Search Results (230)

Background/Objectives: Optimal selection for perioperative therapy in urothelial carcinoma (UC) remains uncertain. We evaluated the efficacy of neoadjuvant and/or adjuvant chemotherapy (NAC/AC) for patients with bladder cancer (BC) and upper tract UC (UTUC), examined the role of lymphovascular invasion (LVI), and considered the implications for adjuvant nivolumab. Methods: We retrospectively analyzed consecutive patients who underwent radical cystectomy or radical nephroureterectomy at a single center (July 1998–April 2021; observation to 31 March 2025). After exclusions, 252 BC and 153 UTUC patients were included. Endpoints were cancer-specific survival, progression-free survival (PFS; BC), non-urinary-tract recurrence-free survival (NUTRFS; UTUC), and overall survival (OS). Survival was estimated by Kaplan–Meier analysis and compared by log-rank tests. Results: For BC, AC did not improve the PFS or OS in the overall pT ≥ 2 population, whereas node-positive (pN+) disease derived significant benefits in both endpoints among NAC-naïve patients (PFS and OS, p = 0.002 and p = 0.008). For UTUC, AC conferred no advantage in NUTRFS or OS for the overall pT ≥ 2 population. However, NUTRFS benefits emerged in the pN+ subset (p = 0.049), although the OS was not improved. Among NAC-treated BC, the outcomes were poorest for ≥ypT3 and ypN+, whereas ypT ≤ 2 fared better. LVI was associated with adverse outcomes and was borderline higher in pN+ versus pT ≥ 2/pN− for BC (p = 0.056) and significantly higher for UTUC (p = 0.012). Conclusions: In this retrospective, single-center cohort, our exploratory analyses suggest that perioperative benefit is largely node-dependent, supporting prioritizing systemic therapy for pN+ disease and cautioning against routine AC for pT2/ypT2 without nodal involvement. After NAC, adjuvant therapy appeared most justified for ≥ypT3/ypN+. Prospective biomarker-integrated validation is warranted and, given the small and underpowered subgroups and the potential for selection and immortal time biases, these observations should be interpreted as hypothesis-generating rather than causal. Full article

Nanosatellites in Low Earth Orbit (LEO) are an attractive platform for commercial and scientific missions, but their downlink capacity is constrained by bandwidth and by low ground station duty cycles (often under 5%). These limitations are particularly acute in heterogeneous cooperative networks, where operators seek to maximize “good-put”: the number of unique messages successfully delivered to the ground. In this paper, we present and evaluate three complementary algorithms for scheduling nanosatellite passes to maximize good-put under realistic traffic and link variability. First, a Cooperative Reception Algorithm uses Shapley value analysis from cooperative game theory to estimate each station’s marginal contribution (considering signal quality, geography, and historical transmission patterns) and prioritize the most valuable upcoming satellite passes. Second, a pair-utility optimization algorithm refines these assignments through local, pairwise comparisons of reception probabilities between neighboring stations, correcting selection biases and adapting to changing link conditions. Third, a weighted bidding algorithm, inspired by the Helium reward model, assigns a price per message and allocates passes to maximize expected rewards in non-commercial networks such as SatNOGS and TinyGS. Simulation results show that all three approaches significantly outperform conventional scheduling strategies, with the Shapley-based method providing the largest gains in good-put. Collectively, these algorithms offer a practical toolkit to improve throughput, fairness, and resilience in next-generation nanosatellite communication systems. Full article

Background/Objectives: The widely recognized potential for biased responses to food frequency questionnaires and non-compliant self-reporting is motivating the search for alternative food intake records. The analysis of fecal DNA has been investigated as a potentially less biased and technically manageable method to replace or complement oral or written dietary surveys. The accuracy of fecal-DNA-based recalls critically depends on the persistence of ingested DNA of dietary origin during digestion. Methods: To inform the implementation of alternative molecular dietary inventories, we quantified the concentration of dietary DNA in the small intestine and in the feces of dogs, and in several sections of the mouse gastro-intestinal tract. Results: Using PCR assays specific for five ingredients used in commercial dog food and in mouse chow, we observed that fish DNA was most sensitive to digestion in the canine GI tract. In both species, DNA from corn and wheat was detectable in intestinal and in fecal samples. Perturbation of the mouse intestinal microbiota with antibiotics delayed the dietary DNA degradation in the GI tract. Conclusions: These results illustrate the limitations of DNA-based dietary recalls, underscoring their potential for generating biased information. Full article

Background/Objectives: Patients with differentiated thyroid cancer (DTC) receive thyroxine substitution targeting thyroid-stimulating hormone (TSH) levels based on their treatment response category. However, variations in prescribing and inter-assay TSH variability may result in over or undertreatment. Methods: We measured TSH in 220 consecutive DTC patients using three automated immunoassay platforms (Elecsys, Atellica, Alinity). Each patient was assigned to a response-to-therapy category (Excellent Response [ER], Indeterminate Response [IndR], Biochemical Incomplete Response [BIR], Structural Incomplete Response [SIR]) by an experienced thyroid oncologist. We defined recommended TSH targets according to the American Thyroid Association (ATA) 2015 guidelines and the response-adapted ATA 2025 framework that allows progressive relaxation of TSH suppression in patients with ER while maintaining tight suppression in those with persistent disease. Analytical agreement between assays was assessed using Passing–Bablok regression and Bland–Altman analysis. Clinical appropriateness was evaluated by classifying each measured TSH value as below, within, or above the recommended range for that patient’s response category. Results: The three immunoassays demonstrated high analytical agreement with only minor biases unlikely to affect clinical interpretation. However, significant deviations from guideline-defined TSH targets were observed. Among ER patients, 37% remained oversuppressed despite the absence of active disease. Conversely, in IndR or BIR patients, 76% had TSH levels above the recommended range, indicating undersuppression where residual disease could not be excluded. SIR patients were generally managed appropriately. The ATA 2025 framework reclassified more ER patients as appropriately managed, but undersuppression persisted in non-ER patients. Conclusions: Guidelines are not uniformly applied in thyroxine dosing for DTC patients. TSH immunoassays have achieved adequate analytical performance. The focus must now shift toward addressing clinical, educational, and systemic factors that prevent optimal levothyroxine management. Full article

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and hold significant potential as biomarkers. Saliva, a non-invasive and easily accessible biofluid, offers a promising alternative to blood for miRNA-based diagnostics. However, miRNA profiling by next-generation sequencing (NGS) is highly influenced by library preparation protocol, which can introduce detection and quantification biases. This study compared four commercial small RNA library preparation kits—QIASeq miRNA library kit (Qiagen), RealSeq-Biofluids Plasma/Serum miRNA library kit (Somagenics), Small RNA-seq library prep kit (Lexogen) and NEBNext multiplex small RNA library prep set for illumina (set 1) (New England BioLabs)—to evaluate their performance in profiling miRNAs from cell-free saliva, plasma and their extracellular vesicles (EVs). Using both synthetic reference and biological samples, we assessed the kits’ efficiency in handling low RNA input, minimizing bias and detecting diverse miRNAs. QIAseq outperformed the others, showing the highest miRNA mapping rates, minimal adapter dimers and the broadest miRNA detection, particularly in saliva. Moreover, substantial overlap between saliva- and plasma-derived miRNAs supports saliva’s diagnostic potential. Overall, this study underscores the critical impact of library preparation on miRNA sequencing outcomes and offers guidance for selecting optimal protocols for biomarker discovery from non-invasive sample matrices. Full article

Background/Objectives: Heart disease (HD) is recently becoming the foremost cause of death worldwide, underlining the importance of early and correct diagnosis to improve patient outcomes. Although Internet of Things (IoT)-enabled machine learning approaches have demonstrated encouraging outcomes in screening, existing approaches often face challenges such as imbalanced dataset handling, influential feature selection identification, and the ability to adapt to evolving HD data forms. To tackle the aforementioned challenges, we present a sequential hybrid approach, RAVE-HD (ResNet And Vanilla RNN Ensemble for HD), that combines a number of cutting-edge techniques to enhance screening. Methods: Preprocessing phase includes duplicates removal and feature scaling for data consistency. Recursive Feature Elimination is employed to extract the most informative features, while a proximity-weighted random synthetic sampling technique addresses class imbalance to reduce class biases. The proposed RAVE model in RAVE-HD approach sequentially integrates a Residual Network (ResNet) for high-level feature extraction and Vanilla Recurrent Neural Network to capture the non-linearity of the feature relationships present in the HDHI medical dataset. Results: Compared to ResNet and Vanilla RNN baselines, the proposed RAVE model attained superior results: 92.06% accuracy and 97.12% ROC-AUC. Stratified 10-fold cross-validation validated the robustness of RAVE, while Sensitivity-to-Prevalence analysis demonstrated stable recall and predictable precision across varying disease prevalence levels. Additional evaluations, including bootstrap and DeLong analyses, showed statistical significance $(p<0.001)$ of the discriminative gains of RAVE. Minimum Clinically Important Difference (MCID) evaluation confirmed clinically meaningful improvements $(\ge$$3\%)$ over strong baselines. Cross-dataset validation using the CVD dataset verified robust generalization (92.4% accuracy). SHAP analysis provided interpretability to build clinical trust. Conclusions: RAVE-HD shows promise as a reliable, explainable, and scalable solution for large-scale HD screening, consistently performing well across diverse evaluations and datasets. Through statistical validation, the RAVE-HD approach emerges as a practical decision-support tool in HD predictive screening results. Full article

Background: T cell regeneration in the thymus is intrinsically linked to the T cell-biased lineage differentiation of hematopoietic stem and progenitor cells (HSPCs). Although nonhuman primates (NHPs) serve as indispensable models for studying thymic output under physiological and pathological conditions, a non-animal technology facilitating efficient TCR-selected T cell development and evaluating T cell output from NHP-derived HSPCs has been lacking. To address this gap, we established a rhesus macaque-specific artificial thymic organoid (RhATO) modeling primary thymus-tissue-free thymopoiesis. Methods: The RhATO was developed by expressing Rhesus macaque (RM) Delta-like Notch ligand 1 in mouse bone marrow stromal cell line (MS5-RhDLL1). The bone marrow-derived HSPCs were aggregated with MS5-RhDLL1 and cultured forming 3D artificial thymic organoids. These organoids were maintained under defined cytokine conditions to support complete T cell developmental ontogeny. T cell developmental progression was assessed by flow cytometry, and TCR-selected subsets were analyzed for phenotypic and functional properties. Results: RhATOs recapitulated the complete spectrum of thymopoietic events, including emergence of thymus-seeding progenitors, CD4⁺CD3⁻ immature single-positive and CD4⁺CD8⁺ double-positive early thymocytes, and mature CD4⁺ or CD8⁺ single-positive subsets. These subsets expressed CD38, consistent with the recent thymic emigrant phenotype, and closely mirrored canonical T cell ontogeny described in humans. RhATO-derived T cells were TCR-selected and demonstrated cytokine expression upon stimulation. Conclusions: This study provides the first demonstration of an NHP-specific artificial thymic technology that faithfully models thymopoiesis. RhATO represents a versatile ex vivo platform for studying T cell development, immunopathogenesis, and generating TCR selected T cells. Full article

The Lomb–Scargle method (LSM) constitutes a robust method for frequency and amplitude estimation in cases where data exhibit irregular or sparse sampling. Conventional spectral analysis techniques, such as the discrete Fourier transform (FT) and wavelet transform, rely on orthogonal mode decomposition and are inherently constrained when applied to non-equidistant or fragmented datasets, leading to significant estimation biases. The classical LSM, originally formulated for univariate time series, provides a statistical estimator that does not assume a Fourier series representation. In this work, we extend the LSM to multivariate datasets by redefining the shifting parameter $\tau$ to preserve the orthogonality of trigonometric basis functions in ${\mathbb{R}}^n$. This generalization enables simultaneous estimation of the frequency, phase, and amplitude vectors while maintaining the statistical advantages of the LSM, including consistency and noise robustness. We demonstrate its application to solar activity data, where sunspots serve as intrinsic markers of the solar dynamo process. These observations constitute a randomly sampled two-dimensional binary dataset, whose characteristic frequencies are identified and compared with the results of solar research. Additionally, the proposed method is applied to an ultrasound velocity profile measurement setup, yielding a three-dimensional velocity dataset with correlated missing values and significant temporal jitter. We derive confidence intervals for parameter estimation and conduct a comparative analysis with FT-based approaches. Full article

Environmental DNA (eDNA) metabarcoding has transformed marine biodiversity monitoring by allowing non-invasive, cost-effective detection of species with high resolution across diverse marine habitats. A systematic literature search was conducted using Google Scholar, Scopus, and the Qatar University Library databases. Relevant peer-reviewed publications were screened and selected based on predefined inclusion criteria to ensure comprehensive coverage of studies. This review synthesizes advances in global and regional eDNA applications, emphasizing the Middle East and North Africa (MENA) region, which faces unique environmental extremes, high endemism, and significant data gaps. eDNA metabarcoding often outperforms traditional methods under comparable sampling effort to traditional surveys in detecting rare, cryptic, and invasive taxa, but technical challenges like incomplete reference databases, primer biases, PCR inhibitors, and inconsistent methodologies limit their effectiveness, particularly in understudied areas such as MENA. Recent developments, including multi-marker approaches, autonomous samplers, and next-generation sequencing, are enhancing detection precision and enabling broader, real-time monitoring. In the MENA region, early studies have revealed eDNA’s potential for habitat distinction, biogeographic research, pollution assessment, and the early discovery of non-indigenous species, although progress is hindered by gaps in reference libraries, infrastructure, and regulation. This review underscores the urgent need for regional collaboration, standardized protocols, and capacity-building. By integrating eDNA with traditional methods and leveraging emerging technologies, the MENA region can establish itself as a leader in marine biomonitoring under extreme environmental conditions, providing actionable insights for conservation and sustainable management of its unique marine ecosystems. Full article

PIWI-interacting RNAs (piRNAs) are small non-coding RNAs that interact with PIWI proteins and play essential roles in genome stability, gonadal development, and gametogenesis in animals. The Japanese flounder (Paralichthys olivaceus) is an important marine culture teleost in North Asia, showing pronounced sexual size dimorphism, where gynogenetic induction of all-female cohorts can markedly enhance production. However, the PIWI/piRNA pathway in gynogenetic diploid P. olivaceus, which often exhibit gonadal dysgenesis, poor gamete quality, and low fertilization rates, remains poorly understood. In this study, RNA-seq and small RNA-seq data from 11 tissues and 6 developmental stages of common P. olivaceus, as well as the gonads of gynogenetic P. olivaceus, were analyzed to characterize the PIWI/piRNA pathway and its roles in transposon and gene regulation within the germline. The results showed that PIWI/piRNA genes were predominantly expressed in gonads and early embryogenesis in common P. olivaceus, with the highest expression in testis. Clustered piRNAs were identified in the testis and early embryos of common P. olivaceus, which targeted multiple transposon and gene families. Intriguingly, gynogenetic P. olivaceus gonads harbored abundant clustered piRNAs not only in the testes but also in the ovaries, both targeting similar transposon families as that in common P. olivaceus. Notably, the DNA transposon Tc1/Mariner family and pim genes were the most heavily targeted by piRNAs in gynogenetic P. olivaceus, with testis-biased expression. Expanded pim genes were identified in P. olivaceus, overlapping with piRNA clusters, and the in vitro test in P. olivaceus testes revealed that the expanded pim genes may be pseudogenes as a piRNA cluster reference to generate piRNAs regulating the conventional pim members. These unique features of the PIWI/piRNA pathway in gynogenetic diploid P. olivaceus may underline their impaired reproductive ability, and have important theoretical and practical implications for teleost gynogenetic breeding. Full article

Transformer-based models have accelerated EEG motor imagery (MI) decoding by using self-attention to capture long-range temporal structures while complementing spatial inductive biases. This systematic survey of Scopus-indexed works from 2020 to 2025 indicates that reported advances are concentrated in offline, protocol-heterogeneous settings; inconsistent preprocessing, non-standard data splits, and sparse efficiency frequently reporting cloud claims of generalization and real-time suitability. Under session- and subject-aware evaluation on the BCIC IV 2a/2b dataset, typical performance clusters are in the high-80% range for binary MI and the mid-70% range for multi-class tasks with gains of roughly 5–10 percentage points achieved by strong hybrids (CNN/TCN–Transformer; hierarchical attention) rather than by extreme figures often driven by leakage-prone protocols. In parallel, transformer-driven denoising—particularly diffusion–transformer hybrids—yields strong signal-level metrics but remains weakly linked to task benefit; denoise → decode validation is rarely standardized despite being the most relevant proxy when artifact-free ground truth is unavailable. Three priorities emerge for translation: protocol discipline (fixed train/test partitions, transparent preprocessing, mandatory reporting of parameters, FLOPs, per-trial latency, and acquisition-to-feedback delay); task relevance (shared denoise → decode benchmarks for MI and related paradigms); and adaptivity at scale (self-supervised pretraining on heterogeneous EEG corpora and resource-aware co-optimization of preprocessing and hybrid transformer topologies). Evidence from subject-adjusting evolutionary pipelines that jointly tune preprocessing, attention depth, and CNN–Transformer fusion demonstrates reproducible inter-subject gains over established baselines under controlled protocols. Implementing these practices positions transformer-driven BCIs to move beyond inflated offline estimates toward reliable, real-time neurointerfaces with concrete clinical and assistive relevance. Full article

Accurate profiling of deep (20–300 cm) soil moisture is crucial for precision irrigation but remains technically challenging and costly at operational scales. We systematically benchmark eight regression algorithms—including linear regression, Lasso, Ridge, elastic net, support vector regression, multi-layer perceptron (MLP), random forest (RF), and gradient boosting trees (GBDT)—that use easily accessible inputs of 0–20 cm surface soil moisture (SSM) and ten meteorological variables to non-invasively infer soil moisture at fourteen 20 cm layers. Data from a typical agricultural site in Wenxi, Shanxi (2020–2022), were divided into training and testing datasets based on temporal order (2020–2021 for training, 2022 for testing) and standardized prior to modeling. Across depths, non-linear ensemble models significantly outperform linear baselines. Ridge Regression achieves the highest accuracy at 0–20 cm, SVR performs best at 20–40 cm, and MLP yields consistently optimal performance across deep layers from 60 cm to 300 cm (R² = 0.895–0.978, KGE = 0.826–0.985). Although ensemble models like RF and GBDT exhibit strong fitting ability, their generalization performance under temporal validation is relatively limited. Model interpretability combining SHAP, PDP, and ALE shows that surface soil moisture is the dominant predictor across all depths, with a clear attenuation trend and a critical transition zone between 160 and 200 cm. Precipitation and humidity primarily drive shallow to mid-layers (20–140 cm), whereas temperature variables gain relative importance in deeper profiles (200–300 cm). ALE analysis eliminates feature correlation biases while maintaining high predictive accuracy, confirming surface-to-deep information transmission mechanisms. We propose a depth-adaptive modeling strategy by assigning the best-performing model at each soil layer, enabling practical non-invasive deep soil moisture prediction for precision irrigation and water resource management. Full article

Understanding how complex biological forms emerge and evolve remains a central question in evolutionary and developmental biology. To explore this complexity, we introduce a minimal two-dimensional, cellular automaton (CA)-based model that captures key features of biological development—such as spatial growth, self-organization, and differentiation—while remaining computationally tractable and evolvable. Unlike most abstract genotype–phenotype mapping models, our approach generates emergent morphological complexity through spatially explicit rule-based interactions governed by a simple genetic vector, resulting in self-organized patterns reminiscent of biological morphogenesis. Using simulations, we show that, as observed in empirical studies, the resulting phenotypic distribution is highly skewed: simple forms are common, while complex ones are rare. The model exhibits a strongly non-linear genotype-to-phenotype mapping in such a way that small genetic changes can lead to disproportionately large morphological shifts. Notably, transitions toward complexity are less frequent than regressions to simplicity, reflecting evolutionary asymmetries observed in natural systems. We further demonstrate that, by allowing for mutations in the generative rules, our model is capable of adaptive evolution and even reproducing generic features of tumoral growth. These findings suggest that even minimal developmental rules can give rise to rich, hierarchical patterns and complex evolutionary dynamics, positioning our CA-based model as a powerful tool for investigating how developmental constraints and biases shape morphological evolution. Full article

Learned systems in the domain of visual recognition and cognition impress in part because even though they are trained with datasets many orders of magnitude smaller than the full population of possible images, they exhibit sufficient generalization to be applicable to new and previously unseen data. Since training data sets typically represent such a small sampling of any domain, the possibility of bias in their composition is very real. But what are the limits of generalization given such bias, and up to what point might it be sufficient for a real problem task? There are many types of bias as will be seen, but we focus only on one, selection bias. In vision, image contents are dependent on the physics of vision and geometry of the imaging process and not only on scene contents. How do biases in these factors—that is, non-uniform sample collection across the spectrum of imaging possibilities—affect learning? We address this in two ways. The first is theoretical in the tradition of the Thought Experiment. The point is to use a simple theoretical tool to probe into the bias of data collection to highlight deficiencies that might then deserve extra attention either in data collection or system development. Those theoretical results are then used to motivate practical tests on a new dataset using several existing top classifiers. We report that, both theoretically and empirically, there are some selection biases rooted in the physics and imaging geometry of vision that challenge current methods of classification. Full article

In artificial neural networks, the activation dynamics of non-trainable variables are strongly coupled to the learning dynamics of trainable variables. During the activation pass, the boundary neurons (e.g., input neurons) are mapped to the bulk neurons (e.g., hidden neurons), and during the learning pass, both bulk and boundary neurons are mapped to changes in trainable variables (e.g., weights and biases). For example, in feedforward neural networks, forward propagation is the activation pass and backward propagation is the learning pass. We show that a composition of the two maps establishes a duality map between a subspace of non-trainable boundary variables (e.g., dataset) and a tangent subspace of trainable variables (i.e., learning). In general, the dataset-learning duality is a complex nonlinear map between high-dimensional spaces. We use duality to study the emergence of criticality, or the power-law distribution of fluctuations of the trainable variables, using a toy and large models at learning equilibrium. In particular, we show that criticality can emerge in the learning system even from the dataset in a non-critical state, and that the power-law distribution can be modified by changing either the activation function or the loss function. Full article