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Figure 2: The microscopy images fo neuronal cells control (a) generated by MWCNT (b) and SWCNT (c)-COMPARATIVE STUDY OF SINGLE- AND MULTI-WALL CARBON NANOTUBES WITH APPLICATION IN CEREBRAL ANEURYSM
<p>Fig. 2 shows the evolution of a network generated by SWCNT and MWCNT. The data show<br> that cells ¯rst aggregate at the NT islands. As they complete this step axons and dendrites begin to form and to build connections.</p>
Fig. 9 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 9 Overall framework of proposed automated malaria diagnosis and species identification. CNN, Convolutional neural network; RBC, red blood cell; YOLO, You Only Look Once (model)
Fig. 8 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 8 Examples of false positive predictions by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)
Fig. 6 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 6 Comparison of detection performance by the original YOLOv4 model and the YOLOv4-RC3_4 model. Red arrows indicate cells not detected by the original YOLOv4 model, green arrows indicate the same cells detected by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)
Fig. 3 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 3 Network structure of YOLOv4. CSP, cross-spatial connection; SPP, spatial pyramid pooling layer; PANet, Path Aggregation Network; CBM, Convolutional, Batch Normalisation, and Activation; CBL, Convolutional, Batch normalisation, and Leaky-ReLU; Conv, convolutional; Concat, concatenation
Fig. 4 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 4 Building blocks of the residual learning module. CBM, Convolutional, Batch normalisation and Mish (modules)
Fig. 5 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 5 Visual representation of the removal of residual blocks from C3 and C4 Res-block body. YOLO,You Only Look Once (model)
Fig. 1 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 1 Comparison of malaria diagnosis using deep learning CNN models and deep learning object detectors. CNN, Convolutional neural network
Fig. 2 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 2 Cropping of infected cells using the coordinates of predictions by the object detectors. RBC, Red blood cell; YOLO,You Only Look Once (model)
single cell RNA seq data of 3 healthy sample from frontal lobe and temporal lobe
<p><span>Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We provided single-nucleus transcriptome analysis in “fresh” human FL and TL which are integrated with genetic susceptibility, gene dysregulation in neuropsychiatric disease, and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB-neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. these data provide prefound insight into brain frontotemporal lobe.</span></p>
Рис. 2. Mukhina elegans (Mukhina, 1981) (самка): А — трофико-сенсорный отΔеΛ теΛа; Б — поΛовая система; В — переΔний конец теΛа; Г — хвост; À — фрагмент кутикуΛы с боковым поΛем бп — боковое поΛе, да — ΔробиΛьный аппарат, в — вуΛьва, зм — заΔняя матка, кб — карΔиаΛьный буΛьбус, кск — кΛетки среΔней кишки, нк — нервное коΛьцо, пм — переΔняя матка, р — ректум, рт — ренетта, с — сперма, ск — среΔняя кишка, сп — семяприемник, ф — фазмиΔы, хс — хейΛостома, эп — экскреторная пора, я — яичник Fig. 2. Mukhina elegans (Mukhina, 1981) (female): A — trophic-sensory part of the body; B — the reproductive system; C — the front end of the body; G — the tail; D — fragment of the cuticle with a side field бп — side field, да — crushing apparatus, в — vulva, зм — posterior uterus, кб — cardial bulb, кск — cells of the mid-intestine, нк — nerve ring, пм — anterior uterus, р — rectum, рт — renetta с — sperm, ск — mid-intestin, сп — seminal receptacle, ф — phasmids, хс — cheilostoma, эп — excretory pore, я — ovary in A New Species Sp. Nov. (Nematoda, Cephalobidae) From Primorsky Region (Russia)
Рис. 2. Mukhina elegans (Mukhina, 1981) (самка): А — трофико-сенсорный отΔеΛ теΛа; Б — поΛовая система; В — переΔний конец теΛа; Г — хвост; À — фрагмент кутикуΛы с боковым поΛем бп — боковое поΛе, да — ΔробиΛьный аппарат, в — вуΛьва, зм — заΔняя матка, кб — карΔиаΛьный буΛьбус, кск — кΛетки среΔней кишки, нк — нервное коΛьцо, пм — переΔняя матка, р — ректум, рт — ренетта, с — сперма, ск — среΔняя кишка, сп — семяприемник, ф — фазмиΔы, хс — хейΛостома, эп — экскреторная пора, я — яичник Fig. 2. Mukhina elegans (Mukhina, 1981) (female): A — trophic-sensory part of the body; B — the reproductive system; C — the front end of the body; G — the tail; D — fragment of the cuticle with a side field бп — side field, да — crushing apparatus, в — vulva, зм — posterior uterus, кб — cardial bulb, кск — cells of the mid-intestine, нк — nerve ring, пм — anterior uterus, р — rectum, рт — renetta с — sperm, ск — mid-intestin, сп — seminal receptacle, ф — phasmids, хс — cheilostoma, эп — excretory pore, я — ovary
Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
<p>Manganese porphyrins reportedly exhibit synergic effects when combined with irradiation. However, an in-depth understanding of intratumoral heterogeneity and immune pathways, as affected by Mn porphyrins, remains limited. Here, we explored the mechanisms underlying immunomodulation of a clinical candidate, MnTnBuOE-2-PyP<sup>5+</sup> (BMX-001, MnBuOE), using single-cell analysis in murine carcinoma<em> </em>model. Mice bearing 4T1 tumors were divided into 4 groups: control, MnBuOE, radiotherapy (RT), combined MnBuOE, and radiotherapy (MnBuOE/RT). In epithelial cells, epithelial-mesenchymal transition, TNF-α signaling via NF-кB, angiogenesis, and hypoxia-related genes were significantly downregulated in the MnBuOE/RT compared to the RT. All subtypes of cancer-associated fibroblasts (CAFs) were reduced in MnBuOE and MnBuOE/RT. Inhibitory receptor-ligand interactions, in which epithelial cells and CAFs interacted with CD8+ T cells, were significantly lower in the MnBuOE/RT than in the RT. Trajectory analysis showed that DC maturation-associated markers were increased in MnBuOE/RT. M1 macrophages were significantly increased in the MnBuOE/RT compared to the RT, whereas myeloid-derived suppressor cells were decreased. CellChat analysis showed that the number of cell-cell communications was the lowest in the MnBuOE/RT. Our study is the first to provide evidence for the combined radiotherapy with a novel Mn porphyrin clinical candidate, BMX-001 from the perspective of each cell type within the tumor microenvironment.</p>
Discrete Flow Cytometry of Depth Profile Samples from the Gradients 5 (2023) Cruise Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected during the Gradients 2023 (Gradients 5/TN412) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for heterotrophic bacteria, picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Sclerostin blockade inhibits bone resorption through PDGF receptor signaling in osteoblast lineage cells
<p>While sclerostin-neutralizing antibodies (Scl-Ab) transiently stimulate bone formation by activating Wnt signaling in osteoblast lineage cells, they exert sustained inhibition of bone resorption, suggesting an alternate signaling pathway by which Scl-Ab control osteoclast activity. Since sclerostin can activate platelet-derived growth factor receptors (PDGFRs) in osteoblast lineage cells in vitro and PDGFR signaling in these cells induces bone resorption through M-CSF secretion, we hypothesized that the prolonged anti-catabolic effect of Scl-Ab could result from PDGFR inhibition. We show here that inhibition of PDGFR signaling in osteoblast lineage cells is sufficient and necessary to mediate prolonged Scl-Ab effect on M-CSF secretion and osteoclast activity in mice. Indeed, sclerostin co-activates PDGFRs independently of Wnt/β-catenin signaling inhibition, by forming a ternary complex with LRP6 and PDGFRs in pre-osteoblasts. In turn, Scl-Ab prevents sclerostin-mediated co-activation of PDGFR signaling and consequent M-CSF up-regulation in pre-osteoblast cultures, thereby inhibiting osteoclast activity in pre-osteoblast/osteoclast co-culture assays. These results provide a new potential mechanism explaining the dissociation between anabolic and anti-resorptive effects of long-term Scl-Ab.</p>
Optimized summary-statistic-based single-cell meta-analysis. Input files
<p>This dataset contains information about the input files used in the Optimized summary-statistic-based single-cell meta-analysis research project. </p> <p> </p>
Genome editing of LKB1 gene by CRISPR/Cas9 in lung cancer cells and evaluation of its role in metformin and cisplatin response.
<pre>LKB1 is an important upstream inhibitor of the mTOR/S6Ks pathway. Loss of LKB1 is often associated with cancer, including in A549 lung cancer cell line, boosting the transformation of pre-malignant neoplasic cells. Metformin, a natural compound derived from Galega officinalis, is mainly used as a treatment for diabetes mellitus type 2 (DM2), but recently, it has been associated to lower incidence of cancer. One of the main mechanisms is by activation of AMPK through different pathways, including LKB1 activation. Activation of AMPK inhibits the mTOR pathway, protein synthesis and thus cell growth. The combination of metformin and cisplatin, a main chemothepeutic drug for lung cancer, has shown to improve treatment of cancer cells to cisplatin and also to hinder the cisplatin associated resistance common in lung cancer. Here we aim to use the CRISPR/Cas9 technology to correct the mutation presented in A549 lung cancer cells and improve their response to metformin or to the combination between metformin and cisplatin. Besides, we will assess the mTOR signaling pathway status, as well as cell growth, proliferation, cell cycle and sensitivity to apoptosis induction by cisplatin. This project may be useful as a proof of principle that in the future therapies must consider the genomic background of cancer cells before administration of a specific anticancer drug.<br><br></pre>
Ultra-sensitive and multiplexed tracking of single cells using whole-body PET/CT
<p><em>In vivo </em>molecular imaging tools are crucially important for elucidating how cells move through complex biological systems, however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upwards of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 Bq/cell, and a streamlined workflow to reliably label single cells with over 50 Bq/cell of <sup>18</sup>F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of over 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.</p>
Raw data for the article entitled ''Evaluation of in-plane architecture in a thermo-electrochemical cell with nanostructured and porous Sb:SnO2 electrodes''
<p>Raw data for the plots in the article entitled ''Evaluation of in-plane architecture in a thermo-electrochemical cell with nanostructured and porous Sb:SnO2 electrodes''.</p>
Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming
<p>During cell fate transitions, cells remodel their transcriptome, chromatin, and epigenome; however, it has been difficult to determine the temporal dynamics and cause-effect relationship between these changes at the single-cell level. Here, we employ the heterokaryon-mediated reprogramming system as a single-cell model to dissect key temporal events during early stages of pluripotency conversion using super-resolution imaging. We reveal that, following heterokaryon formation, the somatic nucleus undergoes global chromatin decompaction and removal of repressive histone modifications H3K9me3 and H3K27me3 without acquisition of active modifications H3K4me3 and H3K9ac. The pluripotency gene OCT4 (POU5F1) shows nascent and mature RNA transcription within the first 24 h after cell fusion without requiring an initial open chromatin configuration at its locus. NANOG, conversely, has significant nascent RNA transcription only at 48 h after cell fusion but, strikingly, exhibits genomic reopening early on. These findings suggest that the temporal relationship between chromatin compaction and gene activation during cellular reprogramming is gene context dependent. </p>
Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provides both common and discordant biological insights
<p>The datasets generated by <em>Cellranger </em>for all 24 samples (.h5 format).<br><br></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.