High-resolution Dataset Research Articles

Location-Based Technology for Real-Time Artifact Recognition in Businesses

The recognition of historical artifacts play a crucial role in sustaining cultural heritage and advancing tourism. Despite advancements in object detection technologies, accurately identifying artifacts in diverse geographical and environmental contexts remains a significant challenge. Existing models often struggle to adapt to region-specific features and the complexity of historical artifacts, limiting their practical applications. To address these limitations, this study evaluates the potential of YOLOv4, YOLOv7-X, and YOLOv9c models for historical artifact recognition, with a particular focus on location-based segmentation. Geographically distinct datasets were utilized for training and evaluation, enabling the models to achieve higher accuracy in region-specific artifact detection. Among the tested models, YOLOv9c demonstrated superior performance, achieving the highest metrics across accuracy (96%), precision (93%), recall (95%), and mean average precision (mAP, 71%), making it the best-performing model. These results highlight YOLOv9c’s robustness and adaptability to complex datasets and diverse artifact characteristics. A user-friendly application interface was also developed, allowing real-time detection and providing detailed historical information about the artifacts. However, challenges such as the high computational cost of training YOLOv9c on high-resolution datasets were observed, particularly when compared to YOLOv4, which was computationally efficient but less accurate. YOLOv7-X offered a balance between performance and computational efficiency. The results demonstrate that location-based segmentation significantly enhances detection accuracy, making this approach highly effective for real-world applications in cultural heritage preservation and tourism.

Forest fires under the lens: needleleaf index - a novel tool for satellite image analysis

Forest fires under the lens: needleleaf index - a novel tool for satellite image analysis

Deep Learning-Based Detection of Abdominal Diseases Using YOLOv9 Models and Advanced Preprocessing Techniques

Artificial intelligence has emerged as a transformative tool in medical imaging, enabling automated diagnosis and analysis across various domains. While significant advancements have been made in abdominal imaging, many studies struggle to achieve robust detection of diseases. The complexity and variability in abdominal structures present unique challenges for traditional machine learning models, necessitating the adoption of more advanced object detection frameworks. Motivated by these challenges, this study focuses on leveraging the YOLOv9 object detection architecture to enhance the identification of abdominal diseases using the TEKNOFEST 2022 Abdomen Dataset. Advanced preprocessing techniques, including CLAHE (Contrast Limited Adaptive Histogram Equalization) and Gaussian noise augmentation, were applied to improve image contrast and model robustness. The dataset was processed into YOLO-compatible formats, and multiple training configurations were evaluated using YOLOv9c and YOLOv9s variants. These configurations included variations in batch size, optimizer type (SGD and Adam), dropout rate, and frozen layers. Among the configurations tested, the YOLOv9s model with 32 batch size, SGD optimizer, and a 35% dropout rate demonstrated the best performance, achieving a Recall of 0.7698, Accuracy of 0.7698, and F1 Score of 0.8228. The highest mAP50 of 0.9385 was observed with the YOLOv9c model trained using the Adam optimizer and a 35% dropout rate. Confusion matrix analysis revealed strong detection capabilities for conditions like acute cholecystitis and abdominal aortic aneurysm. This study highlights the potential of YOLOv9 models in medical imaging and emphasizes the importance of high-resolution datasets and advanced feature extraction techniques for improving diagnostic accuracy in abdominal disease detection. These findings lay a foundation for the development of reliable and efficient AI-driven diagnostic tools.

Modeling Urban Microclimates for High-Resolution Prediction of Land Surface Temperature Using Statistical Models and Surface Characteristics

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R2 = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R2 = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R2 ranging from 0.79 to 0.95.

Establishment and extension of a compact and robust binary feature descriptor for UAV image matching

Abstract Local feature description is a crucial challenge in unmanned aerial vehicle (UAV) images matching. Although scholars have explored a variety of methods for defining local feature descriptions, improving the matching accuracy of UAV images and reducing the memory consumption of descriptor remain worthwhile research issues. A compact and robust binary feature descriptor for UAV image matching is proposed. To achieve rotational invariance of the descriptor, a sampling pattern based on the region overlap error is employed to assign the dominant direction of the feature points. Additionally, a rotation invariant gradient computation method is proposed to reduce the dominant direction assignment errors. Then, the region around the feature point is divided into grids based on the region overlap error, and gradient orientation histogram and average intensity histogram are constructed. Finally, these histograms are binarized to generate a 30-byte binary descriptor. Results from the Oxford dataset, the Iguazu dataset, and a high-resolution UAV image dataset indicate that the proposed descriptor is compact and achieves the highest matching accuracy compared to nine commonly used feature descriptors, including scale-invariant feature transform, speeded up robust feature, binary robust invariant scalable keypoints, KAZE, oriented fast and rotated brief, fast retina keypoint, local intensity order pattern, boosted efficient binary local image descriptor, and triplet-based efficient binary local image descriptor. Notably, on the high-resolution UAV images with translational transformations, scale variations, salt-and-pepper noise, and Gaussian white noise, the accuracies of the proposed descriptor were 87.09%, 80.83%, 92.46%, and 87.42%, respectively. The improvements ranged from 6.95% to 53.20%, 9.32% to 55.90%, 10.39% to 44.29%, and 6.54% to 58.24%.

Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data

The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. A total of 432,230 Landsat scenes were processed, enhancing data quality and accuracy. The dataset was validated using 255,000 samples across 6 geographical regions, showing strong performance in capturing spatiotemporal NDVI variations. Additionally, the dataset effectively addresses Scan Line Corrector-off stripes in Landsat 7 imagery. This dataset enables reliable annual NDVI estimates for China at a 30-m resolution and is available for reuse through an open data repository.

Upscaling Tower-Based Net Ecosystem Productivity to 250 m Resolution with Flux Site Distribution Considerations

Net ecosystem productivity (NEP) is an extremely important flux for terrestrial ecosystems, indicating the value of net ecosystem exchange (NEE) between terrestrial ecosystems and the atmosphere, excluding carbon fluxes from disturbances. Leveraging flux network NEE annual measurements, this study focuses on upscaling the tower-based NEP to a global 250 m resolution dataset with flux site distribution considerations. Firstly, the data augmentation method was presented to address issues related to the uneven spatial distribution of flux sites. Secondly, a random forest model was developed for NEP estimation using the optimized tower-based NEP and remotely sensed and meteorological gridded sample sets, giving an R2 value of 0.73 and an RMSE value of 149.83 gC m−2 yr−1. Finally, a global NEP product at a 250 m resolution was generated (2001–2022, average 13.79 PgC yr−1) and evaluated. In summary, we present a solution to the overestimation of global NEP by data-driven methods, producing a long-time-series, high-resolution NEP dataset that is more comparable to atmospheric inversion results. This dataset enhances comparability with atmospheric inversion results, thereby boosting our confidence in conducting a consistency analysis of terrestrial carbon sinks across different methods within the framework.

Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico

Abstract. Deep learning models have demonstrated remarkable success in fields such as language processing and computer vision, routinely employed for tasks like language translation, image classification, and anomaly detection. Recent advancements in ocean sciences, particularly in data assimilation (DA), suggest that machine learning can emulate dynamical models, replace traditional DA steps to expedite processes, or serve as hybrid surrogate models to enhance forecasts. However, these studies often rely on ocean models of intermediate complexity, which involve significant simplifications that present challenges when transitioning to full-scale operational ocean models. This work explores the application of convolutional neural networks (CNNs) in data assimilation within the context of the HYbrid Coordinate Ocean Model (HYCOM) in the Gulf of Mexico. The CNNs are trained to correct model errors from a 2-year, high-resolution (1/25°) HYCOM dataset, assimilated using the Tendral Statistical Interpolation System (T-SIS). The CNNs are trained to replicate the increments generated by the T-SIS data assimilation package, aiming to correct model forecasts of sea surface temperature (SST) and sea surface height (SSH). The inputs to the CNNs include real satellite observations of SST from the Group for High Resolution Sea Surface Temperature (GHRSST), along-track altimeter SSH observations (ADT), the model background state (previous forecast), and the innovations (differences between observations and background). We assess the performance of the CNNs across five controlled experiments, designed to provide insights into their application in environments governed by full primitive equations, real observations, and complex topographies. The experiments focus on evaluating (1) the architecture and complexity of the CNNs, (2) the type and quantity of observations, (3) the type and number of assimilated fields, (4) the impact of training window size, and (5) the influence of coastal boundaries. Our findings reveal significant correlations between the chosen training window size – a factor not commonly examined – and the CNNs' ability to assimilate observations effectively. We also establish a clear link between the CNNs' architecture and complexity and their overall performance. This research uses artificial intelligence to enhance ocean forecasting in the Gulf of Mexico. By using convolutional neural networks, the study improves predictions of sea temperatures and heights by integrating real satellite data with existing models. Through five comprehensive experiments, the team found that the amount of training data and the design of the neural networks significantly affect accuracy. These insights pave the way for faster, more reliable ocean models, benefiting environmental monitoring and maritime operations.

Three-dimensional structure of entire hydrated murine hearts at histological resolution

Imaging the entire cardiomyocyte network in entire small animal hearts at single cell resolution is a formidable challenge. Optical microscopy provides sufficient contrast and resolution in 2d, however fails to deliver non-destructive 3d reconstructions with isotropic resolution. It requires several invasive preparation steps, which introduce structural artefacts, namely dehydration, physical slicing and staining, or for the case of light sheet microscopy also clearing of the tissue. Our goal is to provide 3d reconstructions of the cardiomyocyte network in entire hydrated murine hearts, and to develop a methodology for quantitative analysis of heart pathologies based on X-ray phase contrast computed tomography (XPCT). We have used XPCT at two beamlines of the extremely brilliant source (EBS) at the European Synchrotron Radiation Facility (ESRF) to scan wild-type murine hearts at high resolution, as well as a series of murine hearts of different pathological models, at reduced resolution and higher throughput. All hearts were obtained from the small animal facility of the university medical center in Göttingen. The hearts were fixed in formalin, stored and measured non-destructively in phosphate buffer solution. The high resolution dataset allows to discern individual cardiomyocytes in the tissue. All datasets have been analyzed using semi-automated image segmentation of the ventricles, rotation into a common coordinate system, classification into different anatomical compartments, and finally the structure tensor approach. A 3d streamline representation of the cardiomyocyte orientation vector field is provided. The different cardiovascular disease models are analysed based on metrics derived from the 3d structure tensor. An entire hydrated murine heart has been covered at an isotropic voxel size of 1.6μm (distributed over several volumes). A binned and fused dataset of this heart is available at 3.2μm, and has been analyzed by the structure tensor approach to yield the ventricular cardiomyocyte network or mesh, i.e. the aggregation of the cardiomyocyte chains in particular in the ventricular wall. Semi-automatic determination of structural metrics is already achieved and the corresponding tools and resulting data are made publically available. XPCT using extremely brilliant undulator radiation is close to achieve single cell reconstruction in an entire small animal organ.

Spatiotemporal estimates of anthropogenic NOx emissions across China during 2015-2022 using a deep learning model.

As one of the significant air pollutants, nitrogen oxides (NOx = NO + NO2) not only pose a great threat to human health, but also contribute to the formation of secondary pollutants such as ozone and nitrate particles. Due to substantial uncertainties in bottom-up emission inventories, simulated concentrations of air pollutants using GEOS-Chem model often largely biased from those of ground-level observations. To address this issue, we developed a new deep learning model to simulate the inverse process of the GEOS-Chem model. This framework was applied to improve the total anthropogenic NOx emission intensity over a five-year period (2015-2019), and then to predict anthropogenic NOx emission intensity for 2020-2022. The deep learning model showed higher R2 value (0.94) based on 10-fold cross-validation, indicating that the model effectively captured spatial features and patterns. Then, NOx emission intensity was calibrated based on the new framework using high-resolution NO2 concentration dataset instead of the simulated NO2 levels derived from GEOS-Chem model. Overall, the top-down inversion result was in agreement with the bottom-up emission inventory at the spatial scale. The top-down emission fluxes were lower in high-emission regions such as central China, Beijing, Shanghai, the Pearl River Delta, and the central part of Liaoning, while posterior estimates were higher in regions with lower prior emission intensity. The posterior NOx emission intensity suggested that some major regions such as Beijing-Tianjin-Hebei (BTH) (-56.4 %) and Pearl River Delta (PRD) (-52.5 %) experienced dramatic NOx emission intensity decreases from 2015 to 2022, whereas some remote regions such as Tibetan Plateau remained relatively stable. This research contributes to the timely tracking of changes in pollutant emission and aids in the formulation of more effective and relevant pollution prevention and control policies.

Enhancing Coffee Leaf Rust Detection Using DenseNet201: A Comprehensive Analysis of the Mbozi and Public Datasets in Songwe, Tanzania

Coffee Leaf Rust (CLR) is a worldwide devastating fungal disease that threatens coffee production, upsetting economic and farmers' livelihoods. Traditional methods of detecting CLR heavily rely on using machine-learning (ML) models trained through weakly collected datasets and physical inspection which is tedious, time-consuming, and subject to human error. This study explores the performance of the DenseNet201 model using three datasets: Mbozi, Public, and Combined (a merger of Mbozi and Public datasets). Machine Learning Theory guided this research. The study objective is to assess the influence of dataset quality on CLR detection, analyze Mbozi and Public datasets using DenseNet201, and enhance robustness by merging the two datasets. A study on coffee leaf rot (CLR) severity was conducted using systematic sampling techniques. Leaves from multiple coffee farms were collected, representing different levels of infection. The Mbozi dataset, sourced from high-resolution images captured from Tanzania's Songwe coffee plantations, was analyzed for quality under controlled conditions, including environmental factors, image clarity, resolution, labeling consistency, and class balance, based on data completeness, image quality score, visual inspection, and model performance. DenseNet201 was trained and validated on each dataset achieving its highest accuracy with the Mbozi dataset at 98.72% and a validation accuracy of 97.65%, demonstrating the importance of consistent image quality and accurate annotations. In contrast, the public dataset suffered from inconsistencies in resolution and labeling, resulting in a lower training and validation accuracy of 96.86% and 96.42% respectively. The Combined dataset, which integrated the strengths of both datasets, exhibited a stronger generalization with an accuracy of 97.48% and validation accuracy of 97.49%, balancing the need for high-quality images with environmental variability. The study shows improved CLR detection speed and accuracy due to high-quality and consistently labeled images from the Mbozi dataset. It recommends future models integrate regionally relevant and high-resolution datasets for robust performance in real-world agricultural conditions, providing coffee farmers with timely disease intervention tools for better production management and economic stability in coffee-growing regions.

Comparative study of CNN-based and conventional fault interpretation methods: a study of the deep-water Orange Basin, South Africa

Image-based deep learning methods, especially convolutional neural networks (CNNs), are gaining traction in seismic interpretation, but their application still demands manual validation. This study compares a U-Net structured CNN, called Fault-Net, with conventional edge-enhancing seismic attributes of variance and chaos, which serve as a scientific baseline. We adopted two seismic interpretation workflows: (1) conventional attributes to enhance fault features, and (2) a deep learning-based workflow for fault segmentation using CNNs. Both workflows were applied to a high-resolution 3D seismic dataset from the structurally complex deep-water Orange Basin (South Africa). While deep learning-based software packages are commercially available, it is unclear whether they are suitable for the Orange Basin and in an academic setting due to their proprietary architectures and generally closed training data. This study provides public evidence of the feasibility of automated structural interpretation in complex seismic datasets using deep learning, revealing both key benefits and limitations. Where high-quality labelled data are available, the deep learning approach is faster and tends to produce a cleaner and more accurate depiction of larger faults compared to conventional methods. The open availability of Fault-Net makes deep learning-based interpretation particularly advantageous for academic settings, offering significant time and resource efficiency while enhancing the understanding of complex subsurface structures.

High-resolution gridded dataset of China’s offshore wind potential and costs under technical change

Assessing the dynamics of offshore wind potential and costs is essential for low-carbon energy policy decision-making and energy modeling, but no open-source, spatial explicit and technologically detailed dataset is available. This study addresses this gap by employing a consistent assessment framework that integrates GIS analysis, a wind reanalysis model, a component-based cost model and scenario analysis. It identifies suitable space for offshore wind deployment considering 12 technical and policy constraints, estimates hourly output curves, capacity factors, and technology cost dynamics by components across 5058 grid points with a 10 km resolution from 2020 to 2035 under three technical change scenarios. The dataset has been validated through comparisons with existing offshore wind projects and datasets, and is stored in two formats (GeoTIFF and NetCDF4). This dataset offers extensive potential for use as an input in climate policy and energy system research.

A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics

This paper presents an open-source dataset intended to enhance the analysis and optimization of photovoltaic (PV) power generation in urban environments, serving as a valuable resource for various applications in solar energy research and development. The dataset comprises measured PV power generation data and corresponding on-site weather data gathered from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals at the inverter-level. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using the Brick schema, which simplifies data comprehension and the development of smart analytics applications. This paper provides a detailed description on the site specifications, data collection method, data records, and data validation. This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.

Regional-specific trends of PM2.5 and O3 temperature sensitivity in the United States

Climate change poses direct and indirect threats to public health, including exacerbating air pollution. However, the influence of rising temperature on air quality remains highly uncertain in the United States, particularly under rapid reduction in anthropogenic emissions. Here, we examined the sensitivity of surface-level fine particulate matter (PM2.5) and ozone (O3) to summer temperature anomalies in the contiguous US as well as their decadal changes using high-resolution datasets generated by machine learning. Our findings demonstrate that in the eastern US, stringent emission control strategies have significantly reduced the positive responses of PM2.5 and O3 to summer temperature, thereby lowering the population exposure associated with warming-induced air quality deterioration. In contrast, PM2.5 in the western US became more sensitive to temperature, highlighting the urgent need to manage and mitigate the impact of worsening wildfires. Our results have important implications for air quality management and risk assessments of future climate change.

Towards Personalized Mapping Through Lumbosacral Spinal Cord Task fMRI

Abstract The lumbosacral spinal cord contains neural circuits crucial for locomotion, organized into rostrocaudal levels with distinct somatosensory and motor neuron pools that project to and from the muscles of the lower limbs. However, the specific spinal levels that innervate each muscle and the locations of neuron pools vary significantly between individuals, presenting challenges for targeted therapies and neurosurgical interventions aimed at restoring locomotion. Non-invasive approaches to functionally map the segmental distribution of muscle innervation – or projectome – are therefore essential. Here, we developed a pipeline dedicated to record blood oxygenation level dependent (BOLD) signals in the lumbosacral spinal cord using functional magnetic resonance imaging (fMRI). We assessed spinal activity across different conditions targeting the extensor/flexor muscles of the right leg (ankle, knee, and hip) in 12 healthy participants. To enhance clinical relevance, we included not only active movements but also two conditions that did not rely on participants’ performance: passive stretches and muscle-specific tendon vibration, which activates proprioceptive afferents of the vibrated muscle. BOLD activity patterns were primarily located on the side ipsilateral to the movement, stretch, or vibration, both at the group and participant levels, indicating the BOLD activity being associated with the projectome. The fMRI-derived rostrocaudal BOLD activity patterns exhibited mixed alignment with expected innervation maps from invasive studies, varying by muscle and condition. While some muscles and conditions matched well across studies, others did not. Significant variability among individual participants underscores the need for personalized mapping of projections for targeted therapies and neurosurgical interventions. To support the interpretation of BOLD activity patterns, we developed a decision tree-based framework that combines reconstruction of neural structures from high-resolution anatomical MRI datasets and muscle-specific fMRI activity to produce personalized projectomes. Our findings provide a valuable proof-of-concept for the potential of fMRI to map the lumbosacral spinal cord’s functional organization, while shedding light on challenges associated with this endeavor.

A diffusion-based super resolution model for enhancing sonar images.

Improved hardware and processing techniques such as synthetic aperture sonar have led to imaging sonar with centimeter resolution. However, practical limitations and old systems limit the resolution in modern and legacy datasets. This study proposes using single image super resolution based on a conditioned diffusion model to map between images at different resolutions. This approach focuses on upscaling legacy, low-resolution sonar datasets to enable backward compatibility with newer, high-resolution datasets, thus creating a unified dataset for machine learning applications. The study demonstrates improved performance for classifying upscaled images without increasing the probability of false detection. The increased probability of detection was 7% compared to bicubic interpolation, 6% compared to convolutional neural networks, and 2% compared to generative adversarial networks. The study also proposes two sonar specific evaluation metrics based on acoustic physics and utility to automatic target recognition.

Reconstructing urban vegetation evolution in China using multimodal deep learning and 30-years Landsat archive

To retrospect the impact of urbanization on vegetation, consistent and reliable maps that provide urban vegetation estimates are required. In this study, we make the first attempt to employ deep learning for urban vegetation mapping of 2120 cities in China using a 30-years Landsat archive (UV-30). We present a multimodal deep learning (MDL) model to combine features from 5 categories, namely reflective bands, remote sensing indices, texture variables, topographical variables, and time labels. We normalized elevation data locally to remove the inter-city differences, while retaining topographic details. We used time labels to account for spectral variability over time and Landsat sensors. The performance of the MDL models was compared with a vanilla deep neural network, random forest, and support vector regression. Results indicated that the fusion of different categories of features improved the prediction accuracy. The MDL performed best in most validation cities, and our results were superior to the fractional maps aggregated from high-resolution datasets in homogeneous urban landscapes. We further analyzed urban vegetation dynamics across the urban core-new town-outskirt gradients of different geographical regions and city sizes. We found a gradually diminishing de-greening trend in urban China, especially in urban cores and new towns. Nevertheless, we observed a more pronounced degradation of urban vegetation in the south regions of China compared to the north, and smaller cities show no evident urban vegetation increase. The suggested framework is valuable for urban vegetation assessments and facilitates a thorough understanding and management of urban ecosystems.

Future projections of precipitation extremes over East Asia based on a deep learning downscaled CMIP6 high-resolution (0.1°) dataset

Future projections of precipitation extremes over East Asia based on a deep learning downscaled CMIP6 high-resolution (0.1°) dataset

Read Mapping for Hi-C Analysis.

Hi-C is a popular ligation-based technique to detect 3D physical chromosome structure within the nucleus using cross-linking and next-generation sequencing. As an unbiased genome-wide assay based on chromosome conformation capture, it provides rich insights into chromosome structure, dynamic chromosome folding and interactions, and the regulatory state of a cell. Bioinformatics analyses of Hi-C data require dedicated protocols as most genome alignment tools assume that both paired-end reads will map to the same chromosome, resulting in large two-dimensional matrices as processed data. Here, we outline the necessary steps to generate high-quality aligned Hi-C data by separately mapping each read while correcting for biases from restriction enzyme digests. We introduce our own custom open-source pipeline, which enables users to select an aligner of their choosing with high accuracy and performance. This enables users to generate high-resolution datasets with fast turnaround and fewer unmapped reads. Finally, we discuss recent innovations in experimental techniques, bioinformatics techniques, and their applications in clinical testing for diagnostics.