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The impact of high standard farmland construction on China’s agricultural resilience
A gated temporal attention based intra prediction framework for robust deepfake video detection
Deep learning based personalized tourism recommendation for behavior analysis and route optimization
Molecular mechanism and structural models of protein-mediated copper transfer to the Arabidopsis thaliana ethylene receptor ETR1 at the ER membrane
Abstract In plants, the gaseous plant hormone ethylene regulates a wide range of developmental processes and stress responses. The small unsaturated hydrocarbon is detected by a family of receptors (ETRs) located in the membrane of the endoplasmic reticulum, which rely on a monovalent copper cofactor to detect this hydrocarbon. The copper-transporting P-type ATPase RAN1 (HMA7), located in the same membrane, is known to be essential for the biogenesis of ETRs. Still, the precise molecular mechanism by which the receptors acquire their copper cofactor remains unclear. A recent study by our laboratory demonstrated a direct interaction between RAN1 and soluble copper chaperones of the ATX1 family with the model ethylene receptor ETR1, providing initial insights into the mechanism by which copper is transferred from the cytosol to the membrane-bound receptors. In this study, we further investigated these interactions with respect to the function of individual domains in complex formation. To this end, we combined biochemical experiments and computational predictions and unraveled the processes and mechanisms by which copper is transferred to ETR1 at the molecular level.
Cluster analysis of articulatory trajectories in fluent nonword productions separates adults who stutter from fluent speakers
Abstract Whether fluent-sounding utterances of adults who stutter are normally articulated is unclear. We asked 15 patients and 17 matched controls to utter a pseudoword while recording real-time MRI at 55 frames per second in a midsagittal plane, and we automatically clustered participants for distances between sites of articulation. Clustering was successful in 80% of the cases, indicating major differences in the movement patterns of fluent sounding utterances in both groups.
Combined effects of restriction factors and transduction adjuvants on lentiviral vector gene transfer efficacy
Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability
Abstract Accurate demand forecasting is essential for informed decision-making in today’s dynamic business environment, where product demand often follows diverse and shifting patterns throughout increasingly shorter life cycles driven by continuous product innovation. This study aims to develop a forecasting framework capable of accurately predicting demand across varying patterns, with particular attention to the decline phase of the product life cycle. Traditional statistical forecasting methods, such as those in the ARIMA family, generally perform well with linear trends over short horizons, whereas machine learning techniques like XGBoost are better suited for capturing complex, nonlinear patterns over longer periods. This paper introduces an adaptive, hybrid forecasting framework that integrates ARIMA-based regression models with XGBoost using a weighted ensemble strategy. Initially, the framework tests linear models; if diagnostic analysis indicates nonlinearity, it incorporates XGBoost to address these complexities. To optimize the ensemble model performance, a grid search algorithm adjusts the ensemble weights by minimizing the root mean square error (RMSE), enabling the framework to dynamically leverage the strengths of both approaches. The proposed method was validated on five datasets representing different phases of the product life cycle. Results demonstrate that the proposed framework achieved MAPE below 13% on most datasets, with up to 80% improvement over ARIMA models in cases involving high variability demand patterns. The results show that the ensemble model enhances both flexibility and accuracy, especially for demand patterns that combine linear and nonlinear components. The framework benefits from the explainability and time-series capabilities of ARIMA while utilizing XGBoost’s power to model nonlinear relationships. This research underscores the practical advantages of hybrid modeling in improving demand forecasting and operational planning across various industry sectors.
Hybrid Wavelet–ML models for regional drought forecasting in Norway
Abstract Drought is a natural disaster that often remains unnoticed until ecosystem impacts become severe. Therefore, monitoring and detecting droughts are important research topics. Consequently, drought indices with different focuses, such as precipitation or soil moisture, have been developed. Yet, the utility of the indices is limited before the beginning of the drought. To overcome this shortcoming, drought forecasting and providing decision-makers with an early warning to mitigate the effects is an important research topic. This study aims to take on the forecasting of the droughts with its novelty on the spatial focus, Norway (Drammen, Hamar, and Lillehammer). We forecast the Effective Drought Index (EDI) across spatially diverse Norwegian regions without hydrological constraints. To achieve this, we have utilized precipitation data between 1980 and 2025 and trained our machine learning models, namely, Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGboost), Long-Short Term Memory network (LSTM), and Categorical Boosting Algorithm (Catboost). Moreover, the latent feature space is extended by wavelet transformation (WT). The innovative aspect of this study and its contribution to the literature is its novel application of the WT to some algorithms. Furthermore, unlike the literature, EDI was chosen as the drought index in this study, further increasing its innovative nature. Our results indicate that long short-term memory networks enhanced by wavelet transformation provide the best forecasts. Here, the best performance, LSTMW-M04, is achieved over Drammen ( r = 0.9765, NSE = 0.9510, KGE = 0.8641, PI = 0.3211, and RMSE = 0.2207). Although LSTM is already an innovative and successful algorithm, we have further improved the model performance. This result will help decision-makers in a future drought study with both the model input structure and the algorithm used.
Therapeutic effects of conditioned medium of immortalized dental pulp stem cells from human exfoliated deciduous teeth against experimental autoimmune neuritis
Theranostic application of radiolabeled HER2-targeting single-domain antibodies (RAD202) in preclinical setting
Abstract HER2 is a well-established target for antibody-based drug delivery. Single-domain antibodies (sdAbs) offer advantages over monoclonal antibodies, including faster clearance, higher antigen-binding affinity, and improved tumor penetration. This study investigates the biodistribution of HER2-targeting sdAbs (with and without HIS-tag) labeled with 68 Ga and their therapeutic efficacy radiolabeled with 177 Lu, in HER2-positive xenograft. Mice received 10 MBq of 68 Ga-labeled HIS-tagged sdAb and underwent PET/CT scans immediately and three hours post-injection. The following day, untagged radioactive sdAb was administered and scanned similarly. Controls were co-injected with an excess of unlabeled sdAb. For therapy study, mice received either four weekly injections or a single dose of 177 Lu-labeled sdAb (without HIS-tag). Radiotracers were synthesized with high radiochemical purity (> 99.8%). PET/CT imaging revealed specific tumor accumulation and low background, except in excretory organs. Co-injection of unlabeled sdAbs reduced tumor uptake, confirming tracer specificity. 177 Lu-labeled sdAb therapy prolonged median survival by 34 d (single dose) and 26 d (fractionated therapy) versus respective control group treated with unlabeled sdAb. The fractionated therapy group exhibited significantly lower tumor volumes and did not exceed 200% of the initial tumor volume. 68 Ga-labeled sdAbs enabled high-contrast PET imaging and 177 Lu-labeled sdAbs, particularly in fractionated regimens, effectively delayed tumor progression and were well tolerated, supporting further clinical translation.
Predator awareness training improves survival of released critically endangered western ringtail possums, pseudocheirus occidentalis
The clinical value and application of augmented reality navigation in brachial plexus schwannoma
Integrated biostratigraphy of the Maastrichtian-Danian sequence in the southern Tethys with insights into paleoenvironmental implications
Abstract The Cretaceous-Paleogene (K/Pg) sequence offers a global perception of the paleoecological evolution in the southern Tethys realm. High-resolution calcareous nannofossil and dinoflagellate cyst analyses were conducted on subsurface Maastrichtian-Danian deposits in the Western Desert of Egypt, to characterize the paleo-conditions. The calcareous nannofossil analysis retrieved four main biostratigraphic zones (UC18-UC20, and NP4). A hiatus at the K/Pg boundary resulted in absence of most Danian nannofossil zones (NP1-NP3). The palynofacies assemblages discriminated three intervals of varying oxygen levels: dysoxic, proximal suboxic-anoxic shelf, and distal suboxic-anoxic basin incorporated by oligo-eutrophic conditions. The species diversity and relative abundance patterns of sensitive nannofossil indicators during this interval are attributed to significant climatic shifts. The surface waters during the Maastrichtian experienced a general cooling trend, interrupted by two warming episodes that may be correlated with the Deccan traps’ two phases (1&2). The biotic productivity indices and geochemical proxy (P, Sr/Al& P/Al) showed that during the earliest Maastrichtian high productivity prevailed, whereas during middle and late Maastrichtian a fluctuating pattern appeared. Statistically, multivariate techniques discriminated the retrieved taxa into five groups, each characterizing a depositional environment and climatic trend. Further, the palynomorphs analysis classified the Maastrichtian-Danian sequence into three offshore environments. A more refined understanding of climatic upheavals is essential for future studies to assess their potential role in driving biotic changes across the K/Pg boundary.
Longitudinal functional connectivity during rest and task is differentially related to Alzheimer’s pathology and episodic memory in older adults
Abstract Changes in functional connectivity (FC) strength involving the medial temporal lobe (MTL) and posteromedial cortex (PMC) are related to early Alzheimer’s pathology and alterations in episodic memory performance in cognitively unimpaired older adults, but their dynamics remain unclear. We examined how longitudinal changes in FC involving MTL and PMC during resting-state, episodic memory encoding, and retrieval relate to subsequent amyloid- and tau-PET burden, longitudinal episodic memory performance, and the APOE4 genotype in 152 cognitively unimpaired older adults from the PREVENT-AD cohort. We found APOE4- and fMRI paradigm-dependent associations of change in FC strength with pathology burden and change in episodic memory performance. Decreasing FC over time, or “hypoconnectivity”, within PMC during rest in APOE4 carriers and during retrieval in APOE4 non-carriers was related to more amyloid and tau, respectively. Conversely, increasing FC over time, or “hyperconnectivity”, within MTL during encoding in APOE4 carriers and between MTL and PMC during retrieval independent of APOE4 status was related to more tau. Further, increasing FC between MTL and PMC during rest, unlike during encoding, was beneficial for episodic memory. Our study highlights that pathology-related episodic memory network changes manifest differently during rest and task and have differential implications for episodic memory trajectories.
Ferroptosis-related gene analysis revealing novel biomarkers and therapeutic targets in diffuse large B-cell lymphoma
Abstract Diffuse large B-cell lymphoma (DLBCL) is an aggressive, heterogeneous non-Hodgkin lymphoma with high relapse rates and drug resistance, which necessitates novel biomarkers. This study aimed to fill a research gap by investigating the function of ferroptosis—an iron-dependent form of programmed cell death—in DLBCL, an area that remains inadequately explored. Employing a comprehensive bioinformatics framework, we analyzed ferroptosis-related genes in The Cancer Genome Atlas-DLBCL dataset using consensus clustering and differential expression, somatic mutation, copy number variation (CNV), gene ontology and pathway enrichment, and immune infiltration analyses. Our analysis identified two DLBCL subtypes, revealing 912 differentially expressed genes, including 24 ferroptosis-related differentially expressed genes (FRDEGs). Enrichment analyses indicated that these genes are involved in crucial biological pathways, including the lipoxygenase pathway and inflammatory regulation, while immune infiltration assessment highlighted significant correlations with specific immune cell types, particularly the positive correlation of IDO1 with M1 macrophages and the negative correlation of IFNG with memory B cells. Further, we established a prognostic risk model incorporating CDKN1A , KLF2 , and IFNG that holds promise for predicting patient outcomes. These findings demonstrate that ferroptosis regulates DLBCL progression and identify potential biomarkers/therapeutic targets requiring validation to develop new therapies.
Impact of tectonic and stratigraphic evolution of Shushan Basin on hydrocarbon accumulation, north Western Desert, Egypt
Abstract Tectonic and stratigraphic evolution of the Shushan Basin in northern Western Desert of Egypt played an important role in hydrocarbon accumulation in the Mesozoic. The purpose of this study is to assess the hydrocarbon prospectivity of the deep Middle Jurassic Safa Member by integration of seismic interpretation, structural analysis, core descriptions, and wireline log evaluation over ten wells. Seismic data also indicate extensive E–W and NW–SE normal fault systems that developed horst–graben structures, hence providing crucial structural traps. Petrographic observation indicates that the Safa reservoir consists of vertically stacked, pebbly, cross-bedded sandstones deposited in a fluvial channel environment. Petrophysical evaluation indicates net pay thicknesses varying from 375 to 500 ft, 10–15% effective porosity, 5–20% shale content, and hydrocarbon saturation varying up to 95%, particularly in Qasr-3 and Qasr-4 wells. These results confirm the Safa Member as a fault compartmentalized, overlying Zahra shale-sealed, high-productivity gas–condensate reservoir. The findings also stress the significance of tectono-stratigraphic evolution in governing reservoir development and offer an exploration predictive model for such similar rift-related basins worldwide.
D-amphetamine alters the dynamic ECoG activity distribution patterns in the rat neocortex
Abstract Amphetamine has widespread effects on multiple neurotransmitter systems, potentially altering the physiological connectivity and network dynamics across various regions of the brain. In this study, we investigated the effects of D-amphetamine using our previously published approach where electrocorticogram (ECoG) recordings from eight cortical areas provided a coarse estimation of the global activity distribution patterns across sets of neuron populations. Changes in these activity distribution patterns were quantified with Principal Component Analysis (PCA) and k-Nearest Neighbors (kNN) classification. We found that D-amphetamine significantly altered the activity distribution patterns both for spontaneous activity and for activity recorded during ongoing tactile stimulation. It also reduced the difference between spontaneous activity and activity during ongoing tactile stimulation, which suggests that amphetamine reduced the organization in the network activity and could potentially explain hallucinations under the influence of amphetamine. Each of these changes were distributed approximately evenly across each dimension of the principal component space. This indicates that amphetamine impacts cortical network dynamics broadly and in multifaceted ways, compatible with the system-wide presence of the receptors that amphetamine interferes with. Our data indicates that relatively low doses of D-amphetamine can induce changes in brain activity distributions that are measurable potentially also by non-invasive EEG electrodes.