Strong Clustering Research Articles

307 Revealing Candidate Inherited Retinal Disease Candidates via Genome-wide Screening of Knockout Mice

OBJECTIVES/GOALS: The goal of this study is to reveal strong candidate genes for inherited retinal diseases (IRDs) in humans to better understand the mechanisms behind IRD development and reveal potential therapeutic targets. We hope these findings will help improve our understanding of IRDs and, subsequently, diagnostic accuracy and prognosis for IRD patients. METHODS/STUDY POPULATION: The goal of the International Mouse Phenotyping Consortium is to identify the function of all protein-coding genes in the mouse genome via generation and phenotypic characterization of single gene knockout (KO) mice. Using this database, we identified all KO strains associated with abnormal retinal phenotypes, removed all RNA coding genes and pseudogenes, converted to human orthologues, and conducted a literature search for existing research regarding candidate IRD genes and retinal function/abnormalities. A similar process was used for RetNet genes. Subsequently, we performed bioinformatics analysis, including functional annotation (e.g. panther-db), pathway analysis (e.g. KEGG), and string-db to visualize known and predicted protein-protein interactions between the two data sets. RESULTS/ANTICIPATED RESULTS: Analysis of the IMPC database revealed, out of 8481 phenotyped genes, 572 unique protein coding genes were associated with 14 categories of retinal abnormalities such as abnormal retinal vasculature and abnormal retinal thickness, 377 of which have never been associated with retinal pathology in humans or mice. Pathway analysis of the IMPC database highlighted a general metabolism pathway as well as PI3K-Akt and MAPK pathways, not found in RetNet pathway results. Unique clusters from functional annotation clustering of the IMPC include DNA methylation and protein ubiquination. Visualization of protein-protein interactions in string-db between the IMPC (mouse) and RetNet (human) revealed 4 clusters of interest with gold standard RetNet IRD proteins interacting with candidate IMPC IRD proteins. DISCUSSION/SIGNIFICANCE: IMPC analysis revealed 572 candidate IRD genes, 377 of which are novel with no existing independent research related to the retina outside of the IMPC. Bioinformatic analysis reveals 4 strong clusters of interest through string-db where a gold standard RetNet gene interacts with candidate IRD genes as well as many functional pathways of interest.

Abstract 3726: Absence of synergistic effects for PARPi and immunotherapy combinations in ex vivo 3D micro tumor assay for ovarian cancer patients

Abstract Introduction: 50% of ovarian cancers are homologous recombination deficient (HRD), with 15% of the patients carrying a BRCA mutation. PARP inhibitors (PARPi) are designed to improve survival in HRD patients, but clinical responses to PARPi often do not match HRD status. In this study, a 3D ex vivo tumor testing platform was applied to quantify patient-specific drug responses to (combinations of) PARPi, chemo- and immunotherapies. Material and Methods: 81 patients with ovarian cancer eligible for chemotherapy were included in the trial between 2019 and 2022 in the Netherlands (IRB P18.032). Tumor clusters were isolated from ascites and solid tissue, seeded and exposed to three PARPi, standard-of-care chemotherapies and immunotherapies, both single agent and in combination. Ex vivo drug sensitivity was quantified using high-throughput 3D imaging and extraction of morphological features. Bliss scores were calculated for synergy analysis. Results: Results were generated in two weeks after sample collection. Ex vivo patient-specific response profiles were observed for all drug classes, with a small percentage of patients (7%) showing strong PARPi sensitivity reflected by strong tumor cluster shrinkage upon treatment. Significant correlation of drug sensitivity profiles was observed comparing the different PARPi between themselves, but also comparing PARPi and platinum responses. Interestingly, ex vivo sensitivity to olaparib and immunotherapy combinations demonstrated additive, but no synergistic effects (21 patients, 79 tests). In addition, olaparib responses were shown to be independent of immune responses (SEA). Conclusion: This study applied a predictive ex vivo 3D micro tumor testing platform to classify patient-specific sensitivity to (a combination of) PARPi, chemo- and immunotherapies in over 80 patients. The current dataset demonstrates the potential of ex vivo testing to identify patients that are responsive to PARPi. No synergistic effects were observed for these drugs in combination with immunotherapies. Discussion: Evaluation of PARPi responses in a functional 3D micro tumor assay could support identification of responsive patients prior to treatment. Distinct responses were observed for PARPi and immunotherapies. No synergistic but additive effects were measured, suggesting that ovarian cancer patients could benefit from PARPi and immunotherapies. Correlation of clinical and ex vivo drug responses to PARPi in combination with available HRD tests will further validate the platform for predicting patient clinical outcome. Citation Format: Lieke J. Ceton, Esmee Koedoot, Timothy J. Sijsenaar, Dieudonné J. van der Meer, Marta Montero Garcia, Jurrian van der Valk, Kristofer Maad, Cor de Kroon, Anne van Altena, Willemijn Vader, Judith Kroep, Nelleke Ottevanger. Absence of synergistic effects for PARPi and immunotherapy combinations in ex vivo 3D micro tumor assay for ovarian 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 3726.

Remote sensing perspective in exploring the spatiotemporal variation characteristics and post-disaster recovery of ecological environment quality, a case study of the 2010 Ms7.1 Yushu earthquake

Natural hazards usually cause heavy casualties and vast economic losses, as well as severe damage to the ecological environment. Quantitative and scientific evaluations of the ecological environment quality (EEQ) and its recovery trend after hazards can provide valuable insights for disaster risk reductions. This study takes the 2010 Ms7.1 Yushu earthquake as an example to explore the spatiotemporal changes and driving mechanisms of EEQ before and after the earthquake using remote sensing and GIScience. First, the Moderate-resolution Imaging Spectroradiometer (MODIS) data was selected to establish the remote sensing based ecological index (RSEI). Then, we analyzed the spatiotemporal characteristics of Yushu’s EEQ from 2001 to 2020 and explored spatial autocorrelation relationships. Last, the driving mechanism of EEQ recovery in Yushu was analyzed using the GeoDetector model. The main conclusions are as follows: (1) From the perspective of the RSEI time series, the EEQ in Yushu County was strongly negatively affected during the earthquake and recovered well after the earthquake. (2) Based on a spatial distribution analysis, it can be observed that the regions in Yushu with relatively high EEQ are primarily concentrated in the central and southern areas. Conversely, the northwestern and southeastern areas display relatively lower quality. Moreover, the EEQ in Yushu County has a strong correlation and clustering, as evidenced by Moran’s I value exceeding 0.7 over the years. (3) Based on the GeoDetector results, elevation and population were found to be the key driving factors affecting changes in post-disaster EEQ. The interaction between elevation and slope plays the most critical role in the process of ecological recovery. This study provides a theoretical basis for the evolution of the post-disaster ecological environment in Yushu County and helps decision-makers better balance the relationship between social development and environmental protection in disaster management and urban planning. It also provides useful reference and guidance for future ecological protection and recovery under similar disasters.

Diverse Genotypes of Cronobacter spp. Associated with Dairy Farm Systems in Jiangsu and Shandong Provinces in China.

Cronobacter spp. are the most concerning foodborne pathogen in infant formula milk powder. Currently, there are many reports on the prevalence of Cronobacter spp. in infant formula milk and its processing environment, but there are few studies on the prevalence of Cronobacter spp. on dairy farms. We have, therefore, undertaken this study to investigate and track genomic epidemiology of Cronobacter spp. isolates from Chinese dairy farms in the provinces of Jiangsu and Shandong. In this study, forty Cronobacter spp. strains, consisting of thirty Cronobacter sakazakii, eight Cronobacter malonaticus, and two Cronobacter dublinensis, were obtained from 1115 dairy farm samples (raw milk, silage, bedding, and feces), with a prevalence rate of 3.57%. These isolates were classified into 10 Cronobacter serotypes and 31 sequence types (STs), including three novel STs which were isolated for the first time. Notably, pathogenic Cronobacter STs 7, 8, 17, 60, and 64, which are associated with clinical infections, were observed. Antimicrobial susceptibility testing showed that all the Cronobacter spp. were highly resistant to cephalothin and fosfomycin, which was consistent with the antimicrobial genotype. All isolates carried core virulence genes related to adherence, invasion, endotoxin, immune evasion, secretion system, and regulation. Approximately half the isolates were also able to produce a strong biofilm. Twenty-one prophages and eight plasmids were detected, with the most common prophage being Cronobacter_ENT47670 and the most common plasmid being IncFIB (pCTU1). In addition, two isolates harbored the transmissible locus of stress tolerance (tLST) which confers high environmental persistence. Phylogenetic analysis showed strong clustering by species level and sequence types. Isolates from different sources or regions with a similar genomic background suggests the cross-contamination of Cronobacter spp. The presence of diverse genotypes of Cronobacter spp. in dairy farms in Jiangsu and Shandong provinces indicates that surveillance of Cronobacter spp. on dairy farms should be strengthened, to prevent and control transmission and ensure the quality and safety of raw dairy products.

Automatic detection of cell-cycle stages using recurrent neural networks.

Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this paper, we propose an approach to study mitotic progression automatically using deep learning. We used neural networks to predict different mitosis stages. We extracted video sequences of cells undergoing division and trained a Recurrent Neural Network (RNN) to extract image features. The use of RNN enabled better extraction of features. The RNN-based approach gave better performance compared to classifier based feature extraction methods which do not use time information. Evaluation of precision, recall, and F-score indicates the superiority of the proposed model compared to the baseline. To study the loss in performance due to confusion between adjacent classes, we plotted the confusion matrix as well. In addition, we visualized the feature space to understand why RNNs are better at classifying the mitosis stages than other classifier models, which indicated the formation of strong clusters for the different classes, clearly confirming the advantage of the proposed RNN-based approach.

Cities and Urbanization: Balancing the Environmental and Socioeconomic Dimensions of Sustainability

AbstractWith an increasingly urbanized world, there is an urgent need to examine how cities may evolve and achieve sustainability. This paper systematically looks at the Greater Bay Area (GBA) and the Poyang Lake Region (PLR) in China to examine the spatial processes for insights into cities and urbanization, balancing the environmental and socio‐economic dimensions. A total of 226 805 cells are analyzed to unveil the relationship between sustainability changes in 2015–2019 period and urban form indicators, considering sociodemographic variables, geographical features, and city size as control variables. Two tree‐based machine learning models (Random Forest and XGBoost) are developed. This study provides evidence that a monocentric urban form and a high share of small activity clusters are not good for sustainability. For each urban form indicator, there is a non‐linear relationship with sustainability. The results of the machine learning models reconfirm the sustainability benefits of having a strong second activity cluster comparable to the largest one. When planning cities, some forms of land use buffering are desirable. There is also support for developing relatively large activity nodes and promoting compactness in urban form. Beyond urban form characteristics, the levels of urbanization, economic development, and population are still highly relevant.

Structure shapes the representation of a novel category.

Concepts contain rich structures that support flexible semantic cognition. These structures can be characterized by patterns of feature covariation: Certain features tend to cluster in the same items (e.g., feathers, wings, can fly). Existing computational models demonstrate how this kind of structure can be leveraged to slowly learn the distinctions between categories, on developmental timescales. However, it is not clear whether and how we leverage feature structure to quickly learn a novel category. We thus investigated how the internal structure of a new category is first extracted from experience, with the prediction that feature-based structure would have a rapid and broad influence on the learned category representation. Across three experiments, novel categories were designed with patterns of feature associations determined by carefully constructed graph structures, with Modular graphs-exhibiting strong clusters of feature covariation-compared against Random and Lattice graphs. In Experiment 1, a feature inference task using verbal stimuli revealed that Modular structure broadly facilitated category learning. Experiment 2 replicated this effect in visual categories. In Experiment 3, a statistical learning paradigm revealed that this Modular benefit relates to high-level structure rather than pairwise feature associations and persists even when category structure is incidental to the task. A neural network model was readily able to account for these effects, suggesting that correlational feature structure may be encoded within rapidly learned, distributed category representations. These findings constrain theories of category representation and link theories of category learning with structure learning more broadly. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The spatial effects of the household's food insecurity levels in Ethiopia: by ordinal geo-additive model.

Food insecurity and vulnerability in Ethiopia are historical problems due to natural- and human-made disasters, which affect a wide range of areas at a higher magnitude with adverse effects on the overall health of households. In Ethiopia, the problem is wider with higher magnitude. Moreover, this geographical distribution of this challenge remains unexplored regarding the effects of cultures and shocks, despite previous case studies suggesting the effects of shocks and other factors. Hence, this study aims to assess the geographic distribution of corrected-food insecurity levels (FCSL) across zones and explore the comprehensive effects of diverse factors on each level of a household's food insecurity. This study analyzes three-term household-based panel data for years 2012, 2014, and 2016 with a total sample size of 11505 covering the all regional states of the country. An extended additive model, with empirical Bayes estimation by modeling both structured spatial effects using Markov random field or tensor product and unstructured effects using Gaussian, was adopted to assess the spatial distribution of FCSL across zones and to further explore the comprehensive effect of geographic, environmental, and socioeconomic factors on the locally adjusted measure. Despite a chronological decline, a substantial portion of Ethiopian households remains food insecure (25%) and vulnerable (27.08%). The Markov random field (MRF) model is the best fit based on GVC, revealing that 90.04% of the total variation is explained by the spatial effects. Most of the northern and south-western areas and south-east and north-west areas are hot spot zones of food insecurity and vulnerability in the country. Moreover, factors such as education, urbanization, having a job, fertilizer usage in cropping, sanitation, and farming livestock and crops have a significant influence on reducing a household's probability of being at higher food insecurity levels (insecurity and vulnerability), whereas shocks occurrence and small land size ownership have worsened it. Chronically food insecure zones showed a strong cluster in the northern and south-western areas of the country, even though higher levels of household food insecurity in Ethiopia have shown a declining trend over the years. Therefore, in these areas, interventions addressing spatial structure factors, particularly urbanization, education, early marriage control, and job creation, along with controlling conflict and drought effect by food aid and selected coping strategies, and performing integrated farming by conserving land and the environment of zones can help to reduce a household's probability of being at higher food insecurity levels.

COOL-LAMPS. VI. Lens Model and New Constraints on the Properties of COOL J1241+2219, a Bright z = 5 Lyman Break Galaxy and its z = 1 Cluster Lens

We present a strong lensing analysis of COOL J1241+2219, the brightest known gravitationally lensed galaxy at z ≥ 5, based on new multiband Hubble Space Telescope (HST) imaging data. The lensed galaxy has a redshift of z = 5.043, placing it shortly after the end of the “Epoch of Reionization,” and an AB magnitude z AB = 20.47 mag (Khullar et al.). As such, it serves as a touchstone for future research of that epoch. The high spatial resolution of HST reveals internal structure in the giant arc, from which we identify 15 constraints and construct a robust lens model. We use the lens model to extract the cluster mass and lensing magnification. We find that the mass enclosed within the Einstein radius of the z = 1.001 cluster lens is M(<5.″77)=1.079−0.007+0.023×1013M☉ , significantly lower than other known strong lensing clusters at its redshift. The average magnification of the giant arc is 〈μ arc〉 = 76−20+40 , a factor of 2.4−0.7+1.4 greater than previously estimated from ground-based data; the flux-weighted average magnification is 〈μ arc〉 = 92−31+37 . We update the current measurements of the stellar mass and star formation rate (SFR) of the source for the revised magnification to log(M⋆/M⊙)= 9.7 ± 0.3 and SFR = 10.3−4.4+7.0 M ⊙ yr−1, respectively. The powerful lensing magnification acting upon COOL J1241+2219 resolves the source and enables future studies of the properties of its star formation on a clump-by-clump basis. The lensing analysis presented here will support upcoming multiwavelength characterization with HST and JWST data of the stellar mass assembly and physical properties of this high-redshift lensed galaxy.

Neighbourhood Health Inequalities between Ethnic Groups in England: An Application of Ecological Inference

Ecological inference has had primarily political science applications, but this study considers an application to assess variations in neighbourhood mental health between population sub-groups. The methodology used has particular utility when neighbourhood health data are available only in aggregate form, without disaggregation (e.g. by ethnic or socioeconomic group). The ecological inference approach is shown to provide insights into contextual effects, where neighbourhood features influence disease variations between sub-groups (e.g. the ethnic density effect on psychosis among non-white groups). The present study also highlights important issues raised by the data’s geographic framework, namely strong spatial clustering in the outcome, and compares spatial error and spatial lag methods to represent this spatial patterning appropriately. The study considers neighbourhood variations in psychosis between four ethnic groups, with a spatial framework provided by 32,844 small areas (Lower Super Output Areas, LSOAs) in England.

A high black-hole-to-host mass ratio in a lensed AGN in the early Universe.

Early JWST observations have uncovered a population of red sources that might represent a previously overlooked phase of supermassive black hole growth1-3. One of the most intriguing examples is an extremely red, point-like object that was found to be triply imaged by the strong lensing cluster Abell 2744 (ref. 4). Here we present deep JWST/NIRSpec observations of this object, Abell2744-QSO1. The spectroscopy confirms that the three images are of the same object, and that it is a highly reddened (AV ≃ 3) broad emission line active galactic nucleus at a redshift of zspec = 7.0451 ± 0.0005. From the width of Hβ (full width at half-maximum = 2,800 ± 250 km s-1), we derive a black hole mass of . We infer a very high ratio of black-hole-to-galaxy mass of at least 3%, an order of magnitude more than that seen in local galaxies5 and possibly as high as 100%. The lack of strong metal lines in the spectrum together with the high bolometric luminosity (Lbol = (1.1 ± 0.3) × 1045 erg s-1) indicate that we are seeing the black hole in a phase of rapid growth, accreting at 30% of the Eddington limit. The rapid growth and high black-hole-to-galaxy mass ratio of Abell2744-QSO1 suggest that it may represent the missing link between black hole seeds6 and one of the first luminous quasars7.

Publisher's Note

In the current work, we study the eigenvalue distribution results of a class of non-normal matrix-sequences which may be viewed as a low rank perturbation, depending on a parameter β>1, of the basic Toeplitz matrix-sequence {Tn(eiθ)}n∈N, i2=−1. The latter of which has obviously all eigenvalues equal to zero for any matrix order n, while for the matrix-sequence under consideration we will show a strong clustering on the complex unit circle. A detailed discussion on the outliers is also provided. The problem appears mathematically innocent, but it is indeed quite challenging since all the classical machinery for deducing the eigenvalue clustering does not cover the considered case. In the derivations, we resort to a trick used for the spectral analysis of the Google matrix plus several tools from complex analysis. We only mention that the problem is not an academic curiosity and in fact stems from problems in dynamical systems and number theory. Additionally, we also provide numerical experiments in high precision, a distribution analysis in the Weyl sense concerning both eigenvalues and singular values is given, and more results are sketched for the limit case of β=1.

Information content of note transitions in the music of J. S. Bach

Music has a complex structure that expresses emotion and conveys information. Humans process that information through imperfect cognitive instruments that produce a gestalt, smeared version of reality. How can we quantify the information contained in a piece of music? Further, what is the information inferred by a human, and how does that relate to (and differ from) the true structure of a piece? To tackle these questions quantitatively, we present a framework to study the information conveyed in a musical piece by constructing and analyzing networks formed by notes (nodes) and their transitions (edges). Using this framework, we analyze music composed by J. S. Bach through the lens of network science, information theory, and statistical physics. Regarded as one of the greatest composers in the Western music tradition, Bach's work is highly mathematically structured and spans a wide range of compositional forms, such as fugues and choral pieces. Conceptualizing each composition as a network of note transitions, we quantify the information contained in each piece and find that different kinds of compositions can be grouped together according to their information content and network structure. Moreover, using a model for how humans infer networks of information, we find that the music networks communicate large amounts of information while maintaining small deviations of the inferred network from the true network, suggesting that they are structured for efficient communication of information. We probe the network structures that enable this rapid and efficient communication of information—namely, high heterogeneity and strong clustering. Taken together, our findings shed light on the information and network properties of Bach's compositions. More generally, our simple framework serves as a stepping stone for exploring further musical complexities, creativity, and questions therein. Published by the American Physical Society 2024

Primordial Orbital Alignment of Sednoids

We examined the past history of the three most detached trans-Neptunian objects (TNOs)—Sedna, 2012 VP113, and Leleakuhonua (2015 TG387)—the three clearest members of the dynamical class known as sednoids, with high perihelia distances q. By integrating backward their nominal (and a set of cloned) orbits for the solar system’s age, we surprisingly find that the only time all their apsidal lines tightly cluster was 4.5 Gyr ago, at perihelion longitude ϖ of 200°. This “primordial alignment” is independent of the observational biases that contribute to the current on-sky clustering in the large-semimajor-axis Kuiper Belt. If future sednoid discoveries confirm these findings, this strongly argues for an initial event during the planet formation epoch, which imprinted this particular apsidal orientation on the early detached TNO population. Their apsidal orientations were then subsequently modified only by the simple precession from the four giant planets (and weakly by the galactic tide). If other sednoids also cluster around the same primordial value, various models suggesting a still present planet in the outer solar system would be incompatible with this alignment. We inspected two scenarios that could potentially explain the primordial alignment. First, a rogue planet model (where another massive planet raises perihelia near its own longitude until ejection) naturally produces this signature. Alternatively, a close stellar passage early in solar system history raises perihelia, but it is poor at creating strong apsidal clustering. We show that all other known 35 < q < 55 au TNOs are either too perturbed or orbits are still too uncertain to provide evidence for or against this paradigm.

Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas

Indonesia's export activities are dominated by non-oil and gas exports consisting of four sectors, namely the processing industry, agriculture, mining, and others. The government must pay attention to non-oil and gas exports for each province because exports can play an essential role in a country's economic growth. This study was conducted to cluster provinces in Indonesia using time series clustering in the non-oil and gas export sector based on data patterns concerning Dynamic Time Warping (DTW) distance. The sectors used in this study are the manufacturing industry sector and the agricultural sector in 34 Indonesian provinces in the period 2017 - 2021. Time series clustering analysis uses the average linkage method with DTW distance and the selection of the optimum number of clusters using the silhouette coefficient method. The results of the analysis in the processing industry sector resulted in 3 optimum clusters, namely cluster 1 consisting of 1 province that has high processing industry exports, cluster 2 consisting of 8 provinces that have medium processing industry exports, and cluster 3 consisting of 25 provinces that have low processing industry exports. As for the agricultural sector, it produces 2 optimum clusters, namely cluster 1 consisting of 5 provinces that have high agricultural industry exports, and cluster 2 consisting of 29 provinces that have low agricultural industry exports. The clustering results in the processing industry sector and the agricultural sectors have a silhouette coefficient value of 0.778 and 0.798, so it is said to have a strong cluster structure.

Synchronize your chrono-brane: testing a variable brane tension model with strong gravitational lensing

Brane world models have shown to be promising to understand the late cosmic acceleration, in particular because such acceleration can be naturally derived, mimicking the dark energy behaviour just with a five dimensional geometry. In this paper we present a strong lensing joint analysis using a compilation of early-type galaxies acting as a lenses, united with the power of the well studied strong lensing galaxy cluster Abell 1689. We use the strong lensing constraints to investigate a brane model with variable brane tension as a function of the redshift. In our joint analysis we found a value n=7.8-0.5+0.9\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n = 7.8^{+0.9}_{-0.5}$$\\end{document}, for the exponent related to the brane tension, showing that n deviates from a Cosmological Constant (CC) scenario (n=6). We obtain a value for the deceleration parameter, q(z) today, q(0)=-1.2-0.8+0.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q(0)=-1.2^{+0.6}_{-0.8}$$\\end{document}, and a transition redshift, zt=0.60±0.06\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z_t=0.60\\pm 0.06$$\\end{document} (when the Universe change from an decelerated phase to an accelerated one). These results are in contrast with previous work that favors CC scenario, nevertheless our lensing analysis is in agreement with a formerly reported conclusion suggesting that the variable brane tension model is able to source a late cosmic acceleration without an extra fluid as in the standard one.

UPREGULATION OF SERPINE1/PAI-1 INDUCED BY IL-33 CONTRIBUTES TO INTESTINAL FIBROSIS IN A CROHN’S DISEASE-LIKE ILEITIS MODEL

Abstract IL-33 is a pleiotropic cytokine known to possess dichotomous roles during gut health and disease. We previously described that IL-33 promotes epithelial restitution/repair in otherwise healthy (C57BL/6) mice that have been challenged with dextran sodium sulphate (DSS) to induce acute colitis, with the end result of efficient resolution of inflammation. However, in SAMP1/YitFc (SAMP) mice that spontaneously develop Crohn’s disease (CD)-like ileitis, elevated IL-33 levels persist as disease progresses, perpetuating chronic gut inflammation and severe fibrosis. While IL-33 has been implicated in the development of inflammation-associated fibrosis, the precise mechanism(s) by which this occurs remains unclear. The aim of this study was to determine IL-33-dependent events leading to intestinal fibrosis in ileitis-prone SAMP mice. Our results show IHC co-localization of IL-33 with fibrotic lesions, specifically in cells morphologically-consistent with both macrophages and subepithelial myofibroblasts (SEMFs). Bulk RNA-Seq analysis of the human SEMF cell line, CCD-18Co, stimulated +/- IL-33 identifies one of the highest-expressing transcripts as SERPINE1, encoding for plasminogen activator inhibitor-1 (PAI-1), which has previously been reported as highly-enriched in IBD patients with active disease that do not respond to anti-TNF therapy. Analysis of publicly-available scRNA-Seq data confirms the increased expression of SERPINE1 in IBD patients that localizes to mesenchymal cell populations. Of note, robust upregulation of SERPINE1 is detected in activated fibroblasts from involved vs. non-involved areas of CD patients. Furthermore, spatial transcriptomics of SAMP ilea reveal a progressive and concomitant increase in both Il33 and Serpine1, with strong clusterization compared to healthy AKR controls, particularly during later time points when fibrosis is evident. Currently, results are pending on the treatment of SAMP mice with the PAI-1 inhibitor, MDI-2268, to determine its direct effect(s) on the development of inflammation-associated intestinal fibrosis. Taken together, these findings suggest IL-33-dependent regulation of Serpine1/PAI-1 that promotes intestinal fibrosis, commonly observed in CD patients, and may provide a novel target to treat IBD patients with fibrostenoic disease.

A full reconstruction of two galaxy clusters intra-cluster medium with strong gravitational lensing

ABSTRACT Whilst X-rays and Sunyaev–Zel’dovich observations allow to study the properties of the intra-cluster medium (ICM) of galaxy clusters, their gravitational potential may be constrained using strong gravitational lensing. Although being physically related, these two components are often described with different physical models. Here, we present a unified technique to derive the ICM properties from strong lensing for clusters in hydrostatic equilibrium. In order to derive this model, we present a new universal and self-similar polytropic temperature profile, which we fit using the X-COP sample of clusters. We subsequently derive an analytical model for the electron density, which we apply to strong lensing clusters MACS J0242.5-2132 and MACS J0949.8+1708. We confront the inferred ICM reconstructions to XMM-Newton and ACT observations. We contrast our analytical electron density reconstructions with the best canonical β-model. The ICM reconstructions obtained prove to be compatible with observations. However they appear to be very sensitive to various dark matter halo parameters constrained through strong lensing (such as the core radius), and to the halo scale radius (fixed in the lensing optimizations). With respect to the important baryonic effects, we make the sensitivity on the scale radius of the reconstruction an asset, and use the inferred potential to constrain the dark matter density profile using ICM observations. The technique here developed should allow to take a new, and more holistic path to constrain the content of galaxy clusters.

Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula

Coastal areas are characterized by high levels of urbanization and also the most active and highly sensitive to geological activities. Due to the impact of global climate change, landslides occur frequently in coastal areas, which have significantly impacted the sustainable socio-economic development of the region. Therefore, studying the spatial distribution of landslides and the factors that drive them holds immense practical significance for the geohazards prevention and mitigation work and optimization of ecological security patterns in coastal areas. This study takes the southern Liaodong Peninsula as the study area and analyzes the spatial distribution characteristics and influence mechanism of landslides. We first applied the Getis-Ord Gi*, kernel density analysis techniques, and Ripley’s K function to explore the spatial distribution characteristics of landslides in the study area at different scales. Then, we employed the information value method to test for the linkage relationships between the driving factors and landslides. Finally, we utilized the geographical detector to reveal the potential impacts and interaction of the driving factors on landslides. The results showed that: (1) landslides in the Southern Liaodong Peninsula exhibit strong spatial clustering characteristics, and have a spatial scale effect; (2) the high susceptibility areas are mainly concentrated in the southern hilly regions and the mountainous regions in the northern parts of the study area; (3) The information value method reveals that there is an optimal combination of factors driving landslides. (4) The factor detector analysis reveals that the primary driver of landslide spatial distribution is the geomorphological types (q-value of 39.10%). (5) The interaction detector indicates that the interplay of all driving factor pairs exhibited an enhanced effect, which is not a simple superposition effect but bivariate and non-linear. The collective influence of natural factors and human activities holds a more substantial influence over landslide development and distribution. The research offers guidance for the execution of landslide prevention initiatives and the establishment of preservation policies for the ecological environment in the southern Liaodong Peninsula region.

Understanding shape and centroid deviations in 39 strong lensing galaxy clusters in various dynamical states

Through observational tests of strong lensing galaxy clusters, we can test simulation derived structure predictions that follow from Λ Cold Dark Matter (ΛCDM) cosmology. The shape and centroid deviations between the total matter distribution, stellar matter distributions, and hot intracluster gas distribution serve as an observational test of these theoretical structure predictions. We measure the position angles, ellipticities, and locations/centroids of the brightest cluster galaxy (BCG), intracluster light (ICL). the hot intracluster medium (ICM), and the core lensing mass for a sample of strong lensing galaxy clusters from the SDSS Giant Arcs Survey (SGAS). We utilize HST WFC3/1R imaging data to measure the shapes/centroids of the ICL and BCG distributions and use Chandra ACIS-I X-ray data to measure the shapes/centroids of the ICM. Additionally, we measure the concentration parameter (c) and asymmetry parameter (A) to incorporate cluster dynamical state into our analysis. Using this multicomponent approach, we evaluate the different components in terms of their ability to trace out the DM halo of clusters in various dynamical states.