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| 2. |
- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Forskningsöversikt (refereegranskat)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 4. |
- Yu, Wenjin, et al.
(författare)
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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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| 6. |
- Shi, Tian-Le, et al.
(författare)
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High-quality genome assembly enables prediction of allele-specific gene expression in hybrid poplar
- 2024
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Ingår i: Plant Physiology. - : Oxford University Press. - 0032-0889 .- 1532-2548. ; 195:1, s. 652-670
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Tidskriftsartikel (refereegranskat)abstract
- Poplar (Populus) is a well-established model system for tree genomics and molecular breeding, and hybrid poplar is widely used in forest plantations. However, distinguishing its diploid homologous chromosomes is difficult, complicating advanced functional studies on specific alleles. In this study, we applied a trio-binning design and PacBio high-fidelity long-read sequencing to obtain haplotype-phased telomere-to-telomere genome assemblies for the 2 parents of the well-studied F1 hybrid “84K” (Populus alba × Populus tremula var. glandulosa). Almost all chromosomes, including the telomeres and centromeres, were completely assembled for each haplotype subgenome apart from 2 small gaps on one chromosome. By incorporating information from these haplotype assemblies and extensive RNA-seq data, we analyzed gene expression patterns between the 2 subgenomes and alleles. Transcription bias at the subgenome level was not uncovered, but extensive-expression differences were detected between alleles. We developed machine-learning (ML) models to predict allele-specific expression (ASE) with high accuracy and identified underlying genome features most highly influencing ASE. One of our models with 15 predictor variables achieved 77% accuracy on the training set and 74% accuracy on the testing set. ML models identified gene body CHG methylation, sequence divergence, and transposon occupancy both upstream and downstream of alleles as important factors for ASE. Our haplotype-phased genome assemblies and ML strategy highlight an avenue for functional studies in Populus and provide additional tools for studying ASE and heterosis in hybrids.
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| 8. |
- Nie, Shuai, et al.
(författare)
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Gapless genome assembly of azalea and multi-omics investigation into divergence between two species with distinct flower color
- 2023
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Ingår i: Horticulture Research. - : Oxford University Press. - 2662-6810 .- 2052-7276. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The genus Rhododendron (Ericaceae), with more than 1000 species highly diverse in flower color, is providing distinct ornamental values and a model system for flower color studies. Here, we investigated the divergence between two parental species with different flower color widely used for azalea breeding. Gapless genome assembly was generated for the yellow-flowered azalea, Rhododendron molle. Comparative genomics found recent proliferation of long terminal repeat retrotransposons (LTR-RTs), especially Gypsy, has resulted in a 125 Mb (19%) genome size increase in species-specific regions, and a significant amount of dispersed gene duplicates (13 402) and pseudogenes (17 437). Metabolomic assessment revealed that yellow flower coloration is attributed to the dynamic changes of carotenoids/flavonols biosynthesis and chlorophyll degradation. Time-ordered gene co-expression networks (TO-GCNs) and the comparison confirmed the metabolome and uncovered the specific gene regulatory changes underpinning the distinct flower pigmentation. B3 and ERF TFs were found dominating the gene regulation of carotenoids/flavonols characterized pigmentation in R. molle, while WRKY, ERF, WD40, C2H2, and NAC TFs collectively regulated the anthocyanins characterized pigmentation in the red-flowered R simsii. This study employed a multi-omics strategy in disentangling the complex divergence between two important azaleas and provided references for further functional genetics and molecular breeding.
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| 9. |
- Pecunia, Vincenzo, et al.
(författare)
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Roadmap on energy harvesting materials
- 2023
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Ingår i: Journal of Physics. - : IOP Publishing. - 2515-7639. ; 6:4
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Tidskriftsartikel (refereegranskat)abstract
- Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.
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| 10. |
- Zhao, Shi-Wei, et al.
(författare)
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Haplotype-resolved genome assembly of Coriaria nepalensis a non-legume nitrogen-fixing shrub
- 2023
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Coriaria nepalensis Wall. (Coriariaceae) is a nitrogen-fixing shrub which forms root nodules with the actinomycete Frankia. Oils and extracts of C. nepalensis have been reported to be bacteriostatic and insecticidal, and C. nepalensis bark provides a valuable tannin resource. Here, by combining PacBio HiFi sequencing and Hi-C scaffolding techniques, we generated a haplotype-resolved chromosome-scale genome assembly for C. nepalensis. This genome assembly is approximately 620 Mb in size with a contig N50 of 11 Mb, with 99.9% of the total assembled sequences anchored to 40 pseudochromosomes. We predicted 60,862 protein-coding genes of which 99.5% were annotated from databases. We further identified 939 tRNAs, 7,297 rRNAs, and 982 ncRNAs. The chromosome-scale genome of C. nepalensis is expected to be a significant resource for understanding the genetic basis of root nodulation with Frankia, toxicity, and tannin biosynthesis.
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