Search Results (169)

Neurodegenerative diseases (NDDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), are becoming more prevalent and still lack effective disease-modifying therapies (DMTs). However, translational efficiency remains critically low. For example, a ClinicalTrials.gov analysis of AD programs (2002–2012) estimated ~99.6% attrition, while PD programs (1999–2019) achieved an overall success rate of ~14.9%. In vitro platforms are assessed, ranging from immortalized neuronal lines and primary cultures to human-induced pluripotent stem cell (iPSC)-derived neurons/glia, neuron–glia co-cultures (including neuroinflammation paradigms), 3D spheroids, organoids, and blood–brain barrier (BBB)-on-chip systems. Complementary in vivo toxin, pharmacological, and genetic models are discussed for systems-level validation and central nervous system (CNS) exposure realism. The therapeutic synthesis focuses on AD, covering symptomatic drugs, anti-amyloid immunotherapies, tau-directed approaches, and repurposed drug classes that target metabolism, neuroinflammation, and network dysfunction. This review links experimental models to translational decision-making, focusing primarily on AD and providing a brief comparative context from other NDDs. It also covers emerging targeted protein degradation (PROTACs). Key priorities include neuroimmune/neurovascular human models, biomarker-anchored adaptive trials, mechanism-guided combination DMTs, and CNS PK/PD-driven development for brain-directed degraders. Full article

Text-to-image (T2I) generation, a core component of generative artificial intelligence(AI), is increasingly important for creative industries and human–computer interaction. Despite impressive progress in realism and diversity, diffusion models still exhibit critical security blind spots particularly in the Transformer key-value mapping mechanism that underpins cross-modal alignment. Existing backdoor attacks often rely on large-scale data poisoning or extensive fine-tuning, leading to low efficiency and limited stealth. To address these challenges, we propose two efficient backdoor attack methods AttnBackdoor and SemBackdoor grounded in the Transformer’s key-value storage principle. AttnBackdoor injects precise mappings between trigger prompts and target instances by fine-tuning the key-value projection matrices in U-Net cross-attention layers (≈5% of parameters). SemBackdoor establishes semantic-level mappings by editing the text encoder’s MLP projection matrix (≈0.3% of parameters). Both approaches achieve high attack success rates (>90%), with SemBackdoor reaching 98.6% and AttnBackdoor 97.2%. They also reduce parameter updates and training time by 1–2 orders of magnitude compared to prior work while preserving benign generation quality. Our findings reveal dual vulnerabilities at visual and semantic levels and provide a foundation for developing next generation defenses for secure generative AI. Full article

Information asymmetry regarding freshness has become a critical issue in the fresh produce supply chain. This study focuses on a fresh produce e-commerce supply chain comprising suppliers, third-party logistics (TPL) providers, and e-commerce platforms. Considering consumer preferences for freshness, it employs a Stackelberg game model to examine the impact of TPL exaggerating freshness preservation efforts on the supply chain. Subsequently, contract design is employed to achieve supply chain coordination. Findings indicate that when TPL misrepresents preservation effort information, profits decline across all supply chain members. A cost-sharing-profit-sharing contract facilitates redistribution of costs and benefits between upstream and downstream entities, thereby increasing preservation effort levels. Although preservation costs increase under this arrangement, contractual terms ultimately enhance profits for all supply chain members. This study incorporates freshness preferences to enhance model realism, providing theoretical foundations for decision-making under information asymmetry regarding freshness preservation efforts. It holds significant practical value for fostering collaboration among members in fresh produce e-commerce supply chains and promoting sustainable supply chain development. Full article

Point cloud data, with its rich information and high-precision geometric details, holds significant value for urban road infrastructure surveying and management. To overcome the limitations of manual classification, this study employs deep learning techniques for automated point cloud feature extraction and classification, achieving high-precision object recognition in road scenes. By integrating the Princeton ModelNet40, ShapeNet, and Sydney Urban Objects datasets, we extracted 3D spatial coordinates from the Sydney Urban Objects Dataset and organized labeled point cloud files to build a comprehensive dataset reflecting real-world road scenarios. To address noise and occlusion-induced data gaps, three augmentation strategies were implemented: (1) Farthest Point Sampling (FPS): Preserves critical features while mitigating overfitting. (2) Random Z-axis rotation, translation, and scaling: Enhances model generalization. (3) Gaussian noise injection: Improves training sample realism. The PointNet++ framework was enhanced by integrating a point-filling method into the preprocessing module. Model training and prediction were conducted using its Multi-Scale Grouping (MSG) and Single-Scale Grouping (SSG) schemes. The model achieved an average training accuracy of 86.26% (peak single-instance accuracy: 98.54%; best category accuracy: 93.15%) and a test set accuracy of 97.41% (category accuracy: 84.50%). This study demonstrates successful road scene point cloud classification, providing valuable insights for point cloud data processing and related research. Full article

In modern high-demand applications, such as real-time video communication, cloud gaming, and high-definition live streaming, achieving both superior transmission speed and high visual fidelity is paramount. However, unstable networks and packet loss remain major bottlenecks, making accurate and low-latency video error concealment a critical challenge. Traditional error control strategies, such as Forward Error Correction (FEC) and Automatic Repeat Request (ARQ), often introduce excessive latency or bandwidth overhead. Meanwhile, receiver-side concealment methods struggle under high motion or significant packet loss, motivating the exploration of predictive models. SimVP-v2, with its efficient convolutional architecture and Gated Spatiotemporal Attention (GSTA) mechanism, provides a strong baseline by reducing complexity and achieving competitive prediction performance. Despite its merits, SimVP-v2’s reliance on 2D convolutions for implicit temporal aggregation limits its capacity to capture complex motion trajectories and long-term dependencies. This often results in artifacts such as motion blur, detail loss, and accumulated errors. Furthermore, its single-modality design ignores the complementary contextual cues embedded in the audio stream. To overcome these issues, we propose A3DSimVP (Audio- and 3D-Enhanced SimVP-v2), which integrates explicit spatio-temporal modeling with multimodal feature fusion. Architecturally, we replace the 2D depthwise separable convolutions within the GSTA module with their 3D counterparts, introducing a redesigned GSTA-3D module that significantly improves motion coherence across frames. Additionally, an efficient audio–visual fusion strategy supplements visual features with contextual audio guidance, thereby enhancing the model’s robustness and perceptual realism. We validate the effectiveness of A3DSimVP’s improvements through extensive experiments on the KTH dataset. Our model achieves a PSNR of 27.35 dB, surpassing the 27.04 of the SimVP-v2 baseline. Concurrently, our improved A3DSimVP model reduces the loss metrics on the KTH dataset, achieving an MSE of 43.82 and an MAE of 385.73, both lower than the baseline. Crucially, our LPIPS metric is substantially lowered to 0.22. These data tangibly confirm that A3DSimVP significantly enhances both structural fidelity and perceptual quality while maintaining high predictive accuracy. Notably, A3DSimVP attains faster inference speeds than the baseline with only a marginal increase in computational overhead. These results establish A3DSimVP as an efficient and robust solution for latency-critical video applications. Full article

High Mountain Asia (25–40° N, 70–100° E) plays a critical role in sustaining water resources for nearly two billion people; however, the accurate estimation of precipitation remains challenging. Numerous gridded products have been developed, yet their performance across the region remains uncertain and is often analyzed only over small areas or short periods. This study provides a comprehensive evaluation of five major gridded precipitation datasets (ERA5, HARv2, GPCC, APHRODITE, and PERSIANN-CDR) over 1983–2007 throughout the entire domain through spatial intercomparison, validation against ground stations, and assessment against observed river discharge. Results show that reanalysis products (ERA5, HARv2) better capture spatial precipitation patterns, particularly along the Himalayas and Kunlun range, with HARv2 more accurately representing elevation-dependent gradients. Gauge-based (GPCC, APHRODITE) and satellite-derived (PERSIANN-CDR) datasets exhibit smoother fields and weaker orographic responses. In catchment-scale evaluations, reanalysis shows a superior performance, with ERA5 achieving the lowest bias, highest Kling–Gupta Efficiency, and best water-balance consistency. GPCC and PERSIANN-CDR underestimate discharge, and APHRODITE performs worst overall. No single dataset is optimal for all applications. Gauge-based datasets and PERSIANN-CDR are suitable for localized climatology in well-instrumented areas, while reanalysis products offer the best compromise between spatial realism and hydrological consistency for large-scale modelling in high-altitude regions where observations are limited. Full article

The widespread use of anticancer drugs (ACDs) in human therapies determines the occurrence of these potent cytotoxic chemicals into aquatic ecosystems. Nowadays, ACDs are ubiquitous contaminants in wastewater effluents and freshwater compartments, raising urgent questions about their environmental impact. Designed to disrupt cellular proliferation, these compounds are inherently bioactive and can exert toxic effects on non-target organisms even at trace concentrations. Conventional fate and toxicity tests provide important initial data but are limited in ecological realism, often focusing on single-specie and single-endpoint under controlled conditions and overlooking complex interactions, trophic dynamics, and long-term chronic exposures. Knowledge of all these aspects is needed for proper monitoring, assessment, and regulation of ACDs. Simulated ecosystem experiments, such as mesocosms, provide intermediate-scale, semi-controlled platforms for investigating real-world exposure scenarios, assessing ACD fate, and identifying both direct and indirect ecological effects. They offer distinct advantages for evaluating the chronic toxicity of persistent pollutants by enabling realistic long-term contamination simulations and supporting the simultaneous collection of comprehensive hazard and exposure endpoints. This perspective underscores the growing concern surrounding the contamination of ACDs, examines the limitations of traditional assessment approaches, and advocates for mesocosm-based studies as a critical bridge between laboratory research and ecosystem-level understanding. By integrating mesocosm experiments into environmental fate and risk evaluation, we can better predict the behavior and ecological consequences of anticancer pharmaceuticals, guiding strategies to mitigate their impact on aquatic life. Full article

Understanding how emotions and psychological states influence both individual and collective actions is critical for expressing the real complexity of biosocial and ecological systems. Recent breakthroughs in mathematical modeling have created new opportunities for systematically integrating these emotion-specific elements into dynamic frameworks ranging from human health to animal ecology and socio-technical systems. This review builds on mathematical modeling approaches by bringing together insights from neuroscience, psychology, epidemiology, ecology, and artificial intelligence to investigate how psychological effects such as fear, stress, and perception, as well as memory, motivation, and adaptation, can be integrated into modeling efforts. This article begins by examining the influence of psychological factors on brain networks, mental illness, and chronic physical diseases (CPDs), followed by a comparative discussion of model structures in human and animal psychology. It then turns to ecological systems, focusing on predator–prey interactions, and investigates how behavioral responses such as prey refuge, inducible defense, cooperative hunting, group behavior, etc., modulate population dynamics. Further sections investigate psychological impacts in epidemiological models, in which risk perception and fear-driven behavior greatly affect disease spread. This review article also covers newly developing uses in artificial intelligence, economics, and decision-making, where psychological realism improves model accuracy. Through combining these several strands, this paper argues for a more subtle, emotionally conscious way to replicate intricate adaptive systems. In fact, this study emphasizes the need to include emotion and cognition in quantitative models to improve their descriptive and predictive ability in many biosocial and environmental contexts. Full article

Low-dose computed tomography (LDCT) has become a widely adopted protocol to reduce radiation exposure during clinical imaging. However, dose reduction inevitably amplifies noise and artifacts, compromising image quality and diagnostic confidence. To address this challenge, this study introduces De-TransGAN, a transformer-augmented Generative Adversarial Network specifically designed for high-fidelity LDCT image denoising. Unlike conventional CNN-based denoising models, De-TransGAN combines convolutional layers with transformer blocks to jointly capture local texture details and long-range anatomical dependencies. To further guide the network toward diagnostically critical structures, we embed channel–spatial attention modules based on the Convolutional Block Attention Module (CBAM). On the discriminator side, a hybrid design integrating PatchGAN and vision transformer (ViT) components enhances both fine-grained texture discrimination and global structural consistency. Training stability is achieved using the Wasserstein GAN with Gradient Penalty (WGAN-GP), while a composite objective function—L1 loss, SSIM loss, and VGG perceptual loss—ensures pixel-level fidelity, structural similarity, and perceptual realism. De-TransGAN was trained on the TCIA LDCT and Projection Data dataset and validated on two additional benchmarks: the AAPM Mayo Clinic Low Dose CT Grand Challenge dataset and a private clinical chest LDCT dataset comprising 524 scans (used for qualitative assessment only, as no NDCT ground truth is available). Across these datasets, the proposed method consistently outperformed state-of-the-art CNN- and transformer-based denoising models. On the LDCT and Projection dataset head images, it achieved a PSNR of 44.9217 dB, SSIM of 0.9801, and RMSE of 1.001, while qualitative evaluation on the private dataset confirmed strong generalization with clear noise suppression and preservation of fine anatomical details. These findings establish De-TransGAN as a clinically viable approach for LDCT denoising, enabling radiation reduction without compromising diagnostic quality. Full article

Generating realistic synthetic gene expression data that captures the complex interdependencies and biological context of cellular systems remains a significant challenge. Existing methods often struggle to reproduce intricate co-expression patterns and incorporate prior biological knowledge effectively. To address these limitations, we propose BioGen-KI, a novel bio-inspired generative network with knowledge integration. Our framework leverages a hybrid deep learning architecture that integrates embeddings learned from biological knowledge graphs (e.g., gene regulatory networks, pathway databases) with a conditional generative adversarial network (cGAN). The knowledge graph embeddings guide the generator to produce synthetic expression profiles that respect known biological relationships, while conditioning on contextual information (e.g., cell type, experimental condition) allows for targeted data synthesis. Furthermore, we introduce a biologically informed discriminator that evaluates not only the statistical realism but also the biological plausibility of the generated data, encouraging the preservation of pathway coherence and relevant gene interactions. We demonstrate the efficacy of BioGen-KI by generating synthetic gene expression datasets that exhibit improved statistical similarity to real data and, critically, better preservation of biologically meaningful relationships compared to baseline GAN models and methods relying solely on statistical characteristics. Evaluation on downstream tasks, such as clustering and differential gene expression analysis, highlights the utility of BioGen-KI-generated data for enhancing the robustness and interpretability of biological data analysis. This work presents a significant step towards generating more biologically faithful synthetic gene expression data for research and development. Full article

This article contends that Sofia Coppola’s Priscilla (2023) uses digital filmmaking to re-animate the commodified image of Priscilla Presley, privileging surface and affect over historical realism. Though Coppola predominantly shoots on film, her decision to film Priscilla digitally—an adaptation of Presley’s memoir—marks a formal shift in her filmography aligned with her ongoing exploration of feminine interiority and aesthetic control. The film traces Priscilla’s life from her first encounter with Elvis Presley to their separation, presenting a visually stylized narrative that immerses viewers in what Walter Benjamin terms a phantasmagoria: a spectacle of commodification divorced from historical consciousness (The Arcades Project). Rather than striving for veracity, Coppola evokes a nostalgic atmosphere that re-members Priscilla through pre-circulated cultural images. This article examines Coppola’s often-criticized “flat” visual style in relation to the Freudian uncanny, i.e., the estrangement of the familiar through temporal and affective distortion. Coppola manipulates digital temporality—looping and flattening time—to produce an oneiric repetition that heightens the artifice of Presley’s image while emotionally distancing viewers. These formal strategies dissipate emotional depth but intensify aesthetic control. Finally, this article considers the political valences of Coppola’s digital aesthetics in a media landscape that both enables visibility and enacts erasure. Full article

Community-based adaptation (CBA) has become a credible remedy to climate change adaptation, emphasizing local participation and community-defined priorities. However, its transformative potential remains limited when structural root causes of vulnerability are insufficiently addressed. This article argues—via the methodology of problematization—that aligning CBA with the broader agenda of social-ecological transformation requires a stronger philosophical foundation. We propose critical realism as a suitable philosophy of science to translate CBA’s emancipatory ambitions into a robust analytical and methodological practice. Critical realism is a practically oriented philosophy facilitating causal analyses coherent with its realist ontology and relativistic epistemology. It illuminates the interplay between agency and structure, enhancing CBA to confront power imbalances and systemic injustices while supporting local agency. By conjoining insights from political ecology and political economy, we show how critical realism offers analytical coherence, methodological robustness, and normative orientation for transformative adaptation practice. We delineate nine key synergies between critical realism and CBA that together provide the conceptual scaffolding for a politically powerful, reflexive, and justice-oriented adaptation science. In doing so, the paper contributes to rethinking CBA as not merely a localized coping mechanism but as part of a structural response to the social-ecological crisis. Full article

Hydro-erosion is a primary driver of soil degradation worldwide, yet accurate catchment-scale prediction remains challenging because sheet, gully, and raindrop-impact detachment processes operate simultaneously at sub-grid scales. We introduce TopEros, a hydro-erosion model that integrates the hydrological framework of TOPMODEL with three distinct erosion modules: sheet erosion, gully erosion, and raindrop-impact detachment. TopEros employs a sub-grid zoning strategy in which each grid cell is partitioned into diffuse-flow (sheet erosion) and concentrated-flow (gully erosion) domains using threshold values of two topographic indices: the topographic index (TI) and the contributing area–slope index (a_itanβ). Applied to the Namatala River catchment in eastern Uganda and calibrated with TI = 15 and a_itanβ = 35, TopEros identified sheet-dominated and gully-prone areas. The simulated specific sediment yields ranged from 95 to 155 Mgha⁻¹yr⁻¹—classified as “high” to “very high”—with gully zones contributing disproportionately large erosion volumes. These results demonstrate the importance of capturing intra-cell heterogeneity: conventional catchment-average approaches can obscure critical erosion hotspots. By explicitly representing multiple soil detachment and transport mechanisms within a unified process-based framework, TopEros has the potential to enhance the realism of catchment-scale erosion estimates and support the precise targeting of soil and water conservation measures. Full article

Positive Body Image (PBI) has been conceptualized as a multidimensional construct encompassing acceptance, functionality appreciation, and self-care, yet little is known about the lived processes through which women move from self-criticism to reconciliation with their bodies. This study aimed to explore how women experience, construct, and sustain PBI in their everyday lives, and to identify the psychological and contextual factors that facilitate its development. Semi-structured interviews were conducted with ten women in Greece (ages 18–62) of diverse body sizes, educational backgrounds, and life circumstances, which were then analyzed using interpretative phenomenological analysis. Participants were recruited through convenience sampling and interviewed online between July and August 2025. Findings revealed three superordinate themes—(1) Catalysts of Realism and Self-Care, (2) From Rejection to Reconciliation, and (3) My Own Positive Body Image—comprising nine subthemes that together illustrated a developmental process of body acceptance and meaning-making. Findings revealed a trajectory from self-rejection to reconciliation, marked by shifts from external appearance to holistic embodiment, and from self-criticism to compassion, functionality appreciation, and intrinsic motivation. Participants described mindful self-care practices—particularly exercise and healthy eating—as acts of self-nurturing, supported by psychotherapy, positive social relationships, and turning points such as illness, aging, or personal maturation. These catalysts facilitated a reorientation of body image away from societal ideals and toward health, resilience, and existential meaning. The study contributes to understanding how women develop sustainable forms of PBI, highlighting the importance of self-compassion, supportive contexts, and body functionality. These insights have implications for interventions aiming to promote wellbeing, resilience, and healthier relationships with the body across the lifespan. Full article

This paper contributes to the discussion on the Moral Argument for the existence of God—an important argument of natural theology which is relevant to science and religion dialogues—by showing that the argument can be formulated in a such way that avoids the lack of comprehensiveness in Andrew Loke’s original formulation and the unnecessarily complicated reformulation offered in Jack et al.’s criticism of Loke. This paper also contributes to the discussion by demonstrating the failure of relaxed (moral) realism proposed by Jack et al. to rebut the Moral Argument and offers replies to their other objections concerning moral obligations and social relations, the law-like character of moral obligations, and moral truths and responsibilities. Full article