Search Results (461)

Autism spectrum disorder (ASD) and intellectual disability (ID) frequently coexist and share heterogeneous cognitive manifestations, yet their specific performance patterns on Wechsler scales remain poorly systematized. This meta-analysis synthesized data from 31 studies using the WISC-IV, WISC-V, WAIS-III, and WAIS-IV to compare cognitive index profiles in individuals with ASD, ID and ASD+ID. Standardized mean differences (Hedges’ g) were calculated using random-effects models, adopting a normative reference of mean 100 and SD 15. Results showed a distinct profile for ASD, with greater impairments in the Processing Speed Index (PSI) and Working Memory Index (WMI), while the Vocabulary Comprehension Index (VCI), Perceptual/Fluid Reasoning Index (PRI/FRI), and Visual Processing Index (VPI) remained close to normative scores. In contrast, ID and ASD+ID exhibited generalized deficits across all indices, with the lowest scores in Full-Scale IQ (FSIQ) and broad effects above g = −2.5. No significant differences emerged between Wechsler versions or age-based test types. Heterogeneity was high in ASD and ID across outcomes, but negligible in ASD+ID due to reduced k. These findings reinforce that ASD presents a specific cognitive pattern, whereas ID and ASD+ID display diffuse impairment, and that Wechsler scales are consistent across versions for identifying these profiles. Full article

Background/Objectives: Children born small for gestational age (SGA) are at increased risk for impaired growth, metabolic disturbances, and neurodevelopmental difficulties. Although previous research has examined cognitive and behavioral outcomes in this population, findings remain inconsistent. Moreover, limited evidence is available regarding the potential effects of recombinant human growth hormone (rhGH) therapy on cognitive development. We aimed to assess cognitive performance, emotional–behavioral functioning, and neonatal predictors of neurocognitive outcomes in term SGA children compared with age- and sex-matched peers born appropriate for gestational age (AGA). We also explored potential differences in cognitive outcomes between rhGH-treated and untreated SGA children. Methods: A total of 18 term SGA children and 23 AGA controls underwent anthropometric measurements, biochemical evaluation, cognitive testing using the Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV), and behavioral assessment through the Child Behavior Checklist (CBCL). Birth weight, length, and head circumference were analyzed as potential predictors of cognitive performance. Results: SGA children demonstrated significantly lower Intelligence Quotient (IQ) scores than AGA peers, with marked weaknesses in Perceptual Reasoning index (PRI) and Processing Speed index (PSI), while Verbal Comprehension and Working Memory were preserved. They also exhibited higher internalizing behavioral symptoms, whereas externalizing behaviors did not differ between groups. Birth head circumference emerged as a strong predictor of PRI and a modest predictor of PSI. No associations were found between rhGH treatment parameters and cognitive outcomes. Larger longitudinal studies are needed to clarify how early growth restriction affects brain development and cognition and whether GH therapy influences these processes. Full article

The escalating complexity, density, and agility of the modern electromagnetic environment (CME) pose unprecedented challenges to radar signal deinterleaving, a cornerstone of electronic intelligence. While traditional methods face significant performance bottlenecks, the advent of artificial intelligence, particularly deep learning, has catalyzed a paradigm shift. This review provides a systematic, comprehensive, and forward-looking analysis of the radar signal deinterleaving landscape, critically bridging foundational techniques with the cognitive frontiers. Previous reviews often focused on specific technical branches or predated the deep learning revolution. In contrast, our work offers a holistic synthesis. It explicitly links the evolution of algorithms to the persistent challenges of the CME. We first establish a unified mathematical framework and systematically evaluate classical approaches, such as PRI-based search and clustering algorithms, elucidating their contributions and inherent limitations. The core of our review then pivots to the deep learning-driven era, meticulously dissecting the application paradigms, innovations, and performance of mainstream architectures, including Recurrent Neural Networks (RNNs), Transformers, Convolutional Neural Networks (CNNs), and Graph Neural Networks (GNNs). Furthermore, we venture into emerging frontiers, exploring the transformative potential of self-supervised learning, meta-learning, multi-station fusion, and the integration of Large Language Models (LLMs) for enhanced semantic reasoning. A critical assessment of the current dataset landscape is also provided, highlighting the crucial need for standardized benchmarks. Finally, this paper culminates in a comprehensive comparative analysis, identifying key open challenges such as open-set recognition, model interpretability, and real-time deployment. We conclude by offering in-depth insights and a roadmap for future research, aimed at steering the field towards end-to-end intelligent and autonomous deinterleaving systems. This review is intended to serve as a definitive reference and insightful guide for researchers, catalyzing future innovation in intelligent radar signal processing. Full article

Background/Objectives: Alzheimer’s disease (AD) is the leading cause of dementia, which is an inevitable consequence of aging. Early detection of AD, or detection during the pre-AD stage, is beneficial, as it enables timely intervention to reduce modifiable risk factors, which may help prevent or delay the progression to dementia. On the one hand, plasma biomarkers have demonstrated great promise in predicting cognitive decline. On the other hand, in recent years, ocular imaging features, particularly the thickness of retinal layers measured by spectral-domain optical coherence tomography (SD-OCT), are emerging as possible non-invasive, non-contact surrogate markers for early detection and monitoring of neurodegeneration. This pilot study aims to identify retinal layer thickness changes across the entire retina linked to plasma AD biomarkers in cognitively healthy (CH) elderly individuals at risk for AD. Methods: Eleven CH individuals (20 eyes total) were classified in the pre-AD stage by plasma β-amyloid (Aβ)42/40 ratio < 0.10 and underwent SD-OCT. A deep-learning-derived automated algorithm was used to segment retinal layers on OCT (with manual correction when needed). Multiple layer thicknesses throughout the entire retina (including the inner retina, the outer retina, and the choroid) were measured in the inner ring (1–3 mm) and outer ring (3–6 mm) of the Early Treatment Diabetic Retinopathy Study (ETDRS). Relationships between retinal layers and plasma biomarkers were analyzed by ridge regression/bootstrapping. Results: Results showed that photoreceptor inner segment (PR-IS) thinning had the largest size effect with neurofilament light chain. Additional findings revealed thinning or thickening of the other retinal layers in association with increasing levels of glial fibrillary acidic protein and phosphorylated tau at threonine 181 and 217 (p-tau181 and p-tau217). Conclusions: This pilot study suggests that retinal layer-specific signatures exist, with PR-IS thinning as the largest effect, indicating neurodegeneration in pre-AD. Further research is needed to confirm the findings of this pilot study using larger longitudinal pre-AD cohorts and comparative analyses with healthy aging adults. Full article

As a sink for microplastics (MPs) in the aquatic environment, sediments have garnered considerable attention. However, the occurrence characteristics of MPs in sediments of different water seasons are not clear, especially for reservoir sediment cores. This study aimed to elucidate the occurrence, spatial and vertical distribution, fragmentation and pollution risk of MPs in the sediment cores of the Xiangxi River, Three Gorges Reservoir (TGR) during different seasons. In sediment cores, the average abundance of MPs was 8.57 × 10³ ± 5.65 × 10³ items/kg DW in the wet season (WS) and 7.98 × 10³ ± 4.00 × 10³ items/kg DW in the dry season (DS), respectively. The abundance of MPs in surface sediments and sediment cores exhibited spatial heterogeneity, reflecting seasonally contrasting hydrodynamic conditions between sites S1 and S3. However, the abundance of MPs in the river estuary was the highest, both in surface sediments and sediment cores. Interestingly, the occurrence characteristics of MPs in surface sediments indicated that in addition to anthropogenic activity, hydrological conditions of the river can also have an impact on the spatial distribution of MP abundance in surface sediments. Polypropylene (PP), polyethylene (PE), polystyrene (PS), and polyethylene-propylene copolymer (EPM) were identified as the dominant polymer types (57–99%), with small-sized microplastics (SMPs, 0–300 μm) being the most prevalent. Water seasons influenced the size distribution of MPs in surface sediments. Using a conditional fragmentation model, MP sources were inferred by comparing fragmentation parameters (λ and α) in sediments with those reported for atmospheric deposition, reservoir water, and water-level fluctuation zone soils. Furthermore, the pollution load index (PLI) exceeded 1, indicating MP accumulation in the sediments. The pollution risk index (PRI) values indicated a considerable (300 < PRI < 1000) pollution risk in two water seasons, primarily due to the presence of polyvinyl chloride (PVC). This study enhances the understanding of MP behavior and associated environmental risks in reservoir sediments, offering valuable insights for future research and pollution mitigation efforts. Full article

Staggered synthetic aperture radar (SAR) is an innovative concept capable of achieving an ultrawide continuous swath with fine azimuth resolution by variable pulse repetition interval. However, the inherent data gaps and nonuniform sampling introduce severe azimuth artifacts, degrading image quality. Existing methods can mitigate these artifacts but struggle to effectively balance imaging quality and computational cost, especially under low oversampling conditions. To address this challenge, this paper proposes a low-artifact preview image generation method for staggered SAR. First, the artifact characteristics are analyzed through the derivation of a staggered SAR signal model. Then, a three-stage processing framework is introduced, consisting of constant-gradient phase extrapolation, artifact-based inverse filtering, and result fusion. Additionally, data nonuniformity is addressed using a weighted nonuniform fast Fourier transform. Simulation results demonstrate that the proposed method significantly improves processing speed compared to existing techniques while maintaining good imaging quality, making it suitable for rapid scene screening in wide-area SAR applications. Full article

Strawberry (Fragaria × ananassa (Weston) Rozier) is a high-value crop whose market success depends on fruit quality traits such as sweetness, firmness, and pigmentation. In sustainable agriculture, wood distillates are gaining interest as natural biostimulants. This study evaluated the effects of foliar application of two commercial wood distillates (WD1 and WD2) and one produced in a pilot plant at the Institute for Bioeconomy of the National Research Council of Italy (IBE-CNR) on strawberry physiology, fruit yield, and fruit quality under greenhouse conditions. Non-destructive ecophysiological measurements were integrated using optical sensors for proximal phenotyping, enabling continuous monitoring of plant physiology and fruit ripening. Leaf gas exchange and chlorophyll fluorescence were measured with a portable photosynthesis system, while vegetation indices and pigment-related parameters were obtained using spectroradiometric sensors and fluorescence devices. To assess the functional relevance of vegetation indices, a linear regression analysis was performed between net photosynthetic rate (A) and the Photochemical Reflectance Index (PRI), confirming a significant positive correlation and supporting PRI as a proxy for photosynthetic efficiency. All treatments improved photosynthetic efficiency during fruiting, with significant increases in net photosynthetic rate, quantum yield of photosystem II, and electron transport rate compared to control plants. IBE-CNR and WD2 enhanced fruit yield, while all treatments increased fruit soluble solids content. Non-invasive monitoring enabled real-time assessment of physiological responses and pigment accumulation, confirming the potential of wood distillates as biostimulants and the value of advanced sensing technologies for sustainable, data-driven crop management. Full article

Spectral indices are widely used to assess vegetation fire severity following wildland fires. Although essential, ground-based assessments of how such indices change due to varying fire intensities remain limited, especially with deciduous tree species that exhibit resprouting. In this paper, we evaluate the efficacy of detecting post-fire physiological change and top kill in quaking aspen (Populus tremuloides) saplings using differenced spectral indices. Saplings (n = 64) were burned under controlled conditions over a range of discrete fire intensity levels from 0 to 4.0 MJ m⁻², and reflectance was collected pre-fire and at six post-fire intervals up to 16 weeks. Ten spectral indices (CCI, CSI, MIRBI, NDVIL8, NBR, NBRL8, PRI, SAVI, SW-NIR_ratio, and SW-SW_ratio) were calculated, differenced from pre-fire, and related to the change in net photosynthesis and top kill. Fire intensity most strongly influenced the observed spectral changes at weeks 1–2 post-fire, especially for ΔCSI, ΔCCI, and ΔPRI. Pre- to post-fire change in net photosynthesis was strongly related (Tjur’s R² > 0.5) with ΔCCI, ΔCSI, ΔNBRL8, and the ΔSW–NIR ratio at one week post-fire. Of the spectral indices assessed, ΔCCI and ΔPRI were most effective at predicting top kill. This study illustrates the potential of spectral indices for monitoring vegetation fire severity in deciduous tree species. Full article

Legumes are sensitive to soil heterogeneity and disease pressure, particularly from Fusarium oxysporum, which causes severe yield losses worldwide. This study examined the relationships between soil properties, disease incidence, and yield variability within management unit zones (MUZs) to support site-specific management strategies. Two field experiments were conducted in central Italy, in two different growing seasons, using synthetic images of bare soil and clusters to delineate MUZs. Soil samples were analyzed for texture, organic carbon, and nitrogen content, while disease incidence and severity were assessed in relation to symptoms on foliar, root, and hypocotyl tissues. Furthermore, pathogen isolations were carried out from the altered hypocotyl and root tissue. Vegetation indices, including NDVI and PRI derived from Sentinel-2 images, were integrated with field observations to map disease and yields spatially. The results highlighted the almost exclusive presence of F. oxysporum on the altered tissues. MUZ-3, characterized by lower organic carbon content and higher sand content, consistently exhibited the highest incidence and severity of Fusarium wilt. In contrast, MUZ-1, richer in clay and organic carbon, supported healthier plant growth and higher productivity. The integration of vegetation indices with field data proved effective in detecting spatial variability, allowing the delimitation of productivity zones and supporting precision farming strategies aimed at mitigating Fusarium-related yield losses. Full article

This systematic review focuses on intelligent navigation as a core enabler of autonomy in smart warehouses, where mobile robots must dynamically perceive, reason, and act in complex, human-shared environments. By synthesizing advancements in AI-driven decision-making, SLAM, and multi-sensor fusion, the study highlights how intelligent navigation architectures reduce operational uncertainty and enhance task efficiency in logistics automation. Smart warehouses, powered by mobile robots and AGVs and integrated with AI and algorithms, are enabling more efficient storage with less human labour. This systematic review followed PRISMA 2020 guidelines to systematically identify, screen, and synthesize evidence from 106 peer-reviewed scientific articles (including pri-mary studies, technical papers, and reviews) published between 2020–2025, sourced from Web of Science. Thematic synthesis was conducted across 8 domains: AI, SLAM, sensor fusion, safety, network, path planning, implementation, and design. The transition to smart warehouses requires modern technologies to automate tasks and optimize resources. This article examines how intelligent systems can be integrated with mathematical models to improve navigation accuracy, reduce costs and prioritize human safety. Real-time data management with precise information for AMRs and AGVs is crucial for low-risk operation. This article studies AI, the IoT, LiDAR, machine learning (ML), SLAM and other new technologies for the successful implementation of mobile robots in smart warehouses. Modern technologies such as reinforcement learning optimize the routes and tasks of mobile robots. Data and sensor fusion methods integrate information from various sources to provide a more precise understanding of the indoor environment and inventory. Semantic mapping enables mobile robots to navigate and interact with complex warehouse environments with high accuracy in real time. The article also analyses how virtual reality (VR) can improve the spatial orientation of mobile robots by developing sophisticated navigation solutions that reduce time and financial costs. Full article

The Internet of Medical Things (IoMT) integrates sensors, medical devices, and Internet of Things (IoT) technologies to provide data-driven healthcare systems. The systems facilitate medical monitoring and decision-making; however, there are significant concerns about data leakage and patient consent. Additionally, a shortage of large, high-quality IoMT datasets to study the surrounding issues is problematic. Federated learning (FL) is a decentralized machine learning approach that potentially offers substantial amounts of capacity, so that compound Smart Healthcare Systems (SHSs) can further personalize and contextualize the secrecy of data and strong system structures. Additionally, to protect against advanced and shifting computational intelligence-based cyber threats, especially in operational health environments, the use of Intruder Detection Systems (IDSs) is quite essential. However, traditional approaches to implementing IDSs are usually computationally costly and inappropriate for the narrow contours of deploying medical IoT devices. To address these challenges, the proposed study introduces PriFed-IDS, a novel, privacy-preserving FL-based IDS framework based on FL and reinforcement learning. The proposed model leverages reinforcement learning to uncover latent patterns in medical data, enabling accurate anomaly detection. A dynamic federation and aggregation strategy is implemented to optimize model performance while minimizing communication overhead by adaptively engaging clients in the training process. Experimental evaluations and theoretical analysis demonstrate that PriFed-IDS significantly outperforms existing benchmark IDS models in terms of detection accuracy and efficiency, underscoring its practical applicability for securing real-world IoMT networks. Full article

Acute myeloid leukemia (AML) is an aggressive and often fatal hematopoietic malignancy, diagnosed predominantly in the elderly. The five-year survival of patients with AML is as low as 30%. Differentiation therapy of a subtype of AML, named acute promyelocytic leukemia (APL), using all-trans retinoic acid (ATRA) was the most successful example of a targeted therapy against AML. Epigenetic-based differentiation therapies for other subtypes of AML are also showing improvements in response and in survival rates. Thus, in this study, we investigated a potential differentiation therapy with a combination of 1,25-dihydroxyvitamin D (1,25D) analog (named PRI5202) and low concentration of Fludarabine. We show that such a combination elicits immunostimulatory and pro-differentiation effects in AML cells, specifically in those with activating mutations in fibroblast growth factor receptor (FGFR) and Janus kinase (JAK) pathways. We show here that both PRI5202 and Fludarabine are potent activators of the transcription of many innate immunity-related genes, and that, in combination, their effects are in many aspects synergistic. We propose that such a low-intensity regimen may be suitable for older patients with AML, who are unfit for intensive chemotherapy. We also present data indicating that PRI5202 induces myeloid differentiation in blasts from patients with myelodysplastic syndrome (MDS), and we propose to further investigate PRI5202 as a differentiation therapy for patients suffering from MDS. Full article

Oral infections caused by Candida spp. represent a major health concern due to the increasing resistance of these fungi to conventional antifungal agents. Photodynamic therapy (PDT) is a treatment based on the use of light at a specific wavelength that activates a photosensitizer (PS) in the presence of oxygen. The activated PS selectively binds to infected cells and induces apoptosis through the generation of reactive oxygen species (ROS). Previous biomolecular studies on Candida albicans have demonstrated that its infection triggers characteristic molecular signals, such as miRNA-146a and miRNA-155, which serve as inflammatory markers. This in vitro study aimed to evaluate the impact of PDT on the expression of their primary transcripts (pri-miRNAs) in a cell culture model of C. albicans infection. Human embryonic kidney (HEK-293) cells were infected with a multidrug-resistant strain of C. albicans (CA97) and subsequently exposed to curcumin-based PDT activated by blue light (470 nm). The expression of pri-miRNAs 146a and 155 was assessed before and after PDT treatment for each experimental group. The expression levels of pri-miRNAs increased approximately 2- to 3.5-fold following C. albicans infection but returned to baseline values after PDT treatment. The evaluation of pri-miRNAs 146a/155 may serve as a valuable research tool for monitoring early inflammatory responses induced by Candida infection, as well as a sensitive biomarker for assessing the effectiveness of photodynamic therapy in an in vitro cell culture model. Full article

Targeted therapies, including treatment with inhibitors of BRAF^V600 and MEK kinases, have improved outcomes in advanced melanoma. However, most patients relapse due to acquired resistance, underscoring the need for new drug targets. This study evaluated PRI-724, a CBP/β-catenin inhibitor, in patient-derived drug-naïve melanoma cells and their trametinib- or vemurafenib-resistant counterparts. While PRI-724 has demonstrated efficacy in preclinical models and clinical trials in different cancer types, its potential in melanoma has not been previously assessed. We found that treatment with PRI-724 downregulated survivin and other CBP/β-catenin target proteins, reduced invasiveness, and induced apoptosis in drug-naïve and trametinib- and vemurafenib-resistant cells. Trametinib-resistant melanoma cells showed the greatest sensitivity to PRI-724, indicating that CBP/β-catenin transcriptional activity may represent a new therapeutic vulnerability. Transcriptomic and immunoblotting analyses revealed the highest survivin levels in vemurafenib-resistant cells, which may underlie their reduced responsiveness to PRI-724. Bioinformatic analyses (TCGA and GSE50509) confirmed that a high survivin level predicts poor prognosis and reduced response to treatment. The results of the study point to the potential of PRI-724 as a chemotherapeutic agent for the treatment of melanoma. Its efficacy might depend on CBP/β-catenin transcriptional activity in melanoma cells, and further evaluation of this signaling with survivin as a biomarker is therefore warranted. Full article

Sunflowers are one of the most important agricultural crops in the world. Given the high importance of sunflower products in the world market and the scale of their cultivation, the introduction of precision farming technologies into its culture can have a significant economic and environmental effect. This study demonstrated the fundamental possibility of developing a technology for rapid, remote, and non-invasive assessment of sunflower seed moisture to determine the optimal timing for desiccation and harvesting. It has been shown that the moisture content of sunflower seeds can be assessed with high accuracy based on the spectral characteristics of the underside of the inflorescences obtained using a hyperspectral camera in the visible and near-infrared range (VNIR) (from 450 to 950 nm). Random forest regression (RFR) was used to predict sunflower seed moisture. The model performed excellently on the training data (R²_c = 1.00; MAE_c = 0.58; RMSE_c = 0.74, MAPE_c = 1.29) and with a high performance on the testing data (R²_t = 0.98, MAE_t = 2.99, RMSE_t = 3.28, MAPE_t = 12.22). The most significant vegetation indices for determining moisture are CCI, Booch, Datt3, Datt4, LSIRed, modPRI, SR5, TCARI, and TCARI2. Full article