Annotation Techniques Research Articles

Unlocking Hidden Treasures: LC-MS/MS Molecular Networks for Exploring Novel Passiflora Species with Pharmaceutical Potential.

In this study, we report the metabolic profiling of several previously uncharacterized Passiflora species native to Brazil, employing molecular networks to delve deeper into chemical constituents. Using the GNPS platform, in silico tools, and substructure annotation techniques, we expanded the chemical annotations. Principal Coordinate Analysis (PCoA) revealed significant metabolic similarities between several species, including P. incarnata, suggesting shared pharmacological potential. Our identification of metabolic compounds facilitated comparisons between understudied species with medicinal properties. Notably, we documented 25 previously uncharacterized species, paving the way for the development of novel products aimed at improving human well-being. This research focused on several native Passiflora species from Brazil, highlighting their unexplored therapeutic potential.

Annotated Dataset for Training Cloud Segmentation Neural Networks Using High-Resolution Satellite Remote Sensing Imagery

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this task has been hindered by the scarcity of specialized datasets and annotation tools. This study addresses this challenge by introducing CloudLabel, a semi-automatic annotation technique leveraging region growing and morphological algorithms including flood fill, connected components, and guided filter. CloudLabel v1.0 streamlines the annotation process for high-resolution satellite images, thereby addressing the limitations of existing annotation platforms which are not specifically adapted to cloud segmentation, and enabling the efficient creation of high-quality cloud segmentation datasets. Notably, we have curated the Annotated Dataset for Training Cloud Segmentation (ADTCS) comprising 32,065 images (512 × 512) for cloud segmentation based on CloudLabel. The ADTCS dataset facilitates algorithmic advancement in cloud segmentation, characterized by uniform cloud coverage distribution and high image entropy (mainly 5–7). These features enable deep learning models to capture comprehensive cloud characteristics, enhancing recognition accuracy and reducing ground object misclassification. This contribution significantly advances remote sensing applications and cloud segmentation algorithms.

Implementasi Strategi Influencer Marketing untuk Membangun Brand Awareness di Industri Skincare Pria

The purpose of this study is to look into how influencer marketing techniques affect brand awareness in the men's skincare market. The method used is a literature review that examines relevant literature from scientific journals, using keywords such as "Advertising," "Strategy Implementation," and "Brand Awareness." Data was collected through Google Scholar and analyzed using bibliographic annotation techniques. The results show that influencer marketing has great potential to enhance brand awareness and consumer purchase intention. The appropriate use of influencers can increase public trust and brand strength, while inappropriate influencer selection can have negative impacts. The study also emphasizes that effective influencer marketing strategies involve selecting influencers that match the product persona and using integrated and sustainable marketing strategies. Thus, companies in the men's skincare industry can utilize influencer marketing to achieve optimal results in building brand awareness and encouraging consumer purchasing decisions.

Human-in-the-loop: Using classifier decision boundary maps to improve pseudo labels

For classification tasks, several strategies aim to tackle the problem of not having sufficient labeled data, usually by automatic labeling or by fully passing this task to a user. Automatic labeling is simple to apply but can fail handling complex situations where human insights may be required to decide the correct labels. Conversely, manual labeling leverages the expertise of specialists but may waste precious effort which could be handled by automatic methods. More specifically, automatic solutions could be improved by combining an active learning loop with manual labeling assisted by visual depictions of a classifier’s behavior. We propose to include the human in the labeling loop by using manual labeling in feature spaces produced by a deep feature annotation (DeepFA) technique. To assist manual labeling, we provide users with visual insights on the classifier’s decision boundaries. Finally, we use the manual and automatically computed labels jointly to retrain the classifier in an active learning (AL) loop scheme. Experiments using a toy and a real-world application dataset show that our proposed combination of manual labeling supported by visualization of decision boundaries and automatic labeling can yield a significant increase in classifier performance with a quite limited user effort.

On Developing a Machine Learning-Based Approach for the Automatic Characterization of Behavioral Phenotypes for Dairy Cows Relevant to Thermotolerance

The United States is predicted to experience an annual decline in milk production due to heat stress of 1.4 and 1.9 kg/day by the 2050s and 2080s, with economic losses of USD 1.7 billion and USD 2.2 billion, respectively, despite current cooling efforts implemented by the dairy industry. The ability of cattle to withstand heat (i.e., thermotolerance) can be influenced by physiological and behavioral factors, even though the factors contributing to thermoregulation are heritable, and cows vary in their behavioral repertoire. The current methods to gauge cow behaviors are lacking in precision and scalability. This paper presents an approach leveraging various machine learning (ML) (e.g., CNN and YOLOv8) and computer vision (e.g., Video Processing and Annotation) techniques aimed at quantifying key behavioral indicators, specifically drinking frequency and brush use- behaviors. These behaviors, while challenging to quantify using traditional methods, offer profound insights into the autonomic nervous system function and an individual cow’s coping mechanisms under heat stress. The developed approach provides an opportunity to quantify these difficult-to-measure drinking and brush use behaviors of dairy cows milked in a robotic milking system. This approach will open up a better opportunity for ranchers to make informed decisions that could mitigate the adverse effects of heat stress. It will also expedite data collection regarding dairy cow behavioral phenotypes. Finally, the developed system is evaluated using different performance metrics, including classification accuracy. It is found that the YoloV8 and CNN models achieved a classification accuracy of 93% and 96% for object detection and classification, respectively.

Osteoporotic vertebral compression fracture (OVCF) detection using artificial neural networks model based on the AO spine-DGOU osteoporotic fracture classification system

BackgroundOsteoporotic Vertebral Compression Fracture (OVCF) substantially reduces a person's health-related quality of life. Computer Tomography (CT) scan is currently the standard for diagnosis of OVCF. The aim of this paper was to evaluate the OVCF detection potential of artificial neural networks (ANN). MethodsModels of artificial intelligence based on deep learning hold promise for quickly and automatically identifying and visualizing OVCF. This study investigated the detection, classification, and grading of OVCF using deep artificial neural networks (ANN). Techniques: Annotation techniques were used to segregate the sagittal images of 1,050 OVCF CT pictures with symptomatic low back pain into 934 CT images for a training dataset (89%) and 116 CT images for a test dataset (11%). A radiologist tagged, cleaned, and annotated the training dataset. Disc deterioration was assessed in all lumbar discs using the AO Spine-DGOU Osteoporotic Fracture Classification System. The detection and grading of OVCF were trained using the deep learning ANN model. By putting an automatic model to the test for dataset grading, the outcomes of the ANN model training were confirmed. ResultsThe sagittal lumbar CT training dataset included 5,010 OVCF from OF1, 1942 from OF2, 522 from OF3, 336 from OF4, and none from OF5. With overall 96.04% accuracy, the deep ANN model was able to identify and categorize lumbar OVCF. ConclusionsThe ANN model offers a rapid and effective way to classify lumbar OVCF by automatically and consistently evaluating routine CT scans using AO Spine-DGOU osteoporotic fracture classification system.

Object Detection in Diverse Environment

This project focuses on the creation of a custom dataset for object detection, employing meticulous annotation techniques and diverse data sources. The dataset is evaluated using various object detection models, including YOLOv8, on both the custom dataset and established benchmarks. Through comparative analysis, the quality, diversity, and generalizability of the custom dataset are assessed. Additionally, the impact of dataset quality on model performance is examined, highlighting areas for improvement. Furthermore, the integration of Generative Adversarial Networks (GANs) to augment dataset diversity is explored. Future work involves refining dataset collection methodologies, exploring novel architectures, and integrating domain adaptation techniques to enhance model robustness and applicability in real-world scenarios.

ScPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement.

In the past decade, single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal method for transcriptomic profiling in biomedical research. Precise cell-type identification is crucial for subsequent analysis of single-cell data. And the integration and refinement of annotated data are essential for building comprehensive databases. However, prevailing annotation techniques often overlook the hierarchical organization of cell types, resulting in inconsistent annotations. Meanwhile, most existing integration approaches fail to integrate datasets with different annotation depths and none of them can enhance the labels of outdated data with lower annotation resolutions using more intricately annotated datasets or novel biological findings. Here, we introduce scPLAN, a hierarchical computational framework designed for scRNA-seq data analysis. scPLAN excels in annotating unlabeled scRNA-seq data using a reference dataset structured along a hierarchical cell-type tree. It identifies potential novel cell types in a systematic, layer-by-layer manner. Additionally, scPLAN effectively integrates annotated scRNA-seq datasets with varying levels of annotation depth, ensuring consistent refinement of cell-type labels across datasets with lower resolutions. Through extensive annotation and novel cell detection experiments, scPLAN has demonstrated its efficacy. Two case studies have been conducted to showcase how scPLAN integrates datasets with diverse cell-type label resolutions and refine their cell-type labels. https://github.com/michaelGuo1204/scPLAN.

Computational Politeness in Natural Language Processing: A Survey

Computational approach to politeness is the task of automatically predicting and/or generating politeness in text. This is a pivotal task for conversational analysis, given the ubiquity and challenges of politeness in interactions. The computational approach to politeness has witnessed great interest from the conversational analysis community. This article is a compilation of past works in computational politeness in natural language processing. We view four milestones in the research so far, viz. supervised and weakly supervised feature extraction to identify and induce politeness in a given text, incorporation of context beyond the target text, study of politeness across different social factors, and study the relationship between politeness and various socio-linguistic cues. In this article, we describe the datasets, approaches, trends, and issues in computational politeness research. We also discuss representative performance values and provide pointers to future works, as given in the prior works. In terms of resources to understand the state of the art, this survey presents several valuable illustrations—most prominently, a table summarizing the past papers along different dimensions, such as the types of features, annotation techniques, and datasets used.

A Prostate Boundary Localization and Edge Denoising Algorithm

This research aimed at presenting a two-step method for prostate segmentation in TRUS images. The research used a prostate boundary localization and prostate edge denoising approach. The proposed method contribution is the use of the optimized Hodge’s method as the boundary operator and the use of the Bidirectional Exponential moving average to perform edge denoising. The results showed that the proposed method is effective in completing the prostate segmentation task. (1) The prostate region is effectively initialized and localized. (2) The recovery of noise points is accomplished and the segmentation result being consistent with the general shape of the prostate. The experimental results showed that this method can improve the overall segmentation accuracy. The process uses a combination of traditional and unsupervised methods, eliminating the need to rely on large data sets compared to current deep learning methods. The proposed method achieved excellent segmentation accuracy, with the Dice similarity coefficient (DICE) value of 0.9679, an average Intersection over Union (IoU) value of 0.9377, and an average False Positive Rate (FPR) of 0.0399. The results obtained from this study have significant implications for clinical practice. Accurate prostate segmentation is crucial for various applications, including radiation therapy planning, image-guided interventions, and computer-aided diagnosis. The proposed method has the potential to improve these applications by providing more precise and reliable prostate segmentations. However, it is important to acknowledge some limitations of this study. First, the proposed method was evaluated on a limited dataset, which may not fully represent the diversity of prostate images encountered in clinical practice. Further validation on larger datasets is necessary to assess its generalizability. Additionally, the proposed method relied on manual annotations for training, which can introduce inter-observer variability. Incorporating automated or semi-automated annotation techniques could enhance the robustness of the method

A review on available proteomic databases, annotation techniques and data projects important in male reproductive physiology research

A review on available proteomic databases, annotation techniques and data projects important in male reproductive physiology research

Exploring the dynamic microbial tapestry of South Asian rivers: insights from the Ganges and Yamuna ecosystems

AbstractThis review meticulously examines the dynamics of river microbiomes, with an emphasis on the Ganges and Yamuna rivers of South Asia. These rivers are vital for both ecological and cultural landscapes and offer to understand the interaction between ecological and anthropogenic factors and their impact on microbial communities and activities. Ecological and hydrological factors such as seasonal changes, water flow and physico‐chemical properties of rivers influence microbial diversity and abundance. The effect of heavy metals from industrial and agricultural sources on the river microbiome and how these pollutants modify microbial community structures and ecosystem health are not understood well yet. This underscores the need for sustainable water treatment and remediations for practical engineering solutions. The study reveals how these interactions, whether symbiotic or competitive, affect the composition and functionality of riverine microbial communities. An innovative aspect of our research is the potential of river microbiomes as indicators of urban sewage contamination. We demonstrate how microbial patterns can signal pollution levels, proving valuable for environmental monitoring, management and mitigation. A special attention to the role of microbes in river ecosystems' biogeochemical cycles has been paid to how these microbes contribute to nutrient recycling, organic matter decomposition and overall ecosystem productivity, underlining their crucial role in maintaining the aesthetic value of the river. Additionally, study evaluates the latest methodologies for analysing microbiome metagenomic data, including functional annotation and microbial community analysis techniques. Findings highlight the key importance of understanding river microbiomes for hydrology, ecology and microbiology researchers.

Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach

The prediction of material properties from a given microstructure and its reverse engineering displays an essential ingredient for accelerated material design. However, a comprehensive methodology to uncover the processing-structure-property relationship is still lacking. Herein, we develop a methodology capable of understanding this relationship for differently processed porous materials. We utilize a multi-method machine learning approach incorporating tomographic image data acquisition, segmentation, microstructure feature extraction, feature importance analysis and synthetic microstructure reconstruction. Enhanced segmentation with an accuracy of about 95% based on an efficient annotation technique provides the basis for accurate microstructure quantification, prediction and understanding of the correlation of the extracted microstructure features and electrical conductivity. We show that a diffusion probabilistic model superior to a generative adversarial network model, provides synthetic microstructure images including physical information in agreement with real data, an essential step to predicting properties of unseen conditions.

On-the-fly point annotation for fast medical video labeling.

In medical research, deep learning models rely on high-quality annotated data, a process often laborious and time-consuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two corners makes the process inherently frame-by-frame. Given the scarcity of experts' time, efficient annotation methods suitable for clinicians are needed. We propose an on-the-fly method for live video annotation to enhance the annotation efficiency. In this approach, a continuous single-point annotation is maintained by keeping the cursor on the object in a live video, mitigating the need for tedious pausing and repetitive navigation inherent in traditional annotation methods. This novel annotation paradigm inherits the point annotation's ability to generate pseudo-labels using a point-to-box teacher model. We empirically evaluate this approach by developing a dataset and comparing on-the-fly annotation time against traditional annotation method. Using our method, annotation speed was faster than the traditional annotation technique. We achieved a mean improvement of AP@50 over conventional method at equivalent annotation budgets on the developed dataset. Without bells and whistles, our approach offers a significant speed-up in annotation tasks. It can be easily implemented on any annotation platform to accelerate the integration of deep learning in video-based medical research.

面向机器阅读理解的医学域数据集MedicalQA

Machine reading comprehension aims to make the computer understand the paragraph semantics and answer the questions raised by users using algorithms. The quality of the dataset used in this task can directly affect the experimental results of the model. In order to enrich the medical domain dataset of machine reading comprehension, this paper constructs MedicalQA, a medical domain dataset for machine reading comprehension, employing a combination of web crawlers and manual annotation techniques. The dataset takes two medical platforms (i.e. Xunyiwenyao Network and 39 Health Network) as main data sources, and includes 19,502 paragraphs and Q & A pairs, covering 9 medical departments, such as internal medicine, surgery, obstetrics and gynecology. The dataset is formatted as an Excel file, organized with 5e columns. The first column denotes the paragraph ID; the second column indicates the department to which the paragraph belongs; the third column contains the paragraph content; the fourth column lists the questions, and the fifth column provides corresponding answers to the questions. The construction of this dataset is conducive to the establishment of machine reading comprehension models in the medical domain, and can also promote the sharing of medical datasets in the field of machine reading comprehension.

Efficient Training of Colorectal Cancer Diagnosis Model through Unsupervised Learning Composite Network

The use of machine learning algorithms for precise and effective colorectal cancer diagnosis has gained popularity as a result of advancements in medical imaging. Through the use of an Unsupervised Learning Composite Network (ULCN) model this study suggests a novel method for training a diagnosis model. Conventional training techniques for these models frequently rely on sizable labelled datasets, which can be labour and resource-intensive to create. The ULCN, on the other hand, incorporates unsupervised learning into the training process, decreasing the need on labelled input. In order to address this issue, our study offers a revolutionary method known as the DL-Kmeans (Deep Learning with Unsupervised K-Means Clustering), which combines the effectiveness of the unsupervised K-means clustering algorithm with the power of deep learning. When compared to manual screening and annotation techniques, the DL-Kmeans ability to more quickly refine medical images serves as evidence of its effectiveness. The use of DL- Kmeans processed photos resulted in a two-fold acceleration in the training process for deep learning models used to diagnose colorectal cancer, which is very notable. Notably, this speeding up did not degrade performance quality; the DL- Kmeans enhanced method showed superior results in terms of training loss and accuracy attained. DL- Kmeans importance goes beyond only improving images. It is a useful tool for handling the growing volume of medical photos, which will help with the later creation of artificial intelligence models.

Joint extraction of entity relations from geological reports based on a novel relation graph convolutional network

Geological reports house a wealth of geological domain knowledge and expert experience knowledge, and the efficient extraction of geological entity relations from these texts holds great significance in constructing a geological knowledge graph. To address challenges such as overlapping entity relations, data imbalance, and the limited incorporation of contextual features in existing deep learning-based entity relation extraction methods, this paper proposes a relational graph convolutional network pointer model based on the pre-trained model of Robustly Optimize Bidirectional Encoder Representation from Transformers Pre-training Approach (RoBERTa). Specifically, our approach tackles the issue of overlapping entity relationships by identifying the primary entity and the subordinate entity associated with a specific relationship. Additionally, by incorporating a relationship graph convolutional network, we can capture multiple relationships among entities. Moreover, we leverage the Biaffine layer to accurately handle entity annotation boundaries, thereby enhancing the precision of entity classification. To address the issue of imbalanced data, we adopt the Focal Loss function during experiments. Furthermore, we construct a geological entity relation dataset comprising 24 types of relationships, which is generated by collecting seven regional geological reports and employing semi-automatic annotation techniques. Finally, the proposed algorithm was tested and comparison with several baseline models, the results demonstrate the superior performance of the proposed model in this paper.

An exploration and practice of an optimized online annotation solution based on PDF slicing and SVG

With the continuous development of network technology, online annotation has become an essential tool in fields such as education, training, and document collaboration. However, traditional annotation techniques are plagued by several limitations, including low security, non-erasable annotations after uploading, and difficulties in communication across different systems. To address these issues, this paper proposes an SVG-based online annotation technique that is built upon PDF file slicing. This approach aims to enhance the deficiencies of traditional annotation techniques and improve user interaction. The research begins by introducing the background and significance of online annotation technology, elucidating the application prospects of PDF slicing technology and SVG in annotation. The paper then provides a detailed description of the annotation algorithm and the implementation process. Additionally, it discusses the experimental setup, results, and analysis. The results indicate that the SVG-based online annotation technology can effectively enhance annotation efficiency and real-time interaction while enabling erasure of historical data and facilitating cross-platform communication. Compared to traditional annotation techniques, the SVG-based approach exhibits higher practicality and flexibility, making it suitable for various types of document annotations and collaborative scenarios.

Interpretable deep learning: evaluating YOLO models and XAI techniques for video annotation

Interpretable deep learning: evaluating YOLO models and XAI techniques for video annotation

MAPS: pathologist-level cell type annotation from tissue images through machine learning

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.