Search Results (76)

Metalloprotein docking is difficult because it requires precise preparation of the grid box, charge assignment, and metal coordination geometry. Many current graphical user interfaces (GUIs) do not support these steps well. Here, we present MBind v2026, a web-based GUI that combines AutoDock Vina, AutoGrid4 and AutoDock4 into a single workflow for standard docking and metalloprotein docking. The interface also has the optional machine learning (ML) docking program GNINA v1.0. The main validated metalloprotein workflow in MBind is based on AutoDock4Zn parameterization for zinc, while preliminary workflows for magnesium, iron, and copper are also included. MBind was benchmarked on eight zinc metalloproteins and eight nonmetalloprotein targets. Its performance was compared with ML pose prediction methods (GNINA v1.0, EquiBind v2026, TankBind v2026, and GAABind v2026), cofolding models (Boltz-2 v2026, and AlphaFold 3 v2026), ML affinity prediction tools (StructureNet v2026, GNNSeq v2026, and PLAIG v2026), and non-ML docking tools (SwissDock v2026, CB-Dock2 v2026, Webina v2026, 1-ClickDock v2026, and MolModa v1.01). Pose accuracy was measured by symmetry-aware ligand RMSD using DockRMSD v1.1 under redocking conditions with co-crystal-defined binding sites. Binding energy trends were evaluated by comparing docking scores with IC50-derived ΔG values using mean absolute error. On the zinc metalloprotein benchmark set, MBind produced a mean RMSD of 0.49 Å. On the nonmetalloprotein set, the mean RMSD was 0.62 Å. These results show that MBind can execute zinc metalloprotein and nonmetalloprotein docking workflows through a web-based interface, while the preliminary Mg, Fe, and Cu workflows require additional validation. The MBind GUI web-based platform is publicly available. Full article

Bird’s-eye-view (BEV) perception has become the dominant paradigm for camera-centric scene understanding in autonomous driving, as well as road occupancy prediction, which involves the dense estimation of which regions of space are occupied and by what has emerged as its most expressive form. Between 2020 and 2026, the field underwent three overlapping transitions: from two-dimensional BEV semantic map segmentation to dense three-dimensional voxel-based 3D semantic occupancy, catalyzed by the 2022 industrial adoption of “occupancy networks,” and, most recently, to efficient, generative, and four-dimensional forecasting formulations. This survey organizes the literature along six orthogonal axes output representation, view-transformation mechanism, input modality, supervision paradigm, temporal scope, and efficiency strategy and uses the representation lineage as a primary spine connecting the 2020 BEV-segmentation works to the 2026 Gaussian and 4D frontier. Alongside the ego-centric mainstream, we review the parallel multi-view and infrastructure-side lineage from multi-view pedestrian occupancy to roadside traffic occupancy, which shares the BEV occupancy-map output and contributes generalization tools the ego-centric thread has yet to absorb. We review the canonical methods at each stage, summarize the standard datasets (CARLA, GMVD, MultiviewX, WildTrack, nuScenes, SemanticKITTI, Occ3D, OpenOccupancy) and evaluation metrics (MODA, mIoU, RayIoU, RayPQ), and consolidate reported results on the Occ3D-nuScenes benchmark into a single comparison. We close by identifying open problems in label efficiency, robustness, temporal forecasting, and deployment. Our intent is to bridge the historically separate BEV-segmentation and 3D-occupancy literatures within a single taxonomy. Full article

This article examines how DressGO, a fashion game developed by DRESSX on Roblox, organizes immersive fashion commerce for youth-oriented audiences through persuasive and gamified interface design. Rather than treating purchase as a discrete transactional event, the study examines how monetization cues are embedded in progression loops, cosmetic status, and habitual return within a platform-mediated retail environment. Methodologically, the article adopts a qualitative single-case study and interface analysis based on a primary corpus of 12 screenshots selected from an initial pool of 34 screenshots collected across six gameplay sessions in January 2026, complemented by observational notes and contextual documentary triangulation and supplementary verification material for Robux/payment-route visibility. The analysis identifies four operational mechanisms: staged unboxing as a sensory gateway to acquisition, daily rewards and quantified tasks as retention infrastructure, rankings and rarity displays as social comparison cues, and accelerators, probability boosters, and waiting timers as conversion pressure mechanisms. The findings indicate that the interface integrates virtual fashion consumption into ordinary play and social visibility, while monetization cues operate through playful aesthetics, repetition, scarcity cues, and low-friction prompts embedded in progression systems. The article contributes to immersive commerce research by examining how gamification, interface design, and symbolic fashion value converge in a youth-oriented virtual retail environment. It further argues that randomized access, temporal friction, and comparative visibility should be understood not only as engagement features, but also as matters of digital fairness, platform trust, and responsible interface design. Full article

Accurate three-dimensional temperature and salinity initial fields are essential for regional short-term ocean forecasting, but subsurface constraints remain limited in the Western Indian Ocean because in situ profiles are sparse and satellite observations mainly describe the sea surface. This study evaluated a method to improve short-term forecasts by assimilating pretrained language model (PLM)-reconstructed thermohaline fields into the Finite Volume Community Ocean Model with a three-dimensional variational data assimilation scheme (FVCOM–3DVAR). The method was applied to the Western Indian Ocean, covering 15° S–10° N, 33° E–60° E. A non-assimilation control experiment (Control_run), Modular Ocean Data Assimilation System assimilation (MODAS_ass), and PLM reconstructed-field assimilation (PLM_ass) were conducted to evaluate the forecast performance with 5-day rolling forecasts. World Ocean Database (WOD) profiles were used for profile-based validation, and Copernicus Marine Environment Monitoring Service (CMEMS) gridded fields were used for spatially continuous reference evaluation in February, May, August, and November 2021. The results demonstrated that PLM_ass produced lower temperature and salinity root-mean-square errors (RMSEs) than Control_run and MODAS_ass at most depths and lead times. Relative to Control_run, PLM_ass reduced mean temperature RMSE by 28.0% at 100–200 m and mean salinity RMSE by 23.5% at 0–100 m; compared with MODAS_ass, the reductions were approximately 16.6% and 14.2%, respectively. Spatial diagnostics showed variable-, depth-, and region-dependent impacts with the most robust improvement in thermocline temperature forecasts. This study provides a feasible pathway for incorporating AI-reconstructed subsurface information into regional ocean forecasting systems under sparse observation conditions. Full article

(1) Background: Creutzfeldt–Jakob disease (CJD) is a progressive neurodegenerative disorder, characterized by cognitive decline, and motor and psychiatric symptoms; it primarily affects the central nervous system; however, peripheral nervous system involvement has rarely been described, particularly as an atypical presentation. (2) Methods: A 78-year-old Caucasian man, a retired farmer with no family history of neurological disease, presented with diarrhea followed by progressive lower limb weakness, which eventually evolved into encephalopathy and generalized areflexia. An initial diagnosis of inflammatory neuropathy was considered; the diagnostic assessment included blood and cerebrospinal fluid testing, a CT whole body scan, brain MRI, neuropsychological testing, electroencephalography, a nerve conduction study and electromyography. (3) Results: Neurophysiological studies demonstrated an acute asymmetrical sensorimotor, predominantly axonal polyneuropathy, initially suggestive of an axonal form of inflammatory polyradiculoneuritis. This pattern was confirmed on follow-up neurophysiological assessment performed three weeks later. Unexpectedly, the diagnostic course ultimately led to a diagnosis of sporadic Creutzfeldt–Jakob disease, confirmed by post-mortem neuropathological examination. Based on these findings, we conducted a literature review to summarize the current evidence on CJD-related neuropathy. (4) Conclusions: Our case emphasizes the importance of maintaining clinical suspicion for CJD even in patients presenting with progressive lower limb weakness and suggests that peripheral neuropathy may be concomitant or even precede the CNS manifestations. Careful consideration is required to avoid misdiagnosis of inflammatory neuropathy in the context of neurodegenerative diseases such as CJD. Full article

Object detection in unmanned aerial vehicle (UAV) imagery remains a crucial yet challenging task due to complex backgrounds, large scale variations, and the prevalence of small objects. Visible-spectrum images lack robustness under all-weather and all-illumination conditions; by contrast, multispectral sensing provides complementary cues (e.g., thermal signatures) that improve detection robustness. However, existing multispectral solutions often incur high computational costs and are therefore difficult to deploy on resource-constrained UAV platforms. To address these issues, SG-YOLO is proposed, a lightweight and efficient multispectral object detection framework that aims to balance accuracy and efficiency. First, a Spectral Gated Downsampling Stem (SGDS) is designed, in which grouped convolutions and a gating mechanism are employed at the early stage of the network to extract band-specific features, thereby maximizing spectral complementarity while minimizing redundancy. Second, a Spectral–Spatial Iterative Attention Fusion (SSIAF) module is introduced, in which spectral-wise (channel) attention and spatial-wise attention are iteratively coupled and cascaded in a multi-scale manner to jointly model cross-band dependencies and spatial saliency, thereby aggregating high-level semantic information while suppressing redundant spectral responses. Finally, a Spatial–Channel Synergistic Fusion (SCSF) module is designed to enhance multi-scale and cross-channel feature integration in the neck. Experiments on the MODA dataset show that SG-YOLOs achieves 72.4% mAP₅₀, outperforming the baseline by 3.2%. Moreover, compared with a range of mainstream one-stage detectors and multispectral detection methods, SG-YOLO delivers the best overall performance, providing an effective solution for UAV object detection while maintaining a favorable trade-off between model size and detection accuracy. Full article

High-precision detection of hazardous gases with low refractive indices ranging from 1.000 to 1.100, specifically including methane, carbon monoxide, and sulfur dioxide, is critical for industrial safety, yet conventional sensors often suffer from limited sensitivity and severe thermal cross-sensitivity. This work presents a Magneto-Optical Differential Photonic Crystals Sensor (MO-DPCS) utilizing indium antimonide (InSb) to address these constraints. Employing the Multi-Objective Dragonfly Algorithm (MODA), the system was inversely optimized to maximize magneto-optical polarization splitting while rigorously maintaining an ultra-high transmission efficiency. Crucially, an angular interrogation architecture operating under oblique incidence was established to maximize the magneto-optical non-reciprocity, where the detection was realized by fixing the terahertz source frequency and monitoring the precise angular displacements of the steep spectral edges. A differential detection technique was employed to utilize the non-reciprocal phase changes wherein Transverse Electric (TE) and Transverse Magnetic (TM) modes display contrasting kinematic characteristics in the presence of an external magnetic field. The findings indicate that with an adjusted magnetic field of 0.033 T, the MO-DPCS attains an exceptional differential sensitivity of 30.8°/RIU, much above the 0.8°/RIU seen in the unmagnetized condition. The differential approach efficiently eliminates common-mode thermal noise, minimizing temperature-induced drift to below 0.35° across a 1 K range. The suggested MO-DPCS offers a robust, self-referencing solution for stable and high-sensitivity gas sensing applications with a detection limit of 4.18 × 10⁻⁴ RIU. Full article

Accurate three-dimensional (3D) salinity fields are crucial for diagnosing freshwater transport and upper-to-intermediate ocean dynamics, yet subsurface salinity observations remain uneven in space and time, especially away from primary observing corridors. Gridded fields derived from sparse measurements can also suffer from regional biases and over-smoothing, which may blur mesoscale signals in energetic regimes. This study presents an empirical orthogonal function-guided U-shaped Vision Transformer (EOF-UViT) to reconstruct daily 3D salinity in the Northwest Pacific (0–50°N, 100–150°E) from multi-source surface remote-sensing factors. Compared with the U-Net baseline and the Modular Ocean Data Assimilation System (MODAS) reconstruction, EOF-UViT produces more realistic horizontal structures and improved vertical consistency, with the largest gains in dynamically active regions. Comparison with collocated in situ profiles further supports the reconstruction skill (RMSE = 0.094 psu; R² = 0.904), with estimates clustering more tightly around the 1:1 line than MODAS. Overall, EOF-UViT provides an efficient, observation-driven route to spatially coherent 3D salinity fields, supporting applications such as model initialization, assimilation background fields, and basin-scale salinity variability analysis. Full article

The “Neuroimaging and Pathology Biomarkers in Parkinson’s Disease” course held on 12–13 September 2025 in Milan, Italy, convened an international faculty to review state-of-the-art biomarkers spanning neurotransmitter dysfunction, protein pathology and clinical translation. Here, we synthesize the four themed sessions and highlights convergent messages for diagnosis, stratification and trial design. The first session focused on neuroimaging markers of neurotransmitter dysfunction, highlighting how positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI) provided complementary insights into dopaminergic, noradrenergic, cholinergic and serotonergic dysfunction. The second session addressed in vivo imaging of protein pathology, presenting recent advances in PET ligands targeting α-synuclein, progress in four-repeat tau imaging for progressive supranuclear palsy and corticobasal syndromes, and the prognostic relevance of amyloid imaging in the context of mixed pathologies. Imaging of neuroinflammation captures inflammatory processes in vivo and helps study pathophysiological effects. The third session bridged pathology and disease mechanisms, covering the biology of α-synuclein and emerging therapeutic strategies, the clinical potential of seed amplification assays and skin biopsy, the impact of co-pathologies on disease expression, and the “brain-first” versus “body-first” model of pathological spread. Finally, the fourth session addressed disease progression and clinical translation, focusing on imaging predictors of phenoconversion from prodromal to clinically overt stages of synucleinopathies, concepts of neural reserve and compensation, imaging correlates of cognitive impairment, and MRI approaches for atypical parkinsonism. Biomarker-informed pharmacological, infusion-based, and surgical strategies, including network-guided and adaptive deep brain stimulation, were discussed as examples of how multimodal biomarkers may inform personalized management. Across all sessions, the need for harmonization, longitudinal validation, and pathology-confirmed outcome measures was consistently emphasized as essential for advancing biomarker qualification in multicentre research and clinical practice. Full article

Digital heritage (DH) research serves as a bridge between technological applications and broader cultural, social, and policy issues. A comprehensive understanding of DH requires the integration of multiple fields. To address this, this work applies a socio-technical systems (STS) perspective to DH as a strategy to bridge the technological and social aspects. It first examines how DH functions as STSs, analyses the dynamic interactions between technological and social subsystems, and explains the need to achieve joint optimisation to tackle the complexity of DH research. Second, a comparative analysis of six STS models is conducted, using the Venice Time Machine project as a representative case, to explore both the potential and limitations of STSs as a theoretical framework for DH. Third, STS theory is applied to emphasise that the approach needs to incorporate cultural expression, technological feasibility, diverse stakeholder interests, and long-term adaptability in order to address the complexity of current DH challenges. Finally, an STS-DH framework is proposed to guide the design, implementation and evaluation of DH projects using the elements identified through the present analysis. This work extends STS theory applications to cultural heritage digitisation; provides stakeholders with new practical tools; recognises the lack of empirical research in this field and highlights the need for further research. Full article

Phytophagous beetles, particularly those within the superfamilies Chrysomeloidea and Curculionoidea, constitute one of the most diverse and ecologically significant groups of insect herbivores. Within this group, the subfamily Bruchinae is especially notable for its close association with leguminous plant seeds. As most Bruchinae species do not feed during the adult stage, the timing and regulation of vitellogenesis remain unclear. Previous studies suggest that vitellogenesis may be triggered by volatile organic compounds emitted by host seeds, which promote juvenile hormone (JH) synthesis. This increase in JH is hypothesized to stimulate vitellogenesis, enhance female attractiveness, and ultimately facilitate fertilization and oviposition. To explore this hypothesis, we investigated the external cues regulating reproductive physiology in the capital breeder Zabrotes subfasciatus. Specifically, we examined the effects of host seeds and male presence on oviposition dynamics, fecundity, ovary activation, and the expression of vitellogenic genes (vg and vgR) throughout adult life. Our results show that females initiate vitellogenesis during the final phases of adult development, enabling oviposition to begin as early as the first day after emergence. Oviposition remains at basal levels throughout adult life unless both host seeds and males are present (p < 0.0001). This oviposition pattern is consistent with ovary activation dynamics, which reveal that vitellogenesis peaks early in the oviposition period and is prolonged by the presence of seeds and males (p < 0.05). Notably, vg and vgR gene expression respond differentially to these cues (p < 0.05). We integrate our findings with previous literature to propose a working model for the regulation of oviposition in the Bruchinae beetle Z. subfasciatus. Full article

The cellular prion protein (PrP^C) is studied in prion diseases, where its misfolded isoform (PrP^Sc) leads to neurodegeneration. PrP^C has also been implicated in several physiological functions. The protein is abundant in the nervous system, and it is critical for cell signaling in cellular communication, where it acts as a scaffold for various signaling molecules. The Reelin signaling pathway, implicated both in Alzheimer’s and prion diseases, engages Dab1, an adaptor protein influencing APP processing and amyloid beta deposition. Here, we show, using Prnp knockout models (Prnp^0/0), that PrP^C modulates Reelin signaling, affecting Dab1 activation and downstream phosphorylation in both neuronal cultures and mouse brains. Notably, Prnp^0/0 mice showed reduced responsiveness to Reelin, associated with altered Dab1 phosphorylation and Fyn kinase activity. Even though no direct interaction between PrP^C and Reelin/ApoER2 was found, Prnp^0/0 neurons showed lower NCAM levels, a well-established PrP^C interactor. Prion infection further disrupted the Reelin signaling pathway, thus downregulating Dab1 and Reelin receptors and altering Reelin processing, like Alzheimer’s disease pathology. These findings emphasize PrP^C indirect role in Dab1 signaling via the NCAM and Fyn pathways, which influence synaptic function and neurodegeneration in prion diseases. Full article

Heterozygous mutations in the GBA1 gene, encoding the enzyme glucocerebrosidase (GCase), are major risk factors for Parkinson’s Disease (PD). Ambroxol, a small chaperone originally used as a mucolytic agent, has been shown to cross the blood–brain barrier, enhance GCase activity, and reduce α-synuclein levels, making it a promising therapeutic candidate for disease-modifying effects in GBA1-associated PD (GBA1-PD). This study aimed to develop a method to quantify ambroxol levels in human plasma and cerebrospinal fluid (CSF) using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Ambroxol was determined by online solid-phase extraction (SPE), coupled with LC-MS/MS, by gradient elution on a monolithic column. Detection employed a 3200 QTRAP tandem mass spectrometer in the positive electrospray ionization mode. Calibration curves exhibited linearity across the analyzed ranges in both plasma and CSF. The recovery rate ranged from 106.7% to 113.5% in plasma and from 99.0% to 103.0% in CSF. No significant matrix effect was observed. Intra-day and inter-day precisions were below 11.8% in both matrices, and accuracy ranged from 89.9% to 103.1% in plasma and from 96.3% to 107.8% in CSF. We evaluated and confirmed the stability of the analyte in plasma and CSF across various storage conditions. The method was successfully validated according to European Medicine Agency (EMA) guidelines and its applicability was confirmed in the context of a multicenter, randomized, double-blind, placebo-controlled, phase II study, designed to monitor the ambroxol levels in the plasma and CSF of GBA1-PD. Full article

This study addresses the challenge of estimating reference crop evapotranspiration (ET_O) in Xizang Plateau irrigation districts with limited meteorological data by proposing a coupled LASSO-BP model that integrates LASSO regression with a BP neural network. The model was applied to three irrigation districts: Moda (MD), Jiangbei (JB), and Manla (ML). Using ET_O values calculated by the FAO-56 Penman–Monteith (FAO-56PM) model as a benchmark, the performance and applicability of the LASSO-BP model were assessed. Short-term ET_O predictions for the three districts were also conducted using the mean-generating function optimal subset regression algorithm. The results revealed significant multicollinearity among six meteorological factors (maximum temperature, minimum temperature, average temperature, average relative humidity, sunshine duration, and average wind speed), as identified through tolerance, variance inflation factor (VIF), and eigenvalue analysis. The LASSO-BP model effectively captured the interannual variation of ET_O, accurately identifying peaks and troughs, with trends closely aligned with the FAO-56PM model. The model demonstrated strong performance across all three districts, with evaluation metrics showing MAE, RMSE, NSE, and R² values ranging from 4.26 to 9.48 mm·a⁻¹, 5.91 to 11.78 mm·a⁻¹, 0.92 to 0.96, and 0.82 to 0.94, respectively. Prediction results indicated a statistically insignificant declining trend in annual ET_O across the three districts over the study period. Overall, the LASSO-BP model is a reliable and accurate tool for estimating ET_O in Xizang Plateau irrigation districts with limited meteorological data. Full article

The study investigated the determinants of safe pesticide handling and application among farmers in rural communities of Oyo State, ssouthwestern Nigeria. A cross-sectional design utilizing 2-stage cluster sampling techniques was used to select Ido and Ibarapa central Local Government Areas and to interview 383 farmers via a structured questionnaire. Data were analyzed using descriptive statistics and logistic regression at p = 0.05. Results showed that 41.8% of the farmers had been working with pesticides on farms for at least 5 years, 33.0% attended training on pesticide application, 73.5% had good safety and health knowledge, and 72.3% had safe pesticide handling and application practices. About half (50.2%) stated that they wear coveralls, gloves, and masks to protect their body, face, and hands when applying pesticides, 9.8% use empty pesticide containers for other purposes in the house/farm, while 11.5% blow the nozzle with their mouth to unclog it if it becomes blocked. The three major health symptoms reported by the participants were skin irritation (65.0%), itchy eyes (51.3%), and excessive sweating (32.5%). Having attended training on pesticide application and use enhanced (OR = 2.821; C.I = 1.513–5.261) practicing safe pesticide handling and application. Farmers with good knowledge (OR = 5.494; C.I = 3.385–8.919) were more likely to practice safe pesticide handling and application than those with poor knowledge about pesticide use. It is essential to develop and deliver mandatory comprehensive training programs for farmers on impacts of pesticides on health and environment, along with sustainable safe handling, application, and disposal of pesticides using proper waste management techniques and recognizing early signs and seeking medical assistance. The urgent need to strengthen policy to regulate pesticide use and limit farmers’ access to banned products is also key. Full article