Search Results (448)

Beyond their canonical role in promoting G1/S progression, the complexes formed by cyclin D and cyclin-dependent kinase (CDK) 4/6 have emerged as contributors to enhanced cell migration. However, a direct link between this complex and cytoskeletal remodeling during cell motility has remained poorly understood. Here, we show that CDK4/6 inhibition in HeLa cells disrupts lamellipodia formation and subsequent focal adhesion assembly, leading to a reduction in cell migration and invasion. Notably, CDK4, but not CDK6, in complex with cyclin D1/D2, localizes to membrane ruffles to facilitate cytoskeletal reorganization. Mechanistically, proteomic and phosphoproteomic analyses revealed that CDK4 inhibition attenuates the transforming growth factor β (TGFβ) pathway via reduced Smad3 phosphorylation at Thr8, downregulating integrin subunits (α5, α6, and β1). Furthermore, CDK4 inhibition significantly decreased focal adhesion kinase (FAK) phosphorylation at Tyr397 and Rac1-GTP levels. Importantly, the resulting migration defect was largely restored by activation of either Rac1 or FAK. Thus, our data support a model in which cyclin D1/D2–CDK4 promotes phosphorylation of Smad3, leading to upregulation of integrin subunits, activation of FAK and Rac1, and consequent lamellipodia formation and cell migration. These findings provide direct evidence that CDK4 regulates actin cytoskeletal reorganization during cell migration and suggest that CDK4/6 inhibitors may dampen cytoskeleton-dependent tumor invasion, in addition to their antiproliferative effects. Full article

Autonomous navigation in unknown environments demands policies that can jointly perceive semantic context and geometric safety. Existing Transformer-enabled deep reinforcement learning (DRL) frameworks, such as the Goal-guided Transformer Soft Actor–Critic (GoT-SAC), rely on temporal stacking of multiple RGB frames, which encodes short-term motion cues but lacks explicit spatial understanding. This study introduces a geometry-aware RGB-D early fusion modality that replaces temporal redundancy with cross-modal alignment between appearance and depth. Within the GoT-SAC framework, we integrate a pixel-aligned RGB-D input into the Transformer encoder, enabling the attention mechanism to simultaneously capture semantic textures and obstacle geometry. A comprehensive systematic ablation study was conducted across five modality variants (4RGB, RGB-D, G-D, 4G-D, and 4RGB-D) and three fusion strategies (early, parallel, and late) under identical hyperparameter settings in a controlled simulation environment. The proposed RGB-D early fusion achieved a 40.0% success rate and +94.1 average reward, surpassing the canonical 4RGB baseline (28.0% success, +35.2 reward), while a tuned configuration further improved performance to 54.0% success and +146.8 reward. These results establish early pixel-level multimodal fusion (RGB-D) as a principled and efficient successor to temporal stacking, yielding higher stability, sample efficiency, and geometry-aware decision-making. This work provides the first controlled evidence that spatially aligned multimodal fusion within Transformer-based DRL significantly enhances mapless navigation performance and offers a reproducible foundation for sim-to-real transfer in autonomous mobile robots. Full article

The development of cancer immunotherapies has transformed cancer treatment paradigms, yet durable and tumour-specific responses remain elusive for many patients. Neoantigens, immunogenic peptides arising from tumour-specific genomic alterations, have emerged as promising cancer vaccine targets. Early-phase clinical trials using different vaccine platforms, including mRNA, peptide, DNA, and viral vector-based personalised cancer vaccines, have demonstrated the feasibility of targeting neoantigens, with early signals of prolonged survival in some patients. Most current vaccine strategies focus on canonical neoantigens, typically derived from exonic single-nucleotide variants (SNVs) and small insertions/deletions (INDELs), yet this represents only a fraction of the potential neoantigen repertoire. Evidence now shows that non-canonical neoantigens, arising mostly from alternative splicing, intron retention, translation of non-coding RNAs, gene fusions, and retroelement activation, broaden the antigenic landscape, with the potential for increasing tumour specificity and immunogenicity. In this review, we explore the biology of non-canonical neoantigens, the technological advances that now enable their systematic detection, and their potential to inform next-generation personalised cancer vaccines. Full article

This paper starts with the transition from classical physics to quantum mechanics which was greatly aided by the concept of phase space. The role of canonical transformations in quantum mechanics is addressed. The Wigner phase-space distribution function is then defined which arises from the formulation of the density matrix, followed by the harmonic oscillator in phase space. Coherent and one- and two-mode squeezed states of light as well as the squeezed vacuum are discussed in the phase-space picture. Attention is also drawn to the fact that squeezed states naturally generate entanglement between the two-modes. Coupled harmonic oscillators are also elucidated in connection with the Wigner phase space. Note that the phase-space picture of quantum mechanics has become an important scientific language for the rapidly expanding field of quantum optics. Here, we mainly focus on the simplest form of the Wigner function, which finds application in many branches of quantum mechanics. We make use of several symmetry groups such as Lorentz groups, the symplectic group in two and four dimensions, and the Euclidean group. The decoherence problem of an optical field is examined through a reformulation of the Poincaré sphere as a further illustration of the density matrix. Full article

To address the limitation of 2D methods in inferring absolute scrap dimensions from images, we propose Scrap-SAM-CLIP (SSC), a vision-language model integrating the segment anything model (SAM) and contrastive language-image pre-training in Chinese (CN-CLIP). The model enables identification of canonical scrap shapes, establishing a foundational framework for subsequent 3D reconstruction and dimensional extraction within the 3D recognition pipeline. Individual modules of SSC are fine-tuned on the self-constructed scrap dataset. For segmentation, the combined box-and-point prompt yields optimal performance among various prompting strategies. MobileSAM and SAM-HQ-Tiny serve as effective lightweight alternatives for edge deployment. Fine-tuning the SAM decoder significantly enhances robustness under noisy prompts, improving accuracy by at least 5.55% with a five-positive-points prompt and up to 15.00% with a five-positive-points-and-five-negative-points prompt. In classification, SSC achieves 95.3% accuracy, outperforming Swin Transformer V2_base by 2.9%, with t-SNE visualizations confirming superior feature learning capability. The performance advantages of SSC stem from its modular assembly strategy, enabling component-specific optimization through subtask decoupling and enhancing system interpretability. This work refines the scrap 3D identification pipeline and demonstrates the efficacy of adapted foundation models in industrial vision systems. Full article

This study examines whether Drawmetrics (DM), an expressive–semantic personality system, can be linked with the Five-Factor Model (Big Five) through an embedding-based mapping approach and network psychometric methods. A total of 185 participants completed both the DM assessment and the IPIP-NEO 120 Big Five inventory. DM term outputs were embedded using a miniLM sentence-transformer and aggregated into 30 facet composites, with six composites per domain. Big Five facet composites were extracted from standardized reports and harmonized to canonical facet names. Analyses focused on the overlap sample (N = 148) with valid scores on both instruments. DM composites demonstrated strong internal structure and high stability indices. Substantial semantic-space alignment was observed between DM term language and Big Five facet language, supporting interpretable linking. However, person-level correlations between DM and Big Five domains were modest (mean |r| ≈ 0.07; Spearman similar), with the largest facet-level association at |r| ≈ 0.26. DM appears to represent a coherent expressive–semantic trait space that is related to, but not isomorphic with, Big Five traits. These findings support a linking rather than equivalence interpretation and highlight the need for future research on scaling, reliability, range restriction, and criterion validation. Full article

This paper introduces a new framework for high-dimensional covariance matrix estimation, the Blockwise Exponential Covariance Model (BECM), which extends the traditional block-partitioned representation to the log-covariance domain. By exploiting the block-preserving properties of the matrix logarithm and exponential transformations, the proposed model guarantees strict positive definiteness while substantially reducing the number of parameters to be estimated through a blockwise log-covariance parameterization, without imposing any rank constraint. Within each block, intra- and inter-group dependencies are parameterized through interpretable coefficients and kernel-based similarity measures of factor loadings, enabling a data-driven representation of nonlinear groupwise associations. Using monthly stock return data from the U.S. stock market, we conduct extensive rolling-window tests to evaluate the empirical performance of the BECM in minimum-variance portfolio construction. The results reveal three main findings. First, the BECM consistently outperforms the Canonical Block Representation Model (CBRM) and the native 1/N benchmark in terms of out-of-sample Sharpe ratios and risk-adjusted returns. Second, adaptive determination of the number of clusters through cross-validation effectively balances structural flexibility and estimation stability. Third, the model maintains numerical robustness under fine-grained partitions, avoiding the loss of positive definiteness common in high-dimensional covariance estimators. Overall, the BECM offers a theoretically grounded and empirically effective approach to modeling complex covariance structures in high-dimensional financial applications. Full article

Background: p-values are ubiquitous in scientific research, yet they fundamentally fail to quantify the strength of evidence for or against competing hypotheses. This limitation is particularly problematic in neuroimaging meta-analyses, where researchers need to assess how strongly the available data support specific and spatially consistent patterns of brain activation across studies. Methods: In this work, we present a practical approach that transforms p-values into their corresponding upper bounds on the Bayes factor, which quantify the maximum plausible evidence in favor of the alternative hypothesis given the observed data. The method is illustrated within the framework of Activation Likelihood Estimation, the most widely used coordinate-based meta-analytic technique in neuroimaging and applied to a reference dataset comprising 73 finger-tapping experiments. Results: The results show that effects traditionally classified as statistically significant using the canonical Activation Likelihood Estimation framework actually span a wide range of evidential strengths, with Bayes factor bounds varying approximately from 46 to 410. This finding reveals substantial heterogeneity in weight of evidence that is concealed by conventional threshold-based inference. Conclusion: By enabling the construction of voxel-wise maps of evidential strength while remaining fully compatible with existing analysis pipelines, the proposed approach helps to avoid common misinterpretations of p-values and improves the interpretability and reliability of neuroimaging meta-analytic conclusions. It therefore provides a conservative, Bayesian-inspired complement to standard significance maps. Full article

Objectivity or material frame indifference is the indifference of material behavior to a Euclidean transformation (a general change of observer). This paper considers the objectivity of turbulent fields under a time-periodic change of the observer. At a given phase, the fluctuating velocity and Reynolds stress tensor fields are shown to be objective. This is further illustrated by presenting one-point statistics of two canonical flows: homogeneous isotropic turbulence and turbulent channel flow. The results also highlight that statistical symmetries such as homogeneity and stationarity found in the objective fields are carried over after a change of observer. The paper concludes with some final thoughts on objectivity and its usefulness for the advancement of turbulent theory. Full article

Pronunciation assessment is central to computer-assisted pronunciation training (CAPT) and speaking tests, yet most systems still adopt a native norm, treating deviations from canonical L1 pronunciations as errors. In contrast, rating rubrics and psycholinguistic evidence emphasize intelligibility for a target listener population and show that listeners rapidly adapt their phonetic categories to new accents. We argue that automatic assessment should likewise be referenced to the target learner group. We build a Transformer-based mispronunciation detection (MD) model that computationally mimics listener adaptation: it is first pre-trained on multi-speaker Librispeech, then fine-tuned on the non-native L2-ARCTIC corpus that represents a specific learner population. Fine-tuning, using either synthetic or human MD labels, constrains updates to the phonetic space (i.e., the representation space used to encode phone-level distinctions, the learned phone/phonetic embedding space, and its alignment with acoustic representations), which means that only the phonetic module is updated while the rest of the model stays fixed. Relative to the pre-trained model, L2 adaptation substantially improves MD recall and F1, increasing ROC–AUC from 0.72 to 0.85. The results support a target-population norm and inform the design of perception-aligned, fairer automatic pronunciation assessment systems. Full article

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) have transformed the treatment landscape for estrogen receptor-positive (ER+) breast cancer, yet resistance remains a major clinical challenge. Although CDK4/6i induce G₁ arrest and therapy-induced senescence (TIS), the exact nature of this senescent state and its contribution to resistance are not well understood. To explore this, we developed palbociclib- (2PR, 9PR, TPR) and abemaciclib- (2AR, 9AR, TAR) resistant ER+ breast cancer sublines through prolonged drug exposure over six months. Resistant cells demonstrated distinct phenotypic alterations, including cellular senescence, reduced mitochondrial membrane potential, and impaired glycolytic activity. Cytokine profiling and enzyme-linked immunosorbent assay (ELISA) validation revealed a non-canonical senescence-associated secretory phenotype (SASP) characterized by elevated growth/differentiation factor 15 (GDF-15) and serpin E1 (plasminogen activator inhibitor-1, PAI-1) and absence of classical pro-inflammatory interleukins, including IL-1α and IL-6. IL-8 levels were significantly elevated, but no association with epithelial–mesenchymal transition (EMT) was observed. Resistant cells preserved their epithelial morphology, showed no upregulation of EMT markers, and lacked aldehyde dehydrogenase 1-positive (ALDH1+) stem-like populations. Additionally, Regulated upon Activation, Normal T-cell Expressed, and Secreted (RANTES) was strongly upregulated in palbociclib-resistant cells. Together, these findings identify a distinct, non-canonical senescence phenotype associated with CDK4/6i resistance and may provide a foundation for identifying new vulnerabilities in resistant ER+ breast cancers through targeting SASP-related signaling. Full article

The present research demonstrates the utility of the linear canonical transform (LCT) in constructing the characteristic function of real random variables. We refer to this construction as the linear canonical characteristic function (LCCF). The proposed LCCF aims to address the limitations of the classical characteristic function in both theoretical and applied aspects. Using this approach, we investigate its properties, such as Hermitian symmetry, continuity, convolution, and derivatives, which are generalized forms of the classical characteristic function in the literature. Finally, we implement the obtained results by calculating several probability density functions in the LCCF domains. Full article

The digitization of the Chinese Buddhist Canon represents a transformative shift in Buddhist textual scholarship, enabling unprecedented access to and analysis of one of East Asia’s most extensive scriptural collections. This review examines the evolution of digital platforms, with a focus on the Chinese Buddhist Electronic Text Association (CBETA) and the SAT Daizōkyō Text Database, which have become foundational resources in the field. It evaluates their respective methodological paradigms—CBETA’s critical edition model and SAT’s interoperable, ecosystem-based approach—while highlighting their shared reliance on the Taishō Tripiṭaka as a base text. The study identifies a persistent “Taishō bottleneck,” wherein the dominance of a single edition obscures the rich textual diversity inherent in the canon’s three major lineages: Central, Southern, and Northern. By surveying newly accessible image databases of key editions such as the Zhaocheng Jin Canon 趙城金藏, Sixi Canon 思溪藏, and Qidan Canon 契丹藏, the paper argues for a paradigm shift toward a multi-lineage collation framework. The integration of artificial intelligence—particularly in OCR, text–image alignment, and semantic analysis—is presented as essential for realizing a “Hybrid Digital Canon.” This model would harmonize genealogical, media, and methodological pluralism, fostering a more nuanced and historically grounded digital philology. Full article

In this paper, we investigate the m-spotty weight enumerators over the mixed alphabet ${\mathbb{Z}}_p{R}_k$. Specifically, we construct the Gray map from ${\mathbb{Z}}_p^{\alpha }\times R_k^{\beta }$ to ${\mathbb{Z}}_p^{\alpha +k\beta }$, where $R_k={\mathbb{Z}}_p+v{\mathbb{Z}}_p+v^2{\mathbb{Z}}_p+\cdots +v^{k-1}{\mathbb{Z}}_p$ with $v^k=0$ and $k\ge 5$. Based on this framework, we establish the MacWilliams identity for the m-spotty weight enumerators between a linear code and its dual over ${\mathbb{Z}}_p{R}_k$, by employing the generalized Hadamard transform and the canonical additive character of ${\mathbb{Z}}_p$. Finally, an example is presented to illustrate and validate the theoretical results. Full article

Background: Accurate recognition of promoter sequences in Escherichia coli is fundamental for understanding gene regulation and engineering synthetic biological systems. However, existing computational methods struggle to simultaneously model long-range genomic dependencies and fine-grained local motifs, particularly the degenerate −10 and −35 elements of $\sigma 70$ promoters. To address this gap, we propose DNABERT2-CAMP, a novel hybrid deep learning framework designed to integrate global contextual understanding with high-resolution local motif detection for robust promoter identification. Methods: We constructed a balanced dataset of 8720 experimentally validated and negative 81-bp sequences from RegulonDB, literature, and the E. coli K-12 genome. Our model combines a pre-trained DNABERT-2 Transformer for global sequence encoding with a custom CAMP module (CNN-Attention-Mean Pooling) for local feature refinement. We evaluated performance using 5-fold cross-validation and an independent external test set, reporting standard metrics including accuracy, ROC AUC, and Matthews correlation coefficient (MCC). Results: DNABERT2-CAMP achieved 93.10% accuracy and 97.28% ROC AUC in cross-validation, outperforming existing methods including DNABERT. On an independent test set, it maintained strong generalization (89.83% accuracy, 92.79% ROC AUC). Interpretability analyses confirmed biologically plausible attention over canonical promoter regions and CNN-identified AT-rich/-35-like motifs. Conclusions: DNABERT2-CAMP demonstrates that synergistically combining pre-trained Transformers with convolutional motif detection significantly improves promoter recognition accuracy and interpretability. This framework offers a powerful, generalizable tool for genomic annotation and synthetic biology applications. Full article