Search Results (37,095)

Background: Gene therapy has experienced significant development since its origin decades ago, resulting in therapies for a wide range of diseases. In this context, optogenetics has emerged as a promising therapy for treating diseases in a precise spatiotemporal way using light. Transporting optogenetic genes to target cells is achieved using viral vectors, specifically AAV vectors. These vectors present limited cargo capacity, and a large percentage of the population carries AAV neutralizing antibodies. In this regard, lipid nanoparticles can overcome some of the previously mentioned problems of AAV vectors, making them prime candidates for optogenetic delivery. Methods: In this study, we evaluated their suitability for the delivery of the ChrimsonR plasmid in neurons in vitro. Results: In rat cortical neurons, in most of the concentrations tested, there was no reduction in several neuron morphological parameters that we measured when compared to another non-viral nanoparticle called lipofectamine. Transfection efficiency was significantly higher compared to lipofectamine in almost all treatments. Further in vitro analysis showed that electrophysiological parameters were altered, with reduced signal amplitudes; however, cell viability assays showed no decline in cell viability. Conclusions: These results demonstrate that lipid nanoparticles represent a promising non-viral platform for optogenetic delivery, though formulation optimization is required to achieve full functional efficacy. Full article

Pine wilt disease (PWD), caused by the pine wood nematode (PWN) Bursaphelenchus xylophilus, is a devastating pine disease that is characterized by rapid transmission, high lethality, and limited control options. In our previous study, the fucosyltransferase gene (fut) which encoded fucosyltransferase (FUT) was found to be a putative virulence determinant in PWN, which regulates pathogenicity of nematodes. To investigate the functional role of the fut gene in PWN, a comprehensive analysis was conducted to understand its molecular structure and biological activity. The full-length open reading frame (ORF) of fut was amplified using reverse transcription PCR (RT-PCR) and successfully ligated into the pET-28a expression vector. Heterologous expression of the recombinant FUT was achieved in Escherichia coli Rosetta (DE3) through induction with 1.0 mM isopropyl-β-D-thiogalactoside (IPTG), followed by purification via nickel-nitrilotriacetic acid (Ni-NTA) affinity chromatography. Biochemical characterization revealed that the recombinant FUT exhibited optimal enzymatic activity at ‌30 °C‌ and ‌pH 8.0‌, respectively. Furthermore, RNA interference (RNAi) validated by RT-qPCR was used to explore the biological functions of fut in PWN, and results indicated that downregulation of the fut gene could significantly reduce the vitality, reproduction, pathogenicity, development, and lifespan of PWN. Furthermore, gallic acid as an inhibitor of FUT displayed a strong inhibitory effect on recombinant FUT activity and nematicidal activity against PWNs in vitro and could alleviate the wilt symptom of pine seedlings inoculated with PWNs at a concentration of 100 μg/mL, indicating that it has the potential to be a novel nematicide. Collectively, these results establish fut as a critical virulence determinant in PWN and highlight its potential as a molecular target for controlling pine wilt disease. Full article

Tick-borne diseases (TBD) pose a significant threat to both animal and public health, particularly in tropical regions. In the Colombian Caribbean region, there is limited knowledge of the epidemiology of TBD in domestic animals and their vectors. In this study, conducted in northern Colombia from 2021 to 2022, we analyzed the molecular diversity of Anaplasmataceae in a total of 1156 ticks and blood samples collected from their infested hosts: 56 cattle and 17 equids (horses and mules). Polymerase chain reaction (PCR) assays were performed, using primers to amplify the mitochondrial 16S rRNA gene for tick identification and bacterial 16S and 23S rRNA to detect Anaplasmataceae. The amplified products were sequenced and analyzed for molecular characterization of species. Four tick species were identified: Dermacentor nitens (55.6%), Rhipicephalus microplus (43.0%), Rhipicephalus sanguineus sensu lato (0.7%), and Amblyomma patinoi (0.7%). Overall, 9.4% of the pooled tick samples were identified as R. microplus, and 64.4% of the blood samples tested positive for Anaplasmataceae. Molecular analyses identified Anaplasma marginale in cattle and several species in ticks, including an Anaplasma sp. closely related to A. platys-like, Ehrlichia ruminantium, and E. muris and Ehrlichia variants closely related to Candidatus E. rustica, E. canis, and E. minasensis. The results indicate high infection rates and the circulation of both well-known and potentially novel Anaplasmataceae species, suggesting complex transmission dynamics among ticks and hosts. Full article

Monitoring surface water quality is essential for assessing water resources and identifying their quality patterns. Traditional monitoring methods, based on conventional point-sampling stations, are reliable but costly and limited in frequency and spatial coverage. These constraints hinder the ability to evaluate water quality parameters at the temporal and spatial scales required to detect the effects of extreme events on aquatic systems. Satellite imagery offers a viable complementary alternative to enhance the temporal and spatial monitoring scales of traditional assessment methods. However, limitations related to spectral, spatial, temporal, and/or radiometric resolution still pose significant challenges to prediction accuracy. This study aimed to propose a methodology for predicting optically active and inactive water quality parameters in lotic and lentic environments using remote-sensing data and machine-learning techniques. Three remote-sensing datasets were organized and evaluated: (i) data extracted from Sentinel-2 imagery; (ii) data obtained from raw PlanetScope (PS) imagery; and (iii) data from PS imagery normalized using the methodology developed by Dias. Data on water quality parameters were collected from 24 monitoring stations located along the Paraopeba River channel and the Três Marias Reservoir, covering the period from 2016 to 2023. Four machine-learning algorithms were applied to predict water quality parameters: Random Forest, k-Nearest Neighbors, Support Vector Machines with Radial Basis Function Kernel, and Cubist. Model performance was evaluated using four statistical metrics: root-mean-square error, mean absolute error, Lin′s concordance correlation coefficient, and the coefficient of determination. Models based on normalized PS data achieved the best performance in parameter estimation. Additionally, decision-tree-based algorithms showed superior generalization capability, outperforming the other models tested. The proposed methodology proved suitable for this type of analysis, confirming not only the applicability of PS data but also providing relevant insights for its use in diverse environmental-monitoring applications. Full article

Despite the growing interest in physiological stress monitoring, an objective measure of stress is currently lacking, especially in clinical and rehabilitation contexts. With the emerging integration of artificial intelligence (AI) in data analytics, heart rate variability (HRV) has gained attention as an effective biomarker; however, the literature remains fragmented across disciplines, stress types, and methodological approaches. This scoping review aims to investigate how AI techniques are applied to HRV analysis for stress detection and prediction in adult populations. Although this review does not focus on a specific subtype of stress, its primary objective is to explore the current methodological state of the art as reported in the literature, without restrictions on stress typology. Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, and Google Scholar for studies published between 2005 and 2025, using MeSH terms including “HRV”, “Rehabilitation”, “SCI” (for Spinal Cord Injury), “Stress”, “Sympathetic”, “Parasympathetic”, “Non-linear”, “Gamification”, “AI” and “Machine Learning”. Inclusion criteria targeted adult human populations and studies employing HRV features as input for AI and machine learning techniques for psychophysical stress assessment. Of the 566 records identified, 15 studies met the eligibility criteria. The reviewed studies exhibit substantial heterogeneity in terms of settings, populations, sensors, and algorithms with most employing supervised methods (e.g., random forest, support vector machine), alongside several applications of deep learning and explainable AI. Only one study focused specifically on physiological stress, none focused on SCI populations, and rehabilitation-related research was scarce, thereby underscoring important gaps in the current literature. Overall, HR variability analysis, especially when combined with artificial intelligence techniques, represents a promising approach for stress assessment; however, the field is methodologically fragmented and clinically underdeveloped in critical areas, underscoring the need for a multidisciplinary methodological framework. Full article

In recent years, Peru’s agricultural sector has expanded steadily despite recurrent external shocks and persistent volatility in global commodity markets. This sustained performance reflects the sector’s exposure to international price dynamics, a connection with direct implications for structural sustainability in a small, open and commodity-dependent economy. In this context, the study examines whether the terms of trade (TOT) sustain a stable long-run relationship with Peru’s agricultural GDP and assesses how this linkage shapes structural sustainability. The analysis applies Johansen’s cointegration method combined with a bivariate Vector Error Correction Model (VECM), enabling the identification of common long-run trends and the estimation of adjustment speeds following external shocks. The results reveal a single cointegrating vector and a negative, highly significant error-correction term in the agricultural equation, indicating that the sector gradually corrects deviations from its long-run equilibrium. In contrast, the TOT display no meaningful adjustment mechanism, behaving as a weakly exogenous driver. Short-run effects of external shocks are small and statistically fragile, suggesting that quarterly disturbances are overshadowed by the longer-run correction process. Beyond quantifying these dynamics, the study offers a structural reading of how volatile imported inputs—fertilisers, fuels and agricultural machinery—influence agricultural performance, even when export prices are favourable. Overall, the findings underscore that long-term sustainability depends not only on global price trajectories but also on domestic productive capacities and gradual technological improvement, highlighting the need for adaptive strategies in an environment of persistent global volatility. Full article

To address flight safety risks from rotor defects in rotorcraft drones operating in complex low-altitude environments, this study proposes a high-precision diagnostic model based on the Multimodal Data Input and Spatio-Temporal Feature Fusion Network (MDI-STFFNet). The model uses a dual-modality coupling mechanism that integrates vibration and air pressure signals, forming a “single-path temporal, dual-path representational” framework. The one-dimensional vibration signal and the five-channel pressure array are mapped into a texture space via phase space reconstruction and color-coded recurrence plots, followed by extraction of transient spatial features using a pre-trained ResNet-18 model. Parallel LSTM networks capture long-term temporal dependencies, while a parameter-free 1D max-pooling layer compresses redundant pressure data, reducing LSTM parameter growth. The CSW-FM module enables adaptive fusion across modal scales via shared-weight mapping and learnable query vectors that dynamically assign spatiotemporal weights. Experiments on a self-built dataset with seven defect types show that the model achieves 99.01% accuracy, improving by 4.46% and 1.98% over single-modality vibration and pressure inputs. Ablation studies confirm the benefits of spatiotemporal fusion and soft weighting in accuracy and robustness. The model provides a scalable, lightweight solution for UAV power system fault diagnosis under high-noise and varying conditions. Full article

Malaria transmission in sub-Saharan Africa is dominated by the An. gambiae complex and An. funestus group, whose distribution varies across ecological settings. Secondary species occur at lower densities, but their role in transmission may differ from one locality to another depending on local conditions. Assessing Anopheles biodiversity using ecological indices is therefore essential to characterise their diversity and relative abundance. This study investigated the biodiversity and spatial distribution of Anopheles species across the three climatic zones of Burkina Faso to guide effective vector control strategies. Indoor resting mosquitoes were collected from 67 health districts across the 13 regions of Burkina Faso between September and December 2022 using pyrethroid spray catches. A total of 30,521 Anopheles mosquitoes were identified, with An. gambiae s.l. dominating (94.4%). The Sudano-Sahelian zone recorded the highest abundance, followed by the Soudanian and Sahelian zones. Biodiversity decreased from humid southern to arid northern areas, with the Soudanian zone showing the highest diversity. Molecular analysis of 2026 An. gambiae s.l. specimens revealed marked heterogeneity: An. coluzzii predominated in Sahelian (74.9%) and Sudano-Sahelian (71.2%) zones, while An. gambiae s.s. was most frequent in the Soudanian zone (53.8%). These results highlight spatial and ecological differences in Anopheles composition across Burkina Faso and emphasize the need for locally adapted malaria vector control strategies. Full article

Nanobodies (single-domain antibodies, VHHs) have emerged as versatile tools for evaluating and treating Alzheimer’s disease (AD). They offer distinct engineering benefits compared with traditional antibodies and small molecules, including small size, stability, and specificity. In AD, nanobodies have been shown in preclinical models to neutralize toxic amyloid-β oligomers, inhibit tau generation and aggregation, and modulate neuroinflammation, thereby demonstrating significant therapeutic potential. However, all nanobody applications in AD are discussed strictly as preclinical therapeutic potential rather than established clinical therapies, and direct clinical evidence in patients with AD is still lacking. Advanced engineering strategies, including intranasal and intrathecal routes, receptor-mediated transport, plasma protein binding with albumin, and focused ultrasound to facilitate brain penetration. Additionally, to improve nanobody delivery precision, half-life, and efficacy, strategies such as integrating nanobodies with nanoparticles, dendrimers, liposomes, and viral vectors are being employed. In fact, nanobodies are applied beyond monotherapy across multiple technological platforms to optimize brain delivery and target multiple targets. Nanobodies have been used on bispecific and trispecific antibody platforms, as well as in CRISPR/Cas9 editing and AI-driven technologies, to expand their applications. Recently, preclinical evidence has been mounting on the efficacy of nanobodies in clearing Aβ and tau, preserving synapses, and normalizing biomarkers. Comparison with FDA-approved anti-Aβ monoclonal antibodies (aducanumab, lecanemab, and donanemab) highlights opportunities and current translational gaps, including safety testing, half-life extension, and delivery optimization. This review critically delineates the current molecular mechanisms, emerging strategies, and delivery platforms, and emphasizes the potential of nanobodies as promising therapeutic and diagnostic molecules in AD therapeutics. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

This study investigates how centralized governance structures undermine the achievement of sustainable development by systematically eliminating local grassroot territorial development vectors and initiatives. It examines how centralization reduces the representation of diverse sustainability strategies as systems transition from local to regional/national level. Using Belarus as a case study, this research discovers the effects of this transition. The study thoroughly explored 47 sustainable development planning documents from Belarus, spanning from 2005 to 2020, and encompassing diverse levels of governance, including Local Agenda 21 plans, municipal strategies, and regional planning documents. The SWOT indicators extracted during the analysis were systematically categorized within the advanced sustainability framework into the following four categories: social, environmental, economic, and institutional/participatory. A quantitative analysis of local development vectors loss was conducted using a novel evaluation tool designed to measure indicator diversity across various planning scales. The findings show that approximately 85% of the diversity of local sustainability vectors is lost due to aggregation/in hierarchical planning processes. This phenomenon can be explained by reference to three mechanisms: administrative inertia (institutional resistance to novel approaches), funding constraints (central budgets default to standardized territorial development vectors), and structural barriers (limited local autonomy despite formal decentralization policies). Social and environmental development vectors demonstrate greater losses than economic ones, indicating that context-specific local solutions are systematically ignored at higher scales. The results indicate that the formal decentralization approach is ineffective in preserving local sustainability without complementary institutional reforms. The study enhances existing knowledge of sustainability science by demonstrating how central governance restricts the implementation of localized solutions to environmental and social challenges. This demonstrates that formal decentralization policies, without institutional reforms, do not lead to sustainable development. The methodology developed here can also be applied to other highly centralized systems. Full article

High-resolution range profile (HRRP) target recognition has garnered significant attention in radar automatic target recognition (RATR) research for its rich structural information and low computational costs. With the rapid advancements in deep learning, methods for HRRP target recognition that leverage deep neural networks have emerged as the dominant approaches. Nevertheless, these traditional closed-set recognition methods require labeled data for every class in training, while in reality, seen classes and unseen classes coexist. Therefore, it is necessary to explore methods that can identify both seen and unseen classes simultaneously. To this end, a semantic- and physical-guided generative network (SPGGN) was innovatively proposed for HRRP generalized zero-shot recognition; it combines a constructed knowledge graph with attribute vectors to comprehensively represent semantics and reconstructs strong scattering points to introduce physical constraints. Specifically, to boost the robustness, we reconstructed the strong scattering points from deep features of HRRPs, where class-aware contrastive learning in the middle layer effectively mitigates the influence of target-aspect variations. In the classification stage, discriminative features are produced through attention-based feature fusion to capture multi-faceted information, while the design of balancing loss abates the bias towards seen classes. Experiments on two measured aircraft HRRP datasets validated the superior recognition performance of our method. Full article

Background and Objective: Changes in female sex hormone levels are closely linked to the development and progression of uterine fibroids (UFs). Clinical approaches to fibroid management vary according to guidelines and depend on patient symptoms, fibroid size, and clinician judgment. Despite available diagnostic tools, surgical decisions remain largely subjective. With the advancement of artificial intelligence (AI) and clinical decision support technologies, clinical experience can now be transferred into data-driven computational models trained with hormone-based parameters. To develop a clinical decision support algorithm that predicts surgical necessity for uterine fibroids by integrating fibroid characteristics and female sex hormone levels. Methods: This multicenter study included 618 women with UFs who presented to three hospitals; 238 underwent surgery. Statistical analyses and artificial intelligence-based modeling were performed to compare surgical and non-surgical groups. Training was conducted with each hormone—follicle-stimulating hormone (FSH), luteinizing hormone (LH), estrogen (E2), prolactin (PRL), and anti-Müllerian hormone (AMH)—and with 126 input combinations including hormonal and morphological variables. Five supervised learning algorithms—support vector machine, decision tree, random forest, and k-nearest neighbors—were applied, resulting in 630 trained models. In addition to this retrospective development phase, a prospective validation was conducted in which 20 independent clinical cases were evaluated in real time by a gynecologist blinded to both the model predictions and the surgical outcomes. Agreement between the clinician’s assessments and the model outputs was measured. Results: FSH, LH, and PRL levels were significantly lower in the surgery group (p < 0.001, 0.009, and <0.001, respectively), while E2 and AMH were higher (p = 0.012 and 0.001). Fibroid volume was also greater among surgical cases (90.8 cc vs. 73.1 cc, p < 0.001). The random forest model using LH, FSH, E2, and AMH achieved the highest accuracy of 91 percent. In the external validation phase, the model’s predictions matched the blinded gynecologist’s decisions in 18 of 20 cases, corresponding to a 90% concordance rate. The two discordant cases were later identified as borderline scenarios with clinically ambiguous surgical indications. Conclusions: The decision support algorithm integrating hormonal and fibroid parameters offers an objective and data-driven approach to predicting surgical necessity in women with UFs. Beyond its strong internal performance metrics, the model demonstrated a high level of clinical concordance during external validation, achieving a 90% agreement rate with an independent, blinded gynecologist. This alignment underscores the model’s practical reliability and its potential to reduce subjective variability in surgical decision-making. By providing a reproducible and clinically consistent framework, the proposed AI-based system represents a meaningful advancement toward the validated integration of computational decision tools into routine gynecological practice. Full article

Background/Objectives: Thalassemia is highly prevalent in Indonesia, and its treatment imposes a significant financial burden. To date, thalassemia management in Indonesia remains largely limited to supportive therapies. This report aims to present the monitoring of the first Indonesian pediatric thalassemia patient to undergo gene therapy. Methods: Medical summaries were gathered across multiple time points. The gene therapy process consisted of several phases: screening, apheresis and cell manufacturing, conditioning, cell infusion, and post-treatment follow-up. The therapy utilized autologous CD34+ hematopoietic stem and progenitor cells (HSPCs), which were genetically modified using a lentiviral vector carrying the beta-globin gene. The primary outcome of this study was transfusion independence, determined through serial assessments of hematological parameters over a six-month period following gene therapy. Results: A 15-year-old female had been diagnosed with thalassemia major at the age of five. DNA analysis revealed compound heterozygous mutations Hb Malay (codon 19, AAC^Asn > AGC^Ser) and IVS1-nt5 (G > C). She had been receiving regular blood transfusions every 3–4 weeks, and hemosiderosis was detected in the liver and pancreas. Given the patient’s age—over 10 years—hematopoietic stem cell transplantation carries increased risks, making gene therapy the most suitable curative option. During the six-month follow-up period after gene therapy, the patient remained transfusion-independent and experienced no complications. Conclusions: In selecting an appropriate curative therapy for thalassemia patients, several factors must be considered. The successful implementation of the first gene therapy in an Indonesian pediatric thalassemia patient should serve as a catalyst for the continued development and expansion of curative treatment options for thalassemia patients across the country. Full article