Set Of Samples Research Articles

Smart City Aquaculture: AI-Driven Fry Sorting and Identification Model

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities.

Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data

Log anomaly detection, critical in identifying system failures and preempting security breaches, finds irregular patterns within large volumes of log data. Modern log anomaly detectors rely on training deep learning models on clean anomaly-free log data. However, such clean log data requires expensive and tedious human labeling. In this paper, we thus propose a robust log anomaly detection framework, PlutoNOSPACE, that automatically selects a clean representative sample subset of the polluted log sequence data to train a Transformer-based anomaly detection model. Pluto features three innovations. First, due to localized concentrations of anomalies inherent in the embedding space of log data, Pluto partitions the sequence embedding space generated by the model into regions that then allow it to identify and discard regions that are highly polluted by our pollution level estimation scheme, based on our pollution quantification via Gaussian mixture modeling. Second, for the remaining more slightly polluted regions, we select samples that maximally purify the eigenvector spectrum, which can be transformed into the NP-hard facility location problem; allowing us to leverage its greedy solution with a (1-(1/e)) approximation guarantee in optimality. Third, by iteratively alternating between the above subset selection, a model re-training on the latest subset, and a subset filtering using dynamic training artifacts generated by the latest model, the data selected is progressively refined. The final sample set is used to retrain the final anomaly detection model. Our experiments on four real-world log benchmark datasets demonstrate that by retaining 77.7% (BGL) to 96.6% (ThunderBird) of the normal sequences while effectively removing 90.3% (BGL) to 100.0% (ThunderBird, HDFS) of the anomalies, Pluto provides a significant absolute F-1 improvement up to 68.86% (2.16% → 71.02%) compared to the state-of-the-art sample selection methods. The implementation of this work is available at https://github.com/LeiMa0324/Pluto-SIGMOD25.

Modified Low-Field NMR Method for Improved Pore Space Analysis in Tight Fe-Bearing Siliciclastic and Extrusive Rocks

Abstract Understanding the filtration and storage properties of tight reservoirs is crucial for efficient resource exploitation, particularly in unconventional formations. This study presents two low-field nuclear magnetic resonance (LF-NMR) techniques: standard cut-off and modified differential approaches combined with mercury injection capillary pressure (MICP) and X-ray diffraction (XRD) studies to evaluate porosity and pore size distribution (PSD) in such formations. The differential technique involves subtracting the dry sample signal from a 100% water-saturated one, allowing the chemically bound water compound to be eliminated and facilitating PSD analysis. Through the application of the percolation theory, we established a power–law relationship between LF-NMR transverse relaxation time (T2) and MICP pore-throat diameter, enabling the derivation of PSD and pseudo capillary pressure curves. Our methodology was validated on a sample set representing tight sandstones, conglomerates, and extrusive rocks with high clay and iron mineral content, demonstrating the superior accuracy of the modified differential method in estimating effective porosity and absolute PSD in comparison with the standard approach. While the use of the percolation theory in PSD conversion was successful for rocks with unimodal distributions, it often failed for rocks with larger voids. The study also revealed that the relationship between the LF-NMR transverse relaxation times and MICP pore sizes is both nonlinear and challenging to describe with a universal equation, especially in the presence of para- and ferro-magnetic elements in the rock matrix. Despite obstacles to the complete elimination of the influence of these minerals on the T2 distribution, employing the modified differential LF-NMR method significantly mitigated this effect and offered a precise and noninvasive way of characterizing the petrophysical properties of tight reservoir rocks. Consequently, our studies offer a significant step toward a more precise assessment of pore structures in unconventional reservoirs that could be translated into more efficient strategies for locating geothermal heat and hydrocarbon resources.

Mangrove mapping in China using Gaussian mixture model with a novel mangrove index (SSMI) derived from optical and SAR imagery

As an important shoreline vegetation and highly productive ecosystem, mangroves play an essential role in the protection of coastlines and ecological diversity. Accurate mapping of the spatial distribution of mangroves is crucial for the protection and restoration of mangrove ecosystems. Supervised classification methods rely on large sample sets and complex classifiers and traditional thresholding methods that require empirical thresholds, given the problems that limit the feasibility and stability of existing mangrove identification and mapping methods on large scales. Thus, this paper develops a novel mangrove index (spectral and SAR mangrove index, SSMI) and Gaussian mixture model (GMM) mangrove mapping method, which does not require training samples and can automatically and accurately map mangrove boundaries by utilizing only single-scene Sentinel-1 and single-scene Sentinel-2 images from the same time period. The SSMI capitalizes on the fact that mangroves are differentiated from other land cover types in terms of optical characteristics (greenness and moisture) and backscattering coefficients of SAR images and ultimately highlights mangrove forest information through the product of three expressions (f(S) = red egde/SWIR1, f(B) = 1/(1 + e-VH), f(W)=(NIR-SWIR1)/(NIR+SWIR1)). The proposed SSMI was tested in six typical mangrove distribution areas in China where climatic conditions and mangrove species vary widely. The results indicated that the SSMI was more capable of mapping mangrove forests than the other mangrove indices (CMRI, NDMI, MVI, and MI), with overall accuracys (OA) higher than 0.90 and F1 scores as high as 0.93 for the other five areas except for the Maowei Gulf (S5). Moreover, the mangrove maps generated by the SSMI were highly consistent with the reference maps (HGMF_2020、LASAC_2018 and IMMA). In addition, the SSMI achieves stable performance, as shown by the mapping results of the other two classification methods (K-means and Otsu’s algorithm). Mangrove mapping in six typical mangrove distribution areas in China for five consecutive years (2019–2023) and experiments in three Southeast Asian countries with major mangrove distributions (Thailand, Vietnam, and Indonesia) demonstrated that the SSMIs constructed in this paper are highly stable across time and space. The SSMI proposed in this paper does not require reference samples or predefined parameters; thus, it has great flexibility and applicability in mapping mangroves on a large scale, especially in cloudy areas.

Facilitating integrative and personalized oncology omics analysis with UCSCXenaShiny.

The continuous generation of multi-omics and phenotype data is propelling advancements in precision oncology. UCSCXenaShiny was developed as an interactive tool for exploring thousands of cancer datasets available on UCSC Xena. However, its capacity for comprehensive and personalized pan-cancer data analysis is being challenged by the growing demands. Here, we introduce UCSCXenaShiny v2, a milestone update through a variety of improvements. Firstly, by integrating multidimensional data and implementing adaptable sample settings, we create a suite of robust TPC (TCGA, PCAWG, CCLE) analysis pipelines. These pipelines empower users to conduct in-depth analyses of correlation, comparison, and survival in three modes: Individual, Pan-cancer and Batch screen. Additionally, the tool includes download interfaces that enable users to access diverse data and outcomes, several features also facilitate the joint analysis of drug sensitivity and multi-omics of cancer cell lines. UCSCXenaShiny v2 is an open-source R package and a web application, freely accessible at https://github.com/openbiox/UCSCXenaShiny .

Expression assays of selected lncRNAs in non-functioning pituitary adenomas

PurposeNon-functioning pituitary adenomas (NFPAs) are a group of these neoplasms originated from the adenohypophyse and do not show evidence of hormonal oversecretion. However, different genes and lncRNAs have been found to be dysregulated in these samples.Material and methodsIn this study, in order to identify novel biomarkers, a set of regulatory lncRNAs for the two important hub genes, i.e. STAT3 and EGFR were selected and subjected to experimental investigation. These lncRNAs were EGFR-AS1 for the EGFR gene, and LINC00240, FALEC and SNHG12 for the STAT3 gene.ResultsAll studied genes were down-regulated in NFPA samples compared with normal tissues adjacent to the tumors (NTATs), except for FALEC whose expression was not different between these two sets of samples. EGFR was the most significantly down-regulated gene in NFPAs (Expression ratio (95% CI) = 0.009 (0.002–0.04), P value < 0.0001). ROC curve analyses proposed that the expression levels of SNHG12, EGFR, EGFR-AS1 and LINC00240 can be used to distinguish NFPAs from NTATs with AUC values of 0.88, 0.83, 0.7 and 0.66, respectively. Spearman's correlation analyses showed significant correlations between FALEC and EGFR-AS1 in both types of tissues, and between FALEC and EGFR in NFPAs. Moreover, expression of LINC00240 was correlated with EGFR-AS1, FALEC and SNHG12 in NFPAs.ConclusionTaken together, EGFR and STAT3-related lncRNAs may be involved in the pathogenesis of NFPA.

Selecting the Best Pharmacokinetic Models for a Priori Model-Informed Precision Dosing with Model Ensembling.

When utilizing population pharmacokinetic (popPK) models for a priori dosage individualization, selecting the best model is crucial to obtain adequate doses. We developed and evaluated several model-selection and ensembling methods, using external evaluation on the basis of therapeutic drug monitoring (TDM) samples to identify the best (set of) models per patient for a priori dosage individualization. PK data and models describing both hospitalized patients (n = 134) receiving continuous vancomycin (26 models) and patients (n = 92) receiving imatinib in an outpatient setting (12 models) are included. Target attainment of four model-selection methods was compared with standard dosing: the best model based on external validation, uninformed model ensembling, model ensembling using a weighting scheme on the basis of covariate-stratified external evaluation, and model selection using covariates in decision trees that were subsequently ensembled. Overall, the use of PK models improved the proportion of patients exposed to concentrations within the therapeutic window for both cohorts. Relative improvement of proportion on target for best model, unweighted, weighted, and decision trees were - 7.0%, 2.3%, 11.4%, and 37.0% (vancomycin method-development); 23.2%, 7.9%, 15.6%, and, 77.2% (vancomycin validation); 40.7%, 50.0%, 59.5%, and 59.5% (imatinib method-development); and 19.0%, 28.5%, 38.0%, and 23.8% (imatinib validation), respectively. The best (set of) models per patient for a priori dosage individualization can be identified using a relatively small set of TDM samples as external evaluation. Adequately performing popPK models were identified while also excluding poor-performing models. Dose recommendations resulted in more patients within the therapeutic range for both vancomycin and imatinib. Prospective validation is necessary before clinical implementation.

Cross-Linguistic Nasalance Comparisons: A Review of Speech Sample Sets and Preliminary Consideration of Effect of Lexical Tone.

Nasalance is an acoustic representation of perceived nasality with proven clinical and research utility. Its validity is contingent on appropriate speech sample sets and distinct normative databases based on known impact factors such as language and phonetic environment, but little is known about the potential effects of lexical tone on nasalance. Its use in international cross-linguistic studies necessitates definition and added considerations of speech sampling protocol. Part I. A methodological review was undertaken using PRISMA (2020), the Scottish Intercollegiate Guidelines Network (SIGN) Methodology Checklist for evaluating risk of bias, and calculation of effect sizes and/or visual displays using tables showing grouping of similar data for synthesis of findings. Part II. A pilot study explored the effect of lexical tone on nasalance in Cantonese, a lexical tone language. Part I. 17 studies met the inclusion criteria. In addressing possible confounders and minimizing risk of bias, 13 studies were assigned an overall quality rating of (+) acceptable and the remaining 4, a high rating (++). For the 9-word string, there was a (non-clinical) difference of 3-5% between Swedish and Brazilian-Portuguese, and a moderate effect size for age (d=0.49); for the consonant-vowel syllables set (CV set), clinical differences across languages were identified for adults, and between 5-10% for children and adolescents. Part II. The pilot study showed a significant effect of lexical tone on nasalance where nasalance for the high-level tone 1 was significantly higher than that for high-rising tone 2, low-rising tone 5, and low-falling tone 4. There is a need for further evidence from other languages, including tonal languages, to better define the evidence and speech methodology of international cross-linguistic nasalance studies.

Influence of environmental humidity during filament storage on the structural and mechanical properties of material extrusion 3D-printed poly(lactic acid) parts

Material extrusion (MEX) is one of the most widely used additive manufacturing techniques these days. This study investigates how the properties of MEX 3D-printed objects depend on the relative humidity (RH) conditions in which filaments are stored before and during the manufacturing process. Poly(lactic acid) (PLA) filament was drawn directly from a humidity-controlled chamber into the MEX 3D printer's nozzle. For each set of samples, the filaments were conditioned under different RH conditions, ranging from 10 % to 90 %. The macrostructure of the fabricated products was characterized using computed tomography, revealing increased porosity at higher RH values (from 0.84 % to 4.42 %). The increased porosity at higher storage RH is attributed to under-extrusion and volatile entrapment due to excess moisture. With growing storage RH, the melt flow rate of PLA also gradually increased, indicating a plasticizing effect of humidity on the biopolymer. Gel permeation chromatography and differential scanning calorimetry analyses were conducted to determine whether hydrolytic chain scission took place when PLA was processed in the presence of excessive moisture. Neither measurement indicated any considerable alteration in molecular integrity and crystalline structure as a function of storage RH. Mechanical tests, however, revealed a reduced load-bearing capacity of the manufactured PLA specimens. Flexural strength decreased from 103.0 to 99.6 MPa, and the impact strength dropped from 18.2 to 16.2 kJ/m2, which is ascribed to the increasing size of pores inside the specimens with increasing storage RH. These findings should be taken into account when designing and processing PLA products by MEX-based additive manufacturing.

Evaluation of Volatile Organic Compounds from Spotted Lanternfly (Lycorma delicatula) Eggs Using Headspace Odor Sampling Methods.

The spotted lanternfly (SLF) is an invasive species native to China. It was first discovered in the United States in Pennsylvania in 2014. It is known to cause great economic damage by destroying various crops, specifically grape vines, and therefore, several efforts have been made to control and mitigate its spread from the Northeast. Canine detection is a useful detection tool; however, it is crucial to understand the volatile organic compounds emitting by this pest to better direct canine training paradigms to prevent false alerts and to understand potential volatile markers of importance indicative of this species. The purpose of this study is to address the gap in research regarding the volatile organic compound (VOC) profile of SLF to better inform pest control mitigation strategies. Instrumental analysis was performed utilizing SPME-GC/MS on cold-killed SLF eggs, dried crickets, and tree bark. Differences in detected VOCs within each sample set depicted distinctive odor profiles for each matrix tested. Storage of these samples also depicted VOC accumulation variation as a function of time, thereby providing implications for long-term storage and sample handling for these types of training aids in canine applications.

Underwater measurement of abalone shell’s size based on stereo vision

In order to cultivate superior varieties of abalone, it is necessary to accurately measure the useful trait data of them. This paper presents a novel underwater binocular system for measuring the size of abalone. The measurement process can be divided into two main stages. In the first stage, underwater images are corrected using our proposed algorithm to eliminate the impact of refraction. In the second stage, the corrected images are utilized for the 3D reconstruction of the abalone shell. The length and width can be calculated by analyzing the contour of the shell. To validate the effectiveness of our method, a set of abalone shell samples are measured with the binocular system. The average measurement errors for the length and the width are 2.02% and 2.54%, respectively.

GenoTriplo: A SNP genotype calling method for triploids.

Triploidy is very useful in both aquaculture and some cultivated plants as the induced sterility helps to enhance growth and product quality, as well as acting as a barrier against the contamination of wild populations by escapees. To use genetic information from triploids for academic or breeding purposes, an efficient and robust method to genotype triploids is needed. We developed such a method for genotype calling from SNP arrays, and we implemented it in the R package named GenoTriplo. Our method requires no prior information on cluster positions and remains unaffected by shifted luminescence signals. The method relies on starting the clustering algorithm with an initial higher number of groups than expected from the ploidy level of the samples, followed by merging groups that are too close to each other to be considered as distinct genotypes. Accurate classification of SNPs is achieved through multiple thresholds of quality controls. We compared the performance of GenoTriplo with that of fitPoly, the only published method for triploid SNP genotyping with a free software access. This was assessed by comparing the genotypes generated by both methods for a dataset of 1232 triploid rainbow trout genotyped for 38,033 SNPs. The two methods were consistent for 89% of the genotypes, but for 26% of the SNPs, they exhibited a discrepancy in the number of different genotypes identified. For these SNPs, GenoTriplo had >95% concordance with fitPoly when fitPoly genotyped better. On the contrary, when GenoTriplo genotyped better, fitPoly had less than 50% concordance with GenoTriplo. GenoTriplo was more robust with less genotyping errors. It is also efficient at identifying low-frequency genotypes in the sample set. Finally, we assessed parentage assignment based on GenoTriplo genotyping and observed significant differences in mismatch rates between the best and second-best couples, indicating high confidence in the results. GenoTriplo could also be used to genotype diploids as well as individuals with higher ploidy level by adjusting a few input parameters.

Effects of a single oral dose of azithromycin in laboring women on antimicrobial resistance (AMR) and the microbiome: a protocol for the Antimicrobial Resistance Sub-Study of the A-PLUS trial

Background The Azithromycin Prevention in Labor Use Study (A-PLUS), a large, multi-national randomized trial, was performed to evaluate the improvement of maternal and newborn outcomes following a single dose of azithromycin (AZ) given during labor. However, the immediate and long-term impact of this single dose on the microbiome and the development or prevalence of antimicrobial resistance are unknown. We designed a sub-study to assess AZ resistance of bacterial isolates from clinical infections and of three target bacteria (Staphylococcus aureus, Streptococcus pneumoniae, and Escherichia coli) from the serial surveillance of the nasopharynx and rectum. Additionally, the serially surveilled samples will be evaluated for changes to the microbiome and the resistome of the nasopharynx and rectum. Methods and Analysis As part of the large, randomized APLUS trial, maternal and neonatal clinical infections were monitored for up to 42 days postpartum, and samples collected for culture using site-specific routine methods. For this sub-study, cultured bacterial isolates will be assessed for AZ resistance using an antibiotic susceptibility method. Additionally, a random subset of maternal-neonatal dyads from the main trial will be selected for serial surveillance with aseptic swab collections of the nasopharynx and the rectum at baseline, and subsequently at 1-week, 6-weeks, 3-months, 6-months, and 12-months postpartum. The serial samples will undergo selective culturing of sentinel bacterial species and screened for AZ resistance. An additional set of serial samples will be stored for future microbiome and resistome analyses. Ethics and Dissemination The study protocol was reviewed, and ethics approval obtained from all the relevant ethical review boards at each research site. All participants will provide informed written consent prior to their enrollment. Results of the trial will be disseminated through peer-reviewed publications, presentations at relevant conferences, and at dissemination meetings with the ministries of health and local stakeholders at each research site. Trial Registration NCT03871491.

Exploring the pyrolytic degradation of seventeen phthalate esters using the non-targeted regions of interest-multivariate curve resolution (ROIMCR) chemometric method

The global use of phthalate esters (PAEs) in plastic production entails a need for developing effective remediation strategies to minimize exposure and health risks. In this study, pyrolysis degradation processes are evaluated for 17 different PAEs that were analyzed by high-resolution gas chromatography-mass spectrometry (Pyr-GC-Orbitrap-MS) at different temperatures (300 °C, 400 °C, 500 °C, 600 °C, 700 °C, 800 °C) both in electron ionization (EI) and positive chemical ionization (PCI) mode. All the analytical data generated have been processed with the Regions of Interest Multivariate Curve Resolution (ROIMCR) chemometrics method. A preliminary selection of the Mass Spectrometry Regions of Interest (ROIs) coupled to a bilinear factor decomposition method (MCR-ALS) allowed the identification of 4 principal components, which were used to define the thermal degradation process. ROIMCR method is a powerful tool for non-targeted analysis which allows the resolution of the elution and spectral profiles of the different constituents present in the analyzed samples, which were confirmed using PCI. Moreover, ROIMCR was used to resolve and identify the different products generated during the studied degradation processes. As a result, 10 new thermal degradation products were identified in the analysis of the different sample sets. Finally, the degradation efficiencies higher than 99.8 % were obtained for all the PAEs at 800 °C, except for benzoic acid-benzyl ester, whose removal efficiency decreased to 94.8 %. As phthalates are widespread and toxic compounds for the environment and for humans, there is a need for new remediation technologies. This study provides new knowledge to understand their elimination through thermal processes. We combine mass spectrometric data with powerful chemometric processing to determine the removal efficiency, to understand the degradation process and to identify the degradation compounds formed through thermal decomposition.

Diagnostic Accuracy of Five Molecular Assays for the Detection of Dengue Virus.

Background and Objectives: The steady spread of dengue virus (DENV) poses a profound public health threat worldwide. Reverse transcription real-time polymerase chain reaction (RT2-PCR) has been increasingly recognized as a reference method for the diagnosis of acute dengue infection. The goal of this study was to assess the diagnostic accuracy of five different RT2-PCR kits for the detection of DENV in a historically processed set of sera samples. Materials and Methods: In this retrospective study, 25 sera samples from routinely processed unique adult patients with a known DENV status (previously tested in both molecular and serological assays) were tested in parallel using four conventional (RealStar Dengue PCR Kit 3.0, Clonit'ngo Zika, Dengue & Chikungunya, BioPerfectus Zika Virus/Dengue Virus/Chikungunya Virus Real Time PCR Kit and Novaplex Tropical fever virus) and one sample-to-result (STANDARD M10 Arbovirus Panel) RT2-PCR assays. Additionally, an end-point dilution analysis was conducted in quintuplicate on six serial dilutions of an RNA preparation obtained from a culture-grown DENV serotype 1 strain for a total of 150 tests. Results: The overall accuracy of the evaluated tests ranged from 84% to 100%. In particular, the sensitivity of three conventional RT2-PCR assays (RealStar, Clonit'ngo and Novaplex) was 100% (95% CI: 79.6-100%), while it was lower (73.3%; 95% CI: 48.1-89.1%) for the BioPerfectus kit. The sample-to-result STANDARD M10 panel performed comparatively well, showing a sensitivity of 92.9% (95% CI: 68.5-98.7%). No false positive results were registered in any assay. The end-point dilution analysis suggested that the RealStar kit had the lowest limit of detection. Conclusions: Available RT2-PCR kits for the detection of DENV are highly specific and generally sensitive and, therefore, their implementation in diagnostic pathways is advisable.

The feasibility of using national‐scale datasets for classifying wetlands in Arizona with machine learning

AbstractThe advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national‐scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland‐specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non‐wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

Abstract C147: Impact of genetic ancestry in triple-negative breast cancer subtypes

Abstract Background: Triple-negative breast cancer (TNBC) is an aggressive BC subtype characterized by significant molecular and clinical heterogeneity, influenced by genetic, phenotypic, and environmental factors, as well as interactions of the tumor with its surrounding milieu. A significant racial disparity has been observed between African American (AA) and European American (EA) women regarding the incidence and clinical progression of TNBC. TNBC frequently occurs in young parous women who self-identify as AA and in women who harbor a germline BRCA1 mutation. Notably, AA women are diagnosed with TNBC at a younger age and exhibit a higher risk of developing stage IV disease. Studies examining gene expression signatures that distinguish AA and EA TNBC have reported distinct tumor-associated immunologic profiles in AA patients. We have developed a novel cell type-driven subtyping system based on a racially diverse TNBC sample set that identified three TNBC subtypes, each with distinct biology and prognosis: CC1- immune signaling/T-cell response; CC2- pro- migratory/inflammatory; CC3- fatty acid and hormone and nuclear receptor signaling. In this study, we propose to examine ancestry-inferred differences within each TNBC subtype between patients with African and European ancestry. Methods: RNA-Sequencing data for the Louisiana Tumor Registry, a TNBC cohort of 250 diverse women [EA: 123 and AA: 127], was obtained from our collaborators at Louisiana State University Health Sciences Center. Genetic admixture was performed on 227/250 patients using the method described by Pakstis, A. J. et al. Differentially expressed genes (DEGs) were identified in pairwise comparisons at p-adj ≤ 0.05. Survival was assessed using log-rank tests. Results: We investigated DEGs within each subtype between patients with &amp;gt;60% African (Aa, n=102) or &amp;gt;60% European ancestry (Ea, n=99) (identified via admixture analysis). In all three subtypes, upregulated genes, and their associated enrichment in Aa were related to immune pathways, while DEGs in Ea patients were related to epithelial cell regulation. In CC1, Aa was associated with the upregulation of immunoglobulin genes involved in immunoglobulin production and antigen recognition, while Ea was associated with the upregulation of embryonic development genes. In CC2, Aa showed enriched complement activation and immunoglobulin genes, while Ea exhibited upregulation of basal cytokeratin and growth factor EGFR. In CC3, Aa was associated with the upregulation of inflammatory genes, whereas Ea showed upregulation of cellular senescence genes. Survival analysis demonstrated favorable outcomes for Aa in CC1 and less favorable outcomes for Aa in CC2. Conclusion: Our study highlights significant ancestry-inferred differences in gene expression within TNBC subtypes, with Aa patients showing immune pathways enrichment and Ea patients exhibiting differential epithelial cell regulation. These findings underscore the importance of considering genetic ancestry in TNBC prognosis and treatment strategies. Citation Format: Zahra Mesrizadeh, Kavitha Mukund, Fokrul Hossain, Jovanny Zabaleta, Denise Danos, Luis De Valle, Xiao-Cheng Wu, Chindo Hicks, Augusto Ochoa, Lucio Miele, Victoria L. Seewaldt, Shankar Subramaniam. Impact of genetic ancestry in triple-negative breast cancer subtypes [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C147.

Transfer learning for well logging formation evaluation using similarity weights

Machine learning has been widely applied in well logging formation evaluation studies. However, several challenges negatively impacted the generalization capabilities of machine learning models in practical implementations, such as the mismatch of data domain between training and testing datasets, imbalances among sample categories, and inadequate representation of data model. These issues have led to substantial insufficient identification for reservoir and significant deviations in subsequent evaluations. To improve the transferability of machine learning models within limited sample sets, this study proposes a weight transfer learning framework based on the similarity of the labels. The similarity weighting method includes both hard weights and soft weights. By evaluating the similarity between test and training sets of logging data, the similarity results are used to estimate the weights of training samples, thereby optimizing the model learning process. We develop a double experts' network and a bidirectional gated neural network based on hierarchical attention and multi-head attention (BiGRU-MHSA) for well logs reconstruction and lithofacies classification tasks. Oil field data results for the shale strata in the Gulong area of the Songliao Basin of China indicate that the double experts’ network model performs well in curve reconstruction tasks. However, it may not be effective in lithofacies classification tasks, while BiGRU-MHSA performs well in that area. In the study of constructing large-scale well logging processing and formation interpretation models, it is maybe more beneficial by employing different expert models for combined evaluations. In addition, although the improvement is limited, hard or soft weighting methods is better than unweighted (i.e., average-weighted) in significantly different adjacent wells. The code and data are open and available for subsequent studies on other lithofacies layers.

Pottery from Motion – A Refined Approach to the Large-Scale Documentation of Pottery Using Structure from Motion

Abstract In recent years, interest in the usage of computer-based methods in archaeology, especially regarding field documentation, has grown significantly. In 2021, Göttlich et al. presented a new large-scale three-dimensional (3D) capture method for the documentation of pottery using structure from motion. This method, however, was only tested on a very small sample set and never truly conducted in a large-scale documentation surrounding. Consequently, we decided to test this workflow on a large-scale basis during three field campaigns (March 2022, November 2022, and March 2023) in Lebanon, documenting more than 4,000 sherds in total. In this article, I will present the results and observations of these campaigns, critically discuss the workflow involved (documentation and processing), and propose a refined workflow for this methodology. This article focuses solely on the relevant documentation and 3D processing.

Polyamine Pathway Inhibitor DENSPM Suppresses Lipid Metabolism in Pheochromocytoma Cell Line.

Pheochromocytomas (PCCs) are tumors arising from chromaffin cells in the adrenal medulla, and paragangliomas (PGLs) are tumors derived from extra-adrenal sympathetic or parasympathetic paraganglia; these tumors are collectively referred to as PPGL cancer. Treatment for PPGL primarily involves surgical removal of the tumor, and only limited options are available for treatment of the disease once it becomes metastatic. Human carriers of the heterozygous mutations in the succinate dehydrogenase subunit B (SDHB) gene are susceptible to the development of PPGL. A physiologically relevant PCC patient-derived cell line hPheo1 was developed, and SDHB_KD cells carrying a stable short hairpin knockdown of SDHB were derived from it. An untargeted metabolomic approach uncovered an overactive polyamine pathway in the SDHB_KD cells that was subsequently fully validated in a large set of human SDHB-mutant PPGL tumor samples. We previously reported that treatment with the polyamine metabolism inhibitor N1,N11-diethylnorspermine (DENSPM) drastically inhibited growth of these PCC-derived cells in culture as well as in xenograft mouse models. Here we explored the mechanisms underlying DENSPM action in hPheo1 and SDHB_KD cells. Specifically, by performing an RNAseq analysis, we have identified gene expression changes associated with DENSPM treatment that broadly interfere with all aspects of lipid metabolism, including fatty acid (FA) synthesis, desaturation, and import/uptake. Furthermore, by performing an untargeted lipidomic liquid chromatography-mass spectrometry (LC/MS)-based analysis we uncovered specific groups of lipids that are dramatically reduced as a result of DENSPM treatment. Specifically, the bulk of plasmanyl ether lipid species that have been recently reported as the major determinants of cancer cell fate are notably decreased. In summary, this work suggests an intersection between active polyamine and lipid pathways in PCC cells.