Dense Clusters Research Articles

Hepatic C-X-C chemokine receptor type 6-expressing innate lymphocytes limit detrimental myeloid hyperactivation in acute liver injury.

Acute liver failure (ALF) is characterized by rapid clinical deterioration and high mortality. Acetaminophen (APAP or paracetamol) overdose is a leading cause of ALF, resulting in hepatocellular necrosis with subsequent inflammation, inflicting further liver damage. Infiltrating myeloid cells are early drivers of liver inflammation. However, the role of the abundant population of liver-resident innate lymphocytes, which commonly express the chemokine receptor CXCR6, is incompletely understood in ALF. We investigated the role of CXCR6-expressing innate lymphocytes using the model of acute APAP toxicity in mice deficient in CXCR6 (Cxcr6gfp/gfp). APAP-induced liver injury was strongly aggravated in Cxcr6gfp/gfp mice compared with wild-type counterparts. Immunophenotyping using flow cytometry revealed a reduction in liver CD4+T cells, natural killer (NK) cells, and most prominently, NKT cells, whereas CXCR6 was dispensable for CD8+ T-cell accumulation. CXCR6-deficient mice exhibited excessive neutrophil and inflammatory macrophage infiltration. Intravital microscopy revealed dense cellular clusters of neutrophils in necrotic liver tissue, with higher numbers of clustering neutrophils in Cxcr6gfp/gfp mice. Gene expression analysis linked hyperinflammation in CXCR6 deficiency to increased IL-17 signaling. Although reduced in overall numbers, CXCR6-deficient mice had a shift in NKT cell subsets with increased RORγt-expressing NKT17 cells as a likely source of IL-17. In patients with ALF, we found a prominent accumulation of IL-17-expressing cells. Accordingly, CXCR6-deficient mice lacking IL-17 (Cxcr6gfp/gfpx Il17-/-) had ameliorated liver damage and reduced inflammatory myeloid infiltrates. Our study identifies a crucial role of CXCR6-expressing liver innate lymphocytes as orchestrators in acute liver injury containing IL-17-mediated myeloid cell infiltration. Hence, strengthening the CXCR6-axis or downstream inhibition of IL-17 could yield novel therapeutics in ALF.

Phase behavior of active and passive dumbbells.

We report phase separation in a mixture of "hot" and "cold" three-dimensional dumbbells which interact by Lennard-Jones potential. We also have studied the effect of asymmetry of dumbbells and the variation of ratio of "hot" and "cold" dumbbells on their phase separation. The ratio of the temperature difference between hot and cold dumbbells to the temperature of cold dumbbells is a measure of the activity χ of the system. From constant density simulations of symmetric dumbbells, we observe that the "hot" and "cold" dumbbells phase separate at higher activity ratio (χ>5.80) compared to that of a mixture of hot and cold Lennard-Jones monomers (χ>3.44). We find that, in the phase-separated system, the hot dumbbells have high effective volume and hence high entropy which is calculated by two-phase thermodynamic method. The high kinetic pressure of hot dumbbells forces the cold dumbbells to form dense clusters such that at the interface the high kinetic pressure of hot dumbbells is balanced by the virial pressure of cold dumbbells. We find that phase separation pushes the cluster of cold dumbbells to have solidlike ordering. Bond orientation order parameters reveal that the cold dumbbells form solidlike ordering consisting of predominantly face-centered cubic and hexagonal-close packing packing, but the individual dumbbells have random orientations. The simulation of the nonequilibrium system of symmetric dumbbells at different ratios of number of hot dumbbells to cold dumbbells reveals that the critical activity of phase separation decreases with increase in fraction of hot dumbbells. The simulation of equal mixture of hot and cold asymmetric dumbbells revealed that the critical activity of phase separation was independent of the asymmetry of dumbbells. We also observed that the clusters of cold asymmetric dumbbells showed both crystalline and noncrystalline order depending on the asymmetry of dumbbells.

Aqueous habitats and carbon inputs shape the microscale geography and interaction ranges of soil bacteria

Earth’s diverse soil microbiomes host bacteria within dynamic and fragmented aqueous habitats that occupy complex pore spaces and restrict the spatial range of ecological interactions. Yet, the spatial distributions of bacterial cells in soil communities remain underexplored. Here, we propose a modelling framework representing submillimeter-scale distributions of soil bacteria based on physical constraints supported by individual-based model results and direct observations. The spatial distribution of bacterial cell clusters modulates various metabolic interactions and soil microbiome functioning. Dry soils with long diffusion times limit localized interactions of the sparse communities. Frequently wet soils enable long-range trophic interactions between dense cell clusters through connected aqueous pathways. Biomes with high carbon inputs promote large and dense cell clusters where anoxic microsites form even in aerated soils. Micro-geographic considerations of difficult-to-observe microbial processes can improve the interpretation of data from bulk soil samples.

The developmental trajectories of L2 lexical-semantic networks

This study explored the developmental trajectories of L2 Lexical-semantic networks by comparing the structure of lexical-semantic networks of Chinese EFL learners at two levels of English: intermediate and advanced. Analyses of data from semantic fluency tasks found that with the improvement of language proficiency, the L2 lexical-semantic network becomes more connected but less centralized. Analyses of community structure revealed that the lexical-semantic network of advanced learners has denser clusters than that of intermediate learners. Examination of individual words in the L2 lexical-semantic network shows that words tend to reorganize themselves with the expanse of the network. In this process, more frequent and connected words tend to remain in the network as central words while less frequent words with fewer connections have the risk of being lost, although a large number of new words keep joining in the network. The results provide empirical evidence for the preferential attachment model of lexical-semantic network growth.

Searching for clues of past binary supermassive black hole mergers in nuclear star clusters

ABSTRACTGalaxy mergers are common processes in the Universe. As a large fraction of galaxies hosts at their centres a central supermassive black hole (SMBH), mergers can lead to the formation of a supermassive black hole binary (SMBHB). The formation of such a binary is more efficient when the SMBHs are embedded in a nuclear star cluster (NSC). NSCs are dense and massive stellar clusters present in the majority of the observed galaxies. Their central densities can reach up to $10^7\, {\rm M_{\odot }}\,{\rm pc^{-3}}$ and their masses can be as large as a few $10^7\, {\rm M_{\odot }}$. The direct detection of an SMBHB is observationally challenging. In this work, we illustrate how the large-scale structural and dynamical properties of an NSC can help to identify nucleated galaxies that recently went through a merger that possibly led to the formation of a central SMBHB. Our models show that the merger can imprint signatures on the shape, density profile, rotation, and velocity structure of the NSC. The strength of the signatures depends on the mass ratio between the SMBHs and on the orbital initial conditions of the merger. In addition, the number of hypervelocity stars produced in the mergers is linked to the SMBHB properties. The merger can also contribute to the formation of the nuclear stellar disc of the galaxy.

Periodic X-ray sources in the massive globular cluster 47 Tucanae: Evidence for dynamically formed cataclysmic variables

ABSTRACT We present a systematic study of periodic X-ray sources in the massive globular cluster 47 Tuc, utilizing deep archival Chandra observations that resolve the cluster core and recently available eROSITA observations that cover the cluster outskirt. By applying the Gregory-Loredo algorithm, we detect 20 periodic signals among 18 X-ray sources, ranging between 205–95731 s. Fourteen periods are newly discovered in the X-ray band. We classify these periodic sources into four quiescent low-mass X-ray binaries, 1 ms pulsar, two coronally-active binaries, and eleven cataclysmic variables (CVs) based on their X-ray temporal and spectral properties, as well as multiband information. Despite a small sample subject to potential selection bias against faint and non-magnetic CVs, the 11 CVs together define an orbital period distribution significantly different from that of the CVs previously found in the solar neighbourhood and the Galactic bulge. In particular, there exists in 47 Tuc an apparent paucity of short-period CVs below the period gap, which might be attributed to a high occupation fraction of non-magnetic CVs. Also characteristic of the 47 Tuc CVs are an overabundance of long-period CVs with a subgiant donor, a substantial fraction of CVs within the period gap, and a steep radial surface density profile. These are best understood as a group of CVs having recently formed via dynamical interactions in the dense cluster core. Despite sufficient sensitivity of the X-ray data, only one periodic source is found between one-third of the half-light radius and the tidal radius, the nature of which is unclear.

The growth of intermediate mass black holes through tidal captures and tidal disruption events

ABSTRACTWe present N-body simulations, including post-Newtonian dynamics, of dense clusters of low-mass stars harbouring central black holes (BHs) with initial masses of 50, 300, and 2000 M⊙. The models are evolved with the N-body code bifrost to investigate the possible formation and growth of massive BHs by the tidal capture of stars and tidal disruption events (TDEs). We model star–BH tidal interactions using a velocity-dependent drag force, which causes orbital energy and angular momentum loss near the BH. About ∼20–30 per cent of the stars within the spheres of influence of the black holes form Bahcall–Wolf cusps and prevent the systems from core collapse. Within the first 40 Myr of evolution, the systems experience 500–1300 TDEs, depending on the initial cluster structure. Most (>95 per cent) of the TDEs originate from stars in the Bahcall–Wolf cusp. We derive an analytical formula for the TDE rate as a function of the central BH mass, density, and velocity dispersion of the clusters ($\dot{N}_{\mathrm{TDE}} \propto M\mathrm{_{BH}}\rho \sigma ^{-3}$). We find that TDEs can lead a 300 M⊙ BH to reach $\sim 7000 \, \mathrm{{M}_{\odot }}$ within a Gyr. This indicates that TDEs can drive the formation and growth of massive BHs in sufficiently dense environments, which might be present in the central regions of nuclear star clusters.

Liquid Crystals as Multifunctional Interfaces for Trapping and Characterizing Colloidal Microplastics.

Identifying and removing microplastics (MPs) from the environment is a global challenge. This study explores how the colloidal fraction of MPs assemble into distinct 2D patterns at aqueous interfaces of liquid crystal (LC) films with the goal of developing surface-sensitive methods for identifying MPs. Polyethylene (PE) and polystyrene (PS) microparticles are measured to exhibit distinct aggregation patterns, with addition of anionic surfactant amplifying differences in PS/PE aggregation patterns: PS changes from a linear chain-like morphology to a singly dispersed state with increasing surfactant concentration whereas PE forms dense clusters at all surfactant concentrations. Statistical analysis of assembly patterns using deep learning image recognition models yields accurate classification, with feature importance analysis confirming that dense, multibranched assemblies are unique features of PE relative to PS. Microscopic characterization of LC ordering at the microparticle surfaces leads to predict LC-mediated interactions (due to elastic strain) with a dipolar symmetry, a prediction consistent with the interfacial organization of PS but not PE. Further analysis leads to conclude that PE microparticles, due to their polycrystalline nature, possess rough surfaces that lead to weak LC elastic interactions and enhanced capillary forces. Overall, the results highlight the potential utility of LC interfaces for rapid identification of colloidal MPs based on their surface properties.

Quiescent and Active Galactic Nuclei as Factories of Merging Compact Objects in the Era of Gravitational Wave Astronomy

Galactic nuclei harbouring a central supermassive black hole (SMBH), possibly surrounded by a dense nuclear cluster (NC), represent extreme environments that house a complex interplay of many physical processes that uniquely affect stellar formation, evolution, and dynamics. The discovery of gravitational waves (GWs) emitted by merging black holes (BHs) and neutron stars (NSs), funnelled a huge amount of work focused on understanding how compact object binaries (COBs) can pair up and merge together. Here, we review from a theoretical standpoint how different mechanisms concur with the formation, evolution, and merger of COBs around quiescent SMBHs and active galactic nuclei (AGNs), summarising the main predictions for current and future (GW) detections and outlining the possible features that can clearly mark a galactic nuclei origin.

The Mechanism of Acentrosomal Spindle Assembly in Human Oocytes

ABSTRACT During somatic cell mitosis and meiosis, spindle assembly is an essential step for ensuring accurate chromosome transmission and is mediated via duplicated centrosomes. In nonsomatic cells, canonical centrosomes are absent in the oocytes of many species, suggesting the mechanism of female meiotic spindle assembly is not conserved between species. Human oocytes lack detectable acentriolar microtubule organizing centers at the meiotic spindle poles and the exact mechanism of acentrosomal spindle assembly in human oocyte remains unclear. This study aimed to utilize 3-dimensional (3D) high-resolution imaging of more than 2000 human oocytes to identify and characterize key structures in the spindle assembly process. First, human oocytes were stained to visualize chromosomes, kinetochores, and microtubules before maturation. Approximately 2 to 4 hours following nuclear envelope breakdown (NEBD), the nucleation of spindle microtubules was initiated from kinetochores. To identify factors driving this spindle microtubule nucleation from kinetochores in human oocytes, 86 human centrosome and microtubule-related proteins were localized by immunofluorescence in more than 1000 fixed human oocytes. Of these, 43 proteins localized in the meiotic spindle, among which 4 proteins (centriolar coiled-coil protein 110 [CCP110], cytoskeleton-associated protein 5 [CKAP5], disrupted in schizophrenia 1 [DISC1], and transforming acidic coiled-coil-containing protein 3 [TACC3]) showed kinetochore and spindle microtubule localization. It was observed that these 4 proteins belong to the same structure (human oocyte microtubule organizing center [huoMTOC]), and a dense microtubule cluster was observed around this structure, which caused asymmetrical microtubule distribution around the nuclear envelope before NEBD. Next, it was observed that TACC3 had over 29-fold greater expression than CCP110, CKAP5, and DISC1, and after being depleted using siRNA, there was complete disruption of the huoMTOC, and the distribution of microtubules was drastically diminished. The other 3 proteins showed similar but less severe diminishment of the huoMTOC when depleted. Next, to visualize the dynamic process of huoMTOC-regulated spindle assembly, 3D time-lapse imaging was used following NEBD. This showed the huoMTOC fragments and relocates on the kinetochores to initiate microtubule nucleation and spindle assembly. Again, each huoMTOC component was down-regulated in human oocytes and brought through this stage, showing that with each component there was significant impairment of spindle microtubule nucleation and spindle assembly compared with the control group (P < 0.001), and spindle microtubules were greatly decreased. It was hypothesized that given the key role of the huoMTOC in human spindle assembly, a disrupted huoMTOC resulting from mutations in CCP110m CKAP5, DISC1, or TACC3 may cause abnormal oocyte maturation in patients. A cohort of 1394 infertile female patients characterized by oocyte maturation arrest was screened for mutations through whole-exome sequencing data sets. Two patients with loss-of-function mutations in TACC3 were identified, and analysis of their retrieved oocytes found a missing huoMTOC, suggesting the disruption of the huoMTOC impaired the nucleation of microtubules in the oocytes of patients. This study identified and characterized a structure, which was named the huoMTOC, which serves as a major site of microtubule nucleation and is required for spindle assembly in human oocytes.

Abstract P4-09-02: A molecular classification system for basal-like breast cancer based on the tumor microenvironment is prognostic for survival

Abstract Recent advancements in molecular profiling have revealed distinct breast cancer subtypes, but many clinical NGS assays rely on gene panels, such as PAM50, limiting their clinical utility. Basal-like breast cancer (BLBC), one of the most aggressive subtypes, has highly variable molecular and clinical characteristics. Now, the tumor microenvironment (TME) is recognized as a vital participant in tumor progression and therapeutic response. The development of more refined classifications based on the TME, capable of accounting for tissue heterogeneity, may improve NGS clinical utility for BLBC. Here, we apply our transcriptomic-based approach, recently described by Bagaev et. al., to classify the BLBC TME into discrete immune portraits, to potentially improve clinical outcomes and facilitate therapeutic decisions. We collected a cohort of 1,708 BLBC samples based on the expression levels of 50 genes (PAM50) from 10 publicly available datasets, with clinical outcomes available (n = 819). Using methodology described by Bagaev et. al., 31 functional gene expression signatures (Fges) were selected, and unsupervised dense Louvain clustering was performed to identify TME subtypes. A novel RNA-seq deconvolution algorithm was used to determine the cell types within the TME. Validation of the histological features, including stroma, tumor infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLS), relative to gene expression patterns in the TME subtypes was performed by automated and manual annotation of BLBC H&E slides (n = 146) from an independent TCGA cohort. Overall survival (OS) analysis was performed using Kaplan-Meier and Cox regression methods. We revealed 5 BLBC subtypes with distinct expression patterns: immune-enriched, non-fibrotic (IE, 19%), B-cell–enriched, TLS-like (TLS, 25.5%), granulocyte-enriched (G, 12.8%), fibrotic (F, 28%), and immune desert (D, 17.7%) (Table). IE tumors featured an active immune TME, with high immune checkpoint expression and T cell activity. The TLS subtype also had an immune-rich TME, presenting high levels of B cells, T helper cells, and TLS (p < 0.001). The TLS subtype exhibited the highest number of stromal TILs on TCGA H&E slides. The G subtype was characterized by high expression of granulocytes and granulocyte traffic molecules. In accordance with our findings, deconvolution predicted the highest percent of neutrophils in the G subtype (p < 0.001). The F subtype demonstrated the highest levels of angiogenesis, stromal Fges, and VEGFR1-3, FGFR1, and EGFR expression. By histological evaluation, 84% of F subtype samples demonstrated a medium or high level of fibrosis. The D subtype showed a high proliferation rate and low stromal and immune Fges. Indicative of proliferation rate, CCNB1 and cyclin B1 were highest in G, D, and IE subtypes. OS analysis revealed a significant association between TME subtypes (TLS = baseline; log HR G = 0.87, p < 0.05; IE = 0.39, p = 0.18; F = 0.99, p < 0.05; D = 1.21, p < 0.05), and survival outcomes. The immune-enriched subtypes, IE and TLS, demonstrated good prognosis and higher expression of immune checkpoint genes, while immune desert D and granulocyte-enriched G subtypes exhibited the worst OS. Using our transcriptomic-based approach, BLBC was classified into 5 distinct subtypes, each with unique therapeutic vulnerabilities. Further investigation of these TME subtypes may lead to potential clinical utility as a prognostic tool to improve clinical decision making. Table. Characteristics of Basal-like breast cancer (BLBC) tumor microenvironment (TME) subtypes. TLS - Tertiary lymphoid structures; CAF - cancer-associated fibroblasts Citation Format: Svetlana Khorkova, Diana Shamsutdinova, Vladimir Kushnarev, Lev Popyvanov, Daniil Dymov, Anastasia Zotova, Ivan Valiev, Zoya Antysheva, Anna Love, Jessica H. Brown, Alexander Bagaev, Nikita Kotlov, Nathan Fowler. A molecular classification system for basal-like breast cancer based on the tumor microenvironment is prognostic for survival [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-02.

Low-frequency Waves Due to Newborn Interstellar Pickup Ions Observed from 43 to 47 au by the Voyager 1 Spacecraft

Interstellar neutral atoms enter the heliosphere at a relatively slow speed corresponding to the motion of the Sun through the local interstellar medium, which is approximately 25 km s−1. Neutral hydrogen atoms enter from the approximate location of the Voyager spacecraft and are eventually ionized primarily by collision with thermal solar wind ions. An earlier analysis by Hollick et al. examined low-frequency magnetic waves observed by the Voyager spacecraft from launch through 1990 that are thought to arise from the scattering of newborn interstellar pickup H+ and He+. We report an analysis of Voyager 1 observations in 1991, which is the last year of high-resolution magnetic field data that are publicly available, and find 70 examples of low-frequency waves with the characteristics that suggest excitation by pickup H+ and 10 examples of waves consistent with excitation by pickup He+. We find a particularly dense cluster of observations at the tail end of what is thought to be a Merged Interaction Region (MIR) that was previously studied by Burlaga & Ness using Voyager 2 observations. This is not unexpected if the MIR is followed by a large rarefaction region, as they tend to be regions of reduced turbulence levels that permit the growth of the waves over the long time periods that are generally required of this instability.

Great balls of FIRE II: The evolution and destruction of star clusters across cosmic time in a Milky Way-mass galaxy

ABSTRACT The current generation of galaxy simulations can resolve individual giant molecular clouds, the progenitors of dense star clusters. But the evolutionary fate of these young massive clusters, and whether they can become the old globular clusters (GCs) observed in many galaxies, is determined by a complex interplay of internal dynamical processes and external galactic effects. We present the first star-by-star N-body models of massive (N ∼ 105–107) star clusters formed in a FIRE-2 MHD simulation of a Milky Way-mass galaxy, with the relevant initial conditions and tidal forces extracted from the cosmological simulation. We select 895 (∼30 per cent) of the YMCs with >6 × 104 M⊙ from Grudić et al. 2022 and integrate them to z = 0 using the cluster Monte Carlo code, CMC. This procedure predicts a MW-like system with 148 GCs, predominantly formed during the early, bursty mode of star formation. Our GCs are younger, less massive, and more core-collapsed than clusters in the Milky Way or M31. This results from the assembly history and age-metallicity relationship of the host galaxy: Younger clusters are preferentially born in stronger tidal fields and initially retain fewer stellar-mass black holes, causing them to lose mass faster and reach core collapse sooner than older GCs. Our results suggest that the masses and core/half-light radii of GCs are shaped not only by internal dynamical processes, but also by the specific evolutionary history of their host galaxies. These results emphasize that N-body studies with realistic stellar physics are crucial to understanding the evolution and present-day properties of GC systems.

Differential roles of CaMKII isoforms in phase separation with NMDA receptors and in synaptic plasticity

Calcium calmodulin-dependent kinase II (CaMKII) is critical for synaptic transmission and plasticity. Two major isoforms of CaMKII, CaMKIIα and CaMKIIβ, play distinct roles in synaptic transmission and long-term potentiation (LTP) with unknown mechanisms. Here, we show that the length of the unstructured linker between the kinase domain and the oligomerizing hub determines the ability of CaMKII to rescue the basal synaptic transmission and LTP defects caused by removal of both CaMKIIα and CaMKIIβ (double knockout [DKO]). Remarkably, although CaMKIIβ binds to GluN2B with a comparable affinity as CaMKIIα does, only CaMKIIα with the short linker forms robust dense clusters with GluN2B via phase separation. Lengthening the linker of CaMKIIα with unstructured "Gly-Gly-Ser" repeats impairs its phase separation with GluN2B, and the mutant enzyme cannot rescue the basal synaptic transmission and LTP defects of DKO mice. Our results suggest that the phase separation capacity of CaMKII with GluN2B is critical for its cellular functions in the brain.

Machine Learning Approach for Detection of Water Overgrowth in Azov Sea with Sentinel-2 Data

The Azov Sea estuaries play an important role in the reproduction of semi-anadromous fish species. Spawning efficiency is closely connected with overgrowing of those species spawning grounds; thus, the objective of the water vegetation research has vital fisheries importance. Thus, the main goal of the research was to develop a machine learning algorithm for the detection of water overgrowth with Phragmites australis based on Sentinel-2 data. The research was conducted based on field botanical and vegetation investigations in 2020–2021 in Soleniy and Chumyanniy firths. Collected field and remote sensing data were processed with the semi-automatic classification plugin for QGIS. For the classification of Azov Sea estuaries, a random forest algorithm was used. The obtained results showed that in 2020 the areas occupied by reeds reached 0.37 km2, while in 2021, they increased to 0.51 km2. There was a high level of Phragmites australis growth in the Soleniy and Chumyanniy firths. The rapid growth of Phragmites australis in the period of 2020–2021, where the area covered by the reed doubled, is primarily attributed to eutrophication. This is due to the nutrient enrichment from agricultural lands located in the northern part of the research area near Novonekrasovskiy village. Additionally, changes in water flows and hydrological conditions can also contribute to the favorable growth of the reed. This can result in a high growth rate of Phragmites australis, which can reach up to 2 m per year and can propagate both through vegetative and sexual means, leading to the formation of large and dense clusters.

Density peaks clustering algorithm based on fuzzy and weighted shared neighbor for uneven density datasets

Uneven density data refers to data with a certain difference in sample density between clusters. The local density of density peaks clustering algorithm (DPC) does not consider the effect of sample density difference between clusters of uneven density data, which may lead to wrong selection of cluster centers; the algorithm allocation strategy makes it easy to incorrectly allocate samples originally belonging to sparse clusters to dense clusters, which reduces clustering efficiency. In this study, we proposed the density peaks clustering algorithm based on fuzzy and weighted shared neighbor for uneven density datasets (DPC-FWSN). First, a nearest neighbor fuzzy kernel function is obtained by combining K-nearest neighbor and fuzzy neighborhood. Then, local density is redefined by the nearest neighbor fuzzy kernel function. The local density can better characterize the distribution characteristics of the sample by balancing the contribution of sample density in dense and sparse areas, in order to avoid the situation that the sparse cluster does not have a cluster center. Finally, the allocation strategy for weighted shared neighbor similarity is proposed to optimize the sample allocation at the boundary of the sparse cluster. Experiments are performed on IDPC-FA, FKNN-DPC, FNDPC, DPCSA and DPC for uneven density datasets, complex morphologies datasets and real datasets. The clustering results demonstrate that DPC-FWSN effectively handles datasets with uneven density distribution.

Observations for Sjögren’s Pigment Epithelial Reticular Dystrophy in a 16-Year-Old Boy—An Extremely Rare Retinal Case Report

The purpose of this publication is to present an extremely rare case of Sjögren's pigment epithelial reticular dystrophy. So far, 10 such publications have been found in world literature. A 16-year-old boy was diagnosed due to a slight loss of visual acuity, confirmed in static perimetry/24-2/. Abnormal dense clusters of retinal pigment epithelium (RPE) cells forming a reticular network pattern (resembling a fishing net) with marked knots were detected by fundoscopy in the macular area and the mid-periphery of the retina. No abnormalities were found in the anterior segment, intraocular pressure, kinetic perimetry, Ishihara or Farnsworth D-15 tests or OCT. Fluorescein angiography confirmed blocked fluorescence from the choroidal vessels caused by the pigment in RPE. An autofluorescence test showed hypofluorescent foci corresponding to symmetrical and bilateral retinal hyperpigmentation with an RPE reticular pattern. Multifocal ERG (mfERG) revealed slight cone photoreceptor and bipolar bioelectrical dysfunction. Electrooculography (EOG) showed significant asymmetry (Arden Ratio 1.8), suggesting bioelectrical dysfunction of RPE/photoreceptors. Flash ERG (ERG) revealed only slight increase in implicit time of the a and b waves of the rod and cone responses and exclude cone-rod dystrophies. This article highlights the importance of the results of ophthalmoscopy, fluorescein angiography, autofluorescence, mfERG, fERG, EOG and genetic tests for Sjögren's reticular dystrophy with a pathogenic variant in the region of the C2 gene-c.841_849+19del (dbSNP rs9332736).

Weakly supervised semantic segmentation of histological tissue via attention accumulation and pixel-level contrast learning

Objective. Histopathology image segmentation can assist medical professionals in identifying and diagnosing diseased tissue more efficiently. Although fully supervised segmentation models have excellent performance, the annotation cost is extremely expensive. Weakly supervised models are widely used in medical image segmentation due to their low annotation cost. Nevertheless, these weakly supervised models have difficulty in accurately locating the boundaries between different classes of regions in pathological images, resulting in a high rate of false alarms Our objective is to design a weakly supervised segmentation model to resolve the above problems. Approach. The segmentation model is divided into two main stages, the generation of pseudo labels based on class residual attention accumulation network (CRAANet) and the semantic segmentation based on pixel feature space construction network (PFSCNet). CRAANet provides attention scores for each class through the class residual attention module, while the Attention Accumulation (AA) module overlays the attention feature maps generated in each training epoch. PFSCNet employs a network model containing an inflated convolutional residual neural network and a multi-scale feature-aware module as the segmentation backbone, and proposes dense energy loss and pixel clustering modules are based on contrast learning to solve the pseudo-labeling-inaccuracy problem. Main results. We validate our method using the lung adenocarcinoma (LUAD-HistoSeg) dataset and the breast cancer (BCSS) dataset. The results of the experiments show that our proposed method outperforms other state-of-the-art methods on both datasets in several metrics. This suggests that it is capable of performing well in a wide variety of histopathological image segmentation tasks. Significance. We propose a weakly supervised semantic segmentation network that achieves approximate fully supervised segmentation performance even in the case of incomplete labels. The proposed AA and pixel-level contrast learning also make the edges more accurate and can well assist pathologists in their research.

Specific absorption rate in quasispherical and elongated aggregates of magnetite nanoparticles: Experimental characterization and numerical simulation

Assemblies of magnetite nanoparticles created by the ultrasonic mechanocavitation in a viscous liquid have been obtained for use in magnetic hyperthermia. The magnetite nanoparticles inherit the perfect crystal structure of the original coarse-grained magnetite powder, with characteristic sizes of 0.2–8 μm, and have a high saturation magnetization, Ms = 92 emu/g, close to the value of pure magnetite. Measurements of the specific absorption rate (SAR) of assemblies in an alternating (ac) magnetic field showed a significant dependence of SAR on the nanoparticle concentration, as well as on the shape of particle macroaggregates. A 4-fold increase in the SAR of elongated clusters of particles oriented parallel to the action of ac magnetic field was found in comparison with an assembly of quasi-spherical clusters. In addition a sharp dependence of the SAR of an assembly of elongated clusters on the ac magnetic field direction with respect to the cluster orientation axis was revealed. The results obtained are in qualitative agreement with the results of numerical simulation of dense clusters of magnetic nanoparticles based on the solution of the stochastic Landau-Lifshitz equation.

Mechanistic insights into CDCP1 clustering on non-small-cell lung cancer membranes revealed by super-resolution fluorescent imaging

CDCP1 is a transmembrane protein that is involved in a variety of important biologicalprocesses and upregulated in a variety of human solid malignancies; however, its spatial distribution and variation at the molecular level remain unclear. To solve this problem, we first analyzed its expression level and prognostic implications in lung cancer. Then, we used super-resolution microscopy to reveal the spatial organization of CDCP1 at different levels, and found that cancer cells generated more and larger CDCP1 clusters than normal cells. Furthermore, we found that CDCP1 can be integrated into larger and denser clusters as functional domains upon activation. Our findings elucidated the significant differences of CDCP1 clustering characteristics between cancer and normal cells, and revealed the relationship between its distribution and function, which will contribute to a comprehensive understanding of its oncogenic mechanism, and will be of great help for the development of CDCP1-targeted drugs for lung cancer.