Damage Hotspots Research Articles

Identifying wild boar (Sus scrofa) crop damage hotspots to mitigate human-wild boar conflicts in northern Iran

Conflict between wild boars and humans, and the ensuing economic losses has been a global challenge for government authorities and conservation biologists for over a decade. Understanding the interplay of ecological and anthropogenic factors driving human–wild boar conflicts is paramount for formulating effective strategies to address this challenge. This study employed species distribution modeling (SDM) to predict the extent and distribution of wild boar damage risk in Taleghan, Iran, where crop cultivation and livestock rearing represent the primary livelihood activities for the local communities. Our analyses revealed that 7.7 % of the overall study area is at high risk of wild boar crop damage. The area of high risk is proportionally smaller in the no-hunting zone (1.6 %) compared to the non-protected zone (14.8 %). Also, the areas at high risk were significantly lower within the no-hunting (1200 ha) area than in non-protected area (9500 ha). The most important determinants of the occurrence of high wild boar damage are presence of orchards (30.7 %), rangeland density (24.4 %), distance to human settlements (19.4 %), and slope (10.7 %). This study successfully mapped wild boar damage risk in northern Iran. The results suggest that areas with gentle slopes, heterogeneous landscapes featuring dense vegetation (i.e., orchards and rangelands) and human settlements with sparse road networks are at the highest risk of wild boar damage. The resulting map of conflict hotspots offers a concise visualization of conflict patterns across the landscape, facilitating prioritization and development of conflict mitigation measures for areas that require immediate attention.

Layer-by-layer reconstruction of fatigue damages in composites from thermal images by a Residual U-Net

In this paper a deep learning model is used to fully reconstruct the 3D distribution of arbitrarily shaped subsurface fatigue damages in a fiber/epoxy composite from synthetic thermal surface images. Synthetic thermal surface images (TIs) of self-heating damage hotspots are produced by thermal finite element analysis which are consequently used to train a Residual U-Net based on recent architectures designed for image segmentation. Different augmentation techniques are employed to mitigate the computational cost of generating training data through thermal finite element analysis. The Residual U-Net model accurately reconstructed – layer by layer – the ground truths and thereby enabled the quantitative assessment of location, size, shape, depth and gradient of an internal fatigue damage distribution. Moreover, the Residual U-Net achieved good predictions for comparatively small training set sizes.

Development and validation of graphics‐based digital twin framework for UAV‐aided post‐earthquake inspection of high‐rise buildings

SummaryTraditional post‐earthquake inspection of civil infrastructure is conducted manually, taking a considerable amount of time and often putting inspectors in harm's way. This problem is exacerbated in modern cities, where millions of people can be left homeless until their residences are deemed safe to reinhabit. Image collection enabled by commercial unmanned aerial vehicles (UAVs) combined with computer vision‐based techniques has provided an alternative with high potential for rapid post‐earthquake inspection. However, the extracted images of the damage alone are inadequate to evaluate the system‐level safety condition of a structure. The quality of the visual information also heavily relies on the effectiveness of the UAV inspection scheme which is susceptible to environmental uncertainties. To this end, a graphics‐based digital twin (GBDT) framework is developed for UAV‐aided post‐earthquake inspection of high‐rise buildings and validated using a high‐rise building in Guangzhou, China. The GBDT is comprised of a finite element (FE) model and a photorealistic computer graphics (CG) model, with the latter being informed by the former, jointly providing as a comprehensive virtual representation of the structure so that every step of the post‐earthquake inspection procedure can be planned and evaluated virtually. First, to avoid the cumbersome nature of constructing the graphical representation of the numerous components in high‐rise buildings, the CG model in the GBDT is created by automatically importing structural components from the FE model and adding nonstructural components according to the dimensions of the as‐built structure. This fast modeling process as well as the accuracy of the virtual presentation are validated by point cloud comparisons between the CG model and the as‐built structure. Subsequently, the GBDT is used to showcase the evaluation of UAV flight schemes for post‐earthquake inspection of high‐rise buildings. To shorten flight time and place more emphasis on potential damage, FE analysis is conducted to determine the earthquake‐induced damage locations. Consistent damage hotspots are then marked on the CG model, along with restrictions from the real environment such as obstacles, weak satellite signal, wind speed, and lighting conditions considered in the synthetic environment. Finally, applying the synthetic environment as the testbed, three UAV‐aided inspection schemes are implemented virtually and the best UAV flight scheme is determined for the assumed field inspection. This example demonstrates the flexibility of the GBDT in representing the real‐world structure and environmental conditions and its efficacy in assisting decision making for rapid and effective structural inspection in the aftermath of an earthquake.

Evaluating a Novel Approach to Detect the Vertical Structure of Insect Damage in Trees Using Multispectral and Three-Dimensional Data from Drone Imagery in the Northern Rocky Mountains, USA

Remote sensing is a well-established tool for detecting forest disturbances. The increased availability of uncrewed aerial systems (drones) and advances in computer algorithms have prompted numerous studies of forest insects using drones. To date, most studies have used height information from three-dimensional (3D) point clouds to segment individual trees and two-dimensional multispectral images to identify tree damage. Here, we describe a novel approach to classifying the multispectral reflectances assigned to the 3D point cloud into damaged and healthy classes, retaining the height information for the assessment of the vertical distribution of damage within a tree. Drone images were acquired in a 27-ha study area in the Northern Rocky Mountains that experienced recent damage from insects and then processed to produce a point cloud. Using the multispectral data assigned to the points on the point cloud (based on depth maps from individual multispectral images), a random forest (RF) classification model was developed, which had an overall accuracy (OA) of 98.6%, and when applied across the study area, it classified 77.0% of the points with probabilities greater than 75.0%. Based on the classified points and segmented trees, we developed and evaluated algorithms to separate healthy from damaged trees. For damaged trees, we identified the damage severity of each tree based on the percentages of red and gray points and identified top-kill based on the length of continuous damage from the treetop. Healthy and damaged trees were separated with a high accuracy (OA: 93.5%). The remaining damaged trees were separated into different damage severities with moderate accuracy (OA: 70.1%), consistent with the accuracies reported in similar studies. A subsequent algorithm identified top-kill on damaged trees with a high accuracy (OA: 91.8%). The damage severity algorithm classified most trees in the study area as healthy (78.3%), and most of the damaged trees in the study area exhibited some amount of top-kill (78.9%). Aggregating tree-level damage metrics to 30 m grid cells revealed several hot spots of damage and severe top-kill across the study area, illustrating the potential of this methodology to integrate with data products from space-based remote sensing platforms such as Landsat. Our results demonstrate the utility of drone-collected data for monitoring the vertical structure of tree damage from forest insects and diseases.

Telomere dynamics and oxidative stress in Arabidopsis grown in lunar regolith simulant.

NASA envisions a future where humans establish a thriving colony on the Moon by 2050. Plants will be essential for this endeavor, but little is known about their adaptation to extraterrestrial bodies. The capacity to grow plants in lunar regolith would represent a major step towards this goal by minimizing the reliance on resources transported from Earth. Recent studies reveal that Arabidopsis thaliana can germinate and grow on genuine lunar regolith as well as on lunar regolith simulant. However, plants arrest in vegetative development and activate a variety of stress response pathways, most notably the oxidative stress response. Telomeres are hotspots for oxidative damage in the genome and a marker of fitness in many organisms. Here we examine A. thaliana growth on a lunar regolith simulant and the impact of this resource on plant physiology and on telomere dynamics, telomerase enzyme activity and genome oxidation. We report that plants successfully set seed and generate a viable second plant generation if the lunar regolith simulant is pre-washed with an antioxidant cocktail. However, plants sustain a higher degree of genome oxidation and decreased biomass relative to conventional Earth soil cultivation. Moreover, telomerase activity substantially declines and telomeres shorten in plants grown in lunar regolith simulant, implying that genome integrity may not be sustainable over the long-term. Overcoming these challenges will be an important goal in ensuring success on the lunar frontier.

Long-term trajectory of ozone impact on maize and soybean yields in the United States: A 40-year spatial-temporal analysis

Understanding the long-term change trends of ozone-induced yield losses is crucial for formulating strategies to alleviate ozone damaging effects, aiming towards achieving the Zero Hunger Sustainable Development Goal. Despite a wealth of experimental research indicating that ozone's influence on agricultural production exhibits marked fluctuations and differs significantly across various geographical locations, previous studies using global statistical models often failed to capture this spatial-temporal variability, leading to uncertainties in ozone impact estimation. To address this issue, we conducted a comprehensive assessment of the spatial-temporal variability of ozone impacts on maize and soybean yields in the United States (1981–2021) using a geographically and temporally weighted regression (GTWR) model. Our results revealed that over the past four decades, ozone pollution has led to average yield losses of −3.5% for maize and −6.1% for soybean, translating into an annual economic loss of approximately $2.6 billion. Interestingly, despite an overall downward trend in ozone impacts on crop yields following the implementation of stringent ozone emission control measures in 1997, our study identified distinct peaks of abnormally high yield reduction rates in drought years. Significant spatial heterogeneity was detected in ozone impacts across the study area, with ozone damage hotspots located in the Southeast Region and the Mississippi River Basin for maize and soybean, respectively. Furthermore, we discovered that hydrothermal factors modulate crop responses to ozone, with maize showing an inverted U-shaped yield loss trend with temperature increases, while soybean demonstrated an upward trend. Both crops experienced amplified ozone-induced yield losses with rising precipitation. Overall, our study highlights the necessity of incorporating spatiotemporal variability into assessments of crop yield losses attributable to ozone pollution. The insights garnered from our findings can contribute to the formulation of region-specific pollutant emission policies, based on the distinct profiles of ozone-induced agricultural damage across different regions.

The Protective Mechanism of TFAM on Mitochondrial DNA and its Role in Neurodegenerative Diseases.

Mitochondrial transcription factor A (TFAM) is a mitochondrial protein encoded by nuclear genes and transported from the cytoplasm to the mitochondria. TFAM is essential for the maintenance, expression, and delivery of mitochondrial DNA (mtDNA) and can regulate the replication and transcription of mtDNA. TFAM is associated with the formation of mtDNA nucleomimetic structures, mtDNA repair, and mtDNA stability. However, the mechanism by which TFAM protects mtDNA is still being studied. This review provides a summary of the protective mechanism of TFAM on mtDNA including the discrete regulatory effects of TFAM acetylation and phosphorylation on mtDNA, the regulation of Ca2+ levels by TFAM to activate transcription in mitochondria, and the increased binding of TFAM to mtDNA damage hot spots. This review also discusses the association between TFAM and some neurodegenerative diseases.

A GIS-based flood damage index for cultural heritage

Evaluating potential consequences of floods on cultural heritage is one of the objectives of the 60/2007/EC Floods Directive. Nevertheless, the peculiarities of cultural heritage in terms of data availability, exposure values and vulnerability, make flood damage and risk analyses rarely applied. This work aims at developing a GIS-based Flood Damage Index for Cultural Heritage (FDICH) that (i) weights exposure based on the level of listing, (ii) weights vulnerability based on ad-hoc taxonomy, (iii) provides a damage density per unit surface instead than a point classification. FDICH is intended for large scale applications, e.g., regional or District ones, to identify damage hotspots and support risk mapping and management. The method is applied to the Po River District Authority, the largest in Italy, within a project framework for flood damage analysis of all exposed assets. In the territory of the district, a new spatial database of cultural heritage counting ca. 125,000 items is created and enriched with exposure and vulnerability attributes. The results allow for identifying 5 main cultural heritage damage hotspots and the calculation of FDICH shows the potential for cross-compare damages at District but also al local scales, for prioritization of mitigation measures.

A Multi-Criteria Analysis Approach to Identify Flood Risk Asset Damage Hotspots in Western Australia

Climate change is contributing to extreme weather conditions, which transform the scale and degree of flood events. Therefore, it is important for relevant government agencies to effectively respond to both extreme climate conditions and their impacts by providing more efficient asset management strategies. Although international research projects on water-sensitive urban design and rural drainage design have provided partial solutions to this problem, road networks commonly serve unique combinations of urban-rural residential and undeveloped areas; these areas often have diverse hydrology, geology, and climates. Resultantly, applying a one-size-fits-all solution to asset management is ineffective. This paper focuses on data-driven flood modelling that can be used to mitigate or prevent floodwater-related damage in Western Australia. In particular, a holistic and coherent view of data-driven asset management is presented and multi-criteria analysis (MCA) is used to define the high-risk hotspots for asset damage in Western Australia. These state-wide hotspots are validated using road closure data obtained from the relevant government agency. The proposed approach offers important insights with regard to factors influencing the risk of damage in the stormwater management system.

Numerical modelling of rolling contact fatigue damage initiation from non-metallic inclusions in bearing steel

Bearing failure is a cause of concern in a variety of machinery such as turbines, transmissions, drills, engines, etc. It is often associated with rolling contact fatigue (RCF) triggered from damage initiation at non-metallic inclusions (NMI’s). Experimental evidence shows that damage initiation lifetime is highly sensitive to the NMI characteristics and its bonding with the steel matrix. This study numerically investigates the role of NMI features and its bonding with the steel matrix on damage initiation lifetime. NMI characteristics modelled in this study are derived from an experimental investigation of a failed bearing. Simulation results highlight a near to instantaneous debonding at the matrix-inclusion interface followed by accelerated crack initiation. The critical depth for damage initiation shifts towards the surface with the increase in friction coefficient between roller and raceway. The simulations also reveal that larger inclusions show earlier damage initiation, indicating a size effect. The damage hotspots from the simulation results were compared with experimental findings and a hypothesis for crack initiation from a NMI is put forward.

Sustainable Tourism and Conservation of Underground Ecosystems through Airflow and Particle Distribution Modeling

Underground ecosystems are often of interest for the tourism industry due to their important naturalistic and cultural heritage. Since these underground ecosystems are almost completely isolated, external agents (such as human presence) can easily disrupt their chemico-physical and biological processes, which can affect, sometimes irrevocably, their natural equilibrium, placing the preservation of such sites at risk. The most sensible managers of caves, catacombs, mines, and all the accessible cultural sites are searching for methods to control these dynamics and the modeling appears to be effective in preventing scenarios of the known impacts as well as suggesting strategies for their mitigation. In this study, by employing finite element analysis by the COMSOL Multiphysics software and reproducing, in a simplified way, a section of the tourist trail of the Pertosa-Auletta Cave (Italy), for the first time we provided a fact-finding survey of the airflow and the scattering and subsequent deposition of particles transported by tourists. Taking into account discontinuities in the pathway, the simulations rebuilt the possible natural airflow line, reproducing the particle movements induced by different tourist loads, whose high numbers increase the swirling movement of air masses, promoting a higher dispersion of particles, even in the remote cave areas. Performed simulations clearly indicated both the speed and direction followed by particles, as well as deposition sites, highlighting potential hotspots of damage, and demonstrating that the employed approach can be an excellent tool for planning the management of these extraordinary ecosystems, foretelling anthropogenic impacts, and supporting managers in decision-making processes.

Machine learning based thermal imaging damage detection in glass-epoxy composite materials

Machine learning (ML) based fatigue damage detection from thermal imaging in glass-epoxy composites is an important component of remote structural health monitoring used for safety assessment and optimization of composite structures and components. However, accurate characterization of fatigue damage hotspots in terms of size, shape, location, hysteretic heat, and local temperature deep inside the material using surface thermal images remains a challenge to date. This work aims at evaluating the theoretical accuracy level of hotspot characterization by training a ML model with artificially generated thermal images from 3D finite element models with increasing complexity. Modelling the fatigue damage as an intrinsic heat source allowed to significantly reduce the influence of thermal image noise and other uncertainties related to heat transfer. It is shown that ML can indeed accurately recover the heat influx, depth, and geometry of the heat source from the original thermal images of the composite materials with prediction accuracies in the range 85%–99%. The effect of training set size and image resolution on the prediction error is also presented. The findings reported in this work contribute to the advancement of accurate and efficient remote fatigue damage detection methods for fibre composite materials.

Human–elephant conflict and land cover change in Sri Lanka

Human–elephant conflict (HEC) is a key environmental issue in number of Asian countries, including Sri Lanka. Incidents of HEC have significantly increased in Sri Lanka between 1991 and 2018, with 1734 human deaths reported in this period (281% increase), 4837 elephant deaths (1172% increase), 1053 human injuries (140% increase) and more than 23,000 property damage reports (1406% increase). In this study we present a Sri Lanka wide analysis to explore the role of land use and land cover change (LULCC) in relation to HEC, using official government data and a land cover change dataset (1993–2018) recently developed by the authors using satellite imagery from the Landsat archive. We investigated rates of HEC over time and compared these to rates of LULCC over the same period. We also present spatial analytics of HEC and LULCC, as well as determining hotspots of HEC and LULCC using a kernel density estimator. Annual HEC incidents were found to broadly increase in line with land use change events (r = 0.43, p < 0.05). Human deaths, elephant deaths, human injuries and property damage hotspots show distinct spatial patterns: human deaths and injuries being more concentrated in the North West, Polonnaruwa and Ampara, wildlife regions; while elephant deaths are spread throughout the HEC region and property damage is high in the Central, Polonnaruwa Anuradhapura, North West, and Southern wildlife regions. We found a strong negative correlation between HEC location and distance to LULCC events. In total, 98% HEC occurred within 1 km of an area that experienced recent LULCC Since 2017, the primary HEC hotspots have shifted to the south and east of the country in concert with LULCC. These countrywide perspectives could help inform HEC mitigation strategies in Sri Lanka and other countries facing similar human-wildlife challenges.

2 Conducting and Communicating Environmental Impacts of Research: Forage Production, Soil Health, Sustainability

Abstract Sustainability research is complex and involves the evaluation of tradeoffs. Environmental, economic, and social aspects should ideally be considered. Even when one only pillar of sustainability is evaluated, results should be assessed within the broader context of overall sustainability. Environmental impacts from forage and livestock systems, as well as confined animal feeding operations, can be positive and negative, depending on the type of operation, environmental setting, weather episodes, and management conditions. These systems also have associated social and economic impacts. As in all agricultural management systems, there are likely to be hotspots of damage due to a particular management style combined with the right soil, landscape, and climatic conditions. Widespread, positive benefits of pasture-based management are possible when forage resources are utilized ideally with best grazing management principles leading to enhanced soil organic carbon storage, high water infiltration, efficient nutrient cycling, and minimal water and nutrient runoff into nearby water bodies. Research to quantify the environmental impacts of livestock management systems can be designed in detail on research stations to understand the direct implications of management differences on plant biomass production that captures atmospheric CO2, on carbon stored in soil organic matter, and on greenhouse gas emissions from ruminant livestock fed controlled diets. On-farm research is also valuable to characterize the impacts of a particular style of management across a diversity of environments using a system-level approach integrated across the farm. Communication strategies engaging major stakeholders from the livestock industry are key to deliver a consistent message to the general public. Perceived benefits, challenges, and opportunities must be science based, seeking opportunities to reduce negative impacts, and enhancing the positive ones. Messaging to the lay audience about environmental impacts can be focused on solutions from positive benefits or problems from negative effects, but should also address economic and social components.

Biallelic mutations in cancer genomes reveal local mutational determinants

The infinite sites model of molecular evolution posits that every position in the genome is mutated at most once1. By restricting the number of possible mutation histories, haplotypes and alleles, it forms a cornerstone of tumor phylogenetic analysis2 and is often implied when calling, phasing and interpreting variants3,4 or studying the mutational landscape as a whole5. Here we identify 18,295 biallelic mutations, where the same base is mutated independently on both parental copies, in 559 (21%) bulk sequencing samples from the Pan-Cancer Analysis of Whole Genomes study. Biallelic mutations reveal ultraviolet light damage hotspots at E26 transformation-specific (ETS) and nuclear factor of activated T cells (NFAT) binding sites, and hypermutable motifs in POLE-mutant and other cancers. We formulate recommendations for variant calling and provide frameworks to model and detect biallelic mutations. These results highlight the need for accurate models of mutation rates and tumor evolution, as well as their inference from sequencing data.

Sensor placement with optimal damage detectability for statistical damage detection

Damage diagnosis based on global structural vibrations critically depends on the sensor layout, in particular when a small number of sensors is used for large structures under unknown excitation. This paper proposes a sensor placement strategy that yields an optimized sensor layout with maximum damage detectability in selected structural components. The optimization criterion is based on the Fisher information, which quantifies the information that the damage-sensitive feature carries on the design parameters of structural components, such as material constants or cross-sectional values. It is evaluated using a finite element model, and considers the statistical uncertainties of the damage-sensitive feature. The methodology is shown for the stochastic subspace-based damage detection method, but can be applied to any damage-sensitive feature whose distribution can be approximated as Gaussian. It is suitable to find the optimal layout for a fixed number of sensors and to choose an appropriate number of sensors. Since the Fisher information is defined component-wise, the sensor layout can be tuned to become more sensitive to damage in local structural components, such as damage hotspots, non-inspectable components, or components that are critical for the safety and serviceability of the structure. For proof of concept, the sensor layout on a laboratory beam is optimized based on numerical simulations, and it is showcased that the optimal sensor layout leads to the highest damage detectability for experimental data.

Multiscale temperature-dependent ceramic matrix composite damage model with thermal residual stresses and manufacturing-induced damage

This work presents a multiscale thermomechanical simulation framework to capture the temperature-dependent damage behavior of woven ceramic matrix composites (CMCs). The framework consists of cooldown simulations, which capture a realistic material initial state, and subsequent mechanical loading simulations to capture temperature-dependent nonlinear stress–strain behavior. The cooldown simulations result in a realistic material initial state with thermal residual stresses and damage hotspots that occur due to constituent property mismatch and post-manufacturing cooldown. A fracture mechanics-informed thermomechanical progressive damage model is extended to capture the manufacturing-induced damage that occurs because of the high thermal residual stresses and to simulate the mechanical response of two-dimensional (2D) plain weave carbon (C) fiber, silicon carbide (SiC) matrix (C/SiC) CMCs at temperatures ranging from room temperature (RT) to 1200 °C. A combination of temperature-dependent material properties and damage model parameters are included in the model to simulate the effects of temperature on deformation and damage behavior. Model calibration was conducted using quasi-static tensile experimental data from the literature for RT, 700 °C, and 1200 °C and the nonlinear, temperature-dependent predictive capabilities of the reformulated model are demonstrated for 1000 °C. The model is also applied to simulate the temperature-dependent thermomechanical response of a 2D woven five harness satin (5HS) SiC/SiC CMC at RT and 1200 °C and shows excellent agreement with experiments.

Brain DNA damage hotspots.

Unexpected high levels of recurrent DNA single-strand break repair were detected in enhancer regions of post-mitotic neurons.

Study on fatigue damage optimization mechanism of deepwater lazy wave risers based on multiple waveform serial arrangement

Lazy wave risers can mitigate the kinematic coupling between floating structure and riser touchdown zone (TDZ), so as to improve its fatigue life. However, due to the introduction of buoyancy blocks, nonlinear dynamic response and fatigue damage prediction will be more complicated. In this paper, a concept of “multiple lazy wave riser”, namely, deepwater lazy wave riser configuration based on multiple waveform serial arrangement, is proposed. According to the improved slender rod theory and equivalent optimization of buoyancy sections, three riser configurations with different heave excitations and currents are structured. The dynamic response characteristics, excitation motion transfer paths and fatigue damage hotspots are well compared. Moreover, the influence law of riser fatigue damage and decoupling mechanism of riser TDZ movements are also explored. The results indicate that: by constructing lazy wave configuration, deepwater risers based on lazy wave configuration can attenuate the kinematic coupled response of floating structure in the first sag bend section, and reduce the normal motion response in the riser TDZ, so as to optimize its anti-fatigue performance; compared with lazy wave riser, multiple lazy wave riser has some competitiveness in the anti-fatigue performance for the riser TDZ. For flexible risers, this competitiveness will become even more prominent.

A streamlined solution for processing, elucidating and quality control of cyclobutane pyrimidine dimer sequencing data.

UV radiation may lead to melanoma and nonmelanoma skin cancers by causing helix-distorting DNA damage such as cyclobutane pyrimidine dimers (CPDs). These DNA lesions, if located in important genes and not repaired promptly, are mutagenic and may eventually result in carcinogenesis. Examining CPD formation and repair processes across the genome can shed light on the mutagenesis mechanisms associated with UV damage in relevant cancers. We recently developed CPD-Seq, a high-throughput and single-nucleotide resolution sequencing technique that can specifically capture UV-induced CPD lesions across the genome. This novel technique has been increasingly used in studies of UV damage and can be adapted to sequence other clinically relevant DNA lesions. Although the library preparation protocol has been established, a systematic protocol to analyze CPD-Seq data has not been described yet. To streamline the various general or specific analysis steps, we developed a protocol named CPDSeqer to assist researchers with CPD-Seq data processing. CPDSeqer can accommodate both a single- and multiple-sample experimental design, and it allows both genome-wide analyses and regional scrutiny (such as of suspected UV damage hotspots). The runtime of CPDSeqer scales with raw data size and takes roughly 4 h per sample with the possibility of acceleration by parallel computing. Various guiding graphics are generated to help diagnose the performance of the experiment and inform regional enrichment of CPD formation. UV damage comparison analyses are set forth in three analysis scenarios, and the resulting HTML pages report damage directional trends and statistical significance. CPDSeqer can be accessed at https://github.com/shengqh/cpdseqer .