Nucleus-independent chemical shift (NICS) values were calculated at several locations for a series of dimethyldihydropyrenes (DDPs). These NICS values were used to assess the relative aromaticities of the dimethyldihydropyrene nucleus (DDPN) of these DDPs and to construct a NICS scale of aromaticity. The NICS and experimentally determined relative aromaticities of these DDPNs are in complete agreement, verifying that NICS can be used not only to classify a compound as aromatic but also to determine the degrees of aromaticity of structurally related systems.

In eukaryotes, the cell has the ability to modulate the size of the nucleus depending on the surrounding environment, to enable nuclear functions such as DNA replication and transcription. From previous analyses of nuclear size scaling in various cell types and species, it has been found that eukaryotic cells have a conserved scaling rule, in which the nuclear size correlates with both cell size and genomic content. However, there are few studies that have focused on a certain cell type and systematically analyzed the size scaling properties in individual species (intra-species) and among species (inter-species), and thus, the difference in the scaling rules among cell types and species is not well understood. In the present study, we analyzed the size scaling relationship among three parameters, nuclear size, cell size, and genomic content, in our measured datasets of terminally differentiated erythrocytes of five Anura frogs and collected datasets of different species classes from published papers. In the datasets of isolated erythrocytes from individual frogs, we found a very weak correlation between the measured nuclear and cell cross-sectional areas. Within the erythrocytes of individual species, the correlation of the nuclear area with the cell area showed a very low hypoallometric relationship, in which the relative nuclear size decreased when the cell size increased. These scaling trends in intra-species erythrocytes are not comparable to the known general correlation in other cell types. When comparing parameters across species, the nuclear areas correlated with both cell areas and genomic contents among the five frogs and the collected datasets in each species class. However, the contribution of genomic content to nuclear size determination was smaller than that of the cell area in all species classes. In particular, the estimated degree of the contribution of genomic content was greater in the amphibian class than in other classes. Together with our imaging analysis of structural components in nuclear membranes, we hypothesized that the observed specific features in nuclear size scaling are achieved by the weak interaction of the chromatin with the nuclear membrane seen in frog erythrocytes.

AC = FNAL.

m, n, Electron micrographs of the brain of Atg7flox/flox; nestin-Cre mice. Inclusion bodies (arrows) were often observed in Atg7flox/flox; nestin-Cre hypothalamus. The boxed region in m is shown in n. Inclusion bodies were not detected in Atg7flox/+; nestin-Cre brain (data not shown). Scale bars, 5 µm (m), 1 µm (n). o, Immunoelectron micrograph of ubiquitin in a representative Atg7flox/flox; nestin-Cre hypothalamus. N, nucleus. Scale bar, 1 µm.. List of tagged entities: Rps27a (uniprot:P62983), inclusion body (go:GO:0016234), nucleus (go:GO:0005634), Atg7 (ncbigene:74244), Electron micrographs,Immunoelectron micrograph

NuCorpus-15M: A Large-Scale DAPI-Stained Cell Nucleus Image Dataset

  Dataset Description

NuCorpus-15M is a large-scale dataset specifically created for nuclear morphology analysis in biomedical research. It consists of 15.52 million high-quality DAPI-stained cell nucleus images, representing a diverse range of human and mouse tissues, both diseased and non-diseased. The dataset spans 15 different organs or tissue types and has been meticulously curated to ensure high… See the full description on the dataset page: https://huggingface.co/datasets/TongjiZhanglab/NuCorpus-15M.

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, in...

# # # Machine Learning Model for identifying Cell Nuclei from Histology Images

Machine learning model for identifying cell nuclei from histology images. The model having the ability to generalize across a variety of lighting conditions, cell types, magnifications, and imaging modalities.Imagine speeding up research for almost every disease, from lung cancer and heart disease to rare disorders. The Data Science Bowl offers to data scientist / practitioner a most ambitious mission i.e. create an algorithm to automate nucleus detection & create an algorithm to detect all non overlapped nuclei from the given test data i.e. It should have the capability for instance segmentation. We’ve all seen people suffer from diseases like cancer, heart disease, chronic obstructive pulmonary disease, Alzheimer’s, and diabetes. Many have seen their loved ones pass away. Think how many lives would be transformed if cures came faster. By automating nucleus detection, you could help unlock cures faster—from rare disorders to the common cold

# ## Why nuclei?

Identifying the cells’ nuclei is the starting point for most analyses because most of the human body’s 30 trillion cells contain a nucleus full of DNA, the genetic code that programs each cell. Identifying nuclei allows researchers to identify each individual cell in a sample, and by measuring how cells react to various treatments, the researcher can understand the underlying biological processes at work.By participating, teams will work to automate the process of identifying nuclei, which will allow for more efficient drug testing, shortening the 10 years it takes for each new drug to come to market

Acknowledgements

The success and final outcome of this project required a lot of guidance and assistance from many people and I am extremely privileged to have got this all along the completion of my project. All that I have done is only due to such supervision and assistance and I would not forget to thank them.I owe my deep gratitude to our project guide C - DAC Noida, who took keen interest on my project work and guided me all along, till the completion of our project work by providing all the necessary information for developing a good system.

Inspiration

The Data Science Bowl, presented by Booz Allen and Kaggle, is the world’s premier data science for social good competition. The Data Science Bowl brings together data scientists, technologists, domain experts, and organizations to take on the world’s challenges with data and technology. It’s a platform through which people can harness their passion, unleash their curiosity, and amplify their impact to effect change on a global scale

'Measurements of the fractional momentum loss ($S_{loss}$ ≡ $\delta p_T/p_T$) of high-transverse-momentum identified hadrons in heavy ion collisions are presented. Using $\pi^0$ in Au+Au and Cu+Cu collisions at $\sqrt{s_{NN}}$ = 62.4 and 200 GeV measured by the PHENIX experiment at the Relativistic Heavy Ion Collider and and charged hadrons in Pb+Pb collisions measured by the ALICE experiment at the Large Hadron Collider, we studied the scaling properties of $S_{loss}$ as a function of a number of variables: the number of participants, $N_{part}$, the number of quark participants, $N_{qp}$, the charged particle density, $dN_{ch}/d \eta$, and the Bjorken energy density times the equilibration time, $\epsilon B_j \tau_0$. We find that the $p_T$ , where Sloss has its maximum, varies both with centrality and collision energy. Above the maximum, Sloss tends to follow a power-law function with all four scaling variables. The data at $\sqrt{s_{NN}}$ = 200 GeV and 2.76 TeV, for sufficiently high particle densities, have a common scaling of $S_{loss}$ with $dN_{ch}/d \eta$ and $\epsilon B_j \tau_0$, lending insight on the physics of parton energy loss.'

Toronto Alexithymia Scale (TAS-20) total score and subscores.

NASA's International Halley Watch (IHW) has created a Comet Halley Archive. The collection of data spans the full wavelength range as submitted by scientists to the IHW. The observations belong to one of the following Disciplines: Amateur, Astrometry, Infrared Studies, Large-Scale Phenomena, Meteor Studies, Near-Nucleus Studies, Photometry and Polarimetry, Radio Studies, and Spectroscopy and Spectrophotometry. The data collected by these nine disciplines were augmented by Spacecraft measurements. The data were submitted to IHW, but the evaluation and selection for the Archive has been the primary responsibility of the Discipline Specialist Teams for each network in cooperation with the Lead Center. The data from the Near Nucleus Studies Network contains 3523 images of Halley. These data span dates 1982 October 16 through 1989 April 12.

FNAL-TEV. NUCLEUS=NUCLEI OF 40 ILFORD G5 EMULSION.

Global lumbar nucleus replacement devices market was valued at USD 168 million in 2024 and is projected to reach USD 472 million by 2031, growing at a CAGR of 15.9% during the forecast period.

(f) Left: confocal images of LC3-positive dots. N, nucleus. Scale bar, 10 μm. Right: percentage of cells containing more than five LC3-positive dots. At least 60 cells were counted in each experiment. Means ± s.d. for three independent experiments. Asterisk, P 0.001 versus cells transfected with the empty vector; ANOVA.. List of tagged entities: MAP1LC3B (uniprot:Q9GZQ8), nucleus (go:GO:0005634), IB3-1 (cellosaurus:CVCL_0338), BECN1 (ncbigene:8678), fluorescence microscopy (bao:BAO_0000450)

The PHENIX Collaboration at the Relativistic Heavy Ion Collider has measured open heavy flavor production in Cu+Cu collisions at $\sqrt{s_{NN}}$=200 GeV through the measurement of electrons at midrapidity that originate from semileptonic decays of charm and bottom hadrons. In peripheral Cu+Cu collisions an enhanced production of electrons is observed relative to $p$+$p$ collisions scaled by the number of binary collisions. In the transverse momentum range from 1 to 5 GeV/$c$ the nuclear modification factor is $R_{AA}$∼1.4. As the system size increases to more central Cu+Cu collisions, the enhancement gradually disappears and turns into a suppression. For $p_T$ > 3 GeV/$c$, the suppression reaches $R_{AA}$∼0.8 in the most central collisions. The $p_T$ and centrality dependence of $R_{AA}$ in Cu+Cu collisions agree quantitatively with $R_{AA}$ in $d$+Au and Au+Au collisions, if compared at similar number of participating nucleons $\langle N_{part} \rangle$.

This dataset contains Giant Nuclei Impactor (GNI) NaCl-equivalent Sea-Salt Size Distributions collected by the NSF/NCAR C-130 during the ICE-T project

The prosthetic disc nucleus market size was over USD 124.5 million in 2024 and is projected to reach USD 374 million by the end of 2037, escalating at a CAGR of 9.6% during the forecast timeline, i.e., 2025-2037. North America industry is predicted to account for the largest share of 46.6% by 2037, owing to well-developed healthcare facilities in the region.

Across the tree of life, cell size varies by orders of magnitude, and organelles scale to maintain cell function. Depending on their shape, organelles can scale by increasing volume, length, or number. Scaling may also reflect demands placed on organelles by increased cell size. The 8,653 species of amphibians exhibit diverse cell sizes, providing a powerful system to investigate organellar scaling. Using transmission electron microscopy and stereology, we analyzed three frog and salamander species whose enterocyte cell volumes range from 228 to 10,593 μm3. We show that the nucleus increases in radius while the mitochondria increase in total network length; the endoplasmic reticulum and Golgi apparatus, with their complex shapes, are intermediate. Notably, all four organelles increase in volume proportionate to cell volume. This pattern suggests that protein concentrations are the same across amphibian species that differ 50-fold in cell size, and that organellar building blocks are inc..., Tissue Sampling, Fixation, Staining, and Imaging Intestinal tissue was chosen for analyses as it is made up of only four cell types, and 80 percent of the total cell population is enterocytes, resulting in a relatively homogenous population of cells (De Santa Barbara et al. 2003). Three species of amphibians were chosen that span much of the range of amphibian genome and cell sizes: the western clawed frog Silurana tropicalis (genome size = 1.2 Gb), the northern gray-cheeked salamander Plethodon montanus (genome size = 35 Gb), and the western waterdog Necturus beyeri (genome size ~100 Gb based on congeners that range from 80.5-120.6 Gb). Silurana tropicalis were obtained from a lab-reared colony following standard husbandry conditions and Necturus beyeri were obtained commercially. Plethodon montanus were field collected between May and August of 2018 in Avery County, North Carolina under the wildlife collection license # 18-SC01250 issued by the North Carolina Wildlife Resources Commis..., , The excel files are measurements of organellar volume (μm3), surface area (μm2/μm3) and the ratio of the 2 different measurements. All measurements were made in the stereology plugin of IMODS. All relevant data has been converted into data frames using .csv files for ease of processing. CSV files marked as V_DF, SA_DF, and Ratio_DF are used to calculate statistics using the the ANOVA_R_Code.txt, while the complimentary CSV files marked with *Graph are used to generate graphs for the relevant data using Graph_R_Code.txt. The columns of the CSV are each denoted in the file but for the sake of further clarity each preffix of the CSV files coincides with what is being measured V_DF is organellar volume (μm3), SA_DF is organellar surface area (μm2/μm3) and Ratio_DF is the ratio of the 2 different measurements.

The nucleator excel file is the volume measurements of random nuclei (μm3) using the nucleator plugin provided by Visiopharm VIS stereology software.

The 2 text documents are the ge...

FNAL. Multiplicity distributions for hadron-nucleon collisions.

(a) TUNEL staining of apoptotic cells in penumbral brain slices in vivo (scale bar, 100 μm) when H3R was inhibited with thioperamide (scale bar, 50 μm) after tMCAO. TUNEL-positive cells are green, and all cells are stained with 4',6-diamidino-2-phenylindole (blue).. List of tagged entities: nucleus (go:GO:0005634), brain (uberon:UBERON:0000955), Thioperamide (CHEBI:81391), middle cerebral artery (uberon:UBERON:0001627), 4',6-diamidino-2-phenylindole,DAPI,TUNEL staining,fluorescence microscopy (bao:BAO_0000450)

(A) Immunofluorescence staining against Iba-1 in Cb of ASMko and wt mice treated or not with PLX for 2 months. DAPI staining shows cell nuclei. Scale bar, 100 μm. (B) Magnified images (from A) showing ramified (left) versus amoeboid (right) morphology in wt and ASMko microglia treated or not with PLX. Scale bars, 30 μm. (C) Mean ± SEM number of Iba-1 positive cells in the Cb of the different mouse groups (n = 7 mice per group, Two-way Anova, Bonferroni post hoc).. List of tagged entities: Aif1 (uniprot:O70200), nucleus (go:GO:0005634), microglial cell (cl:CL:0000129), PLX5622 (pubchem.compound:342606920), Smpd1 (ncbigene:20597), cell number determination (bao:BAO_0000572),fluorescence microscopy (bao:BAO_0000450),imaging assay (obi:OBI_0000185),morphological phenotype (bao:BAO_0170002),staining (obi:OBI_0302887)