Search Results (1,600)

Background/Objectives: Multidrug-resistant (MDR) Escherichia coli resistant to third-generation cephalosporins are a growing One Health concern, but data on extraintestinal pathogenic E. coli (ExPEC) from wildlife in North Africa remain scarce. We aimed to characterize ESBL/AmpC-producing ExPEC from captive wild mammals in Tunisia and to situate these isolates in a global genomic context. Methods: In 2018, 30 fecal samples from 14 captive wild mammals in a private farm were screened on cefotaxime agar. Four cefotaxime-resistant E. coli isolates were recovered from a llama, lion, hyena, and tiger. Antimicrobial susceptibility testing and Illumina whole-genome sequencing were combined with in silico typing, resistome and virulome profiling, plasmid and mobile element analysis, human pathogenicity prediction and core-genome MLST-based minimum-spanning trees. Results: All isolates were MDR but remained susceptible to carbapenems, colistin and tigecycline. Two ST162/B1 isolates from the llama and tiger carried bla_CMY-2, whereas two ST69/D isolates from the lion and hyena harbored bla_CTX-M-15 and qnrS1. Genomes encoded 61–68 antimicrobial resistance genes and 114–131 virulence-associated genes, together with IncF-, IncI1- and IncY-type plasmids and IS26-rich insertion sequence profiles. Mating-out assays yielded cefotaxime-resistant transconjugants, supporting plasmid transferability of bla_CMY-2 or bla_CTX-M-15. PathogenFinder predicted a ≥0.93 probability of human pathogenicity for all isolates. cgMLST-based trees showed that Tunisian ST69 and ST162 clustered within internationally disseminated lineages containing human, animal and food isolates, rather than forming wildlife-restricted branches. Conclusions: Captive wild mammals in Tunisia can harbor high-risk ExPEC lineages combining ESBL/AmpC production, multidrug resistance and extensive virulence and mobility gene repertoires. These findings highlight captive wildlife as potential reservoirs and sentinels of clinically relevant E. coli and underscore the need for integrated WGS-based One Health surveillance at the human–animal–environment interface in North Africa. Full article

Actinidia eriantha is an endemic kiwifruit species in China with high nutritional value and breeding potential. As a typical dioecious fruit tree, most currently bred cultivars are female, while the development of male pollinizer cultivars remains insufficiently studied and reported. Through long-term collection and evaluation of wild germplasm resources, our research team bred a male cultivar ‘Ganxiong 1’ with both ornamental and pollination value. In this study, the phenological traits, floral characteristics, major biological traits, ploidy levels, and genetic diversity of ‘Ganxiong 1’ were systematically analyzed and compared with those of the commonly used pollinizer ‘Moshan 4’. The results showed that ‘Ganxiong 1’ exhibited stable genetic traits, with branch bleeding occurring in late February and flowering in early May, highly overlapping with the flowering period of most female A. eriantha cultivars. It produced bright red flowers arranged in false dichasial cymes, showing high ornamental value. The average number of anthers per flower was 140.24, and the number of pollen grains per anther reached 8.57 × 10⁴, with a pollen viability of 97.64% and a pollen tube length of 127.25 μm, indicating strong pollination potential. Ploidy and SSR analyses revealed that ‘Ganxiong 1’ is a diploid cultivar and is genetically distinct from previously reported A. eriantha cultivars at the DNA level. Regarding pollination effects, the fruit set rate, single fruit weight, seed number, SSC, and AsA content of ‘Ganlv 1’ fruits pollinated with ‘Ganxiong 1’ were significantly higher than those pollinated with ‘Moshan 4’, while the TA content was significantly lower than that of ‘Moshan 4’ pollination. In conclusion, ‘Ganxiong 1’ exhibits high stability and distinctiveness in phenological, morphological, cytological, and genetic characteristics. It can be considered a new ornamental and pollination dual-purpose cultivar of A. eriantha and provides an important parental resource for kiwifruit breeding programs. Full article

Tree architecture is a critical determinant of plant performance, light capture, biomechanical stability, and resource allocation. However, the multidimensional functional trait space and multiscale allometric scaling mechanisms underlying different architectural types in Malus remain poorly understood. This study investigates the multidimensional functional trait space and multiscale allometric scaling relationships among three typical architectural types (weeping, upright, and spreading) in Malus. A total of 206 germplasm accessions were analyzed by integrating nine core functional traits spanning macro-architectural, branch biomechanical, and leaf economic dimensions. Principal component analysis revealed that architectural differentiation is primarily driven by macro-architectural and branch biomechanical traits, alongside coordinated contributions from leaf economic traits. Functional diversity analysis indicated that the upright and spreading types exhibited higher functional richness, while the weeping type displayed the highest functional divergence but minimal or no functional overlap with the upright and spreading type, reflecting strong niche specialization under artificial selection. Multiscale allometric analyses demonstrated significant divergence in resource allocation strategies across hierarchical levels. At the whole-tree level, architectural types differed markedly in height–diameter and height–crown scaling relationships. At the branch level, conserved positive allometric scaling was observed, with the weeping type showing higher intercepts indicative of increased mechanical investment. At the leaf level, consistent negative allometry between petiole length and leaf area suggested optimized resource allocation for light capture. These pronounced differences suggest distinct ecological adaptation strategies: the weeping type prioritizes biomechanical compensation for pendulous branches and optimized light capture in loose canopies; the upright type emphasizes vertical light competition and mechanical compactness; the spreading type balances lateral expansion and spatial filling efficiency, reflecting differentiated resource allocation patterns shaped by artificial selection. Overall, this study reveals that tree architecture in Malus is shaped by coordinated trait interactions across multiple scales, leading to distinct ecological strategies and resource allocation patterns. These findings provide new insights into the structure–function co-evolution of woody plants and offer a theoretical framework for functional trait-assisted breeding of ornamental tree architectures. Full article

The impact of potassium fertilization on disease resistance in Korla fragrant pear trees was evaluated under drip irrigation to determine the optimal application rate. Seven- to eight-year-old trees were subjected to four K treatments: the control (K0, 0 kg/hm) and applications of 75 (K75), 150 (K150), and 225 kg/hm² (K225). Disease resistance indices in current-year shoots and old branches were assessed throughout growth stages, and correlations with branch mineral contents were analyzed. The K75 treatment significantly increased branch Ca and Mg contents and enhanced flavonoid and lignin contents and PAL activity relative to K0. The K150 treatment markedly raised N, P, K, Fe, Mn, and Cu contents, as well as flavonoid, lignin, soluble sugar, PPO, and PAL levels, with optimal effects on flavonoids and old branch PAL activity. The K225 treatment mildly reduced Ca and Mg but strongly elevated total phenols, flavonoids, lignin, soluble sugars, PPO, and PAL, exerting the greatest influence on total phenols, soluble sugars, PPO, and current-year shoot PAL. K rates were significantly correlated with disease resistance indices. Branch mineral contents showed highly significant correlations with resistance indices and yield, but resistance indices were not significantly associated with yield. Potassium directly modulated resistance indices, with mineral elements exerting more pronounced effects in current-year shoots. Application of 150 kg/hm K is proposed as the optimal rate to improve disease resistance, mineral nutrition, tree vigor, survival, and yield in 7–8-year-old Korla fragrant pear orchards. Full article

Forest canopy water storage capacity is a critical component of ecohydrological research. However, because most current studies focus on the plot or stand scale, upscaling these fine-scale measurements to regional spatial scales remains a major challenge. Taking the forest in southern Jiangxi province as a case study, we integrated water immersion experiments, Handheld Laser Scanning (HLS), Unmanned Aerial Vehicle LiDAR (UAV-LiDAR), and optical remote sensing data to construct a spatial upscaling model. This model aims to quantify regional canopy water storage capacity and delineate its spatial patterns. The results indicate that: (1) the water storage capacity of branches and leaves per unit surface area of coniferous trees was significantly higher than that of broad-leaved trees, and the water storage capacity of branches was 6.0–10.7 times that of leaves. The mean canopy water storage capacities of coniferous forests, mixed coniferous and broad-leaved forests, and broad-leaved forests were 1.41 ± 0.27 mm, 1.30 ± 0.45 mm, and 1.26 ± 0.36 mm, respectively. (2) The canopy water storage capacity was significantly positively correlated with canopy volume ($V_C$) and average canopy area ($A_C$) extracted from UAV-LiDAR data, and vegetation structure factors such as normalized difference vegetation index (NDVI) and vegetation cover (FVC) extracted from optical remote sensing, and significantly negatively correlated with altitude and slope. Among them, canopy closure (C), average canopy area ($A_C$), and altitude were key factors affecting canopy water storage capacity. (3) The upscaling prediction models based on UAV-LiDAR data and optical remote sensing factors, respectively, show reliable prediction performance, with R² values of 0.884 and 0.815, RMSE of 0.951 and 0.116 mm, respectively. (4) The canopy water storage in the study area ranged from 0 to 1.76 mm, with a prediction uncertainty ranging from 0.12 to 0.49 mm. Canopy water storage is higher in the continuous middle and low mountain and hill areas within the region, while it is relatively lower in the high elevation ridge areas along the western, eastern, and southern margins. The results provide baseline structural information for understanding the spatial patterns of regional forest canopy interception potential. Full article

Traditional leaf area index (LAI) measurement methods are destructive, time-consuming, and labor-intensive. In this study, 282 four-year-old central-leader apple trees were used as research subjects. Canopy reflectance spectra in the range of 4000−10,000 cm⁻¹ were collected, and the corresponding true LAI values were measured destructively by harvesting all leaves from a representative branch of each tree using a leaf area meter. The dataset was randomly divided into training (70%) and testing (30%) sets. Eight spectral pretreatment methods were compared. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to extract characteristic wavelengths. Subsequently, both a BP neural network and a Support Vector Machine (SVM) model for LAI prediction were constructed. The optimal model was selected based on evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE). The combined preprocessing of MSC and SD yielded the optimal results, screening out 26 characteristic wavelengths. The SVM linear kernel model (c = 5, g = 0.3) constructed based on MSC + SD preprocessing performed best, achieving a validation set R² of 0.90, MAE of 0.2117, MBE of −0.1214, and MAPE of 16.09%. The performance on the training set and validation set was comparable, with no overfitting observed. The MSC + SD preprocessing combined with CARS feature screening and SVM linear kernel modeling enables rapid, non-destructive estimation of apple canopy LAI, providing an effective technical tool for precision orchard management. Full article

Mine ventilation network (MVN) regulation faces severe challenges: strong variable coupling, high search dimensionality, and the inherent conflict between energy conservation and safety constraints. To address these issues, we propose a novel airflow optimization framework integrating a Minimum Influence Spanning Tree (MIST), sensitivity attenuation boundaries, and an Improved Dung Beetle Optimizer (IDBO). Initially, high-influence co-tree chords are strategically extracted via MIST to compress the mathematical optimization dimensionality. Subsequently, effective ventilation resistance search intervals are bounded using sensitivity attenuation, preventing the algorithm from performing invalid searches in high-resistance regions. Furthermore, the standard DBO is enhanced via Fuchs chaotic initialization, Golden Sine and Lens Imaging collaborative learning, and differential mutation to minimize system power consumption. A 46-branch MVN case study validates the approach, identifying an 8-dimensional control combination as the absolute minimum requirement for full compliance. Compared to state-of-the-art baselines (DBO, SSA, WOA, DE), IDBO achieved the lowest power consumption. Post-optimization, the airflow constraint satisfaction rate improved from 89.13% to 100%, and total system power decreased by 11.87% (from 185.03 kW to 163.08 kW). Ultimately, this method robustly achieves Ventilation on Demand (VoD), providing a reliable computational tool for intelligent underground mining. Full article

Avian reovirus (ARV) causes viral arthritis and leads to considerable economic losses in the poultry industry. In this study, six ARV strains of distinct genotypes (FJNP01–FJNP06) were isolated from commercial broiler farms. Through gene sequencing and pathogenicity assessment, we analyzed the genetic evolution and pathogenic characteristics of the σC, P10, σB, μB, and λC genes. Pathogenicity tests revealed that inoculation with FJNP01–FJNP06 by footpad or oral gavage induced symptoms in specific-pathogen-free (SPF) chickens, including mortality and growth retardation. Among the isolates, FJNP04 (genotype IV) showed the highest pathogenicity, causing increased mortality, weight loss, and severe lesions in the footpads and bursa of Fabricius, followed by FJNP05 and FJNP02. The pathogenicity of FJNP06 varied by inoculation route, with enhanced pathogenicity observed following oral gavage. In contrast, FJNP01 and FJNP03 demonstrated relatively low pathogenicity. Identity analysis indicated that σC and P10 were highly variable, σB was relatively conserved, while μB and λC displayed considerable divergence. Phylogenetic analysis placed FJNP01–FJNP06 into genotypes I to VI, respectively, forming six distinct branches on the σC and P10 phylogenetic trees, yet clustering more closely on the σB, μB, and λC trees. The pathogenicity of different genotypes of ARV varies, among which FJNP04 (genotype IV) exhibits the strongest pathogenicity. Genetic sequence analysis revealed that σC and P10 are highly variable, σB is relatively conserved, while μB and λC display a wide range of variation. This study provides insights into the genetic variation and pathogenic characteristics of ARV and serves as a reference for future research. Full article

Tree contact discharge is a key contributing factor to wildfires caused by medium-voltage insulated conductors. Prolonged abrasion of the insulation layer by branches gradually creates weak points in the insulation. When subjected to lightning strikes, these areas are prone to forming lightning-induced pinholes, which can subsequently trigger partial discharge and even ignition. This study systematically investigates the discharge-induced ignition mechanism for 10 kV overhead insulated conductors in tree contact scenarios by establishing an experimental platform integrated with high-speed imaging, ultraviolet detection, and simulation methods. Three types of typical defects were set up in the experiments: complete insulation abrasion, lightning puncture holes accompanied by localized abrasion, and lightning puncture holes without abrasion. The development process and characteristics of different discharge forms were observed and analyzed. The results indicate that the tree contact discharge ignition mechanism can be categorized into two types: thermal accumulation and direct arcing. The former occurs when insulation abrasion or composite defects exist, where sustained partial discharge or a high-resistance current leads to gradual heat accumulation, resulting in an ignition delay lasting tens of seconds. The latter occurs when only small defects such as lightning puncture holes exist in the insulation layer. A concentrated arc forms due to gap breakdown under high voltage, leading to a millisecond-level ignition process. The study found that different discharge forms produce significantly distinct ablation and carbonization patterns on both the insulation layer and the branch surface, reflecting differences in energy transfer pathways. Simulation analysis further indicated that the thickness of the insulation layer affects the electric field distribution in the tree contact gap, with the initial discharge field strength decreasing as the thickness increases. This study provides experimental evidence and classification guidance for tree contact fault monitoring, insulation condition assessment, and wildfire prevention and control in medium-voltage distribution networks. Full article

Horse chestnut (Aesculus hippocastanum L.) is a widely planted ornamental and urban tree valued for its aesthetic and ecological functions. In recent years, declining health of horse chestnut in urban environments has been increasingly reported, often associated with a complex of biotic and abiotic stressors. During a health survey of A. hippocastanum trees growing along an urban road corridor in Warsaw, Poland, extensive bark necrosis and branch dieback were observed. The aim of this study was to identify the causal agent of these symptoms using morphological, cultural, molecular (ITS rDNA), and pathogenicity tests under controlled conditions. Fungal isolates were obtained from necrotic tissues and were consistently identified as Kalmusia variispora based on ITS sequence analysis (99.0–99.6% similarity to GenBank references) and characteristic morphology. Pathogenicity tests fulfilled Koch’s postulates, reproducing necrotic lesions and cambial damage similar to those observed in the field. To our knowledge, this is the first documented report worldwide of K. variispora infecting A. hippocastanum. The findings expand the known host range of this opportunistic Didymosphaeriaceae species and highlight its potential role in bark and wood disease complexes of urban trees. Further research is needed to assess its distribution, genetic diversity, and epidemiological significance in urban forest ecosystems. Full article

Achieving precise fruit localization and fine branch segmentation simultaneously in unstructured orchard environments remains challenging due to variable lighting, occlusion, and complex backgrounds. This study proposed a joint detection–segmentation architecture based on an improved YOLOv11 network for collaborative perception of apples and tree branches. First, a dual-task dataset of spindle-type apple orchards was constructed with bounding-box annotations for fruits and pixel-level polygon masks for branches, encompassing diverse illumination and occlusion conditions. Second, Convolutional Block Attention Modules (CBAMs) are strategically embedded into the YOLOv11 backbone to enhance feature discrimination for slender branch structures while preserving high fruit detection accuracy. The enhanced model achieves precision of 0.981, recall of 0.986, and F1-score of 0.983 for apple detection, and precision of 0.803, recall of 0.715, mAP of 0.698, and IoU of 0.6066 for branch segmentation on the validation set. Comparative experiments against YOLOv8 and baseline YOLOv11 confirm improved segmentation continuity and finer branch delineation. The proposed integrated perception framework provides reliable visual guidance for collision-avoidance robotic harvesting and offers a practical reference for multi-task agricultural vision systems. Full article

Successive cropping frequently causes a decline in Chinese Fir (Cunninghamia lanceolata) biomass, a problem intricately tied to soil nutrient shifts and microbial processes. This research investigates the mechanisms governing biomass carbon partitioning and soil nutrient shifts in these plantations. This study investigated five Chinese Fir clones (‘ck’, ‘b44’, ‘K13’, ‘F13’, and ‘kt13’) across two cultivation regimes: continuous cropping (second-generation plantation, G2) and first-generation plantation (G1). The focus was on their biomass and soil nutrient status. The results showed that: (1) The biomass of different Chinese Fir clones at 25 years of age decreased significantly with increasing generations of continuous cultivation. Tree height showed no significant differences among clones within the same generation; however, the G2 cultivation significantly inhibited diameter at breast height (DBH). (2) The changes in soil nutrients and microbial activity under different successive generations (G1, G2) was closely linked to the decline in Chinese Fir biomass carbon. Analysis revealed that the decreases in dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and Catalase (CAT) activity were significantly positively correlated with the reduction in biomass carbon. Concurrently, the decrease in soil pH showed a significant negative correlation with microbial biomass carbon (MBC) and Sucrase (SUC) activity. (3) Regarding growth traits, although tree height showed no significant differences among clones within the same generation, DBH was generally and significantly inhibited under G2 cultivation. An exception was the ‘K13’ clone, which remained largely unaffected. In terms of carbon accumulation, G2 cultivation led to a universal decline in biomass carbon across clones; however, the magnitude of reduction in different components (leaf, branch, stem, root) and total biomass carbon varied clone-specifically. Notably, ‘K13’ exhibited the strongest tolerance, with a significantly smaller decrease in tree biomass carbon compared to the other four clones, which showed substantially lower tree carbon stocks across all components relative to G1 plantations. This indicates that successive cropping of Chinese Fir likely constrains the carbon sequestration capacity of plantations by altering soil nutrient properties, thereby suppressing tree DBH growth and biomass carbon accumulation, likely through reduced net primary productivity. Among the five clones, ‘K13’ was the least affected, demonstrating its high potential for adaptation to continuous cultivation. These findings provide implications for sustainable forest management by guiding clone selection to mitigate productivity decline under successive cropping. Full article

Plant fungal diseases, such as apple tree canker caused by Valsa mali, have caused severe losses in agricultural production. Traditional chemical fungicides induce drug resistance in pathogens and cause environmental pollution. Therefore, it is of substantial importance to screen efficient and environmentally friendly bacterial strains as potential biocontrol agents. The tea rhizosphere harbors abundant microbial resources, and previous research has identified microorganisms with antifungal activity existing in this environment. Therefore, in this study, we isolated antagonistic bacteria with broad-spectrum biocontrol potential from tea rhizosphere soil. In this study, a strain with strong antagonistic activity against V. mali was isolated from tea rhizosphere soil. Based on morphological characteristics, 16S rRNA gene sequencing, and whole-genome analysis, the isolated strain was identified as Bacillus velezensis and designated as LW-66. This strain demonstrated broad-spectrum antifungal activity against various plant pathogenic fungi, including Valsa mali, Fusarium graminearum, Bipolaris sorokinianum, Alternaria solani, and Exserohilum turcicum. The active extract of B. velezensis maintained strong stability across a wide range of temperatures (25–90 °C) and pH values (2–8), with stability decreasing only when the temperature reached 100 °C or pH ≥ 10. In a preventive assay using detached apple branches inoculated with V. mali, the control efficacy of LW-66 against apple tree canker reached more than 90%. Additionally, in a therapeutic assay using V. mali-infected potted apple seedlings, the LW-66 bone-glue bacterial agent achieved a survival rate of up to 90%. Whole-genome analysis revealed that the genome of LW-66 contains 13 predicted secondary metabolite biosynthetic gene clusters, seven of which showed high homology (≥92% similarity) with known antimicrobial gene clusters, including surfactin, bacillaene, macrolactin H, fengycin, difficidin, bacillibactin, and bacilysin. These gene clusters may be connected to the broad-spectrum antifungal activity of B. velezensis, as well as its ability to disrupt hyphal morphology. The volatile organic compounds produced by LW-66 inhibited V. mali growth by 91.70%. Collectively, these findings demonstrate that B. velezensis LW-66 has a wide antimicrobial range and strong antagonistic effects against multiple plant pathogenic fungi. Therefore, B. velezensis shows promise as a biocontrol agent for managing fungal diseases in plants, providing a basis for developing LW-66-derived biocontrol products aimed at controlling diseases such as apple tree canker. Full article

In the mechanized pruning process of apple trees, reasonably matching cutting parameters is the key to reducing energy consumption and improving pruning quality. The conventional empirical parameter configuration usually ignores the vibration suppression effect of the branch support system, resulting in unstable cutting processes and poor cross-section quality. This study systematically investigated the influences of saw blade rotational speed, feed speed, and active support system on the sawing process of apple branches, aiming to obtain optimal operating parameters through a closed-loop research method of “simulation, optimization, and verification”. An explicit dynamic finite element model was established for multi-branch staggered sawing with three saw blades. The influence trends of each factor were analyzed via single-factor tests. A three-factor, three-level orthogonal experiment was designed based on the Box–Behnken method, and a response surface prediction model of sawing force was constructed. Regression analysis showed that the established model was extremely significant (p < 0.01). The order of factors affecting sawing force from primary to secondary was as follows: feed speed > number of support components > saw blade rotational speed. Multi-objective optimization yielded the optimal parameter combination: rotational speed of 2500 r/min, feed speed of 2 km/h, and five support components. A prototype was manufactured according to these parameters, and field verification tests were carried out in orchards. Taking the qualified rate of cross-section quality and the missed-cut rate as evaluation indexes, the qualified rate under optimized parameters reached 95.07%, which was significantly higher than 83.11% under traditional parameters, and the missed-cut rate decreased from 11.27% to 2.63%. Results indicate that the collaborative optimization mode of “medium-high rotational speed, moderate feed speed, and active support” enables the low-vibration and high-quality sawing of apple branches. The combined method of explicit dynamics, response surface methodology, and field verification provides a systematic solution for intelligent parameter configuration of orchard pruning equipment. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article