Comprehensive Classification System Research Articles

Implementing sustainability taxonomies to redirect capital flows: the case of South Africa

ABSTRACT Sustainability taxonomies are comprehensive classification systems that define what constitutes a sustainable economic activity. They aim to increase transparency in financial markets and redirect capital flows to sustainable investments. Such sustainable finance policies can be important levers for the transformation of the economy towards climate neutrality, as today’s investments will shape economic production processes for decades to come. While sustainability taxonomies have been developed in recent years in many countries, existing research on taxonomies has so far often focused on the European Union and has hardly addressed questions of policy implementation. This case study focuses on South Africa’s Green Finance Taxonomy (GFT) and addresses the question of what factors influence the adoption of sustainability taxonomies by potential users. Based on 44 semi-structured expert interviews with taxonomy stakeholders from the private sector, government, civil society and academia, it finds that one year after its publication, the GFT has hardly been used in practice. Important factors hindering an effective implementation are a lack of regulatory embedding, the absence of a legal recognition of the GFT by the European Union (EU), hesitance among financial market participants to build capacity to collect the necessary data, and fossil-fuel path dependencies in South Africa’s economy. Given the difficulties many jurisdictions currently face in implementing their newly developed sustainability taxonomies, these findings have important policy implications not only for South Africa but also for other countries.

The CHD severity classification system: development of a tool to assist with disease stratification for CHD research.

Complexity stratification for CHD is an integral part of clinical research due to its heterogenous clinical presentation and outcomes. To support our ongoing research efforts into CHD requiring disease severity stratifications, a simplified CHD severity classification system was developed and verified, with potential utility for clinical researchers without specialist CHD knowledge or access to clinical/medical records. A two-tiered analysis approach was undertaken. First-tier analysis included the audit of a comprehensive system based on: i) timing of intervention, ii) cardiac morphology, and iii) cardiovascular physiology using real patient data (n = 30), across 10 common CHD lesions. Second-tier analysis allowed for a simplified version of the classification system using morphology as a stand-alone predictor. Twelve clinicians of varying specialities involved in CHD care ranked 10 common lesions from least to most severe based on typical presentation and clinical course. First-tier analysis identified that cardiac morphology was the principal driver of complexity. Second-tier analysis largely confirmed the ranking and classification of the lesions into the broad CHD severity groups, although some variation was noted, specifically among non-cardiac specialists. This simplified version of the classicisation system, with morphology as a stand-alone predictor of severity, allowed for effective stratification for the purposes of analysis. The findings presented here support this comprehensive and simple CHD severity classification system with broad utility in CHD research, particularly among clinicians and researchers with limited knowledge of CHD. The model may be applied to produce locally relevant research tools.

Conspectus of the vegetation types of Tajikistan and adjacent areas with special focus on phytosociological classes

Vegetation classification is a tool for organizing the patterns and diversity of plant communities. It is used in nature conservation, and helps to understand the role of vegetation in the biosphere. Given the rapidly diminishing opportunity to understand the vegetation diversity of Tajikistan located in the Middle Asian region, phytosociological surveys using the floristic-ecological approach and the Braun-Blanquet cover-abundance scale were undertaken in 2006. Aer 18 years of research, with a total of 55 research expeditions to Tajikistan, Kyrgyzstan, and Uzbekistan, it is time to summarise and make a first attempt to establish a comprehensive classification system for the entire vegetation of Tajikistan and the surrounding areas. As a first step, the Vegetation of Middle Asia (VMA) database with records from three countries: Tajikistan (4,130 relevés), Kyrgyzstan (1,681 relevés) and Uzbekistan (13 relevés) was created. e internal comparison of all the plots and the analysis of the similarities with the vegetation of Iran, Russia, Afghanistan, Mongolia, Pakistan, and China make it possible to present the comprehensive list of plant communities organized in 45 classes, 47 orders, 83 alliances, 297 associations, and 237 rankless units. In addition to those described in our previous works, we propose also several new sytaxa: <i>Eremogono griffithii-Nepetetea podostachyos, Artemisio persicae-Stipetea drobovii, Festucetea alaico-krylovianae, Carici koshewnikowii-Asperuletea oppositifoliae, Trichodesmo incani-Pachypterygietea brevipes, Aceretalia turkestanici, Juniperetalia seravschanicae, Crataegetalia ponticae, Populetalia pruinoso-euphraticae, Salici capusii- Hippophaeetalia rhamnoidis, Carici stenophylloidis-Stipetalia drobovii, Eritrichion pamirico-subjacquemontii, Stipetum kazachstanicae, Caricetum pamirensis</i>, and <i>Bolboschoenetum affinis</i>. Tis systematic compilation of data, based on our fieldwork and literature data, is not definitive, and certainly the rich and diverse vegetation of Middle Asia requires further studies incorporating also modern LIDAR and satellite techniques.

Novel comprehensive unified classification system toward smart standardized built environment knowledge and low-risk international collaborations: UniCCC

The Construction Industry (CI) data is acknowledged to still be unstructured and high-dimensional. Efforts were undertaken to tackle this problem using regional Unified Classification Systems (UCSs). The study emphasizes the necessity for a comprehensive international UCS to manage the growing trend of international collaborations within the CI and associated data risks. Existing UCSs exhibit significant inconsistencies, such as regional omissions, terminological disparities, and diverse data structuring, underscoring the need for a unified approach. Accordingly, using a 3-stage mixed method, the novel UCS ‘Unified Classification of Construction Components_ UniCCC’ was developed, comprising 4 phyla, 9 divisions, and 54 classes, and was shown to have good adaptability/scalability, structure/breakdown, clarity, codifiability, and inclusivity of the 11 key CI aspects. By addressing inconsistencies and smartly structuring CI knowledge from North America, Europe, Africa, and the Middle East, using a faceted classification scheme aligned with ISO 12,006, and being developed using a conventional modeling language, UniCCC represents a significant advancement in UCSs, with promising implications for global CI practices and research, paving the way for new avenues in CI performance, innovation, and sustainability. It demonstrated efficiency in various CI aspects, including risk reduction, automation, and sustainability enhancement. As evidence of its performance, using UniCCC for scheduling might decrease up to 58.17%, 66.85%, and 31.74% of the related time, request for information, and omissions, respectively. The study suggests future research perspectives to explore UniCCC's applicability in different regions, its integration with emerging technologies, its proficiency metrics for different purposes, and strategies for organizational implementation.

Hand eczema: a review of clinical, dermoscopic and histological features.

Hand eczema (HE) is a prevalent inflammatory skin condition, impacting 15-20% of individuals, with a notable incidence rate of approximately 7.3 cases per 1000 person/years. This condition exhibits significant gender-based variability, with a higher prevalence in females. The clinical presentation of HE is characterized by pruritic erythematous, edematous, weeping plaques, vesicles, and/or bullae, showcasing considerable heterogeneity. A literature search was conducted across multiple databases, including Medline, Pubmed, Scopus, and the Cochrane Library. The search was conducted using the following key words and medical subject heading (MeSH) terms: "hand," "eczema," "dermatitis," "dermoscopy," and "histology," employing the Boolean term "AND" to combine the research terms for optimal search precision. PRISMA algorithm has been used for article screening. The search scope included manuscripts published up to October 1, 2023. Up to 50% of HE cases are associated with atopic dermatitis, emphasizing the complex interplay between various dermatological conditions. Common subtypes of HE include irritant contact dermatitis (ICD), allergic contact dermatitis (ACD), atopic hand eczema (AHE), and protein contact dermatitis/contact urticaria (PCD). The chronic nature of HE presents a substantial management challenge, often underestimated, leading to delayed treatment and potential progression to chronic hand eczema (CHE). Beyond individual health implications, HE exerts a profound impact on occupational, domestic, social, and psychological aspects, establishing itself as the most prevalent occupation-related skin disease. This paper seeks to establish a comprehensive classification system for HE, integrating clinical, dermoscopic, and histological elements. Dermoscopy, specifically, proves instrumental in distinguishing HE from palmar psoriasis, revealing characteristic features such as yellow scales and irregular vessels. Histopathological findings underscore the dynamic changes observed from acute to chronic stages, while challenges in differentiating hyperkeratotic HE from psoriasis underscore the necessity for a holistic diagnostic approach. Accurate diagnosis and effective management of HE necessitate a holistic perspective that recognizes the inherent complexities of this inflammatory skin disease. By providing a multidimensional classification system, incorporating clinical, dermoscopic, and histological parameters, this paper aimed to contribute to a more nuanced understanding of HE and facilitate improved approaches to its diagnosis and treatment.

Traumatic Periprosthetic Fractures Following Total Ankle Replacement A Systematic Review and Proposed Classification

Total ankle replacements (TAR) are increasingly utilized, but postoperative traumatic periprosthetic fractures remain a rare yet challenging complication. This systematic review aims to address the gap in literature by proposing a comprehensive classification system for these fractures, considering implant stability, fracture location, and surrounding bone quality. A systematic review identified 13 cases from 9 studies meeting inclusion criteria. Fractures were categorized using the proposed Hill-Brown classification: Type A (talus or fibula), Type B (distal tibial component), and Type C (diaphysis/proximal tibial metaphysis). Implant stability was a key factor, with Type B fractures further classified as B1 (stable), B2 (unstable with adequate bone stock), and B3 (unstable with poor bone stock). Most fractures occurred at or near the distal tibial component (Type B), with implant stability largely dependent on fracture location and bone quality. Surgical fixation, particularly minimally invasive plate osteosynthesis (MIPO) with locking plates, was the preferred treatment for stable implants, showing low complication rates. Unstable implants often required revision TAR or conversion to arthrodesis. Surgical intervention is recommended following all traumatic periprosthetic fractures in the setting of a TAR. Bone quality, particularly in patients with rheumatoid arthritis or osteoporosis, significantly impacted treatment decisions. Our findings emphasize the importance of fracture location, implant stability, and bone quality in managing these fractures. Future multicenter studies are necessary to validate this classification system and refine treatment protocols. Level of Evidence: Level III

Defining an ageing-related pathology, disease or syndrome: International Consensus Statement.

Around the world, individuals are living longer, but an increased average lifespan does not always equate to an increased health span. With advancing age, the increased prevalence of ageing-related diseases can have a significant impact on health status, functional capacity and quality of life. It is therefore vital to develop comprehensive classification and staging systems for ageing-related pathologies, diseases and syndromes. This will allow societies to better identify, quantify, understand and meet the healthcare, workforce, well-being and socioeconomic needs of ageing populations, whilst supporting the development and utilisation of interventions to prevent or to slow, halt or reverse the progression of ageing-related pathologies. The foundation for developing such classification and staging systems is to define the scope of what constitutes an ageing-related pathology, disease or syndrome. To this end, a consensus meeting was hosted by the International Consortium to Classify Ageing-Related Pathologies (ICCARP), on February 19, 2024, in Cardiff, UK, and was attended by 150 recognised experts. Discussions and voting were centred on provisional criteria that had been distributed prior to the meeting. The participants debated and voted on these. Each criterion required a consensus agreement of ≥ 70% for approval. The accepted criteria for an ageing-related pathology, disease or syndrome were (1) develops and/or progresses with increasing chronological age; (2) should be associated with, or contribute to, functional decline or an increased susceptibility to functional decline and (3) evidenced by studies in humans. Criteria for an ageing-related pathology, disease or syndrome have been agreed by an international consortium of subject experts. These criteria will now be used by the ICCARP for the classification and ultimately staging of ageing-related pathologies, diseases and syndromes.

Defining an Ageing-Related Pathology, Disease or Syndrome: International Consensus Statement.

Around the world, individuals are living longer, but an increased average lifespan does not always equate to an increased healthspan. With advancing age, the increased prevalence of ageing-related diseases can have a significant impact on health status, functional capacity, and quality of life. It is therefore vital to develop comprehensive classification and staging systems for ageing-related pathologies, diseases and syndromes. This will allow societies to better identify, quantify, understand, and meet the healthcare, workforce, wellbeing, and socioeconomic needs of ageing populations, while supporting the development and utilisation of interventions to prevent or to slow, halt or reverse the progression of ageing-related pathologies. The foundation for developing such classification and staging systems is to define the scope of what constitutes an ageing-related pathology, disease or syndrome. To this end, a consensus meeting was hosted by the International Consortium to Classify Ageing-Related Pathologies (ICCARP), on February 19 th , 2024, in Cardiff, UK, and was attended by 150 recognised experts. Discussions and voting were centred on provisional criteria that had been distributed prior to the meeting. The participants debated and voted on these. Each criterion required a consensus agreement of ≥70% for approval. The accepted criteria for an ageing-related pathology, disease or syndrome were: Develops and/or progresses with increasing chronological age.Should be associated with, or contribute to, functional decline, or an increased susceptibility to functional decline.Evidenced by studies in humans. Criteria for an ageing-related pathology, disease or syndrome have been agreed by an international consortium of subject experts. These criteria will now be used by the ICCARP for the classification and ultimately staging of ageing-related pathologies, diseases and syndromes.

Application of deep ensemble learning for palm disease detection in smart agriculture

Agriculture has notably become one of the fields experiencing intensive digital transformation. Leveraging state-of-the-art techniques in this domain has provided numerous advantages for agricultural activities. Deep learning (DL) algorithms have proven beneficial in addressing various agricultural challenges. This study presents a comprehensive investigation into applying DL models for palm disease detection and classification in the context of smart agriculture. The research aims to address the limitations observed in previous studies and improve the robustness and generalizability of the results. To achieve this, a two-stage optimization methodology is employed. First, transfer learning and fine-tuning techniques are applied using various pre-trained deep neural network models. The experiments show promising results, with all models achieving high accuracy rates during training and validation. Furthermore, their performance on unseen test data is also assessed to ensure practical applicability. The top-performing models are MobileNetV2 (92.48 %), ResNet (92.42 %), ResNetRS50 (92.30 %), and DenseNet121 (92.01 %). Second, a deep ensemble learning approach is applied to enhance the models' generalization capability further. The best-performing models with different criteria are combined using the ensemble technique, resulting in remarkable improvements in disease detection tasks. DELM1 emerges as the most successful ensemble model, achieving an ROC AUC Score of 99 %. This study demonstrates the effectiveness of deep ensemble learning models in palm disease detection and classification for smart agriculture applications. The findings contribute to advancing disease detection systems and emphasize the potential of ensemble learning. The study provides valuable insights for future research, guiding the application of DL techniques to address critical agricultural challenges and improve crop health monitoring systems. Another contribution is combining various plant diseases and insect pest classes using diverse datasets. A comprehensive classification system is achieved by considering different disease classes and stages within the white scale category, improving the model's robustness.

Mobile forensics tools and techniques for digital crime investigation: a comprehensive review

<p>Extracting and analyzing data from smartphones, IoT devices, and drones is crucial for conducting digital crime investigations. Effective cyberattack mitigation necessitates the use of advanced Android mobile forensics techniques. The investigation necessitates proficiency in manual, logical, hex dump, chip-off, and microread methodologies. This paper provides a comprehensive overview of Android mobile forensics tools and techniques for digital crime investigation, as well as their use in gathering and analyzing evidence. Forensic software tools like Cellebrite UFED, Oxygen Forensic Detective, XRY by MSAB, Magnet AXIOM, SPF Pro by SalvationDATA, MOBILedit Forensic Express, and EnCase Forensic employ both physical and logical techniques to retrieve data from mobile devices. These advanced tools offer a structured approach to tackling digital crimes effectively. We compare dependability, speed, compatibility, data recovery accuracy, and reporting. Mobile-network forensics ensures data acquisition, decryption, and analysis success. Conclusions show that Android mobile forensics tools for digital crime investigations are diverse and have different capabilities. Mobile forensics software offers complete solutions, but new data storage and encryption methods require constant development. The continuous evolution of forensic software tools and a comprehensive tool classification system could further enhance digital crime investigation capabilities.</p>

Comprehensive Classification System for Localized Alveolar Bone Deficiencies in Treatment Planning for Dental Implants: A Proposed Classification and Prevalence Study.

Introduction The current classification of alveolar bone defects remains ambiguous. This study aims to classify and evaluate the prevalence of bone deformities using a comprehensive classification system for localized alveolar bone deficiencies in dental implant treatment planning. Methods This cross-sectional prospective clinical trial included 698 participants (353 females and 345 males), patients with localized alveolar bone deficiencies. The clinical parameters evaluated were keratinized tissue width (KTW) and gingival thickness (GT) at the edentulous site. The width and height of alveolar bone deficiency at the site of implant placement were measured as horizontal deficiency (HD) and vertical deficiency (VD). Results Out of 698 patients, 566 (81.10%) had Subclass A horizontal deficiencies (HD), 99 (14.20%) had Subclass B HD, and 33 (4.70%) had Subclass C HD. Regarding vertical deficiencies (VD), 426 (61%) had Class I VD, 222 (31.80%) had Class II VD, and 50 (7.20%) had Class III VD. Younger individuals (20-30 years) predominantly exhibited Subclass A HD, whereas older participants (above 50 years) showed more severe deficiencies (Subclass B and C HD, and Class II and III VD). Gender analysis indicated no significant difference in HD prevalence but a significant difference in VD, with males more commonly presenting Class I VD and females exhibiting more Class II VD. Conclusion The study reveals significant associations between age and both HD and VD, indicating that older individuals tend to have more severe deficiencies. The study's findings underscore the importance of the proposed classification system in accurately identifying alveolar bone deficiencies and guiding appropriate treatment strategies, thereby improving clinical outcomes in dental implant therapy.

Beyond Trial and Error: A Comprehensive Classification of Metaheuristics along with Metaphor Criterion Development Trend

Objectives: The research aims to develop a comprehensive classification system for metaheuristics, categorize metaphor metaheuristics, and present the development trend and percentage representation of metaphor metaheuristics within each metaphor group. Method: A descriptive-based systematic review was conducted to collect data on studies concerning the classification of metaheuristics and the proposal of new metaheuristics. Data was sourced from Google Scholar, Science Direct, Springer, ResearchGate, and IEEE Xplore. For the first research objective, 148 studies were screened, resulting in the selection of six studies. The second and third research objectives involved screening 1145 studies, of which 654 were ultimately selected. This review considers studies published up to August 2023. The extracted data includes the characteristics of each classification and the name, abbreviation, author, year, and metaphor group for each metaheuristic reviewed. Findings: The results reveal that existing classifications do not cover the full range of metaheuristic characteristics. The data indicates a rising trend in the introduction of new metaheuristics over the years, with the peak occurring in 2020, boasting 68 new approaches, closely followed by 2022 with 57 introductions. However, between 1965 and 1992, progress was limited to only one or two new approaches annually, signifying periods of stagnation in the field. The majority of metaheuristics proposed are in the physics-chemistry metaphor group (20%), followed closely by human metaheuristics (18%). Novelty: The novelty of this study lies in its exhaustive classification of metaheuristics developed from 1965 to August 2023 based on the metaphor criterion, along with the development progression and percentage-wise representation of various metaphor groups using up-to-date data. Keywords: Metaheuristics, Metaphor, Classification, Optimization, Trend

Brain Tumor Classification Using Vanilla Convolutional Neural Networks

Brain tumours are a common and dangerous type of malignant tumour that, if not detected early enough, can cut short a patient's life. The segmentation and classification of brain tumours using solely traditional medical image processing is a difficult and time-consuming task. Various imaging modalities, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound image, are frequently utilized to assess brain, lung, liver, breast, prostate and other tumours. MRI images are specifically utilised in these analyses to detect brain tumours. As a result, developing approaches for detecting, recognizing, and classifying the conditions based on image analysis becomes essential. A comprehensive and automatic classification system is important to saving human lives. The geographical and anatomical heterogeneity of brain tumours makes automatic categorization challenging. This study proposes an automated method for detecting brain tumours using Convolutional Neural Networks (CNNs) classification, with the primary goal of developing a deep learning model that is capable of accurately identifying and classifying images as either having a brain tumour or not. In this paper, we provide a classification model for brain tumours based on a Deep Convolutional Neural Network with a vanilla neural network technique. The proposed method's performance on a publicly available dataset of 3000 Brain MRI Images yielded superior results, with accuracy and F1 score of 98.00 percent and 98.00 percent, respectively. This study shows that the proposed vanilla-CNN model can be used to make it easier for brain tumours to be automatically classified

Multi-Omics Classification of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.

Intrahepatic cholangiocarcinoma (ICC) is a heterogeneous disease characterized by a dismal prognosis. Various attempts have been made to classify ICC subtypes with varying prognoses, but a consensus has yet to be reached. This systematic review aims to gather relevant data on the multi-omics-based ICC classification. The PubMed, Embase, and Cochrane databases were searched for terms related to ICC and multi-omics analysis. Studies that identified multi-omics-derived ICC subtypes and investigated clinicopathological predictors of long-term outcomes were included. Nine studies, which included 910 patients, were considered eligible. Mean 3- and 5-year overall survival were 25.7% and 19.6%, respectively, for the multi-omics subtypes related to poor prognosis, while they were 70.2% and 63.3%, respectively, for the subtypes linked to a better prognosis. Several negative prognostic factors were identified, such as genes' expression profile promoting inflammation, mutations in the KRAS gene, advanced tumor stage, and elevated levels of oncological markers. The subtype with worse clinicopathological characteristics was associated with worse survival (Ref.: good prognosis subtype; pooled hazard ratio 2.06, 95%CI 1.67-2.53). Several attempts have been made to classify molecular ICC subtypes, but they have yielded heterogeneous results and need a clear clinical definition. More efforts are required to build a comprehensive classification system that includes both molecular and clinical characteristics before implementation in clinical practice to facilitate decision-making and select patients who may benefit the most from comprehensive molecular profiling in the disease's earlier stages.

Experimental evaluation of a small-sized continuum robot for grasping tasks

This study explores catheters, small-sized continuum robots (CRs), in medical procedures, utilizing a recently developed comprehensive classification system known as the “CR-based taxonomy”. This work specializes the proposed taxonomy for small-sized CRs with medical applications through semi-structured interviews with researchers in this field. Subsequently, it provides a comprehensive analysis of grasp taxonomy, encompassing aspects such as grasp stability, grasp adaptability, and the inherent characteristics of manipulated objects and tasks. This analysis is conducted within the context of the “CR-based taxonomy” to assess its feasibility in a practical scenario involving a small-sized CR. Based on this analysis, fixed-tip grasp, hook grasp, and power form closure grasp (PFG) with a grasp stability of 18 have the highest stability, while platform/pull/push grasp and expandable grasp have the lowest stability. Moreover, PFG and precision multi-contact grasp have superior adaptability compared to other grasp families. Also, the planar payload of selected grasp families is much higher than the spatial payload of those families; however, the grasp size remains equal for both dimensions. This endeavor lays the groundwork for more in-depth investigations into catheter applications and offers valuable guidance to medical device developers in the creation of enhanced medical instruments.

Classification of variant portal vein anatomy based on three-dimensional CT: surgical implications

PurposesThe purpose of this study was to develop a new and more comprehensive classification system for portal vein (PV) variations using three-dimensional visualization and evaluation (3DVE) and to discuss the prevalence rates and clinical implications of the variants.MethodsThe anatomies of PVs were tracked and analyzed by using three-dimensional visualization of CT images acquired between 2013 and 2022. Scans from 200 adults were evaluated and a total of 178 patients (N = 178) were included in the study. The new classification system, named BLB classification, was developed based on the level of the absent PV branch in each variant anatomy.ResultsUsing the BLB classification system, PVs were divided into thirteen subtypes. Only 82.6–84.8% of the portal veins of the 178 patients were depicted in Atri’s, Cheng’s or Covey’s classification, compared with 100% identified by the BLB classification. The BLB classification was validated against external data sets from previous studies, with 97.0-98.9% of patients classified by the BLB system.ConclusionVariant PV anatomies are more commonly seen based on 3DVE than in previous reports. The BLB classification covers almost all portal vein variants and may be used for planning liver surgery.

Cone‐beam computed tomography‐based descriptive classification for transverse root fracture

AbstractTransverse root fracture (TRF) is classified as a cervical, middle or apical third root fracture on a periapical radiograph. The International Association of Dental Traumatology (IADT) suggests that cone‐beam computed tomography (CBCT) should be considered, when conventional radiographs provide ‘insufficient’ information for diagnosis and treatment planning. Considering that CBCT can divulge additional information, it would be beneficial to have a clinically pertinent three‐dimensional classification for TRF. The proposed alphanumeric classification includes the traumatised tooth number, describes the number of TRF, the facial and lingual location of each fracture line on the anatomic root, and its position relative to the crest of the alveolar bone. Further, diastasis, displacement of the coronal fragment and status of the alveolar bone at the site of TRF are also documented. This comprehensive classification system would provide a standard format for reporting, aid in referral communication and can be applied for future outcome studies on TRF.

Insights into the genetic characteristics, clustering patterns, and phylogeographic dynamics of the JC polyomavirus, 1993 to 2023

The human JC polyomavirus (JCV) is a widespread, neurotropic, opportunistic pathogen responsible for progressive multifocal leukoencephalopathy (PML) as well as other diseases in immunosuppressed individuals, including granule cell neuronopathy, JCV-associated nephropathy, encephalitis, and meningitis in rare cases. JCV classification is still unclear, where the ICTV (International Committee on Taxonomy of Viruses) has grouped all the strains into human polyomavirus 2, with no classification on clade and subclade levels. Therefore, JCV strains were previously classified using different genomic regions, e.g., full-length, VP1, and the V-T intergenic region etc., and the strains were grouped into several types related to various geographic locations and human ethnicities. However, neither of these classifications and nomenclature contemplates all the groups described so far. Herein, we evaluated all the available full-length coding genomes, VP1, and large T antigen nucleotide sequences of JCV reported during 1993–2023 and classified them into four major phylogenetic clades, i.e., GI-GIV, where GI is further grouped into two types GI.1 and GI.2 with five sub-clades each (GI.1/GI.2 a-e), GII into three (GII a-c), GIII as a separate clade, and GIV into seven sub-clades (GIV a-g). Similarly, the phylogeographic network analysis indicated four major clusters corresponding to GI-GIV clades, each with multiple subclusters and mutational sub-branches corresponding to the subclades. GI and GIV clusters are connected via GI.1-e reported from Europe and America, GII, GIII and GIV clusters are connected by GII-b and GII-c strains reported from Africa, while GIV cluster strains are connected to the Russia-Italy JCV haplotype. Furthermore, we identified JCV-variant-GS/B-Germany-1997 (GenBank ID: AF004350.1) as an inter-genotype recombinant having major and minor parents in the GI.1-e and GII-a clades, respectively. Additionally, the amino acid variability analysis revealed high entropy across all proteins. The large T antigen exhibited the highest variability, while the small t antigen showed the lowest variability. Our phylogenetic and phylogeographic analyses provide a new approach to genotyping and sub-genotyping and present a comprehensive classification system of JCV strains based on their genetic characteristics and geographic distribution, while the genetic recombination and amino acid variability can help identify pathogenicity and develop effective preventive and control measures against JCV infections.

Discovery and Classification in Astronomy: Scientific and Philosophical Challenges and the Importance of a Comprehensive and Consistent Classification System

Throughout history, the definition of “class” and the construction of astronomical classification systems has been a deep scientific and philosophical problem: scientific because facts such as physical composition ideally need to be known for proper classification but often are not, philosophical because astronomers need to understand the philosophical assumptions behind their attempts at classification, and because different philosophical ideas such as “natural kinds” often guide classification, even if unconsciously. The primary lesson of history is that the most useful classifications of celestial objects are optimally based on their physical nature. The second lesson is that because discovery is an extended process consisting of detection, interpretation, and understanding, initial classifications may be phenomenological, based on characteristics that may be useful in early “detection” stages of extended discovery. By contrast, final classifications of “the thing itself,” is achieved only after the “understanding” stage of discovery and must have a physical basis. A third lesson is that class status is best determined within a comprehensive classification system in order to determine taxon level, e.g., class, type, subtype. Such a system, encompassing all astronomical objects, illustrates the problems of class and classification, problems that may be applied to exoplanet discoveries.

Understanding precipitation during in-situ and post-heat treatments of Al-Mg-Sc-Zr alloys processed by powder-bed fusion

This study investigates the evolution of Sc-rich precipitates in Scalmalloy® processed using Powder Bed Fusion (PBF) additive manufacturing. By combining microstructural analysis, thermodynamic calculations, and an adapted precipitation model, we examine how precipitates change during solidification, in-situ heat treatment (IHT), and post-heat treatment (PHT). We establish a comprehensive classification system for primary and secondary Sc-rich precipitates based on their origin, location, morphology, composition, interaction with other precipitates, and size. Notably, primary precipitates are observed within the fine-grained (FG) zone, and their characteristics suggest further modification during IHT and PHT. The developed precipitation model, coupled with multi-scale thermal simulations, accurately predicts the formation of small homogeneous secondary L12-Al3Sc precipitates during PBF and PHT, showcasing its potential as a powerful tool for optimizing PBF processes for components with complex geometries and different thermal conditions. Our findings reveal that while the employed IHT conditions did not induce secondary precipitation, subsequent PHT at 400°C for 1 hour led to the formation of secondary precipitates via both continuous and discontinuous mechanisms. Addressing limitations in understanding primary precipitate formation during PBF and incorporating both homogeneous and heterogeneous mechanisms in future models are crucial for a deeper understanding of Scalmalloy® precipitation behavior and PBF process optimization.