Accurate Identification Research Articles

Modal Parameter Identification of Jacket-Type Offshore Wind Turbines Under Operating Conditions

Operational modal analysis (OMA) is essential for long-term health monitoring of offshore wind turbines (OWTs), helping identifying changes in structural dynamic characteristics. OMA has been applied under parked or idle states for OWTs, assuming a linear and time-invariant dynamic system subjected to white noise excitations. The impact of complex operating environmental conditions on structural modal identification therefore requires systematic investigation. This paper studies the applicability of OMA based on covariance-driven stochastic subspace identification (SSI-COV) under various non-white noise excitations, using a DTU 10 MW jacket OWT model as a basis for a case study. Then, a scaled (1:75) 10 MW jacket OWT model test is used for the verification. For pure wave conditions, it is found that accurate identification for the first and second FA/SS modes can be achieved with significant wave energy. Under pure wind excitations, the unsteady servo control behavior leads to significant identification errors. The combined wind and wave actions further complicate the picture, leading to more scattered identification errors. The SSI-COV based modal identification method is suggested to be reliably applied for wind speeds larger than the rated speed and with sufficient wave energy. In addition, this method is found to perform better with larger misalignment of wind and wave directions. This study provides valuable insights in relation to the engineering applications of in situ modal identification techniques under operating conditions in real OWT projects.

Taxonomy and phylogeny of rodents parasitic fleas in southeastern China with description of a new subspecies of Ctenophthalmus breviprojiciens.

Accurate differentiation and identification of flea species are essential for both basic and applied research on fleas, as well as for the diagnosis of flea-borne diseases. However, distinguishing between flea species can be challenging, especially among those with minimal morphological differences. Therefore, some scholars have suggested the necessity of comprehensive revisions to the classification of fleas, incorporating morphological, molecular, and phylogenetic data. In this study, we focused on classifying the rodents' parasitic fleas in southeastern China and provided molecular and phylogenetic data. We also described a new subspecies Ctenophthalmus breviprojiciens fujiansis n. ssp. A total of 392 fleas were collected from 8 species of rodents in 10 counties. Morphologically, they belonged to 10 species, 9 genera and 5 families. Barcode identification based on COI gene and phylogenetic analysis based on five genetic markers (18S rDNA, 28S rDNA, EF-1a, COI, COII) revealed that the molecular and morphological identification of Xenopsylla cheopis, Aviostivalius klossi bispiniformis, Leptopsylla segnis, Monopsyllus anisus and Ctenocephalides felis felis were consistent. The taxonomic status of Neopsylla specialis minpiensis and Peromyscopsylla himalaica sinica as subspecies is questionable due to significant intraspecific genetic distance, and further morphological and molecular data are required to determine if they should be elevated to species level. The molecular identification of C. breviprojiciens n. ssp., N. dispar fukienensis, and Nosopsyllus nicanus could not be completed at this time due to a lack of sequences for related species in existing GenBank databases. Additionally, phylogenetic relationships of 31 species from 9 genera and 5 families of Siphonaptera were inferred based on five molecular markers (18S rDNA, 28S rDNA, EF-1a, COI and COII) using Maximum Likelihood analyses. The analyses revealed that various taxa of Siphonaptera are monophyletic at the subspecies, species, and genus levels. However, at the family level, Leptopsyllidae, Ceratophyllidae, Pulicidae, and Pygiopsyllidae are all monophyletic, while Ctenophthalmidae is paraphyletic. we support the view of some authors that revising the catchall group Ctenophthalmidae and elevating each of its constituent subfamilies to family status.

Auxiliary model maximum likelihood least squares-based iterative algorithm for multivariable autoregressive output-error autoregressive moving average systems

Identification of multivariable systems is of great significance to control systems. This paper focuses on the parameter identification problems for multivariable autoregressive output-error autoregressive moving average (M-AROEARMA) systems. On the basis of the decomposition strategy, the M-AROEARMA model is de- composed into multiple subsystem models. By means of the auxiliary model idea, the auxiliary model least squares-based iterative algorithm is derived. For the purpose of achieving highly accurate parameter identification performance under colored noises interference, an auxiliary model maximum likelihood least squares-based iterative algorithm is proposed by utilizing the maximum likelihood principle. The numerical simulation example demonstrates the effectiveness of the proposed algorithms.

Multi-type stroke lesion segmentation: comparison of single-stage and hierarchical approach.

Stroke, a major cause of death and disability worldwide, can be haemorrhagic or ischaemic depending on the type of bleeding in the brain. Rapid and accurate identification of stroke type and lesion segmentation is critical for timely and effective treatment. However, existing research primarily focuses on segmenting a single stroke type, potentially limiting their clinical applicability. This study addresses this gap by exploring multi-type stroke lesion segmentation using deep learning methods. Specifically, we investigate two distinct approaches: a single-stage approach that directly segments all tissue types in one model and a hierarchical approach that first classifies stroke types and then utilises specialised segmentation models for each subtype. Recognising the importance of accurate stroke classification for the hierarchical approach, we evaluate ResNet, ResNeXt and ViT networks, incorporating focal loss and oversampling techniques to mitigate the impact of class imbalance. We further explore the performance of U-Net, U-Net++ and DeepLabV3 models for segmentation within each approach. We use a comprehensive dataset of 6650 images provided by the Ministry of Health of the Republic of Türkiye. This dataset includes 1130 ischaemic strokes, 1093 haemorrhagic strokes and 4427 non-stroke cases. In our comparative experiments, we achieve an AUC score of 0.996 when classifying stroke and non-stroke slices. For lesion segmentation task, while the performance of different architectures is comparable, the hierarchical training approach outperforms the single-stage approach in terms of intersection over union (IoU). The performance of the U-Net model increased significantly from an IoU of 0.788 to 0.875 when the hierarchical approach is used. This comparative analysis aims to identify the most effective approach and deep learning model for multi-type stroke lesion segmentation in brain CT scans, potentially leading to improved clinical decision-making, treatment efficiency and outcomes.

Volunteer Accuracy in a Benthic Macroinvertebrate Participatory Science Project

Concerns about the accuracy of volunteer-derived aquatic macroinvertebrate identifications and the resulting influence on calculated water quality metrics can limit use of volunteer-generated data in freshwater-focused participatory science programs. To address these concerns, we assessed 357 benthic macroinvertebrate quality control (QC) samples collected by volunteers using leaf packs, kick nets, and visual assessments between 2011 and 2016 for the Environmental Quality Institute (EQI) in North Carolina, USA. We reviewed each sample for quality, and compared macroinvertebrate identifications and water quality metric scores determined by volunteers to identification and metrics determined by an entomologist. About 80% of the QC samples were identified to be of high quality, indicating proper preparation, preservation, and labeling of samples. The majority of samples that received low quality ratings were improperly or poorly preserved. We recommend use of 95% (rather than 70%) ethanol for macroinvertebrate preservation, and increased communication to volunteers during QC sampling periods to enhance their success in properly preserving samples. We observed significant (p < 0.005) linear relationships between volunteer and entomologist-derived water quality metrics including a biotic index, Taxa Richness, and percent intolerant Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa. However, visual assessments – those in which no sampling equipment was used to collect aquatic macroinvertebrates – reduced the goodness of fit between volunteer and entomologist-derived biotic index scores and artificially increased the goodness of fit between volunteer and entomologist derived Taxa Richness and percent EPT scores. We recommend calculating water quality metrics based only on leaf pack and kick net samples collected by volunteers.

Dynamic changes of radiological and radiomics patterns based on MRI in viable hepatocellular carcinoma after transarterial chemoembolization.

This study aims to analyze the magnetic resonance imaging (MRI) change patterns of viable hepatocellular carcinomas (HCCs) following the initial transarterial chemoembolization (TACE). A retrospective analysis of HCC patients' initial TACE from February 2015 to October 2022 across three centers and a clinical trial (NCT03113955) was conducted. The viability of residual HCCs at one and six months after TACE was evaluated using the LI-RADS Treatment Response Algorithm (LR-TRA) v2024. The radiological and radiomics features of post-TACE viable tumors between baseline and one-month, and between one- and six- months were compared using Wilcoxon signed-rank test and McNemar's test. A total of 160 viable tumors were included in the study. Viable tumors at one month after TACE exhibited higher T1WI intensity (P =.024), lower T2WI intensity (P =.005), fewer washout features (P <.001), smaller size (P <.001), and higher ADC values (P <.001) compared to baseline HCC imaging.A significant reduction in DWI intensity (P =.002) and ADC values (P <.001) were observed in viable tumors at one month compared to those at six months. There were 82 (45.1%) radiomics features that changed significantly between the baseline and one-month. Only three radiomics features showed statistically significant difference of viable tumors between one- and six-month. Compared to the baseline, viable HCCs after TACE demonstrated significant changes of imaging characteristics in a series of radiological and radiomics features at one- and six-month follow-ups. Clinically diagnosing of viable HCCs using radiological methods is challenging. A comprehensive analysis of these imaging characteristics can facilitate the accurate identification of viable tumors.

INNOVATIVE AIRSHIP DESIGN FOR REAL-TIME AIR QUALITY MONITORING USING IOT TECHNOLOGY

The effect of poor air quality on human health has been linked to both short-term and long-term exposure to air pollutants. One of the most important steps in reducing emissions is accurate identification of these pollutants. This can be achieved by using Internet of Things equipped remotely operated airships that can survey large areas and gather pollution data. The ability of an airship to function at low altitudes for extended periods is the main design requirement of airships. This study describes a methodology for conceptualizing and building a helium-filled blimp for remote measurement of atmospheric pollutants (Carbon monoxide, Carbon dioxide, and Sulphur dioxide), temperature, and pressure. An onboard telemetry system measures the data while the airship is in flight and transmits them in real time to a ground-based station. The experiment showed that remotely operated airships are capable of gathering air data and their quality, enabling environmental scientists and regulatory agencies to better understand the behaviour of air pollutants and take steps to mitigate them.

PVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.

Neoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example, limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression, and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address. We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select, and export their neoantigen candidates. The tool allows users to visualize candidates at multiple levels of detail including variant, transcript, peptide, and algorithm prediction information. pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings. The application is available as part of the pVACtools software at pvactools.org and as an online server at pvacview.org.

GeM-LR: Discovering predictive biomarkers for small datasets in vaccine studies.

Despite significant progress in vaccine research, the level of protection provided by vaccination can vary significantly across individuals. As a result, understanding immunologic variation across individuals in response to vaccination is important for developing next-generation efficacious vaccines. Accurate outcome prediction and identification of predictive biomarkers would represent a significant step towards this goal. Moreover, in early phase vaccine clinical trials, small datasets are prevalent, raising the need and challenge of building a robust and explainable prediction model that can reveal heterogeneity in small datasets. We propose a new model named Generative Mixture of Logistic Regression (GeM-LR), which combines characteristics of both a generative and a discriminative model. In addition, we propose a set of model selection strategies to enhance the robustness and interpretability of the model. GeM-LR extends a linear classifier to a non-linear classifier without losing interpretability and empowers the notion of predictive clustering for characterizing data heterogeneity in connection with the outcome variable. We demonstrate the strengths and utility of GeM-LR by applying it to data from several studies. GeM-LR achieves better prediction results than other popular methods while providing interpretations at different levels.

Multimodal Biometric Authentication System for Military Weapon Access: Face and ECG Authentication

Unimodal or Single factor biometric systems refer to biometric systems that employ only one form of biometric data to authenticate an individual’s identity. These kinds of biometrics are susceptible to higher error rates and security vulnerabilities because it relays on a single trait for authentication. To overcome this, multimodal biometrics method is proposed. Multi-modal biometric system can authenticate more than once and some advantages include; highaccuracy, low error rate, and large population coverage. These biometrics systems increase integrity and privacy since it will contain several biometric features of every customer. So, here designed a multimodal biometrics project utilizing deep learning to enhance authentication security by combining face and Electrocardiogram(ECG) signals. VGG-16 model, a deep learning architecture used to capture complex patterns in accurate individual identification with both ECG and Facial data. The high-resolution convolutional filters capture the intricate details of the face and ECG waveform, ensuring high accuracy in distinguishing different individuals.

Decomposition dynamics and Calliphoridae succession in forest and urban environments of Constantine (Northeast of Algeria): implications for forensic entomology

ABSTRACT Forensic entomology serves a vital role in criminal investigations, contributing to the determination of time of colonization (TOC), minimum post-mortem interval (minPMI), and other forensic evidence related to the decomposition process. This study aimed to identify Calliphoridae species attracted to carcasses in two distinct locations in Algeria and to understand their role in forensic investigations. Two sites in Constantine were selected: a forest in Jebel Ouahch and an urban area in Chaabat Erssas. Two dog carcasses, an adult and a puppy, were monitored for decomposition stages. The puppy carcass reached the dry stage faster than the adult carcass, with decomposition rates varying between sites. A total of 594 Calliphoridae flies were collected, including Calliphora vicina, Calliphora vomitoria, Calliphora subalpina, Lucilia sericata, Lucilia illustris, Lucilia silvarum, and Chrysomya albiceps. These species were present in both locations, with Ch. albiceps being most prevalent, especially on the puppy carcass. Initial blowfly colonization occurred within four hours, with L. sericata and Ch. albiceps particularly abundant. This study underscores the importance of accurate species identification and environmental reconstruction in improving the precision of forensic estimates. Continued research on species phenology and the impacts of climate change will further enhance the applicability of forensic entomology in Algeria.

Effects of age on accuracy of advanced imaging modalities in identifying intervertebral disc extrusions in Dachshunds.

The aim of this study was to ascertain whether patient age was a significant factor for the accurate identification of compressive intervertebral disc extrusions (IVDE) in Dachshunds using non-contrast computed tomography (CT). This retrospective analysis encompassed 150 Dachshunds evaluated for suspected IVDE at a referral hospital in south-eastern Queensland, Australia. Each case underwent diagnostic evaluation by CT, either with or without myelography, or magnetic resonance imaging (MRI), followed by surgical intervention for an intervertebral disc extrusion. In the <9-year dog cohort, 94.7% (71/75) were diagnosed with IVDE via non-contrast CT and proceeded to surgery. In contrast, the ≥9-year dog cohort, only 56% (42/75) were diagnosed via non-contrast CT, with the remaining 44% (33/75) necessitating CT myelography or MRI for diagnosis. The odds of requiring CT myelography or MRI increased 13.95-fold (95% CI, 4.62-42.13) in Dachshunds aged 9 years or older. Furthermore, each additional year of age was associated with a 0.60 increase in the odds of undergoing these advanced imaging modalities (CT myelography or MRI) (95% CI, 0.49-0.74). Age significantly affected the efficacy of non-contrast CT in diagnosing IVDE in Dachshunds. Dogs aged 9 years and above were substantially more likely to require CT myelography or MRI due to the reduced visibility of disc extrusions on non-contrast CT. Veterinarians performing advanced imaging on older Dachshunds for suspected IVDE should be aware of the limitations of non-contrast CT in obtaining an accurate diagnosis and localisation of IVDE, and be prepared to perform either CT myelography or MRI if required.

A straightforward method for estimating evolutionary power spectral density of non-stationary typhoon wind speed

PurposeA precise estimation of the evolutionary power spectral density (EPSD) of typhoon wind speed is a difficult and significant undertaking in the analysis of turbulence effects on large-expansive structures. A majority of the prevailing EPSD estimation techniques rely on complex signal processing methodologies, such as wavelet decomposition, Hilbert–Huang transformation and time-varying autoregressive moving average (ARMA) model. However, these approaches often pose challenges in terms of comprehensibility and practical implementation for engineers. In light of this issue, the present study introduces a straightforward and effective EPSD estimation method tailored specifically for typhoon wind speed, aiming to facilitate its understanding and application in engineering contexts.Design/methodology/approachFirstly, the mathematical model of a uniformly modulated non-stationary process is employed to represent the typhoon wind speed. Secondly, the reverse arrangement test serves as an auxiliary tool in conjunction with wavelet transform or empirical mode decomposition, aiding in the determination of the optimal slowly varying mean wind speed. Thirdly, Kernel regression technique is utilized to discern the time-dependent standard deviation of wind speed fluctuations. Finally, the power spectral density (PSD) of wind speed residuals is computed to facilitate the estimation of the EPSD.FindingsFirstly, the reverse arrangement test-assisted approach enables the determination of an optimal time-dependent mean from the candidate results obtained through discrete wavelet transform (DWT) and empirical mode decomposition (EMD). Secondly, the application of the Kernel regression technique facilitates accurate identification of the time-dependent variance from the fluctuating wind speed data. Thirdly, due to the influence of the extreme weather, the Gaussianity of the reduced turbulent fluctuations in typhoon wind is easily disturbed, resulting in the obvious non-Gaussian features.Originality/valueThis paper employs the mathematical model of uniformly modulated non-stationary process to characterize typhoon wind speeds and then proposes a straightforward and efficient method for estimating the EPSD of typhoon wind. The accuracy and efficacy of the presented estimation method are verified using the field-measured wind speed data from Typhoon Rammasun. The proposed EPSD estimation method for typhoon wind exhibits suitability for engineering applications owing to its simplicity and computational efficiency.

Comparing options for screening of reading difficulties in middle school: Do teacher ratings improve accuracy?

Reading problems may emerge beyond the primary grades when the linguistic and cognitive demands of reading comprehension increase in middle school. The accurate identification of students requiring supplemental reading instruction is critical to provide remediation and decrease the prevalence and likelihood of reading problems in secondary settings and beyond. Nevertheless, research guidance on middle school reading screening is scarce. This study analyzed data from 193 sixth-grade students across 12 classrooms to examine (a) how well various reading screeners predicted proficiency on the year-end state assessment, (b) what combinations of reading screeners were most accurate, (c) the extent to which a brief teacher rating improved classification accuracy, and (d) the agreement rates between the most accurate combinations of screeners. Screeners included the Sight Word Efficiency, oral reading fluency (ORF), maze, and a multiple-choice reading comprehension (MCRC) assessment. Results from logistic regressions and receiver operating characteristic curve analyses suggested that no single screener was appropriate for use and that combinations of two or three screeners assessing different reading skills improved classification accuracy (i.e., ORF + MCRC, ORF + maze + MCRC). Moreover, teacher ratings further improved classification accuracy but its predictive value depended on the combination of screeners. Finally, there was a high agreement regarding which students were identified as needing intervention between these combinations of screeners. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Complete chloroplast genomes of three Polygala species and indel marker development for identification of authentic polygalae radix (Polygala tenuifolia).

In Korea, only Polygala tenuifolia is registered as Polygalae Radix in the pharmacopoeia, while in China, both P. tenuifolia and P. sibirica are used equally. Accurate identification of herbal medicines is crucial for their safety and efficacy, but commercial products are typically sold in dried form, making morphological distinction difficult. Therefore, a quick and accurate method to distinguish P. tenuifolia is necessary for proper utilization of medicinal herb. We aimed to identify specific molecular markers for P. tenuifolia to avoid confusion regarding its pharmacological efficacy and to evaluate the classification of Polygala using plastid phylogenetic data. We analyzed the sequences of three species distributed in Korea, P. tenuifolia, P. japonica, and P. sibirica, and assembled their chloroplast genome sequences. Comparative analysis revealed regions of local divergence, and six molecular markers were developed from these hotspots. Additionally, a phylogenetic tree was constructed to determine the phylogenetic positions of the three Polygala species. The marker successfully identified the three Polygala species, and all commercial products and breeding lines tested were confirmed to be P. tenuifolia and recognized as authentic. Phylogenetic analysis revealed that P. tenuifolia forms a distinct cluster from P. sibirica and P. japonica. We determined the chloroplast genomes of the three Polygala species and performed phylogenetic tree analysis and marker development. Indel markers were developed to identify the critical herbal species, P. tenuifolia. This comprehensive study of the Polygala chloroplast genome provides useful information for P. tenuifolia identification.

OGC SO21 - Role of Staging Laparoscopy on oesophageal and junctional cancer management

Abstract Background Staging laparoscopy and peritoneal cytology is currently recommended as a staging tool required for oesophageal and gastro-oesophageal junction (OGJ) adenocarcinomas. However, evidence is variable regarding the utility of this investigation, leading to heterogenous practice in the UK. With improved staging sensitivity of CT and PET imaging and inclusion of routine MDT discussion for upper GI cancer patients, staging laparoscopy may not be necessary for all patients with oesophago-gastric cancer, and in fact may delay their treatment. We thus aimed to assess the impact of staging laparoscopy on the treatment algorithm of patients with oesophago-gastric cancer. Method All staging laparoscopies undertaken between January 2022 and March 2024 were identified from our surgical database. Patients were included if they had oesophago-gastric adenocarcinoma, with a plan for curative treatment. Data collected included age, gender, location of primary tumour, CT/PET staging, laparoscopy findings, impact on treatment intention, neoadjuvant treatment, and final histology. Primary outcome was incidence of positive microscopic or macroscopic peritoneal disease for oesophageal and OGJ malignancy. Results 127 patients were identified - 69 (55%) oesophageal, 25 (20%) GOJ, and 32 (24.4%) gastric cancer. 1/70 (1.4%) patient diagnosed endoscopically with oesophageal cancer had positive peritoneal disease, which was suspected due to pre-operative imaging findings of ascites and suspicions of linitis plastica. Of patients diagnosed with GOJ cancer, 4/25 patients (16%) had peritoneal disease present. None of the 39 patients with oesophageal/junctional tumours with clinical T1-2 disease were found to have peritoneal disease on staging laparoscopy. 6/32 (19%) patients with gastric cancer were found to have unexpected peritoneal disease present. Conclusion This study found minimal impact staging laparoscopy has on the management of primary oesophageal cancers. Consideration can be made of limiting staging in oesophageal cancer to patients with suspicious findings on CT/PET imaging. For GOJ tumours, there may be a role for limiting staging laparoscopy for Siewert 1 tumours to T3/4 tumours based on CT/PET findings. Importantly, accurate identification on primary endoscopy of the location of the tumour in relation to the gastro-oesophageal junction is necessary to help guide ongoing management.

Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications.

Accurate identification between pathologic (e.g., tumors) and healthy brain tissue is a critical need in neurosurgery. However, conventional surgical adjuncts have significant limitations toward achieving this goal (e.g., image guidance based on pre-operative imaging becomes inaccurate up to 3cm as surgery proceeds). Hyperspectral imaging (HSI) has emerged as a potential powerful surgical adjunct to enable surgeons to accurately distinguish pathologic from normal tissues. We review HSI techniques in neurosurgery; categorize, explain, and summarize their technical and clinical details; and present some promising directions for future work. We performed a literature search on HSI methods in neurosurgery focusing on their hardware and implementation details; classification, estimation, and band selection methods; publicly available labeled and unlabeled data; image processing and augmented reality visualization systems; and clinical study conclusions. We present a detailed review of HSI results in neurosurgery with a discussion of over 25 imaging systems, 45 clinical studies, and 60 computational methods. We first provide a short overview of HSI and the main branches of neurosurgery. Then, we describe in detail the imaging systems, computational methods, and clinical results for HSI using reflectance or fluorescence. Clinical implementations of HSI yield promising results in estimating perfusion and mapping brain function, classifying tumors and healthy tissues (e.g., in fluorescence-guided tumor surgery, detecting infiltrating margins not visible with conventional systems), and detecting epileptogenic regions. Finally, we discuss the advantages and disadvantages of HSI approaches and interesting research directions as a means to encourage future development. We describe a number of HSI applications across every major branch of neurosurgery. We believe these results demonstrate the potential of HSI as a powerful neurosurgical adjunct as more work continues to enable rapid acquisition with smaller footprints, greater spectral and spatial resolutions, and improved detection.

Phylogenomic analyses of hamsters (Cricetinae) inferred from GBS data and mitochondrial genomes.

Accurate species delimitation and identification is crucial for species conservation, providing a foundation for studies on evolutionary biology, ecology, and essentially all biological disciplines. The subfamily Cricetinae (Cricetidae, Rodentia), known as hamsters, is widely distributed in the Palearctic region. At present, there are nine genera and 18 species of hamsters are recognized worldwide, although the taxonomic status of certain taxa remains unclear. In this study, we collected 146 hamster specimens representing 14 species and generated new mitochondrial genomes and nuclear genome-wide single nucleotide polymorphisms (SNPs) to explore their relationships among these hamsters using multiple species delimitation approaches. Results showed: (1) strong phylogenetic support for the classification of Urocricetus, Nothocricetulus, and Cansumys as separate genera; (2) Urocricetus contained two separate species, U. kamensis and U. lama, with U. alticola and U. tibetanus considered synonyms of U. lama; (3) U. kamensis and U. lama are separated by the Nujiang River, with the matching divergence time suggesting that the formation of the river was the primary evolutionary factor driving the species differentiation, and (4) genetic differentiation occurred within the Tscherskia genus, which included two cryptic species.

Abstract A043: Tumor-informed whole genome sequencing of ctDNA from lymph and plasma detects molecular residual disease in HPV-negative head and neck cancer patients

Abstract Introduction: Recurrence after tumor resection is a major cause of failure in HPV-negative head and neck squamous cell carcinoma (HNSCC), with up to 50% of patients recurring within 2 years. There is unmet need for an accurate diagnostic test that can predict risk of recurrence prior to adjuvant therapy selection. We previously demonstrated that ctDNA in lymphatic exudate collected via surgical drains (“lymph”) accurately identifies molecular residual disease (MRD) and outperforms plasma in HNSCC patients using a targeted sequencing (TS) approach. While TS assays are robust and sensitive, they have key limitations, namely limited ctDNA features, higher cost, longer turnaround times, and lower throughput. Here we evaluate whole genome sequencing (WGS) for detection of MRD in both lymph and plasma sampled 24 hours after surgery. Methods: Lymph, plasma, and whole blood were collected 24 hours postoperatively from 30 HPV-negative HNSCC patients (stages I – IV) along with resected tumor. To detect ctDNA, we utilized WGS analyses of patient matched tumor, germline (blood), and cfDNA from lymph or plasma samples at approximately 150x, 30x, and 30x depth, respectively. Tumor- specific single nucleotide variants were identified from tumor and germline datasets, from which a candidate variant set was used to determine the presence of ctDNA within lymph or plasma DNA through a random forest machine learning model. ctDNA status was determined based on the level of signal compared to a reference population of noncancerous donor plasma samples (n=80). 5 tumors and 1 lymph sample failed QC; in total, 24 patients remained, yielding 10 patients with disease recurrence and 14 with no evidence of disease with &amp;gt;1 year of follow-up. The Kaplan-Meier (KM) estimator with log-rank test and Cox proportional-hazards model were used for survival analyses. Results: KM survival analyses indicated that WGS of lymph ctDNA accurately predicts recurrence (sensitivity (SN) = 50%, specificity (SP) = 93%; p = 0.003, Hazard ratio (HR) = 6.0), consistent with previously published results using a TS MRD approach in this cohort (SN = 80%, SP = 64%, p = 0.02, HR = 5.3). WGS of lymph ctDNA outperformed plasma at this timepoint (SN = 30%, SP = 93%, p = 0.03, HR = 4.2). WGS of lymph ctDNA also showed enhanced performance in locoregional relapse (n=21, SN = 57%, SP = 93%; p = 0.006, HR = 6.7). Finally, the ctDNA fraction of proximal lymph was higher than paired plasma in all locoregional recurrences (n=7), while peripheral plasma had a higher ctDNA fraction in 2 out of 3 distant recurrences. Conclusions: Tumor-informed WGS analysis of lymph ctDNA represents a robust MRD approach in HPV-negative HNSCC. Lymph outperforms time-matched plasma for prediction of recurrence, particularly in patients with locoregional relapse. Accurate MRD identification in the immediate post-surgical timeframe suggests that lymph analysis has potential to augment pathology and longitudinal plasma testing to provide more personalized adjuvant treatment decision-making in patients with HPV-negative HNSCC. Citation Format: Zhuosheng Gu, Seka Lazare, Noah Earland, Marra S Francis, Adam Harmon, Megan Long, Amy Greer, Ellen Verner, Andrew Georgiadis, Mark Sausen, Shakti Ramkissoon, Jose P Zevallos, Aadel A Chaudhuri, Taylor J Jensen, Wendy Winckler. Tumor-informed whole genome sequencing of ctDNA from lymph and plasma detects molecular residual disease in HPV-negative head and neck cancer patients [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A043.

DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation

Plant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation (HDMR) to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems.