Search Results (498)

Various fields within biological and psychological inquiry recognize the significance of exploring long-range temporal correlations to study phenomena. However, these fields face challenges during this transition, primarily stemming from the impracticality of acquiring the considerably longer time series demanded by canonical methods. The Bayesian Hurst–Kolmogorov (HK) method estimates the Hurst exponents of time series—quantifying the strength of long-range temporal correlations or “fractality”—more accurately than the canonical detrended fluctuation analysis (DFA), especially when the time series is short. Therefore, the systematic application of the HK method has been encouraged to assess the strength of long-range temporal correlations in empirical time series in behavioral sciences. However, the Bayesian foundation of the HK method fuels reservations about its performance when artifacts corrupt time series. Here, we compare the HK method’s and DFA’s performance in estimating the Hurst exponents of synthetic long-range correlated time series in the presence of additive white Gaussian noise, fractional Gaussian noise, short-range correlations, and various periodic and non-periodic trends. These artifacts can affect the accuracy and variability of the Hurst exponent and, therefore, the interpretation and generalizability of behavioral research findings. We show that the HK method outperforms DFA in most contexts—while both processes break down for anti-persistent time series, the HK method continues to provide reasonably accurate H values for persistent time series as short as $N=64$ samples. Not only can the HK method detect long-range temporal correlations accurately, show minimal dispersion around the central tendency, and not be affected by the time series length, but it is also more immune to artifacts than DFA. This information becomes particularly valuable in favor of choosing the HK method over DFA, especially when acquiring a longer time series proves challenging due to methodological constraints, such as in studies involving psychological phenomena that rely on self-reports. Moreover, it holds significance when the researcher foreknows that the empirical time series may be susceptible to contamination from these processes. Full article

The tilapia (Oreochromis) aquaculture industry in Malaysia has expanded rapidly to meet the increasing demand for animal protein. However, this growth is challenged by microbial infections, particularly those caused by the emerging pathogen Aeromonas dhakensis. This study aimed to investigate the microbial community composition across four distinct tilapia farming systems and assess associated water physicochemical parameters, with a focus on detecting the presence of A. dhakensis and elucidating its environmental associations. Water physicochemical parameters were measured to evaluate environmental conditions, microbial communities were characterized through 16S rDNA metabarcoding, and A. dhakensis was detected using both microbiological and molecular approaches. Principal component analysis (PCA) and canonical correspondence analysis (CCA) were employed to explore the influence of environmental variables and microbial community dynamics on pathogen occurrence. Our results indicated that floating cages exhibited higher levels of temperature, ammonium, and fecal coliform, while cement tanks showed signs of nutrient accumulation. PCA revealed that both systems were associated with degraded water quality. A total of 45 A. dhakensis strains with distinct fingerprints were isolated. The 16S metabarcoding revealed Proteobacteria, Actinobacteria, and Planctomycetota as the dominant phyla. Alpha diversity did not differ significantly among pond systems, while beta diversity revealed variations in microbial assemblages across aquaculture systems. CCA identified dissolved oxygen, temperature, macronutrients (phosphate, ammonium, nitrate, and nitrite), and turbidity as significant environmental drivers shaping the microbial community structure across the pond systems. In conclusion, this study highlights the importance of environmental factors, particularly dissolved oxygen, temperature, and nutrient levels, in shaping microbial community composition and potentially influencing the presence of pathogenic bacteria such as A. dhakensis. These findings underscore the need for improved environmental management in tilapia aquaculture to mitigate disease risks and support fish health. Full article

Big head croaker (Collichthys lucidus) is a dominant fish species in the Yangtze River estuary, with significant economic and ecological value in the local ecosystem. In this study, the dietary composition of big head croaker in the Yangtze River estuary from 2022 to 2023 was determined using stomach content analysis. Statistical methods such as cluster analysis and canonical correspondence analysis were also applied to study the ontogenetic variation in the feeding habits of big head croaker and their relationships with environmental factors. The results indicated that big head croaker in the Yangtze River estuary fed primarily on 15 prey groups and 33 prey species. Copepods were the dominant prey group, followed by mysids, shrimp, and fish. The dominant prey species included Acanthomysis longirostris, Neomysis awatschensis, and Calanus sinicus. Compared with historical studies, the proportion of large prey such as fish and crustaceans in the diet of big head croaker has increased since the implementation of the “10-Year Fishing Ban” on the Yangtze River, which reflects the improved aquatic habitat for organisms in the Yangtze River estuary to some extent. The feeding habits of big head croaker exhibited clear ontogenetic and seasonal variations. The empty stomach rate gradually decreased as the body size of big head croaker increased and their main prey shifted from small individuals such as Acetes chinensis and A. longirostris to larger individual fishes and Brachyura. In addition, big head croaker primarily fed on N. awatschensis in spring, A. longirostris in summer and autumn, and Acrocalanus gibber in winter. Canonical correspondence analysis indicated that salinity and length were the factors most strongly correlated with the feeding habits of big head croaker, followed by latitude and longitude. Full article

Degenerative changes are characterized by the formation of vertebral osteophytes, the hypertrophy of facet joints, and narrowing of the intervertebral space. This study aimed to investigate the concentrations of trace elements (Al, As, Se, Zn, Fe, Mo, Cu) in spinal tissues (intervertebral discs, muscle, and bone) of patients with degenerative lumbar spine disease (DLSD) and their potential associations with the disease. The research involved 13 patients undergoing surgery for symptomatic degenerative spine disease. The trace element concentrations were analyzed using chemical and radiographic assessments, with a statistical analysis performed through a Mann–Whitney U-test, Spearman’s rank correlation test, principal component analysis (PCA), and canonical discriminant analysis (CDA). The results showed significant variations and correlations among the trace elements across different spinal tissues, suggesting their roles in metabolic and oxidative processes and the pathology of spinal degeneration. Full article

The canonical model of vertebrate sex chromosome evolution predicts a one-way trend toward degradation. However, most sex chromosomes in lower vertebrates are homomorphic. Recent progress in studies of sex determination has resulted in the discovery of more than 30 master sex determination (MSD) genes, most of which are from teleost fish. An analysis of MSD gene acquisition, recombination suppression, and sex chromosome-specific sequences revealed correlations in the modes of MSD gene acquisition and the evolution of sex chromosomes. Sex chromosomes remain homomorphic with MSD genes acquired by simple mutations, gene duplications, allelic variations, or neofunctionalization; in contrast, they become heteromorphic with MSD genes acquired by chromosomal inversion, fusion, and fission. There is no recombination suppression with sex chromosomes carrying MSD genes gained through simple mutations. In contrast, there is extensive recombination suppression with sex chromosomes carrying MSD genes gained through chromosome inversion. There is limited recombination suppression with sex chromosomes carrying MSD genes gained through transposition or translocation. We propose a cause–effect model that predicts sex chromosome evolution as a consequence of the acquisition modes of MSD genes, which explains the evolution of sex chromosomes in various vertebrates. A key factor determining the trend of sex chromosome evolution is whether non-homologous regions are created during the acquisition of MSD genes. Chromosome inversion creates inversely homologous but directly non-homologous sequences, which lead to recombination suppression but retain recombination potential. Over time, recurrent recombination in the inverted regions leads to the formation of strata and may cause the degradation of sex chromosomes. Depending on the nature of deletions in the inverted regions, sex chromosomes may evolve with dosage compensation, or the selective retention of haplo-insufficient genes may be used as an alternative strategy. Full article

This study investigates the anatomical adaptations and ecological plasticity of C. ambiguus in extreme environmental conditions by analyzing the structural characteristics of its leaves and annual shoots collected from 12 populations in the arid regions of Mangystau, including Western Karatau, Northern Aktau, and the Tyubkaragan Peninsula. Microscopic and statistical analyses revealed significant variability in key anatomical traits, including epidermal thickness, collenchyma, primary cortex, and vascular bundle area, highlighting the species’ adaptive responses to drought, high solar radiation, and limited water availability. The epidermal thickness ranged from 14.85 µm (Pop_12 Botakan) to 22.51 µm (Pop_6 Samal), demonstrating xeromorphic adaptations for reducing transpiration. At the same time, the vascular bundle area varied from 286.06 × 10⁻³ mm² (Pop_3 Emdikorgan) to 528.51 × 10⁻³ mm² (Samal), indicating differences in water transport efficiency across populations. Despite substantial anatomical variation, the low coefficients of variation (0.31%–6.31%) suggested structural stability, reinforcing C. ambigua’s ability to maintain functional integrity under environmental stress. Canonical Correlation Analysis (CCA) confirmed that environmental factors such as soil type, elevation, and water availability significantly influenced anatomical traits. Floristic analysis revealed distinct patterns of species richness, with the highest diversity recorded in Pop_4 and Pop_7, while Pop_12 and Pop_9 exhibited lower diversity, indicating potential vulnerability. Furthermore, the identified anatomical traits could serve as key markers for selecting drought-resistant genotypes in afforestation and restoration programs. This study also highlighted the need for the long-term monitoring of C. ambigua populations to assess the impact of climate change on structural adaptations. These findings offer a framework for integrating ecological and genetic studies to refine conservation strategies for xerophytic species. Full article

This study investigates the phytochemical composition, anti-inflammatory, antioxidant, and antiproliferative activities of A. absinthium and A. annua flowers and leaf ethanol extracts in acute rat inflammation model. Polyphenolic compounds were analyzed quantitatively (total phenolic (TPC) and total flavonoids (TFCs)) and qualitatively by HPLC-ESI MS analysis. The antioxidant activity was evaluated in vitro (by DPPH, FRAP, H₂O₂, and NO scavenging tests), and in vivo (by total oxidative status (TOS), total antioxidant capacity (TAC), oxidative stress index (OSI), and key oxidative damage markers). Inflammation was evaluated via nuclear factor-kB-p65 (NfkB-p65), and canonical NLRP3 inflammasome activation (with IL-1β, IL-18, caspase-1, and gasdermin D). The antiproliferative activity against human ovarian tumor cells (A2780cis, OVCAR-3, and OAW-42) was evaluated by the MTT assay, focusing on the modulation of multidrug resistance (MDR) pumps and the PARP-1 enzyme. Liver and renal toxicity were tested by measuring transaminases (ALT and AST), creatinine, and urea. The study results indicated that A. absinthium and A. annua flowers and leaf ethanol extracts have rich polyphenol content and moderate in vitro antioxidant activity. Tested extracts display an important antiproliferative activity against the ovarian tumor cell lines A2780cis, OVCAR-3, and OAW-42 based on chemoresistance countering and apoptotic mechanisms. There were differences related to the cell type and plant extract type. The tested plant extracts had significant and dose-dependent in vivo anti-inflammatory and antioxidant activity, with the A. annua flowers extract having the lowest efficiency. The anti-inflammatory and antioxidant activity biomarkers correlated with the extracts’ chemical composition. There was no inflammation-induced hepatotoxicity, but renal dysfunction was associated. Only AANL improved the renal function. These results can be used to design and develop remedies with combined anti-inflammatory, antioxidant, and anti-proliferative activities. Full article

The accurate, robust, and efficient prediction of transition in viscous flows is a significant challenge in computational fluid dynamics. We present a coupled high-fidelity iterative approach that leverages the FUN3D flow solver and the LASTRAC stability code to predict transition in low-disturbance environments, initiated by the linear growth of boundary-layer instability modes. Our method integrates the ability of FUN3D to compute mixed laminar–transitional–turbulent mean flows via transition-sensitized Reynolds-Averaged Navier–Stokes equations with the ability of LASTRAC to perform linear stability analysis, all within an automated framework that requires no intermediate user involvement. Unlike conventional frameworks that rely on classical stability theory or reduced-order metamodels, our approach employs parabolized stability equations to provide more accurate and reliable estimates of disturbance growth for multiple instability mechanisms, including Tollmien–Schlichting, Kelvin–Helmholtz, and crossflow modes. By accounting for the effects of mean-flow nonparallelism as well as the surface curvature, this approach lays the foundation for improved N-factor correlations for transition onset prediction in a broad class of flows. We apply this method to three distinct flow configurations: (1) flow over a zero-pressure-gradient flat plate, (2) the NLF-0416 airfoil with both natural and separation-induced transition, and (3) a 6:1 prolate spheroid, where transition is primarily driven by crossflow instability. For two-dimensional cases, a formulated intermittency distribution is used to model the transition zone between the laminar and fully turbulent flows. The results include comparisons with experimental measurements, similar numerical approaches, and transport-equation-based models, demonstrating good agreement in surface pressure coefficients, transition onset locations, and skin-friction coefficients for all three configurations. In addition to contributing a couple of new insights into boundary-layer transition in these canonical cases, this study presents a powerful tool for transition modeling in both research and design applications in aerodynamics. Full article

Modeling the brain dependence network is central to understanding underlying neural mechanisms such as perception, action, and memory. In this study, we present a broad range of statistical methods for analyzing dependence in a brain network. Leveraging a combination of classical and cutting-edge approaches, we analyze multivariate hippocampal local field potential (LFP) time series data concentrating on the encoding of nonspatial olfactory information in rats. We present the strengths and limitations of each method in capturing neural dynamics and connectivity. Our analysis begins with exploratory techniques, including correlation, partial correlation, spectral matrices, and coherence, to establish foundational connectivity insights. We then investigate advanced methods such as Granger causality (GC), robust canonical coherence analysis, spectral transfer entropy (STE), and wavelet coherence to capture dynamic and nonlinear interactions. Additionally, we investigate the utility of topological data analysis (TDA) to extract multi-scale topological features and explore deep learning-based canonical correlation frameworks for connectivity modeling. This comprehensive approach offers an introduction to the state-of-the-art techniques for the analysis of dependence networks, emphasizing the unique strengths of various methodologies, addressing computational challenges, and paving the way for future research. Full article

Capsicum annuum L. var. glabriusculum is a semi-domesticated species of economic importance; however, its establishment in commercial plantations has been hampered by the low germination and emergence rates of its seeds. The aim of this study was to evaluate the effect of the fruit ripening stage on seed germination and seedling emergence in C. annuum var. glabriusculum. Seeds were extracted from fruits with six different ripening stages. The evaluated traits were the germination and emergence percentages, germination and emergence rates, and 17 physical traits of the seeds. According to the results, seeds extracted from red, orange, and pinto fruits presented better germination and seedling emergence percentages (85, 86, and 82% and 95, 93, and 94%, respectively). A principal component analysis showed that some differences in the physical traits of the seed were associated with the fruit ripening stages and seed development. A canonical discriminant analysis showed a high correlation between the fruit ripening stages and the physical and physiological characteristics of the seed, allowing the formation of four groups. The fruit ripening stages (pinto, orange, and red) influence the germination of the seeds and the emergence of the seedlings of C. annuum L. var. glabriusculum, so obtaining seeds from physiologically ripe fruits allows for obtaining seeds of better quality. Full article

The riverine fish species are highly vulnerable and responsive to large-scale water diversion projects. These adverse impacts are more pronounced in the plateau river ecosystems, which may change the environmental conditions of fish habitats and community structure. We investigated the effects of various environmental factors on fish diversity in seven rivers of the Western Sichuan Plateau, which is the planned area of China’s South-to-North Water Diversion Project. Twenty-two fish species, including eight exotic species, were collected during September 2023 (Autumn) and May 2024 (Spring). The fish communities exhibited no significant difference between seasons but had prominent variations among different rivers. The heterogeneity of fish communities was significantly and positively correlated with the geographical distance between the sampling sites (based on a projected coordinate system). Furthermore, the canonical correspondence analysis (CCA) illustrated that altitude contributed more to the distribution of fish species than other physicochemical factors, such as channel width, conductivity, and water temperature. Rivers at low altitudes are likely to be vulnerable to invasion of exotic fish. Our results demonstrated that the dispersal limitation by geographical distance and altitudinal gradient were the primary regulatory factors on the spatial differentiation of fish communities in the rivers of the study area, which reflected a high dependence of fish species on local habitats. As the water diversion project is implemented, more attention is expected to be paid to protecting fish habitats and regime shifts in fish communities. Additionally, the risk assessment of biological invasion under inter-basin water transfers and human activities should be carried out as soon as possible. Full article

The aim of the research was to comprehensively evaluate the impact resistance of conveyor belts. Initially, variables were identified that describe the input conditions of the experiment (weight of impacting material, impact height, type and strength of conveyor belt) and subsequent dependent variables that describe the result of the experiment (impact force, increase in tension force, relative amount of absorbed energy, degree of damage). For each dependent variable, its dependence on input variables was monitored through multiple regression analysis. In the next step, through canonical correlation analysis, correlations were observed between the created dummy canonical variables that are a linear combination of the original variables. Based on the results, strong relationships between new canonical variables were demonstrated. A test of significance of canonical correlation using Wilk’s lambda showed that the first to third canonical correlations were statistically significant. It turns out that in the first pair of canonical variables, the strength and type of conveyor belt are strongly negatively correlated with the increase in tension force and the relative amount of energy absorbed. In the second pair, the impact height and weight of the landing material are strongly positively correlated with the impact force. Full article

For the evaluation of food efficacy, in vitro experiments and cell and animal models are heavily relied on, with a need for quick and non-invasive monitoring methods. In this study, the fecal odor of aging mice supplemented with goat whey powder was obtained by an E-nose, and the correlation between odor information and the antioxidant indexes, serum antibody, cytokine, and intestinal bacteria were analyzed, aiming to establish a non-invasive method for monitoring and differentiating the effect of goat whey powder. As a result, the fecal odor differed with intervention groups and intervention time, and most of the sensor responses were significantly correlated with weight gain rate, SOD activity, and MDA content. For serum antibodies, cytokines, IL-2, and IL-6 were negatively correlated with the responses of sensor S7. A strong correlation was found between the E-nose sensor responses and the dominant intestinal bacteria. The E-nose could differentiate aging mice of different intervention times and intervention groups with canonical discriminate analysis (CDA). The effective predictive model was built by multiple linear regression (MLR) and a multilayer perceptron neural network (MLP) for SOD, MDA, and weight gain rate, with R² ranging from 0.1571 to 0.6361. These results indicated that E-nose technology could be used in the tracking of goat whey powder intervention in aging mice. Full article

To investigate the effects of biochar on the availability of trace elements (Fe, Mn, Cu, and Zn) in soils with different properties, biochar derived from wheat straw (WSBC) and peanut shells (PSBC) was added to red and yellow-brown soils for pot experiments. The results showed that WSBC and PSBC significantly increased the red and yellow-brown soils’ organic matter (SOM) and available potassium (AK), C, and C/N, especially with WSBC in red soil. The total and available amounts of trace elements in red and yellow-brown soil decreased after biochar was applied, where the effect of WSBC on the available of Fe, Mn, and Zn was greater than that of PSBC and the effect on the available contents of Fe, Mn, and Zn was less than that of PSBC. WSBC and PSBC decreased the contents of Fe, Mn, and Zn in the grains in both soils, while they increased the content of Cu in the grains. According to the results of a canonical correlation analysis, there was a competitive relationship between Mn and Cu in the grains. Fe and Zn in the grains were negatively correlated with AP in red soil and positively correlated with AP in yellow-brown soil. This study evaluates the effect of biochar on soil nutrient cycles, ultimately maximizing the application of biochar in the field of agriculture. Full article

Epipactis helleborine (L.) Crantz is considered a challenging and phenotypically difficult species to identify due to its wide range of morphological variability. This variability is mainly observed in the perianth parts but also extends to the gynostemium structure, which has so far been considered one of the most useful diagnostic characteristics. As a result, a simple graphic illustrating the structural pattern of gynostemium morphology has appeared in 10 different forms in available European taxonomic keys, which significantly complicates the identification of this species. A total of 122 flowers of E. helleborine were collected from four natural populations in the Lower Silesia region (Poland) between 2017 and 2019 and analysed for gynostemium morphological variation. Geometric morphometric analyses, including Procrustes ANOVA, PCA, and CVA, were used to examine gynostemium shape, with statistical tests assessing variation in size and stigma inclination angle among populations, individual plants (ramets), and years of research. Statistical analysis revealed significant positive correlations between gynostemium width and height, with significant variation in size and angle of stigma inclination, primarily driven by population, while ramet and year of research had a lesser impact. Geometric morphometric analyses indicated significant population-level variation in gynostemium shape, with principal component analysis identifying the ventral view as the most informative for discriminating these differences. The first two principal components explained the major shape variation, and canonical variate analysis confirmed that this view is most important for species identification. Full article