Search Results (799)

In this contribution, we introduce the concept of Fourier ambiguity resolution. We show how it is rooted in the principle of integer equivariant (IE) estimation and in its periodic representation. As a result, we present a general Fourier representation of IE-estimators. As the IE-class is the largest class of estimators used in GNSS ambiguity resolution, the periodic representation opens up a broad spectrum of new applications, both in the field of parameter estimation and in that of statistical testing. The representation also applies to the integer class, with its popular estimators of integer-rounding, integer-bootstrapping, and integer least-squares, as well as to their integer-aperture variants. In this contribution, we consider the periodic representation of the best integer equivariant (BIE) estimator. It is shown how this minimum mean squared error IE-estimator can be represented in both the spatial and frequency domains and how preference for one of the two representations should be based on the GNSS carrier-phase ambiguity precision. We also present a hybrid form of the BIE-estimator and show how the spatial and frequency representations can be mixed so as to do justice to the practical situation when carrier-phase ambiguity vectors consist of ambiguities having a wide range of varying precision. Full article

Genotyping is a key step in molecular breeding. Due to its cost-effectiveness, accuracy, and flexibility, genotyping by target sequencing (GBTS) has become a preferred technology for medium-density genotyping. In this study, a new GBTS array for fresh edible maize was developed using resequencing data from 477 lines. The array contains 5759 SNPs evenly distributed across the maize genome, with average minor allele frequency (MAF) and polymorphism information content (PIC) values of 0.40 and 0.36, respectively. These SNPs are closely associated with 1566 functional genes. Cluster analysis of 198 maize lines based on the GBTS array was consistent with their pedigree relationships. Furthermore, 277 fresh waxy maize lines were genotyped and used for genomic selection analyses of hundred-kernel weight, kernel length, and kernel width. Comparative evaluation of different models indicated that Ridge Regression Best Linear Unbiased Prediction (rrBLUP) was the optimal model, with prediction accuracies of 0.33, 0.64, and 0.36, respectively. Additional analyses using different marker densities based on the rrBLUP model showed that prediction accuracy did not increase when the number of markers exceeded 2000, indicating that this array provides sufficient marker density for genetic analysis and genomic selection. Overall, this array provides a useful tool for genetic studies of fresh edible maize and facilitates the application of genomic selection in breeding programs. Full article

We introduce the concept of a one-way, broadband information package, the Cosmic Illuminating Gift, intended to provide distant intelligences with fundamental empirical data about the Universe. Unlike previous messaging to extraterrestrial intelligences (METI) that emphasized greetings or cultural identity, the Gift aims to transmit unbiased, universally interpretable information that recipients could not otherwise obtain due to their distinct spacetime position and epoch. By emphasizing raw observations, rather than human interpretations or cosmological models, the Gift aspires to serve as a neutral and enduring resource. A central assumption of the project is that any potential recipients are likely to possess a level of intelligence and technological sophistication far beyond our own. Accordingly, the content and encoding of the Gift are not designed to “teach” fundamentals, but to deliver compact, logically structured packets that such civilizations could decode even at extremely low signal-to-noise levels. This perspective shifts the challenge from brute-force transmission to ensuring that photons arrive in spectrally quiet windows and that the format is unmistakably artificial and distinguishable from astrophysical backgrounds. We outline strategies for content selection, encoding, and transmission that reflect this assumption. Practical implementation is feasible with current or near-term infrastructure, and future advances will only improve the quality of subsequent Gifts. Ultimately, the endeavor is unique among scientific projects in that it anticipates no feedback or measurable result within the span of our civilization’s timeline. Its significance lies instead in the act of contribution itself: offering a durable, universal dataset as a gesture of intellectual solidarity across cosmic distances. Full article

Unbiased stereology represents the most accurate approach for estimating the total number of neurons of specific brain regions; however, its reliability critically depends on the use of rigorously defined and anatomically appropriate sampling parameters. The brain nucleus Locus Coeruleus (LC) plays a key role in several brain functions. LC impairment has been associated with a range of disorders affecting individuals across the lifespan, from infancy to adulthood. In animal models of these conditions, precise estimation of LC neuronal number is essential. The LC analysis poses specific methodological challenges due to its small size, indistinct anatomical boundaries, and age-dependent changes in neuronal density. In this study, we present a detailed and reproducible stereological workflow for the quantification of LC neurons in the mouse brain across the lifespan. Using C57BL/6J mice at postnatal, adult, and aged stages, we optimized all key components of the Optical Fractionator method, LC neurons were identified by immunoperoxidase staining for tyrosine hydroxylase (TH) and quantified using systematic-random sampling implemented in Stereo Investigator^® software. We show that age-specific adjustment of stereological parameters is necessary to obtain reliable estimates, particularly at early postnatal stages characterized by high neuronal packing density. With the optimized protocols described here, TH+ LC neuron counts consistently met accepted precision criteria, as assessed by the Gundersen coefficient of error. Full article

Alzheimer’s disease is a devastating progressive neurodegenerative disorder characterized by extracellular amyloid-beta plaques and intracellular tau tangles. Despite recent advancements in amyloid-beta-targeting immunotherapies, achieving safe and definitive disease control remains a profound clinical challenge. The CRISPR/Cas9 system has emerged as a powerful technology for precision neurogenetics, offering significant potential to address the fundamental questions behind Alzheimer’s disease. This comprehensive review delineates the trajectory of CRISPR applications in Alzheimer’s disease research and therapeutics. First, we explore the integration of CRISPR in engineering high-fidelity in vitro models, such as isogenic induced pluripotent stem cells and three-dimensional cerebral organoids, alongside advanced in vivo mammalian models. Second, we examine how these platforms facilitate unbiased high-throughput genetic screening to uncover molecular underpinnings regulating tau, lipid metabolism, and neuroinflammation. Third, we critically evaluate precision editing strategies targeting core risk genes (APP, MAPT, APOE, and TREM2), explicitly highlighting the severe physiopathological trade-offs between therapeutic efficacy and loss-of-function toxicity. Finally, we address the ultimate translational bottlenecks impeding clinical application. By dissecting the packaging limits of adeno-associated viral vectors and the physical barricade of the blood–brain barrier, we underscore the necessity of transitioning toward next-generation base editors and non-viral lipid nanoparticles to realize safe and efficacious in vivo clinical gene therapies against Alzheimer’s disease. Full article

G protein-coupled receptor kinase 5 (GRK5) is an important therapeutic target involving cardiovascular diseases, cancer, and inflammatory disorders. However, the features of its activation as an essential function regulation process have been poorly understood, limiting related drug development. The work utilizes a molecular dynamics simulation coupled with an interpretable machine learning model to identify key structure and dynamics determinants distinguishing the active and inactive states of GRK5. Benefiting from the unbiased and data-driven framework, the work reveals that the active site tether (AST) is a dominant activation-associated feature, acting as a conformational switch that regulates kinase domain movements. Beyond this canonical element, we also uncover two previously underappreciated structure modules contributing to GRK5 activation, such as the coupling interaction between the α10/α11 helix interface with the N-terminal lipid-binding domain (NLBD) in the active state, and the α5 helix region that facilitates large-scale RH domain reorientation. Conformation dynamics analyses further indicate that GRK5 activation involves disruption of the interdomain interactions and interaction coupling between AST, αN-helix, kinase domain N-lobe, NLBD, and α10/α11 hinge. These observations provide valuable insights into understanding the GPK5 activation mechanism and also highlight the power of machine learning in capturing functionally conformational changes, and in turn offering a methodological guideline for the studying of the protein function mechanism. Full article

This paper presents a multimodal transformer-based framework for the joint prediction of indoor thermal comfort and energy efficiency using real-world building management system (BMS) datasets. Unlike traditional comfort models that rely on fixed physical assumptions and subjective surveys, the proposed approach adopts physics-guided, data-driven learning to capture nonlinear and time-dependent interactions among environmental conditions, HVAC operation, and occupancy-related variables. Thermal comfort labels are computed using the PMV–PPD formulation defined by ASHRAE Standard 55, assuming standard metabolic rate and clothing insulation due to the lack of direct measurements in routine BMS data. A temperature-driven baseline HVAC energy proxy is derived using change-point regression. The proposed transformer architecture fuses multivariate temporal sequences to jointly predict both comfort and energy baseline targets through a dual-head regression formulation. The model is validated on two complementary datasets representing steady-state and dynamically perturbed thermal conditions. The proposed approach consistently outperforms linear regression, random forest, and LSTM baselines, achieving mean absolute errors below 0.03 and $R^2$ values exceeding 0.98 with corresponding RMSE values below 0.035 for both targets. Residual and calibration analyses confirm stable, unbiased prediction behavior across wide temperature ranges. The results highlight the strong potential of attention-based multimodal learning for future comfort-aware building energy optimization and digital twin integration. Full article

Accurate capacity prediction is paramount for ensuring the operational safety and reliability of lithium-ion battery management systems (BMS). Nevertheless, contemporary data-driven approaches often grapple with limited feature representation—frequently relying solely on aggregate charging duration or noise measures—which compromises the robustness of these approaches. To address these limitations, this study proposes a robust framework integrating multi-point voltage temporal sampling (MVTS) with an adaptive gated hybrid ensemble learning strategy. The MVTS method is first used to extract high-dimensional geometric features from the constant-current (CC) charging phase (3.9 V–4.15 V), effectively capturing subtle degradation patterns. Subsequently, an unsupervised isolation forest algorithm is incorporated for automated anomaly detection and rectification, thereby augmenting data stability prior to training. In the fusion stage, a heterogeneous hybrid model comprising eXtreme gradient boosting (XGBoost) and long short-term memory (LSTM) is constructed. An adaptive gating mechanism based on random forest (RF) is added to dynamically weight the base learners. To mitigate data leakage during the stacking process, this study employs an out-of-fold (OOF) training strategy based on leave-one-battery-out (LOBO) cross-validation to generate unbiased meta-features for the gating model. This mechanism dynamically modulates fusion weights contingent upon the multi-point voltage features and model discrepancies, thereby accommodating diverse aging stages and capacity degradation patterns. Experimental results from the NASA battery aging dataset demonstrate that the proposed framework significantly outperforms single-model baselines in terms of RMSE and ${\mathrm{R}}^2$, exhibiting superior adaptability and predictive precision. Full article

Artificial Intelligence (AI) is being increasingly used for equid health and welfare. This study aimed to establish consensus on where and how AI should be developed to achieve maximum benefit in this field. A workshop involving 41 stakeholders generated statements about current welfare concerns, areas for AI development, and barriers and solutions to AI use. Statements were circulated through Delphi surveys (acceptance set at 75% agreement). One-hundred-and-six statements reached agreement. Ethological needs not being met and poor equid management practices were key welfare concerns. Participants identified that insufficient owner/carer knowledge and understanding were important factors contributing to welfare concerns. Priority areas for AI development included assessment of equid wellbeing, as well as individual and population-level monitoring. Barriers included limited understanding of both equine behaviour and AI, biased, unethical, or insufficient data collection, difficulties developing accurate models, challenges to validation, and uncertainty around interpretation. Proposed solutions included development of evidence-based, unbiased AI systems, following best practice guidelines, requiring approval/regulation of AI tools, collaboration, and education of AI users. This is the first study to identify stakeholders’ opinions about where AI is likely to have the greatest benefit for equids, potential barriers, and solutions. The findings should be used to prioritise funding and development. Full article

The current study proposes an improved DenseNet-based Sequential Multimodal Biometric Authentication System, involving face and ear modality for better human identification. The architecture is composed of three convolutional layers and two dense layers, which are optimized for obtaining the discriminative spatial representations in 200 × 200 pixel facial and ear images. Evaluation is performed based on strict 5-fold subject disjoint cross-validation data to ensure the unbiased assessment. The model proposed attained a steady classification accuracy of 97.1 ± 0.79%, and balanced values for Precision, Recall and F1-score under controlled validation conditions, while the Performance analysis including False Acceptance (FAR), False Rejection (FRR) and Equal Error Rate (EER) showed that the EER found is around 1.05% at the optimum operating value. Comparative experiments between parallel feature concatenation and sequential verification techniques show that the sequential framework yields decreased FAR, when compared to the parallel framework, without having a detrimental effect on overall accuracy, while the Statistical validation by analysis of variance shows that the incremental architectural improvements have a significant impact on performance improvements. Findings of this analysis show a “score distribution” that both “single-trait and traditional multifactor systems” exceed the presentation of a novel method for Nex-G authentication solutions. This study advances biometric security by demonstrating how multimodal fusion may address the increasing global demand for robust and privacy-aware authentication methods, thereby setting a standard for intelligent multimodal recognition systems. Full article

Triple-negative breast cancer (TNBC) tumors are typically heterogeneous, predominantly epithelial tissues with discrete patches of mesenchymal-like TNBC cells that differ in their invasiveness, proliferation potential and response to treatment. However, the impact of mesenchymal-like and epithelial TNBC cells on the persistence of chemotherapy-resistant disease remains poorly understood. Mesenchymal-like and epithelial TNBC cell types were detected by multiplex fluorescent immunohistochemistry using antibodies against vimentin, Ki67, and Annexin A6 (AnxA6). Chemotherapy drug-resistant mesenchymal-like and epithelial TNBC cell populations were established by pulse exposure and stepwise dose escalation and validated by 3D cultures and unbiased antibody arrays. Analysis of the response of TNBC tumors treated with six common chemotherapy regimens resulted in 36% complete response and 64% partial response with residual tumor sizes ranging from 0.5 to 37.0 mm. Treatment of TNBC cells with chemotherapy agents led to distinct resistance signatures including downregulation of survivin and upregulation of M-CSF and CXCL8/IL-8 in the model mesenchymal-like TNBC cells, and upregulation of CCL2/MCP-1, CTSS and DKK-1 in model epithelial TNBC cells. The inhibitory phosphorylation of GSK-3β (p-S9) increased in paclitaxel-resistant epithelial cells but decreased in resistant mesenchymal-like TNBC cells. Finally, chemotherapy resistance also activated p90 ribosomal S6 kinases (RSK1/2) in both cell types, while activation of mitogen- and stress-activated kinases (MSK1/2) was only observed in chemotherapy-resistant epithelial TNBC cells. These data reveal that chemotherapy resistance of epithelial and mesenchymal-like TNBC cellular subsets led to distinct profiles of proinflammatory and immune cell chemotactic cytokines and modulated the activities of GSK-3β, p90 RSK1/2 and the related MSK1/2. Targeting these factors and/or the associated signaling pathways may help overcome chemotherapy resistance in TNBC. Full article

The 2025 approval of the selective NaV1.8 blocker suzetrigine for acute pain marked a pivotal advance in analgesic drug development. Yet the subsequent failure of Vertex’s next-generation NaV1.8 inhibitor VX993 to demonstrate clinical analgesia underscores enduring challenges in translating mechanistic promise into patient benefit. This review examines why promising targets and compounds, spanning NaV and TRP channels, often falter and outlines a path toward more reliable target selection and validation. I first summarize the pain pathway, from nociceptor transduction through spinal processing to cortical perception, emphasizing how inflammation and peripheral sensitization reshape excitability. Historically serendipitous, pain drug discovery now prioritizes molecular precision. Most approved chronic pain therapies act in the CNS and are limited by modest efficacy and adverse effects. Nociceptor-enriched targets (NaV1.7/1.8/1.9; TRP channels) remain attractive, yet redundancy among NaV subtypes and the necessity of blocking targets at the correct anatomical sites complicate translation. Human genetics and multi-omics provide a powerful, unbiased engine for target discovery. Rare high-impact variants offer strong causal hypotheses, while common polygenic contributions illuminate broader susceptibility. Large biobanks increasingly reveal a mismatch between legacy pain targets and genetically supported candidates across neuronal and non-neuronal cells. Human DRG transcriptomics highlight NaV channel redundancy. Human in vitro electrophysiology and PK/PD analyses show suzetrigine achieves ~90–95% NaV1.8 engagement, yet neurons can still fire unless additional channels are blocked. Species differences and drug distribution (including BBB/PNS penetration and P-gp efflux) critically influence efficacy; centrally accessible blockade (e.g., for NaV1.7 or TRPA1) may be necessary to achieve robust analgesia, challenging peripherally restricted strategies. Osteoarthritis illustrates how obesity-driven metabolic inflammation, synovial immune activation, subchondral bone remodeling, and specific nociceptor subtypes converge to drive mechanical pain. Multi-omic integration across diseased human tissues can pinpoint causal processes and cell types, enabling more selective and safer target choices. I propose a practical framework for target validation that integrates: (i) rigorous human genetic support; (ii) cell-type and site-of-action mapping; (iii) human-relevant electrophysiology and PK/PD with verified target engagement; (iv) species-appropriate models; (v) consideration of modality (small molecule, biologic, RNA, targeted protein degradation). Advancing genetically and anatomically aligned targets, tested at the right sites and exposures, offers the best path to genuinely effective, better-tolerated pain therapeutics. Full article

Obesity is a persistent public health issue, often resulting in metabolic complications such as insulin resistance (IR). The secretion of pro-inflammatory cytokines from adipose tissue (AT) is increased during obesity, contributing to the impairment of systemic insulin sensitivity. While interventions in animal models have shown that reducing inflammation restores insulin sensitivity, human studies reducing systemic inflammation have produced inconsistent results. We recently demonstrated that three months of high-dose (4 g/daily) ω-3PUFA (fish oil, FO) supplementation improved insulin sensitivity, and decreased systemic and AT inflammation in individuals with obesity (BMI  ≥  30 kg/m²). Given recent studies highlighting the involvement of microRNA (miRNA) in inflammatory cytokine production, we investigated the effect of ω-3PUFA supplementation on AT miRNA expression in this cohort. AT biopsies were collected before and after ω-3PUFA supplementation. miRNA was processed on the Affymetrix miRNA 4.0 GeneChip and analyzed using existing inflammatory gene sets sourced from MSigDB. Unbiased, differentially expressed miRNA analysis identified miR-4498 and miR-5689 as significantly increased after three months of ω-3PUFA supplementation. Real-time PCR confirmed bioinformatic analysis findings. Our study reports the modulation of miRNA in AT, reductions in systemic and AT markers of inflammation, and the improvement of IR post ω-3PUFA supplementation. Further research is needed to elucidate the link between miR-4498, miR-5689, and whole-body insulin sensitivity. Full article

GWASs have successfully identified numerous genetic variants linked to complex traits, but traditional univariate approaches often fail to capture shared genetic architecture across multiple phenotypes. As the scale of genomic data continues to increase, the demand for more efficient multi-phenotype analysis methods has become particularly critical. In addition, the issue of population structure must also be properly addressed to ensure robust and unbiased results. Multivariate methods for multi-phenotype analysis, such as GAMMA, address this by combining linear mixed models with multivariate distance matrix regression to account for population structure; however, since these methods utilize computationally intensive models, developing efficient implementations is essential for practical analysis. Although GAMMA is a well-designed and effective tool, its original implementation relies on multiple programming environments and requires frequent data exchanges between components. These factors increase computational burden and complicate installation and execution for users unfamiliar with programming, making practical applications, particularly for high-dimensional datasets, challenging. Here, we present GAMMA-RAY, a high-performance C++ implementation that streamlines the computational pipeline, leverages parallel processing, and employs efficient matrix operations to achieve substantial reductions in runtime and memory usage. GAMMA-RAY provides both a user-friendly web-based interface for non-programmers and a standalone version for secure local execution. We further applied GAMMA-RAY to a yeast dataset and identified putative trans-eQTLs, in which several variants overlapped with previously reported cis- and trans-eQTLs. In addition, functional enrichment analysis revealed that the associated trans-eGenes are enriched, a conclusion consistently supported by biological annotation resources and underscoring the biological significance of these results. Full article

In autonomous driving, deep convolutional neural networks face a core conflict between computational efficiency and safety-critical robustness on resource-constrained onboard computing units. Dominant structural pruning, based on weight magnitude or geometric statistics, fails in long-tailed traffic scenarios by equating parameter magnitude with feature importance and pruning critical filters in the tail classes. To address this, we propose a structural pruning framework that evaluates the semantic utility of features using weighted copula entropy rather than relying solely on their magnitude. Our novel approach integrates Elastic Net regularization for inducing sparsity and weighted copula entropy for unbiased information-theoretic feature selection. By incorporating inverse class frequency weighting into empirical Copula estimation, we decouple feature relevance from sample abundance, ensuring the preservation of rare-class discriminators based on their information content rather than occurrence frequency. Furthermore, this metric is embedded into an enhanced max-relevance and min-redundancy algorithm to eliminate semantic redundancy while maintaining representational diversity. Extensive experiments on the BDD100K dataset with YOLOv5l and YOLOv8l architectures demonstrate that, at a 50% pruning rate, the proposed method reduces FLOPs and parameters by nearly 50%, with only 0.09% mAP@0.5 loss for YOLOv5l and 0.14% mAP@0.5 loss for YOLOv8l, while significantly improving the mAP of the extreme tail class Train from 0% to 3.84% and 2.76% to 5.12%, respectively. It achieves a more favorable trade-off between detection accuracy and computational efficiency than mainstream pruning approaches. This work provides a lightweight scheme for autonomous driving perception models and a new information-theoretic perspective for structured network pruning. Full article