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Using historic data sets, we quantified the degree to which global change over 120 years disrupted plant-pollinator interactions in a temperate forest understory community in Illinois, USA. We found degradation of interaction network structure and function and extirpation of 50% of bee species. Network changes can be attributed to shifts in forb and bee phenologies resulting in temporal mismatches, nonrandom species extinctions, and loss of spatial co-occurrences between extant species in modified landscapes. Quantity and quality of pollination services have declined through time. The historic network showed flexibility in response to disturbance; however, our data suggest that networks will be less resilient to future changes.

Enceladus and Europa, icy moons of Saturn and Jupiter, respectively, are believed to be habitable with liquid water oceans and therefore are of interest for future life detection missions and mission concepts. With the limited data from missions to these moons, many studies have sought to better constrain these conditions. With these constraints, researchers have, based on modeling and experimental studies, hypothesized a number of possible metabolisms that could exist on Europa and Enceladus if these worlds host life. The most often hypothesized metabolisms are methanogenesis for Enceladus and methane oxidation/sulfate reduction on Europa. Here, we outline, review, and compare the best estimated conditions of each moon’s ocean. We then discuss the hypothetical metabolisms that have been suggested to be present on these moons, based on laboratory studies and Earth analogs. We also detail different detection methods that could be used to detect these hypothetical metabolic reactions and make recommendations for future research and considerations for future missions.

Background:A loss of tolerance to self-antigens leads to increased levels of autoantibodies against nuclear components (ANAs) prior to clinical disease onset. However, only about 4-8% develop autoimmune disease. Patients with incomplete lupus erythematosus (ILE) exhibit some clinical symptoms with most never progressing to Systemic Lupus Erythematosus (SLE). Exact mechanisms involved in T cell dysregulation and progression of autoimmune disease remain unclear.Objectives:Investigate whether alterations in T cell populations and activation of cellular pathways are dysregulated during autoimmunity development.Methods:PBMCs from 64 subjects, divided evenly among ancestry (African, European American) and disease group: healthy (ANA-), healthy with autoantibodies (ANA+), ILE, SLE, were sorted with microfluidic flow cytometer to remove dead cells and used for multiomics single-cell analysis with 5’scRNA-seq/137-plex Total-seq, BCR/TCR repertoire to identify distinct disease-associated clusters, differential gene signatures and dysregulated pathways. Cell counts were confirmed via CyTOF. Serum soluble biomarkers levels were obtained via Olink Proximity Extension Assay (Explore HT).Results:We obtained profiles for ~650,000 cells across all PBMCs. Differences in T cell fractions were observed by disease group. Analysis of differentially expressed genes revealed the importance of metabolic processes, such as autophagy and oxidative phosphorylation; downregulation of mitochondrial dysfunction in ANA+ and upregulation of MAPK and receptor kinase signaling in ILE and SLE. Pathway analysis indicates downregulation of TNFR Signaling in SLE compared to ILE and cytokine storm signaling in ANA+ compared to ANA-. These finding were confirmed by protein. Gene set enrichment analysis of serum soluble biomarkers indicated upregulation of T cell activation, proliferation, antigen presentation, MAPK cascade and receptor kinase signaling in ILE and SLE. We observed upregulation of MAP2K6 and MAP3K5 proteins in ILE compared to ANA+ (non-parametric test; pad <0.05). Furthermore, we identified a CD4+ T cell population (CTL) with elevated expression of cytotoxic markers PRF1, GZMB, NKG7, CCL5 and transcription factors: ZNF683, IKZF1, TBX21, ZEB2. Individuals with that population express higher level of IFN related genes. Pathway analysis of CTL indicates upregulation of antiviral response, cellular cytotoxicity and exhaustion in ILE and SLE witth IFNG, STAT3 and IL10 determined as activated upstream regulators. Olink assay confirmed these results and revealed IFNB1 upregulation and viral response. TCR analysis indicates both CTL and CD8+ cytotoxic T cells have largest fraction of expanded clonotypes, with increased levels of TRAV19, TRAV8, TRAV38. Clonotypes similar transcriptionally, restricted to those two populations, are associated with higher expression of TXNIP, TMSB4X, HLA, GZMB and shared among ANA+ and ILE individuals.Conclusion:Dysregulation of signaling in T cell activation appears to be manifesting in increased oxidative phosphorylation, dysregulation of MAPK kinases or alterations in apoptotic pathways and might be suggestive of a preclinical autoimmunity development trajectory and associated with clonal expansion. Alterations of these processes vary by ancestral background, reflecting the heterogeneity of SLE presentation.REFERENCES:[1] Dorner, T. and R. Furie, Novel paradigms in systemic lupus erythematosus. Lancet, 2019[2] Slight-Webb, S., et al., Autoantibody-positive healthy individuals with lower lupus risk display a unique immune endotype. J Allergy Clin Immunol, 2020Figure 1.A. UMAP projection of distinct T cell clusters. B. T cell density for total population, by ancestry and disease groups. C. Pathway analysis of CD8+, CD4+ T cells with most distinct differences between ANA+ compared to ILE. D. Expression of cytotoxic markers across T cell clusters with CTL population highlighted. E. Fractions of CTL by disease group. F. Pathway analysis of CTL. G. Clonal expansion by T cell population (blue – singleton, orange – clonotypes with 2 cells, green – >3 cells)Acknowledgements:NIL.Disclosure of Interests:None declared.

Background:AI-enabled algorithms can increase the speed and accuracy of identifying key histological features and enable researchers and clinicians to more readily and thoroughly understand the tissue collected from their patients. Sjogren’s Disease (SJD), in particular, is ripe with opportunity given the reliance on tissue reads for focus scores and overall histological examinations of patients.Objectives:To develop an AI-enabled algorithm that automatically identifies key histological features of the minor salivary gland of SJD patients, and then test this algorithm on samples from a diverse patient population.Methods:Minor salivary glands from control and SJD patients were collected via standard-of-care and formalin fixed and paraffin embedded. 5micron sections were collected from blocks containing 3-5 minor salivary glands from a single patient, stained with H&E, then imaged on a whole-slide scanner. Images were loaded into HALO-AI v4.0 (Indica Labs). 5 cases and 5 controls were fully annotated under the guidance of a trained pathologist for background (no tissue), adipose/connective tissue, stroma, glandular tissue, and immune infiltrates. The classes were trained via HALO-AI’s DenseNet v2 for >10,000 iterations with a final entropy of <1 (a measure of agreement between annotation input and AI-prediction). After successful training and implementation on the training set on 10 cases/controls, the algorithm was applied to a set of 40 cases to evaluate performance (n=50 total). Percent area of all classes were then calculated.Results:The algorithm accurately identifies all 5 classes across varying degrees of disease severity across SJD patients and controls, as well as differences in staining intensity. Though inaccuracies are observed, the overall results were better than previous machine-learning methodologies (data not shown/published) and were quickly applied to samples (approximately 30sec of analysis time per sample). Resulting data show a high degree in variability in percent immune infiltration and gland.Conclusion:Though pathologist reads are important for the understanding SjD and clinical workups, it is time consuming and costly to annotate entire images manually in order to measure all major histological features in the minor salivary gland of SJD patients. Though the AI-Tissue classifier produced here would not outperform a trained pathologist in accuracy, it is much faster and much more cost effective to run. Therefore, this algorithm in combination with current focus scores (and other clinical data) could provide novel insights and key findings for both clinicians and researchers concerning SJD progression, severity, or other meaningful metrics.REFERENCES:NIL.Figure 1.AI-Tissue Classifier Results of Example Trainer and Test Images.Figure 2.Percent Histological Features as Identified by the AI-Tissue Classifier and Stratified by Percent Immune Infiltration (High to low, top 50% of immune infiltrate)Acknowledgements:NIL.Disclosure of Interests:None declared.

Background:Identifying immune populations in Lupus Nephritis (LN) kidney has the potential to greatly aid in defining disease processes. Immunohistochemistry is the gold standard for antibody staining in microscopy, though a lack of multiplexing limits its utility in the use of discovery. Here we demonstrate a serial Immunohistochemistry (sIHC) staining workflow and analysis pipeline that allows for multiplexing of 18 antibodies on a single section.Objectives:To develop a multiplex methodology and analyses pipeline in order to evaluate immune populations in LN kidney, then apply these methodologies on a large set of LN Kidney samplesMethods:Samples were identified and submitted for preliminary analyses from the 127 clinically indicated kidney biopsies recruited as part of the LN Accelerating Medicines Partnership (AMP) project.Staining: 10μm sections from FFPE blocks of LN kidney biopsies were collected. Sections were deparaffinized, rehydrated, and antigen retrieved with boiling citrate buffer. Slides were blocked for peroxidases, washed, and incubated with primary antibody and secondary HRP reagents. Sections were counterstained with AEC-Red Chromogen and Hematoxylin, then imaged at 40X magnification on a whole-slide scanner. Slides were decolored in 90% Ethanol, stripped in an antibody elution buffer, and then cycled for all markers.Image Stacks: Images were imported into HALO-AI v3.5 (Indica Labs) for deconvolution, pseudo-coloring, co-registration, and alignment. Registered images were fused to create a single composite image with all 18 markers and a representative DNA and counterstain channel (20-channels in total). Tissue regions were identified by a DenseNet V2 AI tissue classifier trained on a co-registered IHC image and individual cell objects were identified by nuclear detection and segmented by cell distance boundaries.Analysis: Single-cell marker expression datasets were analyzed in R. Cells located on the edge of the tissue or in artificial wrinkles in the tissue were removed. Marker expression values underwent CLR-normalization and standardization within each sample. All data were then combined and harmonized by Harmony to further minimize batch effect. PCA and UMAP were used for dimensional reduction, and KNN and SNN algorithms were applied for clustering similar cell phenotypes. Candidate immune cell clusters were defined as clusters of cells with CD45 expression levels > 2 standard deviations. Cell clusters with high keratin marker expression were removed to eliminate renal tubular epithelial cells and remaining clusters were then re-clustered via KNN and SNN to identify distinct immune cell types via heatmaps and feature plots.Results:By producing co-registered and fused images all markers could be visualized within the same image/tissue section. After single-cell segmentation and cluster phenotyping, cells were identified as either immune or non-immune population within key histological regions within the tissue. All cells were classified by phenotype and location across all samples for better evaluation of LN kidney immune processes.Conclusion:For the first time, we demonstrate imaging and analysis of 20 markers via sIHC on a single section, which produced a large, spatially informed, clinically-relevant dataset of LN Kidney Biopsies. sIHC can be successfully employed to perform multiplexed whole-slide analysis harnessing both the reliability and the subcellular resolution of IHC.REFERENCES:NIL.Figure 1.sIHC Staining and Imaging PipelineFigure 2.Single-Marker IHC, sIHC Composites, Cell-Segmentation, and AI-Based Tissue Classification Example ImagesAcknowledgements:NIL.Disclosure of Interests:None declared.

Background:10X Visium spatial transcriptomics evaluates mRNA-binding tiles (55μm diameter) of a sectioned tissue, yielding heterogeneous cell sampling. The SpatialPCA algorithm was developed to identify like tissue regions and determine the cellular context of spatial coordinates using homogenous tissue types with distinct boundaries [1] However, while proficient in analyzing homogenous tissue types, SpatialPCA is less effective at differentiating like tiles from heterogenous tissue types.Objectives:To develop a novel analysis pipeline, HistoSpatialPCA, that leverages spatially aware dimensional reduction to model spatially correlated structures across tiles from heterogeneous tissue types such as a target tissue of Sjögren’s Disease (SjD), the minor salivary gland (MSG). Then, to apply HistoSpatialPCA to MSGs biopsied from SjD patients and healthy controls (HC) to identify disease-specific differential gene expression (DE) and pathway dysregulation in the salivary gland.Methods:MSG sections were arranged on 10X Visium capture slide chambers. Nuclei segmentation and classification was performed, followed by images annotation by tissue type (fibrosis, glandular, inflammatory, fat) (HALO Image Analysis Platform). Imaging data were extracted from tiles and integrated with spatial coordinates using HistoPCA. After quality control to filter low-quality and non-tissue tiles, SpatialPCA was performed [1]. Subsequently, data integration (Harmony) and UMAP with KMeans clustering were performed. SjD case-control differential expression (DE) was analyzed using pseudo-bulk gene expression. Finally, DE transcripts were analyzed by Ingenuity Pathway Analysis.Results:HistoSpatialPCA, followed by UMAP with KMeans clustering, detected 34,948 tiles from n=41 subjects, resulting in 8 distinct clusters in the MSG (Figure 1A,B). Comparison of dysregulated genes and pathways revealed cluster-specific differences between Ro+ and Ro- SjD cases verses HCs (Figure 1C). Ro+ SjD cases exhibited dysregulation across all clusters, whereas Ro- cases showed no significant dysregulated pathways in clusters 0, 2, and 7 and fewer altered pathways in clusters 1 and 6. Rank order of the dysregulated pathways also differed between Ro+ and Ro- SjD cases. Interferon gamma was the top pathway in all SjD cases and Ro+ across all clusters, but was only dysregulated in Ro- cluster 5 and modestly in clusters 1 and 3. Cluster 5 was the most similar between Ro+ and Ro- and showed the highest percentage of inflammation (upregulation of many proinflammatory pathways; downregulation of CTLA4, IL-10, and PD-1 signaling).Conclusion:HistoSpatialPCA successfully grouped like tiles from spatial transcriptomic analysis of heterogeneous MSG. Cluster annotation, followed by DE and pathway analyses revealed dysregulation of tiles across all clusters in Ro+ SjD cases, while Ro- cases exhibited the most pronounced dysregulation in cluster 5, 4, and 3. Notably, cluster 5 demonstrated the highest inflammation, sharing many dysregulated pathways between Ro+ and Ro- SjD cases. This spatially aware technology will provide new insights into the role of different cell/tissue types in SjD pathobiology of the salivary gland.REFERENCES:[1] Shang L, et al. Nat Commun. 2022; 13:7203.Acknowledgements:National Institutes of Health (NIH): R01ARO7385503 (CJL); R21 DE029302 (ADF).Disclosure of Interests:Songyuan Yao: None declared, Rick Wilbrink: None declared, Paulina Czarnota: None declared, Matthew Caleb Marlin: None declared, Bhuwan Khatri: None declared, Anna M Stolarczyk: None declared, Cherilyn Pritchett Frazee: None declared, Chuang Li: None declared, Kyle Wright: None declared, Kandice L Tessneer: None declared, Judith A. James: None declared, R Hal Scofield Received consulting fees from Johnson and Johnson Innovative Medicine (formerly Janssen) and Merk Pharmaceuticals., Indra Adrianto: None declared, Astrid Rasmussen: None declared, Joel M Guthridge: None declared, A Darise Farris Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/23)., Christopher J Lessard Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/23).

Background:SjD is a chronic, heterogeneous autoimmune disease characterized by signs of oral and ocular dryness in patients who are either positive for anti-Ro/SSA and/or focus score positive on biopsies from labial/parotid salivary glands. Although involvement of salivary glands is a distinguishing disease feature, little is known about the transcriptomics of discrete cell populations in the glands.Objectives:To determine the optimal dissociation approach and single-cell (sc) transcriptomics platform for the use of matched viably frozen and fixed minor salivary glands (MSG) biopsied from SjD patients and healthy controls (HCs); then, to expand the dataset using the optimal approach.Methods:Viably frozen MSG (n=7; 2 SjD and 5 HCs) were thawed, dissociated, and counted, showing >90% viable cells. Samples were split and captured using 3’ and 5’ scRNA-seq, targeting to capture 8000-10000 cells. In addition, 25μm sections from formalin-fixed paraffin-embedded (FFPE) tissues containing 4-5 labial salivary glands (LSG) each were dissociated, counted, labeled following the 10X Flex/scFFPE protocol, and captured, yielding 8000-20000 cells per subject. Raw sequencing data were analyzed using 3’, 5’, or scFFPE analysis pipelines in CellRanger. Ambient RNAs were corrected (SoupX) and doublets detected (scDblfinder). Cells with feature counts <200 & >5000, mitochondrial percent >5%, and doublets were removed. Samples were merged, integrated, and batch corrected using Harmony (Seurat). Data were normalized, scaled, and dimensional reduction performed using UMAP (Seurat). Cell clusters were annotated using CellTypist and MSG reference data1. Cells were separated into immune and non-immune clusters and annotated using reference data from CellTypist or MSG reference data1. Differentially expressed (DE) genes in each cell type in SjD compared to HC were identified using pseudobulk approach (DESeq2). DE genes (p<0.05) were subjected to Ingenuity Pathway Analysis (IPA).Results:Overall quality of the 3’ and 5’ approaches were similar. In comparison, scFFPE yielded similar feature/gene counts per cell (median = 5186 (3’), 7452 (5’), 4571 (scFFPE)), but was superior in the reduction of ambient RNA (or soup) and in identifying cell populations that were very low in 3’ and 5’ assays: seromucous acini, basal cells, macrophages, myoepithelial cells, among others. The scFFPE dataset was expanded to include 8 Ro+ SjD and 9 HCs, yielding 477,071 cells before QC, 371,454 after QC, among 23 cell states (Figure 1A). It revealed significantly DE genes in Ro+ SjD compared to HCs that mapped to common and different pathways between immune (Figure 1B and C) and glandular cells (Figure 1B and D). For example, many cell types showed dysregulation of Type I interferon, including downregulation (plasma cells, plasmacytoid DCs, naive B cells), upregulation (migratory DCs, DC1, serous and mucus acini, macrophages, mast cells), or no dysregulation (DC2, memory B, cytotoxic T, helper T, and natural killer cells).Conclusion:scFFPE yielded superior single-cell data compared to 3’ and 5’ using viable cells. Further, the data analysis method permitted discrimination of targeted/affected cell types from effector cell types. Expansion of the scFFPE pilot study showed many DE genes and dysregulated pathways. Ongoing work with AMP-AIM STAMP projects will further expand this dataset.REFERENCES:[1] Huang N, et al. SARS-CoV-2 infection of the oral cavity and saliva. Nat Med. 2021 May; 27(5):892-903.Acknowledgements:National Institutes of Health: UM2 AR067678, UC2 AR081023, UC2 AR081032, UC2 AR081032-S1, UC2 AR081032-02S1, UC2 DE032254, UC2 AR081033, P30 AR073750, U54 GM104938, NIDCR 15-D-0051; Presbyterian Health Foundation; OMRF Institutional Funds; NIH, NCI Intramural Program; Jerome L. Greene Foundation.Disclosure of Interests:Bhuwan Khatri: None declared, Anna M Stolarczyk: None declared, Matthew Caleb Marlin: None declared, Miles Smith: None declared, Cherilyn Pritchett Frazee: None declared, Margaret Beach: None declared, Eileen Pelayo: None declared, Zohreh Khavandgar: None declared, Paola Pérez: None declared, David E Kleiner: None declared, Stephen E Hewitt: None declared, Kevin Wei Received a sponsored research agreement from Gilead Sciences and 10X Genomics., Erin M Theisen: None declared, Kandice L Tessneer: None declared, Soumya Raychaudhuri: None declared, Michael B Brenner: None declared, Johann E. Gudjonsson: None declared, Nir Hacohen: None declared, Judith A. James: None declared, R Hal Scofield Received consulting fees from Johnson and Johnson Innovative Medicine (formerly Janssen) and Merk Pharmaceuticals., Stephen Shiboski: None declared, Astrid Rasmussen: None declared, Alan Baer Received consulting fees from Bristol Myers Squibb (BMS) and iCell Gene Therapeutics., A Darise Farris Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/2023)., Caroline Shiboski: None declared, Blake M Warner Receives funding to support research from Pfizer, Inc., and Mitobridge, Inc., a subsidiary of Astellas Bio., Joel M Guthridge: None declared, Christopher J Lessard Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/2023).

Thermal water of Thermopylae and from other geothermal fields located in the southern part of the Sperchios basin (central Greece) are characterized by high salinity (total dissolved salts, or TDS, range from 1.2 to 30.3 g L-1) associated with a degassing of CO2. To determine the mineralization processes, geochemical and isotopic investigations (major elements, 18O, 2H and 13C) have been carried out upon 17 thermal waters from springs and boreholes. This study emphasizes that all the thermal waters result from the mixing of a seawater end-member, several fresh water components depending on the field location, and a mantle-derived CO2 rising upward through an E-W fault system. The seawater identified in the thermal mixture is likely to be evolved Aegean seawater (ASW). Once intruded into the basin sediments, the trapped seawater has its chemical content modified by both water-rock interactions and massive dissolution of the deep CO2 (pCO2 of 100.5 atm). The modelling performed with PHREEQC indicates that the anomalous major ion ratios measured in the so-called evolved ASW are explained by the dissolution of calcite and dolomitization process associated to precipitation of gypsum within the thermal aquifer. (PUBLICATION ABSTRACT)

The volume variation of a glacier is the actual indicator of long term and short term evolution of the glacier behaviour. In order to assess the volume evolution of the Austre Lovénbreen (79° N) over the last 47 years, we used multiple historical datasets, complemented with our high density GPS tracks acquired in 2007 and 2010. The improved altitude resolution of recent measurement techniques, including phase corrected GPS and LiDAR, reduces the time interval between datasets used for volume subtraction in order to compute the mass balance. We estimate the sub-metre elevation accuracy of most recent measurement techniques to be sufficient to record ice thickness evolutions occurring over a 3 year duration at polar latitudes. The systematic discrepancy between ablation stake measurements and DEM analysis, widely reported in the literature as well as in the current study, yields new questions concerning the similarity and relationship between these two measurement methods. The use of Digital Elevation Model (DEM) has been an attractive alternative measurement technique to estimate glacier area and volume evolution over time with respect to the classical in situ measurement techniques based on ablation stakes. With the availability of historical datasets, whether from ground based maps, aerial photography or satellite data acquisition, such a glacier volume estimate strategy allows for the extension of the analysis duration beyond the current research programmes. Furthermore, these methods do provide a continuous spatial coverage defined by its cell size whereas interpolations based on a limited number of stakes display large spatial uncertainties. In this document, we focus on estimating the altitude accuracy of various datasets acquired between 1962 and 2010, using various techniques ranging from topographic maps to dual frequency skidoo-tracked GPS receivers and the classical aerial and satellite photogrammetric techniques.

As a follow-up to the observations of Charles Robertson from 1884 to 1916, we revisited the Carlinville, Illinois, area between 18 August 1970 and 13 September 1972 to sample and identify bee species (Hymenoptera: Apoidea). We concentrated on collecting nonparasitic bees (and excludedApisandBombus) visiting 24 plant species that bloomed at various times of the year, and upon which Charles Robertson found many bee species. For example, we collected most intensively on spring-bloomingClaytonia virginicaand fall-bloomingAster pilosus, upon which Robertson reported 58 and 90 bee visitors, respectively. Bees were also collected on an opportunistic basis at some other plants. We updated the species names used by Robertson for revisions and synonymies. This paper summarizes a comparison of the two collections, made about 75 years apart at the same small geographic location. The study considers 214 valid bee species that Robertson collected plus an additional 14 species found by us but not by Robertson. Of these 214, we collected 140 species. The absence of most of the remaining 74 species that we did not collect can be explained by examining their plant preferences. Robertson did not record 47 of these 74 species on the 24 plant species where we collected intensively, and he observed 19 more species on only one or two of the 24 plant species. Additionally, he observed 21 of them on only one of the 441 plants he studied. Of the bee species found by Robertson on the 24 plant species, we collected 82% on the same plant species. The land uses and land cover on Macoupin County’s 225,464 ha (558,080 acres), which bear directly on the type and availability of habitat for bees and their host plants, varied considerably over two centuries. For example, in the early 1800s, land cover was about 73% prairie and 27% forest. The estimated 59,792 ha (148,000 acres) of forested land in 1820 diminished to 24,644 ha (61,000 acres) by 1924. It then grew to 34,340 ha (85,000 acres) by 1962. Agriculture is the predominant land use; in 1967, 59% of the land was in harvested crops (primarily row crops) and 15% was in pasture. Despite habitat changes and the passage of 75 years, our 1970 and 1972 Carlinville collections show a high degree of similarity with those of Robertson, possibly because diverse habitats within the agricultural matrix contained the host plants and nesting sites required by the bees. We recommend that a third survey of this area be undertaken as part of a long-term study made possible by the meticulous 19th century records of Charles Robertson, which must be preserved.