Search Results (227)

Predicting the Remaining Useful Life (RUL) of railway wheels is challenging because wheel–rail degradation is cumulative, stochastic, and influenced by operating conditions. This study evaluates a multi-indicator prognostic framework using real in-service measurements acquired with a CALIPRI C42 optical profilometer (NextSense GmbH, Graz, Austria). The database comprises 80 wheels from ten vehicles of the same rolling-stock type, monitored during five monthly measurement campaigns, and includes flange width (Fw), flange height (Fh), and the flange-gradient dimension (qR). The Gamma process and first-passage formulation are established tools; the contribution of this work is their common application to all three indicators on the same in-service fleet and the benchmarking of long-horizon probabilistic results against an AR(1) short-term predictor embedded in the First-Passage Auto-Regressive (FP-AR) framework using the same dataset. Median Gamma-based RUL values were 21.2–22.0 months for Fw, 13.7–17.6 months for Fh, and 5.7–9.5 months for qR, with qR showing the largest relative percentile dispersion. For one-step prediction, the FP-AR benchmark achieved global MAE/RMSE values of approximately 0.368/0.502 mm for Fw and 0.0187/0.0216 mm for Fh; qR was more difficult to predict, with global MAE/RMSE values of approximately 0.575/0.991 mm. Under the adopted intervention thresholds, these results identify qR as the most variable and operationally constraining indicator under the studied Fès–Marrakech service conditions. The proposed dual-model analysis therefore provides a position-specific, uncertainty-aware basis for comparing wheel-profile degradation indicators, while its maintenance implications remain fleet- and route-specific pending validation in additional operating contexts. Full article

Protease-activated receptor-2 (PAR-2; encoded by F2RL1) is emerging as a context-dependent driver of gastrointestinal cancer. Activated by irreversible N-terminal cleavage, its signalling output is not fixed but is calibrated by the identity and source of the activating protease, the receptor cleavage state, cellular context and biased coupling to G-protein αq (Gαq), G-protein α12/13 (Gα12/13) and β-arrestin. PAR-2 is best understood not as a simple inflammatory receptor but as a proteolytic rheostat that converts diverse coagulation, inflammatory, microbial and stromal protease inputs into distinct oncogenic programmes. Colorectal cancer and pancreatic ductal adenocarcinoma provide complementary models: in colorectal cancer, PAR-2 links mucosal inflammation and coagulation to proliferation, metastatic competence and resistance to epidermal growth factor receptor (EGFR)-targeted therapy, whereas in pancreatic cancer, it is embedded in a tissue-factor-rich desmoplastic microenvironment that promotes invasion, immune exclusion and chemoresistance. Therapeutic strategies suggested by this framework include direct and biased PAR-2 modulators, upstream protease and factor Xa (FXa) inhibition, statin repurposing and activated-fragment biomarkers such as the PAR-2 activation neoepitope (PRO-PAR2). These strategies must be applied under biomarker guidance, since PAR-2 blockade may benefit inflammation-dominant tumours yet prove counterproductive where PAR-2 sustains antitumour immunity. Full article

Magnetic-field effects have been reported in controlled biological systems, but the relevance of weak geomagnetic variability to vegetation under natural conditions remains uncertain. We examined temporal associations between satellite-derived solar-induced chlorophyll fluorescence (SIF) and geomagnetic disturbance derived from the Disturbance Storm Time (Dst) index. We defined the sign-inverted Dst index, SII, as the sign-inverted daily mean Dst, so that stronger negative Dst excursions corresponded to larger positive SII values. The primary exposure was the trailing 28-day mean SII, excluding the day of the SIF observation. The primary analysis used Orbiting Carbon Observatory-2 (OCO-2) SIF at 771 nm with leave-one-year-out harmonic adjustment for the annual cycle and linear calendar-time trend. Sensitivity and robustness analyses examined a 21-day exposure, a more flexible cyclic-spline seasonal adjustment, spatial cluster bootstrap, and three temporal-surrogate null models. We also examined temperature and vegetation strata, land-cover and geographic controls, adjustment for an ECMWF Reanalysis version 5 (ERA5) surface solar radiation downwards (SSRD)-derived photosynthetically active radiation (PAR) energy proxy and vapour-pressure deficit, and comparisons with planetary Kp index, 10.7 cm solar radio flux index (F10.7), and supplementary SIF wavelengths. In the primary analysis, the 28-day trailing mean of SII was weakly negatively associated with SIF anomalies (Spearman ρ = −0.051; 95% cluster-bootstrap CI, −0.053 to −0.050), with an estimated linear change of −0.0381 residual-SIF units per 100 nT. Empirical p-values were 0.084 for year permutation, 0.011 for circular shift, and 0.001 for 30-day block permutation. The association remained negative at 21 days and in persistently vegetated cells, but its magnitude was substantially reduced with cyclic-spline adjustment (ρ = −0.013 in the pairwise-matched sample). Negative estimates were found in several vegetated land-cover classes, whereas estimates for the Barren land-cover class and Sahara geographic control were close to zero. The contribution of SII to explained variance remained below one percentage point across the examined temperature regimes. Overall, the results show a weak temporal association whose magnitude depends on analytical choices. Independent observational replication and controlled experiments are needed to determine whether it reflects a biological response to natural geomagnetic variability. Full article

Micro-sprinkler irrigation is commonly used to optimize plant growing environments and prevent growth inhibition and yield losses caused by high air temperatures (T_a) in greenhouses. Nevertheless, instantaneous photosynthetic rate measurements suffer from time lag, and the temporally dynamic microclimate alterations induced by micro-sprinkling make it difficult to reproduce the real ambient conditions for crop growth. Therefore, two treatments, namely micro-sprinkling combined with drip irrigation (MSDI) and conventional drip irrigation control (DI), were established in a Venlo-type greenhouse. Continuous chlorophyll fluorescence (ChlF) monitoring combined with rapid light curves under fixed photosynthetically active radiation was adopted to investigate the diurnal alleviation effects of micro-sprinkling on tomatoes under high-temperature stress. This study found that Φ_PSII was more sensitive than Fv/Fm in detecting changes in PSII photochemical performance under high-temperature stress. Micro-sprinkling showed greater mitigation effects under moderate heat stress, with the highest enhancement in Φ_PSII (approximately 0.12) observed when leaf temperature (T_l) was around 34.5 °C. However, the improvement effect decreased under extreme heat conditions, and Φ_PSII increased by only 0.017 when T_a exceeded 38 °C. The slope of the fitted line between Φ_PSII and PAR on sunny days increased with increasing heat stress, indicating the enhanced sensitivity of PSII photochemical regulation to thermal stress. Compared with DI, MSDI increased tomato yield by 31.2% and 47.6% in 2021 and 2022, respectively, while improving fruit quality by increasing single fruit weight, fruit shape index, and soluble sugar content. In addition, MSDI increased SPAD, Fv/Fm, Φ_PSII, and ETR by 9.6–15.6%, 8.8–14.8%, 10.3–13.3%, and 10.3–19.6%, respectively, indicating improved PSII photochemical performance under high-temperature conditions. In conclusion, micro-sprinkling mitigated part of the negative effects of high-temperature stress and significantly improved tomato yields and fruit quality, which could be used in agricultural production. Full article

Wind gusts are known to significantly influence wildfire behavior, yet their direct role in ignition dynamics remains underexplored in laboratory settings. This study investigates how controlled wind gusts affect ignition behavior, combustion transitions, and heat re-lease characteristics of wildland fuels using a bench-scale wind tunnel. Three fuel types, Excelsior, wild oat (Avena), and Wheatgrass were exposed to heated stainless-steel par-ticles under varying wind speeds (1.0 and 2.0 m/s) and gust frequencies (0.03, 0.05, and 0.07 Hz). Key ignition parameters, including ignition temperature, ignition delay, smol-dering-to-flaming (StF) transition, burnout time, and heat release rate (HRR), were measured and analyzed. The results show that increasing gust frequency consistently impacted ignition behavior which reduces ignition and transition times across all fuels while raising ignition temperatures and HRR. For instance, StF transition times in Avena dropped from 58 to 42 s and flaming ignition temperatures rose from ~415 °C to ~498 °C as gust frequency increased from 0.03 Hz to 0.07 Hz at 2.0 m/s wind speed. Also, for the same set of experiments, HRR rose from 1674 J/s to 2372 J/s with increasing gusts. These findings indicate that gusty winds enhance convective heat transfer and oxygen availability, accelerating fire initiation and intensifying combustion. The results offer valuable insights for improving predictive fire spread models, ignition risk assessments, and wildfire mitigation strategies under transient wind conditions. Full article

Distinguishing authentic gold products from high-quality counterfeits is a challenging fine-grained computer vision problem; counterfeit items are engineered to replicate surface texture, hallmark engravings, color, and geometry with remarkable fidelity, making visual discrimination unreliable even for trained professionals. In this paper, we address the problem of visual gold authentication from unconstrained smartphone imagery in three main contributions. First, we introduce GoldNet, a public benchmark dataset designed for this task, comprising 2127 real-world images of authentic and counterfeit gold items collected under diverse real-world conditions. Second, we evaluate fourteen classification architectures spanning classical handcrafted texture descriptors, convolutional neural networks (CNNs), and vision transformers under a rigorous transfer learning protocol, establishing the first comprehensive baseline for this problem. Third, we propose GoldFormer, a hybrid dual-stream algorithm that combines the local texture representations of ResNet-50 with the global contextual modeling capability of the Swin Transformer (Swin-T) through a newly designed Texture-Aware Attention Gate (TAAG) module. The TAAG dynamically modulates Swin feature dimensions using CNN-derived texture energy, providing improved discriminability and per-prediction interpretability without requiring post hoc attribution. Experimental results show that, under matched-resolution 5-fold cross-validation, the proposed GoldFormer attains the highest overall accuracy (95.02%, F1-score 0.9502) at roughly half the FLOPs of its higher-resolution setting, statistically tied with the strongest individual backbone (ViT-B/16, 94.31%; McNemar $p=0.23$) and on par with a training-free soft-voting ensemble (94.92%), while significantly improving on its own Swin-T backbone (93.65%) and adding built-in, attribution-free texture-gate interpretability. GoldFormer surpasses trained human-expert performance (89.80%) by approximately 5 percentage points. Full article

Proactive robotic assistance in human–robot collaboration (HRC) requires systems that can perceive evolving task contexts, anticipate user needs, and intervene appropriately without disrupting human workflow. We present the Agentic Unified Robotic Assistance (AURA) Framework, which couples Large Language Model (LLM) reasoning grounded by Standard Operating Procedures (SOPs) with a modular layer of specialized Intent, Motion, Perception, Sound, Affordance, and Performance Monitors that supply structured context to a central decision-making module, making the framework reconfigurable and auditable without retraining or re-prompting. We introduce a human-in-the-loop teleoperation data collection methodology and an offline evaluation scheme with an Appropriateness Score (A-Score) tailored to proactive intervention timing, and release a benchmark dataset of annotated multimodal HRC episodes containing workspace and robot wrist camera videos, robot joint states, and labeled intervention events. Across three tasks of varying complexity, we observe progressive gains in intent prediction and decision-making as the modules are supplied with richer grounded context (prior-state memory and tracked object locations), with Combined F1 rising by over 20 points between context-poor and context-rich conditions. The structured grounding allows lightweight multimodal backbones such as Gemini 3.1 Flash Lite to perform on par with heavier reasoning-tier models at roughly one-fifth the inference latency. Together, these contributions establish a scalable framework, benchmark, and evaluation methodology for advancing proactive robotic assistance in collaborative environments. Full article

Background: Thrombotic disorders remain one of the leading causes of global mortality, necessitating the discovery of anticoagulants with broader therapeutic windows and multi-target efficacy. This study aimed to identify FDA-approved drugs capable of simultaneously inhibiting three critical nodes of the coagulation cascade: Factor X (F10), Proteinase-activated receptor 1 (PAR1) and Prothrombin (F2). Methods: High-confidence 3D structures of coagulation cascade proteins were established using AlphaFold2 and validated via MolProbity (Favored regions > 91%). A library of 1657 compounds from the Zinc database was screened using PyRx, followed by rigorous ADMET profiling to evaluate pharmacokinetic viability. The structural integrity and binding kinetics of the top candidate drugs were further analyzed through Molecular Dynamics simulation for 100 ns. Results: Virtual screening and downstream analysis identified 30 multi-target drugs. Avodart and Naldemedine were observed to have superior pharmacokinetic equilibrium. Compared to the other two drugs (Digoxin and Ledipasvir), Avodart and Naldemedine showed high affinity, higher adherence to drug likeness, lower metabolic inhibition risks and lack of acute toxicity, and were therefore the most suitable candidates. The 100 ns MD simulations revealed Avodart and Naldemedine to have the highest level of interaction stability and favorable MM-GBSA energies with Factor X, whereas Ledipasvir and Digoxin exhibited significant structural instability. Conclusions: The study proposes Avodart and Naldemedine as promising candidates for drug repurposing in antithrombotic therapy. This study provides a computational blueprint for the development of next-generation, broad-spectrum anticoagulants. Full article

Net primary productivity (NPP) is an important indicator of ecosystem carbon accumulation capacity and vegetation productivity potential, and its accurate estimation is of great significance for agricultural management and regional carbon cycle research. To address the problem that the temporal continuity of single-source optical remote sensing data is easily affected by cloud cover, this study used Sentinel-2 imagery and the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as data sources and constructed an NDVI time series with high spatial and temporal resolution for the study area based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) method. On this basis, the Simple Ratio (SR) index was incorporated to supplement canopy information, and the key parameters of the Carnegie–Ames–Stanford Approach (CASA) model were differentially optimized for different crop types, thereby enabling remote sensing-based estimation of crop NPP. The results showed that the fused NDVI effectively compensated for observation gaps caused by cloud interference, and its temporal variation was generally consistent with the crop growth process. In addition, the Fraction of Photosynthetically Active Radiation (FPAR) improved with the fused NDVI, which effectively characterized phenological differences among crops. Compared with the unoptimized model, the improved model significantly improved NPP estimation accuracy for both maize and rice. Specifically, for maize, the coefficient of determination ($R^2$) increased from 0.75 to 0.88, and the mean absolute percentage error (MAPE) decreased from 67.00% to 34.68%. For rice, the MAPE decreased from 78.51% to 23.43%, while the mean absolute error (MAE) decreased from 345.1 $\mathrm{gC}·{\mathrm{m}}^{-2}·{\mathrm{a}}^{-1}$ to 95.6 $\mathrm{gC}·{\mathrm{m}}^{-2}·{\mathrm{a}}^{-1}$. These results indicate that constructing a highly continuous vegetation index time series through spatiotemporal fusion, together with optimizing the CASA model by incorporating the SR index and crop-specific parameterization, can effectively improve the stability and accuracy of NPP estimation for agricultural crops. Full article

Rheumatoid arthritis (RA) is an autoimmune disorder characterized by synovial inflammation and progressive joint destruction. There is no cure, and patient responses to current therapies vary, reflecting underlying pathogenic heterogeneity. Leveraging single-cell RNA sequencing (scRNA-seq) of RA synovium, we identified a PLAUR/uPAR-high myeloid subset that co-expresses pathogenic mediators, including IL1B and CXCL8. To target these cells, we developed anti-uPAR antibody–drug conjugates (ADCs) and evaluated various payloads in vitro and in vivo. ADCs bearing BCL-2 family inhibitors selectively induced apoptosis in proinflammatory human monocytes and macrophages with elevated uPAR, while sparing unstimulated monocytes with low basal uPAR in vitro. The treatment also reduced CXCL8 secretion. Given that murine myeloid cells exhibited lower uPAR expression and reduced sensitivity to BCL-2 family inhibitors, we used a monomethyl auristatin F (MMAF) payload to demonstrate in vivo proof-of-concept. In an air-pouch model, the anti-uPAR–MMAF conjugate reduced uPAR^highCD11b⁺F4/80⁺ macrophages by 39% compared with the isotype control. Together, our study underscores the potential of ADCs to eliminate disease-relevant cell types with inducible cell surface markers. This work opens new avenues for exploring cytotoxic ADCs as targeted therapies for autoimmune and inflammatory diseases. Full article

Whole-genome sequencing followed by comprehensive genomic analyses was used to characterize 16 Salmonella isolates from water-overlying sediments in Conococheague Creek (PA), an agricultural irrigation water source. Our goal was to characterize the genomic profiles and diversity of these Salmonella isolates. We identified eight distinct serotypes, including Newport, the most prevalent (43.8%), providing environmental context relevant to agricultural water systems. Genomic surveys showed various Salmonella Pathogenicity Island (SPI) profiles. Although widespread antimicrobial resistance (AMR) genes were not detected, the consistent presence of the aac(6’)-Iaa gene across all isolates and a parC (T57S) mutation in 14 isolates were identified as inherent genotypic markers. Six distinct plasmid replicon types were observed in over 60% of isolates. Replicons for IncF and IncI2 plasmids, frequently associated with β-lactamase genes, were found, documenting the presence of mobile genetic elements despite a lack of acquired AMR genes. Restriction-Modification (RM) systems and CRISPR/Cas loci were also detected, suggesting Salmonella genomic plasticity. Our study showed that sediment-associated Salmonella, notably serotype Newport, harbored diverse virulence-associated genomic features. These findings contributed to the genomic baseline for irrigation water quality and food safety. Full article

Background: Antimicrobial-resistant Escherichia coli and Klebsiella pneumoniae represent an increasing challenge in community-acquired pediatric diarrheal infections. Understanding the genomic basis and dissemination of resistance in outpatient settings is essential for guiding antimicrobial use. Methods: Eighteen Gram-negative isolates obtained from pediatric outpatients with acute diarrhea were analyzed using selective culture methods, antimicrobial susceptibility testing, and whole-genome sequencing. Multilocus sequence typing, serotyping, virulence profiling, antimicrobial resistance gene detection, plasmid replicon typing, mobile genetic element analysis, and core genome-based phylogenetic analysis were performed. Phenotypic resistance profiles were correlated with genomic resistance determinants. Results: Klebsiella pneumoniae (55.56%) and Escherichia coli (44.44%) were identified, with all isolates exhibiting putative multidrug resistance-associated genomic profiles. Extended-spectrum β-lactamase genes, particularly blaCTX-M variants, were strongly associated with resistance to third-generation cephalosporins. In contrast, fluoroquinolone resistance correlated with gyrA and parC mutations and plasmid-mediated qnr genes. Phylogenetic analysis revealed diverse lineages harboring resistance determinants. In silico plasmid analysis revealed that key resistance genes co-occurred with IncF-type plasmids and mobile genetic elements, including ISEcp1, IS26, and class 1 integrons, suggesting putative plasmid association rather than confirmed localization. Conclusions: These findings highlight the small scale of plasmid-mediated antimicrobial resistance among E. coli and K. pneumoniae causing pediatric community-acquired diarrhea. The integration of phenotypic and genomic analyses underscores the need for continuous resistance surveillance to support rational antibiotic use in outpatient settings. Full article

Background: Klebsiella pneumoniae is a World Health Organization-listed critical priority pathogen and a major cause of healthcare-associated infections, driven by the global emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) lineages and their alarming convergence with hypervirulence. Methods: In this study, 152 clinical specimens, including urine, blood, pus, wound swabs, and respiratory samples, were collected from tertiary care hospitals in Peshawar, Pakistan. Standard microbiological and biochemical methods identified 55 K. pneumoniae isolates. Antimicrobial susceptibility testing (AST) was performed using the Kirby–Bauer disk diffusion and broth microdilution methods, with results interpreted according to Clinical and Laboratory Standards Institute (CLSI) guidelines. MDR and XDR phenotypes were defined based on European Centre for Disease Prevention and Control (ECDC) criteria. Whole-genome sequencing (WGS) was conducted on 16 phenotypically confirmed MDR/XDR isolates, followed by comprehensive bioinformatic analyses to characterize sequence types (STs), acquired antimicrobial resistance genes, resistance-associated chromosomal mutations, virulence determinants, plasmid replicons, and phylogenetic relationships. Results: Among 55 confirmed K. pneumoniae isolates, 19 (34.5%) were classified as MDR and 10 (18.2%) as XDR. WGS revealed substantial genomic heterogeneity, identifying 11 distinct STs, with ST39 being the most prevalent. Resistance to multiple antibiotic classes was mediated by the combined presence of plasmid-borne carbapenemases and extended-spectrum β-lactamases, alongside chromosomal mutations affecting outer membrane porins (OmpK35/OmpK36), fluoroquinolone targets (gyrA/parC), efflux regulation (ramR, marR), and lipid A modification pathways associated with colistin resistance (mgrB, pmrA/pmrB, arnC, crrB). IncF-family plasmids predominated and frequently co-occurred with additional resistance-associated replicons. Notably, one isolate exhibited an expanded virulence gene repertoire, including multiple siderophore systems and a complete type II secretion system, consistent with a hypervirulence-associated genomic profile. Phylogenetic analyses demonstrated close relatedness to international lineages from Asia, the Middle East, and Europe, indicating regional and transnational dissemination. Conclusions: This study highlights the complex interplay between plasmid-mediated gene acquisition and chromosomal adaptive mutations driving MDR and XDR phenotypes in K. pneumoniae circulating in Peshawar, Pakistan. The identification of hypervirulence-associated genetic features within an MDR background underscores the growing threat posed by convergent lineages and emphasizes the need for sustained WGS-based surveillance to inform infection control and antimicrobial stewardship strategies. Full article

Background/Objectives: Refractory macular holes (MHs) that persist after conventional internal limiting membrane (ILM) peeling pose a significant surgical challenge. In this study, we analyzed the anatomical and functional outcomes of epiretinal human amniotic membrane (hAM) transplantation in patients with MHs. Methods: This retrospective study included 10 eyes of 10 patients with refractory MHs. All patients underwent 25-gauge pars plana vitrectomy, epiretinal cryopreserved hAM transplantation, and C3F8 gas tamponade. The large hAM graft was placed over the macula with the stromal side facing the retina. Preoperative and postoperative best-corrected visual acuity (BCVA), optical coherence tomography (OCT) findings, and MH dimensions were recorded. Results: The mean follow-up period was 7 months (range: 3–14 months). The mean preoperative minimum linear diameter and base diameter of the MHs were 715 ± 212 μm and 1114 ± 258 μm, respectively. Anatomical closure was achieved in all patients (100%). Postoperative OCT revealed rearrangement of the inner and other retinal layers in 7 out of 10 patients (70%), with partial restoration of the outer retinal layers. The mean logMAR BCVA improved significantly from 1.60 ± 0.37 preoperatively to 1.00 ± 0.45 postoperatively (p < 0.001). No graft dislocation, rejection, or other significant complications were observed. Conclusions: Our preliminary results suggest that epiretinal human amniotic membrane transplantation is a feasible and promising surgical technique for achieving anatomical closure and functional improvement in refractory macular holes in which conventional ILM peeling has failed. Full article

Direct methods for solving protein crystal structures from X-ray diffraction data provide an essential approach for validating predicted models while avoiding external model bias. Nevertheless, traditional iterative projection algorithms, including the widely used Difference Map (DiffMap), are often limited by modest phase retrieval success rates. To address this limitation, we introduce a novel Hybrid Difference Map (HDM) algorithm that synergistically combines the strengths of DiffMap and the Hybrid Input–Output (HIO) method through six distinct iterative update rules. HDM retains an optimized DiffMap-style relaxation term for fine-grained density modulation in protein regions while adopting HIO’s efficient negative feedback mechanism for enforcing the solvent flatness constraint. Using the transmembrane photosynthetic reaction center 2uxj as a test case, the first HDM formula, HDM-f1, successfully recovered an atomic-resolution structure directly from random phases under a conventional full-resolution phasing scheme, demonstrating the robust phasing capability of the approach. Systematic evaluation across 22 protein crystal structures (resolution 1.5–3.0 Å, solvent content ≥ 60%) revealed that all six HDM variants outperformed DiffMap, achieving 1.8–3.5× higher success rates (average 2.8×), performing on par with or exceeding HIO under a conventional phasing scheme. Further performance gains were achieved by integrating HDM with advanced strategies: resolution weighting and a genetic algorithm-based evolutionary scheme. The genetic evolution strategy boosted the success rate to nearly 100%, halved the median number of iterations required for convergence, and reduced the final phase error to approximately 35° on average across test structures through averaging of multiple solutions. The resulting electron density maps were of high interpretability, enabling automated model building that produced structures with a backbone RMSD of less than 0.5 Å when compared to their PDB-deposited counterparts. Collectively, the HDM algorithm suite offers a robust, efficient, and adaptable framework for direct phasing, particularly for challenging cases where conventional methods struggle. Our implementation supports all space groups providing an accessible tool for the broader structural biology community. Full article