Kaposi sarcoma (KS) is among the most common childhood cancers in Eastern and Central Africa. Pediatric KS has a distinctive clinical presentation compared with adult KS, which includes a tendency for primary lymph node involvement, a considerable proportion of patients lacking cutaneous lesions, and a potential for fulminant disease. The molecular mechanisms or correlates for these disease features are unknown.This was a cross-sectional study. All cases were confirmed by IHC for KS-associated herpesvirus (KSHV) LANA protein. Baseline blood samples were profiled for HIV and KSHV genome copy numbers by qPCR and secreted cytokines by ELISA. Biopsies were characterized for viral and human transcription, and KSHV genomes were determined when possible.Seventy participants with pediatric KS were enrolled between June 2013 and August 2019 in Malawi and compared with adult patients with KS. They exhibited high KSHV genome copy numbers and IL-6/IL-10 levels. Four biopsies (16%) had a viral transcription pattern consistent with lytic viral replication.The unique features of pediatric KS may contribute to the specific clinical manifestations and may direct future treatment options.US National Institutes of Health U54-CA-254569, PO1-CA019014, U54-CA254564, RO1-CA23958. Childhood cancers are infrequent in the US and Europe, with an incidence of 13–16 cases per 100,000 children overall and leukemia being the most common diagnosis. The situation is starkly different in sub-Saharan Africa (SSA), where Kaposi sarcoma-associated herpesvirus (KSHV) is endemic. KSHV is transmitted from mother to child before puberty. In SSA, early-age immune challenges, such as malaria and, more recently, HIV infection, are common. Burkitt lymphoma is the most recognized pediatric viral cancer in SSA, ever since Denis Burkitt described it in 1958 in Uganda and Michael Anthony Epstein and Yvonne Barr associated this endemic childhood neoplasia with Epstein-Barr virus (EBV) (reviewed in ref. – classic KS and pediatric KS (HIV+ and HIV–) (Kaposi sarcoma (KS) emerged as an AIDS-defining disease in the US and Europe in the 1980s . It was nd HIV–) . KSHV wand HIV–) , 6. SincPediatric KS is one of SSA’s most common childhood cancers, with a median incidence of 67.35 per 100,000 HIV-infected children , as 90% + and HIV) constitutes a gap in our knowledge that this study aims to close.Pediatric KS can have a similar clinical presentation as adult KS, with visceral, oral, and skin lesions ; howeverEarlier, we proposed a pediatric-specific KS classification . PediatrThis report describes a cohort of 70 pediatric patients with KS enrolled between June 2013 and August 2019 at Kamuzu Central Hospital in Lilongwe, Malawi. Clinical and laboratory conditions are compared with adult KS encountered at the same location and time. This comparison highlights the broad spectrum of pediatric KS clinical presentation, typically higher KSHV viral load but a similar transcription profile. We also obtained the first 11 complete KSHV genomes from pediatric KS tumors and showed they cluster with adult KSHV strains from the region. These studies provide biological plausibility to transferring adult treatment regimens, such as pomalidomide and paclitaxel , 29, to https://doi.org/10.1172/jci.insight.167854DS1). HIV status was ascertained per Malawi guidelines using a rapid test or PCR.Seventy participants with pediatric KS were enrolled between June 2013 and August 2019 at Kamuzu Central Hospital in Malawi. Diagnostic biopsies were subjected to LANA staining to confirm the KS diagnosis. All samples, regardless of the biopsy site, skin, lymph, or tonsil, showed robust LANA staining (– (n = 14) and HIV+ (n = 56) participants separately.The characteristics of the cohort are presented in n = 34) and adult patients with KS seen at the same location between 2008 and 2019 (n = 207). Like in the US . Hence, it is crucial to consider the total cell count of these patients. This measure was available for only a subset of participants. When the HIV viral load was compared with the percent of CD4 cells rather than total CD4 counts, a negative correlation was observed .P value = 0.003) with a difference between means of (pediatric versus adults) 14,505 ± 4,815 copies/mL , IL-10 at 96.87 ± 170.7 pg/mL  at baseline, respectively  to adult KS biopsies (n = 77) (+ KS lesions clustered in 2 subtypes: group 1 (blue) and group 2 (yellow). These are described in 2 fold change greater than 10 and an FDR-adjusted –log10P value greater than 99  . Samples than 99 . The g:P than 99  was used than 99 . This wa than 99  due to aWe also determined the complete genomic sequence of KSHV from 11 biopsies . The reaThe 65,846 bp long unique region (LUR) region of 100 full-length KSHV genomes presently available in GenBank was used to construct a phylogenetic tree using Beast . The 11 KS is about as common in SSA as prostate cancer is in the US. This is due to the high prevalence and early acquisition of KSHV and the unresolved HIV epidemic on the African continent  are considered the first-line therapy (+ KS (IL-6 and IL-10 were often but not consistently elevated in pediatric KS. These 2 cytokines are deeply involved in KSHV pathobiology. They are part of the diagnostic criteria for multicentric Castleman disease (MCD) and KICS, which are seen concurrently with HIV+ KS , 24, 55. therapy , althoug therapy . Followi therapy , 59, andpy  as per our prior publications .Tumor DNA was extracted using the QIAamp DNA FFPE Tissue Kit (Qiagen) per manufacturer protocol with 3 hours of proteinase K digestion (20 μL/sample) to increase DNA yield. PBMC DNA was extracted using a Roche MagNA Pure Compact Instrument  and Nucleic Acid Isolation Kit I -Large Volume . To serve as a control for DNA extraction efficiency, 10 μg of a control plasmid, Fly 2.0 , was added to each sample.IL-6, IL-8, and IL-10 were quantified as previously described  using thFollowing DNA extraction, real-time qPCR was performed to determine the genome copy number. Primers were obtained from Eurofins Genomics and used at 125 nM final concentration: KSHV, LANA78-F GAGCCCATAATCTTGCACGG and LANA78(2)-R GCCTCATACGAACTCCAGGT; EBV , EBNA3C-Total DNA was quantified by Qubit 3.0 dsDNA HS Assay (Life Technologies). Ion AmpliSeq primer pools (Life Technologies) were designed to amplify the viral genome based on KSHV BAC16 . Samples were sequenced on the Ion Torrent S5 (Life Technologies) with default parameters. Ion Torrent barcodes and adapters were removed. Next, reads were trimmed by low-quality bps , including 40 nucleotides at the 5’ terminal and 10 at the 3’ terminal. Reads shorter than 50 nucleotides were filtered out. Longer reads were mapped to the KSHV genome (GenBank accession number NC_009333) using CLC Genomics Workbench v20.0.3 (Qiagen) with default parameters (length fraction of 0.5 and similarity 0.8). Nonspecific reads were ignored. Duplicate mapped reads were collapsed. SNVs, multiple nucleotide variants (MNVs), and InDels were called using the Basic Variant Detection tool with default parameters. SNVs with a Phred score greater than 20, a minimum frequency greater than 60%, a minimum coverage greater than 40, and a minimum forward/reverse balance greater than 0.05 are reported. Consensus sequences were built on CLC Genomics Workbench (v20.0.3) based on quality score voting, with low-coverage regions being defined as regions with a minimum of 3 reads. Low-coverage regions were filled from the reference sequence (NC_009333) and annotated.Biopsy Tissues were stored in RNAlater at –80°C. Tubes were thawed and approximately 30 mg tissue was lysed in 2 mL screw-top tubes containing 300 μL RLT , 0.05% DX Reagent  and 3.0 mm (about 0.12 in.) stainless steel balls. Tissues were homogenized for 3 minutes in a TissueLyser  for an additional 2 minutes, if necessary. Quantity and quality were accessed by Nanodrop (Thermo Fisher Scientific), Qubit 3 (Life Technologies), and 4200 TapeStation (Agilent Technologies). Ribosomal RNA was removed using RiboMinus Eukaryote System v2 and/or Low Input RiboMinus Eukaryote System v2 . Library preparation was performed using the Ion Total RNA-Seq Kit v2 protocol . Libraries were sequenced on the Ion Gene Studio S5 Prime System. Both adult and pediatric KS cases were sequenced simultaneously in the same facility; however, while the adult cases were analyzed using a human exome target approach , the pediatric cases were analyzed using complete RNA-Seq  according to the manufacturer’s recommendations.P value of less than 0.05.Analyses steps included quality control using bbduk version 37.25, mapping to reference genome , with read counting on genes . Raw counts were converted to RPKM. Differential gene expression was calculated using DESeq2, working with a simple interaction term for the model: design = ~ drug + clone × number. PCA, distance heat maps, and other figures were generated based on RPKM in R using DESeq2  and ggplot2. Genes were considered differentially expressed by the Wald test, with an adjusted (Benjamini-Hochberg) The phylogenetic tree was constructed using previously published KSHV genomes of African isolates Zambia , Uganda t test with Welch correction  was used to compare groups.Results are reported as mean ± SD. All cellular experiments were repeated, resulting in 3 complete biological replicates. An unpaired 2-tailed This study was approved by the Malawi National Health Sciences Research Committee, protocol 17/03/1750, and the BCM IRB, protocol H-41274. For children above 5 years of age, we obtained assent from the child as well as consent from the parental guardian. Both the consent and assent forms were available in English and Chichewa, the national language of Malawi.https://bitbucket.org/dittmerlab/2021_naderpediadricks/src/master/ Values for all data points in graphs are reported in the The raw sequencing data were deposited in the NCBI’s Sequence Read Archive (SRA BioProject PRJNA975091). The adult KS data are available under Bioproject PRJNA947563. Viral genome sequences have been submitted to GenBank . All computations are available in the bitbucket repository: CCV designed and performed experiments, analyzed data, and wrote the manuscript. AJ performed experiments. AP and RM performed the formal analysis of the study data. TT and CLK provided laboratory samples. AS, WK, JV, CLM, RM, ECPG, LRC, PSM, and PNK were part of the research and investigation process. CEA, MES, and NWO provided financial support for the project. DPD and NKEM designed experiments, analyzed data, provided financial support, and contributed to the final version of the manuscript. All authors approved the final manuscript.


The natural growth rate of populations enables them to produce more offspring that can be supported by the environment. This idea provoked In ecology, as Fundamentally, organisms are self-replicating dissipative structures . To moreThe central process in all nature  is dissipation of free energy according to the second law of thermodynamics, and evolution therefore is better conceptualized as the emergence of self-replicating dissipative structures that through natural selection become increasingly more efficient at degrading free energy and reproducing themselves. Thermodynamically, organisms do not struggle in a competition for survival. Organisms emerge from the cooling of the Universe as a result of the process of dissipation of free energy. As The scope of this Research Topic includes entropy in biological systems, information theory in evolution, linkage between complexity and entropy in biological systems, and entropy and dissipation in adaptation. The goal of the Research Topic is to focus attention on the fundamental role of thermodynamics, and in particular the second law, in biological evolution. Despite what we consider to be the central importance of this Research Topic to the future of evolutionary biology, our Research Topic was only able to successfully obtain four published contributions. This, we believe, reflects the entrenched subdisciplinary focus of evolutionary biology, with major branches focusing on molecular mechanisms of evolution, population biology, and linking genomic and epigenomic variation to fitness in different environmental contexts. All of these are highly relevant, but a paradigm shift to refocusing evolutionary biology within a thermodynamic context has not yet occurred. The contributions in this Research Topic, however, we hope will spur new attention on this subject and provoke research at the synergistic margins between the fields of molecular biology, informatics, ecology, and thermodynamics.Fibla et al. reported on geohistorical boundaries among human populations, with a particular focus on populations in the rural Catalan Pyrenees. The authors analyzed 726,718 autosomal single nucleotide variants in 435 individuals from the Catalan Pyrenees. Their results show that at a macro-geographic scale there is a consistent genetic gradient across Spain. However, their analysis also identified finer-scale, nested substructure among the sampled individuals which did not correlate with geographic barriers, such mountain ranges. The fine scale structure was correlated, conversely, with historical administrative units, which suggests that socio-cultural factors have strongly shaped the genetic diversity observed in rural populations. This result is important in investigating and separating geographical, historical and sociological factors that influence the genetic structure of human populations. This kind of spatial analysis of genetic variation is important in understanding the history of population migration and the factors that drive it, which is a fundamental question in landscape genetics, and a key to understanding the interplay between drift, gene flow and selection in evolution , the authors examined the global population structure, finding strong genetic homogeneity in some species across large regions of the ocean that appear to be subdivided by physical barriers, while also finding substructure in other species across areas showing little apparent physical discontinuity. Consistent with the findings of Fibla et al., this spatial analysis of morphological and genetic divergence in relation to the structure of the physical environment is a fundamentally important question in spatial evolutionary theory. As Johnson et al. provided novel and insightful analysis of spatial patterns of evolutionary divergence in related taxa across ocean gradients. The linkage between these patterns and thermodynamic ideas was not formally explored in the paper, however.Cruz-Rosas and Miramontes more directly focused on the ideas of spatial information, complexity and biological evolution. The authors began by noting that the information that describe living systems reflects the complex interactions between internal organization and the functional performance of organisms. They then focused on the so-called origin of life problem, considering how prebiotic dynamics of matching and transfer of molecular shapes may emerge as a flow of information in prebiotic assemblages. This is a direct application of the paradigm of self-replicating dissipative structures as the focal Research Topic in evolutionary biology. The authors argued that propagation of resilient conformations could be the substrate for structural maintenance through a dynamical molecular scaffolding. They argued that this framework enables a theoretical integration from non-structured populations of polymers, to a theoretical framework that integrates the active role of these polymers in the emergence of adaptive dissipative structures, life and organisms. Response in systems that manage conformational information flow. The ideas proposed by Cruz-Rosas and Miramontes provide a framework for considering the emergence of order through the action of dissipative structures evolving increased information content through heritability and natural selection. As such, they are important and exciting contributions to the thermodynamic perspective of biological evolution as the emergence and adaptation of self-replicating dissipative structures.The third paper in the Research Topic De la Fuente et al., who explored self-organization and information processing at scales ranging from basic enzymatic activities to complex adaptive cellular behavior. They noted that understanding the origin of molecular structure and organization that underlies the complex dynamic architecture of cellular life is a fundamental question in biology and evolution. They proposed a system of biomolecular order and complexity spanning from the most elementary levels of molecular activity to the emergence of cellular systemic behaviors that integrates complexity, information theory and thermodynamic processes underlying the evolution of life. They formally focused on dissipative self-organization, which they correctly and profoundly identified as the principal source of molecular order in the cell. They then proposed that molecular information processing is the second fundamental source of biochemical order, which emerges from the biochemical dissipative self-organization. The authors suggested that effective connectivity based on Transfer Entropy is a useful framework to quantify biomolecular information flows in dissipative metabolic networks, which they proposed as a mechanism providing self-regulatory control of metabolism. They concluded that the functional structures of the cell emerge as a consequence of both main sources of order, and that quantitative analyses of dissipative metabolic networks can provide a powerful paradigm to explore the basic units of life. De la Fuente et al. also described how this dissipative metabolic structure has been verified experimentally in both prokaryotic and eukaryotic cells, and discussed using Artificial Intelligence and advanced tools of Statistic Mechanics to describe these systemic dissipative metabolic networks. This is a remarkable paper in terms of the scope and vision of its effort to integrate thermodynamic processes of dissipative structures into theoretical models of the emergence and evolution of molecular and cellular structure within the context of energetics.This theme of self-organization of thermodynamic systems as a focal paradigm for evolutionary biology was further developed in In the thermodynamic perspective organisms are complex systems of self-replicating dissipative structures  that have emergent properties of dissipative structures themselves . OrganisIt is indeed the action of the second law through the process of emerging thermodynamic dissipative structures that allows all biological structure and processes. Boltzmann himself (widely seen as the father of the modern study of entropy) realized this profound implication very early in the study of thermodynamic entropy. He wrote “The general struggle for existence of animate beings is not a struggle for raw materials, these for organisms are air water and soil, all abundantly available, nor for energy which exists in plenty in the Sun and any hot body in the form of heat, but rather a struggle for entropy [exergy], which becomes available through the transition of energy from the hot Sun to the cold earth”. In this quote Boltzmann explicitly frames life as a thermodynamic system driven by entropy. He also wrote: “Thermodynamics, correctly interpreted, does not just allow Darwinian evolution; it favors it”. and “Available energy is the main object at stake in the struggle for existence and the evolution of the world”. Boltzmann emphatically stated that “In my view all salvation for philosophy may be expected to come from Darwin’s theory”. because “All structures and events correspond to the evolution of the Universe through successive states of increasing probability”. This is the deep underlying process that drives all natural change, and the emergence of all order and structure, including life itself. To rephrase the iconic last sentence of the Origin of Species into the paradigm of dissipative structures and the second law: There is a grandeur to this view of things, with its simple, undirected process underlying all creation and all structure, and whilst the cosmos goes on unwinding, according to the second law of thermodynamics, endless forms are being and will be emergent.


Why does online distrust  continue to grow despite numerous mitigation efforts? We analyzed changing discourse within a Facebook ecosystem of approximately 100 million users who were focused pre-pandemic on vaccine (dis)trust. Post-pandemic, their discourse interconnected multiple non-vaccine topics and geographic scales within and across communities. This interconnection confers a unique, system-level  resistance to mitigations targeting isolated topics or geographic scales—an approach many schemes take due to constrained funding. For example, focusing on local health issues but not national elections. Backed by numerical simulations, we propose counterintuitive solutions for more effective, scalable mitigation: utilize “glocal” messaging by blending (1) strategic topic combinations  and (2) geographic scales . The pandemic exacerbated this issue as many people turned to their trusted online communities for advice and to share distrust of official health messaging18. Within a month of the U.S. national emergency declaration19, Facebook—the largest and most widely used social media platform—saw a 50% increase in messaging and 70% increase in time spent20, driving its monthly active users to 2.6 billion.Distrust and its associated mis/disinformation—however defined—is now a widespread threat to public health ), science , election processes and even national security38. For instance, Facebook has funded internal efforts like adding misinformation labels to posts25, while The Mercury Project, spearheaded by the Social Science Research Council with support from philanthropic organizations like The Rockefeller Foundation, Robert Wood Johnson Foundation, and Craig Newmark Philanthropies, has funded vaccine promotion campaigns28. Depending on a mitigation scheme's funding source, its focus is typically on a specific topic , and geographical scale, such as a specific state, national , or worldwide . However, despite the diversity of mitigation schemes, distrust continues to be widespread.To combat the growth of online distrust and mis/disinformation, myriad ingenious mitigation strategies have been introduced and implemented on social media platforms39. Given that such schemes also operate independently, this suggests widespread distrust would remain resilient even if these schemes were implemented at mass scale. We show this in Figs. 40. Combining this with an agent-based simulation, Fig. Here we provide an answer to this question, and the counterintuitive solution that this answer suggests. Specifically, we show that post-pandemic distrust has developed a massive glocal web that—within individual communities and across interconnected communities—blends distinct topics, locations, and geographic scales. This makes it resilient to current mitigation schemes that only focus on a specific topic or geographic scale (e.g. due to funding mandate)40 that had centered around vaccines and comprised interlinked anti-, pro-, and neutral-vaccination Facebook pages. Our full methodology is given in the SI and follows Ref.40. Each Facebook page is a community  are mentioned, their frequency is generally much lower . To categorize the discourse topics within each community, we developed word filters for five non-vaccine topics: COVID-19, mpox, abortion, elections, and climate change. By 'non-vaccine' we mean topics not centered on vaccines in a broad sense; although the COVID-19 and mpox filters included some vaccination-related terms , the intent was to capture discourse specifically about these diseases, not vaccines generally. After all, if we had been filtering for general vaccine discussion rather than disease-specific discourse, it would have been difficult to reliably distinguish COVID-19-related posts from mpox-related posts in an automated manner without additional human classification. The filters combined regular expressions and keyword searches across post content, descriptions, image tags, and link text in multiple languages  7—however, our analysis and findings are not dependent on this.Of the 1356 communities, 342 identify as local and have around 3.1 million individuals, while the remaining 1014 communities are global with around 83.7 million individuals. In terms of geography, a global community is a page with broad, worldwide focus that is not tied to a specific location, while a local community is focused on a specific geographic area, such as a neighborhood, city, county, state, or country . In terms of topic, a global community discusses diverse issues broadly, whereas a local community has a narrow topical focus . The size of each community can be estimated by the number of likes, given that the average user only likes one Facebook page on averageThus, the terms ‘global’ and ‘local’ are applied in two dimensions—geographic and topic—to highlight the interconnection between geographic and topic glocality. This dual usage of ‘global’ and ‘local’ elucidates how geographic and topic glocality may interrelate and allow communities to occupy different glocal positions. For instance, a community focused on a narrow topic within a small locality embodies hyperlocality on both dimensions, while one that discusses many topics worldwide embodies hyperglobality. Our analysis aims to demonstrate that post-pandemic, vaccine skepticism discourse expanded beyond just hyperlocal geography and topics to encompass more hyperglobal geography and topics. The terms ‘global’ and ‘local’ effectively convey this evolution across both dimensions.38. For instance, Facebook has funded internal efforts like adding misinformation labels to posts, while the Mercury Project has conducted vaccine promotion campaigns28. Many strategies operate at a national level, like the UK's ‘Don't Feed the Beast’ campaign23 while others have a global focus, like the United Nations' VERIFIED initiative26. The strategies aim to limit the spread and impact of misinformation by encouraging critical thinking, accurate reporting, and authoritative voices. However, persistent exposure to narratives via social connections can enable reactivation of silenced misinformation.In our numerical simulations, ‘mitigation’ refers to efforts that aim to counter mis/disinformation, such as fact-checking, verification, public awareness campaigns, collaborative initiatives, research, and global programs, rather than banning communities. These strategies have been implemented to address false narratives related to topics like COVID-19, public health, elections, and climate changeThus, ‘deactivation’ refers to temporarily 'silencing' communities by removing their network links as part of a mitigation campaign focused on a particular topic or locality. However, 'reactivation' allows deactivated communities to resume discussing the targeted topic based on continued exposure to narratives via their remaining network connections. This represents the limited, temporary effectiveness of real-world debunking efforts, with misinformation narratives persisting over time. Our agent-based model thus simulates the effects of different mitigation strategies by ‘deactivating’ communities through removing their network connections, then allowing ‘reactivation’ based on remaining links. The geography-focused simulation tests the impact of messaging targeted at either local or global communities, whereas the topic-focused simulation compares single versus multi-topic debunking  the U.S. Supreme Court's reversal of Roe v. Wade57; (3) President Biden signing into law the Inflation Reduction Act58; (4) primary and run-off elections ahead of the November midterms59. Communities that share more links appear visually closer together and have a higher likelihood of exerting influence on each other through shared content and infiltration. This is because the layout results from a color-agnostic physical calculation (ForceAtlas2) in which nodes repel each other with a force that decays with separation, and linked nodes have an additional attractive spring force60.Figure This web-of-distrust entangles 5 dominant topics within and across communities: abortion, mpox, COVID-19, climate change, elections (panels C–G). This means that within a given community, distrust in one topic can immediately be reinforced by distrust of another topic(s) as well as by the collective distrust of other communities to which it is linked. Hence post-pandemic intervention on one topic and scale invites pushback from distrust on other topics and across scales, making the distrust ecosystem resilient to mitigation schemes that focus on a particular topic and geographic scale. For example, distrust of state elections within a community associated with a small U.S. city is reinforced by distrust in the U.S. federal government on mpox, which is then reinforced by distrust over climate change from another community representing itself as the U.K. mainstream. This global-local entanglement across topics and scales adds new resilience to the distrust ecosystem.Figure Moreover, traditional anti-vax communities—a third of which are local—have a lower interest in abortion Fig. C. ClimatFigure Figure 62: the strategic blending of topics helps suppress distrust around other issues. By examining the relative size of communities over time, we uncover occasional breakthroughs where pro-groups temporarily gain control of specific topic interactions, despite their smaller overall numbers at the 2-topic level (SI Sect. The heatmaps in Fig. SI Sect. .Figure Nodes can then reactivate and resume targeted discussions based on network connections to other active nodes, representing the limited effectiveness of real-world debunking. Specifically, reactivation likelihood is determined by the proportion of a node's connections still actively discussing the targeted topic or locality. This reactivation component, based on ongoing content exposure through network links, captures the stubborn persistence of narratives despite isolated mitigation attempts. Both simulations were performed over 1500 iterations, with results averaged (see SI Sect. In Fig. We thus find that post-pandemic, the Facebook communities originally focused on vaccines strongly entangle multiple non-vaccine topics and geographic scales both within and across communities. As demonstrated by our simulations, this gives the current distrust ecosystem a unique system-level resistance to mitigations that target a specific topic or geographic scale—which is the case of many current schemes due to their funding focus. The geographic scale simulation Fig. A shows i38. Instead, effective mitigation may require “glocal” messaging combining strategic topic and scale mixes. For example, pairing diseases with climate change, or local and national focuses.Our results reveal new insights into the structure and shifts within the online vaccine distrust ecosystem on Facebook, pointing to potential improvements in mitigation strategies. We analyzed a large Facebook ecosystem of ~ 100 million users focused pre-pandemic on vaccine attitudes. Post-pandemic, their conversations blended multiple topics and geographic scales, conferring system-level resistance to targeted interventions. This highlights gaps in current approaches, which are often constrained by the narrow funding focuses of supporting entities. For example, government-funded efforts typically target misinformation only on certain topics relevant to public health or elections63, however SI Sect. Our dataset represents only select languages popular on FacebookSupplementary Information.


An immense volume of digital documents exists online and offline with content that can offer useful information and insights. Utilizing topic modeling enhances the analysis and understanding of digital documents. Topic modeling discovers latent semantic structures or topics within a set of digital textual documents. The Internet of Things, Blockchain, recommender system, and search engine optimization applications use topic modeling to handle data mining tasks, such as classification and clustering. The usefulness of topic models depends on the quality of resulting term patterns and topics with high quality. Topic coherence is the standard metric to measure the quality of topic models. Previous studies build topic models to generally work on conventional documents, and they are insufficient and underperform when applied to web content data due to differences in the structure of the conventional and HTML documents. Neglecting the unique structure of web content leads to missing otherwise coherent topics and, therefore, low topic quality. This study aims to propose an innovative topic model to learn coherence topics in web content data. We present the HTML Topic Model (HTM), a web content topic model that takes into consideration the HTML tags to understand the structure of web pages. We conducted two series of experiments to demonstrate the limitations of the existing topic models and examine the topic coherence of the HTM against the widely used Latent Dirichlet Allocation (LDA) model and its variants, namely the Correlated Topic Model, the Dirichlet Multinomial Regression, the Hierarchical Dirichlet Process, the Hierarchical Latent Dirichlet Allocation, the pseudo-document based Topic Model, and the Supervised Latent Dirichlet Allocation models. The first experiment demonstrates the limitations of the existing topic models when applied to web content data and, therefore, the essential need for a web content topic model. When applied to web data, the overall performance dropped an average of five times and, in some cases, up to approximately 20 times lower than when applied to conventional data. The second experiment then evaluates the effectiveness of the HTM model in discovering topics and term patterns of web content data. The HTM model achieved an overall 35% improvement in topic coherence compared to the LDA. Topic modeling aims to discover topics within a collection of digital textual documents. Several fields, such as the Internet of Things, Blockchain, recommender systems, software engineering, digital humanities, and political science, apply topic modeling to efficiently detect topics in large collections of data . ApplyinExisting topic models have three types of design: a specific design, a generic design, and an integrative design. The specific topic model works best on what they have been designed for, such as Twitter , news Y, reviewsThis study aims to propose an innovative topic model to learn topics in web content data. We present the HTML Topic Model (HTM) that takes into consideration the HTML tags to understand the structure of web pages. HTML tags, such as <title>, <metadata>, ,
,
,
, and , contain very short textual contents. Unlike conventional text documents, combining textual contents from HTML tags results in a sparse and incoherent pattern . The HTMThis study has four research questions: Do the benchmark topic models perform well and generate coherent topics when applied on web content data? Which of the benchmark model have steady performance when applied to web content data? Does considering HTML tags increase the quality of the generated topics applied on web content data? Does the proposed HTM model have better metrics values than the other topic models? In order to answer these research questions, we conducted two experiments. For both experiments, we used topic coherence for performance evaluation. Topic coherence measures the consistency and quality of each individually generated topic. The first experiment examines the performance of the benchmark topic models when working with the web content data and highlights their underperformance. The results indicate the essential need for a web content topic model to generate more coherent topics for web pages. When applied to web data, the overall performance dropped up to approximately 19 times lower than when applied to conventional data. The result of this experiment answers the first and second research questions of this study. The second experiment evaluates the enhancements made by the HTM in discovering topics and term patterns of web content data. The HTM yields more meaningful topics given a number of metrics and achieved an overall 36.7% improvement in topic coherence compared to the LDA. The result of this experiment answers the third and fourth research questions of this study.The organization of the article is as follows. Section 2 follows with a description of the existing topic models. Section 3 describes the proposed HTML topic model (HTM). The methodology and results of evaluating the existing topic models and the proposed HTM are presented in Section 4 and 5. Section 6 concludes and summarizes the article.An immense volume of hypertext and digital documents exist online and offline with content that can offer useful information and insights. Analyzing and understanding the collection of such enormous content, however, is nearly impossible for an individual with traditional methods. Topic modeling, a text mining tool, aims to discover latent semantic structures or topics within a set of textual digital documents. Topic models are widely applied on the Internet of Things, Blockchain, chatbots, bioinformatics, recommender system, spam filtering, summarization, sentiment analysis, text categorization, text similarity, service matchmaking, and classification .Researchers designed several topic models, some of which are generic and comprehensive, such as the LDA and the HLDA. Other models, in contrast, are designed to work with particular topics or specific tasks, such as the Author-Topic Model and the Twitter-LDA. Despite design differences and applications, topic models are Vector Space Model (VSM) based. This section categorizes the VSM topic models based on their underlying statistical model, as The Latent Semantic Indexing (LSI) model was the first statistical model for grouping co-occurrence terms in documents. The LDA and its variants are prominent models in the field of topic modeling due to their outstanding performance. Several LDA variants exist for specific tasks, such as modeling tweets , web serGeneric variant models of the LDA have also been proposed and applied in various tasks and applications. A few studies address the application of the LDA or its variant models on web content data . Those uIt is worth mentioning that several other models, such as BERTopic and word-embedding models, are somewhat related to the benchmark models of this study. However, several key factors support excluding them from this study. The BERTopic model relies on pre-trained Bidirectional Encoder Representations from Transformers (BERT) and employs a unique strategy for word representation. This study focuses on modelling topic distribution with LDA and its variants, whereas BERTopic captures contextual semantics. The fundamental differences between mathematical foundations and topic representation methods preclude them from our experiments. Moreover, despite their effectiveness in clustering words, word embeddings are not explicitly designed for topic identification. This study seeks to propose a novel topic model for the analysis of coherent web content while highlighting the limitations of established benchmark models. By adhering to our research objectives, we provide valuable insight into the need for innovative topic modelling techniques in the field of web mining.Web mining is discovering and extracting information and knowledge from website documents using data mining methods and techniques . It is aWeb content mining focuses on the contents of a website itself. Websites contain several data types; text, hypertext, image, video, and audio, which together constitute a topic or more inside the webpage. Literature has utilized topic modeling on web content data for various applications, such as clustering , classifSeveral studies have proven the benefit of topic modeling in classifying and clustering web pages and websites . The traTopic modeling is also utilized to discover trending topics in a specific subject area or speciality. Several recent studies have used the LDA topic model to discover the popularity of topics in a specific field, such as the IoT and Industry 4.0 , terms oTopic modeling also identifies the most common topics that are being discussed in Question and Answer (Q&A), rating, and review websites. A common drawback of the abovementioned studies is that they have only utilized topic modeling based on textual documents that have been extracted from web pages. However, they have not considered the different structures of web content data. Neglecting the unique structure of web content leads to missing the otherwise topics and, therefore, low topic quality, as the first experiment demonstrates in Section 5.1.A few recent studies, however, have addressed the unique structure of web content when applying topic modeling. A web page represents web contents in a hypertext document provided by a website, usually comprising several web pages. HTML tags represent webpages, which constitute their hyperlinked structures and their textual contents. These tags, such as <title>, <metadata>, , ,
,
,
, and  normally contain very short textual contents. Unlike conventional text documents, combining these textual contents from these tags results in a sparse and incoherent document. The sparseness and incoherence create challenges and cause the traditional text mining and topic model methods to be ineffective for web content mining .The HTM topic model is based on the LDA and aims to enhance the performance of topic modeling for web content-based data. The HTM topic model considers the structure of the textual contents within the HTML tags to extract topics from a web page. HTML tags are usually used to add textual content to the webpage. The HTML tag element consists of a start tag, end tag, attribute name, attribute value, and textual content, as This section describes the problem formally and addresses the used notations in the following subsections. The definition of the problem is as follows:Consider a collection of webpages WPi is the i-th webpage of a dataset collection D, and p is the number of web pages in the collection. Each of these web pages is composed of HTML tags where TGi is the i-th HTML tag of a webpage WP and t is the number of HTML tags in the webpage.where An HTML tag topic in a given webpage is the distribution of all words relating to this webpage and can be represented as, Taking a sports news webpage as an example, which includes many HTML tags, each may include different sub-topics. However, some tags in the webpage may include some other recent news and the side of the webpage for users to read. This news can be related to sports as well as other topics such as political news, health news, and many others. In this case, taking the webpage as one piece could give low topic coherence and, therefore, generate low topic quality. The webpage topic modeling problem aims at finding topics that occur on a webpage and ensures that the generated topics are semantically coherent. The final goal of this article is to learn coherence topics in web content data for a given webpage.Before introducing the generative process and the mathematical explanation of the model, 1, …, Wℓ) of length ℓ ∼Poisson(ξ), where ξ is pre-specified. For this webpage WP, a K-dimensional probability vector θ with non-negative coordinates summed to one is used to model the topic mixture. Three probability distributions are assumed to be multinomial distributions: p(z—wp), p(z—tg), and p(w—z). Therefore, the topic distributions in all web pages share the common Dirichlet prior α, and the word distributions of topics share the common Dirichlet prior β. Given α and β as the parameters for webpage WP, parameter θwp of a multinomial distribution over K topics is constructed from Dirichlet distribution Dir(θwp— α). The alpha is initiated parameter for the webpage (theta_{wp}), and for the simplicity of the distribution, the same parameter is then used for the tags within a specific webpage. Notice that the vector size of alpha will differ within each webpage based on its tags. Therefore, parameter θtg of a multinomial distribution over K topics is constructed from Dirichlet distribution Dir(θtg— α). For topic t, parameter φt of a multinomial distribution over the set of words in the vocabulary (V) is derived from Dirichlet distribution Dir(φt— β). The Dirichlet distribution is a convenient choice as a prior and can simplify the statistical inference in the HTM model. The likelihood is multiplied through all the web pages and maximized with the technique of variational inference for the estimation of α and β.As the LDA topic model, the HTM model is based on a generative statistical model, and it uses latent factors to capture the semantic similarities of words and documents. The generative process of the HTM model is as follows. Firstly, we need to specify the optimal number of topics represented by (K). Then randomly choose a distribution over topics . A specific webpage (WP) is modelled as a sequence of words W =  introduce each dataset and describe its features and sources. Both datasets are then preprocessed for evaluation, as subsection 3 illustrates.https://dumps.wikimedia.org/enwiki/latest/). We chose the latest English page articles at the time of obtaining the dataset (22-Feb-2023). The size of the file was 20,580,560,896 bytes, containing more than 7 million articles. These articles include topics from various Wikipedia categories such as art and culture, geography and places, health and fitness, history and events, mathematics and abstractions, natural sciences and nature, people and self, philosophy and thinking, religion, social sciences and society, and technology and applied sciences category. For the first experiment, the study randomly chooses 50,000 articles to represent the Conventional Document-based (CD-based) dataset. The CD dataset contains 55,775,941 total words with V = 584,132 unique words. The average of words per document was about 1,115.Recent studies have used articles from well-known sources such as Wikipedia, Reuters, BBC, and the New York Times to evaluate topic models . WikipedV = 400,230 unique words. The average of words per document was about 446.This study uses web content data to evaluate the performance of the benchmark topic models and the HTM model. We use an available dataset containing 2 million web pages collected from about 7,000 websites . These wWebpages, however, contain HTML content, which requires an additional step to extract the textual content. Extracting webpage content requires a web scrap agent to crawl and parse the URL of the webpage. Web crawling aims to index the entire web pages contained in a specific website by systematically browsing the World Wide Web. The parsing HTML code extracts relevant web page contents such as paragraphs, images, bold texts, web page titles, refs, and metadata.Python offers the most flexible and powerful way to do it. A few Python libraries support web crawling and scraping, such as BeautifulSoup, LXML, MechanicalSoup, Requests, Scrapy, and Urllib. In fact, building an automatic and systematic website crawler and scrapper requires using a combination of these libraries. This study uses a Python library called CrawlerScraper to create the dataset from the collected websites. The CrawlerScraper is an open-source library for the solution of efficient and easy web crawling and data scraping  tagging.This section shows the results of both experiment series of this study. As aforementioned, the first experiment series demonstrates only the benchmark models’ performance limitation when applied only to web content data. The following subsections firstly address this experiment’s results based on this study’s performance evaluation metrics. The second experiment evaluates the enhancements made by the HTM model in discovering coherent patterns of web content data. The following subsection then illustrates this experiment’s performance evaluation metrics’ results.UMass, CUCI, CNPMI, and CV metrics. Using these metrics, the performance of the models for each dataset and given the number of topics K ∈{1…,100} will then be compared in the following subsections. Section 5.1.4 then discusses the results of compassion.We used the Python programming language with the Anaconda distribution platform’s help to evaluate CTM, DMR, HLDA, LDA, PTM, and sLDA based on both CD-based and WC-based datasets. We used the topic coherence metrics to evaluate a topic model’s quality and performance. This section evaluates the topic models using CUCI and CNPMI topic coherence appear to prefer fewer topics on conventional documents data. In contrast, the CV topic coherence favours a higher number of topics for all models except the HLDA model. The results suggest that the optimal number of topics may vary depending on the evaluation metric used and the type of dataset being analyzed.The results, as depicted in UMass, CUCI, and CNPMI metrics. This result answers the second research question of this study.An interesting observation was made regarding the LDA model’s performance on both datasets. Although it performed the worst for the CD-based dataset, it performed the best for WC-based datasets, achieving the highest topic coherence score in comparison to other models. This indicates that the LDA model is the most stable model among the benchmark topic models based on the CNPMI metric, the sLDA and DMR models achieved the highest topic coherence scores given different numbers of topics for CD-based datasets, whilst the PTM model scored the lowest. It is worth mentioning that the sLDA scores are similar to the DMR scores given all metrics and in both datasets, which is likely due to their methodological similarities . We used the Python programming language with the help of the Gensim library to perform data preprocessing and implement the LDA model and the HTM topic model.This study presents an analysis of the topic modeling of web content data using the LDA and HTM models. The goal of the HTM topic model is to improve the performance of the LDA model, which is a widely used topic modeling algorithm. The study evaluates both models using four coherence metrics: CV metric, B is based on CUMass metric, C is based on CNPMI metric, and D is based on CUCI metric), indicate that the HTM model outperforms the LDA model in terms of overall performance. The CUMass coherence score shows that the HTM model has a significantly better performance than the LDA model, with a steady value over the number of topics, while the LDA model’s value decreases as the number of topics increases. The improvement of the HTM model was slightly more than 89% of the LDA using the CUMass metric. This is due to the UMass metric, which considers preceding and succeeding terms in the list and allows the HTM model to relate topics within each HTML tag and among tags on each webpage. Similarly, the CV coherence score shows that the HTM model outperforms the LDA model for any number of topics, with a CV value of ≥ 0.9 when the number of topics exceeded 4. The improvement of the HTM model was slightly more than 36% of the LDA using the CV metric. These results indicate that the HTM model learns better web content topics than the LDA model, which may enhance human interpretability, as some previous studies argued  between the words in the topic, while CNPMI normalizes PMI by dividing it by the negative logarithm of the probability of the word pair occurring together by chance. Both CUCI and CNPMI coherence metrics are based on the PMI, which often indicates how well the topic model captures local patterns in the corpus  in topic modeling. It would also be very interesting to utilize the HTM topic model in web mining methods and techniques such as classification, clustering, and association rules. Future work can also address additional evaluation of the HTM topic model on the different webpages categories, such as social media, news and magazines, blogs, directories, landing, portfolios, homepages, and e-commerce webpages. Finally, it might be beneficial to include the web structure mining concept in the HTM topic model. Considering the parent, siblings, and children pages may enhance the quality of the generated topics of a webpage.10.7717/peerj-cs.1459/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj-cs.1459/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj-cs.1459/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj-cs.1459/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj-cs.1459/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj-cs.1459/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj-cs.1459/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj-cs.1459/supp-8Supplemental Information 8Click here for additional data file.


The results obtained showed that DNA from fungal spores and from yeast was only obtained after the application of the enrichment method, while mycelium was always lysed. After validation, we compared the diversity of the total and lysis-resistant fractions in the polyextreme environment of the Salar de Huasco, a high-altitude athalassohaline wetland in the Chilean Altiplano. Environmental samples were collected from the salt flat and from microbial mats in small surrounding ponds. Both the lake sediments and microbial mats were dominated by Ascomycota and Basidiomycota, however, the diversity and composition of each environment differed at lower taxonomic ranks. Members of the phylum Chytridiomycota were enriched in the lysis-resistant fraction, while members of the phylum Rozellomycota were never detected in this fraction. Moreover, we show that the community composition of the lysis-resistant fraction reflects the diversity of life cycles and survival strategies developed by fungi in the environment. To the best of our knowledge this is the first time that the fungal diversity is explored in the Salar de Huasco. In addition, the method presented here provides a simple and culture independent approach to assess the diversity of fungal lysis-resistant cells in the environment.At particular stages during their life cycles, fungi use multiple strategies to form specialized structures to survive unfavorable environmental conditions. These strategies encompass sporulation, as well as cell-wall melanization, multicellular tissue formation or even dimorphism. The resulting structures are not only used to disperse to other environments, but also to survive long periods of time awaiting favorable growth conditions. As a result, these specialized fungal structures are part of the microbial seed bank, which is known to influence the microbial community composition and contribute to the maintenance of diversity. Despite the importance of the microbial seed bank in the environment, methods to study the diversity of fungal structures with improved resistance only target spores dispersing in the air, omitting the high diversity of these structures in terms of morphology and environmental distribution. In this study, we applied a separation method based on cell lysis to enrich lysis-resistant fungal structures  to obtain a proxy of the composition of the fungal seed bank. This approach was first evaluated The online version contains supplementary material available at 10.1186/s12866-023-02809-w. Fusarium oxysporum are as resistant to antifungals as vegetative cells [Aspergillus spinosus, Paecilomyces variotii, and Talaromyces macrosporus, show a heat-resistance similar to that of bacterial endospores [The vast majority of fungal species form at a particular stage of their life cycle, single or multicellular specialized structures to reproduce or as a strategy to survive unfavorable environmental conditions. These structures are central in the life cycle of most fungal species. The most common strategy for survival or dispersal is the formation of spores. Fungal spores are specialized dormant cells that serve different purposes: reproduction, dispersal, and survival –3. Accorve cells , whereasdospores .Verticillium spp.) [Fungi are also remarkable in the morphological diversity of their vegetative phases, which may be unicellular, such as yeasts, or pluricellular, such as in the case of mycelial fungi. Some fungi can even transit from the yeast to the (pseudo)-hyphal stage, an ability termed dimorphism. This dimorphism is also part of their resistance to abiotic or biotic stresses. In this case, the yeast stage may be considered as an intermediate between spores and an actively metabolizing filamentous vegetative phase in regard to resistance. This is in particular the case of the so-called black fungi, which are considered poly-extremophiles and among the most stress-resistant eukaryotes on Earth , 9. Finaum spp.) .Up to now, the majority of studies about the taxonomic diversity of single or multicellular specialized fungal structures providing resistance in the environment have been focused on fungal spores suspended in the air –15. On tUnicellular and multicellular specialized structures that promote survival can remain viable for long periods of time in the environment, becoming part of microbial seed banks, which have been shown to influence the microbial composition of an environment and to contribute to the maintenance of diversity , 26. ThuAspergillus niger, Ulocladium alternata, Coprinopsis cinerea, Mortierella antarctica, and Candida albicans. These fungi were selected to represent different life cycles and types of specialized resistant structures. After validation, we applied the method to environmental samples, with the aim of assessing the abundance and diversity of fungal lysis-resistant structures. The samples were collected in the polyextreme environment of the Salar de Huasco. The Salar de Huasco is a high-altitude athalassohaline wetland located in the Chilean Altiplano at an altitude of 3,800 m.a.s.l. In addition to the high altitude, daily temperature changes from—10° to + 25 °C, high solar radiation < 1100 W/m2, and a negative water balance, are other extreme environmental conditions in this ecosystem [The use of the method for enriching lysis-resistant cells has been recently extended to study lysis-resistant communities in archaea. While in bacteria, lysis-resistance was shown to be restricted to a few groups, in archaea, resistance to lysis was widely spread . This iscosystem , 38. Furcosystem –41, whilcosystem .A. niger (Ascomycota); melanized mycelium and asexual spores of U. alternata (Ascomycota); non-melanized mycelium and sexual and asexual spores of C. cinerea (Basidiomycota); non-melanized mycelium and asexual spores of M. antarctica (Mucoromycota); and yeast cells of C. albicans (Ascomycota).Representatives of different fungal species were selected to produce a diversity of morphological structures including: non-melanized mycelium and asexual melanized spores of A. niger and U. alternata, after the lysis-resistant enrichment treatment, DNA was obtained from spores only  was recovered after the lysis-resistant enrichment method. Moreover, DNA from the mycelium samples was only obtained after the direct DNA extraction method  was recovered using the direct DNA extraction method, while only a small amount of DNA (0.086 ng/μl) was recovered using the lysis-resistance method  assigned to the Rozellomycota phylum. ASVs assigned to the phyla Mucoromycota, Monoblepharomycota, and Glomeromycota, were only present in the microbial mats, but their relative abundance was low . In contrast, the PCoA analysis of the lysis-resistant community showed an overlap of the two environments . This indicated that the lysis-resistant fraction is partially similar in both environments. Accordingly, the Venn Diagram in Fig. From this first assessment, we decided to evaluate each environment separately, to reduce the effect of the environment type and to better assess the effect of the enrichment method on the characterization of the lysis resistant community. This decision was supported by a Principal Coordinates Analysis (PCoA) of the total community, which showed a clear separation of both environments Fig. A, with tnts Fig. B and theversus the lysis-resistant fractions, only samples that could be paired  were retained. For the lake sediments, sample 5 could not be analyzed as there was not enough sediment to perform the analysis. Furthermore, amplicons could not be generated from two samples of the lake sediments  and from three samples of microbial mats . These samples were removed to only consider paired samples  for the comparative analysis. A PCoA of the lake sediment and microbial mats is shown respectively in Fig. versus the lysis-resistant fraction were not statistically different .The community analysis (PCoA) was performed using all samples, but for the comparison of the total versus the lysis-resistant fractions was plotted for the lake sediments . However, two known halotolerant and halophilic species, Hortaea werneckii and Aureobasidium pullulans [Assessing the most abundant ASVs of a community leaves a large fraction of the diversity out of the analysis. Thus, the enrichment of all the different phyla at the class level was assessed separately. In the lake sediments, the only identified class within the Chytridiomycota phylum was Lobulomycetes. This class and unidentified sequences were enriched in the lysis-resistant fraction. Similarly, in the Rozellomycota phylum, the only class identified was the Rozellomycotina _cls_Incertae_sedis, which was enriched in the lysis-resistant fraction, while unidentified sequences were enriched in the total fraction. In the Aphelidiomycota phylum, the Aphelidiomycetes class (the only class described so far for this group) was enriched in the total fraction. Furthermore, in the Ascomycota phylum Fig. A, only tullulans , were prA. pullulans [In the microbial mats, the Rozellomycota phylum was represented only by unidentified sequences enriched in the total fraction, similarly the Chytridiomycota phylum was represented only by unidentified sequences (enrichment in the lysis-resistant fraction). Furthermore, the classes Mortierellomycetes (Mortierellomycotina sub-phylum), Mucoromycetes (Mucoromycota phylum), Sanchytriomycetes (Monoblepharomycota phylum), and Glomeromycetes (Glomeromycotina sub-phylum) were the only identified classes in their respective phylum, and were all enriched in the total fraction. On the other hand, the Aphelidiomycetes class was enriched in the lysis-resistant fraction. In the case of the Ascomycota phylum, only the classes Taphrinomycetes and Saccharomycetes were enriched in the lysis-resistant fraction Fig. C. As in ullulans  was presSome differences were observed when comparing the lake sediments and the microbial mats in Fig. The predominance of unclassified sequences limited our ability to assess the effect of the lysis-resistant enrichment method on the identification of a seed bank for the fungal community. Thus, we calculated a lysis-resistant enrichment index Fig. E and 6F.U. alternata, C. cinerea and A. niger only after the method to enrich lysis-resistant cells. This demonstrates the utility of the enrichment method to detect fungi, especially in extreme environments where the probability to find them under the form of spores (or other resistant cells) is high. We further demonstrate that DNA extracted from mycelium is not detected using the enrichment method, but mostly using a direct DNA extraction method. This suggest that DNA detected using the enrichment method should originate from structures more resistant than typical mycelium. However, in the cases of A. niger and U. alternata, DNA from the mycelium was also not extracted using the direct DNA extraction method. This is puzzling and might indicate that the initial quantity of fungal material was probably too low. Moreover, in the case of C. albicans, our results show that non-melanized yeasts will be better detected with a direct DNA extraction method, while only a small fraction of the DNA of these yeast cells can be detected after enrichment. Therefore, the combination of both types of DNA extractions (extraction with no pre-treatment and a second extraction on the lysis-resistant fraction only) may improve the detection of fungi in different environments and broaden our understanding of the conditions that lysis-resistant fungal cells might tolerate.The comparison of different types of fungal structures  allowed us to evaluate the applicability of the lysis-resistance separation method for the fungal kingdom. Our results show that, as expected, it is more difficult to extract DNA from fungal spores than from vegetative structures (mycelia and yeast cells). Indeed, DNA was extracted from spores of To the best of our knowledge, this is the first time that the diversity of the fungal community has been assessed in the Salar de Huasco. In addition, we did not restrict our analysis to the assessment of the fungal diversity overall, but we also compared this diversity to the diversity of lysis-resistant fungal cells. The latter was used as a proxy of specialized resistant cells  that withstand better chemical and mechanical lysis. A large proportion of unidentified sequences was present in the data set, especially in the microbial mats. This is to be expected given that fungal diversity has been less studied than bacterial diversity. Currently, 120,000 fungal species have been described, but it is estimated that over 93% of the fungal diversity is still unknown. This suggests that the number of fungal species could be between 2.2 and 3.8 million . AdditioOur results showed that the fungal community of both the lake sediments and the microbial mats samples were dominated by members of Ascomycota and Basidiomycota, while members of basal lineages had a lower relative abundance. This is in accordance with the overall diversity currently described in fungi, with 98% of the described species belonging to the subkingdom Dikarya. Similar results have been reported regarding the fungal diversity in the Atacama Desert . FurtherThe application of the method to enrich lysis-resistant cells to assess the diversity of fungal spores and spore-like cells, indicated an overall high similarity in the composition of the total and lysis-resistant fractions. This differs from previous studies in Bacteria, in which the production of a lysis-resistant cell appears to be restricted to some clades –56. HoweAlthough the lake sediments and microbial mats differed in biotic and abiotic factors, two phyla showed similar enrichment patterns. The Rozellomycota was enriched in the total community, while the Chytridiomycota was enriched in the lysis resistant fraction. The Rozellomycota phylum is constituted by mainly uncultured unicellular fungi, considered to be the most basal clade of fungi , 58. TheCaloplaca and Acarospora) are not known to inhabit aquatic environments, thus the presence of lichen propagules and/or spores in the lake sediments could be linked to dispersal strategies from lichens present in the surroundings of the lake. Both these genera are worldwide distributed and have been previously found in the Atacama Desert [As a whole, the most abundant phyla, Ascomycota and Basidiomycota were not enriched in either fraction in the lake sediments nor in the microbial mats. This is not surprising as, all together, these two phyla represent most of the diversity described to date. However, when considering lower taxonomic ranks, a specific enrichment of a few classes within both phyla are worth mentioning. In the Ascomycota phylum, the Saccharomycetes class was enriched in the lysis-resistant fraction of both the lake sediments and the microbial mats. This class is constituted of budding yeasts often considered to be “true yeasts”, which can undergo meiosis and sporulation under nutrient-poor conditions , 71. Thea Desert , 74. Thea Desert . The preAt the class level, more classes within Basidiomycota were enriched in the lysis-resistant fraction than for the Ascomycota phylum. The higher number of enriched classes in Basidiomycota compared to Ascomycota, might be due to their complex life cycles. Four of the six enriched classes in the Basidiomycota  are members of the Pucciniomycotina subclass. This subclass accommodates a diverse array of fungi, ranging from plant pathogenic rust fungi, to insect associated fungi, mycoparasitic fungi, and dimorphic species , 77. AddParatritirachium curvibasidium have been shown to be resistant to high temperatures (75 °C) [C. albicans used for the validation step. On the contrary, the dimorphic class Agaricostilbomycetes was enriched in the total fraction. Members of this class might have been present in this environment as mycelium and not yeast, or alternatively as non-melanized cells. These results illustrate the diversity of lysis-resistance in fungi and also that an enrichment in the total community or in the lysis-resistant fraction might better reflect the complexity of fungal life cycles rather than the presence/absence of given taxa.In the lake sediments, the most enriched Basidiomycota class in the lysis-resistant fraction was the Pucciniomycetes. The Pucciniomycetes class is composed of mainly rust fungi, which are obligate plant pathogens . Its enr (75 °C) . The Ust (75 °C) . The enr (75 °C)  or the h (75 °C) , and the (75 °C)  might inIn conclusion, the results presented here corroborate our initial hypothesis. The widespread distribution of lysis-resistance in fungi reflects not only the presence of resistance structures, but also the multitude and complexity of life cycles in fungi. Furthermore, although lysis-resistance is broadly distributed in fungi, the application of the method to enrich for lysis-resistant cells showed that most of the ASVs are either enriched in the lysis-resistant fraction or in the total community. Thus, lysis-resistance is only characteristic for some taxa or for a particular stage in the life cycle of a given organism. In order to better understand the composition and dynamics of the fungal seedbank, molecular studies still have to be coupled with culture-dependent methods and/or microscopy to provide additional information about the morphological characteristics of the fungal lysis-resistant fraction in the environment. Regrettably, in this study, this was not possible as the amount of environmental sample was limited, which is often the case when evaluating extreme environments. Nevertheless, to the best of our knowledge this is the first study evaluating the fungal diversity in the extreme environment of the Salar de Huasco. Fungi are often overlooked in environmental studies as compared to procaryotes, especially in extreme environments. This is surprising as fungi are an integral component of every environment and representing an important resource to take into account to address major global challenges , 85, 86.C. cinerea were collected from in-vitro mature fruiting bodies. A single fruiting body was immersed in 5 ml 0.9% NaCl and homogenized for a few seconds. Then, the fruiting body was removed, resulting in a suspension of sexual spores. The spore biomass of this suspension was further filtered through a 0.22 μm nitrocellulose membrane . Finally, to obtain the yeast stage of C. albicans, 2 colonies grown on an agar-based medium were suspended in 5 ml 0.9% NaCl before being filtered through the membrane as previously described for all other samples. All filters were kept at -20 °C before further processing. For each fungal strain, each type of biomass sample was prepared in experimental duplicates.The different structures for each fungal strain were produced using the growth media and temperatures summarized in Table To one-half filter containing biomass, 900 µl of 1 × TE (Tris–EDTA) buffer were added and the biomass was resuspended by vortexing. The second half of the filter was used for DNA extraction without enrichment. For the enrichment, the sample was incubated at 65 °C for 10 min under constant agitation at 80 rpm. Afterwards, 100 µl of 20 mg/ml lysozyme were added and the sample was incubated at 37 °C for 60 min under constant agitation at 80 rpm. Later, 250 µl of 3 N sodium hydroxide (NaOH) and 250 µl of 6% sodium dodecyl sulfate (SDS) solutions were added and the sample was incubated at room temperature for 60 min under constant agitation at 80 rpm. Next, the sample was filtered on a 0.22 µm sterile nitrocellulose filter, which was then washed twice with 2 ml of sterile physiological water to remove any residue of the solutions used. The filter was then left to dry under a laminar flow hood. Once the filter was dry, 450 µl of sterile ultrapure water, 50 µl of 1 × DNase reaction buffer, and 0.5 µl Dnase enzyme were added and incubated at room temperature for 15 min directly on the filter, to degrade the DNA released by the easy-to-lyse cells prior to the DNA extraction from the lysis-resistant fraction. After 15 min, the solution was filtered and the residue was washed once more with 1 ml of physiological water to wash away any degraded DNA as well as the DNAase solution. Finally, the filters (containing the lysis resistant fraction only) were stored at -20 °C until the DNA extraction .First of all, the same DNA extraction method was used for both fractions, the total community  and the lysis-resistant fraction  and images were generated with a Leica Microscope Camera DFC7000T .The sampling at Salar de Huasco took place in September of 2019 and all samples were collected within 24 h. The lake sediment samples were collected following a saline gradient, with salinity ranging from 53 to 0.706 PSU  for targeted amplicon sequencing of the fungal ITS2 region using an Illumina MiSeq platform , generating 300 bp paired-end reads. The ITS2 region was amplified using the universal primers ITS3 KYO2 (5’-GAT GAA GAA CGY AGY RAA-3’) and ITS4 (5’-TCC TCCGCT TAT TGA TAT GC-3’) . DemultiAll statistical analysis were performed with RStudio version 1.3.1093 , the comAdditional analyses were performed to evaluate the contribution of unclassified ASVs to the composition of the lysis-resistant community. For this, an “enrichment index” was calculated for each individual ASV by comparing the relative contribution of the abundance in the lysis-resistant fraction  as compared to the total abundance of the ASV ). At the end, an index ranging from 0  to 1 (ASVs only detected in the lysis-resistant fraction) is obtained for each environmental sample. The results were represented as cumulative graphs, in which the proportion of ASVs displaying the index value were grouped together.Additional file 1: Figure 1. Lake sediments 50 most abundant ASVs per sampling point at the phylum level. Left, lysis-resistant fraction and right total community, different colors represent different phyla.Additional file 2: Figure 2. Microbial mats 50 most abundant ASVs per sampling point at the phylum level. Left, lysis-resistant fraction and right total community, different colors represent different phyla.Additional file 3: Figure 3. Heatmap representing the enrichment index calculated for the ASVs for which the enrichment index can be calculated in all individual samples Additional file 4: Figure 4. Graphical representation of the method used. Left, graphical description of the validation method and right, graphical representation of the method used for the analysis of the fungal community.Additional file 5: Table 1. Raw values of the DNA quantification after DNA extraction. DNA concentration in ng/µl.Additional file 6: Table 2. Information on the abiotic parameters measured in situ for the different sampling locations. %OD= dissolved oxygen.


The salivary gland section in the 5th edition of the World Health Organization Classification of Head and Neck Tumours includes a description of several new entities. In addition, numerous tumor variants were described and new concepts proposed, most of which have been based on recent molecular discoveries. However, there are still some controversial issues that remain to be resolved, and some of them are discussed in this review. Salivary gland tumor pathology is one of the most challenging areas in all head and neck surgical pathology. The salivary gland section in the 5th edition of the World Health Organization Classification (WHO) of Head and Neck Tumours covers 15 benign and 21 malignant epithelial neoplasms, one benign mesenchymal tumor, and two non-neoplastic epithelial lesions, vast majority of which have been discussed and accepted unanimously during WHO Editorial Board Meeting [NCOA4::RET rearrangements in the majority of cases with intercalated duct differentiation, TRIM27::RET fusions in cases with hybrid features, and a complex genetic profile including HRAS and PIK3CA hotspot mutations in the apocrine subtype.Intraductal carcinoma (IDC) of the salivary glands is rare and enigmatic tumor, the understanding of which is rapidly evolving particularly due to availability of molecular testing. Emerging molecular data suggest that IDC comprises at least three distinct subtypes, characterized by recurrent NCOA4::RET fusion casts doubt on its current name “intraductal carcinoma”.Although much has been learned about this IDC in the recent years, unanswered questions remain. Most controversial issue is the terminology and the biological potential of IDC. For example, the description of frankly invasive and metastasizing IDCs with canonical AKT1 p.Glu17Lys  gene mutation, and therefore, it was originally proposed as a subtype within the spectrum of low-grade MA in the 5th edition of the WHO Classification of Head and Neck Tumours. Consensus has not been reached, and after a difficult discussion of the WHO Editorial Board, finally IPMN was not included. It is a matter of controversy if IPMN represents a low-grade form of MA, a non-invasive precursor neoplasm, or distinct entity.Intraductal papillary mucinous neoplasms (IPMNs) were recently described as a distinct minor salivary gland tumor entity. Mucinous adenocarcinoma (MA) shows variable morphology and is defined as a primary salivary gland carcinoma that displays prominent intracellular and/or extracellular mucin. IPMN, however, shares many similarities with low-grade, papillary-cystic forms of MA, including the identical activating There is no consensus about the existence of oncocytic carcinoma. Oncocytic appearance is a common change encountered in many different salivary gland tumors. Molecular studies have now shown many such tumors to be oncocytic variants of other salivary carcinomas. Therefore, for the 5th edition of the WHO classification, oncocytic adenocarcinoma does not have special category and has only been included in the emerging entity chapter.In contrast, salivary carcinosarcoma has remained a separate entity despite controversial concept of the clonal relationship between highly heterogeneous carcinomatous and sarcomatous components.Intraductal carcinoma (IDC) is a salivary gland malignancy characterized by papillary, cribriform, and solid proliferations with predominantly intraductal but also invasive growth . PreviouNCOA4::RET  , 19. IPMst cases   is characterized by variable combination of malignant epithelial and sarcomatous tumor components, which may develop from a pre-existent pleomorphic adenoma (PA) or de novo . It has AKT1 p.E17K mutation, which can be interpreted as supportive of merging these entities under one umbrella or as simply a case of two separate entities having the same mutation. Similarly, the molecular testing has allowed reclassification of most oncocytic carcinomas into other salivary carcinoma types leading to an unresolved question of whether there are any true oncocytic salivary carcinomas, and thus, if the entity should be retained as a standalone entity in the classification. Since the answer to this question is unknown, the entity has been placed in the emerging and provisional entities chapter for now. In case of IDC, the molecular discoveries suggest that it is a heterogenous group of tumors; however, whether they should all be considered subtypes of IDC or as separate entities remain to be seen. Finally, the recent molecular profiling of SCS suggests that the entity is likely a carcinoma ex-PA (CEPA) with sarcomatous transformation rather than a true sarcoma in most if not all cases, and this may lead to its reclassification as a variant of CEPA in the future editions of the WHO classification.Salivary pathology is a rapidly evolving field in which novel molecular discoveries have driven multiple changes in the tumor classification. While in most instances, the discovery of the molecular changes have helped clarify the classification, in certain instances, they had the opposite effect of obscuring the boundaries between entities and creating controversies. This is well illustrated in the case of IPMN versus MA, which both share the same