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Shao, Xin, Jie Liao, Xiaoyan Lu, Rui Xue, Ni Ai, and Xiaohui Fan. "scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data." iScience 23, no. 3 (March 2020): 100882. http://dx.doi.org/10.1016/j.isci.2020.100882.
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Doddahonnaiah, Deeksha, Patrick J. Lenehan, Travis K. Hughes, David Zemmour, Enrique Garcia-Rivera, A. J. Venkatakrishnan, Ramakrishna Chilaka, et al. "A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets." Genes 12, no. 6 (June 10, 2021): 898. http://dx.doi.org/10.3390/genes12060898.
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Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p = 6.15 × 10−76, r = 0.24; cohen’s D = 2.6). Building on this, we developed an augmented annotation algorithm (single cell Annotation via Literature Encoding, or scALE) that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 133 clusters from nine datasets of human breast, colon, heart, joint, ovary, prostate, skin, and small intestine tissues. With the optimized settings, the true cellular identity matched the top prediction in 59% of tested clusters and was present among the top five predictions for 91% of clusters. scALE slightly outperformed an existing method for reference data driven automated cluster annotation, and we demonstrate that integration of scALE can meaningfully improve the annotations derived from such methods. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of well-studied cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature-derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.
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Pham, Son, Tri Le, Tan Phan, Minh Pham, Huy Nguyen, Loc Lam, Nam Phung, et al. "484 Bioturing browser: interactively explore public single cell sequencing data." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (November 2020): A520. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0484.
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BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A
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Lian, Qiuyu, Hongyi Xin, Jianzhu Ma, Liza Konnikova, Wei Chen, Jin Gu, and Kong Chen. "Artificial-cell-type aware cell-type classification in CITE-seq." Bioinformatics 36, Supplement_1 (July 1, 2020): i542—i550. http://dx.doi.org/10.1093/bioinformatics/btaa467.
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Abstract Motivation Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping. Results We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq. Availability and implementation http://github.com/QiuyuLian/CITE-sort. Supplementary information Supplementary data is available at Bioinformatics online.
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Patino, Cesar A., Prithvijit Mukherjee, Vincent Lemaitre, Nibir Pathak, and Horacio D. Espinosa. "Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform." SLAS TECHNOLOGY: Translating Life Sciences Innovation 26, no. 1 (January 15, 2021): 26–36. http://dx.doi.org/10.1177/2472630320982320.
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Single-cell delivery platforms like microinjection and nanoprobe electroporation enable unparalleled control over cell manipulation tasks but are generally limited in throughput. Here, we present an automated single-cell electroporation system capable of automatically detecting cells with artificial intelligence (AI) software and delivering exogenous cargoes of different sizes with uniform dosage. We implemented a fully convolutional network (FCN) architecture to precisely locate the nuclei and cytosol of six cell types with various shapes and sizes, using phase contrast microscopy. Nuclear staining or reporter fluorescence was used along with phase contrast images of cells within the same field of view to facilitate the manual annotation process. Furthermore, we leveraged the near-human inference capabilities of the FCN network in detecting stained nuclei to automatically generate ground-truth labels of thousands of cells within seconds, and observed no statistically significant difference in performance compared to training with manual annotations. The average detection sensitivity and precision of the FCN network were 95±1.7% and 90±1.8%, respectively, outperforming a traditional image-processing algorithm (72±7.2% and 72±5.5%) used for comparison. To test the platform, we delivered fluorescent-labeled proteins into adhered cells and measured a delivery efficiency of 90%. As a demonstration, we used the automated single-cell electroporation platform to deliver Cas9–guide RNA (gRNA) complexes into an induced pluripotent stem cell (iPSC) line to knock out a green fluorescent protein–encoding gene in a population of ~200 cells. The results demonstrate that automated single-cell delivery is a useful cell manipulation tool for applications that demand throughput, control, and precision.
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Balzategui, Julen, Luka Eciolaza, and Daniel Maestro-Watson. "Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network." Sensors 21, no. 13 (June 25, 2021): 4361. http://dx.doi.org/10.3390/s21134361.
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Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types of faults. The experimental results using 1873 Electroluminescence (EL) images of monocrystalline cells show that (a) the anomaly detection scheme can be used to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order to train a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels are comparable to the ones obtained from the models trained with manual labels.
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Friedmann, Drew, Albert Pun, Eliza L. Adams, Jan H. Lui, Justus M. Kebschull, Sophie M. Grutzner, Caitlin Castagnola, Marc Tessier-Lavigne, and Liqun Luo. "Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network." Proceedings of the National Academy of Sciences 117, no. 20 (May 1, 2020): 11068–75. http://dx.doi.org/10.1073/pnas.1918465117.
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The projection targets of a neuronal population are a key feature of its anatomical characteristics. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.
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Mai, Yun, Kyeryoung Lee, Zongzhi Liu, Meng Ma, Christopher Gilman, Minghao Li, Mingwei Zhang, et al. "Phenotyping of clinical trial eligibility text from cancer studies into computable criteria in electronic health records." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 6592. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.6592.
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6592 Background: Clinical trial phenotyping is the process of extracting clinical features and patient characteristics from eligibility criteria. Phenotyping is a crucial step that precedes automated cohort identification from patient electronic health records (EHRs) against trial criteria. We establish a clinical trial phenotyping pipeline to transform clinical trial eligibility criteria into computable criteria and enable high throughput cohort selection in EHRs. Methods: Formalized clinical trial criteria attributes were acquired from a natural-language processing (NLP)-assisted approach. We implemented a clinical trial phenotyping pipeline that included three components: First, a rule-based knowledge engineering component was introduced to annotate the trial attributes into a computable and customizable granularity from EHRs. The second component involved normalizing annotated attributes using standard terminologies and pre-defined reference tables. Third, a knowledge base of computable criteria attributes was built to match patients to clinical trials. We evaluated the pipeline performance by independent manual review. The inter-rater agreement of the annotation was measured on a random sample of the knowledge base. The accuracy of the pipeline was evaluated on a subset of randomly selected matched patients for a subset of randomly selected attributes. Results: Our pipeline phenotyped 2954 clinical trials from five cancer types including Non-Small Cell Lung Cancer, Small Cell Lung Cancer, Prostate Cancer, Breast Cancer, and Multiple Myeloma. We built a knowledge base of 256 computable attributes that included comorbidities, comorbidity-related treatment, previous lines of therapy, laboratory tests, and performance such as ECOG and Karnofsky score. Among 256 attributes, 132 attributes were encoded using standard terminologies and 124 attributes were normalized to customized concepts. The inter-rater agreement of the annotation measured by Cohen’s Kappa coefficient was 0.83. We applied the knowledge base to our EHRs and efficiently identified 33258 potential subjects for cancer clinical trials. Our evaluation on the patient matching indicated the F1 score was 0.94. Conclusions: We established a clinical trial phenotyping pipeline and built a knowledge base of computable criteria attributes that enabled efficient screening of EHRs for patients meeting clinical trial eligibility criteria, providing an automated way to efficiently and accurately identify clinical trial cohorts. The application of this knowledge base to patient matching from EHR data across different institutes demonstrates its generalization capability. Taken together, this knowledge base will be particularly valuable in computer-assisted clinical trial subject selection and clinical trial protocol design in cancer studies based on real-world evidence.
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Englbrecht, Fabian, Iris E. Ruider, and Andreas R. Bausch. "Automatic image annotation for fluorescent cell nuclei segmentation." PLOS ONE 16, no. 4 (April 16, 2021): e0250093. http://dx.doi.org/10.1371/journal.pone.0250093.
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Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully automate the annotation process of a custom fluorescent cell nuclei image dataset. By that we are able to reduce nuclei labelling time by up to 99.5%. The output of our system provides high quality training data for machine learning applications to identify the position of cell nuclei in microscopy images. Our experiments have shown that the automatically annotated dataset provides coequal segmentation performance compared to manual data annotation. In addition, we show that our system enables a single workflow from raw data input to desired nuclei segmentation and tracking results without relying on pre-trained models or third-party training datasets for neural networks.
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Magidey, Ksenia, Ksenya Kveler, Rachelly Normand, Tongwu Zhang, Michael Timaner, Ziv Raviv, Brian James, et al. "A Unique Crosstalk between Tumor Cells and Hematopoietic Stem Cells Reveals a Myeloid Differentiation Pattern Signature Contributing to Metastasis." Blood 134, Supplement_1 (November 13, 2019): 2465. http://dx.doi.org/10.1182/blood-2019-128126.
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Metastasis is the major cause of death in cancer patients. Recent studies have demonstrated that the crosstalk between different host and tumor cells in the tumor microenvironment regulates tumor progression and metastasis. Specifically, immune cell myeloid skewing is a prominent promoter of metastasis. While previous studies have demonstrated that the recruitment of myeloid cells to tumors is a critical step in dictating tumor fate, the reservoir of these cells in the bone marrow (BM) compartment and their differentiation pattern has not been explored. Here we utilized a unique model system consisting of tumor cell clones with low and high metastatic potential (met-low and met-high, respectively) derived from melanoma and breast carcinoma cell lines. Hematopoietic stem cells (HSCs) and their early progenitor subset were defined as Lin-/Sca1+/CD117+, representing LSK cells. BM transplantation experiments using GFP-positive LSK cells derived from met-low and met-high tumor bearing mice were carried out to study lineage differentiation. The genetic signatures of LSK cells were analyzed by single cell RNA-sequencing (scRNA-seq). This analysis included unbiased automated annotation of individual cell types by correlating single-cell gene expression with reference transcriptomic data sets (SingleR algorithm) in order to evaluate the proportions of cell types in BM and reveal cell type-specific differentially expressed genes. Expression patterns of proteins originated from tumor cells were analyzed using a range of multi-omics techniques including nanostring, protein array, and mass spectrometry analysis. Tumor proteomic data was integrated with differential receptor expression patterns in BM cell types to reveal novel crosstalk between tumor cells and HSCs in the BM compartment. Mice bearing met-high tumors exhibited a significant increase in the percentage of LSK cells in the BM in comparison to tumor-free mice or mice bearing met-low tumors. These results were confirmed by functional CFU assays of peripheral blood of met-high compared to met-low tumor bearing mice. In addition, mice that underwent BM transplantation with GFP-positive LSK cells obtained from met-high inoculated donors exhibited an increased percentage of circulating GFP-positive myeloid cells in comparison to counterpart mice transplanted with LSK cells from met-low inoculated donors. Moreover, scRNA-seq analysis of LSK cells obtained from the BM of met-low and met-high tumor bearing mice revealed that met-high tumors induce the enrichment of monocyte-dendritic progenitor population (MDP), confirmed also by flow cytometry. To uncover the possible factors involved in myeloid programming of LSK cells, we performed a proteomic screen of tumor conditioned medium and integrated the results with the scRNA-seq data analysis. This analysis revealed that the IL-6-IL-6R axis is highly active in LSK-derived MDP cells from mice bearing met-high tumors. An adoptive transfer experiment using MDP-GFP+ cells obtained from BM of met-high tumor bearing mice demonstrated that met-high tumors directly dictate HSC fate decision towards myeloid bias, resulting in increased metastasis. Evidently, blocking IL-6 in mice bearing met-high tumors reduced the number of MDP cells, and consequently decreased metastasis. Our study reveals a unique crosstalk between tumor cells and HSCs. It provides new insight into the mechanism by which tumors contribute to the presence of supporting stroma. Specifically, tumors secreting IL-6 dictate a specific genetic signature in HSCs that programs them towards myeloid differentiation, thereby inducing a metastatic switch. Disclosures No relevant conflicts of interest to declare.
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Wu, Yan, Robin Gandhi, and Harvey Siy. "Semi-Automatic Annotation of Natural Language Vulnerability Reports." International Journal of Secure Software Engineering 4, no. 3 (July 2013): 18–41. http://dx.doi.org/10.4018/jsse.2013070102.
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Those who do not learn from past vulnerabilities are bound to repeat it. Consequently, there have been several research efforts to enumerate and categorize software weaknesses that lead to vulnerabilities. The Common Weakness Enumeration (CWE) is a community developed dictionary of software weakness types and their relationships, designed to consolidate these efforts. Yet, aggregating and classifying natural language vulnerability reports with respect to weakness standards is currently a painstaking manual effort. In this paper, the authors present a semi-automated process for annotating vulnerability information with semantic concepts that are traceable to CWE identifiers. The authors present an information-processing pipeline to parse natural language vulnerability reports. The resulting terms are used for learning the syntactic cues in these reports that are indicators for corresponding standard weakness definitions. Finally, the results of multiple machine learning algorithms are compared individually as well as collectively to semi-automatically annotate new vulnerability reports.
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Wei, Ziyang, and Shuqin Zhang. "CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data." Bioinformatics 37, Supplement_1 (July 1, 2021): i51—i58. http://dx.doi.org/10.1093/bioinformatics/btab286.
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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell-type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation. Results In this article, we propose a semi-supervised learning method for cell-type annotation called CALLR. It combines unsupervised learning represented by the graph Laplacian matrix constructed from all the cells and supervised learning using sparse logistic regression. By alternately updating the cell clusters and annotation labels, high annotation accuracy can be achieved. The model is formulated as an optimization problem, and a computationally efficient algorithm is developed to solve it. Experiments on 10 real datasets show that CALLR outperforms the compared (semi-)supervised learning methods, and the popular clustering methods. Availability and implementation The implementation of CALLR is available at https://github.com/MathSZhang/CALLR. Supplementary information Supplementary data are available at Bioinformatics online.
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Radziszewski, Adam, Marek Maziarz, and Jan Wieczorek. "Shallow syntactic annotation in the corpus of Wrocław University of Technology." Cognitive Studies | Études cognitives, no. 12 (November 24, 2015): 129–47. http://dx.doi.org/10.11649/cs.2012.010.
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Shallow syntactic annotation in the corpus of Wrocław University of TechnologyIn this paper we present shallow syntactic annotation of The Wrocław University of Technology Corpus. We discuss some theoretical and practical considerations related to shallow parsing of Polish, then we present our annotation guidelines. The proposed annotation scheme includes chunking – four chunk types are defined with reference to the notion of accommodation and syntactic connotation, as well as annotation of four inter-chunk predicate-argument relations. Until now almost 18k chunk and 4k relation instances have been annotated. We believe that both the corpus and the annotation guideliness will prove their applicability in construction of automatic shallow parsers.
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Kim, Wan, Sung Min Yoon, and Sangsoo Kim. "A semi-automatic cell type annotation method for single-cell RNA sequencing dataset." Genomics & Informatics 18, no. 3 (September 30, 2020): e26. http://dx.doi.org/10.5808/gi.2020.18.3.e26.
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BABARCZY, ANNA, JOHN CARROLL, and GEOFFREY SAMPSON. "Definitional, personal, and mechanical constraints on part of speech annotation performance." Natural Language Engineering 12, no. 1 (December 6, 2005): 77–90. http://dx.doi.org/10.1017/s1351324905003803.
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For one aspect of grammatical annotation, part-of-speech tagging, we investigate experimentally whether the ceiling on accuracy stems from limits to the precision of tag definition or limits to analysts' ability to apply precise definitions, and we examine how analysts' performance is affected by alternative types of semi-automatic support. We find that, even for analysts very well-versed in a part-of-speech tagging scheme, human ability to conform to the scheme is a more serious constraint than precision of scheme definition. We also find that although semi-automatic techniques can greatly increase speed relative to manual tagging, they have little effect on accuracy, either positively (by suggesting valid candidate tags) or negatively (by lending an appearance of authority to incorrect tag assignments). On the other hand, it emerges that there are large differences between individual analysts with respect to usability of particular types of semi-automatic support.
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Sprugnoli, Rachele, and Sara Tonelli. "Novel Event Detection and Classification for Historical Texts." Computational Linguistics 45, no. 2 (June 2019): 229–65. http://dx.doi.org/10.1162/coli_a_00347.
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Event processing is an active area of research in the Natural Language Processing community, but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts Particularly in the current era of massive digitization of historical sources: Research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work, while having an impact also on the field of Natural Language Processing. Our work aims at shedding light on the complex concept of events when dealing with historical texts. More specifically, we introduce new annotation guidelines for event mentions and types, categorized into 22 classes. Then, we annotate a historical corpus accordingly, and compare two approaches for automatic event detection and classification following this novel scheme. We believe that this work can foster research in a field of inquiry as yet underestimated in the area of Temporal Information Processing. To this end, we release new annotation guidelines, a corpus, and new models for automatic annotation.
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Kabra, Mayank, Alice A. Robie, Marta Rivera-Alba, Steven Branson, and Kristin Branson. "JAABA: interactive machine learning for automatic annotation of animal behavior." Nature Methods 10, no. 1 (January 2013): 64–67. http://dx.doi.org/10.1038/nmeth.2281.
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Brunner, Annelen. "Redewiedergabe – Schritte zur automatischen Erkennung." Zeitschrift für germanistische Linguistik 47, no. 1 (April 8, 2019): 216–48. http://dx.doi.org/10.1515/zgl-2019-0007.
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Abstract This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.
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Buhl, S., B. Neumann, S. C. Schäfer, and A. L. Severing. "Automatic cell segmentation in strongly agglomerated cell networks for different cell types." International Journal of Computational Biology and Drug Design 7, no. 2/3 (2014): 259. http://dx.doi.org/10.1504/ijcbdd.2014.061641.
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Hemphill, Edward E., Asav P. Dharia, Chih Lee, Caroline M. Jakuba, Jason D. Gibson, Frederick W. Kolling, and Craig E. Nelson. "SCLD: a stem cell lineage database for the annotation of cell types and developmental lineages." Nucleic Acids Research 39, suppl_1 (October 22, 2010): D525—D533. http://dx.doi.org/10.1093/nar/gkq941.
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Feng, Zhanying, Xianwen Ren, Yuan Fang, Yining Yin, Chutian Huang, Yimin Zhao, and Yong Wang. "scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization." Bioinformatics 36, no. 8 (December 17, 2019): 2474–85. http://dx.doi.org/10.1093/bioinformatics/btz936.
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Abstract Motivation Single cell RNA-seq data offers us new resource and resolution to study cell type identity and its conversion. However, data analyses are challenging in dealing with noise, sparsity and poor annotation at single cell resolution. Detecting cell-type-indicative markers is promising to help denoising, clustering and cell type annotation. Results We developed a new method, scTIM, to reveal cell-type-indicative markers. scTIM is based on a multi-objective optimization framework to simultaneously maximize gene specificity by considering gene-cell relationship, maximize gene’s ability to reconstruct cell–cell relationship and minimize gene redundancy by considering gene–gene relationship. Furthermore, consensus optimization is introduced for robust solution. Experimental results on three diverse single cell RNA-seq datasets show scTIM’s advantages in identifying cell types (clustering), annotating cell types and reconstructing cell development trajectory. Applying scTIM to the large-scale mouse cell atlas data identifies critical markers for 15 tissues as ‘mouse cell marker atlas’, which allows us to investigate identities of different tissues and subtle cell types within a tissue. scTIM will serve as a useful method for single cell RNA-seq data mining. Availability and implementation scTIM is freely available at https://github.com/Frank-Orwell/scTIM. Supplementary information Supplementary data are available at Bioinformatics online.
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O'Connor, Karen, Abeed Sarker, Jeanmarie Perrone, and Graciela Gonzalez Hernandez. "Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines." Journal of Medical Internet Research 22, no. 2 (February 26, 2020): e15861. http://dx.doi.org/10.2196/15861.
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Background Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse–related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. Methods We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes—abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. Results Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). Conclusions Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks.
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Horgan, G. W., A. J. Travis, and Ji Liang. "Automatic recognition of maize cell types using context information." Micron 36, no. 2 (February 2005): 163–67. http://dx.doi.org/10.1016/j.micron.2004.09.002.
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Sakaguchi, Keisuke, Courtney Napoles, Matt Post, and Joel Tetreault. "Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality." Transactions of the Association for Computational Linguistics 4 (December 2016): 169–82. http://dx.doi.org/10.1162/tacl_a_00091.
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The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics. One unvisited assumption, however, is the reliance of GEC evaluation on error-coded corpora, which contain specific labeled corrections. We examine current practices and show that GEC’s reliance on such corpora unnaturally constrains annotation and automatic evaluation, resulting in (a) sentences that do not sound acceptable to native speakers and (b) system rankings that do not correlate with human judgments. In light of this, we propose an alternate approach that jettisons costly error coding in favor of unannotated, whole-sentence rewrites. We compare the performance of existing metrics over different gold-standard annotations, and show that automatic evaluation with our new annotation scheme has very strong correlation with expert rankings (ρ = 0.82). As a result, we advocate for a fundamental and necessary shift in the goal of GEC, from correcting small, labeled error types, to producing text that has native fluency.
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Zinoveva, Anastasiia Yu, Svetlana O. Sheremetyeva, and Ekaterina D. Nerucheva. "THE ANALYSIS OF AMBIGUITY IN CONCEPTUAL ANNOTATION OF RUSSIAN TEXTS." Tyumen State University Herald. Humanities Research. Humanitates 6, no. 3 (2020): 38–60. http://dx.doi.org/10.21684/2411-197x-2020-6-3-38-60.
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Properly annotated text corpora are an essential condition in constructing effective and efficient tools for natural language processing (NLP), which provide an operational solution to both theoretical and applied linguistic and informational problems. One of the main and the most complex problems of corpus annotation is resolving tag ambiguities on a specific level of annotation (morphological, syntactic, semantic, etc.). This paper addresses the issue of ambiguity that emerges on the conceptual level, which is the most relevant text annotation level for solving informational tasks. Conceptual annotation is a special type of semantic annotation usually applied to domain corpora to address specific informational problems such as automatic classification, content and trend analyses, machine learning, machine translation, etc. In conceptual annotation, text corpora are annotated with tags reflecting the content of a certain domain, which leads to a type of ambiguity that is different from general semantic ambiguity. It has both universal and language- and domain-specific peculiarities. This paper investigates conceptual ambiguity in a case study of a Russian-language corpus on terror attacks. The research methodology combines automated and manual steps, comprising a) statistical and qualitative corpus analysis, b) the use of pre-developed annotation resources (a terrorism domain ontology, a Russian ontolexicon and a computer platform for conceptual annotation), c) ontological-analysis-based conceptual annotation of the corpus chosen for the case study, d) corpus-based detection and investigation of conceptual ambiguity causes, e) development and experimental study of possible disambiguation methods for some types of conceptual ambiguity. The findings obtained in this study are specific for Russian-language terrorism domain texts, but the conceptual annotation technique and approaches to conceptual disambiguation developed are applicable to other domains and languages.
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Aghili, Maryamossadat, and Ruogu Fang. "Mining Big Neuron Morphological Data." Computational Intelligence and Neuroscience 2018 (June 24, 2018): 1–13. http://dx.doi.org/10.1155/2018/8234734.
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The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.
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Chen, Liang, Yuyao Zhai, Qiuyan He, Weinan Wang, and Minghua Deng. "Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation." Genes 11, no. 7 (July 14, 2020): 792. http://dx.doi.org/10.3390/genes11070792.
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As single-cell RNA sequencing technologies mature, massive gene expression profiles can be obtained. Consequently, cell clustering and annotation become two crucial and fundamental procedures affecting other specific downstream analyses. Most existing single-cell RNA-seq (scRNA-seq) data clustering algorithms do not take into account the available cell annotation results on the same tissues or organisms from other laboratories. Nonetheless, such data could assist and guide the clustering process on the target dataset. Identifying marker genes through differential expression analysis to manually annotate large amounts of cells also costs labor and resources. Therefore, in this paper, we propose a novel end-to-end cell supervised clustering and annotation framework called scAnCluster, which fully utilizes the cell type labels available from reference data to facilitate the cell clustering and annotation on the unlabeled target data. Our algorithm integrates deep supervised learning, self-supervised learning and unsupervised learning techniques together, and it outperforms other customized scRNA-seq supervised clustering methods in both simulation and real data. It is particularly worth noting that our method performs well on the challenging task of discovering novel cell types that are absent in the reference data.
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Sepúlveda-Torres, Robiert, Alba Bonet-Jover, and Estela Saquete. "“Here Are the Rules: Ignore All Rules”: Automatic Contradiction Detection in Spanish." Applied Sciences 11, no. 7 (March 30, 2021): 3060. http://dx.doi.org/10.3390/app11073060.
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This paper tackles automatic detection of contradictions in Spanish within the news domain. Two pieces of information are classified as compatible, contradictory, or unrelated information. To deal with the task, the ES-Contradiction dataset was created. This dataset contains a balanced number of each of the three types of information. The novelty of the research is the fine-grained annotation of the different types of contradictions in the dataset. Presently, four different types of contradictions are covered in the contradiction examples: negation, antonyms, numerical, and structural. However, future work will extend the dataset with all possible types of contradictions. In order to validate the effectiveness of the dataset, a pretrained model is used (BETO), and after performing different experiments, the system is able to detect contradiction with a F1m of 92.47%. Regarding the type of contradictions, the best results are obtained with negation contradiction (F1m = 98%), whereas structural contradictions obtain the lowest results (F1m = 69%) because of the smaller number of structural examples, due to the complexity of generating them. When dealing with a more generalistic dataset such as XNLI, our dataset fails to detect most of the contradictions properly, as the size of both datasets are very different and our dataset only covers four types of contradiction. However, using the classification of the contradictions leads us to conclude that there are highly complex contradictions that will need external knowledge in order to be properly detected and this will avoid the need for them to be previously exposed to the system.
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Ji, Meng. "A statistical intra-genre analysis of cross-national environmental news translation." Newspaper Research Journal 39, no. 3 (September 2018): 326–38. http://dx.doi.org/10.1177/0739532918796231.
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This study investigates the instrumental role of translated environmental news in informing public opinions on environmental issues among Chinese-speaking communities. Its contribution to methodology is exploring the automatic corpus annotation tools, that is, semantic analysis system. Its contribution to theory is identifying and distinguishing among three recurrent sub-news-types of translated environmental news published on BBC China, that is, governance; international relations and environmental science. Discourse features attributed to these subtypes of environmental news underscore BBC China’s reporting styles and strategies and largely explain its wide appeal and credibility among the target audiences.
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Scius-Bertrand, Anna, Michael Jungo, Beat Wolf, Andreas Fischer, and Marc Bui. "Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples." Applied Sciences 11, no. 11 (May 26, 2021): 4894. http://dx.doi.org/10.3390/app11114894.
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The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.
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Juhl Jensen, L., and S. Knudsen. "Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation." Bioinformatics 16, no. 4 (April 1, 2000): 326–33. http://dx.doi.org/10.1093/bioinformatics/16.4.326.
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Jan, Rafiya, and Afaq Alam Khan. "Emotion Mining Using Semantic Similarity." International Journal of Synthetic Emotions 9, no. 2 (July 2018): 1–22. http://dx.doi.org/10.4018/ijse.2018070101.
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Social networks are considered as the most abundant sources of affective information for sentiment and emotion classification. Emotion classification is the challenging task of classifying emotions into different types. Emotions being universal, the automatic exploration of emotion is considered as a difficult task to perform. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this article, the authors present the technique of semantic relatedness for automatic classification of emotion in the text using distributional semantic models. This approach uses semantic similarity for measuring the coherence between the two emotionally related entities. Before classification, data is pre-processed to remove the irrelevant fields and inconsistencies and to improve the performance. The proposed approach achieved the accuracy of 71.795%, which is competitive considering as no training or annotation of data is done.
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Moore, Ryan M., Amelia O. Harrison, Sean M. McAllister, Shawn W. Polson, and K. Eric Wommack. "Iroki: automatic customization and visualization of phylogenetic trees." PeerJ 8 (February 26, 2020): e8584. http://dx.doi.org/10.7717/peerj.8584.
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Phylogenetic trees are an important analytical tool for evaluating community diversity and evolutionary history. In the case of microorganisms, the decreasing cost of sequencing has enabled researchers to generate ever-larger sequence datasets, which in turn have begun to fill gaps in the evolutionary history of microbial groups. However, phylogenetic analyses of these types of datasets create complex trees that can be challenging to interpret. Scientific inferences made by visual inspection of phylogenetic trees can be simplified and enhanced by customizing various parts of the tree. Yet, manual customization is time-consuming and error prone, and programs designed to assist in batch tree customization often require programming experience or complicated file formats for annotation. Iroki, a user-friendly web interface for tree visualization, addresses these issues by providing automatic customization of large trees based on metadata contained in tab-separated text files. Iroki’s utility for exploring biological and ecological trends in sequencing data was demonstrated through a variety of microbial ecology applications in which trees with hundreds to thousands of leaf nodes were customized according to extensive collections of metadata. The Iroki web application and documentation are available at https://www.iroki.net or through the VIROME portal http://virome.dbi.udel.edu. Iroki’s source code is released under the MIT license and is available at https://github.com/mooreryan/iroki.
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Peng, Jian, Ai-li Sheng, Qi Xiao, Libing Shen, Xiang-Chun Ju, Min Zhang, Si-Ting He, Chao Wu, and Zhen-Ge Luo. "Single-cell transcriptomes reveal molecular specializations of neuronal cell types in the developing cerebellum." Journal of Molecular Cell Biology 11, no. 8 (January 25, 2019): 636–48. http://dx.doi.org/10.1093/jmcb/mjy089.
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Abstract The cerebellum is critical for controlling motor and non-motor functions via cerebellar circuit that is composed of defined cell types, which approximately account for more than half of neurons in mammals. The molecular mechanisms controlling developmental progression and maturation processes of various cerebellar cell types need systematic investigation. Here, we analyzed transcriptome profiles of 21119 single cells of the postnatal mouse cerebellum and identified eight main cell clusters. Functional annotation of differentially expressed genes revealed trajectory hierarchies of granule cells (GCs) at various states and implied roles of mitochondrion and ATPases in the maturation of Purkinje cells (PCs), the sole output cells of the cerebellar cortex. Furthermore, we analyzed gene expression patterns and co-expression networks of 28 ataxia risk genes, and found that most of them are related with biological process of mitochondrion and around half of them are enriched in PCs. Our results also suggested core transcription factors that are correlated with interneuron differentiation and characteristics for the expression of secretory proteins in glia cells, which may participate in neuronal modulation. Thus, this study presents a systematic landscape of cerebellar gene expression in defined cell types and a general gene expression framework for cerebellar development and dysfunction.
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Lavitt, Falko, Demi J. Rijlaarsdam, Dennet van der Linden, Ewelina Weglarz-Tomczak, and Jakub M. Tomczak. "Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines." Applied Sciences 11, no. 11 (May 27, 2021): 4912. http://dx.doi.org/10.3390/app11114912.
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In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell counting is a laborious and time-consuming process, and its automatization is highly demanded. Here, we propose use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count. First, unlike most of the related work, we formulate the problem of cell counting as the regression task rather than the classification task. This allows not only to reduce the required annotation information (i.e., the number of cells instead of pixel-level annotations) but also to reduce the burden of segmenting potential cells and then classifying them. Second, we propose use of xResNet, a successful convolutional architecture with residual connection, together with transfer learning (using a pretrained model) to achieve human-level performance. We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images). We show that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods. It achieves the test mean absolute error equal 12 (±15) against 32 (±33) obtained by the deep learning without transfer learning, and 41 (±37) of the best-performing machine learning pipeline (Random Forest Regression with the Histogram of Gradients features).
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Cabibbo, Sergio, Agostino Antolino, Giovanni Garozzo, Carmelo Fidone, and Pietro Bonomo. "Clinical Effects of Chronic Red Blood Cell Exchange and Different Types of Red Cell Concentrates in Patients with Sickle Cell Disease." Blood 112, no. 11 (November 16, 2008): 4823. http://dx.doi.org/10.1182/blood.v112.11.4823.4823.
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Abstract For patients with severe SCD not eligible for hydroxyurea, two major therapeutic options are currently available: blood transfusion, and bone marrow transplantation. Either urgent or chronic red blood cell transfusion therapy, is widely used in the management of SCD but determines a progressive increase of ferritin level and is also limited by the development of antibodies to red cell antigens. The introduction of chronic red blood cell exchange and prestorage filtration to remove leucocytes and the use of techniques for multicomponent donation could be a good solutions. Thus, the aims of our studies were to evaluate the clinical effects of the different blood components in terms of annual transfusion needs and the intervals between transfusion, moreover we evaluated the efficacy of chronic red blood cell exchange (manual or automatic with cell separator) in preventing SCD complications and limiting iron overload. In our center we follow 78 patients affected by Sickle Cell Disease. We selected 36 patients occasionally treated with urgent red blood cell exchange because they had less than 2 complications/Year, and 42 patients regularly treated with chronic red blood cell exchange because they had more than 2 complications/Year with Hospital Admission. Moreover among these we selected 10 patients for fulfilling the criteria of continuous treatment at the Centre for at least 48 months with no interruptions, even sporadic and absolute transfusion dependency. All 10 patients were evaluated for a period of 4 years, during which two different systems of producing RCC were used. In the second two the patients were transfused with RCC obtained from filtering whole blood prestorage or with RCC from apheresis filtered prestorage. These products differed from those used in the preceding two years, during which the leucodepletion was obtained by bed-side filtration For all the patients we performed 782 automatic red blood cell exchanges and 4421 units of RCC were transfused. The exchange procedures were extremely well-tolerated by the patients and adverse effects were limited to symptoms of hypocalcaemia during automatic red blood cell exchange with cell separator. After every red blood cell exchange we obtained HbS level < 30%. The10 patients selected received respectively a mean of 6.9 and 6.1 units of RBCs exchanged per automatic procedure, in the first two years and in the second two years. Alloantibody developed in 14 patients but only 2 clinically significant and about the observed frequency of transfusion reactions it was very low. All patients treated with chronic red blood cell exchange had an improvement of the quality of life with a reduced number of complications/year (<2/year) and good compliance and moreover patients had limited iron overload making chelating therapy easier. In conclusion this study was focused on the most suitable characteristics of blood components for use in sickle cell disease patients and the choice of systematically adopting prestorage filtration of whole blood, enabled us to have RCC with a higher Hb concentration than standard. Moreover chronic manual or automatic red blood cell exchange as an alternative approach to simple long-term RBC transfusions give many advantages by being more rapid and tolerable as well as clinically safe and effective and minimize the development of iron overload especially when procedure was carried out with an automatic apparatus. To note that the clinical advantages for patients derived from good selection of the donor and good practices in the production of the blood components
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Farooq, Muhammad, Abul Doulah, Jason Parton, Megan McCrory, Janine Higgins, and Edward Sazonov. "Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment." Nutrients 11, no. 3 (March 13, 2019): 609. http://dx.doi.org/10.3390/nu11030609.
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Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants’ food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (±standard deviation (STD)) kappa value of 0.74 (±0.02) and for food intake annotation the average kappa (Light’s kappa) of 0.82 (±0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (±0.10) and 0.78 (±0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters’ annotations and AIM predictions (p-value = 0.19). These results suggest that the AIM provides accuracy comparable to video observation and may be used to reliably detect food intake in multi-day observational studies.
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Zhao, Xinlei, Shuang Wu, Nan Fang, Xiao Sun, and Jue Fan. "Evaluation of single-cell classifiers for single-cell RNA sequencing data sets." Briefings in Bioinformatics 21, no. 5 (October 23, 2019): 1581–95. http://dx.doi.org/10.1093/bib/bbz096.
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Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.
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Fedjajevs, Andrejs, Willemijn Groenendaal, Carlos Agell, and Evelien Hermeling. "Platform for Analysis and Labeling of Medical Time Series." Sensors 20, no. 24 (December 19, 2020): 7302. http://dx.doi.org/10.3390/s20247302.
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Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.
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Bird, Benjamin, Melissa J. Romeo, Max Diem, Kristi Bedrossian, Nora Laver, and Stephen Naber. "Cytology by infrared micro-spectroscopy: Automatic distinction of cell types in urinary cytology." Vibrational Spectroscopy 48, no. 1 (September 2008): 101–6. http://dx.doi.org/10.1016/j.vibspec.2008.03.006.
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Chen, Shengquan, Qiao Liu, Xuejian Cui, Zhanying Feng, Chunquan Li, Xiaowo Wang, Xuegong Zhang, Yong Wang, and Rui Jiang. "OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions." Nucleic Acids Research 49, W1 (May 17, 2021): W483—W490. http://dx.doi.org/10.1093/nar/gkab337.
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Abstract Chromatin accessibility, as a powerful marker of active DNA regulatory elements, provides valuable information for understanding regulatory mechanisms. The revolution in high-throughput methods has accumulated massive chromatin accessibility profiles in public repositories. Nevertheless, utilization of these data is hampered by cumbersome collection, time-consuming processing, and manual chromatin accessibility (openness) annotation of genomic regions. To fill this gap, we developed OpenAnnotate (http://health.tsinghua.edu.cn/openannotate/) as the first web server for efficiently annotating openness of massive genomic regions across various biosample types, tissues, and biological systems. In addition to the annotation resource from 2729 comprehensive profiles of 614 biosample types of human and mouse, OpenAnnotate provides user-friendly functionalities, ultra-efficient calculation, real-time browsing, intuitive visualization, and elaborate application notebooks. We show its unique advantages compared to existing databases and toolkits by effectively revealing cell type-specificity, identifying regulatory elements and 3D chromatin contacts, deciphering gene functional relationships, inferring functions of transcription factors, and unprecedentedly promoting single-cell data analyses. We anticipate OpenAnnotate will provide a promising avenue for researchers to construct a more holistic perspective to understand regulatory mechanisms.
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Poulet, Axel, Ben Li, Tristan Dubos, Juan Carlos Rivera-Mulia, David M. Gilbert, and Zhaohui S. Qin. "RT States: systematic annotation of the human genome using cell type-specific replication timing programs." Bioinformatics 35, no. 13 (November 22, 2018): 2167–76. http://dx.doi.org/10.1093/bioinformatics/bty957.
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Abstract Motivation The replication timing (RT) program has been linked to many key biological processes including cell fate commitment, 3D chromatin organization and transcription regulation. Significant technology progress now allows to characterize the RT program in the entire human genome in a high-throughput and high-resolution fashion. These experiments suggest that RT changes dynamically during development in coordination with gene activity. Since RT is such a fundamental biological process, we believe that an effective quantitative profile of the local RT program from a diverse set of cell types in various developmental stages and lineages can provide crucial biological insights for a genomic locus. Results In this study, we explored recurrent and spatially coherent combinatorial profiles from 42 RT programs collected from multiple lineages at diverse differentiation states. We found that a Hidden Markov Model with 15 hidden states provide a good model to describe these genome-wide RT profiling data. Each of the hidden state represents a unique combination of RT profiles across different cell types which we refer to as ‘RT states’. To understand the biological properties of these RT states, we inspected their relationship with chromatin states, gene expression, functional annotation and 3D chromosomal organization. We found that the newly defined RT states possess interesting genome-wide functional properties that add complementary information to the existing annotation of the human genome. Availability and implementation R scripts for inferring HMM models and Perl scripts for further analysis are available https://github.com/PouletAxel/script_HMM_Replication_timing. Supplementary information Supplementary data are available at Bioinformatics online.
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Xing, F., D. J. Foran, L. Yang, and X. Qi. "A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology." Methods of Information in Medicine 51, no. 03 (2012): 260–67. http://dx.doi.org/10.3414/me11-02-0015.
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SummaryBackground: Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms.Objectives: In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging.Methods: It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts’ annotation and those generated using the automatic segmentation algorithm.Results: The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation.Conclusion: The proposed segmentation algorithm can accurately detect and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation.
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O'Donovan, Ruth, Michael Burke, Aoife Cahill, Josef van Genabith, and Andy Way. "Large-Scale Induction and Evaluation of Lexical Resources from the Penn-II and Penn-III Treebanks." Computational Linguistics 31, no. 3 (September 2005): 329–66. http://dx.doi.org/10.1162/089120105774321073.
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We present a methodology for extracting subcategorization frames based on an automatic lexical-functional grammar (LFG) f-structure annotation algorithm for the Penn-II and Penn-III Treebanks. We extract syntactic-function-based subcategorization frames (LFG semantic forms) and traditional CFG category-based subcategorization frames as well as mixed function/category-based frames, with or without preposition information for obliques and particle information for particle verbs. Our approach associates probabilities with frames conditional on the lemma, distinguishes between active and passive frames, and fully reflects the effects of long-distance dependencies in the source data structures. In contrast to many other approaches, ours does not predefine the subcategorization frame types extracted, learning them instead from the source data. Including particles and prepositions, we extract 21,005 lemma frame types for 4,362 verb lemmas, with a total of 577 frame types and an average of 4.8 frame types per verb. We present a large-scale evaluation of the complete set of forms extracted against the full COMLEX resource. To our knowledge, this is the largest and most complete evaluation of subcategorization frames acquired automatically for English.
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Louis, Annie, and Ani Nenkova. "A corpus of science journalism for analyzing writing quality." Dialogue & Discourse 4, no. 2 (May 17, 2013): 87–117. http://dx.doi.org/10.5087/dad.2013.205.
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We introduce a corpus of science journalism articles, categorized in three levels of writing quality. The corpus fulï¬lls a glaring need for realistic data on which applications concerned with predicting text quality can be developed and evaluated. In this article we describe how we identiï¬ed, guided by the judgements of renowned writers, samples of extraordinarily well-written pieces and how these were expanded to a larger set of typical journalistic writing. We provide details about the corpus and the text quality evaluations it can support. Our intention is to further extend the corpus with annotations of phenomena that reveal quantiï¬able differences between levels of writing quality. Here we introduce two of the many types of annotation on the sentence level that distinguish amazing from typical writing: text generality/speciï¬city and communicative goal. We explore the feasibility of acquiring annotations automatically, and verify that such features are indeed predictive of writing quality. We ï¬nd that the annotation of general/speciï¬c on sentence level can be performed reasonably accurately fully automatically, while automatic annotations of communicative goal reveals salient characteristics of journalistic writing but does not align with categories we wish to annotate in future work.
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Cheung, Leonard Y. M., Akima S. George, Stacey R. McGee, Alexandre Z. Daly, Michelle L. Brinkmeier, Buffy S. Ellsworth, and Sally A. Camper. "Single-Cell RNA Sequencing Reveals Novel Markers of Male Pituitary Stem Cells and Hormone-Producing Cell Types." Endocrinology 159, no. 12 (October 17, 2018): 3910–24. http://dx.doi.org/10.1210/en.2018-00750.
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Abstract Transcription factors and signaling pathways that regulate stem cells and specialized hormone-producing cells in the pituitary gland have been the subject of intense study and have yielded a mechanistic understanding of pituitary organogenesis and disease. However, the regulation of stem cell proliferation and differentiation, the heterogeneity among specialized hormone-producing cells, and the role of nonendocrine cells in the gland remain important, unanswered questions. Recent advances in single-cell RNA sequencing (scRNAseq) technologies provide new avenues to address these questions. We performed scRNAseq on ∼13,663 cells pooled from six whole pituitary glands of 7-week-old C57BL/6 male mice. We identified pituitary endocrine and stem cells in silico, as well as other support cell types such as endothelia, connective tissue, and red and white blood cells. Differential gene expression analyses identify known and novel markers of pituitary endocrine and stem cell populations. We demonstrate the value of scRNAseq by in vivo validation of a novel gonadotrope-enriched marker, Foxp2. We present novel scRNAseq data of in vivo pituitary tissue, including data from agnostic clustering algorithms that suggest the presence of a somatotrope subpopulation enriched in sterol/cholesterol synthesis genes. Additionally, we show that incomplete transcriptome annotation can cause false negatives on some scRNAseq platforms that only generate 3′ transcript end sequences, and we use in vivo data to recover reads of the pituitary transcription factor Prop1. Ultimately, scRNAseq technologies represent a significant opportunity to address long-standing questions regarding the development and function of the different populations of the pituitary gland throughout life.
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Sun, Dongqing, Jin Wang, Ya Han, Xin Dong, Jun Ge, Rongbin Zheng, Xiaoying Shi, et al. "TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment." Nucleic Acids Research 49, no. D1 (November 12, 2020): D1420—D1430. http://dx.doi.org/10.1093/nar/gkaa1020.
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Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.
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Wiebe, J. M., T. P. O'Hara, Thorsten Ohrstrom-Sandgren, and K. J. McKeever. "An Empirical Approach to Temporal Reference Resolution." Journal of Artificial Intelligence Research 9 (November 1, 1998): 247–93. http://dx.doi.org/10.1613/jair.523.
Full textAbstract:
Scheduling dialogs, during which people negotiate the times of appointments, are common in everyday life. This paper reports the results of an in-depth empirical investigation of resolving explicit temporal references in scheduling dialogs. There are four phases of this work: data annotation and evaluation, model development, system implementation and evaluation, and model evaluation and analysis. The system and model were developed primarily on one set of data, and then applied later to a much more complex data set, to assess the generalizability of the model for the task being performed. Many different types of empirical methods are applied to pinpoint the strengths and weaknesses of the approach. Detailed annotation instructions were developed and an intercoder reliability study was performed, showing that naive annotators can reliably perform the targeted annotations. A fully automatic system has been developed and evaluated on unseen test data, with good results on both data sets. We adopt a pure realization of a recency-based focus model to identify precisely when it is and is not adequate for the task being addressed. In addition to system results, an in-depth evaluation of the model itself is presented, based on detailed manual annotations. The results are that few errors occur specifically due to the model of focus being used, and the set of anaphoric relations defined in the model are low in ambiguity for both data sets.
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Dori, Martina, Leila Haj Abdullah Alieh, Daniel Cavalli, Simone Massalini, Mathias Lesche, Andreas Dahl, and Federico Calegari. "Sequence and expression levels of circular RNAs in progenitor cell types during mouse corticogenesis." Life Science Alliance 2, no. 2 (March 29, 2019): e201900354. http://dx.doi.org/10.26508/lsa.201900354.
Full textAbstract:
Circular (circ) RNAs have recently emerged as a novel class of transcripts whose identification and function remain elusive. Among many tissues and species, the mammalian brain is the organ in which circRNAs are more abundant and first evidence of their functional significance started to emerge. Yet, even within this well-studied organ, annotation of circRNAs remains fragmentary, their sequence is unknown, and their expression in specific cell types was never investigated. Overcoming these limitations, here we provide the first comprehensive identification of circRNAs and assessment of their expression patterns in proliferating neural stem cells, neurogenic progenitors, and newborn neurons of the developing mouse cortex. Extending the current knowledge about the diversity of this class of transcripts by the identification of nearly 4,000 new circRNAs, our study is the first to provide the full sequence information and expression patterns of circRNAs in cell types representing the lineage of neurogenic commitment. We further exploited our data by evaluating the coding potential, evolutionary conservation, and biogenesis of circRNAs that we found to arise from a specific subclass of linear mRNAs. Our study provides the arising field of circRNA biology with a powerful new resource to address the complexity and potential biological significance of this new class of transcripts.
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Palmer, Martha, Daniel Gildea, and Paul Kingsbury. "The Proposition Bank: An Annotated Corpus of Semantic Roles." Computational Linguistics 31, no. 1 (March 2005): 71–106. http://dx.doi.org/10.1162/0891201053630264.
Full textAbstract:
The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated. We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus. We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty “trace” categories of the treebank.