Simplified Data Research Articles

Leveraging SAP BW4HANA for Scalable Data Warehousing in Large Enterprises

In the era of big data, large enterprises face the challenge of efficiently managing and analyzing vast volumes of information to derive actionable insights. SAP BW/4HANA emerges as a powerful solution that facilitates scalable data warehousing, enabling organizations to harness their data for strategic decision-making. This paper explores the key features and advantages of SAP BW/4HANA, highlighting its capability to integrate real-time data processing, advanced analytics, and enhanced data modeling. By leveraging the in-memory computing power of HANA, enterprises can significantly reduce data processing times and improve query performance, thus enabling faster insights and informed decision-making. The paper further discusses the architectural enhancements that BW/4HANA offers, such as simplified data flows, streamlined data integration, and the use of modern user interfaces, which collectively contribute to a more agile data warehousing environment. Moreover, the flexibility of deploying BW/4HANA on-premise or in the cloud allows organizations to scale their data warehousing capabilities in alignment with their growth strategies. Through case studies and real-world examples, this research underscores the effectiveness of SAP BW/4HANA in addressing the complexities of data warehousing in large enterprises, emphasizing its role in fostering a data-driven culture. Ultimately, this paper posits that leveraging SAP BW/4HANA can significantly enhance operational efficiency and support organizations in achieving their strategic objectives in an increasingly data-centric business landscape.

Investigating the Surface Damage to Fuzhou’s Ancient Houses (Gu-Cuo) Using a Non-Destructive Testing Method Constructed via Machine Learning

As an important part of traditional Chinese architecture, Fuzhou’s ancient houses have unique cultural and historical value. However, over time, environmental factors such as efflorescence and plant growth have caused surface damage to their gray brick walls, leading to a decline in the quality of the buildings’ structure and even posing a threat to the buildings’ safety. Traditional damage detection methods mainly rely on manual labor, which is inefficient and consumes a lot of human resources. In addition, traditional non-destructive detection methods, such as infrared imaging and laser scanning, often face difficulty in accurately identifying specific types of damage, such as efflorescence and plant growth, on the surface of gray bricks and are easily hampered by diverse surface features. This study uses the YOLOv8 machine learning model for the automated detection of two common types of damage to the gray brick walls of Fuzhou’s ancient houses: efflorescence and plant growth. We establish an efficient gray brick surface damage detection model through dataset collection and annotation, experimental parameter optimization, model evaluation, and analysis. The research results reveal the following. (1) Reasonable hyperparameter settings and model-assisted annotation significantly improve the detection accuracy and stability. (2) The model’s average precision (AP) is improved from 0.30 to 0.90, demonstrating good robustness in detecting complex backgrounds and high-resolution real-life images. The F1 value of the model’s gray brick detection efficiency is improved (classification model performance index) from 0.22 to 0.77. (3) The model’s ability to recognize the damage details of gray bricks under high-resolution conditions is significantly enhanced, demonstrating its ability to cope with complex environments. (4) The simplified data enhancement strategy effectively reduces the feature extraction interference and enhances the model’s adaptability in different environments.

Rapid estimation of carbon footprints in agrochemical development: correlation of process mass intensity with CO2 emissions.

The agricultural sector faces a challenge in balancing increasing food demand while minimizing environmental impacts. Crop protection products are crucial for achieving high crop yields and ensuring food security. However, life cycle assessment (LCA), the standard framework for evaluating environmental impact, is time-consuming and costly, especially during early product development. To address this, a novel tool correlating process mass intensity (PMI) with greenhouse gas (GHG) emissions has been developed as a streamlined alternative. A strong linear correlation (R2 = 0.95) was identified between PMI and product GHG emissions, enabling rapid carbon footprint estimation using simplified PMI data. The model was validated using 13 small molecule active ingredients (AIs), showing a mean absolute error (MAE) of 55 g CO₂/kg AI and a root mean square error (RMSE) of 64 kg CO₂/kg AI. Residual analysis demonstrated random distribution, suggesting reliable predictions. The PMI-based tool provides rapid, accurate estimates of product carbon footprint (PCF), supporting early-stage decision-making in research and development for agrochemical processes. Its simplicity makes it applicable across various chemical sectors and valuable for sustainability efforts. © 2024 Society of Chemical Industry.

Development and Initial Evaluation of a Digital Phenotype Collection System for Adolescents: Proof-of-Concept Study.

The growing concern on adolescent mental health calls for proactive early detection and intervention strategies. There is a recognition of the link between digital phenotypes and mental health, drawing attention to their potential use. However, the process of collecting digital phenotype data presents challenges despite its promising prospects. This study aims to develop and validate system concepts for collecting adolescent digital phenotypes that effectively manage inherent challenges in the process. In a formative investigation (N=34), we observed adolescent self-recording behaviors and conducted interviews to develop design goals. These goals were then translated into system concepts, which included planners resembling interfaces, simplified data input with tags, visual reports on behaviors and moods, and supportive ecological momentary assessment (EMA) prompts. A proof-of-concept study was conducted over 2 weeks (n=16), using tools that simulated the concepts to record daily activities and complete EMA surveys. The effectiveness of the system was evaluated through semistructured interviews, supplemented by an analysis of the frequency of records and responses. The interview findings revealed overall satisfaction with the system concepts, emphasizing strong support for self-recording. Participants consistently maintained daily records throughout the study period, with no missing data. They particularly valued the recording procedures that aligned well with their self-recording goal of time management, facilitated by the interface design and simplified recording procedures. Visualizations during recording and subsequent report viewing further enhanced engagement by identifying missing data and encouraging deeper self-reflection. The average EMA compliance reached 72%, attributed to a design that faithfully reflected adolescents' lives, with surveys scheduled at convenient times and supportive messages tailored to their daily routines. The high compliance rates observed and positive feedback from participants underscore the potential of our approach in addressing the challenges of collecting digital phenotypes among adolescents. Integrating observations of adolescents' recording behavior into the design process proved to be beneficial for developing an effective and highly compliant digital phenotype collection system.

Gated recurrent units for modelling time series of soil temperature and moisture: An assessment of performance and process reflectivity

Soil temperature and moisture are important variables controlling ecological processes, but continuous high-resolution data are rarely available. Therefore, we used the correlation with widely accessible meteorological variables, including air temperature and precipitation, to develop models that predict time series of soil temperature and moisture. To model high-resolution time series, predictor and target variables had a temporal resolution of 1 h. We tested the applicability of Gated Recurrent Units with time series from one exemplary site. The models showed a high predictability on the four years test set with a mean absolute error of 0.87°C for soil temperature and 3.20% volumetric water content for soil moisture. We further investigated the plausibility of the models by passing simplified synthetic data to the trained models and thereby proved their ability to reflect known processes. Finally, we showed the potential to apply the models to other sites and soil depths using transfer learning.

Mobile Devices in Forest Mensuration: A Review of Technologies and Methods in Single Tree Measurements

Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single tree-level information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMATM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection.

A Practical Solver for Scalar Data Topological Simplification.

This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field f and a set of "signal" persistence pairs to maintain, our approaches produces an output field g that is close to f and which optimizes (i) the cancellation of "non-signal" pairs, while (ii) preserving the "signal" pairs. In contrast to pre-existing simplification algorithms, our approach is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our approach enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. We also show how our approach can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes.

Disabled Individuals’ Negative Perceptions of Mobile Health Applications: Design Guidance for HFE Practitioners

Disabled individuals experience significant inequities in healthcare access, healthcare quality, social determinants of health, and health outcomes compared to non-disabled individuals. By including people with disabilities in the co-design of mobile health (MHealth) applications, we gain valuable insights to improve alignment between users’ needs and MHealth technologies. Through a secondary analysis of mixed-methods data, this paper investigates negative perceptions of disabled individuals regarding MHealth applications to inform MHealth application design guidance for human factors practitioners. Fifty participants with physical, cognitive, sensory, and/or mental health-related disabilities interacted with three MHealth applications during task analysis sessions, revealing concerns about data security, data privacy, and unnecessary patient work. Recommendations include clear articulation of security measures, clarifications that patient profiles are not publicly available, and simplified data entry processes. Implementing these recommendations may enhance the accessibility and usefulness of MHealth applications, promoting healthcare access for a broader range of individuals.

3D textured mesh scene data fusion method considering multiple scales and resolutions

ABSTRACT The fusion of 3D textured mesh scene data can effectively and seamlessly integrate multiple spatially related 3D geographical scene models into larger and more comprehensive datasets. Existing methods usually have different degrees of texture distortion and unreduced texture problems in the splicing area, and these methods do not consider the current situation of Level of Detail(LoD) data organisation in the 3D textured mesh model. We propose a 3D textured mesh scene data fusion method considering multiple scales and resolutions. First, seamless extraction of splicing areas was achieved based on spatial relationship constraints and orthogonal inverse projection principles. Second, the sparse scene structure concept was employed to generate sparse summary scene images. Finally, a dual simplification operator for both mesh and texture was proposed to construct multi-resolution 3D textured mesh models, simultaneously reconstructing triangular facets and textures within the splicing regions. We used multiple 3D textured mesh data constructed from aerial obliques to conduct data fusion experimental verification. The results indicate that our method exhibits minimal texture distortion during grid reconstruction in splicing areas and model simplification, effectively reducing the amount of simplified texture data. Additionally, our method adeptly addresses multilevel detailed 3D textured mesh models, demonstrating good feasibility and advancement.

Compact Class-conditional Attribute Category Clustering: Amino Acid Grouping for Enhanced HIV-1 Protease Cleavage Classification.

Categorical attributes are common in many classification tasks, presenting certain challenges as the number of categories grows. This situation can affect data handling, negatively impacting the building time of models, their complexity and, ultimately, their classification performance. In order to mitigate these issues, this research proposes a novel preprocessing technique for grouping attribute categories in classification datasets. This approach combines the exact representation of the association between categorical values in a Euclidean space, clustering methods and attribute quality metrics to group similar attribute categories based on their contribution to the classification task. To estimate its effectiveness, the proposal is evaluated within the context of HIV-1 protease cleavage site prediction, where each attribute represents an amino acid that can take multiple possible values. The results obtained on HIV-1 real-world datasets show a significant reduction in the number of categories per attribute, with an average reduction percentage ranging from 74% to 81%. This reduction leads to simplified data representations and improved classification performances compared to not preprocessing. Specifically, improvements of up to 0.07 in accuracy and 0.19 in geometric mean are observed across different datasets and classification algorithms. Additionally, extensive simulations on synthetic datasets with varied characteristics are carried out, providing consistent and reliable results that validate the robustness of the proposal. These findings highlight the capability of the developed method to enhance cleavage prediction, which could potentially contribute to understanding viral processes and developing targeted therapeutic strategies.

OnetoMap Meta-Data: Healthcare Analytics Through Research.

Big Data has revolutionized healthcare research through the three Vs: volume, veracity, and variety. This study introduces the OnetoMap meta-data repository, a centralized inventory developed in collaboration with the University of South Florida's Department of Surgery. The repository offers extensive details about each database, including its primary purpose, available variables, and examples of high-impact research utilizing these databases. It aims to create a centralized inventory, enabling researchers to locate and link relevant datasets efficiently. Each dataset is described using standardized criteria to ensure clarity and usability, such as data type, source, collection methods, and potential linkages to other datasets. Results: Currently, the OnetoMap repository contains descriptions of 49 datasets, with ongoing updates to include new datasets and additional data years. These datasets include a range of data types, including cross-sectional and longitudinal, gathered through claims, registries, electronic health records, and surveys. The repository is hosted on GitHub, enabling version control, collaboration, and open access. Effective search functionalities and descriptive categorization enhance the findability of datasets. The data repository includes comprehensive records of patient health statuses, socioeconomic profiles, hospital structures, and physician practices, enabling nuanced interventions and addressing complex healthcare needs. It also promotes interdisciplinary research and accelerates novel discoveries by providing a centralized source of diverse data and facilitating collaboration among research teams. The OnetoMap meta-data repository represents a significant advancement in healthcare research by providing a centralized, detailed, and easily accessible repository of clinical research databases. Future directions include implementing automatic annual updates of datasets, exploring automatic dataset linkage, providing monthly updates on published research, creating a user chat space for enhanced collaboration, and developing code applets for simplified data analysis. These efforts will ensure that the repository remains current, functional, and accessible, ultimately facilitating new discoveries and insights in healthcare outcomes research.

Genome-Wide Association and RNA-Seq Analyses Reveal a Potential Candidate Gene Related to Oil Content in Soybean Seeds.

Soybean is a crucial crop globally, serving as a significant source of unsaturated fatty acids and protein in the human diet. However, further enhancements are required for the related genes that regulate soybean oil synthesis. In this study, 155 soybean germplasms were cultivated under three different environmental conditions, followed by phenotypic identification and genome-wide association analysis using simplified sequencing data. Genome-wide association analysis was performed using SLAF-seq data. A total of 36 QTLs were significantly associated with oil content (-log10(p) > 3). Out of the 36 QTLs associated with oil content, 27 exhibited genetic overlap with previously reported QTLs related to oil traits. Further transcriptome sequencing was performed on extreme high-low oil soybean varieties. Combined with transcriptome expression data, 22 candidate genes were identified (|log2FC| ≥ 3). Further haplotype analysis of the potential candidate genes showed that three potential candidate genes had excellent haplotypes, including Glyma.03G186200, Glyma.09G099500, and Glyma.18G248900. The identified loci harboring beneficial alleles and candidate genes likely contribute significantly to the molecular network's underlying marker-assisted selection (MAS) and oil content.

Riemannian Geodesic Discriminant Analysis–Minimum Riemannian Mean Distance: A Robust and Effective Method Leveraging a Symmetric Positive Definite Manifold and Discriminant Algorithm for Image Set Classification

This study introduces a novel method for classifying sets of images, called Riemannian geodesic discriminant analysis–minimum Riemannian mean distance (RGDA-MRMD). This method first converts image data into symmetric positive definite (SPD) matrices, which capture important features related to the variability within the data. These SPD matrices are then mapped onto simpler, flat spaces (tangent spaces) using a mathematical tool called the logarithm operator, which helps to reduce their complexity and dimensionality. Subsequently, regularized local Fisher discriminant analysis (RLFDA) is employed to refine these simplified data points on the tangent plane, focusing on local data structures to optimize the distances between the points and prevent overfitting. The optimized points are then transformed back into a complex, curved space (SPD manifold) using the exponential operator to enhance robustness. Finally, classification is performed using the minimum Riemannian mean distance (MRMD) algorithm, which assigns each data point to the class with the closest mean in the Riemannian space. Through experiments on the ETH-80 (Eidgenössische Technische Hochschule Zürich-80 object category), AFEW (acted facial expressions in the wild), and FPHA (first-person hand action) datasets, the proposed method demonstrates superior performance, with accuracy scores of 97.50%, 37.27%, and 88.47%, respectively. It outperforms all the comparison methods, effectively preserving the unique topological structure of the SPD matrices and significantly boosting image set classification accuracy.

Multi-fidelity design framework integrating compositional kernels to facilitate early-stage design exploration of complex systems

Abstract Early-stage design of complex systems is considered by many to be one of the most critical design phases because that is where many of the major decisions are made. The design process typically starts with low-fidelity tools, such as simplified models and reference data, but these prove insufficient for novel designs, necessitating the introduction of high-fidelity tools. This challenge can be tackled through the incorporation of multi-fidelity models. The application of MF models in the context of design optimization problems represents a developing area of research. This study proposes incorporating compositional kernels into the autoregressive scheme (AR1) of Multi-Fidelity Gaussian Processes, aiming to enhance the predictive accuracy and reduce uncertainty in design space estimation. The effectiveness of this method is assessed by applying it to 5 benchmark problems and a simplified design scenario of a cantilever beam. The results demonstrate significant improvement in the prediction accuracy and a reduction in the prediction uncertainty. Additionally, the paper offers a critical reflection on scaling up the method and its applicability in early-stage design of complex engineering systems, providing valuable insights into its practical implementation and potential benefits.

Impact of Color Space and Color Resolution on Vehicle Recognition Models.

In this study, we analyze both linear and nonlinear color mappings by training on versions of a curated dataset collected in a controlled campus environment. We experiment with color space and color resolution to assess model performance in vehicle recognition tasks. Color encodings can be designed in principle to highlight certain vehicle characteristics or compensate for lighting differences when assessing potential matches to previously encountered objects. The dataset used in this work includes imagery gathered under diverse environmental conditions, including daytime and nighttime lighting. Experimental results inform expectations for possible improvements with automatic color space selection through feature learning. Moreover, we find there is only a gradual decrease in model performance with degraded color resolution, which suggests the need for simplified data collection and processing. By focusing on the most critical features, we could see improved model generalization and robustness, as the model becomes less prone to overfitting to noise or irrelevant details in the data. Such a reduction in resolution will lower computational complexity, leading to quicker training and inference times.

Enhancing Kurdistan's manufacturing companies' sustainable waste management: A norm activation approach to green accounting, CSR, and environmental auditing oversight

The significance of accurate energy production prediction cannot be overstated, especially in the context of achieving carbon neutrality and balancing traditional and clean energy sources. Unlike conventional models with simplified assumptions or limited data inputs hindering energy usage optimization, waste reduction and efficient resource allocation, we introduced a novel structural equation modelling approach to eight manufacturing industries' sustainable waste management practices (SWMPs) in Iraq. This comprehensive analysis, conducted with Smart PLS software on 375 responses aims to enhance energy production predictions’ accuracy and support sustainability goals contribute to achieving carbon neutrality goals and promote a balanced energy mix that supports sustainability and environmental stewardship. The findings reveal noteworthy insights: notably, chemical manufacturing companies exhibit a substantial advantage from green accounting practices, witnessing a 78.1 % and 45.8 % improvement in environmental auditing oversight and SWMPs, respectively, compared to other manufacturing sectors. Compared to conventional grey models, our model demonstrates that a 1-unit improvement in CSR enhances environmental auditing oversight effectiveness by 33.4 % and sustainable waste management by 56.9 % across industries. By leveraging these data-driven insights and innovative approaches, we can drive positive change towards a more sustainable and resilient energy future, collectively contributing to a more resilient, efficient, and sustainable energy ecosystem that benefits societies, economies, and the environment. The heightened accuracy of energy production prediction facilitated by our novel model empowers stakeholders at regional and global levels to make informed decisions, mitigate risks, support policy development, achieve sustainability goals, formulate effective policies and foster collaboration.

Depth over RGB: automatic evaluation of open surgery skills using depth camera

PurposeIn this paper, we present a novel approach to the automatic evaluation of open surgery skills using depth cameras. This work is intended to show that depth cameras achieve similar results to RGB cameras, which is the common method in the automatic evaluation of open surgery skills. Moreover, depth cameras offer advantages such as robustness to lighting variations, camera positioning, simplified data compression, and enhanced privacy, making them a promising alternative to RGB cameras.MethodsExperts and novice surgeons completed two simulators of open suturing. We focused on hand and tool detection and action segmentation in suturing procedures. YOLOv8 was used for tool detection in RGB and depth videos. Furthermore, UVAST and MSTCN++ were used for action segmentation. Our study includes the collection and annotation of a dataset recorded with Azure Kinect.ResultsWe demonstrated that using depth cameras in object detection and action segmentation achieves comparable results to RGB cameras. Furthermore, we analyzed 3D hand path length, revealing significant differences between experts and novice surgeons, emphasizing the potential of depth cameras in capturing surgical skills. We also investigated the influence of camera angles on measurement accuracy, highlighting the advantages of 3D cameras in providing a more accurate representation of hand movements.ConclusionOur research contributes to advancing the field of surgical skill assessment by leveraging depth cameras for more reliable and privacy evaluations. The findings suggest that depth cameras can be valuable in assessing surgical skills and provide a foundation for future research in this area.

A time-saving fault diagnosis using simplified fast GAN and triple-type data transfer learning

Existing intelligent fault diagnosis approaches demand substantial data for training diagnostic models. However, factors such as the inherent characteristics of bearings, operating conditions, and privacy security make collecting comprehensive fault-bearing data very difficult. Although generating synthetic data through generative adversarial networks (GANs) is feasible, the data generation of GANs is a time-consuming process. To address these challenges, a fault diagnosis framework based on GAN and deep transfer learning (DTL) is proposed, termed the simplified fast GAN triple-type data transfer learning (SFGAN-TDTL) method. Initially, an SFGAN is proposed as a replacement for traditional GANs. The time-frequency image data generated by SFGAN serve to augment the training dataset, offering faster and higher-quality data generation compared to traditional GANs. To further reduce time consumption for GAN-based methods, the TDTL method is proposed. Differing from DTL, which utilizes synthetic data to construct a pre-trained model and conducts targeted fine-tuning with real data, TDTL employs open-source data, synthetic data, and real data to fill the weights of the task-insensitive layer, task-sensitive layer, and fully connected layer, respectively. Numerical results demonstrate that SFGAN-TDTL maintains higher diagnostic accuracy while significantly reducing time consumption.

Abstract 1670: Mechanistic insight into mTOR signaling identifies RNA splicing machinery as a potential therapeutic target for cancer

Abstract Aberrant signal transduction regulating cell growth and proliferation is a hallmark of many cancers. Activation of mTOR signaling is common and is an attractive therapeutic target with mechanistically different mTOR inhibitors in clinical use. One consequence of exposure to the mTOR inhibitor rapamycin is an alteration of gene expression through a global increase in alternative exon inclusion (Cheng et al. 2022, doi.org/10.3390/ijms232012416). To examine the mechanisms underlying this effect, we utilized the TempO-Seq targeted sequencing platform to profile both the whole transcriptome and a selection of alternatively spliced RNA targets in the rapamycin sensitive and resistant breast cancer cell lines MCF-7 and MDA MB 231 following exposure to 13 mTOR inhibitors, some closely related and some from distinct chemical classes. Recent publications have demonstrated that high quality dose response data can be obtained using this sequencing platform (Harrill et al. 2021, doi: 10.1093/toxsci/kfab009; Ramaiahgari et al. 2019, doi: 10.1093/toxsci/kfz065) and TempO-Seq has been widely adopted in toxicology for deriving compound-specific expression signatures to identify mechanisms of action and long-term toxicity. Treated cells were also profiled using TempO-LINC, a novel high-throughput single-cell assay based on the same platform. We observed that certain mTOR inhibitors induced both common and cell-specific differential expression as well as widespread alternative exon inclusion, as expected. Intriguingly, transcripts with the most significant changes in splicing upon treatment were those encoding members of the SR family of splicing factors, often overexpressed or amplified in breast cancers (Park, et al. 2019, doi: 10.1016/j.celrep.2019.10.110). This is significant because SR protein expression is autoregulated by alternative inclusion of an exon containing a premature stop codon in a feedback loop leading to nonsense-mediated decay (NMD) and lowered protein levels. Indeed, NMD is known to be inhibited by rapamycin (Martinez-Nunez, et al. 2017, doi: 10.1093/nar/gkw1109), which would lead to the observed accumulation of these transcripts. Thus, monitoring a targeted selection of alternative splicing events and regulators revealed mTOR regulation of mRNA isoform abundance consistent with regulation of the NMD RNA turnover process and with overexpression of SR proteins in breast cancers. Importantly, targeted bulk and single-cell TempO-Seq assays enable efficient sequencing and simplified data analysis without the expense and bioinformatic burden of RNAseq, making them particularly well-suited for drug discovery. These results support the use of this approach for further characterization of cell-specific effects of mTOR inhibitors and their mechanisms of action. Citation Format: Gaelle Rondeau, Megan Opichka, Kevin White, Joanne M. Yeakley, Dennis J. Eastburn, Bruce Seligmann. Mechanistic insight into mTOR signaling identifies RNA splicing machinery as a potential therapeutic target for cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1670.

UAV-Based Wetland Monitoring: Multispectral and Lidar Fusion with Random Forest Classification

As sea levels rise and temperatures increase, vegetation communities in tropical and sub-tropical coastal areas will be stressed; some will migrate northward and inland. The transition from coastal marshes and scrub–shrubs to woody mangroves is a fundamental change to coastal community structure and species composition. However, this transition will likely be episodic, complicating monitoring efforts, as mangrove advances are countered by dieback from increasingly impactful storms. Coastal habitat monitoring has traditionally been conducted through satellite and ground-based surveys. Here we investigate the use of UAV-LiDAR (unoccupied aerial vehicle–light detection and ranging) and multispectral photogrammetry to study a Florida coastal wetland. These data have higher resolution than satellite-derived data and are cheaper and faster to collect compared to crewed aircraft or ground surveys. We detected significant canopy change in the period between our survey (2020–2022) and a previous survey (2015), including loss at the scale of individual buttonwood trees (Conocarpus erectus), a woody mangrove associate. The UAV-derived data were collected to investigate the utility of simplified processing and data inputs for habitat classification and were validated with standard metrics and additional ground truth. UAV surveys combined with machine learning can streamline coastal habitat monitoring, facilitating repeat surveys to assess the effects of climate change and other change agents.