Clear Clustering Research Articles

Genetically differentiated populations of Ceratocystis fimbriata species complex points to host specialization in Brazil

AbstractCeratocystis fimbriata is an important pathogen with a wide host range that causes xylem lesions, wilt and death in different plant species. More adapted populations of C. fimbriata have probably been selected to specific hosts and high genetic and physiological variability can be found within the pathogen populations. Thus, molecular analysis and inoculation studies were performed to investigate the genetic and physiological variability of isolates of C. fimbriata complex obtained from different host species and geographic regions in Brazil. The minimum spanning tree analysis based on 14 simple‐sequence repeat (SSR) markers of C. fimbriata exhibited a clear clustering of isolates according to host, where each groups of isolates differed in at least five loci. In addition, inoculation of eight hosts with 10 isolates revealed a wide variation in aggressiveness. By assessing the length of xylem lesions caused by C. fimbriata isolates, Ficus carica was found to be the most susceptible host, followed by Mangifera indica. Only the isolates from Theobroma cacao and Carapa guianensis proved to be host specialized. Overall, the isolates tested were more aggressive to the hosts from which they were sampled.

Identification of characteristic volatiles of Zhenjiang aromatic vinegar using HS‐SPME‐GC/MS coupled with multivariate analysis

AbstractThis study employed headspace solid phase micro‐extraction gas chromatography mass spectrometry (HS‐SPME‐GC–MS) coupled with multivariate statistical analysis to identify volatile markers in various levels of Zhenjiang Aromatic Vinegar (ZAV). A full range of 40 volatile organic compounds (VOCs) were identified and categorized into acids, alcohols, esters, aldehydes, ketones, heterocyclic compounds and others. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) demonstrated a clear clustering effect among different levels of ZAV, indicating distinct volatile characteristics at the relative content level. Furthermore, using Orthogonal partial least squares discrimination analysis (OPLS‐DA), 20 significant volatile organic compounds (VOCs) were identified as markers for distinguishing between the various levels of ZAV. To differentiate among the levels of ZAV, two volatile compounds, acetic acid and n‐hexanol, were identified as characteristic volatile compounds using a combination of Venn diagram, analysis of variance (ANOVA) and boxplot analysis. The results showed notable variations in VOCs among ZAV samples of different grades. Acetic acid and n‐hexanol can serve as reliable markers for grade distinction, providing valuable technical and theoretical support for the production and quality control of ZAV.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH.

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

Infant vocal category exploration as a foundation for speech development.

Non-random exploration of infant speech-like vocalizations (e.g., squeals, growls, and vowel-like sounds or "vocants") is pivotal in speech development. This type of vocal exploration, often noticed when infants produce particular vocal types in clusters, serves two crucial purposes: it establishes a foundation for speech because speech requires formation of new vocal categories, and it serves as a basis for vocal signaling of wellness and interaction with caregivers. Despite the significance of clustering, existing research has largely relied on subjective descriptions and anecdotal observations regarding early vocal category formation. In this study, we aim to address this gap by presenting the first large-scale empirical evidence of vocal category exploration and clustering throughout the first year of life. We observed infant vocalizations longitudinally using all-day home recordings from 130 typically developing infants across the entire first year of life. To identify clustering patterns, we conducted Fisher's exact tests to compare the occurrence of squeals versus vocants, as well as growls versus vocants. We found that across the first year, infants demonstrated clear clustering patterns of squeals and growls, indicating that these categories were not randomly produced, but rather, it seemed, infants actively engaged in practice of these specific categories. The findings lend support to the concept of infants as manifesting active vocal exploration and category formation, a key foundation for vocal language.

Grid Model of Energy Consumption Using Random Forest by Integrating Data on the Nighttime Light, Population, and Urban Impervious Surface (2000–2020) in the Guangdong–Hong Kong–Macau Greater Bay Area

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China’s high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption.

Microheterogeneity in the Kinetics and Sex-Specific Response to Type I IFN.

The response to type I IFNs involves the rapid induction of prototypical IFN signature genes (ISGs). It is not known whether the tightly controlled ISG expression observed at the cell population level correctly represents the coherent responses of individual cells or whether it masks some heterogeneity in gene modules and/or responding cells. We performed a time-resolved single-cell analysis of the first 3 h after invivo IFN stimulation in macrophages and CD4+ T and B lymphocytes from mice. All ISGs were generally induced in concert, with no clear cluster of faster- or slower-responding ISGs. Response kinetics differed between cell types: mostly homogeneous for macrophages, but with far more kinetic diversity among B and T lymphocytes, which included a distinct subset of nonresponsive cells. Velocity analysis confirmed the differences between macrophages in which the response progressed throughout the full 3 h, versus B and T lymphocytes in which it was rapidly curtailed by negative feedback and revealed differences in transcription rates between the lineages. In all cell types, female cells responded faster than their male counterparts. The ISG response thus seems to proceed as a homogeneous gene block, but with kinetics that vary between immune cell types and with sex differences that might underlie differential outcomes of viral infections.

Meta-analysis in Plant Pathology: Publication Patterns, Methodological Trends and Potential Future Directions.

Meta-analysis (MA) is increasingly adopted in agricultural and ecological sciences, fields at the interface with plant pathology. Our review on the use of MA in the field, since 1999, identified 79 original research papers. Fifty percent of these works were published in two APS journals, emphasizing their central role in disseminating MA research. Analysis of the number of authors per study and the authorship network revealed a tightly knit field. The network shows a few "hub" authors or groups that significantly influence research directions, with a clear geographical clustering in North and South America. Most of the MA papers focused on the effects of disease control, where fungicide was the main product. The MA investigations often focus on response variables like yield and disease severity for which the analyzed effect sizes differ, but the log of the ratio of means and untransformed means are predominant. The study selection to be included in the MA often follows systematic review standards or when clear quantitative criteria are used for study inclusion. Yet, the inclusion of data from reports, newsletters, and research trial data adds breadth. Frequentist rather than Bayesian inference is most common and SAS and R are the preferred software. Our review underscores the escalating importance of MA in plant pathology, especially for determining the mean and variability of the effects of different control methods on disease and yield. Efforts should continue to improve the detailed reporting of MA methods used, data sharing and reporting of measures of heterogeneity among studies.

Comparative analysis of oropharyngeal microbiota in healthcare workers post-COVID-19.

To date, more than 770 million individuals have become coronavirus disease 2019 (COVID-19) convalescents worldwide. Emerging evidence highlights the influence of COVID-19 on the oral microbiome during both acute and convalescent disease phases. Front-line healthcare workers are at an elevated risk of exposure to viral infections, and the effects of COVID-19 on their oral microbiome remain relatively unexplored. Oropharyngeal swab specimens, collected one month after a negative COVID-19 test from a cohort comprising 55 healthcare workers, underwent 16S rRNA sequencing. We conducted a comparative analysis between this post-COVID-19 cohort and the pre-infection dataset from the same participants. Community composition analysis, indicator species analysis, alpha diversity assessment, beta diversity exploration, and functional prediction were evaluated. The Shannon and Simpson indexes of the oral microbial community declined significantly in the post-COVID-19 group when compared with the pre-infection cohort. Moreover, there was clear intergroup clustering between the two groups. In the post-COVID-19 group, the phylum Firmicutes showed a significant increase. Further, there were clear differences in relative abundance of several bacterial genera in contrast with the pre-infection group, including Streptococcus, Gemella, Granulicatella, Capnocytophaga, Leptotrichia, Fusobacterium, and Prevotella. We identified Gemella enrichment in the post-COVID-19 group, potentially serving as a recovery period performance indicator. Functional prediction revealed lipopolysaccharide biosynthesis downregulation in the post-COVID-19 group, an outcome with host inflammatory response modulation and innate defence mechanism implications. During the recovery phase of COVID-19, the oral microbiome diversity of front-line healthcare workers failed to fully return to its pre-infection state. Despite the negative COVID-19 test result one month later, notable disparities persisted in the composition and functional attributes of the oral microbiota.

Abstract PR002: Sarcomatoid Histological Variants of Canine Muscle-Invasive Bladder Cancer: Transcription Factor Activation Highlights Pathways of Epithelial-Mesenchymal Transformation Similar to Humans

Abstract The histologic heterogeneity of human Muscle Invasive Bladder Cancer (MIBC) is one of the main contributing factors to poor treatment outcomes. Recent reports on MIBC histological variants have highlighted increased levels of transcription factors (TF) associated with epithelial-mesenchymal transformation (EMT) and epigenetic regulation in the sarcomatoid histology variant of MIBC. Dogs with spontaneously developing MIBC have been shown to display similar histological and molecular features, in addition to closely mimicking the clinical behavior of human MIBC. Here, we add to previously reported data on histological variants in canine MIBC by evaluating 21 tumor biopsies for variant histology and performing bulk RNAseq analyses on 3 organoid cell lines derived from one sarcomatoid subvariant and two conventional urothelial carcinoma (UC). MIBC biopsies from 21 dogs were reviewed for variant histology by a human urologic pathologist. Organoids derived from the urine of canine MIBC patients were cultured, preserved in RNA later, and sequenced for mRNA expression to identify TF associated with sarcomatoid variants versus conventional UC. All canine MIBC biopsies were classified as invasive, high-grade UC. Histologic variants were identified as glandular differentiation in 7/21, sarcomatoid differentiation in 3/21, squamous differentiation in 3/21, micropapillary carcinoma in 1/21 MIBC samples and small nested variants in 1/21 samples; with the rest of the tumors classified as conventional UC. Raw RNAseq analyses revealed no clear clustering of expression profiles between the three organoid cell lines, indicating distinct RNA expression profiles between the sarcomatoid variants and conventional UC of canine MIBC. To evaluate differences in the expression of TF targets between the organoid cell lines, pathway analyses and VIPER algorithms were used. Organoids derived from sarcomatoid variant UC showed increased expression of Snail familiy transcription repressor 1 (SNAI1) along with decreased expression of cadherin 1 (CDH1), indicating a signature of EMT, similar to that previously identified in human MIBC organoids with sarcomatoid variant histology. Canine MIBC organoids derived from conventional UC histology samples showed a TF profile associated with proliferation, metastasis, and progression, such as increased Zinc finger E-box binding homeobox 1 (ZEB1), E2F transcription factor 3 (E2F3), and centromere protein F (CENPF), as previously described in humans with high grade, invasive UC. In conclusion, the frequency of some histologic variants in canine MIBC, such as sarcomatoid UC, appear to be higher than reported in human patients. TF analyses of canine MIBC-derived organoid cell lines showed evidence of EMT, similar to the sarcomatoid variants of MIBC in humans. Organoids derived from canine MIBC could potentially serve as a valuable model for evaluating the efficacy of novel treatment modalities for the rare histologic variants of UC. Citation Format: Karin Allenspach, Mohamed Elbadawy, Hannah Nocholson, Christopher Zdyrski, John Cheville, Eugene Douglass, Elizabeth W. Howerth, Lilian Oliveira, Jonathan P. Mochel. Sarcomatoid Histological Variants of Canine Muscle-Invasive Bladder Cancer: Transcription Factor Activation Highlights Pathways of Epithelial-Mesenchymal Transformation Similar to Humans [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR002.

AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment

BackgroundSeveral pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9–68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent. ObjectiveThis study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery. MethodsA YouTube search for "Incontinence after cancer surgery" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results. ResultsThe quality scales presented a high level of correlation one with each other (p < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram. ConclusionsYouTube videos on "Incontinence after Cancer Surgery" present a "High" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.

Pronominal variation in Arabic among the grammarians, Qurʾānic reading traditions and manuscripts

ABSTRACT In the early Islamic period, the literary form of Arabic had an astounding amount of variation in its pronominal morphology. This paper examines the pronominal morphology of especially the third person masculine singular and plural pronouns, as they are described by the early Arabic grammarians, how they appear in the the descriptions of the canonical and non-canonical reading traditions and finally the pronominal systems as they appear in actual early Qurʾānic manuscripts. It is shown that the early grammarians were much more permissive of morphological variation than one would expect from strict norms that appear later. It is also shown that the pronominal systems of the reading traditions go well beyond the kinds of variation described by these grammarians. The systems of these readers show clear clusters (a Kufan, Basran, Hijazi and a ‘Classical’ cluster), even though in the details reading traditions from a single region still often differ from one another. Finally, it is shown that while some of the pronominal systems known in the reading tradition literature also show up in the Qurʾānic manuscripts, there is a significant number of manuscripts that have pronominal systems unlike any of the described systems in the literary sources.

Composition of Stream Dissolved Organic Matter Across Canadian Forested Ecozones Varies in Three Dimensions Linked to Landscape and Climate

AbstractDissolved organic matter (DOM) is a key variable influencing aquatic ecosystem processes. The concentration and composition of DOM in streams depend on both the delivery of DOM from terrestrial sources and on aquatic DOM production and degradation. However, there is limited understanding of the variability of stream DOM composition at continental scales and the influence of landscape characteristics and disturbances on DOM across different regions. We assessed DOM composition in 52 streams at seven research sites across six forested ecozones in Canada in 2019–2022 using 26 indices derived from five analytical approaches: absorbance and fluorescence spectroscopy, liquid chromatography—organic carbon detection, Fourier‐transform ion cyclotron resonance mass spectrometry, and asymmetric flow field‐flow fractionation. Combined analyses showed clear clustering and redundancy across analytical techniques, and indicated that compositional variations were primarily related to three axes of DOM composition: (a) aromaticity, which was greater in low‐relief, wetland‐dominated catchments, (b) oxygenation, which was greater in colder and drier ecozones, and (c) biopolymer content, which was greater in lake‐influenced catchments. Variability in DOM composition among research sites was greater than variability of streams within a site and variability over time within a stream. Forest harvesting and wildfire disturbances had no common influence on DOM composition across research sites, emphasizing the need for regional studies. Our study provides a broad understanding of the variability of stream DOM composition and its associations with landscape and catchment characteristics at a subcontinental scale, and provides key insights for the choice and interpretation of DOM indices from various analytical approaches.

Study on Transportation Green Efficiency and Spatial Correlation in the Yangtze River Economic Belt

The Yangtze River Economic Belt (YREB), a crucial transportation corridor spanning China’s east and west and linking coastal and inland regions, is not only pivotal in the nation’s strategic development but also drives regional economic and social progress through its transportation industry. Despite rapid growth, the industry faces challenges such as low efficiency, resource supply–demand imbalances, and environmental issues. To advance green and sustainable progress, this study establishes a regional transportation green efficiency evaluation system. Using principal component analysis (PCA) to refine input data, the undesirable super-SBM model quantitatively assesses green transportation efficiency (GTE) in YREB provinces and cities, revealing regional disparities. The study also explores spatial correlations and distribution characteristics of GTE. Results indicate that ① YREB’s GTE shows a U-shaped trend, with significant differences between upper, middle, and lower reaches, being stronger in the east and weaker in the west (lower &gt; middle &gt; upper reaches); ② GTE exhibits spatial correlation in YREB regions, with clear clustering; and ③ cold and hot spots of GTE in the middle reaches are relatively stable, with upstream areas generally cold or sub-cold, and hot spots mainly downstream.

Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into the understanding of the functional organization of the tissue. However, most such clustering methods involve some dimension reduction that leads to a loss of the inherent dependency structure among genes at any spatial location in the tissue. This destroys valuable insights of gene co-expression patterns apart from possibly impacting spatial clustering performance. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial coordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a joint Bayesian approach to simultaneously estimate these gene and spatial cell correlations. These estimates provide data summaries for downstream analyses. We illustrate our method with simulations and analysis of several real spatial transcriptomic datasets. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells. Furthermore, our analysis reveals that downstream spatial-differential analysis may aid in the discovery of unknown cell types from known marker genes.

Genome analysis of Streptococcus spp. isolates from animals in pre-antibiotic era with respect to antibiotic susceptibility and virulence gene profiles.

Lyophilized Streptococcus spp. isolates(n = 50) from animal samples submitted to the diagnostic laboratory at the University of Connecticut in the 1940s were revivified to investigate the genetic characteristics using whole-genome sequencing (WGS). The Streptococcus spp. isolateswere identified as follows; S. agalactiae (n = 14), S. dysgalactiae subsp. dysgalactiae (n = 10), S. dysgalactiae subsp. equisimils (n = 5), S. uberis (n = 8), S. pyogenes (n = 7), S. equi subsp. zooepidemicus (n = 4), S. oralis (n = 1), and S. pseudoporcinus (n = 1). We identified sequence types (ST) of S. agalactiae, S. dysgalactiae, S. uberis, S. pyogenes, and S. equi subsp. zooepidemicus and reported ten novel sequence types of those species. WGS analysis revealed that none of Streptococcus spp. carried antibiotic resistance genes. However, tetracycline resistance was observed in four out of 15 S. dysgalactiae isolates and in one out of four S. equi subsp. zooepidemicus isolate. This data highlights that antimicrobial resistance is pre-existed in nature before the use of antibiotics. The draft genome sequences of isolates from this study and 426 complete genome sequences of Streptococcus spp. downloaded from BV-BRC and NCBI GenBank database were analyzed for virulence gene profiles and phylogenetic relationships. Different Streptococcus species demonstrated distinct virulence gene profiles, with no time-related variations observed. Phylogenetic analysis revealed high genetic diversity of Streptococcus spp. isolates from the 1940s, and no clear spatio-temporal clustering patterns were observed among Streptococcus spp. analyzed in this study. This study provides an invaluable resource for studying the evolutionary aspects of antibiotic resistance acquisition and virulence in Streptococcus spp.

Application of Polarized Projective Mapping combined with Ultra Flash Profiling to a complex − high fatigue product category: The case of Greek grape marc spirits

Grape marc spirits in general and Greek grape marc spirits specifically, is an alcoholic drinks category with very limited information on its sensory properties, despite its commercial importance. Polarized Projective Mapping (PPM), a method first introduced by Ares et al. in 2013, is promising but has not been applied in combination with Ultra Flash Profiling (UFP) to sets of many and complex-high fatigue products such as spirits. Our objective was to employ UFP combined to PPM in order to profile a set of 39 grape marc spirits from Greece and relate the results to several production information of the samples such as raw materials, origin region and distillation style, and to evaluate the combination of those methods in such a category. Projective Mapping was used for pole selection and five sessions of PPM followed for the evaluation of all samples. Unlike application in previous studies we had assessors carry out the UFP task first before each PPM session. ‘Global PPM’ and ‘Global UFP’ product spaces gave 39.7% and 43.1% explained variances in the first two axes respectively, when data from all sessions and assessors were analyzed in STATIS. The biggest impact for classification of the products was from the grape varieties used as raw material, even though distillation style had a serious impact as well. Clear product clusters were obtained and combination of those methods worked well for that category. Nevertheless, there is still a need for a global method of defining the “poles” in large sample sets.

A New Approach to Improving Search Efficiency in Digital Libraries

The development of Internet technologies influences the activities of libraries and changes their nature. The volume of content collected in digital libraries is growing rapidly. This requires the use of new technologies to search and obtain electronic materials (text, video, images, sound files) stored in the e-library. Today, using the new capabilities of network technologies and intelligent systems, the proper organization of the digital library, and increasing the efficiency of library services are the main factors leading to an increase in the number of readers and their satisfaction. The main objectives of digital libraries are to ensure efficient retrieval of electronic resources and collaboration between users. While researching various scientific articles on library and information sciences (LIS), we did not encounter approaches using cluster analysis in combination with wiki technologies. To collaborate users in digital libraries and their involvement in organizing electronic resources, we propose using an open database managed by wiki technologies. To effectively search for electronic resources in these open databases, it is proposed to use the clear clustering method. The clear clustering method also allows you to control the quality of clustering. The proposed method is important when creating intelligent (smart) libraries that are easy to manage and automate certain tasks. The research aims to create not just a smart library, but a smart library based on wiki technologies.

Assessing the Stability of Clusters of Neuropsychiatric Symptoms in Alzheimer's Disease and Mild Cognitive Impairment.

The aim of the study was to investigate the factors that underpin neuropsychiatric symptoms and how they might evolve over time in people with Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) dementia. Neuropsychiatric symptoms are psychiatric and behavioural manifestations that occur in people with AD. These are highly prevalent along the continuum of the disease, including at the stage of MCI, as well as before cognitive decline. Various small- and large-scale projects have investigated the underlying factors that underpin these symptoms; however, the identification of clear clusters is still a matter of debate; furthermore, no study has investigated how the clusters might change across the development of AD pathology by comparing different time points. Our objective was to investigate the factors that underpin neuropsychiatric symptoms in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) and to assess how the loadings might differ based on considerations such as the disease stage of the samples. Data was obtained from the Alzheimer's Disease Neuroimaging Initiative database (adni. loni.usc.edu), using scores from the Neuropsychiatric Inventory, followed up yearly from baseline until month 72. Participant groups included those with MCI or AD dementia, or a mixture of both, with all participants presenting with at least one neuropsychiatric symptom. A series of exploratory Principal Component and Factor (Principal Axis) Analyses were performed using Direct Oblimin rotation. The best-fitting structure was interpreted for each time point. A consistent, unique structure could not be identified, as the factors were unstable over time, both within the MCI and AD groups. However, some symptoms showed a tendency to load on the same factors across most measurements (i.e., agitation with irritability, depression with anxiety, elation with disinhibition, delusions with hallucinations). Although the analyses revealed some degree of co-occurrence of neuropsychiatric symptoms across time points/samples, there was also considerable variation. In the AD group, more discrete syndromes were evident at the early time points, whereas a more complex picture of co-occurring symptoms, with differences likely reflecting disease staging, was seen at later time points. As a clear and distinctive factor structure was not consistently identified across time points/ samples, this highlights the potential importance of sample selection (e.g., disease stage and/or heterogeneity) when studying, for example, the neurobiological underpinnings of neuropsychiatric symptoms.

Abstract 7005: Distinct DNA methylation patterns between early-onset and average-onset colorectal cancer patients

Abstract Objective: Early onset colorectal cancer (EO CRC) is rising at an alarming rate. While average onset colorectal cancer (AO CRC) affects patients mostly above 65 years of age, EO CRC affects patients under 50 years of age, disproportionately affecting males from low-income communities and racial/ethnic minorities. Chronic inflammation has been identified as a strong contributor to pathogenesis. We hypothesize that methylation of DNA may serve as a potential mechanism associated with chronic inflammation that contributes to the etiology differentially between EO and AO CRC. Methods: Formalin-fixed paraffin-embedded (FFPE) tissue specimens from four AO and EO CRC patients each were retrieved from the Tissue Repository. Paraffin curls containing tumor and adjacent normal tissues were generated from appropriate EO and AO CRC patient tissues. The Qiagen GeneRead DNA FFPE genomic DNA kit was then used to extract the DNA from the paraffin curls. Illumina Methylation EPIC v2.0 kit was used to evaluate DNA methylation (DNAm) patterns after the restoration process from deamination in the fixed/embedded sample using the Uracil-N-Glycosylase enzyme. Unreliable and non-specific probes were removed before statistical analysis for differentially methylated CpG sites. Age acceleration for each tissue was calculated by comparing the estimated DNAm age with chronological age using the Horvath approach. Results: A clear clustering of DNAm patterns by age of onset were observed for both normal and tumor tissues. However, there was no difference when comparing tumor and normal tissues after multiple corrections. Four-way ANCOVA adjusting for race, gender, BMI, and tissue type suggested that age onset was the most significant factor. We also found that age acceleration in EO CRC tissue is greater than in AO CRC. After applying Bonferroni correction, significant differentially methylated regions (DMRs) were found in the promoter and the gene body of SNTB1, NFATC3, SLC25A24, RABGAP1L, WDSUB1, WWOX, SELB, SLC28A3, and PPP6R2 in EO CRC patients when compared to AO CRC. SNTB1 regulates colorectal cancer progression and stemness, while NFATC3 has been associated with inflammation and cancer. Conclusions: The preliminary results from this study showed a significant difference in the DNAm profile of FFPE samples between AO and EO CRC. The finding suggests that epigenetics may play a role in the etiology of CRC, potentially involved in the chronic inflammation pathway. The analysis of a larger patient number is underway to strengthen these findings. Citation Format: L. Joseph Su, Ping-Ching Hsu, Aniruddha Rathod, Yulun Liu, Jacopo Ferruzzi, Syed Kazmi, Emina Huang. Distinct DNA methylation patterns between early-onset and average-onset colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7005.

A core genome multi-locus sequence typing scheme for Streptococcus uberis: an evolution in typing a genetically diverse pathogen.

Streptococcus uberis is a globally endemic and poorly controlled cause of bovine mastitis impacting the sustainability of the modern dairy industry. A core genome was derived from 579 newly sequenced S. uberis isolates, along with 305 publicly available genome sequences of S. uberis isolated from 11 countries around the world and used to develop a core genome multi-locus sequence typing (cgMLST) scheme. The S. uberis core genome comprised 1475 genes, and these were used to identify 1447 curated loci that were indexed into the cgMLST scheme. This was able to type 1012 of 1037 (>97 %) isolates used and differentiated the associated sequences into 932 discrete core genome sequence types (cgSTs). Analysis of the phylogenetic relationships of cgSTs revealed no clear clustering of isolates based on metadata such as disease status or year of isolation. Geographical clustering of cgSTs was limited to identification of a UK-centric clade, but cgSTs from UK isolates were also dispersed with those originating from other geographical regions across the entire phylogenetic topology. The cgMLST scheme offers a new tool for the detailed analysis of this globally important pathogen of dairy cattle. Initial analysis has re-emphasized and exemplified the genetically diverse nature of the global population of this opportunistic pathogen.