Reference Dataset Research Articles

Chairside 3-D printed impression trays: a new approach to increase the accuracy of conventional implant impression taking? An in vitro study

PurposeA high transfer accuracy of the intraoral implant position to a model is required, to manufacture implant-supported restorations. However, clinically relevant deviations persist between the intraoral implant position and the model obtained, even for the benchmark conventional custom implant impressions with polyether. Thus, new approaches using 3-D printed impression trays may increase the transfer accuracy of implant impressions. The ability to adjust parameters such as the thickness of the layers and the influence of the openings in the impression tray could potentially affect accuracy.MethodsFour different types of impression trays (n = 10 for each group) for the conventional impression technique were investigated: conventional custom impression tray, customized foil tray, chairside 3-D printed impression tray with the SHERA system, and the Primeprint system using an implant master model with four implants in the posterior region and a reference cube. After plaster model casting, all models were measured using a coordinate measuring machine, and the deviation from the reference dataset was determined. A statistical ANOVA analysis was performed (p < 0.05).ResultsChairside 3-D printed impression trays showed the best results, followed by conventional custom impression trays. Implant impressions obtained using a customized foil tray exhibited the lowest accuracy. Statistically significant differences were observed between 3-D printed impression trays and conventional custom impression and customized foil trays (p < 0.05). Whereas, the implant position did not have any significant influence on accuracy (p > 0.05).ConclusionsChairside 3-D printed impression trays significantly increase the transfer accuracy for implant impression taking.Graphical

Numerical treatment for the desirability of Hall current and activation energy in the enhancement of heat transfer in a nanofluidic system

The growing attractiveness of Artificial Neural Networks (ANNs) derives from their exceptional effectiveness in handling difficult and exceptionally nonlinear mathematical ideas. In complicated disciplines such as fluid mechanics, biological computation, and the field of biotechnology ANNs provide a diverse computing framework that is extremely valuable. This article's major aim is to harness the capabilities of the Levenberg-Marquardt technique with backpropagation intelligent neural networks (LM- BPINNs) to study there is still a lack of clarity regarding the mechanics underlying the increased heat transfer caused by dispersed nanoparticles. The using proposed LM-BPINNs to improve the heat transmission use activation energy and Hall current phenomena in nanofluid (HTAHCNF). The data set is obtained by using Lobatto-III. A method and then ANNs is applied. The LM- BPINNs technique is applied by utilizing reference datasets, with 80% of the dataset devoted to training, 10% to testing, and 10% to verification. The precision/accuracy and converging of developed LM- BPINNs are validated based on the obtained reliability via efficient fitness achieved on mean squared error (MSE), comprehensive regression analysis, and appropriate error histogram visualizations. A diminished MSE indicates that the model's predictions are more reliable. The outcome is consistent with getting a minimal absolute error close to zero, exhibiting the effectiveness of the proposed approach.

Intelligent Levenberg–Marquardt neural network solution to flow of carbon nanotubes in a nozzle of liquid rocket engine

In this research, we utilized artificial neural networks along with the Levenberg–Marquardt algorithm (ANN-LMA) to interpret numerical computations related to the efficiency of heat transfer in a regenerative cooling channel of a rocket engine. We used a mixture of Kerosene and carbon nanotubes (CNTs) for this purpose, examining both single-wall carbon nanotubes and multi-wall carbon nanotubes. The primary equations were converted into a dimensionless form using a similarity transformation technique. To establish a reference dataset for ANN- LMA and to analyze the movement and heat transfer properties of CNTs, we employed a numerical computation method called bvp4c, which is a solver for boundary value problems in ordinary differential equations using finite difference schemes combined with the Lobatto IIIA algorithm in MATLAB mathematical software. The ANN- LMA method was trained, tested and validated using these reference datasets to approximate the solutions of the flow model under different scenarios involving various significant physical parameters. We evaluated the accuracy of the proposed ANN- LMA model by comparing its results with the reference outcomes. We validated the performance of ANN- LMA in solving the Kerosene-based flow with CNTs in a rocket engine through regression analysis, histogram studies, and the calculation of the mean square error. The comprehensive examination of parameters undertaken in this research endeavor is poised to provide invaluable support to aerospace engineers as they endeavor to craft regenerative equipment with optimal efficiency. The pragmatic implications of our study are wide-ranging, encompassing domains as diverse as aerospace technology, materials science, and artificial intelligence. This research holds the potential to catalyze progress across multiple sectors and foster the evolution of increasingly efficient and sustainable systems.

Unveiling Hypothalamic Molecular Signatures via Retrograde Viral Tracing and Single-Cell Transcriptomics

Despite the importance of hypothalamic neurocircuits in regulating homeostatic and survival-related behaviors, our understanding of the intrinsic molecular identities of neural components involved in these complex multi-synaptic interactions remains limited. In this study, we constructed a Cre recombinase-dependent pseudorabies virus (PRVs) capable of crossing synapses, coupled with transcriptome analysis of single upstream neurons post-infection. By utilizing this retrograde nuclear Connect-seq (nuConnect-seq) approach, we generated a single nuclei RNA-seq (snRNA-seq) dataset of 1,533 cells derived from the hypothalamus of CRH-IRES-Cre (CRH-Cre) mice. To ensure the technical validity of our nuConnect-seq dataset, we employed a label transfer technique against an integrated reference dataset of postnatal mouse hypothalamus comprising 152,524 QC-passed cells. The uniqueness of our approach lies in the integration of diverse datasets for validation, providing a more nuanced diversity of hypothalamic cell types. The presented validated dataset may deepen our understanding of hypothalamic neurocircuits and underscore the essential role of comprehensive integrated transcriptomic data for technical validity.

Suaeda salsa spectral index for Suaeda salsa mapping and fractional cover estimation in intertidal wetlands

Suaeda Salsa (S. salsa), with short and red-purplish plants, is a typical pioneer saltmarsh species in the intertidal wetlands of temperate East Asia. It has important ecological, economic, and recreational values. In the past few decades, S. salsa has severely degraded in coastal China, which has brought widespread attention from regional and local governments. As a result, extensive S. salsa restoration projects have been initiated in recent years. It is urgently needed to develop satellite-based methods for both S. salsa mapping and fractional cover (FC) estimation because degradation and recovery of S. salsa are manifested by changes in both area and plant cover. However, accurate mapping and FC estimation of S. salsa are challenging because (1) S. salsa in intertidal areas have low FC and (2) heterogeneous soil backgrounds in wetlands greatly impact the spectral reflectance observed by satellites. To address these issues, this study proposed a new Suaeda Salsa Spectral Index (SSSI) to support accurate detection and FC estimation of S. salsa. The SSSI was designed based on the laboratory spectral measurements by considering variations in wetland soil moisture and by taking advantage of the reddish color of S. salsa. It consists of two components, one of which utilized blue, green and red bands to separate S. salsa from green vegetation, and the other component utilized a modification of the Soil Adjusted Vegetation Index (SAVI) to reduce the impact of soil background and maintain a linear relationship with S. salsa FC. SSSI was then applied on Sentinel-2/GF-1 images over the Yellow River Delta (YRD) and Liao River Delta (LRD), China. Based on SSSI, a simple thresholding approach was used to identify S. salsa, and a linear regression model was used to estimate FC. With reference datasets provided from field investigations, Unmanned Aerial Vehicle multispectral images and high-spatial resolution satellite images, our results show that the SSSI was able to detect low-coverage S. salsa (FC > 20 % in YRD and FC > 10 % in LRD), and the S. salsa maps had an overall accuracy over 94%. The SSSI-FC models achieved good estimation accuracies (R2 = 0.77 ∼ 0.86, RMSE = 7.55 % ∼ 9.79 %). Compared to the Normalized Difference Vegetation Index (NDVI) and SAVI, SSSI alleviated the impacts from soil backgrounds and provided better S. salsa FC estimations, particularly for low-coverage S. salsa. SSSI has great potential in supporting continuous monitoring of S. salsa dynamics and evaluating the effectiveness of S. salsa restoration projects.

A brain‐wide risk score of Alzheimer’s disease based on multimodal neuroimaging predicts cognition in large N&gt;37000 population data

AbstractBackgroundAlzheimer’s disease (AD) is the most frequent age‐related dementia impairing memory, thinking, and social skills. Much research has been conducted in recent years to investigate the genetic risk of AD, but little effort has been made to find a brain imaging‐based AD risk evaluation. Here, we proposed a multimodal neuroimaging‐based measure of AD risk, called brain‐wide risk score (BRS).MethodFunctional network connectivity (FNC) and voxelwise gray matter (GM) from the Open Access Series of Imaging Studies (OASIS)‐3 (N = 1302 samples) cohort was used as a reference dataset to define mild cognitive impairment (MCI) and control (CN)group to evaluate individuals in the UK Biobank (N = 37,784) dataset. We calculated the mean of FNC and GM of MCI and CN. Next, we removed the mean FNC and GM from each sample FNC and GM of the target dataset. Then, we calculated the distance between the target dataset and CN (i.e., dist_CN) and MCI (i.e., dist_MCI) of the reference dataset using Euclidian distance. Finally, we calculated Δdiff = dist_CN‐dist_MCI as a BRS of AD, where the positive values are associated with higher AD BRS. Using two BRSs estimated based on FNC and GM for target dataset, we identified four AD biotypes. Samples with two negative Δdist represent low AD risk (Biotype1), samples with two positive Δdist represent high AD risk (Biotype4), and samples with one negative and one positive Δdist represent moderate AD risk (Biotype2/3). Next, we compared cognitive scores including fluid intelligence (FI) and reaction time (RT) across four biotypes.ResultFig.1 shows the 2D histogram of two BRS estimated from FNC and GM. We identified 11704 participants as biotype1, 10910 as biotype2, 7804 as biotype3 and 7366 as biotype4. We also found that Biotype1, with lower AD BRS, significantly shows a higher FI score than Biotype3 and Biotype4 (Fig.2, FI panel). Additionally, Biotype 4, with the highest BRS, had the most increased RT among all four AD biotypes (Fig.2, RT panel).ConclusionWe develop a new multimodal neuroimaging‐based BRS based which we identified four AD biotypes. We found that BRS predict cognitive scores in the identified biotypes.

A full-body motion capture gait dataset of 138 able-bodied adults across the life span and 50 stroke survivors

This reference dataset contains biomechanical data of 138 able-bodied adults (21–86 years) and 50 stroke survivors walking bare-footed at their preferred speed. It is unique due to its size, and population, including adults across the life-span and over 70 years, as well as stroke survivors. Full-body kinematics (PiG-model), kinetics and muscle activity of 14 back and lower limbs muscles was collected with a Vicon motion capture system, ground-embedded force plates, and a synchronized surface EMG system. The data is reliable to compare within and between groups as the same methodology and infrastructure were used to gather all data. Both source files (C3D) and post-processed ready-to-use stride-normalized kinematics, kinetics and EMG data (MAT-file, Excel file) are available, allowing high flexibility and accessibility of analysis for both researchers and clinicians. These records are valuable to examine ageing, typical and hemiplegic gait, while also offering a wide range of reference data which can be utilized for age-matched controls during normal walking.

What time window do AD trajectories depart from normal? Estimating age‐related trajectories to chart preclinical phase of Alzheimer’s disease and select trial‐ready cohorts

AbstractBackgroundThe preclinical phase of Alzheimer’s disease (AD), where pathology slowly accumulates years before cognitive impairment becomes apparent, could offer a treatment window with the greatest potential to preserve cognitive function before downstream pathological processes gather momentum. Characterizing when biomarker trajectories deviate from normal ageing, and the heterogeneity therein, could facilitate targeted trial recruitment and improved biomarker‐based evidence of disease modification. However, reliably identifying early abnormal changes can be challenging due to various confounds, such as age and vascular factors, as well as disease heterogeneity.MethodData from the following cohorts were analysed: (1) individuals at risk for, or affected by, autosomal dominant AD (ADAD), including the Dominantly Inherited Alzheimer Network; (2) Insight 46, a neuroimaging substudy of the MRC National Survey of Heath and Development; (3) pre‐randomization study data from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4); and (4) the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Linear regression assessed differences between at‐risk groups (biomarker‐ or mutation‐positive) and normal ageing. Longitudinal changepoint models estimated when atrophy rates deviate from normal ageing trajectories. Normative modelling, based on large reference datasets, identified outliers of regional atrophy assessed within‐group heterogeneity at the individual level. Data‐driven disease progression models (DPMs) were used to estimate quantitative signatures of AD, including imaging biomarkers.ResultDPMs reveal that amyloid markers deviate first in ADAD, roughly ten years before neurodegenerative markers. Changepoint models indicate that atrophy rates in ADAD become abnormal 3‐8 years before expected symptom onset, with longitudinal tau PET data suggesting a similar trend. While cross‐sectional volumetric measures showed no evidence of association with amyloid markers, atrophy rates were independently related to both amyloid positivity and markers of cerebrovascular disease, indicating similar, additive effects. Both DPMs and normative modelling found evidence of heterogeneity within imaging biomarkers in ADNI and A4, which explained variability in cognitive decline observed in at‐risk participants.ConclusionBiomarker data from preclinical AD suggests a long temporal gap between amyloidosis and subsequent changes. This represents a treatment opportunity to remove amyloid while minimising irreversible damage. There is evidence of heterogeneity within the biomarker trajectories, which could impact the ability to detect disease modification.

Molecular Subtypes in BCR::ABL1-Positive ALL Are Defined By Cellular Origins and Cooperating Genomic Events

Two developmental trajectories are acknowledged in BCR::ABL1-positive acute lymphoblastic leukemia (ALL) as distinct diagnostic entities by the ICC classification: ‘lymphoid-only’ with BCR::ABL1 restricted to the leukemic B precursor compartment vs. ‘multilineage’ with BCR::ABL1 involvement also in other hematopoietic lineages. Diagnostic standards for establishing this distinction are lacking and associated biological and clinical features are insufficiently characterized. To establish developmental trajectories, biological phenotypes, and clinical impact we analyzed n=277 BCR::ABL1-positive ALL patients (age: 2-84 years, median: 46) including our GMALL reference data set (n=113) and two independent validation cohorts (MLL: n=61; St Jude's: n=103; Gu Z, et al. Nat Genet. 2019). Unsupervised gene expression analysis (RNA-Seq: n=277) identified two major gene expression clusters with two further sub-clusters each (Figure A). This clustering was confirmed by a machine learning classifier trained on the GMALL data set which achieved similar grouping of samples in the external validation cohorts. BCR:: ABL1-FISH on FACS-sorted bone marrow/peripheral blood samples revealed the presence of BCR::ABL1 in leukemic as well as myeloid cells in n=18/18 samples from the 1 st major cluster (termed 'multilineage'). In the 2 nd major cluster ('lymphoid'), BCR::ABL1 was restricted to the lymphoid lineage in n=13/16 samples (p&amp;lt;0.001), with n=3/16 cases harboring BCR::ABL1-positive myeloid cells at lower frequencies compared to the multilineage cluster. In each cluster, two patients also harbored BCR::ABL1-positive mature B cells. T cells remained BCR::ABL1-negative. Diagnostic FACS data and analysis of proximity to normal human lymphoid gene expression confirmed more frequent myeloid co-expression and higher proximity to normal pro-B cells in the multilineage cluster, whereas lymphoid cases had increased lymphoid surface marker expression and a stronger proximity to normal pre-B I cells. Genomic profiling using WGS/SNParrays (n=160) revealed significant enrichment for genomic events in the four gene expression sub-clusters: focal deletions comprising exons 1 and 2 of HBS1-like translational GTPase ( HBS1L) or monosomy 7 were strongly enriched in the two multilineage sub-clusters ('delHBS1L'; n=20/27 vs. n=3/133 in remaining cohort or 'del7'; n=16/25 vs. n=10/135; p&amp;lt;0.001). Remarkably, a novel alternative HBS1L transcript with a putative TSS in intron 3 was highly expressed in both multilineage sub-clusters but not in BCR::ABL1 negative ALL cases or healthy B lymphoid progenitors, suggesting this transcript as novel cooperating event in multilineage BCR::ABL1-positive ALL. One lymphoid sub-cluster was enriched for homozygous deletions in IKZF1 ('IKZF1'; n=11/55 vs. n=4/105; p=0.008), whereas the other was enriched for homozygous CDKN2A/B deletions (n=21/53 vs. n=3/107; p&amp;lt;0.001) and PAX5 deletions (n=33/53 vs n=26/107; p&amp;lt;0.001; 'CDKN2A/PAX5'). Hyperdiploid karyotypes were exclusive to the lymphoid main cluster, mostly in CDKN2A/PAX5. We analyzed the clinical implications of these newly established BCR::ABL1-positive ALL subtypes in our homogenously treated GMALL adult patient cohort (n=98, first diagnosis 2014-2021) including TKI treatment combined with age-adapted chemotherapy, MRD monitoring and allo-SCT in 1 st CR (n=84/98, 86%). Overall survival (OS) at 3 years was uniformly high in multilineage and lymphoid cases (70%±6% vs. 70%±8%; p=0.890; Figure B). However, analysis of the sub-clusters revealed an inferior 3-years OS for the IKZF1 -/- enriched cluster in contrast to excellent outcome in hyperdiploid cases and intermediate outcomes in the remaining sub-clusters (Figure B). These data highlight that transcriptomic signatures in BCR::ABL1-positive ALL are driven by developmental disease origins (‘lymphoid’ vs. ‘multilineage‘) and specific patterns of corresponding genomic events. Novel molecular subtypes of BCR::ABL1-positive ALL have distinct outcomes in the context of current GMALL treatment protocols. Gene-expression based subtype definitions enable classification of BCR::ABL1-positive ALL according to ICC definitions based on RNA-Seq data alone. These definitions have been implemented in ALLCatchR - our freely available tool for ALL subtype allocation - to facilitate validation and application in clinical diagnostics.

Single Cell Transcriptomics of Bronchiolitis Obliterans Syndrome

Bronchiolitis obliterans syndrome (BOS) is a manifestation of pulmonary chronic graft versus host disease (cGVHD), a serious complication of allogeneic stem cell transplantation (ASCT). BOS also occurs following bi-orthotopic lung transplant (BOLT), where it is termed chronic lung allograft dysfunction (CLAD)-BOS and is the leading cause of long-term allograft failure. In both contexts, fibrotic obliteration of the small airways leads to progressive respiratory failure. Since the molecular mechanisms leading to fibrotic airway remodeling in humans are poorly understood, we used single cell RNA-sequencing to map intercellular interactions between immune (CD45+ve) and non-immune (CD45-ve) cells of lung explants from patients undergoing first lung transplantation for cGVHD-BOS (n=1) or second lung transplantation for CLAD-BOS (n=2) compared to normal controls (NC) (n=3). BOS patients ranged in age from 44 to 61 years old; NC were similar in age and died of cerebrovascular accidents. Lungs were surgically removed at the time of BOLT (BOS samples) or at autopsy (NC). CD45+ve and CD45-ve cells were magnetically sorted and freshly processed. Single cell libraries were prepared using the 10X Genomics Chromium Controller and sequenced on an Illumina Novaseq 6000 instrument. Quality control filtering, transcript count normalization, integration, PCA, UMAP projection, and cell clustering were performed with Seurat in R v4.2.1. CD45+ve and CD45-ve cells were annotated in Azimuth using the Human Lung Cell Atlas and LungMAP Cell Cards reference data sets, respectively. Differences in gene expression between conditions were evaluated with the Seurat FindMarkers function. Inter-cellular communications were characterized with CellChat. Interaction strength was calculated as a function of ligand/receptor pair expression, and incoming and outgoing signaling was classified using network centrality analysis. We identified 45,932 CD45+ve cells (28,241 in BOS and 17,691 in NC) and 33,428 CD45-ve cells (23,390 in BOS and 10,038 in NC) including 17 distinct cell types in the CD45+ve fractionand 24 in the CD45-ve fraction. As samples came from patients with end-stage disease, we hypothesized that CD45+ve cells would be highly activated while CD45-ve cells would exhibit strongly fibrogenic programs. To test this hypothesis, we analyzed the incoming and outgoing signal strength for each cell type. Stromal cells such as type 1 (AF1) and type 2 alveolar fibroblasts (AF2), secondary crest myofibroblasts (SCMF), and pericytes had the strongest outgoing signals in BOS ( Figure 1). Except for AF2, incoming and outgoing signaling from stromal cells was stronger in BOS than in NC. These observations support stromal cells as important drivers of BOS pathogenesis. Next, we characterized signaling directionality between stromal cells and CD45+ve cells. In general, incoming signals to stromal cells from CD45+ve cells were weaker in BOS than in NC, while outgoing signals from stromal cells to CD45+ve cells were stronger. For example, signaling from stromal cells expressing THY1, which may attenuate fibrosis in fibrotic lung disease, to integrins on monocytes and macrophages was stronger in BOS. Likewise, BOS was enriched for signaling from collagens and fibronectin on stromal cells to CD44 on CD45+ve cells. As expected, genes implicated in fibrosis and wound healing were upregulated in stromal cells in BOS patients. In AF1 ( Figure 2), these included genes encoding collagens (COL6A3, COL14A1), actins (ACTA2), matricellular proteins (POSTN, TNC, SPARC), protease inhibitors (SERPINE1, TIMP1), hyaluronic acid synthetases (HAS2), proteoglycans (LUM, VCAN), and proteases (MMP19, ADAMTS9). Interestingly, we found evidence for active anti-fibrotic programs such as MMP19, PTGS2, and THY1. To our knowledge, this is the first study employing single cell transcriptomics of lung explants from patients with cGVHD-associated BOS. Robust signaling from stromal cells to CD45+ve cells may support their role in modifying the inflammatory infiltrate in BOS. We identified outgoing, stromal derived signaling associated with fibrotic (collagen and fibronectin) and anti-fibrotic programs (THY1). Our findings illustrate an interplay between pro- and anti-fibrotic programs even in end-stage BOS lungs, raising the tantalizing possibility to intervene therapeutically in this dyshomeostatic balance.

Defining the Drivers of Idelalisib-Related Early Autoimmune Toxicity in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) patients with disease resistant to BTK and BCL-2 inhibitors can potentially be treated with PI3K inhibitors to overcome resistance and improve outcomes. PI3K inhibitors such as idelalisib and duvelisib have shown promising efficacy in CLL patients, but severe toxicities remain a significant hurdle to their implementation in the clinic (PMID: 24615777). By investigating the molecular mechanisms that lead to severe early autoimmune toxicity with idelalisib, we hope to predict or prevent toxicity, thus expanding the use of this efficacious PI3K inhibitor class of drugs. To this end, the Brown lab showed that idelalisib-induced hepatitis is autoimmune. CyTOF analysis in CLL patients with early severe toxicity in a front-line idelalisib clinical trial cohort showed a significant decrease in CD4+ T regulatory cells (Treg) with a relative shift in favor of T effectors in peripheral blood. In addition, the Brown lab showed that the Th17 pathway is more active at baseline, with a further increase in patients with toxicity (PMID: 27247136, 36732326). Since the CYTOF data is restricted to fewer mostly T-cell markers in our studies, an unbiased single cell RNA sequencing (scRNA-seq) approach is warranted to explore the full effects of idelalisib on immune cells and CLL cells in patients with and without autoimmune toxicity. The question remains what molecular mechanisms drive the idelalisib-induced decrease in the Treg population, its effect on natural killer (NK) and CD8 T-cell function, and the activation of the Th17 pathway in CLL patients with early toxicity. We therefore carried out the first systematic effort to understand the mechanisms of idelalisib toxicity in molecular detail using combined scRNA and scTCR-seq. In this study, we profile T-cell enriched PBMCs of matched pre-treatment and on-idelalisib treatment samples from CLL patients with early severe toxicity and without any toxicity (n=4 CLL patients from each group, a total of 16 samples) using 10X single cell transcriptomic and TCR profiling. Raw sequencing data files were processed using the Cell Ranger suite (version 7.1.0) from 10x Genomics. Sample identification based on SNP information was done using the Popscle suite. Seurat package (version 4.9.9.9) was used for the single cell analysis. After filtering cells with fewer than 600 features, less than 1000 UMIs, mitochondrial content higher than 15%, 51,487 cells were left for the downstream analysis. To explore transcriptional heterogeneity and perform initial cell clustering, principal component analysis and nonlinear dimensional reduction using UMAP were applied using 20 dimensions. Leiden algorithm was used for cluster identification. Cell types were identified using Azimuth with the PBMC reference dataset and confirmed with expression plots. The initial observations of the cell proportions show that baseline samples from patients with early idelalisib toxicity have fewer Tregs, NK, and CD4+ cytotoxic T (CTL) cells compared to patients without toxicity. In addition, we observed a decline in Tregs, gamma-delta (γδ) T, and CD4+ CTL cells and a concurrent increase in proliferative CD4+ and CD8+ T-cells and proliferative NK on idelalisib treatment in the early toxicity group. On the contrary, CD4+ CTL and CD4+ T effector memory (TEM) cells are enriched with idelalisib therapy in patients without toxicities. At the same time, more CD4+ T-Cell Memory (TCM), γδ T, CD8+ naive T, CD8+ TCM, and Mucosal-associated invariant T (MAIT) cells are present in pretreatment samples from patients with toxicity compared to patients without toxicity. Furthermore, proliferative T-cells are depleted with idelalisib therapy in patients without toxicity whereas they are enriched in patients with early toxicity. To further characterize idelalisib-induced depletion of Tregs, we reclustered them to identify distinct subgroups and found particular clusters of Treg were depleted in patients with early toxicity. In the non-toxicity group, more diversity of Tregs was identified with depletion of some clusters but also an increase in a cluster. At present, we are integrating the scRNA &amp; scTCR-seq data to characterize the cellular diversity and heterogeneous phenotypic states of the T-cell populations and updated results unraveling molecular mechanisms that drive early severe toxicity in CLL patients with idelalisib treatment will be presented at the meeting.

Single-Cell RNA-Seq Analysis Reveals Distinct Tumor and Immunosuppressive T Cell Phenotypes in CLL Patients Treated with Ibrutinib

Introduction: The development of Bruton tyrosine kinase inhibitors (BTKIs) and their introduction into clinical practice represents a major advance in the treatment of chronic lymphocytic leukemia (CLL). Monotherapy with ibrutinib or other BTKIs generally do not induce complete remissions or undetectable minimal residual disease (uMRD) even with extended therapy. The reason why some patients relapse, and some have minimal residual disease is unknown. Therefore, there is a need to understand the differences between ibrutinib sensitive and resistant CLL cells along with the immune microenvironment to identify novel therapeutic targets. Methods: Here, we investigate the cellular heterogeneity of peripheral blood mononuclear cells from patients with CLL treated with ibrutinib using single-cell RNA sequencing. To understand ibrutinib resistance mechanisms, samples were collected from 8 patients at baseline (before treatment) (1), where BTK C481S is present with low variant allele frequency (VAF) (2) and at time of relapse (3). Data from these were compared to ibrutinib sensitive patient samples at baseline (1) (before treatment), 3yr (2) and 5yr (3) on treatment timepoints. Single cells were isolated using the 10X Genomics 5' immune profiling kit. Data were processed with CellRanger v3.1.0. Poor quality cells were filtered out based on a high proportion of mitochondrial reads and low overall feature count. Sample variation was reduced using Batchelor to aid clustering. Clustering was performed using the partitioning and Leiden methods. Cluster top markers were calculated using functions from monocle3. Identity mapping was performed using Seurat and a published PBMC reference dataset. Results: Single-cell RNA sequencing revealed transcriptional heterogeneity within the B cell cluster (Fig 1A). Three subpopulations of B cells were defined based on differential representation in the ibrutinib-sensitive and resistant patients. Ibrutinib sensitive cells showed enrichment of B cell populations with upregulation of MHC I molecules and TNF family members. We also identified that inflammatory response and metabolic-related pathways were decreased, whereas cellular response to stress and DNA repair programs were increased in the ibrutinib resistance samples. Gene module analysis showed that ibrutinib sensitive samples had an increase in T cell activation, RNA splicing, cell adhesion and migration programs during therapy. We hypothesized that the emergence of BTK C481S mutation in ibrutinib-resistant cells would lead to a distinct transcriptional profile. Long-read sequencing was performed using Oxford Nanopore technology. BTK-mutant cells were identified based on the presence of two or more unique RNA molecules detected with the C481S mutation and these were predominantly identified in the low-VAF and relapse samples. However, there was no significant difference between BTK-mutant cells and the remaining B cells. To see if T cell phenotypes reflected the differences observed in B cell transcriptional state, we characterized and quantified T cell populations according to ibrutinib response and investigated the changes in the T cell population. Ibrutinib markedly increased CD4+ and CD8+ T cell numbers in both resistant and sensitive CLL patients. However, there was a significant (p&amp;lt;0.05) enrichment of Tregs in the ibrutinib-resistant samples. We found a connection between the immunosuppressive phenotype in the microenvironment demonstrating tumor's intrinsic immunological reactive program with exhausted T cells. Also, we found significant difference in the expression of checkpoint molecules and suppressive genes in T cells (Fig 1B) and NK cells. Conclusion: Our findings demonstrate that the B cell population in ibrutinib-resistant and ibrutinib sensitive CLL patients exhibit distinct transcriptional states. At the single-cell level, our findings demonstrate a systematic picture of diverse malignancy and microenvironmental changes in CLL. Overall, these findings provide an insight into the complex molecular and cellular interactions of CLL that may have a crucial role in BTKI resistance.

Impact of Gamma-Secretase Inhibition on the Multiple Myeloma Immune Microenvironment

Background: Therapy with monoclonal antibodies, chimeric antigen receptor (CAR) T-cells, and bispecific T-cell engagers (BiTE) has significantly changed the treatment landscape for multiple myeloma (MM). B-cell maturation antigen (BCMA), a cell surface protein highly expressed in malignant plasma cells, has been established as an effective MM cell target, however, achieving long-term responses in relapsed or refractory patients remains a challenge. Resistance to BCMA targeting may result from the enzymatic cleavage of BCMA by surface membrane gamma-secretase resulting in reduced target antigen density on MM cells, and from the soluble BCMA ectodomain that may hinder tumor cell recognition through binding to circulating therapeutic antibodies or to CAR T-cells. Methods: We have previously demonstrated that gamma-secretase inhibitors (GSIs) abrogate enzymatic function and increase BCMA surface density on plasma cells with a concomitant decrease in soluble BCMA concentrations [Pont, MJ. et al. Blood, 134(2019)]. In a phase I, first-in-human clinical trial (n=18; relapsed/refractory MM) combining the GSI, crenigacestat, with anti-BCMA CAR T-cell therapy (FCARH143), we recently demonstrated that plasma cell BCMA antibody-binding capacity increased a median of 12-fold among 17/18 (94%) of participants after they received a 5-day GSI “run-in” (25 mg orally administered QOD for 3 doses) [Cowan AJ, et al. Lancet Oncology. 24(7) 2023]. Overall, 78% of participants experienced a very good partial response or better to GSI with FCAR143, but the duration of response was significantly longer in patients without prior anti-BCMA exposure. Here, we report results from an analysis of the bone marrow aspirates collected at enrollment and again within 12 hours of the third “run-in” dose. We leveraged single-cell RNA sequencing (scRNAseq) coupled with the single-cell assay for transposase-accessible chromatin sequencing (scATACseq) to profile the transcriptome and chromatin accessibility of individual cells within the bone marrow microenvironment. Results: Single-cell multi-omic analysis was performed on 32 bone marrow mononuclear cell samples from 16 patients collected before and within 12 hours after the third oral dose of GSI. Six patients had previously been treated with BCMA-directed therapy, including antibody-drug conjugates, CAR T-cells, or BiTEs. After quality filtering, including compensation for ambient RNA, 142,498 cells were analyzed (median 6,373 cells/sample, range 613-17,150 cells/sample). Cells were automatically classified into 92 immune cell types using Celltypist and a reference dataset trained on immune sub-populations from 20 tissues in 18 studies. Comparing samples pre- and post-GSI exposure revealed a significant decrease in the abundance of plasmablasts, non-classical monocytes, germinal center B cells, mid-erythroid cells, and central memory/naïve cytotoxic T cells after GSI treatment. Additionally, gene expression analysis detected increased expression of Notch signaling genes, NEURL1 and HES1, in non-classical monocytes following GSI treatment. Although RNA expression of TNFRSF17 ( BCMA) did not significantly change, chromatin accessibility was considerably higher in patients without a history of prior BCMA-directed therapy following GSI treatment. In non-plasma cells, we observed increased chromatin accessibility after GSI treatment involving genes encoding proteins known to be cleaved by gamma-secretase, such as ERBB4, NOTCH1, NOTCH2, and TNFRSF17. Accessibility of CD38, the target of daratumumab, was significantly increased in B cells, and SLAMF7, the target of elotuzumab, was significantly increased in plasma cells. Conclusions: BCMA cleavage from myeloma cells' surface is a putative resistance mechanism to BCMA-targeting immunotherapy. This study assessed the single-cell transcriptome and chromatin accessibility in the bone marrow environment of 16 patients given GSI monotherapy to ultimately enhance the efficacy of subsequent anti-BCMA CAR T-cell therapy. We found that prior BCMA-targeted therapy resulted in reduced chromatin accessibility within the BCMA epigenome. Surprisingly, GSI administration also modified the epigenome of other gamma-secretase sensitive genes. These data provide insight into the molecular consequences of prior BCMA-targeted therapy and GSI exposure that may inform future studies.

Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance

BackgroundGenomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome.ResultsWe established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data.ConclusionsThe Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

Comprehensive evaluation of lossless compression algorithms in a real use case for smart grid applications

The so-called “energy digitalization” is pervading the power and energy industry by providing many state-of-the-art digital technologies to collect, store, and analyze the very heterogeneous information managed in modern power systems. Requirements in terms of sampling frequency, temporal and user aggregation, measured quantities, measurement aggregations, involved players, and applications can be very different. Compression strategies, removing redundancy and over-specification in collected samples, can play a relevant role in efficiently move and store such heterogeneous data. In this work, a reference dataset from a real-world use case has been collected for comparing the performance of 62 lossless compression algorithms derived from the LZ77/LZSS/LZMA strategies. Compression of the six-day long acquisition, including active power measurements of a prosumer’s system equipped with a Photovoltaic (PV) and a Battery Energy Storage System, highlights the different obtainable performance when the aggregation interval is changed from 15 min to 24 h. In particular, once the top performing algorithms have been selected, it has been possible to stress the impact of night–day cycle, mainly due to the different sparsity of PV-related data. The obtained results demonstrate that, globally, the compression ratio increases by increasing the aggregation interval, by reaching values close to 9.7. In particular, when offline operation is tolerated, optimal compression schemes can be easily applied, leading to consistent improvement of the compression ratio (up to 24%, depending on the actual algorithm and aggregation interval), which can be very significant when large number of data sources is considered.

CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations

Abstract. CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible and InfraRed Imager), was released in December 2022. It is based on observations from SEVIRI, on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high-quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe; Africa; the Atlantic Ocean; and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 min) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterisation and process studies focusing especially on the diurnal cycle and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website at https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003 (Meirink et al., 2022).

Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.

Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.

LHYMO: A new Water Framework Directive‐compliant multimetric index to assess lake hydromorphology and its application to French lakes

Abstract Hydromorphology provides a physical framework for aquatic biocenoses. Its condition directly affects the quality of habitats available for fauna and flora, and its assessment is therefore useful for biodiversity and water quality conservation, as well as for restoration programmes required by European and national directives. In this study, a site‐specific index of Lake HYdroMOrphology, LHYMO, was developed to provide a quantitative assessment of the hydromorphological status of natural and non‐natural lakes that is consistent with European Committee for Standardization (CEN) standards and that can be used for different purposes, including the implementation of the Water Framework Directive (WFD) and of the Habitats Directive. This new LHYMO index includes nine metrics related to the morphological WFD quality elements (QEs) and six metrics related to the hydrological WFD QEs, all of which are considered to support biological elements. The reference conditions were defined for each metric using an original approach: the degree of alteration is measured in relation to the natural characteristics of each lake, relative to a state that ‘would be expected in the absence of disturbances’. Besides its use for regulatory purposes, this index is also a suitable tool for monitoring the efficacy of hydromorphological restoration projects or to help target effective conservation measures on lakes. Application of this index to 72 French lakes provides the first quantitative and homogeneous assessment of the hydromorphological quality of lakes over a whole territory and gives the first overview of the hydromorphological status of lakes in France, with classification into five classes ranging from ‘high’ to ‘bad’. LHYMO is already operational for large French lakes as it relies mostly on reference datasets available at a national scale, and it may also be used in a wider scope through gathering or completing data from other sources.

The Immunoarchitecture of Human Extraocular Muscles.

The purpose of this study was to describe the immunoarchitecture of normal extraocular muscles (EOMs) in terms of presence, distribution, and organization of various immune cells. We performed unilateral orbital exenterations in six fresh human cadavers from elderly patients, followed by dissection of the medial, lateral, superior and inferior rectus, superior and inferior oblique, and superior palpebral levator muscle in their entirety. We further cross sectioned each EOM in an anterior, central, and posterior third. After immunohistochemical staining for CD3, CD8, CD20, CD138, CD68, and podoplanin, quantitative analysis was performed. We found all EOMs (rectus, oblique, and levator muscles) to harbor both T- and B-lymphocytes, with a B-lymphocyte dominance and an absence of plasma cells. The highest prevalence of immune cells was seen in the muscle bellies, with, on average, 488 ± 63 CD3+ T-lymphocytes and 44 ± 110 CD20+ B-lymphocytes per mm2, and significant differences from the anterior (T-lymphocytes) and posterior (T- and B-lymphocytes) thirds. T- and B-lymphocytes were primarily organized in hotspots in the vicinity of blood vessels. In addition, a small resident population of macrophages scattered throughout the specimens was detected. No lymphatic vessels were found in any of the EOMs. These findings can serve as a reference dataset in the assessment of EOM biopsies in the diagnostic process of inflammatory orbital and systemic disorders. Moreover, from a regenerative perspective, our results highlight the importance of taking into account the presence of a resident immune cell population when studying the host immune response on transplanted tissues or engineered constructs.

Intercomparing the quality of recent reanalyses for offshore wind farm planning in Germany's exclusive economic zone of the North Sea

Abstract. In order to facilitate offshore wind farm tenders, Deutscher Wetterdienst (DWD, Germany's national meteorological service) provides reanalysis data and quality assessments to Bundesamt für Seeschifffahrt und Hydrographie (BSH, Federal Maritime and Hydrographic Agency). The regional reanalysis COSMO-REA6 is used besides the global reanalysis ERA5. New reanalyses and derived products getting available are (i) the regional reanalysis CERRA (C3S), (ii) COSMO-R6G2, a successor of COSMO-REA6 which is currently produced by DWD and (iii) HoKliSim-De, a convection-permitting climate simulation for Germany with COSMO-CLM as a regional downscaling of ERA5. In the present study, the quality of the different data sets for offshore wind energy application is compared using in-situ measurements of the wind speed and wind direction from the top anemometer and vane of the FINO1 research platform and satellite-based data of the near-surface wind speed from the Copernicus Marine Environment Monitoring Service (CMEMS) and the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). Evaluation at FINO1 focuses on the time period prior to the installation of nearby wind farms to avoid wake effects. COSMO-REA6, CERRA and HoKliSim-De show only small biases and resemble the observed distribution of the wind speed at FINO1 whereas ERA5 shows slightly lower values of the wind speed at 100 m. All model-based products tend to slightly underestimate the occurrence of south-westerly wind directions and overestimate wind directions from West to Northwest. Smallest directional biases are analysed for COSMO-REA6. Analysis of the windstorm CHRISTIAN suggests that ensemble information is required for the representation of individual extreme events. Evaluation of the near-surface wind speed using satellite-based data is performed for an area around the German Exclusive Economic Zone (EEZ) of the North Sea. The median bias of ERA5 and COSMO-REA6 is close to zero. CERRA shows a systematic overestimation of the near-surface wind speed compared to the satellite-based reference datasets. By contrast, a slight underestimation is analysed for HoKliSim-De. The bias distribution analysed for a first simulation stream of COSMO-R6G2 is similar to COSMO-REA6 which provides initial indication for the applicability of the new product.