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  • Nutrient transporters can be rapidly removed from cell surface via substrate-stimulated endocytosis as a way to control nutrient influx, but the molecular underpinnings have not been well understood. In this work, we focused on zinc-dependent endocytosis of human ZIP4 (hZIP4), a zinc transporter essential for dietary zinc uptake. Structure-guided mutagenesis and internalization assay revealed that hZIP4 per se acts as the exclusive zinc sensor with the transport site being responsible for zinc sensing. In an effort of seeking sorting signal, a scan of the longest cytosolic loop (L2) led to identification of a conserved LQL motif essential for endocytosis. Partial proteolysis of purified hZIP4 demonstrated a structural coupling between the transport site and the L2 upon zinc binding, which supports a working model of how zinc ions at physiological concentration trigger a conformation-dependent endocytosis of the zinc transporter. This work provides a new paradigm on post-translational regulation of nutrient transporters.
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  • Raw data for 2-choice food preference assays, FLIC assays, immunofluorescence staining, pharyngeal calcium imaging, and optogenetics.
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  • Large surveys of peptides naturally presented on major histocompatibility class I (MHC I) proteins have enabled improved MHC I ligand prediction by dramatically expanding the available data for many MHC I alleles. However, it is unclear to what extent antigen processing signals can also be learned from these datasets. Here, we developed a predictor of antigen processing by training neural networks to discriminate mass spec-identified MHC I ligands from unobserved peptides, where both classes of peptides are predicted to be strong MHC I binders. The resulting predictor shows qualitative consistency with established preferences for the transporter associated with antigen processing, proteasomal cleavage, and endoplasmic reticulum aminopeptidases. When we combined the antigen processing predictor with a novel pan-allele MHC I binding predictor in a logistic regression model, the combination model significantly outperformed the two components alone as well as the NetMHCpan 4.0 and MixMHCpred 2.0.2 tools at predicting mass spec-identified MHC I ligands. Our predictors are implemented in the open source MHCflurry package, version 1.6.0 (github.com/openvax/mhcflurry).
    Data Types:
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  • The barred knifejaw, Oplegnathus fasciatus (Teleostei: Centrarchiformes Oplegnathidae), is an important species in marine cage culture and fish stocking for marine ranching in East Asia. The males of Oplegnathidae (O. fasciatus and O. punctatus) species are characterized by an X1X2Y system with a neo-Y chromosome based on male karyotype analyses. Release of the chromosome-level reference genome of female O. fasciatus has facilitated insights into the origin of the X1X2Y system of male O. fasciatus. In the present study, we applied PacBio long-read sequencing and high-throughput chromosome interaction mapping (Hi-C) to assemble a chromosome-level genome of male O. fasciatus. A highly contiguous genome with a size of 795 Mb, 2,295 contigs, and a contig N50 of 2.13 Mb was obtained. The 1,355 ordered contigs combined with the draft genome were further assembled into 23 chromosomes approximately 762 Mb in length with a contig and scaffold N50 length of 2.18 and 32.43 Mb, respectively. A large neo-chromosome (Ch9) of 94.2 Mb was assembled from 444 contigs, and found to be more than three times larger than the rest chromosomes in O. fasciatus genome. In addition, 63.1 Mb of the Ch9 sequences of male O. fasciatus had high identity (~99.0%) to the Ch8 and Ch10 sequences of female O. fasciatus based on a whole-genome synteny analysis, showing that the neo-Y chromosome shared significant homology with Ch8 and Ch10 based on male/female genome comparison. Significant fission tracks at the terminal point of the chromosomes were also identified between Ch9 and Ch8/Ch10 using synteny analyses, which showed chromosome rearrangements events had happened in the neo-chromosome Ch9. Our present results accurately demonstrated that the X1X2Y system of male O. fasciatus originated from the fusions of the non-homologous chromosomes Ch8 and Ch10. According to the synteny analyses and previous karyotypes results, which characterized acrocentric chromosomes, we suggested that a centric fusion of acrocentric chromosomes Ch8 and Ch10 was responsible for the formation of the X1X2Y system of male O. fasciatus.
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  • The retinal OCT images ofr rare diseases were extracted by using the Google image and Google dataset search that included English keywords including central serous chorioretinopathy, macular telangiectasia, macular hole, Stargadt’s disease, retinitis pigmentosa. These rare diseases were selected according to a previous review article about OCT diagnosis. The images possessing rare diseases were manually classified by two board-certified ophthalmologists, and ambiguous images were removed to clarify the image domains. Additional file "Segmentation_manual.zip" offers manually segmentedOCT images for pathologic lesions.
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  • We presents a dataset of 100 fundus digital images of retina. The retinal images are taken from Armed Forces Institute of Ophthalmology (AFIO), Rawalpindi, Pakistan and annotated with the help of four expert ophthalmologists for the purpose of computer aided diagnosis of hypertensive retinopaty, diabetic retinopathy and papilledema. This dataset contains retinal blood vessels network, segmented artery/ vein network to calculate Arteriovenous Ratio (AVR), annotation of Optic Nerve Head (ONH) and various retinal abnormalities such as hard exudates (HE) and cotton wool spots. The dataset is valuable for those researchers who are developing automated systems for vessels segmentation, artery/ vein classification, diagnosis of hypertensive retinopathy, diabetic retinopathy and papilledema. Please cite the following article if you want to use this dataset: Muhammad Usman Akram, Shahzad Akbar, Taimur Hassan, Sajid Gul Khawaja, Ubaidullah Yasin, Imran Basit, "Data on fundus images for vessels segmentation, detection of hypertensive retinopathy, diabetic retinopathy and papilledema", Data in Brief, Volume 29, 105282, ISSN 2352-3409, 2020.
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  • The detailed data of this mentioned manuscript.
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  • This data repository holds the raw data traces of the bouton responses presented in Bilz et al "Visualization of a distributed synaptic memory code in the Drosophila brain". We analyzed synaptic boutons of Kenyon cells of the Drosophila mushroom body γ-lobe, two to five by expressing a fluorescent Ca2+ sensor in single Kenyon cells so that axonal boutons could be assigned to distinct cells. Single files in this data set represent single cells in the described experiments. The data set consists of all cells included in the publication and is devided into a raw dataset presented here as xlsx files and an analysed data set as MatLab files. The Matlab scripts used to analyse the data can be found here: https://github.com/zerotonin/KCC_KenyonCellCorrelator . Data organization of the xlsx files =========================== Each file contains the responses of one cell. The data inside the file is split onto two sheets: Sheet 1 contains pre learning phase data, Sheet 2 contains post learning data. The data on each sheet is saved as a two mxn dimensional matrix, where m represents the number of acquired frames and n the number of identified boutons on the cell. The first row of the sheet contains stimulus inforation. All xlsx files can be found in xlsxData.zip Data organization of the MatLab files ============================== The data organization of the Matlab files is described in "Steps to reproduce" .
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  • This Dataset Contains augmented X-ray Images for COVID-19 for COVID-19 Disease Detection Using Chest X-Ray images. The dataset is collected from two online available datasets (https://github.com/ieee8023/covid-chestxray-dataset and https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia). The dataset contains two folders one for COVID-19 Augmented images while Non-COVID-19 is not augmented and the other folder contains augmented images for both COVID-19 and Non-COVID-19.
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  • VASP input and output data for the layers and stackings of fluorographane and related materials
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