| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
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| 3. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 4. |
- Gou, De Hai, et al.
(författare)
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Inhibition of copper transporter 1 prevents α-synuclein pathology and alleviates nigrostriatal degeneration in AAV-based mouse model of Parkinson's disease
- 2021
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Ingår i: Redox Biology. - : Elsevier BV. - 2213-2317. ; 38
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Tidskriftsartikel (refereegranskat)abstract
- The formation of α-synuclein aggregates is a major pathological hallmark of Parkinson's disease. Copper promotes α-synuclein aggregation and toxicity in vitro. The level of copper and copper transporter 1, which is the only known high-affinity copper importer in the brain, decreases in the substantia nigra of Parkinson's disease patients. However, the relationship between copper, copper transporter 1 and α-synuclein pathology remains elusive. Here, we aim to decipher the molecular mechanisms of copper and copper transporter 1 underlying Parkinson's disease pathology. We employed yeast and mammalian cell models expressing human α-synuclein, where exogenous copper accelerated intracellular α-synuclein inclusions and silencing copper transporter 1 reduced α-synuclein aggregates in vitro, suggesting that copper transporter 1 might inhibit α-synuclein pathology. To study our hypothesis in vivo, we generated a new transgenic mouse model with copper transporter 1 conditional knocked-out specifically in dopaminergic neuron. Meanwhile, we unilaterally injected adeno-associated viral human-α-synuclein into the substantia nigra of these mice. Importantly, we found that copper transporter 1 deficiency significantly reduced S129-phosphorylation of α-synuclein, prevented dopaminergic neuronal loss, and alleviated motor dysfunction caused by α-synuclein overexpression in vivo. Overall, our data indicated that inhibition of copper transporter 1 alleviated α-synuclein mediated pathologies and provided a novel therapeutic strategy for Parkinson's disease and other synucleinopathies.
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| 5. |
- Huang, Xiaoli, et al.
(författare)
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High-temperature superconductivity in sulfur hydride evidenced by alternating-current magnetic susceptibility
- 2019
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Ingår i: National Science Review. - : Oxford University Press. - 2095-5138 .- 2053-714X. ; 6:4, s. 713-718
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Tidskriftsartikel (refereegranskat)abstract
- The search for high-temperature superconductivity is one of the research frontiers in physics. In the sulfur hydride system, an extremely high Tc (∼200 K) has been recently developed at pressure. However, the Meissner effect measurement above megabar pressures is still a great challenge. Here, we report the superconductivity identification of sulfur hydride at pressure, employing an in situ alternating-current magnetic susceptibility technique. We determine the superconducting phase diagram, finding that superconductivity suddenly appears at 117 GPa and Tc reaches 183 K at 149 GPa before decreasing monotonically with increasing pressure. By means of theoretical calculations, we elucidate the variation of Tc in the low-pressure region in terms of the changing stoichiometry of sulfur hydride and the further decrease in Tc owing to a drop in the electron–phonon interaction parameter λ. This work provides a new insight into clarifying superconducting phenomena and anchoring the superconducting phase diagram in the hydrides.
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| 6. |
- Kristan, Matej, et al.
(författare)
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The first visual object tracking segmentation VOTS2023 challenge results
- 2023
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Ingår i: 2023 IEEE/CVF International conference on computer vision workshops (ICCVW). - : Institute of Electrical and Electronics Engineers Inc.. - 9798350307443 - 9798350307450 ; , s. 1788-1810
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website1
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| 7. |
- Kristanl, Matej, et al.
(författare)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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| 8. |
- Liao, Xiwen, et al.
(författare)
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Comprehensive investigation of key biomarkers and pathways in hepatitis B virus-related hepatocellular carcinoma
- 2019
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Ingår i: Journal of Cancer. - : IVYSPRING INT PUBL. - 1837-9664. ; 10:23, s. 5689-5704
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Our study is aim to explore potential key biomarkers and pathways in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) using genome-wide expression profile dataset and methods. Methods: Dataset from the GSE14520 is used as the training cohort and The Cancer Genome Atlas dataset as the validation cohort. Differentially expressed genes (DEGs) screening were performed by the limma package. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), gene ontology, the Kyoto Encyclopedia of Genes and Genomes, and risk score model were used for pathway and genes identification. Results: GSEA revealed that several pathways and biological processes are associated with hepatocarcinogenesis, such as the cell cycle, DNA repair, and p53 pathway. A total of 160 DEGs were identified. The enriched functions and pathways of the DEGs included toxic substance decomposition and metabolism processes, and the P450 and p53 pathways. Eleven of the DEGs were identified as hub DEGs in the WGCNA. In survival analysis of hub DEGs, high expression of PRC1 and TOP2A were significantly associated with poor clinical outcome of HBV-related HCC, and shown a good performance in HBV-related HCC diagnosis. The prognostic signature consisting of PRC1 and TOP2A also doing well in the prediction of HBV-related HCC prognosis. The diagnostic and prognostic values of PRC1 and TOP2A was confirmed in TCGA HCC patients. Conclusions: Key biomarkers and pathways identified in the present study may enhance the comprehend of the molecular mechanisms underlying hepatocarcinogenesis. Additionally, mRNA expression of PRC1 and TOP2A may serve as potential diagnostic and prognostic biomarkers for HBV-related HCC.
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| 9. |
- Liao, Xiwen, et al.
(författare)
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Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma
- 2019
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Ingår i: Journal of Cancer. - : Ivyspring International Publisher. - 1837-9664. ; 10:14, s. 3267-3283
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The goal of our study is to identify a competing endogenous RNA (ceRNA) network using dysregulated RNAs between HCC tumors and the adjacent normal liver tissues from The Cancer Genome Atlas (TCGA) datasets, and to investigate underlying prognostic indicators in hepatocellular carcinoma (HCC) patients. Methods: All of the RNA- and miRNA-sequencing datasets of HCC were obtained from TCGA, and dysregulated RNAs between HCC tumors and the adjacent normal liver tissues were investigated by DESeq and edgeR algorithm. Survival analysis was used to confirm underlying prognostic indicators. Results: In the present study, we constructed a ceRNA network based on 16 differentially expressed genes (DEGs), 7 differentially expressed microRNAs and 34 differentially expressed long non-coding RNAs (DELs). Among these dysregulated RNAs, three DELs (AP002478.1, HTR2A-AS1, and ERVMER61-1) and six DEGs (enhancer of zeste homolog 2 [EZH2], kinesin family member 23 [KIF23], chromobox 2 [CBX2], centrosomal protein 55 [CEP55], cell division cycle 25A [CDC25A], and claspin [CLSPN]) were used for construct a prognostic signature for HCC overall survival (OS), and performed well in HCC OS (adjusted P<0.0001, adjusted hazard ratio = 2.761, 95% confidence interval = 1.838-4.147). Comprehensive survival analysis demonstrated that this prognostic signature may be act as an independent prognostic indicator of HCC OS. Functional assessment of these dysregulated DEGs in the ceRNA network and gene set enrichment of this prognostic signature suggest that both were enriched in the biological processes and pathways of the cell cycle, cell division and cell proliferation. Conclusions: Our current study constructed a ceRNA network for HCC, and developed a prognostic signature that may act as an independent indicator for HCC OS.
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| 10. |
- Hsiung, Shih-Yi, et al.
(författare)
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Machine learning-based monosaccharide profiling for tissue-specific classification of Wolfiporia extensa samples
- 2023
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Ingår i: Carbohydrate Polymers. - : Elsevier. - 0144-8617 .- 1879-1344. ; 322
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) has been used for many clinical decision-making processes and diagnostic procedures in bioinformatics applications. We examined eight algorithms, including linear discriminant analysis (LDA), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), Naïve Bayes classifier (NB), and artificial neural network (ANN) models, to evaluate their classification and prediction capabilities for four tissue types in Wolfiporia extensa using their monosaccharide composition profiles. All 8 ML-based models were assessed as exemplary models with AUC exceeding 0.8. Five models, namely LDA, KNN, RF, GBM, and ANN, performed excellently in the four-tissue-type classification (AUC > 0.9). Additionally, all eight models were evaluated as good predictive models with AUC value >0.8 in the three-tissue-type classification. Notably, all 8 ML-based methods outperformed the single linear discriminant analysis (LDA) plotting method. For large sample sizes, the ML-based methods perform better than traditional regression techniques and could potentially increase the accuracy in identifying tissue samples of W. extensa.
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