PEPCK) genes for a total of 2,308 bp in length across 38 species from six extant diogenid genera. Molecular data were combined with 41 morphological characters to estimate the largest phylogeny of diogenid hermit crabs to date with the aim of testing the proposed taxonomic scheme of Diogenidae and addressing intergeneric relationships within this family. Despite conflicts between mitochondrial and nuclear DNA trees, the combined-data tree reflects the contributions of each dataset, and improves tree resolution and support for internal nodes. Contrary to traditional classification, our total evidence revealed a paraphyletic Diogenidae based on internally nested representatives of Coenobitidae Dana, 1851. Within Diogenidae, the studied diogenid hermit crabs were split between two clades with high support, which contradicts recent morphological classification scheme for Diogenidae sensu lato based on fossil records. The genus Diogenes Dana, 1851 was found nested inside Paguristes Dana, 1851, which formed a clade being separated from the remainder, pointing towards paraphyly in Paguristes. In another clade, Dardanus Paulson, 1875 occupied a basal position relative to the other diogenids, while Calcinus Dana, 1851 and Clibanarius Dana, 1852 showed sister relationships and formed a cluster with Ciliopagurus Forest, 1995. Among the morphological characters examined, carapace shield and telson were identified as phylogenetically significant for grouping diogenid genera, while phylogenetic insignificance of gill number was evidenced by its mosaic pattern in diogenid phylogeny. The present study sheds light on the controversial generic phylogeny of Diogenidae and highlights the necessity for thorough taxonomic revisions of this family as well as some genera (e.g., Paguristes) to reconcile current classifications with phylogenetic relationships.

Improved pore structure characterization and classification of strong diagenesis sandstones by data-mining analytics in Tazhong area, Tarim Basin.

The Silurian system in Tazhong area is characterized by extensive, low-abundance lithological reservoirs with strong diagenesis, resulting in significant heterogeneity. The complex pore structure in this area significantly impacts fluid control, making accurate characterization and classification of pore structures crucial for understanding reservoir properties and their influence on oil and gas distribution. Based on 314 Mercury Injection Capillary Pressure (MICP) samples in combination with core slices and thin casting slices observation, a pipeline of characterization and classification scheme by data-mining analytics of strong diagenesis sandstone pore structure types in the study zone is established, and the characteristics of different pore structures are clarified. According to the pore structure parameter abstracted by MICP data compression and variable analysis based on hierarchical clustering and principal component analysis (PCA) analysis, the variables are reasonably evaluated and screened, and the screened variables can be divided into three groups: mean pore throat radius-maximum pore throat radius-median pore throat radius-pore throat diameter mean variable group, microscopic mean coefficient variable group, and median pressure displacement pressure-relative sorting coefficient variable group. The combination of classification schemes analysed by decision tree model and linear discriminant analysis (LDA) model was determined. In the two-dimensional projection diagram of LDA model, a relatively obvious distribution of low displacement pressure, middle displacement pressure and high displacement pressure was obtained, and three distribution lines were nearly parallel. Based on the relevant information, 6 combined classification schemes suitable for final pore structure modelling were determined verified by microscopic observation. The correct characterization and classification of pore structure can be applied to the prediction of pore type, which can be used to improve the prediction of oil and gas distribution and oil and gas recovery in the future.

Knowledge representation to support EMDAP implementation in advanced reactor licensing applications

The Data, Information, Knowledge, and Wisdom (DIKW) pyramid depicts the general principle of a decision-making process. Various levels of abstraction are required to formalize the available data/information/knowledge with respect to the decision being supported. The Evaluation Model Development and Assessment Process (EMDAP) was developed by the United States Nuclear Regulatory Commission (US NRC) to guide the development and assessment of Evaluation Models (EMs) for transient and accident analysis of nuclear power plants under design basis accidents. It concerns the decision of adequacy of an EM with respect to the purpose of analysis (NPP application). However, lack of data, scaling issues, modeling limitations, computation, and experimental overhead are some of the key factors that challenge the EM adequacy decision in EMDAP. Phenomena Identification and Ranking Table (PIRT) which is used for complexity resolution can also introduce noise due to bias and variance in expert opinion and judgment. The uncertainty in the decision due to these issues and challenges can be minimized by enhancing the EMDAP from the DIKW perspective. In this work, we formulate a systematic scheme for knowledge representation, and classification and characterization of evidence to enhance clarity and transparency in the EMDAP implementation. The proposed scheme is governed by the concept of the value of information, where we contextualize, classify, characterize, and evaluate the evidence (relevant data/knowledge/information related to EM development, verification, validation, and uncertainty quantification) with respect to the relevant decision attributes or elements of EMDAP. We make use of the Predictive Capability Maturity Model (PCMM), Argumentation technique, and process quality assurance for knowledge representation to support EMDAP implementation in advanced reactor licensing applications. The main objective of the proposed scheme is to support decision-making by making it more transparent, traceable, and accountable. The illustration of the proposed scheme is provided using a system simulation code (SAS4A/SASSYS-1) as the primary EM and a generic sodium fast reactor under loss of flow accident as the target reactor application.EMDAP involves different elements that require expert input and insights. However, the process of expert elicitation may require significant resources and time. To address these issues, we identify generative artificial intelligence models as potential tool which (after systematic testing and capabilities analysis) can be used for domain specific expert elicitation and information extraction for efficient implementation of EMDAP.

Advancing Refugee Health Data Management: The Implementation of ICD-11 in UNRWA's Primary Care System.

The United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) was one of the earliest healthcare systems globally to implement the International Classification of Diseases, Eleventh Revision (ICD-11) across its 140 clinics serving 5.9 million Palestine refugees. This paper discusses the integration of ICD-11 into UNRWA's cloud-based electronic medical record (EMR) system, identifying both the barriers and facilitators involved and analyzing trends in clinical documentation and healthcare utilization. The key challenges included data privacy provisions, integration into a coordinated care model, complex classification schema for primary care settings, frequent staff turnover, and limited data analysis capabilities. Conversely, facilitators included physician-tailored training and on-site support, system compatibility, a multidisciplinary team approach, policy support from UNRWA and the World Health Organization (WHO), and leadership commitment and effective change management. Medical officers (MOs) using ICD-11 reported greater satisfaction with the system's capabilities in managing and visualizing health information. This article contributes to the discourse on health data management in complex humanitarian settings, offering insights into the benefits and challenges of implementing advanced classification systems like ICD-11. Future research should explore longitudinal impacts and further integration with global health systems, ensuring that the advancements in classification continue to support the overarching goal of health equity and access in vulnerable and hard-to-reach populations.

Long-term evaluation of the initial response to therapy in 60 dogs with chronic inflammatory enteropathy.

Dogs with chronic inflammatory enteropathy (CIE) are typically classified into food-responsive enteropathy (FRE), antibiotic-responsive enteropathy (ARE), immunomodulator-responsive enteropathy (IRE), and nonresponsive enteropathy (NRE) based on response to therapy(ies). Reassessment of initial categorization (especially IRE and NRE) is lacking. Investigate validity of categorization scheme when reassessed at least 1 year after diagnosis. Sixty client-owned dogs with CIE. Retrospective study. Clinical information was gathered from records and owners from time of diagnosis (TD), time of initial response (TIR), and at least 1 year after diagnosis (T≥1y). Category change was defined as a switch between groups. Median disease activity index (CIBDAI) at TD was 9 and reduced significantly to 1 at T≥1y (P < .0001). At TIR, dogs were categorized as: FRE 27/60 (45%, 95% binomial confidence intervals [CI], 0.32-0.58), IRE 30/60 (50%, CI 0.37-0.63), ARE 0/60 (0%), NRE 3/60 (5%, CI -0.01 to 0.11). Seventeen of 27 (63%) FRE dogs had previously had at least 1 unsuccessful diet trial. At T≥1y, categorization changed to FRE 44/60 (73%, CI 0.62-0.85), IRE 14/60 (23%, CI 0.13-0.34), ARE 0/60 (0%), NRE 2/60 (3%, CI -0.01 to 0.08). Group changes were found for 24/60 (40%) dogs, largest change was from IRE to FRE (19/24, 79%). Immunosuppressive dosages were administered as sole treatment in 1/30 (3%) IRE dogs at TIR. Chronic inflammatory enteropathy categorization based on initial response to therapy needs reassessment after 1 year. Frequent change from IRE to FRE suggests that dogs initially categorized as IRE might have been initially categorized as FRE if multiple dietary trials had been performed. In our study, antibiotics were not needed to achieve satisfying clinical responses.

LearningTuple: A packet classification scheme with high classification and high update

Packet classification is widely used in network infrastructures and is the key technique that supports security and other functions. The real-time nature of network services naturally demands high classification speed, while the emerging SDN makes rule changes more flexible, thus placing higher demands on the performance of rule update in classification schemes. In this paper, Learning Tuple(LT) is proposed to achieve high classification performance for packets while maintaining the high update characteristics of tuple space-based schemes. Specifically, to solve the issue of excessive tuples and rule overlap due to merging tuples, LT iteratively divides the space by using rule overlap and hash collisions as negative feedback and applies a reinforcement learning algorithm, SARSA, at each level to ensure its reasonableness. Efficient space partitioning guides the construction of tuples, and an excellent rule mapping method called PLR is designed, which improves classification performance. Experimental results demonstrate that compared with classic and advanced classification schemes TSS, TupleMerge, MultilayerTuple, PartitionSort, HybridTSS, and TupleTree, LT achieves average classification performance improvements of 9.23x, 1.74x, 1.45x, 2.85x, 1.37x and 1.25x, as well as average update performance improvements of 1.83x, 6.75x, 1.22x, 6.16x, 1.21x, 10.66x, respectively.

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.