cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8283191.v2
title: WDR45 contributes to neurodegeneration through regulation of ER homeostasis and neuronal death

description: Mutations in the macroautophagy/autophagy gene <i>WDR45</i> cause β-propeller protein-associated neurodegeneration (BPAN); however the molecular and cellular mechanism of the disease process is largely unknown. Here we generated constitutive <i>wdr45</i> knockout (KO) mice that displayed cognitive impairments, abnormal synaptic transmission and lesions in several brain regions. Immunohistochemistry analysis showed loss of neurons in prefrontal cortex and basal ganglion in aged mice, and increased apoptosis in prefrontal cortex, recapitulating a hallmark of neurodegeneration. Quantitative proteomic analysis showed accumulation of endoplasmic reticulum (ER) proteins in KO mouse. At the cellular level, accumulation of ER proteins due to WDR45 deficiency resulted in increased ER stress and impaired ER quality control. The unfolded protein response (UPR) was elevated through ERN1/IRE1 or EIF2AK3/PERK pathway, and eventually led to neuronal apoptosis. Suppression of ER stress or activation of autophagy through MTOR inhibition alleviated cell death. Thus, the loss of WDR45 cripples macroautophagy machinery in neurons and leads to impairment in organelle autophagy, which provides a mechanistic understanding of cause of BPAN and a potential therapeutic strategy to treat this genetic disorder. 7-ADD: 7-aminoactinomycin D; ASD: autistic spectrum disorder; ATF6: activating transcription factor 6; ATG: autophagy-related; BafA1: bafilomycin A<sub>1</sub>; BCAP31: B cell receptor associated protein 31; BPAN: β-propeller protein-associated neurodegeneration; CCCP: carbonyl cyanide m-chlorophenylhydrazone; CDIPT: CDP-diacylglycerol–inositol 3-phosphatidyltransferase (phosphatidylinositol synthase); DDIT3/CHOP: DNA-damage inducible transcript 3; EIF2A: eukaryotic translation initiation factor 2A; EIF2AK3/PERK: eukaryotic translation initiation factor 2 alpha kinase 3; ER: endoplasmic reticulum; ERN1/IRE1: endoplasmic reticulum to nucleus signaling 1; GFP: green fluorescent protein; HIP: hippocampus; HSPA5/GRP78: heat shock protein family A (HSP70) member 5; KO: knockout; LAMP1: lysosomal-associated membrane 1; mEPSCs: miniature excitatory postsynaptic currents; MG132: N-benzyloxycarbonyl-L-leucyl-L-leucyl-L-leucinal; MIB: mid-brain; MTOR: mechanistic target of rapamycin kinase; PCR: polymerase chain reaction; PFA: paraformaldehyde; PFC: prefrontal cortex; PRM: parallel reaction monitoring; RBFOX3/NEUN: RNA binding protein, fox-1 homolog [C. elegans] 3; RTN3: reticulon 3; SEC22B: SEC22 homolog B, vesicle trafficking protein; SEC61B: SEC61 translocon beta subunit; SEM: standard error of the mean; SNR: substantia nigra; SQSTM1/p62: sequestosome 1; TH: tyrosine hydroxylase; Tm: tunicamycin; TMT: tandem mass tag; TUDCA: tauroursodeoxycholic acid; TUNEL: terminal deoxynucleotidyl transferase dUTP nick-end labeling; UPR: unfolded protein response; WDR45: WD repeat domain 45; WT: wild type; XBP1: X-box binding protein 1.
DOI: 10.6084/m9.figshare.8283206
title: The tissue- and developmental stage-specific involvement of autophagy genes in aggrephagy

description: Genetic screens have identified two sets of genes that act at distinct steps of basal autophagy in higher eukaryotes: the pan-eukaryotic <i>ATG</i> genes and the metazoan-specific <i>EPG</i> genes. Very little is known about whether these core macroautophagy/autophagy genes are differentially employed during multicellular organism development. Here we analyzed the function of core autophagy genes in autophagic removal of SQST-1/SQSTM1 during <i>C. elegans</i> development. We found that loss of function of genes acting at distinct steps in the autophagy pathway causes different patterns of SQST-1 accumulation in different tissues and developmental stages. We also identified that the calpain protease <i>clp-2</i> acts in a cell context-specific manner in SQST-1 degradation. <i>clp-2</i> is required for degradation of SQST-1 in the hypodermis and neurons, but is dispensable in the body wall muscle and intestine. Our results indicate that autophagy genes are differentially employed in a tissue- and stage-specific manner during the development of multicellular organisms. <b>Abbreviations:</b><i>ATG</i>: autophagy related; CLP: calpain family; <i>EPG</i>: ectopic PGL granules; ER: endoplasmic reticulum; ESCRT: endosomal sorting complex required for transport; GFP: green fluorescent protein; LGG-1/LC3: LC3, GABARAP and GATE-16 family; MIT: microtubule interacting and transport; PGL: P granule abnormality protein; SQST-1: sequestosome-related; UPS: ubiquitin-proteasome system
DOI: 10.6084/m9.figshare.25279723
title: Raw Data - Smartphone and Motor Learning

description: Raw data from an experiment investigating mental fatigue induced by smartphone use on motor learning.
DOI: 10.6084/m9.figshare.8283206.v1
title: The tissue- and developmental stage-specific involvement of autophagy genes in aggrephagy

description: Genetic screens have identified two sets of genes that act at distinct steps of basal autophagy in higher eukaryotes: the pan-eukaryotic <i>ATG</i> genes and the metazoan-specific <i>EPG</i> genes. Very little is known about whether these core macroautophagy/autophagy genes are differentially employed during multicellular organism development. Here we analyzed the function of core autophagy genes in autophagic removal of SQST-1/SQSTM1 during <i>C. elegans</i> development. We found that loss of function of genes acting at distinct steps in the autophagy pathway causes different patterns of SQST-1 accumulation in different tissues and developmental stages. We also identified that the calpain protease <i>clp-2</i> acts in a cell context-specific manner in SQST-1 degradation. <i>clp-2</i> is required for degradation of SQST-1 in the hypodermis and neurons, but is dispensable in the body wall muscle and intestine. Our results indicate that autophagy genes are differentially employed in a tissue- and stage-specific manner during the development of multicellular organisms. <b>Abbreviations:</b><i>ATG</i>: autophagy related; CLP: calpain family; <i>EPG</i>: ectopic PGL granules; ER: endoplasmic reticulum; ESCRT: endosomal sorting complex required for transport; GFP: green fluorescent protein; LGG-1/LC3: LC3, GABARAP and GATE-16 family; MIT: microtubule interacting and transport; PGL: P granule abnormality protein; SQST-1: sequestosome-related; UPS: ubiquitin-proteasome system
DOI: 10.6084/m9.figshare.11953242
title: Additional file 5 of A systematic analysis of natural α-glucosidase inhibitors from flavonoids of Radix scutellariae using ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology

description: Additional file 5. Degree values of CTP network, node strength and centrality indices of PPI network.
DOI: 10.6084/m9.figshare.11953242.v1
title: Additional file 5 of A systematic analysis of natural α-glucosidase inhibitors from flavonoids of Radix scutellariae using ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology

description: Additional file 5. Degree values of CTP network, node strength and centrality indices of PPI network.
DOI: 10.6084/m9.figshare.22439010
title: Microwave drying process and mechanism analysis of viscous germanium-containing residue

description: Tannin germanium residue (TGR), which has high viscosity and water content, is one of the most important germanium-containing enrichments in the germanium extraction process. In this study, the microwave drying process of a TGR was studied using a high-temperature dielectric testing device, combined with a thermal drying analysis method and response surface methodology (RSM). The experimental results indicated that the water in TGR consisted of free water, crystalline water, and structural water. Owing to the excellent dielectric properties of water in TGR, microwave drying can save 91.7% of the drying time compared to the conventional method. Under the optimal microwave drying conditions, the dehydration rate of TGR was 40.3%. Microwave drying is advantageous in increasing the specific surface area of the TGR particles by 50.75% and pore volume by 160.42%. The high temperature dielectric properties and thermogravimetric curve analysis of raw materials showed that the dielectric constant of water contained in TGR was 12.5 times that of other components, and microwave drying saves 91.7% of the drying time compared with conventional drying. BET analysis showed that the specific surface area of microwave drying TGR increased by 50.75% and the pore volume increased by 160.42% compared with conventional drying. When the microwave power is 225 W and the drying time is 7 min, TGR has the best drying effect, and the germanium concentrate prepared by roasting has no overburning phenomenon.
DOI: 10.6084/m9.figshare.8283245
title: DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in <i>Saccharomyces cerevisiae</i>

description: Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (<i>ATG</i>) genes in <i>Saccharomyces cerevisiae</i> and obtained 1,944 confocal images containing &gt; 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 <i>atg</i> knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 ﬂuorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in <i>S. cerevisiae</i> but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy. <b>Abbreviations:</b><i>Ac</i>: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; <i>ATG</i>: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; <i>MCC</i>: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; <i>Pr</i>: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; <i>Sn</i>: sensitivity; <i>Sp</i>: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.
DOI: 10.6084/m9.figshare.8283245.v1
title: DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in <i>Saccharomyces cerevisiae</i>

description: Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (<i>ATG</i>) genes in <i>Saccharomyces cerevisiae</i> and obtained 1,944 confocal images containing &gt; 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 <i>atg</i> knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 ﬂuorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in <i>S. cerevisiae</i> but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy. <b>Abbreviations:</b><i>Ac</i>: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; <i>ATG</i>: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; <i>MCC</i>: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; <i>Pr</i>: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; <i>Sn</i>: sensitivity; <i>Sp</i>: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.
DOI: 10.6084/m9.figshare.8283248
title: RETRACTED ARTICLE: MicroRNA-422a functions as a tumor suppressor in non-small cell lung cancer through SULF2-mediated TGF-β/SMAD signaling pathway

description: We, the Editors and Publisher of the journal <i>Cell Cycle</i>, have retracted the following article: Wei-Qiang Li, Jian-Peng Zhang, Yan-Yu Wang, Xin-Zhen Li and Lin Sun. MicroRNA-422a functions as a tumor suppressor in non-small cell lung cancer through SULF2-mediated TGF-β/SMAD signaling pathway. Cell Cycle. 2019;18(15):1727-1744. DOI: 10.1080/15384101.2019.1632135 Since publication, significant concerns have been raised about the integrity of the data and reported results in the article. When approached for an explanation, the authors did not provide their original data or any necessary supporting information. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article. The corresponding author listed in this publication has been informed. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.
DOI: 10.6084/m9.figshare.8283248.v1
title: MicroRNA-422a functions as a tumor suppressor in non-small cell lung cancer through SULF2-mediated TGF-β/SMAD signaling pathway

description: MicroRNAs (miRNAs) have been demonstrated to participate in a variety of human cancers by functioning as post-transcriptional regulators of oncogenes or antioncogenes including non-small cell lung cancer (NSCLC). The aim of the current study was to identify the role of miR-422a in NSCLC <i>via</i> sulfatase 2 (SULF2) to further elucidate the mechanism of NSCLC. Initially, the expression of miR-422a and SULF2 was determined in NSCLC tissues and cells. The role of miR-422a in NSCLC was identified in relation with a miR-422a mimic or inhibitor, siRNA against SULF2 and TGF-β1. The regulatory effects of miR-422a were examined following detection of the related epithelial mesenchymal transition (EMT)-related genes, and the apoptosis-related genes and evaluation of their cellular biological functions. The expression pattern of miR-422a, SULF2, and the TGF-β/SMAD pathway-related genes was detected to elucidate the mechanism by which miR-422a influences the progression of NSCLC. Finally, xenograft tumors in nude mice were observed for tumorigenicity evaluation purposes. Our results showed that miR-422a was poorly expressed while SULF2 was highly expressed in NSCLC. Dual luciferase reporter gene assay further verified that miR-422a targeted SULF2. Altogether, this study demonstrated that miR-422a downregulated SULF2 to inhibit the TGF-β/SMAD pathway. NSCLC cell proliferation, migration, invasion, colony formation, EMT and tumorigenesis were all inhibited while apoptosis was promoted upon restoration of miR-422a or silencing of SULF2. However, the activation of the TGF-β/SMAD pathway was determined to reverse the tumor-suppressive effects of si-SULF2. miR-422a restoration, which ultimately inhibited the progression of NSCLC by suppressing the TGF-β/SMAD pathway <i>via</i> SULF2.
DOI: 10.6084/m9.figshare.8283248.v2
title: RETRACTED ARTICLE: MicroRNA-422a functions as a tumor suppressor in non-small cell lung cancer through SULF2-mediated TGF-β/SMAD signaling pathway

description: We, the Editors and Publisher of the journal <i>Cell Cycle</i>, have retracted the following article: Wei-Qiang Li, Jian-Peng Zhang, Yan-Yu Wang, Xin-Zhen Li and Lin Sun. MicroRNA-422a functions as a tumor suppressor in non-small cell lung cancer through SULF2-mediated TGF-β/SMAD signaling pathway. Cell Cycle. 2019;18(15):1727-1744. DOI: 10.1080/15384101.2019.1632135 Since publication, significant concerns have been raised about the integrity of the data and reported results in the article. When approached for an explanation, the authors did not provide their original data or any necessary supporting information. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article. The corresponding author listed in this publication has been informed. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.
DOI: 10.6073/pasta/7f0a7bf9e1425d9f7ae6a75f0b986233
title: Greenhouse mixed culture experiment from August 2002 to April 2003 (FCE): Evaluate the effect of salinity and hydroperiod on interspecific mangrove seedlings growth rate (mixed culture) / Morphometric variables

description: A greenhouse experiment (mixed culture experiment) was performed for 8 months to evaluate the effect of salinity and hydroperiod on seedling growth rates of 2 mangrove species( Laguncularia racemosa and Rizhophora mangle). Data analyses are currently being performed.
DOI: 10.6084/m9.figshare.19817765
title: Additional file 5 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 5. Shared proteins between 7 and 15 dpi.
DOI: 10.6084/m9.figshare.19817756
title: Additional file 2 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 2. Th9 cell-binding proteins identified by LC–MS/MS. In vitro: the proteins identified from the in vitro study. 7 dpi: the proteins identified from a goat infected with H. contortus at 7 dpi. 15 dpi: the proteins identified from a goat infected with H. contortus at 15 dpi. 35 dpi: the proteins identified from a goat infected with H. contortus at 35 dpi. 50 dpi: the proteins identified from a goat infected with H. contortus at 50 dpi.
DOI: 10.6084/m9.figshare.19817756.v1
title: Additional file 2 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 2. Th9 cell-binding proteins identified by LC–MS/MS. In vitro: the proteins identified from the in vitro study. 7 dpi: the proteins identified from a goat infected with H. contortus at 7 dpi. 15 dpi: the proteins identified from a goat infected with H. contortus at 15 dpi. 35 dpi: the proteins identified from a goat infected with H. contortus at 35 dpi. 50 dpi: the proteins identified from a goat infected with H. contortus at 50 dpi.
DOI: 10.6084/m9.figshare.19817759
title: Additional file 3 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 3. Shared proteins between in vivo and in vitro experiments.
DOI: 10.6084/m9.figshare.19817759.v1
title: Additional file 3 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 3. Shared proteins between in vivo and in vitro experiments.
DOI: 10.6084/m9.figshare.19817765.v1
title: Additional file 5 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 5. Shared proteins between 7 and 15 dpi.
DOI: 10.6084/m9.figshare.19817768
title: Additional file 6 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 6. Shared proteins between 15 and 35 dpi.
DOI: 10.6084/m9.figshare.19817750
title: Additional file 10 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 10. The sequences of HcDR and HcGATA.
DOI: 10.6084/m9.figshare.19817768.v1
title: Additional file 6 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 6. Shared proteins between 15 and 35 dpi.
DOI: 10.6084/m9.figshare.19817771
title: Additional file 7 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 7. Shared proteins between 35 and 50 dpi.
DOI: 10.6084/m9.figshare.19817762
title: Additional file 4 of Identification of excretory and secretory proteins from Haemonchus contortus inducing a Th9 immune response in goats

description: Additional file 4. Shared proteins among different developmental stages (7, 15, 35 and 50 dpi).