Clustering In Nuclei Research Articles

Neuronal VCP loss of function recapitulates FTLD-TDP pathology.

SUMMARYThe pathogenic mechanism by which dominant mutations in VCP cause multisystem proteinopathy (MSP), a rare neurodegenerative disease that presents as fronto-temporal lobar degeneration with TDP-43 inclusions (FTLD-TDP), remains unclear. To explore this, we inactivate VCP in murine postnatal forebrain neurons (VCP conditional knockout [cKO]). VCP cKO mice have cortical brain atrophy, neuronal loss, autophago-lysosomal dysfunction, and TDP-43 inclusions resembling FTLD-TDP pathology. Conditional expression of a single disease-associated mutation, VCP-R155C, in a VCP null background similarly recapitulates features of VCP inactivation and FTLD-TDP, suggesting that this MSP mutation is hypomorphic. Comparison of transcriptomic and proteomic datasets from genetically defined patients with FTLD-TDP reveal that progranulin deficiency and VCP insufficiency result in similar profiles. These data identify a loss of VCP-dependent functions as a mediator of FTLD-TDP and reveal an unexpected biochemical similarity with progranulin deficiency.

The Uchuu simulations: Data Release 1 and dark matter halo concentrations

ABSTRACT We introduce the Uchuu suite of large high-resolution cosmological N-body simulations. The largest simulation, named Uchuu, consists of 2.1 trillion (12 8003) dark matter particles in a box of side-length 2.0 $\, h^{-1} \, \rm Gpc$, with particle mass of 3.27 × 108$\, h^{-1}\, \rm M_{\odot }$. The highest resolution simulation, Shin-Uchuu, consists of 262 billion (64003) particles in a box of side-length 140 $\, h^{-1} \, \rm Mpc$, with particle mass of 8.97 × 105$\, h^{-1}\, \rm M_{\odot }$. Combining these simulations, we can follow the evolution of dark matter haloes and subhaloes spanning those hosting dwarf galaxies to massive galaxy clusters across an unprecedented volume. In this first paper, we present basic statistics, dark matter power spectra, and the halo and subhalo mass functions, which demonstrate the wide dynamic range and superb statistics of the Uchuu suite. From an analysis of the evolution of the power spectra, we conclude that our simulations remain accurate from the baryon acoustic oscillation scale down to the very small. We also provide parameters of a mass–concentration model, which describes the evolution of halo concentration and reproduces our simulation data to within 5 per cent for haloes with masses spanning nearly eight orders of magnitude at redshift 0 ≤ z ≤ 14. There is an upturn in the mass–concentration relation for the population of all haloes and of relaxed haloes at z ≳ 0.5, whereas no upturn is detected at z < 0.5. We make publicly available various N-body products as part of Uchuu Data Release 1 on the Skies & Universes site.1 Future releases will include gravitational lensing maps and mock galaxy, X-ray cluster, and active galactic nucleus catalogues.

Cluster‐assembled materials: Ordered structures with advanced properties

AbstractCluster‐assembled materials (CAMs) present delicate structures and tailored properties, which have gained extensive interest in recent years. Due to the noncovalent linkage and size of sub‐1 nm, clusters present polymer‐like solution behaviors distinct from conventional nanocrystals, whereby a variety of CAMs with different constructions have been fabricated. To our knowledge, the morphologies and properties of CAMs are closely connected, which are strongly dependent on the structure and coordination state of cluster building blocks. For the CAMs with superior performances, the specific arrangement and surroundings of clusters at subnanometer scale usually result in well‐organized structures. In this review, we will summarize the recent progress of CAMs with highly ordered structures and advanced properties, where polyoxometalate cluster assemblies as well as cluster–nuclei co‐assemblies are involved. The application potentials of CAMs are then discussed, especially on the cluster arrangements and interactions at subnanometer scale. However, current researches are far from reaching the point, and future efforts are needed before the nature of cluster‐based nanostructures can be sufficiently revealed. We hope this article can offer unique insights and instructive understanding to the CAMs.

Abstract PO-009: Towards solving overlapping nuclei segmentation: Sequential CNNs for one to many mapping of pixels to objects

Abstract Automated cell nucleus segmentation is key to quantifying cell features and functionality to identify the disease state of tissue and its likely biological future development. Despite considerable advances in automated segmentation, it is a challenging task to split overlapping clustered nuclei where pixels can belong to more than one nuclei. To substantially improve upon current cell/nuclei segmentation methods our group has developed a promising nuclear segmentation method using sequential rounds of modified UNet CNNs which we denote SUNet. This sequential application of two separate CNNs is required to allow the reparsing the original image into multiple overlapping sub images which explicitly allows for the SUNet approach to perform a one pixel mapping to many objects that is missing from other CNN based segmentation methods in Digital Pathology. We initially constructed two human annotated 8000 overlapping nuclei training sets one each from lung and prostate tissue sections quantitatively stained for DNA. These were used to train two SUNets. Their performance was evaluated on ~20,000 nuclei from multiple tissues for each tissue type. The average correct visual nuclei segmentation rate was observed to be ~84%. A subset of the training images were hand annotated by multiple observers and the Jaccard similarity coefficient (JSC) was used to quantify the similarity between these annotations. Four human observers have annotated the same 1065 nuclei from 100 nuclei clusters. For the 6 pairwise comparisons of these individuals, the average JSC was found to be 0.69 (range 0.63-0.75). This low number reflects the inability of humans to exactly trace the same boundary by hand and that not all observers recognized the same nuclei within the highly complex clusters. The SUNets JSC was similar to the average JSC and within the range of the human observers. Indicating that for these images the SUNet performs as well as a human at segmenting nuclei within complex clusters of nuclei. Comparing the performance of SUNet with a published mask_RCNN based nuclei segmentation and a recently published nucleAIzer method on a non-complex (not a lot of overlapping nuclei) image; we find the three are almost equivalent. However, the two published methods miss a few overlapping nuclei that are found by SUNet. On images with highly complex overlapping clusters of nuclei SUnet correctly segments more (10-20%, image complexity dependent) nuclei than the other methods in areas of high complexity. In addition, SUNet recognizes and segments large numbers of nuclei completely missed by the other methods. An added benefit of allowing a one to many assignment of pixels, is that it correctly preserves the complete shape of the nuclei as it is able to assign the same pixel(s) in an image to the multiple nuclei involved in the area of their overlap as apposed to assigning it to one of the nuclei involved. In summary, the sequential CNN proposed approach can segment nuclei in complex cluster with an accuracy equivalent to human observers. Citation Format: Calum E. MacAulay, Kouther Noureddine, Martial Guillaud, Paul Gallagher. Towards solving overlapping nuclei segmentation: Sequential CNNs for one to many mapping of pixels to objects [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-009.

Single Nuclei Sequencing Reveals Novel Insights Into the Regulation of Cellular Signatures in Children With Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is a leading cause of death in children with heart failure. The outcome of pediatric heart failure treatment is inconsistent, and large cohort studies are lacking. Progress may be achieved through personalized therapy that takes age- and disease-related pathophysiology, pathology, and molecular fingerprints into account. We present single nuclei RNA sequencing from pediatric patients with DCM as the next step in identifying cellular signatures. We performed single nuclei RNA sequencing with heart tissues from 6 children with DCM with an age of 0.5, 0.75, 5, 6, 12, and 13 years. Unsupervised clustering of 18 211 nuclei led to the identification of 14 distinct clusters with 6 major cell types. The number of nuclei in fibroblast clusters increased with age in patients with DCM, a finding that was confirmed by histological analysis and was consistent with an age-related increase in cardiac fibrosis quantified by cardiac magnetic resonance imaging. Fibroblasts of patients with DCM >6 years of age showed a profoundly altered gene expression pattern with enrichment of genes encoding fibrillary collagens, modulation of proteoglycans, switch in thrombospondin isoforms, and signatures of fibroblast activation. In addition, a population of cardiomyocytes with a high proregenerative profile was identified in infant patients with DCM but was absent in children >6 years of age. This cluster showed high expression of cell cycle activators such as cyclin D family members, increased glycolytic metabolism and antioxidative genes, and alterations in ß-adrenergic signaling genes. Novel insights into the cellular transcriptomes of hearts from pediatric patients with DCM provide remarkable age-dependent changes in the expression patterns of fibroblast and cardiomyocyte genes with less fibrotic but enriched proregenerative signatures in infants.

Theoretical studies on α decays of even-even nuclei with the improved square-well cluster model

<p indent=0mm>α decay is one of the main decay modes of some unstable nuclei. It provides rich information on nuclear structures and is crucial for the experimental identification of new nuclides and new elements in heavy and superheavy regions. It is important to develop theoretical models for α-decay researches. Gamow, Gurney, and Condon proposed the first theoretical model to explain α decay within the framework of quantum tunneling in 1928, which is the first application of quantum mechanics in the subatomic systems. After that, several new theoretical models are proposed to get a better understanding of α decay. They provide preliminary descriptions of α decays in medium-mass and heavy nuclei. Square-well cluster model is an important model for α decay studies. It has several valuable advantages and gives a good description of the experimental α-decay half-lives of some even-even nuclei. It assumes that the effective potential between α cluster and daughter nucleus is given by the square well + the Coulomb potential at the surface. The Pauli blocking between α cluster and daughter nucleus is simulated by the Wildermuth condition, which prevents the α cluster from occupying the orbits inside the daughter nucleus. The theoretical α-decay half-lives are given by the WKB approximation. One of the shortcomings of the square-well cluster model is that different α-emitters are assumed to have exactly the same potential-well depth, which is disfavored by, e.g., the double-folding model and is an oversimplification of realistic effective potentials. In this work, we improve the square-well cluster model by taking into consideration that the depth of the square well depends on the mass number and the proton number. Such an improvement preserves, at the same time, many important merits of the original square-well cluster model. The Pauli blocking effect is simulated by the Wildermuth condition. The theoretical α-decay half-lives are calculated by the WKB approximation. The free parameters in the improved square-well cluster model are determined by fitting the experimental α-decay half-lives of the medium-mass, heavy, and superheavy even-even nuclei, respectively. We then study systematically α decays of even-even nuclei in different mass regions. The logarithmic hindrance factors and root-mean-square deviations between the experimental and theoretical results are calculated. We find that the theoretical α-decay half-lives given by the improved square-well cluster model are in better agreement with the experimental data than those given by the square-well cluster model. There are more theoretical data points agreeing with the experimental data within a factor of 1.5 in the improved square-well cluster model than the squared-well cluster model. For instance, the theoretical α-decay half-life of ²⁸⁸Fl is about three times the experimental data in the square-well cluster model, but only deviates from the experimental data by 3% in the improved square-well cluster model. This shows the advantages of the improved square-well cluster model by introducing the mass-number and proton-number dependence of the square-well depth. These results could be useful for experimental and theoretical studies of α decays.

Contribution of stripped nuclei to the ultracompact dwarf galaxy population in the Virgo cluster

ABSTRACT We use the hydrodynamical EAGLE simulation to predict the numbers, masses, and radial distributions of tidally stripped galaxy nuclei in massive galaxy clusters, and compare these results to observations of ultracompact dwarf galaxies (UCDs) in the Virgo cluster. We trace the merger trees of galaxies in massive galaxy clusters back in time and determine the numbers and masses of stripped nuclei from galaxies disrupted in mergers. The spatial distribution of stripped nuclei in the simulations is consistent with those of UCDs surrounding massive galaxies in the Virgo cluster. Additionally, the numbers of stripped nuclei are consistent with the numbers of M &amp;gt; 107 M⊙ UCDs around individual galaxies and in the Virgo cluster as a whole. The mass distributions in this mass range are also consistent. We find that the numbers of stripped nuclei surrounding individual galaxies correlate better with the stellar or halo mass of individual galaxies than the total cluster mass. We conclude that most high mass (M &amp;gt; 107 M⊙) UCDs are likely stripped nuclei. It is difficult to draw reliable conclusions about low mass (M &amp;lt; 107 M⊙) UCDs because of observational selection effects. We additionally predict that a few hundred stripped nuclei below a mass of 2 × 106 M⊙ should exist in massive galaxies that will overlap in mass with the globular cluster population. Approximately 1–3 stripped nuclei in the process of forming also exist per massive galaxy.

Triangular symmetry in cluster nuclei

In this contribution, we present evidence for the occurrence of triangular symmetry in cluster nuclei. We discuss the structure of rotational bands for 3α and 3α +1 configurations with triangular D 3h symmetry by exploiting the double group and study the application to 12 C and 13C. The structure of rotational bands can be used as a fingerprint of the underlying geometric configuration of α particles.

Evolution of {0kl} 〈1 0 0〉 texture and microstructure in preparation of ultra-thin grain-oriented silicon steel

In this paper, an ultra-thin grain-oriented silicon steel (UTGO steel) strip was prepared via rolling a commercial glassless grain-oriented silicon steel plate to 0.075 mm and then annealing it at 850 °C for less than 30 min in a protective atmosphere. The resultant UTGO steel strip has magnetic induction B800 higher than 1.80 T and iron loss P1.5/400 lower than 12.0 W/kg. These superior magnetic properties are due to predominant {0kl} 〈1 0 0〉 texture and appropriate microstructure. The study shows that preferential {0kl} 〈1 0 0〉 nucleation occurs either inside shear bands or at in-grain deformation-induced boundaries. The nucleation behavior varies at different nucleation sites. Nuclei inside shear bands are denser and show preferential transitional orientation along {0kl} 〈1 0 0〉, while new grains dispersed at in-grain split boundaries have shown orientations which are suggested to inherit corresponding initial grain orientation prior to rolling. Nucleation behavior is also highly dependent on the orientation of the roll-deformed matrix. Our study finds that shear banding nucleation of {0kl} 〈1 0 0〉 (including Goss and {0 2 1} 〈1 0 0〉) is reduced when the orientation of the deformed matrix deviates from {1 1 1} 〈1 1 2〉. With the extension of annealing time, the growth of {0kl} 〈1 0 0〉 nuclei in cluster is restricted because of orientation pinning effect, whereas the growth of grains with orientations other than {0kl} 〈1 0 0〉 is promoted. This phenomenon weakens the dominance of {0kl} 〈1 0 0〉 texture, causing deterioration in magnetic properties of the final silicon steel.

Cross sections for the production of radionuclides via natCu(p,X) spallation reactions for proton energies from 250 MeV to 2 GeV

Spallation cross sections for 250 MeV to 2 GeV protons bombarding thin targets of natural copper were determined via the foil activation technique. This energy range is important for cosmogenic radionuclide production in interplanetary space via bombardment by galactic cosmic-ray protons. Cross sections are reported for the production of 60Co, 58Co, 57Co, 56Co, 56Ni, 59Fe, 54Mn, 52Mng, 51Cr, 48V, 47Sc, 46Sc, 44Scm, and 7Be. The 58Co, 54Mn, 46Sc, and 44Scm cross section measurements provide values at energies not currently reported in the EXFOR cross section database. All measurements are compared to cross sections calculated with the INCL-XX intranuclear cascade reaction code, coupled to the GEMINI, ABLA, and SMM evaporation models. The models generally predict the absolute magnitude and energy-dependent variability of the cross sections to within a factor of two, however the accuracy of the models decreases with increasing mass difference between Cu and the reaction product. The ABLA and GEMINI de-excitation codes performed significantly better than SMM, with the GEMINI deexcitation model also accurately predicting the production of the light cluster nucleus 7Be. The large differences in the predictions for 7Be production between the three deexcitation models demonstrate that the deexcitation stage of spallation reactions is an important contributor to the production of light clusters.

Results on quantum phase transitions within the semimicroscopic algebraic cluster model and extension to deformed clusters

The properties of quantum phase transitions in the semimicroscopic algebraic cluster model are investigated. The order of the quantum phase transition is shown to depend on the path taken in the parameter space and a novel procedure to obtain the Maxwell set separatrix is discussed. The first steps toward a description of systems of deformed cluster and heavy nuclei are presented.

X-ray and Gamma-ray Variability of NGC 1275

Gamma-ray emission from the bright radio source 3C 84, associated with the Perseus cluster, is ascribed to the radio galaxy NGC 1275 residing at the centre of the cluster. Study of the correlated X-ray/gamma-ray emission from this active galaxy, and investigation of the possible disk-jet connection, are hampered because the X-ray emission, particularly in the soft X-ray band (2–10 keV), is overwhelmed by the cluster emission. Here we present a method to spectrally decouple the cluster and active galactic nucleus (AGN) emission in imaging X-ray detectors. We use three sets of simultaneous Niel Gehrels Swift XRT and NuStar data. These observations were made during the period 2015 November to 2017 February, when a huge increase in the gamma-ray emission was observed. We find that the gamma-ray emission shows a very high degree of variability (40%–50%) on time scales of 1–10 days, whereas the hard X-ray emission, associated with the AGN, shows a low variability (∼15%–30%), on various time scales in the range of 0.01–60 days.

Novel imprint cytological classification is correlated with tumor spread through air spaces in lung adenocarcinoma

ObjectivesSpread through air spaces (STAS) is a risk factor for local recurrence after sublobar resection in lung cancer patients. We recently proposed the novel Nakayama-Higashiyama imprint cytological classification (N-H classification) based on small-sized lung adenocarcinoma surgical specimens, which correlated with histological patterns and nodal involvement. This study aimed to evaluate the correlation between STAS and the N-H classification and to validate the N-H classification as an intraoperative predictor of the presence of STAS. Materials and methodsWe retrospectively analyzed 164 intraoperative imprint cytologies and their paired histologic specimens from patients undergoing surgical resection for lung adenocarcinoma in our institute in 2017–2019. Using the NH classification, imprint cytological findings were classified into 5 groups (Groups I to V) based on cell cluster shape, cell and nucleus size, and the existence of necrosis. We examined the characteristics of imprint cytology and STAS in the resected tissues and analyzed the relationship between them. ResultsTumor STAS was observed in 29 (17.7 %) cases. The presence of STAS was significantly associated with the NH classification (P < 0.0001). STAS was present in 6 of 57 cases (10.5 %) in NH classification Group II, 11 of 42 cases (26.2 %) in Group III, and 12 of 28 cases (42.9 %) in Group IV/V; STAS was not observed in any case in Group I. Logistic regression analysis revealed that tumors with a ground glass opacity rate of <50 % on computed tomography (P = 0.00867) and Groups III–V of the NH classification (P = 0.00201) were significant independent predictors for STAS. ConclusionIntraoperative imprint cytology with the N-H classification for lung adenocarcinoma is well correlated with the STAS status of the tumor and might have applications as an intraoperative predictive marker of STAS. This classification may be useful for intraoperative detection of STAS and in the decision-making process for the surgical procedure.

Clustered nuclei splitting based on recurrent distance transform in digital pathology images

The accuracy of the applied technique for automated nuclei segmentation is critical in obtaining high-quality and efficient diagnostic results. Unfortunately, multiple objects in histopathological images are connected (clustered) and frequently counted as one. In this study, we present a new method for cluster splitting based on distance transform binarized with the recurrently increased threshold value and modified watershed algorithm. The proposed method treats clusters separately, splitting them into smaller sub-clusters and conclusively into separate objects, based solely on the shape feature, making it independent of the pixel intensity. The efficiency of these algorithms is validated based on the labeled set of images from two datasets: BBBC004v1 and breast cancer tissue microarrays. Results of initial nuclei detection were significantly improved by applying the proposed algorithms. Our approach outperformed the state-of-the-art techniques based on recall, precision, F1-score, and Jaccard index. The proposed method achieves very low amount of under-segmented, as well as over-segmented objects. In summary, we provide novel and efficient method for dividing the clustered nuclei in digital images of histopathological slides.

A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images.

Pap smear is often employed as a screening test for diagnosing cervical pre-cancerous and cancerous lesions. Accurate identification of dysplastic changes amongst the cervical cells in a Pap smear image is thus essential for rapid diagnosis and prognosis. Manual pathological observations used in clinical practice require exhaustive analysis of thousands of cell nuclei in a whole slide image to visualize the dysplastic nuclear changes which make the process tedious and time-consuming. Automated nuclei segmentation and classification exist but are challenging to overcome issues like nuclear intra-class variability and clustered nuclei separation. To address such challenges, we put forward an application of instance segmentation and classification framework built on an Unet architecture by adding residual blocks, densely connected blocks and a fully convolutional layer as a bottleneck between encoder-decoder blocks for Pap smear images. The number of convolutional layers in the standard Unet has been replaced by densely connected blocks to ensure feature reuse-ability property while the introduction of residual blocks in the same attempts to converge the network more rapidly. The framework provides simultaneous nuclei instance segmentation and also predicts the type of nucleus class as belonging to normal and abnormal classes from the smear images. It works by assigning pixel-wise labels to individual nuclei in a whole slide image which enables identifying multiple nuclei belonging to the same or different class as individual distinct instances. Introduction of a joint loss function in the framework overcomes some trivial cell level issues on clustered nuclei separation. To increase the robustness of the overall framework, the proposed model is preceded with a stacked auto-encoder based shape representation learning model. The proposed model outperforms two state-of-the-art deep learning models Unet and Mask_RCNN with an average Zijdenbos similarity index of 97 % related to segmentation along with binary classification accuracy of 98.8 %. Experiments on hospital-based datasets using liquid-based cytology and conventional pap smear methods along with benchmark Herlev datasets proved the superiority of the proposed method than Unet and Mask_RCNN models in terms of the evaluation metrics under consideration.

Quercetin improves abnormal nephron formation in a mouse model of renal dysplasia

Kidney disease in children often results from the abnormal development of the kidney, termed renal dysplasia (RD). This abnormal development is often associated with poorly formed nephrons, leading to reduced nephron number and poor kidney function. β‐catenin is a dual function protein that complexes with E‐cadherin to form adherens junctions and regulates gene transcription. In human RD, β‐catenin is overexpressed in the nuclei of metanephric mesenchyme (MM), the precursor cells that will form nephrons. Our mouse models of RD where β‐catenin is overexpressed in the MM, termed βcatMM(over), have dysregulated expression of genes involved in nephron formation, resulting in abnormally developed nephrons with poorly formed adherens junctions. Quercetin is a flavonoid that reduces β‐catenin levels in diseases such as cancer and kidney fibrosis. The objective of our study is to test whether quercetin reduces nuclear β‐catenin levels and improves abnormal nephron formation in RD. Dysplastic kidneys from E13.5 βcatMM(over) embryos were cultured in the presence or absence of 40uM quercetin. Immunofluorescence (IF) for Pax2 and Six2 in untreated βcatMM(over) kidneys demonstrated abnormal kidney patterning, clusters of uninduced MM cells, stalled nephrons, and a near absence of normally developing nephrons. Immunohistochemistry (IHC) revealed β‐catenin overexpression in the nuclei of the MM clusters and in the abnormally forming nephrons. In contrast, quercetin treated βcatMM(over) kidneys demonstrated improved kidney patterning, a distinct nephrogenic zone containing Six2 positive MM cells, and a cortical and medullary area containing Pax2 positive developing and maturing nephrons. A 44% increase in normally developing nephrons was observed in the treated kidneys (untreated: 13.25±1.30, N=7 vs. treated: 19.14±2.01, N=8, p=0.03). IHC demonstrated β‐catenin expression was reduced in the nuclei of the MM and developing nephrons and increased in the cell membrane, and this β‐catenin expression pattern was similar to that observed in wildtype kidneys. Quercetin treated kidneys also showed reduced expression levels of β‐catenin target genes: Pax2 (1.5‐fold, p=0.01), Six2 (1.2‐fold, p=0.46) and Gdnf (1.2‐fold, p=0.28). IHC on untreated βcatMM(over) kidneys demonstrated E‐cadherin localization in the cytoplasm of the stalled nephrons instead of the cell membrane. In contrast, quercetin treated βcatMM(over) kidneys demonstrated E‐cadherin expression exclusively in the cell membrane. Analysis of human fetal dysplastic tissue also demonstrated cytoplasmic E‐cadherin localization in abnormally developing nephrons, which was not observed in normally developing nephrons in normal human fetal kidneys. E13.5 wildtype kidneys treated with 40uM, 80uM and 160uM quercetin showed no change in β‐catenin protein levels by Western blot. However, IHC and IF showed that increasing quercetin dose resulted in β‐catenin and E‐cadherin expression being limited to the cell membrane. In conclusion, our data supports that quercetin is effective in improving nephron formation in RD by reducing nuclear β‐catenin and redistributing β‐catenin and E‐cadherin to the cell membrane which improves epithelial integrity during nephron formation.Support or Funding InformationNSERC, CIHR, KFOC

RESEARCH OF ALPHA-PARTICLE TRANSFER REACTIONS

This paper presents the capabilities of nuclear physics for the development of nuclear cluster physics. Frequencies of several nucleons, acting as component clusters, are one of the key aspects of the nuclear device. Therefore, the basis for the clustering was helium nuclei (α-particles). This article explores the different types of cluster ensembles and different types of unmanaged nuclei as the fundamental components of new quantum environments. The experimental results of the α-particle transfer reactions under the complete synthesis of some of the cluster nuclei show the formula for those reactions. Specifically, this work focuses on the so-called "cluster nucleus model" - a model that describes the excited states of the nuclei, ie helium nuclei. One of the most important goals of this work is to carefully analyze the author's available data on research of alpha-particle reactions and to compare them with today's promising models of nuclei, to identify and explain new patterns. The author of the article received experimental data from various publications in journal articles and "personal contact". Relevant sources in the article have reference to sources.

Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement

Automated nuclear segmentation in histopathological images is a prerequisite for a computer-aided diagnosis framework. However, it remains a challenging problem due to the nucleus occlusion or overlapping, shape variation, and image background complexity. Recently, deep learning techniques are widely used in analyzing digital histopathology. We present a computerized image-based method for automatically segmenting nuclei using an integration of a deep learning model and an improved concave point detection algorithm. A modified atrous spatial pyramid pooling U-Net (ASPPU-Net) is derived to capture multi-scale nuclei features and obtain nuclei context information without reducing the spatial resolution of feature map. A weighted binary cross entropy loss function with Dice loss function is used to better handle the data unbalance problem. An accelerated concave point detection method allows to effectively and accurately segmenting highly overlapping nuclei. Our ASPPU-Net based method was tested on four independent data cohorts and achieved the highest Dice similarity coefficient of 0.83, and pixel wise accuracy of 0.95. The experimental results suggested that the combination of ASPPU-Net model and concave point detection method was able to gain improved performance in separating both isolated and touching clustered nuclei in histopathology.

Cluster decay half-lives using modified generalized liquid drop model (MGLDM) with different pre-formation factors

Cluster decay from 212–240Pa, 219–245Np, 228–246Pu, 230–249Am, and 232–252Cm has been studied by including three different pre-formation factors that depend on cluster size, atomic number of cluster and daughter nuclei, and Q value in MGLDM, an approach developed by modifying GLDM with proximity 77 potential. In MGLDM, where Q value-dependent pre-formation factor is added, the standard deviation of logarithmic half-lives was found to be 0.83, and the experimental half-lives were successfully reproduced using MGLDM with cluster size-dependent, and atomic number of cluster and daughter nuclei-dependent pre-formation factor with a standard deviation of 0.58 and 0.63, respectively. The half-lives of heavy elements in trans-lead region emitting 22Ne, 24Ne, 26Mg, 28Mg, 32Si and 33Si clusters, which have not been experimentally determined so far, have been predicted by incorporating three types of pre-formation factors to MGLDM. As all the predicted half-lives are below 1030s and are within the experimentally measurable range, we hope that our present predictions would be helpful for future studies in this field.

Cosmological Simulation of Galaxy Groups and Clusters. I. Global Effect of Feedback from Active Galactic Nuclei

Abstract In this study we quantify the properties of the gas and dark matter around active galactic nuclei (AGNs) in simulated galaxy groups and clusters and analyze the effect of AGN feedback on the surrounding intracluster (group) medium. Our results suggest downsizing of AGN luminosity with host halo mass, supporting the results obtained from clustering studies of AGNs. By examining the temperature and density distribution of the gas in the vicinity of AGNs we show that due to feedback from the central engine, the gas gets displaced from the center of the group/cluster resulting in a reduction of the density but an enhancement of temperature. We show that these effects are pronounced at both high and low redshifts and propose new observables to study the effect of feedback in higher-redshift galaxies. We also show that the average stellar mass is decreased in halos in the presence of AGN feedback confirming claims from previous studies. Our work for the first time uses a fully cosmological hydrodynamic simulation to evaluate the global effects of AGN feedback on their host dark matter halos as well as galaxies at scales of galaxy groups and clusters.