Search Results (143)

The chemotaxonomic classification of Cannabis sativa L. has historically relied on the Δ⁹-tetrahydrocannabinol (THC) to cannabidiol (CBD) ratio, yielding canonical chemotypes I, II, and III. However, this binary framework overlooks the chemical diversity contributed by the minor cannabinoids. High-performance liquid chromatography (HPLC) following the AOAC Official Method 2018.10 was employed to quantify nine cannabinoids (THCA, THC, CBDA, CBD, CBGA, CBG, CBC, CBDV, and CBN) across 36 commercially and medicinally relevant cannabis varieties. Quantitative profiling revealed substantial phytochemical heterogeneity, with total THC ranging from 0.41% to 15.64% and total CBD ranging from 0.09% to 12.32% (w/w). Unsupervised principal component analysis (PCA) demonstrated that the first two principal components explained 62.7% of the total variance. PC1 (37.6%) captured the THCA–CBDA polarity axis, while PC2 (25.1%) was dominated by minor cannabinoids (CBC; loading 0.417), CBGA (0.314), and CBG (0.258). Supervised partial least squares discriminant analysis (PLS-DA) using only the nine cannabinoid variables achieved 94.2% cross-validated accuracy and 100% test-set accuracy in predicting the chemotype class, with CBC identified as the third most discriminatory variable (variable importance in projection, VIP = 1.34). Hierarchical clustering resolved three principal clades and further subdivided THC-dominant accessions into CBC-enriched (Sour Diesel, Cinderella Jack) and CBGA-enriched (Mother Gorilla, Auto Lemon Kix) subclusters. A multivariate “metabolic coordinate” system based on PC1/PC2 scores is proposed as a quantitative and reproducible alternative to the traditional Type I/II/III and sativa/indica nomenclatures. This study introduces an empirically grounded framework for variety authentication, quality control, and enhanced precision breeding in the rapidly growing medicinal cannabis sector, for both human and veterinary applications. Full article

It is widely accepted that modern humans display distinctive vertebral and intervertebral disc (IVD) morphologies that evolved to meet the biomechanical demands of habitual terrestrial bipedalism. This study synthesizes macro- and microstructural differences in the lumbar spine to clarify how human specializations compare with those of extant apes. The skeletal sample consisted of 240 humans, 20 chimpanzees, and 25 gorillas. The CT scan sample comprised 180 humans and eight chimpanzees. Histological analysis of the IVD was performed on 10 humans and four ape specimens. Vertebral bodies and discs were measured. Histological analyses employed hematoxylin–eosin, Von Kossa, and Van Gieson staining. Statistical analyses included ANOVA with Bonferroni-corrected t-tests or Welch’s ANOVA and Games–Howell post hoc tests. Regression analyses were performed using ordinary least-squares estimation, and differences between regression lines were assessed using ANCOVA. Humans and chimpanzees differed significantly in vertebral body proportions, bone volume fraction, IVD thickness, apophyseal ring thickness, annulus fibrosus lamellar organization, endplate and subchondral bone thickness, and vascularization at the bone–endplate interface. These results indicate substantial evolutionary modification of the human vertebral body and IVD, enhancing rotational mobility and resistance to axial loading, key functional requirements for maintaining upright posture and efficient bipedal locomotion. Full article

Precise assessment of rock brittleness is a prerequisite for effective wellbore integrity and successful reservoir stimulation in drilling programs. To achieve precise prediction of rock brittleness index (BI), this study proposes an improved optimization algorithm for an artificial gorilla troops optimizer (GTO), called a Bernoulli Differential Evolution Gorilla Troops Optimizer (BDEGTO). In the BDEGTO, Bernoulli mapping is introduced during the population initialization process, and the differential evolution is embedded after the exploration stage of the GTO. These modifications effectively address the early-stage optimization weaknesses and the susceptibility to local optima that are commonly encountered in a traditional GTO. To evaluate the performance of the BDEGTO, comparisons are made with other optimization algorithms based on 91 datasets from 32 rock types. The results demonstrate the significant advantages of the BDEGTO over other algorithms. Furthermore, the BDEGTO is applied to the optimization process of Least Squares Boosting (LSB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM). A comparison is made with Support Vector Regression (SVR), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) algorithms for predicting rock brittleness based on input parameters such as P-wave velocity (V_p), point load index (Is₅₀), and unit weight (UW). The findings indicate that BDEGTO-XGB achieves the best prediction performance for BI. Additionally, through SHapley Additive exPlanations (SHAP) analysis, it is determined that among the three input parameters, Is₅₀ has the most significant influence. These research results provide valuable guidance for the brittleness assessment of similar rocks. Full article

Gabon harbors one of Africa’s richest assemblages of non-human primates (NHPs), yet integrated national-scale evidence on their conservation status remains limited. To inform conservation strategies, we conducted the first nationwide assessment integrating habitat dynamics, the geographic distribution of species, and the effectiveness of the protected-area network in the country. We harmonized 300 m land-cover maps (ESA CCI 1992; Copernicus 2022), compiled 481 georeferenced occurrences, and identified concentration areas using kernel density estimation and Getis–Ord Gi* analysis. We quantified land-cover transitions with a per-pixel transition matrix and assessed protected-area capture using Monte Carlo randomization. Ten fully protected species are confirmed, including Gorilla gorilla and Pan troglodytes. Occurrences concentrate mainly in the Ogooué-Ivindo and Haut-Ogooué Provinces; ~10% of the national territory lies above the 90th kernel density percentile (≈26,700 km²), and 1.5% of cells qualify as hotspots at the 99% threshold. Primate records are strongly associated with evergreen broadleaved forests (87.9% of points), which remained persistent from 1992 to 2022 (forest-to-forest = 223,476 km²; 98.13%) with a net decline (−2571.66 km²; −1.19%). Gross losses (4046.58 km²) were mainly attributable to agricultural conversion (68.63%; χ² = 31,525; p < 0.001). Over 90% of records fall in areas stable across 1992–2022. Protected areas (PAs) captured more occurrences (observed 40.1% vs. expected 18.47%; p < 0.001), yet gaps remain for some taxa (e.g., Allochorocebus solatus, 86% outside PAs). Overall, Gabon retains an extensive core of suitable habitat, but targeted action outside PAs and maintenance of landscape connectivity are needed to secure populations where agricultural expansion and fragmentation are intensifying. Full article

Background/Objectives: Developmental prosopagnosia (DP) is a neurodevelopmental condition characterized by lifelong difficulties in face recognition. Although substantial work has examined identity-processing impairments in DP, less is known about whether these difficulties extend to other aspects of social cognition, including implicit trait judgements from faces. Prior research using Implicit Association Task (IAT) paradigms shows that neurotypical observers can automatically associate facial composites with personality traits such as extraversion. Although some studies report preserved explicit social evaluations in DP, to our knowledge, no previous work has assessed whether individuals with DP can form implicit personality trait impressions from faces. Methods: Using a cross-sectional experimental design, the present study examined whether adults with DP (N = 36) exhibit implicit extraversion trait associations, using a validated extraversion IAT online via Gorilla, following institutional ethics approval. Results: Group-level analyses showed a significant IAT effect, indicating sensitivity to congruent face–trait pairings. Single-case analyses using Crawford and Garthwaite’s modified t-test showed that no participant scored significantly below the normative neurotypical range. Conclusions: These findings indicate that implicit trait inference performance can remain within the normative range in DP despite severe identity recognition impairments, consistent with relative independence between social-evaluative and identity-related face-processing mechanisms. Full article

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) caused a global public health emergency in humans from 2020 to 2023 and was associated with over 7 million human deaths. Besides humans, SARS-CoV-2 has been detected in a wide range of animals, including companion, farm, zoo, and wild animals. At least 61 animal species from 29 animal families of 12 animal orders have tested SARS-CoV-2 positive. Documented evidence reported that not only human-to-animal transmission but also animal-to-human transmission events occurred. During the course of the pandemic progression in humans, SARS-CoV-2 strains in animals evolved in parallel with those in humans. Continued monitoring of SARS-CoV-2 in animals is needed to safeguard both human and animal health. In this study, we report investigation of two outbreaks of SARS-CoV-2 Omicron variant infection in tigers and gorillas in two zoological institutions. In the first zoo, six tigers tested positive by SARS-CoV-2 real-time RT-PCR and shed viral nucleic acid in feces for up to two weeks. Three of the tigers showed intermittent shedding patterns, while the other tigers shed only for 7–10 days. No other species, including cheetah, otter, lion, anteater, gibbon, and tamarin, tested positive. During the outbreak at the second zoo, a total of six gorillas were tested positive for SARS-CoV-2, while other primates housed in the same building (colobus and orangutan) tested negative. Follow-up testing revealed that two gorillas tested positive for SARS-CoV-2 over a one-month period (30 and 33 days, respectively), while the other four gorillas had positive SARS-CoV-2 PCR results for 14 to 25 days. Four gorillas had intermittent shedding patterns. Notably, compared to tigers, gorillas had a prolonged duration of fecal viral shedding. Sequencing was performed on the positive samples, and analysis indicated that strains detected in tigers and gorillas belonged to SARS-CoV-2 Omicron BQ.1.10 and XBB.1.16, respectively. Overall, this study offers valuable insights into the duration of viral RNA shedding for SARS-CoV-2 Omicron in zoo animals, facilitating accurate diagnostic evaluation and management of infected tigers and gorillas. Full article

Nighttime temperatures in the Afro-alpine zone (>2050 m) of Kahuzi-Biega National Park frequently fall below 5 °C. However, the thermal advantages provided by night nests of Grauer’s gorilla, Gorilla beringei graueri along this elevation gradient have yet to be quantified. From 3 January to 7 January 2025, 80 night nests were located along the Mt. Kahuzi–Biega ridge (2000–2650 m above sea level); 66 with complete data were analyzed. Nest-interior and ambient temperatures were measured using calibrated mercury thermometers, while canopy openness was assessed through sky-facing photographs analyzed with ImageJ. Canopy openness ranged from 18% at 2050 m (dense bamboo) to 83% at 2625 m (open ericaceous scrub), with a mean of 50.5 ± 18.8%. The interiors of the nests consistently exhibited warmer temperatures than the humid ambient air, with a mean temperature difference of 2.03 ± 0.37 °C, ranging from 1.39 to 2.68 °C. Linear mixed-model analysis (n = 66) indicated a significant reduction in thermal buffering correlated with increasing elevation (β = −7.4 × 10⁻⁴ °C m⁻¹, 95% CI −8.9 × 10⁻⁴ to −5.9 × 10⁻⁴, p < 0.001) and greater canopy openness (β = −0.020 °C per %, p < 0.001); fog density and precipitation from the previous night did not exhibit a significant effect. The model explained 78% of the variance in ΔT (marginal R²). Over a 650 m Afro-alpine gradient, Grauer’s gorillas create a 2.0 °C thermal refuge, which decreases by approximately 30% near the summit. This study represents the first quantitative evidence that canopy density can mitigate the elevation penalty for any African great ape. Canopy retention is the only terrestrial mechanism that can mitigate accelerated warming at high altitudes, which is occurring at a rate of +0.45 °C per decade. Without canopy retention, national conservation strategies for the Democratic Republic of Congo must allocate funds for extended energy subsidies at elevations exceeding 2500 m. Full article

Background/Objectives: The rapid development of safe and efficacious vaccines is often hindered by extensive, mandated non-clinical safety evaluations in animals. With the aim to provide scientific evidence supporting a “vaccine platform approach”, here we present the complete non-clinical studies for two investigational vaccines, GRAd-COV2 and GRAdHIVNE1, based on GRAd, a gorilla-derived group C adenoviral vector. Methods: The biodistribution of GRAd genomes following the intramuscular administration of the vaccines was assessed in rats by a sensitive qPCR method. Local tolerance and systemic toxic effects were evaluated in single- and repeated-dose toxicity studies in rabbits. Results: GRAd-COV2 and GRAdHIVNE1 were well-tolerated. Distribution was highly confined to the injection site and draining lymph nodes, and toxicity profile consisted of transient, non-adverse inflammatory responses, while the expected immune responses to the encoded antigens were successfully induced. Notably, both vaccines demonstrated a consistent safety profile despite transgene and backbone differences, comparable to other replication-defective adenoviral vectors. Conclusions: The established non-clinical safety profile of the GRAd platform provides a robust foundation for a more efficient and streamlined regulatory pathway. By leveraging this prior knowledge, future GRAd-based vaccines can achieve accelerated clinical development while fully adhering to the ethical principles of replacement, reduction, and refinement of animal use in research. Full article

The low efficiency of photovoltaic (PV) systems arises from their nonlinear current-voltage characteristics, necessitating the use of maximum power point tracking (MPPT) techniques. Conventional MPPT methods are popular for their simplicity and low cost but exhibit poor performance under rapidly changing atmospheric conditions, leading to considerable energy losses. Under uniform solar irradiation, these traditional approaches can locate the maximum power Point (MPP), yet their reliance on small, fixed step sizes causes oscillations and output ripple. In dynamic environmental conditions, they often fail to accurately track the true MPP. To address these challenges, this paper proposes an MPPT strategy based on the artificial Gorilla Troops Optimizer (GTO) to enhance PV performance under partial shading conditions (PSCs) and fast climatic variations. An enhanced version of the algorithm (EnGTO) was developed to further improve MPPT efficiency. Comparative simulations with the perturb and observe (P&O) method and the classic GTO demonstrate that the proposed approach achieves rapid response to environmental changes and higher accuracy and lower oscillations under PSCs, reaching efficiencies of up to 99.96% (STCs) and 99.81% (PSCs). Full article

Background/Objectives: Segmental duplications (SDs) are major drivers of genome evolution and structural variation in primates, particularly within acrocentric chromosomes, where rDNA arrays and duplicated sequences are densely clustered. However, the evolutionary dynamics of rDNA-linked SDs across great ape lineages have remained poorly characterized due to longstanding technical limitations in genome assembly. Here, we investigate the organization, copy number variation, and evolutionary conservation of acrocentric SDs in great apes by integrating fluorescence in situ hybridization (FISH) with comparative analyses of telomere-to-telomere (T2T) genome assemblies. Methods: Using eight human-derived fosmid probes targeting SD-enriched regions flanking rDNA arrays, we analyzed multiple individuals from chimpanzee, bonobo, gorilla, and both Bornean and Sumatran orangutans. Results: Our FISH analyses revealed extensive lineage-specific variation in SD copy number and chromosomal distribution, with pronounced heteromorphism in African great apes, particularly gorillas, and more conserved patterns in orangutans. Several SDs showed fixed duplications across species, while others exhibited high levels of polymorphism and individual-specific organization. Conclusions: Comparison with T2T assemblies confirmed consistent genomic localization for a subset of probes, whereas others displayed partial discordance, highlighting the persistent challenges in resolving highly repetitive and structurally dynamic regions even with state-of-the-art assemblies. Genome-wide analyses further revealed species-specific enrichment of SDs on rDNA-bearing chromosomes, with chimpanzees and bonobos showing higher proportions than gorillas, and contrasting patterns between the two orangutan species. Overall, our results demonstrate that rDNA-linked SDs represent highly dynamic genomic compartments that have undergone differential expansion and remodeling during great ape evolution. These regions contribute substantially to inter- and intra-species structural variation and provide a mechanistic substrate for lineage-specific genome evolution, underscoring the importance of integrating cytogenetic and T2T-based approaches to fully capture the complexity of duplicated genomic landscapes. Full article

Designing solid-state circuit breakers (SSCBs) involves a large discrete design space spanning MOSFET type, bypass configuration, and heatsink selection. This work formulates SSCB design as a multi-objective combinatorial optimisation problem that minimises conduction loss and material cost subject to electrothermal feasibility constraints. A validated electrothermal model was developed using experimentally measured R_DSon(T) data and thermal-impedance characterisation, allowing rapid and accurate evaluation of candidate configurations. Because the full design space exceeds one million combinations, five representative metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Grey Wolf Optimisation (GWO), Ant Colony Optimisation (ACO), and Gorilla Troops Optimisation (GTO), were benchmarked under an identical computational budget of 2000 evaluations. Sobol sequence initialisation was used to enhance search diversity. Each algorithm was executed 100 times, and its performance was quantitatively assessed using hypervolume, generational distance (GD), inverted generational distance (IGD), Hausdorff distance, overlapping-point score (OP), overall spread (OS), and distribution metrics (DM). GA consistently produced the closest approximation to the true Pareto front obtained from brute-force enumeration, achieving superior accuracy, coverage, and robustness. GTO offered strong secondary performance, while PSO, GWO, and ACO delivered partial front reconstruction. The results demonstrate that metaheuristic optimisation, particularly GA, can reduce SSCB design time significantly while retaining high fidelity, offering a scalable and efficient framework for future power-electronics design tasks. Full article

The mechanical reliability of glass substrates is a key challenge for their adoption in advanced semiconductor packaging. This study employs finite element analysis to systematically evaluate the risk of edge crack propagation in large glass panels during redistribution layer (RDL) fabrication. The influence of critical factors—including crack location, number of RDLs, glass material and thickness, dielectric ABF properties, Cu content, and edge clearance—was examined. Results revealed that top-edge crack near the RDL/glass interface pose the highest failure risk due to elevated peeling stress and increased energy release rate (ERR). The risk of propagation intensifies with more RDLs and thinner glass, while high CTE (coefficients of thermal expansion) glasses such as D263, Gorilla, and ceramic glass markedly suppress crack growth compared with borofloat 33 and fused silica. Among ABF dielectrics, GZ-41 demonstrated superior crack resistance owing to its low CTE and moderate stiffness. Although higher Cu content slightly reduced ERR, its effect remained limited. Edge clearance strongly affects reliability, with ≥300 µm providing effective suppression of crack propagation. These findings provide quantitative design guidelines for glass interposer structures, emphasizing the optimization of dielectric material selection, glass substrate and thickness, and layout constraints such as edge clearance. The proposed methodology and results will contribute to establishing reliable strategies for deploying ultra-thin glass panels in advanced semiconductor packaging. Full article

Hydrological runoff prediction serves as the core technological foundation for water resource management and flood/drought mitigation. However, the nonlinear, non-stationary, and multi-temporal scale characteristics of runoff data result in insufficient accuracy of traditional prediction methods. To address the challenges of single decomposition methods’ inability to effectively separate multi-scale components and single deep learning models’ limitations in capturing long-range dependencies or extracting local features, this study proposes an Informer-GRU runoff prediction model based on STL-CEEMDAN secondary decomposition and Gorilla Troops Optimizer (GTO). The model extracts trend, seasonal, and residual components through STL decomposition, then performs fine decomposition of the residual components using CEEMDAN to achieve effective separation of multi-scale features. By combining Informer’s ProbSparse attention mechanism with GRU’s temporal memory capability, the model captures both global dependencies and local features. GTO is introduced to optimize model architecture and training hyperparameters, while a multi-objective loss function is designed to ensure the physical reasonableness of predictions. Using daily runoff data from the Liyuan Basin in Yunnan Province (2015–2023) as a case study, the results show that the model achieves a coefficient of determination (R²) and Nash-Sutcliffe efficiency coefficient (NSE) of 0.9469 on the test set, with a Kling-Gupta efficiency coefficient (KGE) of 0.9582, significantly outperforming comparison models such as LSTM, GRU, and Transformer. Ablation experiments demonstrate that components such as STL-CEEMDAN secondary decomposition and GTO optimization enhance model performance by 31.72% compared to the baseline. SHAP analysis reveals that seasonal components and local precipitation station data are the core driving factors for prediction. This study demonstrates exceptional performance in practical applications within the Liyuan Basin, providing valuable insights for water resource management and prediction research in this region. Full article

Background/Objectives: Accurate classification of brain tumors and reliable prediction of patient survival are essential in neuro-oncology, guiding clinical decisions and enabling precision treatment planning. However, conventional machine learning and deep learning methods often struggle with challenges such as data scarcity, class imbalance, limited model interpretability, and poor generalization across diverse clinical settings. This study presents GlioSurvQNet, a novel reinforcement learning-based framework designed to address these limitations for both glioma grading and survival prediction. Methods: GlioSurvQNet is built upon a DuelContextAttn Deep Q-Network (DQN) architecture, tailored for binary classification of low-grade vs. high-grade gliomas and multi-class survival prediction (short-, medium-, and long-term categories). Radiomics features were extracted from multimodal MRI scans, including FLAIR, T1CE, and T2 sequences. Feature optimization was performed using a hybrid ensemble of metaheuristic algorithms, including Harris Hawks Optimization (HHO), Modified Gorilla Troops Optimization (mGTO), and Zebra Optimization Algorithm (ZOA). Subsequently, SHAP-based feature selection was applied to enhance model interpretability and robustness. Results: The classification module achieved the highest accuracy of 99.27% using the FLAIR + T1CE modality pair, while the survival prediction model attained an accuracy of 93.82% with the FLAIR + T2 + T1CE fusion. Comparative evaluations against established machine learning and deep learning models demonstrated that GlioSurvQNet consistently outperformed existing approaches in both tasks. Conclusions: GlioSurvQNet offers a powerful and interpretable AI-driven solution for brain tumor analysis. Its high accuracy and robustness make it a promising tool for clinical decision support in glioma diagnosis and prognosis. Full article

Karst springs play a critical strategic role in regional economic and ecological sustainability, yet their spatiotemporal heterogeneity and hydrological complexity pose substantial challenges for flow prediction. This study proposes FMD-mGTO-BiGRU-KAN, a four-stage hybrid deep learning architecture for daily spring flow prediction that integrates multi-feature signal decomposition, meta-heuristic optimization, and interpretable neural network design: constructing an Feature Mode Decomposition (FMD) decomposition layer to mitigate modal aliasing in meteorological signals; employing the improved Gorilla Troops Optimizer (mGTO) optimization algorithm to enable autonomous hyperparameter evolution, overcoming the limitations of traditional grid search; designing a Bidirectional Gated Recurrent Unit (BiGRU) network to capture long-term historical dependencies in spring flow sequences through bidirectional recurrent mechanisms; introducing Kolmogorov–Arnold Networks (KAN) to replace the fully connected layer, and improving the model interpretability through differentiable symbolic operations; Additionally, residual modules and dropout blocks are incorporated to enhance generalization capability, reduce overfitting risks. By integrating multiple deep learning algorithms, this hybrid model leverages their respective strengths to adeptly accommodate intricate meteorological conditions, thereby enhancing its capacity to discern the underlying patterns within complex and dynamic input features. Comparative results against benchmark models (LSTM, GRU, and Transformer) show that the proposed framework achieves 82.47% and 50.15% reductions in MSE and RMSE, respectively, with the NSE increasing by 8.01% to 0.9862. The prediction errors are more tightly distributed, and the proposed model surpasses the benchmark model in overall performance, validating its superiority. The model’s exceptional prediction ability offers a novel high-precision solution for spring flow prediction in complex hydrological systems. Full article