Search Results (77)

Call Detail Records (CDRs) from mobile networks offer valuable insights into both network performance and user behavior. With the growing importance of data analytics, analyzing CDRs has become critical for optimizing network resources by forecasting demand across spatial and temporal dimensions. In this study, we examine publicly available CDR data from Telecom Italia to explore the spatiotemporal dynamics of mobile network activity in Milan. This analysis reveals key patterns in traffic distribution highlighting both high- and low-demand regions as well as notable variations in usage over time. To anticipate future network usage, we employ both Automated Machine Learning (AutoML) and the transformer-based TimeGPT model, comparing their performance against traditional forecasting methods such as Long Short-Term Memory (LSTM), ARIMA and SARIMA. Model accuracy is assessed using standard evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R²). Results show that AutoML delivers the most accurate forecasts, with significantly lower RMSE (2.4990 vs. 14.8226) and MAE (1.0284 vs. 7.7789) compared to TimeGPT and a higher R² score (99.96% vs. 98.62%). Our findings underscore the strengths of modern predictive models in capturing complex traffic behaviors and demonstrate their value in resource planning, congestion management and overall network optimization. Importantly, this study is one of the first to Comprehensively assess AutoML and TimeGPT on a high-resolution, real-world CDR dataset from a major European city. By merging machine learning techniques with advanced temporal modeling, this study provides a strong framework for scalable and intelligent mobile traffic prediction. It thus highlights the functionality of AutoML in simplifying model development and the possibility of TimeGPT to extend transformer-based prediction to the telecommunications domain. Full article

Chronic pain affects a significant portion of the population, with fewer than 30% achieving adequate relief from existing treatments. This study describes the humanization methodology and characterization of an effective non-opioid single-chain fragment variable (scFv) biologic that reverses pain-related behaviors, in this case by targeting P2X4. After nerve injury, ATP release activates/upregulates P2X4 receptors (P2X4R) sequestered in late endosomes, triggering a cascade of chronic pain-related events. Nine humanized scFv (hscFv) variants targeting a specific extracellular 13-amino-acid peptide fragment of human P2X4R were generated via CDR grafting. ELISA analysis revealed nanomolar binding affinities, with most humanized molecules exhibiting comparable or superior affinity compared to the original murine antibody. Octet measurements confirmed that the lead, HC3-LC3, exhibited nanomolar binding kinetics (KD = 2.5 × 10⁻⁹ M). In vivo functional validation with P2X4R hscFv reversed nerve injury-induced chronic pain-related behaviors with a single dose (0.4 mg/kg, intraperitoneal) within two weeks. The return to naïve baseline remained durably reduced > 100 days. In independent confirmation, the spared nerve injury (SNI) model was similarly reduced. This constitutes an original method whereby durable reversals of chronic nerve injury pain, anxiety and depression measures are accomplished. Full article

The postmortem interval (PMI) is defined as the time interval between physiological death and the examination of the corpse, playing a critical role in forensic investigations. Traditional PMI estimation methods are often influenced by subjective and environmental factors. Circular RNAs (circRNAs), known for their stability, abundance, and conservation in brain tissue, show promise as biomarkers for PMI estimation. However, research on circRNAs in this context remains limited. This study aimed to develop PMI estimation models using circRNAs across multiple temperatures. By employing semi-quantitative reverse transcription-PCR, circTulp4, circSlc8a1, and circStrn3 were identified as reliable biomarkers for mouse brain tissue. Mathematical models were constructed using the reference genes 28S rRNA, mt-co1, and circCDR1as. At 4 °C, most equations had p-values below 0.05, with the equation using circSlc8a1 as a marker exhibiting the highest goodness of fit. Validation results indicated that the equation using circTulp4 as the reference gene had the highest accuracy. When applying the combined aforementioned three circRNAs, the equation using circCDR1as as the reference gene showed better accuracy. At 25 °C, all equations had R² values greater than 0.86, but most cubic equations had p-values above 0.05. Validation results demonstrated that the circTulp4/mt-co1 equation had the highest accuracy. When applying combined circRNAs, the R² values improved, and long-term PMI estimation was more accurate than short-term PMI estimation. At 35 °C, the linear equations had significantly poorer goodness of fit compared to nonlinear equations, and nonlinear equations exhibited better accuracy than linear equations. When applying the combined aforementioned three circRNAs, the accuracy of the three reference genes was similar, and the accuracy of long-term PMI estimation was consistently higher than that of short-term estimation. For the three-dimensional models, all R² values exceeded 0.75 with p-values significantly below 0.0001. Validation results demonstrated higher accuracy at 25 °C and 35 °C, with superior performance for long-term PMI estimation. In summary, this study constructed PMI estimation models under multiple temperature conditions based on highly expressed circRNAs in mouse brain tissue, highlighting circTulp4, circSlc8a1, and circStrn3 as novel biomarkers. These findings offer a complementary tool for PMI estimation, particularly for long-term PMI estimation. Full article

The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, India, with an emphasis on understanding the impacts of climate change. This study employed both conceptual and machine learning models to assess how changing precipitation patterns and temperature variations influence streamflow dynamics. Seven satellite precipitation products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation with Stations (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated in a gridded precipitation evaluation over the Godavari River basin. Results of Multi-Source Weighted-Ensemble Precipitation (MSWEP) had a Nash–Sutcliffe efficiency (NSE), coefficient of determination (R²), and root mean square error (RMSE) of 0.806, 0.831, and 56.734 mm/mon, whereas the Tropical Rainfall Measuring Mission had 0.768, 0.846, and 57.413 mm, respectively. MSWEP had the highest accuracy, the lowest false alarm ratio, and the highest Peirce’s skill score (0.844, 0.571, and 0.462). Correlation and pairwise correlation attribution approaches were used to assess the input parameters, which included a two-day lag of streamflow, maximum and minimum temperatures, and several precipitation datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). CMIP6 datasets that had been adjusted for bias were used in the modeling process. R, NSE, RMSE, and R² assessed the model’s effectiveness. RF and M5P performed well when using CMIP6 datasets as input. RF demonstrated adequate performance in testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6) and extremely good performance in training (0.75 < NSE < 1 and 0.7 < R < 1). Likewise, M5P demonstrated good performance in both training and testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6). While RF was the best performer for both datasets, Indian Meteorological Department outperformed all CMIP6 datasets in streamflow modeling. Using the Indian Meteorological Department gridded precipitation, RF’s NSE, R, R², and RMSE values during training were 0.95, 0.979, 0.937, and 30.805 m³/s. The test results were 0.681, 0.91, 0.828, and 41.237 m³/s. Additionally, the Multi-Layer Perceptron (MLP) model demonstrated consistent performance across both the training and assessment phases, reinforcing the reliability of machine learning approaches in climate-informed hydrological forecasting. This study underscores the significance of incorporating climate change projections into hydrological modeling to enhance water resource management and adaptation strategies in the Godavari basin and similar regions facing climate-induced hydrological shifts. Full article

Background/Objectives: The Children’s Depression Rating Scale—Revised (CDRS-R) is a well-established tool to evaluate depressive symptoms in adolescents, yet its psychometric properties in China have not been thoroughly validated. The present research aimed to assess the Chinese version of the CDRS-R in adolescents from China. Methods: This study included 360 adolescents: 180 were diagnosed with major depressive disorder (MDD) and 180 were healthy controls (HCs). Internal consistency, convergent validity, and factor structure were evaluated, while receiver operating characteristic (ROC) analysis was employed to establish cutoff scores. Results: The Chinese CDRS-R demonstrated high internal reliability (Cronbach’s α = 0.966) and strong correlations with related measures, confirming its convergent validity. Confirmatory factor analysis supported the original four-factor structure. ROC analysis indicated that the optimal cutoff score for diagnosing MDD was 48, effectively distinguishing MDD from HCs. Conclusions: The findings confirm that the Chinese CDRS-R is a reliable instrument for assessing depressive symptoms in Chinese adolescents, making it suitable for both clinical and research purposes. Full article

Background/Objectives: Subjective cognitive decline (SCD) may serve as an early indicator of Alzheimer’s disease (AD). This study investigates regional cerebral blood flow (rCBF) alterations in individuals with SCD using single photon emission computed tomography (SPECT). To characterize rCBF patterns in SCD patients compared to healthy controls and examine the relationship between rCBF and cognitive function. Methods: We compared rCBF in 20 SCD patients and 20 age- and sex-matched healthy controls using 99mTc-ECD SPECT imaging. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Geriatric Depression Scale (GDS), and Cognitive Abilities Screening Instrument (CASI). Results: SCD patients demonstrated significantly reduced rCBF in the right superior temporal gyrus (rSTG) (mean uptake ratio [UR] = 0.864 ± 0.090 vs. 1.030 ± 0.074, p < 0.001) and right caudate (mean UR = 0.783 ± 0.068 vs. 0.947 ± 0.062, p < 0.001) compared to controls. Additionally, negative correlations were observed between rCBF in these regions and CDR scores, particularly in the memory domain (rSTG: r = −0.37, p = 0.016; right caudate: r = −0.39, p = 0.011). Conclusions: Reduced rCBF in the rSTG and right caudate may represent early biomarkers for SCD, which could aid in the early detection of AD. These findings suggest that SPECT imaging might be a valuable tool for identifying individuals at risk of cognitive decline, potentially allowing for earlier intervention and targeted preventive strategies in the management of AD. Full article

Glaucoma represents a significant global contributor to blindness. Accurately segmenting the optic disc (OD) and optic cup (OC) to obtain precise CDR is essential for effective screening. However, existing convolutional neural network (CNN)-based segmentation techniques are often limited by high computational demands and long inference times. This paper proposes an efficient end-to-end method for OD and OC segmentation, utilizing the lightweight MobileNetv3 network as the core feature-extraction module. Our approach combines boundary branches with adversarial learning, to achieve multi-label segmentation of the OD and OC. We validated our proposed approach across three public available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The outcomes reveal that the Dice coefficients for the segmentation of OD and OC within these datasets are 0.974/0.900, 0.966/0.875, and 0.962/0.880, respectively. Additionally, our method substantially lowers computational complexity and inference time, thereby enabling efficient and precise segmentation of the optic disc and optic cup. Full article

In cattle, colostral maternal immunoglobulins and lymphocytes transfer across the neonate’s intestinal epithelium to provide protection against pathogens. This study aimed to compare repertoires of B cell populations in blood and colostrum in cows for the first time, with an emphasis on ultralong complementarity determining region 3 (CDR3, ≥40 amino acids). Blood mononuclear cells (BMCs, n= 7) and colostral cells (n = 7) were isolated from Holstein-Friesian dairy cows. Magnetic-activated cell sorting was used to capture IgM and IgG B cells from BMCs. Colostral cells were harvested by centrifugation. RNA was extracted and cDNA was produced; IgM and IgG transcripts were amplified using polymerase chain reactions. Amplicons were sequenced using the Nanopore Native barcoding kit 24 V14 and MinION with R10.4 flow cells. In colostrum, there was a significantly greater percentage of IgM B cells with ultralong CDR3s (8.09% ± 1.73 standard error of the mean) compared to blood (4.22% ± 0.70, p = 0.05). There was a significantly greater percentage of IgG B cells in colostrum with ultralong CDR3s (12.98% ± 1.98) compared to blood (6.61% ± 1.11, p = 0.05). A higher percentage of IgM and IgG B cells with ultralong CDR3s in colostrum may be indicative of a potential role in protecting the neonate. Full article

Unmanned aerial vehicle (UAV) formation flying is an efficient and economical operation mode for air transportation systems. To improve the effectiveness of synergetic formation control for UAVs, this paper proposes a pairwise conflict resolution approach for UAV formation through mathematical analysis and designs a dynamic pairing and deep reinforcement learning framework (P-DRL formation control framework). Firstly, a new pairwise UAV formation control theorem is proposed, which breaks down the multi-UAVs formation control problem into multiple sequential control problems involving UAV pairs through a dynamic pairing algorithm. The training difficulty of Agents that only control each pair (two UAVs) is lower compared to controlling all UAVs directly, resulting in better and more stable formation control performance. Then, a deep reinforcement learning model for a UAV pair based on the Environment–Agent interaction is built, where segmented reward functions are designed to reduce the collision possibility of UAVs. Finally, P-DRL completes the formation control task of the UAV fleet through continuous pairing and Agent-based pairwise formation control. The simulations used the dynamic pairing algorithm combined with the DRL architectures of asynchronous advantage actor–critic (P-A3C), actor–critic (P-AC), and double deep q-value network (P-DDQN) to achieve synergetic formation control. This approach yielded effective control results with a strong generalization ability. The success rate of controlling dense, fast, and multi-UAV (10–20) formations reached 96.3%, with good real-time performance (17.14 Hz). Full article

A nearly continuous daily, global Environmental Science Data Record of NASA Standard MODerate-resolution Imaging Spectroradiometer (MODIS) snow-cover extent (SCE) data products has been available since 2000. When the MODIS record ends, the ‘moderate resolution’ SCE record will continue with NASA Standard Visible Infrared Imaging Radiometer Suite (VIIRS) SCE data products. The objective of this work is to evaluate and quantify the continuity between the MODIS and VIIRS SCE data products to enable the merging of the data product records. A climate data record (CDR) could be developed when 30 years of daily global moderate-resolution SCE become available if the continuity of the MODIS and VIIRS records can be established. Here, we focus on the daily cloud-gap-filled MODIS and VIIRS SCE NASA standard data products, MOD10A1F and VNP10A1F, respectively, for a case study in the Great Basin of the western United States during a period of sensor overlap. Using the methodologies described herein (daily percent of snow cover, duration of snow cover, average monthly number of days (Ndays) of snow cover, and trends in Ndays of snow cover, we show that the snow maps display excellent agreement. For example, the average monthly number of days of snow cover in the Great Basin calculated using MOD10A1F and VNP10A1F agrees with a Pearson’s correlation coefficient of r = 0.99 for our 11-year study period from WY 2013 to 2023. Additionally, the SCE derived from each data product agrees very well with meteorological station data, with a Pearson’s correlation coefficient of r = 0.91 and r = 0.92 for MOD10A1F and VNP10A1F, respectively. Our results support the eventual creation of a CDR. Full article

Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very-High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR-CDR) provides a nearly 40-year global dataset that has the potential to fill the gap in long-term snow cover fractional monitoring. Our study selects the Qinghai–Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with the MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface Reflectance at 0.64 μm (SR1) and Surface Reflectance at 0.86 μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land-cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm and improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1, ground snow-depth measurements and ERA5. The results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land-cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. The improved algorithm results in better consistency with MOD10A1 than with the vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2000 m or above 6000 m and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. The results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land-cover type and topographic factors are important to the algorithm. Our study lays the foundation for a global snow cover percentage product based on AVHRR-CDR and furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset. Full article

Correct classification of irritability is extremely important to assess prognosis and treatment indications of juvenile mood disorders. We assessed factors associated with low versus high parent- and self-rated irritability using the affective reactivity index (ARI) in a sample of 289 adolescents diagnosed with a bipolar or a major depressive disorder. Bivariate analyses were followed by multilinear logistic regression model. Factors significantly and independently associated with high versus low parent-rated ARI score were: more severe emotional dysregulation and bipolar disorders diagnosis. Factors significantly and independently associated with high versus low self-rated ARI score were: lower children depression rating scale (CDRS-R) difficulty of having fun item score, greater children depression inventory (CDI-2) self-report score, more severe emotional dysregulation, and greater CDRS-R appetite disturbance item score. High parent-rated irritability was strictly related with a bipolar disorder diagnosis, whereas high youth-rated irritability was related to depressive phenotype characterized by appetite/food-intake dysregulation, mood lability, and less anhedonia and apathy. Full article

Long-term exposure to lead (Pb) can result in chronic damage to the body through accumulation in the central nervous system (CNS) leading to neurodegenerative diseases, such as Alzheimer’s disease (AD). This study delves into the intricate role of miR-671/CDR1as regulation in the etiology of AD-like lesions triggered by chronic Pb exposure in adult mice. To emulate the chronic effects of Pb, we established a rodent model spanning 10 months of controlled Pb administration, dividing 52 C57BL/6J mice into groups receiving varying concentrations of Pb (1, 2, or 4 g/L) alongside an unexposed control. Blood Pb levels were monitored using serum samples to ensure accurate dosing and to correlate with observed toxicological outcomes. Utilizing the Morris water maze, a robust behavioral assay for assessing cognitive functions, we documented a dose-dependent decline in learning and memory capabilities among the Pb-exposed mice. Histopathological examination of the hippocampal tissue revealed tell-tale signs of AD-like neurodegeneration, characterized by the accumulation of amyloid plaques and neurofibrillary tangles. At the molecular level, a significant upregulation of AD-associated genes, namely amyloid precursor protein (APP), β-secretase 1 (BACE1), and tau, was observed in the hippocampal tissue of Pb-exposed mice. This was accompanied by a corresponding surge in the protein levels of APP, BACE1, amyloid-β (Aβ), and phosphorylated tau (p-tau), further implicating Pb in the dysregulation of these key AD markers. The expression of CDR1as, a long non-coding RNA implicated in AD pathogenesis, was found to be suppressed in Pb-exposed mice. This observation suggests a potential mechanistic link between Pb-induced neurotoxicity and the dysregulation of the CDR1as/miR-671 axis, which warrants further investigation. Moreover, our study identified a dose-dependent alteration in the intracellular and extracellular levels of the transcription factor nuclear factor-kappa B (NF-κB). This finding implicates Pb in the modulation of NF-κB signaling, a pathway that plays a pivotal role in neuroinflammation and neurodegeneration. In conclusion, our findings underscored the deleterious effects of Pb exposure on the CNS, leading to the development of AD-like pathology. The observed modulation of NF-κB signaling and miR-671/CDR1as regulation provides a plausible mechanistic framework for understanding the neurotoxic effects of Pb and its potential contribution to AD pathogenesis. Full article

(1) Background: Growing evidence suggests impairment of ocular blood flow in open-angle glaucoma (OAG) pathology, but little is known about the effect of an impaired cardiovascular supply on the structural and vascular parameters of the retina. This study aims to investigate the variations of these parameters in OAG patients compared to patients undergoing cardiac surgery (CS) with cardiopulmonary bypass. (2) Methods: Prospective observational study with 82 subjects (30 controls, 33 OAG patients, and 19 CS patients) who underwent ophthalmological assessment by swept-source OCT and CDI in one randomly selected eye. (3) Results: In the CS group, OA and SPCA PSV and EDV were significantly lower, OA and SPCA RI were significantly higher compared to the OAG and healthy subjects (p = 0.000–0.013), and SPCA EDV correlated with linear CDR (r = −0.508, p = 0.027). Temporal ONH sectors of GCL++ and GCL+ layers in the CS group did not differ significantly compared to the OAG patients (p = 0.085 and p = 0.220). The CS patients had significantly thinner GCL++ and GCL+ layers in the inner sectors (p = 0.000–0.038) compared to healthy subjects, and these layers correlated with the CRA PSV, EDV, and RI and SPCA PSV (p = 0.005–0.047). (4) Conclusions: CS patients had lower vascular and structural parameters in the ONH, and macula compared to the healthy controls that were similar to persons with OAG. Full article

The Coronavirus disease 2019 (COVID-19) pandemic caused by Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) is an ongoing threat to global public health. To this end, intense efforts are underway to develop reagents to aid in diagnostics, enhance preventative measures, and provide therapeutics for managing COVID-19. The recent emergence of SARS-CoV-2 Omicron variants with enhanced transmissibility, altered antigenicity, and significant escape of existing monoclonal antibodies and vaccines underlines the importance of the continued development of such agents. The SARS-CoV-2 spike protein and its receptor binding domain (RBD) are critical to viral attachment and host cell entry and are primary targets for antibodies elicited from both vaccination and natural infection. In this study, mice were immunized with two synthetic peptides (Pep 1 and Pep 2) within the RBD of the original Wuhan SARS-CoV-2, as well as the whole RBD as a recombinant protein (rRBD). Hybridomas were generated, and a panel of three monoclonal antibodies, mAb CU-P1-1 against Pep 1, mAb CU-P2-20 against Pep 2, and mAb CU-28-24 against rRBD, was generated and further characterized. These mAbs were shown by ELISA to be specific for each immunogen/antigen. Monoclonal antibody CU-P1-1 has limited applicability other than in ELISA approaches and basic immunoblotting. Monoclonal antibody CU-P2-20 is shown to be favorable for ELISA, immunoblotting, and immunohistochemistry (IHC), however, not live virus neutralization. In contrast, mAb CU-28-24 is most effective at live virus neutralization as well as ELISA and IHC. Moreover, mAb CU-28-24 is active against rRBD proteins from Omicron variants BA.2 and BA.4.5 as determined by ELISA, suggesting this mAb may neutralize live virus of these variants. Each of the immunoglobulin genes has been sequenced using Next Generation Sequencing, which allows the expression of respective recombinant proteins, thereby eliminating the need for long-term hybridoma maintenance. The synthetic peptides and hybridomas/mAbs and quantitative antigen-binding data are under the intellectual property management of the Clemson University Research Foundation, and the three CDRs have been submitted as an invention disclosure for further patenting and commercialization. Full article