Abstract Background: HER2 targeted therapy (tx) has transformed the trajectory of HER2+ metastatic breast cancer (MBC), with a subgroup of pts remaining on first line (1L) tx for many years (yrs). Detection of minimal residual disease (MRD) via ctDNA to guide tx is promising but data supporting its use in HER2+ MBC are virtually absent. Methods: We identified 70 pts with HER2+ MBC treated with 1L HER2 targeted tx consented to the EMBRACE cohort study with archival tissue, buffy coat and plasma at ≥1 key timepoints while on 1L tx (baseline, first-on-tx, year [Y] 1 landmark [L], Y2L, Y3L), or at time of disease progression (PD). We employed MAESTRO, an ultrasensitive whole-genome, tumor-informed, mutation enrichment sequencing assay, in a pooled testing format (MAESTRO-Pool). Exceptional responders were pts without PD or death for ≥3 yrs from 1L start (real-world progression-free survival [rwPFS] ≥3 yrs). Conventional responders were pts with PD or death within 3 yrs of 1L start (rwPFS<3 yrs). Local- or brain-only PD that did not lead to tx switch were not considered events. The primary objective was the association between Y1L MRD status (plasma samples collected 1 yr from 1L start +/- 3 months) and exceptional response. Sixty-three pts (90%) (39 exceptional and 24 conventional responders) had sufficient samples and successful assay design; MAESTRO-Pool was run on 147 samples. Results: More exceptional than conventional responders had de novo MBC (69.2% vs 41.7%, p=0.03). High (3+ by IHC) HER2 expression (87.2% vs 75.0%), estrogen receptor (ER)+ status (38.5% vs 33.3%) and visceral disease (61.5% vs 62.5%) were similar between exceptional and conventional responders. Most pts in both groups had 1L chemotherapy plus trastuzumab/pertuzumab (82.1% vs 70.8%) and maintenance endocrine tx if ER+ (86.7% vs 62.5%). With 284.4 person-yrs of follow up, 6/39 exceptional responders had late PD. In a landmark analysis at Y3, the 2-yr rwPFS was 97.4% (95%CI 82.8%-99.6%) (5 yrs from 1L start); 5-yr overall survival (OS) was 91.5% (95%CI 68.7%-97.9%) (8 yrs from 1L start). For conventional responders, total follow up was 110.5 person-yrs, median rwPFS was 1.65 yrs (95%CI 1.1-2.51), median OS was 4.58 yrs (95%CI 3.78-6.95). A median of 1,370 (range 136-4,635) tumor-specific mutations were tracked per patient. MRD was detected in 50 (34%) samples (median tumor fraction (TFx) 1352.8 ppm; range 4-206,399 ppm), including 21 (42%) samples with TFx <500 ppm, the limit of detection (LOD) of tumor-agnostic MBC assays, and 14 (28%) with TFx <100 ppm, the LOD of 1st generation tumor-informed MRD tests. After excluding baseline and PD samples, MRD was detected in 7 (7.4%) and 28 (80%) samples from exceptional and conventional responders, respectively. All exceptional responders who remained progression-free were always MRD- (n=30) or cleared MRD by Y1L (n=3). Indeed, Y1L MRD status was associated with exceptional vs conventional response (0/26 [0%] vs 10/13 [76.9%], p<0.001). Four of the 6 pts who had late PD had a Y3L sample, which was MRD+ in 3 cases (lead time range 2.77-13.47 yrs) and MRD- in one case of breast-only PD. For 20/24 conventional responders, the last sample collected before PD was MRD+. Of the 4 MRD- cases, one pt had 2 primary breast cancers, one had brain-only PD. Conclusions: Here we report, to our knowledge, the first data showing a significant association between MRD detection and duration of response to 1L tx for HER2+ MBC. MRD status at Y1L was highly predictive of exceptional response. Only 2 conventional responders had distant PD without a prior MRD+ sample. All exceptional responders with late distant PD were MRD+ at Y3L. Fourteen (28%) MRD+ samples had TFx<100ppm and would have been misclassified by 1st generation MRD tests, supporting the value of ultrasensitive MRD tracking to predict outcomes and its potential to guide tx de-escalation in prospective clinical trials. Citation Format: S. Morganti, C. Song, N. Zhou, K. Santos, P. Jain, L. Walsh, R. Li, J. Rhoades, K. Gilligan, G. Kirkner, C. Stever, A. Patel, M. E. Hughes, N. Priedigkeit, I. E. Krop, G. Curigliano, E. P. Winer, S. M. Tolaney, N. Tayob, H. Heiling, K. Xiong, N. U. Lin, V. A. Adalsteinsson, H. A. Parsons. Ultrasensitive ctDNA Tracking Predicts Exceptional Response in HER2+ Metastatic Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS5-11.

Underwater vehicles performing sampling tasks often encounter significant buoyancy variations due to payload adjustments and environmental changes, which severely challenge the stability and accuracy of controllers. To address this issue, this paper proposes a hybrid control framework that integrates Proximal Policy Optimization (PPO) with adaptive PID tuning. The framework employs PPO to dynamically adjust PID parameters online while incorporating output saturation, stepwise quantization, and dead zone filtering to ensure control safety and actuator longevity. A dual-error state representation—combining instantaneous error and its derivative—along with actuator command buffering is introduced to compensate for system lag and inertia. Comparative simulations and experimental tests demonstrate that the proposed method achieves faster convergence, lower steady-state error, and smoother control signals compared to both conventional PID and pure PPO-based control. The framework is validated through pool tests and field trials, confirming its robustness under realistic hydrodynamic disturbances. This work provides a practical and safe solution for adaptive depth control of sampling-capable AUVs operating in dynamic underwater environments.

Accurate motion prediction of floating platforms is critical for ensuring operational safety in offshore engineering applications or marine equipment testing. However, the strong nonlinearity and non-stationary characteristics induced by complex marine environments pose significant challenges to conventional prediction models. This study proposes a reinforced hybrid neural network (CNN-BiLSTM-Attention) integrated with advanced signal processing techniques to address these challenges. The methodology combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for multi-scale signal analysis, coupled with temporal feature engineering through sliding window optimization. And the architecture innovatively integrates convolutional neural networks for spatial pattern extraction, bidirectional long short-term memory networks for temporal dependency modeling, and attention mechanisms for dynamic feature weighting. By analyzing datasets generated via hydrodynamic simulations, this study elucidates the model’s physical interpretability and establishes a closed-loop validation framework between data-driven methods and physics-based models. Finally, the predictive performance of the model is evaluated using motion datasets of the proportional platform in the water pool test under different working conditions, demonstrating its broad applicability and transferability by assessed using a dual-stage EWMA control line. Overall, the proposed CNN-BiLSTM-Attention model and its data-physics integrated validation method provide a reliable, interpretable and transferable solution for floating platform motion prediction, which can break through the limitations of single analysis methods, and provide a new research idea for integrating data-driven and physics-based methods in the field of ocean engineering.

The proliferation of Internet of Things (IoT) devices has created unprecedented security challenges characterized by resource constraints, heterogeneous network architectures, and severe class imbalance in attack detection datasets. This paper presents a comprehensive benchmark evaluation of five Generative Adversarial Network (GAN) architectures for energy-aware intrusion detection: Standard GAN, Progressive GAN (PGAN), Conditional GAN (cGAN), Graph-based GAN (GraphGAN), and Wasserstein GAN with Gradient Penalty (WGAN-GP). Our evaluation framework introduces novel energy-normalized performance metrics, including Accuracy-per-Joule (APJ) and F1-per-Joule (F1PJ), that enable principled architecture selection for energy-constrained deployments. We propose an optimized WGAN-GP architecture incorporating diversity loss, feature matching, and noise injection mechanisms specifically designed for classification-oriented data augmentation. Experimental results on a stratified subset of the BoT-IoT dataset (approximately 1.83 million records) demonstrate that our optimized WGAN-GP achieves state-of-the-art performance, with 99.99% classification accuracy, a 0.99 macro-F1 score, and superior generation quality (MSE 0.01). While traditional classifiers augmented with SMOTE (i.e., Logistic Regression and CNN1D-TCN) also achieve 99.99% accuracy, they suffer from poor minority class detection (77.78–80.00%); our WGAN-GP improves minority class detection to 100.00% on the reported test split (45 of 45 attack instances correctly identified). Furthermore, WGAN-GP provides substantial efficiency advantages under our energy-normalized metrics, achieving superior accuracy-per-joule performance compared to Standard GAN. Also, a cross-dataset validation across five benchmarks (BoT-IoT, CICIoT2023, ToN-IoT, UNSW-NB15, CIC-IDS2017) was implemented using 250 pooled test attacks to confirm generalizability, with WGAN-GP achieving 98.40% minority class accuracy (246/250 attacks detected) compared to 76.80% for Classical + SMOTE methods, a statistically significant 21.60 percentage point improvement (). Finally, our analysis reveals that incorporating diversity-promoting mechanisms in GAN training simultaneously achieves best generation quality AND best classification performance, demonstrating that these objectives are complementary rather than competing.

Background: Molecular diagnosis using the Xpert MTB/RIF Ultra assay enables rapid detection of Mycobacterium tuberculosis and rifampicin resistance. Objectives: This study evaluated the diagnostic performance and cost efficiency of a two-sample pooled testing approach compared with standard individual testing. Materials and methods: A cross-sectional diagnostic study was conducted among 3,504 presumptive TB patients. Sputum specimens were tested by Xpert Ultra individually, with those results serving as the reference standard, and in two-sample pools. The pooling protocol utilized 2.0 mL from each specimen to maximize load volume. Deconvolution, which required retesting both individual specimens, was mandatory for all positive and invalid pooled results. Performance metrics, RIF resistance concordance, and cost-effectiveness modeled on cartridge consumption and direct cost 550 Thai Baht per unit were compared. Results: The pooled method showed excellent concordance with the individual method, yielding 240 concordant positives and 3,249 concordant negatives, with no statistically significant difference in MTB detection (McNemar χ²=0.267, p=0.605). The pooled approach achieved a sensitivity of 96.39% (95% CI, 93.25-98.33) and a specificity of 99.82% (95% CI, 99.60-99.93). The assay maintained reliable detection of RIF resistance, indicating that pooling did not compromise molecular accuracy. In practice, 1,752 pooled runs were performed, with 105 (5.7%) error results and 246 positive pools requiring deconvolution. Including repeats and deconvolution, total cartridge use was 2,349 compared with 3,504 for individual testing, corresponding to an actual cost reduction of 32.96% (approx. 180,000 Thai Baht saved per 1,000 tests). Conclusion: Two-sample pooled Xpert Ultra testing demonstrated high diagnostic accuracy and doubled analytical throughput. Although deconvolution limited cost savings to approximately 33%, the strategy proved highly cost-effective and operationally feasible. This method offers a practical, scalable approach for optimizing molecular TB diagnostics and resource utilization, especially in low- to moderate-prevalence settings.

Despite the growing global prominence of elite open-water (OW) swimming, little is known about how seasonal variation influences the physiological and kinematic determinants of maximal performance. This study aimed (1)to evaluate the seasonal changes in performance, physiological, and kinematic factors in maximal incremental swimming tests in elite OW swimmers and (2)to examine the influence of physiological and kinematic factors on the maximal swimming performance. Eighteen world-class and elite (12 males [25.4 (3.3y)] and 6 females [26.4 (3.9)y]) OW swimmers voluntarily participated. A total of 57 (40 male and 17 female) intermittent incremental tests (7 × 400m) in a 50-m pool were analyzed at 4 different moments (October 2022, February and October 2023 and March 2024). Heart rate; blood lactate concentration ([La-]); aerobic (AeT) and lactate thresholds (LT); swimming speed; and stroke rate, length, and index were assessed. The OW swimmers showed no changes in performance or physiological factors between tests. In males, the stroke length and index changed in both AeT and LT, whereas no kinematic changes were observed in females. The maximum swimming speed was positively associated with speed at AeT and LT in both sexes, while only males showed association between [La-] at LT. Stroke length and index at AeT and LT in males and stroke rate at AeT and LT were positively associated with maximum swimming speed. Despite stable performance and physiological factors, seasonal changes in stroke technique highlight the importance of monitoring kinematics to guide training or competition of elite swimmers.

To extend the near-seabed survey operation duration of deep-sea Autonomous Underwater Vehicles (AUVs), this paper proposes a deep-sea bottom-landing and dwelling technical scheme integrating the drive of a variable buoyancy adjustment mechanism with the support of a “biped” telescopic bottom-landing mechanism. This scheme offers a flexible, low-cost, multi-site repeatable bottom-landing process, and sensitive water area-applicable dwelling solution for marine surveys. Firstly, for hard seabed sediments, the mechanical response of AUVs during hard landing under different driving forces and attitudes is solved through simulation analysis, and the local optimal solution of reasonable driving forces is obtained to provide input for the design of the variable buoyancy mechanism. Secondly, for soft seabeds, the variation law of the bottom-leaving adsorption force with different length-to-width ratios (L/B) under the same bottom-landing plate area is studied to provide design input for the telescopic bottom-landing mechanism. Subsequently, the bottom-landing criteria and calculation formulas for flat and uneven seabeds are established, and the bottom-landing and bottom-leaving control strategies are constructed. Finally, the two sets of mechanisms are integrated into the AUV platform. Verification via pool, lake, and sea tests has demonstrated favorable results, and scientific test data of 56 dives within 1 m of the near-seabed are obtained. Traditional technical solutions primarily rely on jettisonable ballast weights or ballast tanks for operations, enabling only a single dive, bottom-landing, and bottom-leaving process. Their concealment and operational depth are often limited. The technical achievement proposed in this paper supports the ABLUV in performing multiple repeated bottom-landing and bottom-leaving operations in deep-sea environments without the need for jettisoning ballast throughout the entire process.

Level 1 polysomnography (PSG) is considered the gold standard test for diagnosing obstructive sleep apnea (OSA). The Belun Ring platform, a recently FDA-approved wearable device, is a potential alternative to PSG for diagnosing OSA. We conducted this systematic review and meta-analysis to assess the diagnostic accuracy of the Belun Ring Platform compared to PSG in adults. A systematic search of PubMed and Embase was conducted from database inception till 8th July 2025 to identify studies comparing the Belun Ring Platform with PSG in adults. A meta-analysis was performed to estimate the pooled diagnostic test characteristics. Four prospective studies involving 339 patients (mean age 54.6 years; 62% males) were included. For diagnosing moderate-to-severe OSA (AHI ≥ 15), the Belun Ring showed pooled sensitivity of 0.92 [95% confidence interval (CI): 0.86-0.96], specificity of 0.81 [95% CI: 0.73-0.87], positive likelihood ratio of 4.84 [95% CI: 3.33-7.03], negative likelihood ratio of 0.10 [95% CI: 0.05-0.17], and diagnostic odds ratio of 50.36 [95% CI: 24.7-102.65]. For AHI ≥ 5, sensitivity was 0.97 [95% CI: 0.91-0.99], but specificity was lower at 0.18 [95% CI: 0.05-0.50]. For severe OSA (AHI ≥ 30), sensitivity was 0.66 [95% CI: 0.40-0.85], with a high specificity of 0.95 [95% CI: 0.91-0.97]. We conclude that the Belun Ring platform demonstrates high sensitivity for detecting OSA and excellent specificity (though poor sensitivity) for severe OSA, supporting its utility as an effective tool for diagnosing OSA in adults with suspected moderate to severe disease. It shows good diagnostic accuracy for moderate-to-severe OSA, but limited specificity for mild OSA, supporting its use as an adjunct to type I PSG for diagnosis of OSA.

Prostate MRI enables detection of clinically significant prostate cancer (csPCa), yet variability in PI-RADS scoring limits reproducibility and throughput. Here, we report the development and validation of an automated MRI-based decision aid (ProAI) that estimates patient-level risk of csPCa from biparametric MRI and supports routine reporting. Training, internal validation, and external testing spanned 7849 examinations across six centres and two public datasets. On pooled external tests, the system achieved a patient-level AUC of 0.93 (95% CI, 0.91-0.95), comparable to PI-RADS while improving inter-case consistency. In a multi-reader, multi-case study involving nine clinicians, assistance increased accuracy from 0.80 to 0.86 and reduced reading time. Prospective implementation in 1978 consecutive examinations-maintained performance (AUC 0.92) and was associated with a 32% reduction in radiology workload. Performance generalised to the TCIA cohort (AUC 0.83). These findings indicate that an automated MRI-based decision aid can standardise reporting and enhance efficiency across prostate cancer care pathways. This study was registered at ClinicalTrials. Trial number: ChiCTR2400092863.

IntroductionIn professional cycling, the technical characteristics of race stages significantly influence group dynamics and performance variability among competitors. However, stage classifications have traditionally been subjective, lacking a robust empirical foundation. This study aimed to develop an objective, technical classification of professional cycling stages using unsupervised learning (KMeans) and analyze how these categories relate to collective performance variability, measured by the coefficient of variation (CV) of finish times.MethodsTechnical data and official results from 439 international race stages conducted between 2017 and 2023 were analyzed. The technical variables included distance, total vertical gain, average relative elevation, and percentages of paved and unpaved surfaces.ResultsCluster validation via Bootstrap analysis demonstrated high stability (mean silhouette index = 0.62 ± 0.03), confirming six clearly distinct technical stage groups. Results indicated that stages characterized by higher relative elevation and greater proportions of unpaved surfaces exhibited higher performance variability (higher CV),whereas less technically demanding stages showed lower variability; relative elevation emerged as the strongest predictor of CV (β = 0.42, p < 0.001), followed by unpaved percentage (β = 0.23, p < 0.01), distance (β = 0.18, p < 0.05), and vertical gain (β = 0.11, p < 0.05). Across 2017–2023, a broadly downward pattern in CV was observed, although a pooled linear-trend test with cluster fixed effects did not reach statistical significance (p = 0.315).DiscussionThe lack of physiological data and possible confounding from unmeasured stage and team factors (e.g., weather, stage order, team tactics) limit causal inference. This empirical typology provides a valuable quantitative tool to optimize competitive strategies, plan targeted training based on stage type, and prevent cumulative fatigue and performance-related injuries in high-performance cycling. Future research incorporating direct physiological data is recommended to further explore the relationship between external and internal load in professional cycling.

ABSTRACT This study explores the feasibility and potential advantages of using a computer adaptive testing (CAT) paradigm for surveys consisting of items that most respondents endorse, such as when assessing socio-emotional outcomes. Such constructs oftentimes produce item responses concentrated at one end of the Likert scale (in psychometric parlance, none of the items are “difficult”). Perhaps given these challenges, CAT versions of socio-emotional surveys are rarely if ever produced. As a result, researchers could be missing an opportunity to harness advantages of CAT, which include shorter but more reliable and more personalized measures. By simulating data using Monte Carlo methods, this study compares CAT with traditional fixed-form surveys, investigating factors such as item pool size, item difficulty, and test length on bias and precision of scores. The results indicate that CAT significantly improves score precision while reducing the number of items administered.

Pooled testing represents a resource-conserving diagnostic strategy, and we propose enterovirus-D68 (EV-D68) as an attractive target for pooling (especially locally). To validate an RT-qPCR-based approach, retrospective pools were contrived from EV-D68-positive nasopharyngeal specimens and non-EV-D68-enterovirus-positive specimens. Five/nine-fold pools demonstrated 90.9 %/88.6 % sensitivity versus-unpooled testing (no false-positives), with commensurate results observed prospectively.

BackgroundIncarceration is a leading driver of tuberculosis in Latin America. Active case-finding in prisons may reduce population-wide tuberculosis burden, but optimal strategies and cost-effectiveness remain uncertain.Methods and findingsUsing dynamic transmission models calibrated to Brazil, Colombia, and Peru, we simulated annual or biannual (twice-yearly) prison-wide screening, alone or combined with entry and exit screening from 2026-2035. We evaluated four algorithms: 1) symptom screening, 2) chest X-ray with computer-aided detection (CXR-CAD), 3) symptoms and CXR-CAD (follow-up testing if either is positive) and 4) GeneXpert Ultra with pooled sputum. Individuals screening positive then received individual Xpert. We projected impacts on within-prison and population-level tuberculosis incidence in 2035, along with discounted costs (2023 USD) and disability-adjusted life years (DALYs). Model projections showed that combined entry, exit, and biannual screening with CXR-CAD was highly impactful and cost-effective across countries, reducing tuberculosis incidence by 62-87% in prisons and 18-28% population-wide. Compared to only biannual CXR-CAD (the next best strategy), the incremental cost per DALY averted of adding entry and exit screening was $2984 (Brazil), $2925 (Colombia), and $645 (Peru). Adding symptom screening to CXR-CAD marginally increased benefit and was only cost-effective in Peru’s higher-incidence prisons. Biannual screening alone remained cost-effective at prison incidence levels well below national averages. In settings without CXR-CAD, pooled Xpert was an impactful, cost-effective alternative.ConclusionsThese modeling results support immediate national-level adoption of prison-wide tuberculosis screening twice-yearly and at entry and exit. Screening should begin with available methods while building capacity for CXR-CAD, the most cost-effective algorithm.

The rapid and precise quantification of viral copies in clinical samples at extremely low concentrations is crucial for controlling infections and epidemics. The widely used nucleic acid detection methods require specialized equipment, high-containment laboratories, and complex amplification processes, which render them inefficient for point-of-care testing (POCT) and population-scale screening. Herein, we propose a sensing system that integrates a triangular prism DNA nanostructure (TPDN) with a carbon nanotube-based field-effect transistor (FET) for the rapid and ultrasensitive detection of viral RNA. Three flexible single-stranded probes targeting distinct viral RNA loci were anchored to a rigid DNA framework, enabling the efficient capture of long-strand viral RNA fragments. A suitable positive gate voltage was applied to the TPDN-FET to orient the DNA probe vertically and extend the Debye screening length. Consequently, this results in a 4-fold increase in the sensitivity of the FET-based biosensor compared with those of the sensors incorporating only a single probe. The TPDN-FET sensor completed the detection process in 40 s, enabling the accurate perception of respiratory syncytial virus (RSV) at concentrations as low as 0.1 copies per microliter. The sensor identified all 10 positive cases (cycle thresholds 28.9-32.2) among 20 individual nasopharyngeal swabs and detected 6 positives in 12 pooled tests without RNA extraction or nucleic acid amplification. Thus, the rapid, ultrasensitive, user-friendly, and pooled-sample-compatible sensing device developed in this study has great potential in the POCT and in vitro diagnosis of respiratory virus and other potential epidemic-causing pathogens.

Screening for carbapenemase-producing Enterobacterales (CPE) and vancomycin-resistant Enterococcus (VRE) colonization among hospitalized patients is a standard infection control procedure that also guides appropriate antibiotic treatment in healthcare settings. Extensive CPE screening in low-prevalence regions imposes a considerable laboratory workload and substantial costs that can be mitigated through the utilization of pool testing. In this study, we evaluated PCR pooling for the detection of CPE and VRE colonization from rectal swabs collected in our hospital, using the Xpert Carba-R and Xpert vanA/vanB assays. CPE pooling demonstrated excellent performance, with 94.44% sensitivity (74.2-99.0 CI 95%), 100% specificity (99.1-100), 100% positive predictive value (81.6-100), and 99.78% (98.7-100) negative predictive value (NPV). In contrast, VRE detection showed lower performance, with 75.86% (63.5-85.0 CI 95%) specificity and 100% (92.0-100) NPV, primarily due to false-positive results for the vanB gene. Overall, the pooling technique showed great potential for CPE screening in low-prevalence settings. Further studies are needed to evaluate VRE pooling and clarify the clinical utility of PCR results for vanB, which currently requires culture confirmation.IMPORTANCECarbapenemase-producing Enterobacterales (CPE) and vancomycin-resistant enterococci (VRE) pose a major threat to healthcare systems due to their role in hospital-acquired infections and limited treatment options. Routine screening for CPE/VRE colonization is essential for preventing transmission, yet conventional culture-based methods are labor-intensive and time-consuming. This study demonstrates that PCR pooling is a highly effective and resource-efficient approach for CPE screening in low-prevalence settings, maintaining high sensitivity and specificity while reducing laboratory workload. The performance of VRE pooling, particularly for vanB detection, requires further evaluation. These findings support the adoption of pooling strategies for CPE surveillance and highlight the need for improved molecular diagnostics for VRE, offering a promising solution to address growing challenges in clinical microbiology diagnostics.

ABSTRACT Biodiversity monitoring is crucial for understanding ecosystem dynamics and species distributions, particularly in the context of anthropogenic impacts and climate change. Cetaceans, as key indicator species of marine ecosystems, face increasing threats from human activities, highlighting the need for effective, non‐invasive monitoring methods. In the present study, we developed novel Cetacea‐specific primer sets to enhance the detection efficiency of cetacean species through environmental DNA (eDNA) metabarcoding, while minimizing the amplification of non‐target vertebrates, such as fish and humans. We retrieved mitochondrial genomes of 71 cetacean species from a public database and designed 20 candidate primer sets, which were assessed in silico for their specificity and capacity to differentiate cetacean sequences. Four primer sets with the best in silico performance were selected for empirical validation using DNA from tissue samples and eDNA from seawater collected from aquarium pools and Hong Kong coastal waters. All four primer sets effectively amplified cetacean DNA from tissue samples. However, in the aquarium pool tests, three primer sets failed to accurately identify one or more cetacean species due to a lack of interspecific variation within the amplified region. From these, we selected one primer set targeting a 267 bp region of the mitochondrial 12S rRNA gene, named μCeta, and applied it to water samples collected from Hong Kong coastal waters, where the iconic Indo‐Pacific humpback dolphin ( Sousa chinensis ) was observed. μCeta successfully detected S. chinensis eDNA while avoiding amplification of non‐target species such as fish or humans. Our results demonstrate that μCeta is a reliable tool for cetacean eDNA detection in Hong Kong waters, contributing to cetacean conservation and enhancing our understanding of marine biodiversity.

BackgroundEstimating an individual’s ethnicity from genetic data is crucial for analyzing disease association studies, making informed medical decisions, conducting forensic investigations, and tracing genealogical ancestry.ResultsThis work combines non-adaptive group testing using the mathematical field of compressed sensing and standard short-read sequencing to allow an up to 4-fold increase in the number of samples in large-scale ethnicity estimates. The method requires no prior knowledge regarding the tested individuals and provides almost identical results compared to testing each individual independently. Our results are based on simulated data, and on simulations based on experimental data from the 1000 Genomes Project and the Human Genome Diversity Project.ConclusionsOur computational approach aims to reduce the costs of large-scale ancestry testing by up to 4-fold in many real-life scenarios while not compromising accuracy. We hope this method will allow more efficient large-scale ethnicity screenings.

Orthoflavivirus nilense, formerly known as West Nile Virus (WNV), has become endemic to the United States since its introduction in 1999. Current surveillance methods rely primarily on mosquito pool testing, which is both costly and time-intensive. Wastewater-based epidemiology (WBE) has proven an effective method for the surveillance of various pathogens, including other orthoflaviviruses such as Dengue. WBE for WNV represents a potentially valuable surveillance approach that has so far been underexplored. A targeted droplet digital reverse transcription-polymerase chain reaction (RT-PCR) approach (ddRT-PCR) was used to measure WNV concentrations in wastewater retrospectively from five locations and in over 600 samples. Three of these locations were in communities with multiple confirmed WNV infections, while two were not. Samples were collected during periods corresponding to typical WNV seasonality (spring to fall). SARS-CoV-2 RNA was measured simultaneously to assess nucleic acid degradation during sample storage. Publicly available confirmed WNV case data were compiled from the California and Nebraska departments of public health and their weekly arboviral reports. WNV RNA was detected in wastewater samples during periods of known viral circulation within a community. The adopted ddRT-PCR assay is highly specific and sensitive, and detections in wastewater solids correspond to the occurrence of cases in the season and location of sampling. WNV was detected in nine samples in three locations with known WNV clinical cases-wastewater positivity rates in these locations ranged from 3.3% to 13%. The results suggest that wastewater monitoring could serve as an effective complement to traditional surveillance methods, particularly for sentinel surveillance in locations which do not have extensive mosquito and clinical testing systems.

One method used to carry out internal quality control at the analytical stage is to use control serum. . For some clinical laboratories, especially primary clinical laboratories where the number of clinical examinations is still small, the use of commercial control serum for quality control is not affordable because the cost is very expensive. Production of control materials from pooled blood serum can be used as an alternative to expensive production controls. The aim of this study was to find out whether serum collected at home can be used as a control material to carry out quality tests by determining its homogeneity and stability.The implementation process includes taking samples, sample from 48 person and then making pooled serum, examining pooled serum, homogeneity and stability tests, determining reference value ranges, and analyzing results. The combined serum that has been made is homogeneous and stable in the parameters of glucose and total cholesterol, while it is unstable in uric acid. The pooled serum value range for glucose is 49-86 mg/dL, total cholesterol 196-288 mg/dL and uric acid 1.36-3.70 mg/dL. The results of making pooled serum as a control material are declared homogeneous and stable in terms of glucose and total cholesterol parameters so that it can be used as quality control in daily activities. Suggestions for future researchers to add other parameters related to clinical chemistry examinations.

EGFR mutation testing across the osimertinib clinical program.