Search Results (2,205)

Substance use during pregnancy is an increasingly important yet under-recognized threat to maternal and child health. This narrative review synthesizes the current evidence available on the epidemiology, pathophysiology, clinical management, and policy landscape of prenatal exposure to alcohol, tobacco, opioids, benzodiazepines, cocaine, cannabis, methamphetamines, and other synthetic drugs. All major psychoactive substances readily cross the placenta and can remain detectable in breast milk, leading to a shared cascade of obstetric complications (hypertensive disorders, placental abruption, pre-term labor), fetal consequences (growth restriction, structural malformations), and neonatal morbidities such as neonatal abstinence syndrome and sudden infant death. Mechanistically, trans-placental diffusion, oxidative stress, inflammatory signaling, and placental vascular dysfunction converge to disrupt critical neuro- and cardiovascular developmental windows. Early identification hinges on the combined use of validated screening questionnaires (4 P’s Plus, CRAFFT, T-ACE, AUDIT-C, TWEAK) and matrix-specific biomarkers (PEth, EtG, FAEE, CDT), while effective treatment requires integrated obstetric, addiction, and mental health services. Medication for opioid use disorders, particularly buprenorphine, alone or with naloxone, confers superior neonatal outcomes compared to methadone and underscores the value of harm-reducing non-punitive care models. Public-health strategies, such as Mexico’s “first 1 000 days” framework, wrap-around clinics, and home-visiting programs, demonstrate the potential of multisectoral interventions, but are hampered by structural inequities and punitive legislation that deter care-seeking. Research gaps persist in polysubstance exposure, culturally tailored therapies, and long-term neurodevelopmental trajectories. Multigenerational, omics-enabled cohorts, and digital longitudinal-care platforms represent promising avenues for closing these gaps and informing truly preventive perinatal health policies. Full article

Digital city planning increasingly relies on data-driven approaches to address complex urban sustainability challenges through innovative network analysis methodologies. This study introduces a comprehensive spatiotemporal network framework to examine digital innovation networks as fundamental infrastructure for urban sustainable development, focusing on the Yellow River Basin as a representative case study. Utilizing digital patent data as innovation indicators across 57 urban centers, we employ advanced network analysis techniques including Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP) to investigate the spatiotemporal evolution patterns and underlying driving mechanisms of regional digital innovation networks. The methodology integrates big data analytics with urban planning applications to provide evidence-based insights for digital city planning strategies. Our empirical findings reveal three critical dimensions of urban sustainable development through digital innovation networks: First, the region demonstrated significant enhancement in digital innovation capacity from 2012 to 2022, with accelerated growth patterns post 2020, indicating robust urban resilience and adaptive capacity for sustainable transformation. Second, the spatial network configuration exhibited increasing interconnectivity characterized by strengthened urban–rural linkages and enhanced cross-regional innovation flows, forming a hierarchical centrality pattern where major metropolitan centers (Xi’an, Zhengzhou, Jinan, and Lanzhou) serve as innovation hubs driving coordinated regional development. Third, analysis of network formation mechanisms indicates that spatial proximity, market dynamics, and industrial foundations negatively correlate with network density, suggesting that regional heterogeneity in these characteristics promotes innovation diffusion and strengthens inter-urban connections, while technical human capital and governmental interventions show limited influence on network evolution. This research contributes to the digital city planning literature by demonstrating how data-driven network analysis can inform sustainable urban development strategies, providing valuable insights for policymakers and urban planners implementing AI technologies and big data applications in regional development planning. Full article

Reconstruction-based unsupervised detection methods have demonstrated strong generalization capabilities in the field of tablet anomaly detection, but there are still problems such as poor reconstruction effect and inaccurate positioning of abnormal areas. To address these problems, this paper proposes an unsupervised Diffusion-based Tablet Defect Detection (DTDD) method. This method uses an Assisted Reconstruction (AR) network to introduce original image information to assist in the reconstruction of abnormal areas, thereby improving the reconstruction effect of the diffusion model. It also uses a Scale Fusion (SF) network and an improved anomaly measurement method to improve the accuracy of abnormal area positioning. Finally, the effectiveness of the algorithm is verified on the tablet dataset. The experimental results show that the algorithm in this paper is superior to the algorithms in the same field, effectively improving the detection accuracy and abnormal positioning accuracy, and performing well in the tablet defect detection task. Full article

Background: Clear cell renal cell carcinoma with rhabdoid features (ccRCC-R) is a highly aggressive variant of renal cell carcinoma that carries a poor prognosis and limited treatment options. Methods: To better define the clinicopathologic and molecular landscape of ccRCC-R, we conducted an integrated clinicopathologic and molecular study of 17 tumors of ccRCC-R, utilizing comprehensive histomorphologic evaluation, immunohistochemistry, and targeted next-generation sequencing (NGS). Results: Histologically, all tumors demonstrated classic clear cell renal cell carcinoma morphology with focal to extensive rhabdoid differentiation, characterized by eccentrically located nuclei, prominent nucleoli, abundant eosinophilic cytoplasm, and paranuclear intracytoplasmic inclusion. Architectural alterations, including solid/sheet-like, alveolar/trabecular, and pseudopapillary growth patterns, were frequently observed. Immunohistochemically, tumors commonly exhibited loss of PAX8 and Claudin4 expression, preserved cytokeratin AE1/AE3 staining, and diffuse membranous CAIX expression. Frequent loss of SMARCA2 with retained SMARCA4 supported aberrations in chromatin remodeling. Unsupervised hierarchical clustering based on pathway-specific somatic mutations identified four distinct molecular subgroups defined by recurrent alterations in (1) DNA damage repair (DDR) genes, (2) chromatin remodeling genes, (3) PI3K/AKT/mTOR signaling components, and (4) MAPK pathway genes. Clinicopathologic correlation revealed that each subgroup was associated with unique biological characteristics and suggested distinct therapeutic vulnerabilities. Conclusions: Our findings underscore the molecular heterogeneity of ccRCC-R and support the utility of pathway-based stratification for guiding precision oncology approaches and biomarker-informed clinical trial design. Full article

As manufacturing and logistics-oriented supply chains continue to expand in scale and complexity, and the coupling between their physical execution layers and information–decision layers deepens, the resulting high interdependence within the system significantly increases overall fragility. Driven by key technological barriers, economies of scale, and the trend toward resource centralization, supply chains have increasingly evolved into centralized structures, with critical functions such as decision-making highly concentrated in a few focal firms. While this configuration may enhance coordination under normal conditions, it also significantly increases dependency on focal nodes. Once a focal node is disrupted, the intense task, information, and risk loads it carries cannot be effectively dispersed across the network, thereby amplifying load spillovers, coordination imbalances, and information delays, and ultimately triggering large-scale cascading failures. To capture this phenomenon, this study develops a coupled network model comprising a Physical Network and an Information and Decision Risk Network. The Physical Network incorporates a tri-load coordination mechanism that distinguishes among theoretical operational load (capacity), actual production load (production output), and actual delivery load (order fulfillment), using a load sensitivity coefficient to describe the asymmetric propagation among them. The Information and Decision Risk Network is further divided into a communication subnetwork, which represents transmission efficiency and delay, and a decision risk subnetwork, which reflects the diffusion of uncertainty and risk contagion caused by information delays. A discrete-event simulation approach is employed to evaluate system resilience under various failure modes and parametric conditions. The results reveal the following: (1) under a centralized structure, poorly allocated redundancy can worsen local imbalances and amplify disruptions; (2) the failure of a focal firm is more likely to cause a full network collapse; and (3) node failures in the Communication System Network have a greater destabilizing effect than those in the Physical Network. Full article

Background/Objectives: Idiopathic pulmonary fibrosis (IPF) registries are established to enhance understanding of its natural history. Methods: Serbia (RS) participated in the EMPIRE (European Multi-Partner IPF Registry) from June 2015 to October 2022, involving four centers. The registry included patients over 18 diagnosed with IPF based on the 2011 international criteria. We aimed to gather key clinical, functional, and survival data, along with treatment information for IPF patients in RS, using a centralized electronic case report for consistency. Results: 188 RS patients participated (median age at diagnosis 65, 63.8% male, 51% smoking history, 56% radiological usual interstitial pneumonia (UIP) pattern). At the diagnosis, median forced vital capacity (FVC) was 73.7% and diffusion capacity for carbon monoxide (DL_CO) was 38%. At initiation of antifibrotic therapy, median FVC was 73.2% (71.5% for deceased, 75.8% for survivors (p = 0.455), and DL_CO was 33.8% (19.9% for deceased, and 35.6% for survivors (p = 0.046)). The median long-term survival from diagnosis was 29.4 months (95% CI: 22.6–36.2 months), and 9.4 months (95% CI: 5.9–12.9 months) from the initiation of therapy, with no difference in the duration of antifibrotic treatment between survivors and deceased (p = 0.598). Conclusions: The RS EMPIRE cohort represents a younger, less comorbid population with fewer smokers and more probable UIP, factors linked to a favorable prognosis. Nevertheless, survival was poorer than expected, mainly due to advanced disease severity at the time of antifibrotic initiation, as indicated by lower DL_CO. These findings highlight the importance of earlier diagnosis and treatment before significant physiological decline to improve outcomes. Full article

In low-light optical imaging, the scarcity of incident photons and the inherent limitations of imaging sensors lead to challenges such as low signal-to-noise ratio, limited dynamic range, and degraded contrast, severely compromising image quality and optical information integrity. To address these challenges, we propose a novel low-light image enhancement technique, LightenResDiff, which combines a residual diffusion model with the discrete wavelet transform. The core innovation of LightenResDiff lies in it accurately restoring the low-frequency components of an image through the residual diffusion model, effectively capturing and reconstructing its fundamental structure, contours, and global features. Additionally, the dual cross-coefficients recovery module (DCRM) is designed to process high-frequency components, enhancing fine details and local contrast. Moreover, the perturbation compensation module (PCM) mitigates noise sources specific to low-light optical environments, such as dark current noise and readout noise, significantly improving overall image fidelity. Experimental results across four widely-used benchmark datasets demonstrate that LightenResDiff outperforms existing methods both qualitatively and quantitatively, surpassing the current state-of-the-art techniques. Full article

In the context of chlorate’s application as a cathodic reagent of power sources, the mechanism of its electroreduction has been studied in electrochemical cells under diffusion-limited current conditions with operando spectrophotometric analysis. Prior to electrolysis, the electrolyte is represented as an aqueous mixed NaClO₃ + H₂SO₄ solution (both components being non-electroactive within the potential range under study), without addition of any external electroactive catalyst. In the course of potentiostatic electrolysis, both the cathodic current and the ClO₂ concentration demonstrate a temporal evolution clearly pointing to an autocatalytic mechanism of the process (regions of quasi-exponential growth and of rapid diminution, separated by a narrow maximum). It has been substantiated that its kinetic mechanism includes only one electrochemical step (chlorine dioxide reduction), coupled with two chemical steps inside the solution phase: comproportionation of chlorate anion and chlorous acid, as well as chlorous acid disproportionation via two parallel routes. The corresponding set of kinetic equations for the concentrations of Cl-containing solute components (ClO₃⁻, ClO₂, HClO₂, and Cl⁻) has been solved numerically in a dimensionless form. Optimal values of the kinetic parameters have been determined via a fitting procedure with the use of non-stationary experimental data for the ClO₂ concentration and for the current, taking into account the available information from the literature on the parameters of the chlorous acid disproportionation process. Predictions of the proposed kinetic mechanism agree quantitatively with these experimental data for both quantities within the whole time range, including the three characteristic regions: rapid increase, vicinity of the maximum, and rapid decrease. Full article

With the rapid advancement of information technology, as critical information carriers, images are confronted with significant security risks. To ensure the image security, this paper proposes an image encryption algorithm based on a dynamic rhombus transformation and digital tube model. Firstly, a two-dimensional hyper-chaotic system is constructed by combining the Sine map, Cubic map and May map. The analysis results demonstrate that the constructed hybrid chaotic map exhibits superior chaotic characteristics in terms of bifurcation diagrams, Lyapunov exponents, sample entropy, etc. Secondly, a dynamic rhombus transformation is proposed to scramble pixel positions, and chaotic sequences are used to dynamically select transformation centers and traversal orders. Finally, a digital tube model is designed to diffuse pixel values, which utilizes chaotic sequences to dynamically control the bit reversal and circular shift operations, and the exclusive OR operation to diffuse pixel values. The performance analyses show that the information entropy of the cipher image is 7.9993, and the correlation coefficients in horizontal, vertical, and diagonal directions are 0.0008, 0.0001, and 0.0005, respectively. Moreover, the proposed algorithm has strong resistance against noise attacks, cropping attacks, and exhaustive attacks, effectively ensuring the security of images during storage and transmission. Full article

This study presents a new steganography method for message transmission based on style transfer and denoising diffusion probabilistic model (DDPM) techniques. Different types of object images are used to represent the messages and are arranged in order from left to right and top to bottom to generate a secret image. Then, the style transfer technique is employed to embed the secret image (content image) into the cover image (style image) to create a stego image. To reveal the messages, the DDPM technique is first used to inpaint the secret image from the stego image. Then, the YOLO (You Only Look Once) technique is utilized to detect objects in the secret image for the message decoding. Two security mechanisms are included: one uses object images for the message encoding, and the other hides them in a customizable public image. To obtain the messages, both mechanisms need to be cracked at the same time. Therefore, this method provides highly secure information protection. Experimental results show that our method has good confidential information transmission performance. Full article

(1) Background: Bispecific antibodies (BsAbs) for the treatment of relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL) can be delivered in ambulatory healthcare settings; however, the safe and effective management of potential side effects, such as cytokine release syndrome (CRS), requires protocolized monitoring and management. (2) Methods: An Expert Working Group (EWG) of nine hematologists from across Canada, with experience in leading BsAb program implementation, combined a review of published literature, a comparison of national/provincial/regional guidance documents and protocols, and their professional experiences to produce an informed framework for BsAb program implementation in various healthcare settings. (3) Results: The EWG supports and recommends the progression of BsAb provision from predominantly inpatient hospital settings to community/ambulatory care settings closer to the patient’s home. A seven-step implementation process is outlined to support the safe and effective establishment of such programs, from establishing leadership, through customization of protocols, to education and execution. Strategies and considerations are offered to overcome potential barriers and empower healthcare professionals who are working to establish or improve BsAb programs across Canada. (4) Conclusions: For patients with R/R DLBCL, the safe and effective provision of BsAbs closer to home is both feasible and preferred. This guidance is intended to support the efficient and effective setup or enhancement of BsAb programs in lymphoma. Full article

We introduce CAESAR, a new framework for scientific data reduction that stands for Conditional AutoEncoder with Super-resolution for Augmented Reduction. The baseline model, CAESAR-V, is built on a standard variational autoencoder with scale hyperpriors and super-resolution modules to achieve high compression. It encodes data into a latent space and uses learned priors for compact, information-rich representations. The enhanced version, CAESAR-D, begins by compressing keyframes using an autoencoder and extends the architecture by incorporating conditional diffusion to interpolate the latent spaces of missing frames between keyframes. This enables high-fidelity reconstruction of intermediate data without requiring their explicit storage. By distinguishing CAESAR-V (variational) from CAESAR-D (diffusion-enhanced), we offer a modular family of solutions that balance compression efficiency, reconstruction accuracy, and computational cost for scientific data workflows. Additionally, we develop a GPU-accelerated postprocessing module which enforces error bounds on the reconstructed data, achieving real-time compression while maintaining rigorous accuracy guarantees. Experimental results across multiple scientific datasets demonstrate that our framework achieves up to 10× higher compression ratios compared to rule-based compressors such as SZ3. This work provides a scalable, domain-adaptive solution for efficient storage and transmission of large-scale scientific simulation data. Full article

Infrared-visible image fusion quality is critical for nighttime perception in autonomous driving and surveillance but suffers severe degradation under extreme low-light conditions, including irreversible texture loss in visible images, thermal boundary diffusion artifacts, and overexposure under dynamic non-uniform illumination. To address these challenges, a Decomposition–Disentanglement–Dynamic Compensation framework, D3Fusion, is proposed. Firstly, a Retinex-inspired Decomposition Illumination Net (DIN) decomposes inputs into enhanced images and degradative illumination maps for joint low-light recovery. Secondly, an illumination-guided encoder and a multi-scale differential compensation decoder dynamically balance cross-modal features. Finally, a progressive three-stage training paradigm from illumination correction through feature disentanglement to adaptive fusion resolves optimization conflicts. Compared to State-of-the-Art methods, on the LLVIP, TNO, MSRS, and RoadScene datasets, D3Fusion achieves an average improvement of 1.59% in standard deviation (SD), 6.9% in spatial frequency (SF), 2.59% in edge intensity (EI), and 1.99% in visual information fidelity (VIF), demonstrating superior performance in extreme low-light scenarios. The framework effectively suppresses thermal diffusion artifacts while mitigating exposure imbalance, adaptively brightening scenes while preserving texture details in shadowed regions. This significantly improves fusion quality for nighttime images by enhancing salient information, establishing a robust solution for multimodal perception under illumination-critical conditions. Full article

Multi-modality image fusion (MIF) aims to integrate complementary information from diverse imaging modalities into a single comprehensive representation and serves as an essential processing step for downstream high-level computer vision tasks. The existing deep unfolding-based processes demonstrate promising results; however, they often rely on deterministic priors with limited generalization ability and usually decouple from the training process of object detection. In this paper, we propose Semantic-Aware Deep Unfolded Network with Diffusion Prior (SEND), a novel framework designed for transparent and effective multi-modality fusion and object detection. SEND consists of a Denoising Prior Guided Fusion Module and a Fusion Object Detection Module. The Denoising Prior Guided Fusion Module does not utilize the traditional deterministic prior but combines the diffusion prior with deep unfolding, leading to improved multi-modal fusion performance and generalization ability. It is designed with a model-based optimization formulation for multi-modal image fusion, which is unfolded into two cascaded blocks: a Diffusion Denoising Fusion Block to generate informative diffusion priors and a Data Consistency Enhancement Block that explicitly aggregates complementary features from both the diffusion priors and input modalities. Additionally, SEND incorporates the Fusion Object Detection Module with the Denoising Prior Guided Fusion Module for object detection task optimization using a carefully designed two-stage training strategy. Experiments demonstrate that the proposed SEND method outperforms state-of-the-art methods, achieving superior fusion quality with improved efficiency and interpretability. Full article

Network trace is a comprehensive record of data packets traversing a computer network, serving as a critical resource for analyzing network behavior. However, in practice, the limited availability of high-quality network traces, coupled with the presence of sensitive information such as IP addresses and MAC addresses, poses significant challenges to advancing network trace analysis. To address these issues, this paper focuses on network trace synthesis in two practical scenarios: (1) data expansion, where users create synthetic traces internally to diversify and enhance existing network trace utility; (2) data release, where synthesized network traces are shared externally. Inspired by the powerful generative capabilities of latent diffusion models (LDMs), this paper introduces NetSynDM, which leverages LDM to address the challenges of network trace synthesis in data expansion scenarios. To address the challenges in the data release scenario, we integrate differential privacy (DP) mechanisms into NetSynDM, introducing DPNetSynDM, which leverages DP Stochastic Gradient Descent (DP-SGD) to update NetSynDM, incorporating privacy-preserving noise throughout the training process. Experiments on five widely used network trace datasets show that our methods outperform prior works. NetSynDM achieves an average 166.1% better performance in fidelity compared to baselines. DPNetSynDM strikes an improved balance between privacy and fidelity, surpassing previous state-of-the-art network trace synthesis method fidelity scores of 18.4% on UGR16 while reducing privacy risk scores by approximately 9.79%. Full article