Search Results (53,338)

In this paper, we study the detour eccentric sum index (DESI) to obtain the Quantitative Structure–Property Relationship (QSPR) for different molecular structures. We establish theoretical bounds for this index and compute its values across fundamental graph families. Through correlation analyses between the physicochemical properties of molecular structures representing anti-malarial and breast cancer drugs, we show the high predictive value of two topological parameters, detour diameter (DD) and detour radius (DR). Specifically, DR shows strong positive correlations with boiling point, enthalpy, and flash point (up to 0.94), while DD is highly correlated with properties such as molar volume, molar refraction, and polarizability (up to 0.97). The DESI was then selected for detailed curvilinear regression modeling and comparison against the established eccentric distance sum index. For anti-malarial drugs, the second-order model yields the best fit. The DESI provides optimal prediction for boiling point, enthalpy, and flash point. In breast cancer drugs, the second-order model is again favored for properties except for melting point, best described by a third-order model. The results highlight how well the index captures subtle structural characteristics. Full article

Cereal-based complementary foods are widely consumed by children, yet limited data exist on their elemental composition and potential health risks. This study quantified As, Cd, Co, Cr, Cu, Fe, K, Mn, Mg, Mo, Ni, P, Pb, Se, Si, V, and Zn in eight commercial cereal-based products collected in Campo Grande, Brazil, using inductively coupled plasma optical emission spectrometry (ICP OES). Arsenic, cadmium, cobalt, and chromium were consistently below the detection limit. Phosphorus and potassium were the predominant elements across brands, followed by Fe, Mg, and Zn, with significant inter-brand variability (Kruskal–Wallis, p < 0.05). Lead was detected in Brands 1–5 (0.11–0.41 mg/kg), but it was below the limit of detection (LOD = 0.003 mg/L) in the other samples. Estimated daily intake (ID) values at 30 g/day and 90 g/day showed that Fe, Zn, Mn, and Se frequently met or exceeded dietary reference intakes for children aged 1–3 years, while Cu, Ni, and P remained below tolerable levels. Comparison with tolerable upper intake levels and ATSDR minimal risk levels indicated that higher consumption (90 g/day) could result in excess intake of Mn, Zn, and Se, with Pb contributing to cumulative hazard indices above the safety threshold (HI > 1). These findings emphasize the dual role of cereal-based foods as important nutrient sources and potential contributors to excessive trace element exposure in young children. Full article

The overturning resistance of trees under lateral loads depends on the interaction between their root system and the surrounding soil, with leeward lateral roots being particularly important. This study presents a parametric investigation into the behaviour of leeward lateral roots during tree overturning using the finite element method (FEM) based on a beam-on-nonlinear-Winkler-foundation (BNWF) approach. The model efficiently simulates large root–soil deformations using non-linear p-y connectors, the properties of which were calibrated against 2D plane-strain continuum FEM simulations and validated against analytical solutions for pipeline bearing capacity (an analogous problem). Simulations varied in root diameter, length, and material properties. A critical root length was identified, beyond which further increases in length do not enhance the root’s contribution to tree moment capacity, defining an optimal root length for peak resistance. The study further demonstrates that moment capacity is profoundly more sensitive to root diameter than to length. Initial rotational stiffness, which is highly relevant to non-destructive field-based winching tests, was also found to be primarily controlled by diameter and independent of length for most practical cases. A direct comparison between leeward and windward roots under specified rotation conditions confirmed the greater mechanical contribution of leeward roots to anchorage, which is consistent with field observations. Full article

Localized ¹H magnetic resonance spectroscopy (MRS) is a powerful tool in pre-clinical and clinical neurological research, offering non-invasive insight into neurochemical composition in localized brain regions. Zebrafish (Danio rerio) are increasingly being utilized as models in neurological disorder research, providing valuable insights into disease mechanisms. However, the small size of the zebrafish brain and limited MRS sensitivity at low magnetic fields hinder comprehensive neurochemical analysis of localized brain regions. Here, we investigate the potential of ultra-high-field (UHF) MR systems, particularly 28.2 T, for this purpose. This present study pioneers the application of localized ¹H spectroscopy in zebrafish brain at 28.2 T. Point resolved spectroscopy (PRESS) sequence parameters were optimized to reduce the impact of chemical shift displacement error and to enable molecular level information from distinct brain regions. Optimized parameters included gradient strength, excitation frequency, echo time, and voxel volume specifically targeting the 0–4.5 ppm chemical shift regions. Exceptionally high-resolution cerebral metabolite spectra were successfully acquired from localized regions of the zebrafish brain in voxels as small as 125 nL, allowing for the identification and quantification of major brain metabolites with remarkable spectral clarity, including lactate, myo-inositol, creatine, alanine, glutamate, glutamine, choline (phosphocholine + glycerol-phospho-choline), taurine, aspartate, N-acetylaspartyl-glutamate (NAAG), N-acetylaspartate (NAA), and γ-aminobutyric acid (GABA). The unprecedented spatial resolution achieved in a small model organism enabled detailed comparisons of the neurochemical composition across distinct zebrafish brain regions, including the forebrain, midbrain, and hindbrain. This level of precision opens exciting new opportunities to investigate how specific diseases in zebrafish models influence the neurochemical composition of specific brain areas. Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

In this study, we analyze a multi-species mutualistic Lotka–Volterra model with Lévy jumps and regime-switching. A defining feature of the work lies in modeling the random environment through state-dependent switching in an infinite countable state space. Our main objective is to establish the sufficient conditions of the extinction and stochastic permanence of the model. First, we analyze the existence and uniqueness of the model’s solution, followed by an examination of the solution’s stochastic ultimate boundedness. Moreover, the challenges arising from state-dependent switching are addressed using the stochastic comparison method. Due to the presence of the jump component, more complex conditions are required to achieve a finite partition of the countably infinite space. Furthermore, the M-matrix theory is also used to obtain the stochastic permanence property. Finally, two specific examples are provided to illustrate the conclusions in this paper. Full article

Bovine respiratory disease is a significant health concern in dairy calves, impacting short-term growth and potentially long-term productivity. While previous studies have linked early-life lung consolidation, often subclinical and diagnosed by lung ultrasonography, to reduced preweaning average daily gain, its extended effects on production and reproductive parameters remain less studied, particularly in specific geographical contexts. This study presents a follow-up analysis on a cohort of dairy calves originally monitored weekly from birth to weaning for lung consolidation via TUS in an Iranian dairy herd. Two years post-weaning, comprehensive production and reproduction data were collected and analyzed for these same animals. Our objective was to investigate the long-term associations between early-life lung consolidation and subsequent outcomes, including mature equivalent milk yield, corrected milk yield, culling risk before and during lactation, age at first breeding, age at first calving, and reproduction parameters such as services per conception and conception rate (the percentage of cows or heifers that become pregnant after a single insemination or breeding attempt). Data analysis, employing descriptive statistics, survival analysis, and non-parametric tests, revealed that while early-life lung consolidation (defined by ≥1 cm or ≥3 cm depth, and number of episodes) did not show significant associations with culling probability or mature equivalent milk yield, interesting numerical trends were observed. The study highlights the complexities of establishing clear long-term links, suggesting that while subclinical BRD can have immediate growth impacts, its chronic effects on later production and reproduction may be subtle and require larger cohorts or more targeted analysis to achieve statistical significance. The findings reinforce the need for cautious interpretation of p-values in the context of multiple comparisons and underscore the challenges in quantifying long-term economic consequences of early-life respiratory health. Full article

In this work, a time–frequency analysis of two railway bridges included in the InBridge4EU project database is presented. The study focuses on the identification of modal parameters from free responses after train passages and their comparison with estimations obtained from ambient vibration data. The wavelet transform is introduced as a valuable tool for detecting both free and forced bridge responses due to different train passages, as well as for conducting time–frequency analysis. This approach is particularly relevant for the identification of structural damping, given its dependence on vibration amplitude, as it enables the estimation of realistic values representative of bridge behavior under operational conditions. Additionally, the paper examines the complementary use of free vibrations for identifying natural frequencies and comparing them with results from ambient vibration tests. Wavelet analysis further reveals the predominant frequencies in the structural response before, during, and after train crossings, thereby capturing the influence of the moving vehicle on bridge dynamics. Full article

Pacific pygmy octopus Paroctopus digueti is a promising model for cephalopod research and aquaculture; its feeding and nutritional biology remain poorly understood. The anterior salivary glands (ASG), posterior salivary glands (PSG), and digestive gland (DG) are central to these processes, but molecular comparisons are lacking. To address this gap, we performed a transcriptomic study to explore the enzymatic repertoire and functional specialization of these tissues. Total RNA was extracted from ASG, PSG, and DG of three pre-adult individuals collected in La Paz Bay, Mexico. RNA-Seq libraries were sequenced, and a non-redundant multi-tissue transcriptome was assembled. The ASG displayed high expression of neuropeptides, playing a role in neuroendocrine regulation. The PSG showed elevated protease expression, supporting its function in extracellular digestion, alongside toxins that reinforce its role as a venom gland. The DG was enriched in proteins linked to biomolecule catabolism and antimicrobial peptides, alluding to metabolic specialization and immune defense. These results were validated by qPCR, and target genes were also amplified in Octopus maya and O. hubbsorum, showing some similarities in expression patterns. Overall, our findings suggest strong glandular specialization in P. digueti, providing insights into cephalopod digestive physiology and supporting its value as a model species. Full article

Urban air pollution in Kazakhstan poses persistent risks; this study synthesizes measured concentrations, source evidence, and policy responses to inform mitigation in cold, inversion-prone cities. We compile national monitoring (Kazhydromet), community PM_2.5 sensors, emissions inventories and recent CEMS provisions, and appraise modeling approaches (Gaussian screening, Eulerian CTMs, and data-driven forecasting). Seasonal descriptive comparisons are performed for Astana using 56,944 observations (2023–2024), partitioned into heating and non-heating periods, and published receptor apportionment is integrated. Across major cities, annual PM_2.5 generally exceeds WHO guidelines and winter stagnation drives episodes. In Astana, the heating season means rose relative to non-heating equivalents—PM_2.5 12.3 vs. 10.6 μg m⁻³ (+16%) and SO₂ 21.9 vs. 14.8 μg m⁻³ (+23%)—while NO was unchanged; higher means but lower medians indicate episodic winter peaks. Receptor analyses attribute large shares of PM_2.5 to traffic (spark-ignition engines 30% and diesel 7%) and coal-related contributions including secondary nitrate (15%), consistent with power/heat and vehicle dominance. Evidence supports prioritizing clean heating (coal-to-gas and efficiency), transport emission controls, and dense monitoring to enable accountability within Kazakhstan’s Environmental Code and decarbonization strategy. A tiered modeling workflow can quantify intervention impacts and deweather trends; the near-term focus should be on reducing winter exposures. Full article

The increasing demand for data utilization has renewed attention to the trade-off between privacy protection and data utility, particularly concerning pseudonymized datasets. Traditional methods for evaluating re-identification risk and utility often rely on fragmented and incompatible metrics, complicating the assessment of the overall effectiveness of pseudonymization strategies. This study proposes a novel quantitative metric—Relative Utility–Threat (RUT)—which enables the integrated evaluation of safety (privacy) and utility in pseudonymized data. Our method transforms various risk and utility metrics into a unified probabilistic scale (0–1), facilitating standardized and interpretable comparisons. Through scenario-based analyses using synthetic datasets that reflect different data distributions (balanced, skewed, and sparse), we demonstrate how variations in pseudonymization intensity influence both privacy and utility. The results indicate that certain data characteristics significantly affect the balance between protection and usability. This approach relies on simple, lightweight computations—scanning the data once, grouping similar records, and comparing their distributions. Because these operations naturally parallelize in distributed environments such as Spark, the proposed framework can efficiently scale to large pseudonymized datasets. Full article

Background: Medial-compartment knee osteoarthritis with varus alignment is treated surgically by realignment (high tibial osteotomy, HTO), resurfacing (unicompartmental knee arthroplasty, UKA), or proximal fibular osteotomy (PFO), which aims to indirectly unload the medial tibial plateau. Methods: We conducted a structured narrative review (PubMed/MEDLINE, Google Scholar; 2000–2025; last search 30 August 2025) of comparative clinical, biomechanical and safety data for HTO, UKA and PFO, including prior meta-analyses and mechanistic reports. One hundred fourteen studies met prespecified criteria. Results: HTO reliably corrects coronal alignment and unloads the medial compartment; long-term survivorship varies by selection and technique, and complications include hinge fracture, delayed/nonunion and hardware problems. UKA typically yields faster early pain relief and recovery in pooled analyses, with implant-specific failure risks and mid-term revision dependent on design and surgical experience. PFO cohorts consistently report early pain and function gains with plausible biomechanical rationale, but evidence is dominated by small, heterogeneous series with short follow-up and limited comparative data. Adjusted head-to-head comparisons generally favor UKA for early pain yet show HTO and UKA can achieve similar patient-reported improvements in selected younger cohorts; robust comparative trials including PFO are lacking. Conclusions: HTO and UKA are established, mechanistically distinct options best matched to patient age, alignment, activity goals, and comorbidity. PFO is a low-burden, promising alternative with uncertain durability; longer-term, controlled evaluation and registry surveillance are required before broad adoption. Findings should inform shared decision-making while acknowledging differences in evidence maturity. Full article

Background/Objectives: Sleep disturbances are increasingly recognized as dynamic biomarkers of cognitive decline; however, longitudinal and multimodal studies directly comparing amnestic (aMCI) and non-amnestic mild cognitive impairment (naMCI) remain limited. Methods: In a three-wave longitudinal design (~24 months), 179 older adults (46 healthy controls [HCs], 75 aMCI, 58 naMCI; mean age = 70.2 years, education = 12.3 years) were assessed with actigraphy and validated questionnaires (Athens Insomnia Scale, Pittsburgh Sleep Quality Index, STOP-BANG). Mixed ANOVAs and structural equation modeling tested group, time and mediation effects. Results: Subjective measures revealed a progressive worsening of insomnia and sleep quality in MCI, with naMCI exhibiting the steepest decline, while HCs remained largely stable. STOP-BANG trajectories indicated increasing sleep-disordered breathing risk across groups, most pronounced in naMCI. Objective indices corroborated these findings: total sleep time (TST) and sleep efficiency (SE) declined significantly in MCI, especially naMCI, while wake after sleep onset (WASO) increased longitudinally. By the third assessment, naMCI consistently showed the shortest TST and lowest SE. Mediation analyses identified SE as a central predictor of future subjective complaints, with indirect contributions from WASO and PSQI. Conclusions: Longitudinal trajectories, rather than cross-sectional comparisons, best differentiated MCI subtypes. NaMCI demonstrated the most aggressive deterioration in both objective and subjective sleep measures, highlighting its heightened vulnerability to sleep dysregulation and potential relevance for neurodegenerative progression. Clinically, sustained monitoring of SE, TST, and sleep-disordered breathing risk may provide prognostic value and inform early, targeted interventions in at-risk populations. Full article

Background/Objectives: Salivary volatile organic compounds (VOCs) are promising noninvasive biomarkers for a wide range of diseases. While glandular saliva, secreted by salivary glands, is a relatively pure biofluid, whole saliva is a complex mixture containing oral microbiota, food debris, and desquamated epithelial cells. Therefore, a comprehensive comparison of the VOC profiles of these two types of saliva is essential to identify biologically relevant compounds. In this study, we aimed to establish a reliable method for VOC profiling from small saliva volumes and identify VOCs that reflect the biological differences between glandular and whole saliva. Methods: We developed a protocol combining MonoTrap extraction with dichloromethane, allowing the analysis of VOCs from just 100 µL of saliva. To address the issue of sampling-derived artifacts, we implemented a two-step blank analysis to systematically exclude compounds originating from the collection device. Results: Our analysis successfully identified a total of 72 VOCs. Following blank analysis, we systematically excluded 15 artifacts originating from the sampling device. Subsequent orthogonal partial least squares discriminant analysis (OPLS-DA) and Wilcoxon signed-rank test (using variable importance for prediction (VIP) > 1.0 and q < 0.05) identified 10 key VOCs that were significantly higher in whole saliva than in glandular saliva. These compounds included isobutyric acid, isovaleric acid, 4-methylvaleric acid, 3-phenylpropionic acid, indole, skatole, methyl mercaptan, 1-propanol, δ-valerolactam, and acetaldehyde. Most of these compounds originate from the metabolic activities of the oral microbiome, suggesting that the distinct VOC profile of whole saliva is predominantly influenced by microbial activity. Conclusions: Our findings demonstrated the effectiveness of this method for identifying biologically relevant VOCs from relatively small sample volumes. The identified VOC profiles highlight the contribution to the discovery of non-invasive biomarkers for oral health and serve as a solid foundation for future research into clinical applications. Full article

This research represents the first in the Republic of Kazakhstan focusing on Afghan war veterans identified as being at risk for Post-Traumatic Stress Disorder (PTSD). A comprehensive investigation was undertaken regarding the comorbidity of PTSD symptoms, somatic diseases (cardiovascular and others), and mental disorders. The study’s sample comprised 293 Afghan war veterans, while the control group consisted of 149 males without combat experience. The research methodology incorporated data extracted from medical records, a demographic questionnaire, the Mississippi PTSD Scale (military and civilian versions), the Spielberger–Khanin Anxiety Inventory, the Beck Depression Inventory (BDI), and the SCL-90-R questionnaire. Subclinical symptoms indicative of chronic PTSD were identified in 25.2% of veterans, as assessed by the Mississippi PTSD Scale. According to the Spielberger–Khanin questionnaire, veterans with subclinical PTSD exhibited a moderate level of situational (44.96 ± 6.39 points) and personal anxiety (44.14 ± 5.49 points), which were significantly higher than those observed in the control group (29.87 ± 6.50, p₂ < 0.001, 36.13 ± 6.82, p₂ = 0.003). Furthermore, based on the BDI, these veterans demonstrated an elevated level of depression (13.32 ± 1.36) in comparison to veterans without PTSD (8.61 ± 0.65, p < 0.001) and the control group (4.06 ± 0.75, p < 0.0001). The findings indicate that veterans exhibiting a high level of PTSD are characterized by a more pronounced manifestation of psychopathological indicators, as assessed by the SCL-90-R questionnaire. Compared to the control group, veterans exhibiting PTSD symptoms were found to have a comorbidity with somatic diseases: stage 3 hypertension in 45.2% vs. 21.3% (p₂ < 0.001), coronary heart disease in 45.5% vs. 13.4% (p₂ < 0.001), and type 2 diabetes mellitus in 20.3% vs. 6.1% (p₂ = 0.001). Consequently, Afghan war veterans should be regarded as a high-risk group for cardiovascular diseases (including hypertension and coronary heart disease), cerebrovascular diseases, and mental health disorders. Accordingly, a comprehensive approach is necessary to identify PTSD symptoms alongside concomitant anxiety–depressive disorders and somatic diseases in individuals who have experienced traumatic events. Full article