Search Results (467)

Light influence on shoot regeneration in Prunus salicina is a complex interaction that has been studied for the first time. Japanese plum plants were regenerated from calli and seeds of the scion cultivar ‘Victoria’. The effect of four different light spectra (white, blue, red, and mixed), along with three 6-benzyladenine (BA) concentrations (1, 1.5, and 2 mg L⁻¹), was studied in these two sources of explants. Organogenic calli were derived from the base of stem explants of the scion cultivar ‘Victoria’, whereas cotyledons and embryogenic axis slices were used as seed explants. Calli cultured with 2 mg L⁻¹ of BA and mixed light or 2.5 mg L⁻¹ of BA and control light showed the highest regeneration rates, with no significant differences compared to other treatments. Seed explants exposed to 2.5 mg L⁻¹ of BA and red light exhibited significantly higher organogenesis. In comparison, those in 1.5 mg L⁻¹ of BA with blue light or 2.5 mg L⁻¹ of BA with mixed/control light showed no regeneration. BA concentration did not have a significant effect in the induction of somatic shoots from any explant source. In contrast, a strong interaction between light and BA was noticed. This work presents a protocol that can be applied in transformation and editing research as light spectrum studies continue to advance. Full article

Objectives: Shoulder injury prevalence appears to be the highest among all injuries in CrossFit (CF) athletes. Nevertheless, there is no evidence deriving from prospective studies to explain this phenomenon. The purpose of this study was to document shoulder injury incidence in CF participants over a 12-month period and prospectively investigate the risk factors associated with their demographic, epidemiological, and functional characteristics. Methods: The sample comprised 109 CF athletes in various levels. Participants’ data were collected during the baseline assessment, using a specially designed questionnaire, as well as active range of motion, muscle strength, muscle endurance, and sport-specific tests. Non-parametric statistical tests and inferential statistics were employed, and in addition, linear and regression models were created. Logistic regression models incorporating the study’s continuous predictors to classify injury occurrence in CF athletes were developed and evaluated using the Area Under the ROC Curve (AUC) as the performance metric. Results: A shoulder injury incidence rate of 0.79 per 1000 training hours was recorded. Olympic weightlifting (45%) and gymnastics (35%) exercises were associated with shoulder injury occurrence. The most frequent injury concerned rotator cuff tendons (45%), including lesions and tendinopathies, exhibiting various severity levels. None of the examined variables individually showed a statistically significant correlation with shoulder injuries. Conclusions: This is the first study that has investigated prospectively shoulder injuries in CrossFit, creating a realistic profile of these athletes. Despite the broad spectrum of collected data, the traditional statistical approach failed to identify shoulder injury predictors. This indicates the necessity to explore this topic using more sophisticated techniques, such as advanced machine learning approaches. Full article

Automatic modulation classification (AMC) is a fundamental technique in wireless communication systems, as it enables the identification of modulation schemes at the receiver without prior knowledge, thereby promoting efficient spectrum utilization. Recent advancements in deep learning (DL) have significantly enhanced classification performance by enabling neural networks (NNs) to learn complex decision boundaries directly from raw signal data. However, many existing NN-based AMC methods employ deep or specialized network architectures, which, while effective, tend to involve substantial structural complexity. To address this issue, we present a simple NN architecture that utilizes features derived from Hankelized matrices to extract informative signal representations. In the proposed approach, received signals are first transformed into Hankelized matrices, from which informative features are extracted using singular value decomposition (SVD). These features are then fed into a compact, fully connected (FC) NN for modulation classification across a wide range of signal-to-noise ratio (SNR) levels. Despite its architectural simplicity, the proposed method achieves competitive performance, offering a practical and scalable solution for AMC tasks at the receiver in diverse wireless environments. Full article

In order to investigate bioactive natural products derived from the endophytic fungus Penicillium ochrochloron SWUKD4.1850, a comprehensive study focusing on secondary metabolites was conducted. This research led to the isolation of twenty distinct compounds, including a novel nortriterpenoid (compound 20), alongside nineteen compounds that had been previously characterized (compounds 1–19). The chemical structures of these compounds were elucidated using spectroscopic techniques and nuclear magnetic resonance (NMR) analyses. Compounds 1–17 were isolated for the first time as metabolites of P. ochrochloron. Except for compounds 1–14, significant structural similarity was discerned between the metabolites of the endophytic fungus and those of the host plant. Compound 20 is noted as the inaugural instance of a naturally occurring 27-nor-3,4-secocycloartane schinortriterpenoid, while compound 17 was identified in fungi for the first time. An antifungal assay showed that compound 10 displayed a broader antifungal spectrum and a stronger inhibitory effect towards four important plant pathogens, at inhibitory rates of 74.9 to 85.3%. The in vitro radical scavenging activities of compounds 1, 3, 8, 15, and 16 showed higher antioxidant activity than vitamin C. Moreover, a cytotoxic assay revealed that compound 20 had moderate cytotoxicity against the HL-60, SMMC-7721, and MCF-7 cell lines (IC₅₀ 6.5–17.8 μM). Collectively, these findings indicate that P. ochrochloron has abundant secondary metabolite synthesis ability in microbial metabolism and that these metabolites have good biological activity and have the potential to enhance plant disease resistance. Full article

Background: The benzamide MMV030666 from MMV’s Malaria Box Project, the starting point of herein presented study, was initially tested against various Plasmodium falciparum strains as well as Gram-positive and Gram-negative bacteria. It exhibits multi-stage antiplasmodial potencies and lacks resistance development. Methods: The favorable structural features from previous series were kept while the influence of the N-Boc-piperazinyl substituent per se, as well as its ring position and its replacement by various heteroaromatic rings, was evaluated. Thus, this paper describes the preparation of the MMV030666-derived 4′-(piperazin-1-yl)benzanilides for the first time, exhibiting broad-spectrum activity not only against plasmodia but also various bacterial strains. Results: A series of insightful structure–activity relationships were determined. Furthermore, pharmacokinetic and physicochemical parameters of the new compounds were determined experimentally or in silico. Drug-likeliness according to Lipinski’s rules was calculated as well. Conclusions: A diarylthioether derivative of the lead compound was promisingly active against P. falciparum and exhibited broad-spectrum antibacterial activity against Gram-positive as well as Gram-negative bacteria. It is considered for testing against multi-resistant bacterial strains and in vivo studies. Full article

Soil heavy metal pollution threatens agricultural safety and human health, with Cd exceeding standards being the most common problem in contaminated farmland. The development of hyperspectral remote sensing technology has provided a novel methodology of quickly and non-destructively monitoring heavy metal contamination in soil. This study aims to explore the potential of an interpretable Stacking ensemble learning model for the estimation of soil Cd contamination in farmland hyperspectral data. We assume that this method can improve the modeling accuracy. We chose Zhangjiagang City, Jiangsu Province, China, as the study area. We gathered soil samples from wheat fields and analyzed soil spectral data and Cd level in the lab. First, we pre-processed the spectra utilizing fractional-order derivative (FOD) and standard normal variate (SNV) transforms to highlight the spectral features. Second, we applied the competitive adaptive reweighted sampling (CARS) feature selection algorithm to identify the significant wavelengths correlated with soil Cd content. Then, we constructed and compared the estimation accuracy of multiple machine learning models and a Stacking ensemble learning method and utilized the Optuna method for hyperparameter optimization. Ultimately, the SHAP method was used to shed light on the model’s decision-making process. The results show that (1) FOD can further highlight the spectral features, thereby strengthening the correlation between soil Cd content and wavelength; (2) the CARS algorithm extracted 3.4–6.8% of the feature wavelengths from the full spectrum, and most of them were the wavelengths with high correlation with soil Cd; (3) the optimal estimation precision was achieved using the FOD1.5-SNV spectral pre-processing combined with the Stacking model (R² = 0.77, RMSE = 0.05 mg/kg, RPD = 2.07), and the model effectively quantitatively estimated soil Cd contamination; and (4) SHAP further revealed the contribution of each base model and characteristic wavelengths in the Stacking modeling process. This research confirms the advantages of the interpretable Stacking model in hyperspectral estimation of Cd contamination in farmland wheat soil. Furthermore, it offers a foundational reference for the future implementation of quantitative and non-destructive regional monitoring of heavy metal contamination in farmland soil. Full article

Background/Objectives: The increasing prevalence of urinary tract infections (UTIs) caused by extended-spectrum β-lactamase (ESBL)-producing organisms poses a significant clinical challenge worldwide due to limited oral treatment options. Tebipenem (TBPM), an oral carbapenem antibiotic, is currently approved only for pediatric use in Japan, with no adult indication established. International studies have shown promising results for ESBL-producing infections, but optimal dosing regimens for Japanese adults with varying renal function have not been established. This study aimed to determine the optimal TBPM dosing regimens for ESBL-producing Enterobacterales UTIs in Japanese patients stratified by renal function, providing evidence for potential adult approval applications in Japan. Methods: Monte Carlo simulations (MCSs) were performed using pharmacokinetic parameters derived from clinical trials in Japanese subjects. Various dosing regimens were evaluated across different creatinine clearance (CCR) ranges and minimum inhibitory concentrations (MICs). The pharmacokinetic/pharmacodynamic target was set at fAUC_0–24/MIC·1/tau ≥ 34.58, with a ≥90% probability of target attainment (PTA) considered optimal. Results: For patients with severe renal impairment (CCR < 30 mL/min), 150 mg q12 h achieved a >90% PTA against ESBL-producing organisms with an MIC of 0.03 mg/L. For moderate-to-severe renal impairment (30 ≤ CCR < 50 mL/min) and moderate renal impairment (50 ≤ CCR < 80 mL/min), 300 mg q8 h maintained a >90% PTA. For normal renal function (CCR ≥ 80 mL/min), 600 mg q8 h was required to achieve the target PTA. Conclusions: This first Japanese PK/PD analysis of TBPM in ESBL-producing UTIs provides evidence-based dosing recommendations across various renal function levels. TBPM, with appropriate renal-adjusted dosing, may offer an effective oral treatment option for patients who have traditionally required hospitalization for parenteral therapy. Full article

This study presents the MSPAT (Multispectral Soil Plant Analysis Tool), a device designed for assessing leaf nitrogen concentrations in maize crops under field conditions. The MSPAT includes the AS7265x sensor, which has 18 bands and covers the spectrum from 410 to 940 nm. This device was designed to be portable, using the ESP32 microcontroller and incorporating such functionalities as data storage on a MicroSD card, communication with a smartphone via Wi-Fi, and geolocation of acquired data. The MSPAT was evaluated in an experiment conducted at the Federal University of Ceará (UFC), where maize was subjected to different doses of nitrogen fertiliser (0, 60, 90, 120, 150, and 180 kg·ha⁻¹ N). Spectral readings were taken at three phenological stages (V5, V10, and R2) using the MSPAT, an SPAD-502 chlorophyll meter, and a FieldSpec PRO FR3 spectroradiometer. After the optical measurements were taken, the nitrogen concentrations in the leaves were determined in a laboratory by using the Kjeldahl method. The data analysis included the calculation of normalised ratio indices (NRIs) using linear regression and the application of multivariate statistical methods (PLSR and PCR) for predicting leaf nitrogen concentrations (LNCs). The best performance for the MSPAT index (NRI) was obtained using the 900 nm and the 560 nm bands (R² = 0.64) at stage V10. In the validation analysis, the MSPAT presented an R² of 0.79, showing performance superior to that of SPAD-502, which achieved an R² of 0.70. This confirms the greater potential of the MSPAT compared to commercial equipment and makes it possible to obtain results similar to those obtained using the reference spectroradiometer. The PLSR model with data from the FieldSpec 3 provided important validation metrics when using reflectance data with first-derivative transformation (R² = 0.88, RMSE = 1.94 and MAE = 1.28). When using the MSPAT, PLSR (R² = 0.75, RMSE = 2.77 and MAE = 2.26) exhibited values of metrics similar to those for PCR (R² = 0.75, RMSE = 2.78 and MAE = 2.26). This study validates the use of MSPAT as an effective tool for monitoring the nutritional status of maize to optimize the use of nitrogen fertilisers. Full article

The search for probiotics to help limit antibiotic resistance is a major scientific challenge. The exploration of Lactobacillus postbiotics represents a promising approach to prevent pathogen invasion. With this aim, Limosilactobacillus fermentum Lf53, with a broad-spectrum of antagonistic activity, was characterized as a candidate probiotic strain with promising transit tolerance and broad spectrum of activity. A study on growth and postbiotic production in modified MRS broth with different carbohydrates and its vegan variant was carried out. This study presents a comprehensive approach to characterizing the anti-virulence properties of postbiotics derived from Lf53. The promising antibacterial, antibiofilm, and anti-quorum sensing activities of the cell-free supernatants (CFS) were assessed as part of the probiotic’s barrier mechanisms. Biofilm inhibition of P. aeruginosa revealed remarkable suppressive effects exerted by the three tested postbiotics, two of which (nCFS and aCFS) exhibited over 50% inhibition and more than 60% for lysates. The postbiotics’ influence on the production of violacein and pyocyanin pigments of Chromobacterium violaceum and Pseudomonas aeruginosa, which are markers for quorum sensing, highlighted their potential in regulating pathogenic mechanisms. The Lf53 lysates showed the most significant inhibition of violacein production across multiple assays, showing 29.8% reduction. Regarding pyocyanin suppression, the postbiotics also demonstrated strong activity. These are the first reported data on complex postbiotics (metabiotics and parabiotics) demonstrating their potential as anti-virulence agents to help combat pathogens associated with antibiotic-resistant infections. Full article

This study highlights the potential of Fourier-transform near-infrared (FT-NIR) spectroscopy for the on-site, nondestructive detection of spoilage caused by bacterial action in raw salmon (Salmo salar) fillets. A stepwise multiple linear regression model with first-derivative spectrum transformation was combined with the standard normal variate and detrend preprocessing techniques. The model achieved correlation values of 0.97 in both the calibration and validation sample sets, with root mean square error values of 0.18 and 0.20 log CFU/mL, respectively. These accurate results reveal the precision of FT-NIR spectroscopy for assessing the spoilage caused by bacteria. The most informative wavelengths (885.27 nm, 1026.27 nm, 1039.93 nm, 1068.38 nm, 1257.55 nm, 1267.75 nm, and 1453.49 nm) related to the total bacterial count’s identification were obtained. The innovative, cost-effective, and feasible approach outlined in this article is a promising methodology for enhancing the safety and quality standards of various fishery products. Full article

To enhance the signature of an extended target in a SAR image, a robust waveform design method is presented for spectrally dense environments. First, the problem is formulated by maximizing the worst-case signal-to-clutter ratio (SCR) over the uncertainty set of statistics for both target and background scattering characteristics, subject to energy, similarity, and spectrum constraints. Second, the closed-form solutions for the uncertain statistics are derived. The problem of maximizing worst-case SCR is boiled down to a nonconvex fractional quadratically constrained quadratic problem (QCQP). Resorting to the Dinkelbach’s algorithm and Lagrange duality, the QCQP is split into a series of solvable semidefinite programming problems. A convergence analysis is conducted, where a sufficient condition for global convergence is derived. Finally, numerical examples are presented to demonstrate the performance of the proposed scheme. Full article

Nowadays, antimicrobial resistance (AMR) is a growing global health threat, with carbapenem-resistant Enterobacteriaceae (CRE) posing particular concern due to limited treatment options. In fact, CRE have been classified as a critical priority by the World Health Organization (WHO). Carbapenem resistance results from complex mechanisms, often combining the production of hydrolytic enzymes such as β-lactamases with reduced membrane permeability and efflux system induction. The Ambler classification is an effective tool for differentiating the characteristics of serine-β-lactamases (SβLs) and metallo-β-lactamases (MβLs), including ESβLs (different from carbapenemases), KPC, NDM, VIM, IMP, AmpC (different from carbapenemases), and OXA-48. Recently approved inhibitor drugs, such as diazabicyclooctanones and boronic acid derivatives, only partially address this problem, not least because of their ineffectiveness against MβLs. However, compared with taniborbactam, xeruborbactam is the first bicyclic boronate in clinical development with a pan-β-lactamase inhibition spectrum, including the IMP subfamily. Recent studies explore strategies such as chemical optimization of β-lactamase inhibitor scaffolds, novel β-lactam/β-lactamase inhibitor combinations, and siderophore–antibiotic conjugates to enhance bacterial uptake. A deeper understanding of the mechanistic properties of the active sites enables rational drug design principles to be established for inhibitors targeting both SβLs and MβLs. This review aims to provide a comprehensive overview of current therapeutic strategies and future perspectives for the development of carbapenemase inhibitor drug candidates. Full article

Double-column self-centering segmentally assembled bridges (SC-SABs) present greater design complexity compared to single-column systems, primarily due to vertical stiffness discontinuities at segmental spandrel abutments, which critically affect the refinement of their seismic design methods. To address these challenges, this study conducts a systematic investigation into the mechanical behavior and seismic performance of double-column SC-SAB. First, leveraging fundamental mechanical principles and stress-strain relationships, the coupling mechanism between the two columns is analytically established. An analytical expression for the elastic stiffness of a double-column SC-SAB, when simplified to an equivalent single-column system, is derived. This establishes the equivalent stiffness conditions for reducing a double-column system to a single-column model, and the overall equivalent stiffness of the double-column system is formulated. To validate the theoretical framework, a finite element model of the double-column SC-SAB is developed using OpenSees (1.0.0.1 version). An equivalent single-column model is constructed based on the derived stiffness equivalence conditions. By comparing the peak displacement and bearing capacity between the double-column and equivalent single-column models, the accuracy and feasibility of the simplification approach are confirmed. The numerical results further validate the derived overall equivalent stiffness, providing a robust theoretical foundation for simplified engineering applications. Additionally, pushover analysis and hysteretic response analysis are performed to systematically evaluate the influence of key design parameters on the seismic performance of double-column SC-SAB. The results demonstrate that the prestressed twin-column system exhibits excellent self-centering capability, effectively controlling residual displacements, aligning with seismic resilience goals. This research advances the seismic design methodology for SC-SAB by resolving critical challenges in stiffness equivalence and joint behavior quantification. The findings of this study can be utilized to derive equivalent damping ratios and equivalent periods. Based on the displacement response spectrum, the pier-top displacement and maximum force can be determined, thereby enabling a displacement-based seismic design approach. This research holds significant theoretical and practical value for advancing seismic design methodologies for self-centering segmental bridge piers and enhancing the seismic safety of bridge structures. Full article

Autism Spectrum Disorder (ASD) poses significant challenges in diagnosis due to its diverse symptomatology and the complexity of early detection. Atypical gait and gesture patterns, prominent behavioural markers of ASD, hold immense potential for facilitating early intervention and optimising treatment outcomes. These patterns can be efficiently and non-intrusively captured using modern computational techniques, making them valuable for ASD recognition. Various types of research have been conducted to detect ASD through deep learning, including facial feature analysis, eye gaze analysis, and movement and gesture analysis. In this study, we optimise a dual-stream architecture that combines image classification and skeleton recognition models to analyse video data for body motion analysis. The first stream processes Skepxels—spatial representations derived from skeleton data—using ConvNeXt-Base, a robust image recognition model that efficiently captures aggregated spatial embeddings. The second stream encodes angular features, embedding relative joint angles into the skeleton sequence and extracting spatiotemporal dynamics using Multi-Scale Graph 3D Convolutional Network(MSG3D), a combination of Graph Convolutional Networks (GCNs) and Temporal Convolutional Networks (TCNs). We replace the ViT model from the original architecture with ConvNeXt-Base to evaluate the efficacy of CNN-based models in capturing gesture-related features for ASD detection. Additionally, we experimented with a Stack Transformer in the second stream instead of MSG3D but found it to result in lower performance accuracy, thus highlighting the importance of GCN-based models for motion analysis. The integration of these two streams ensures comprehensive feature extraction, capturing both global and detailed motion patterns. A pairwise Euclidean distance loss is employed during training to enhance the consistency and robustness of feature representations. The results from our experiments demonstrate that the two-stream approach, combining ConvNeXt-Base and MSG3D, offers a promising method for effective autism detection. This approach not only enhances accuracy but also contributes valuable insights into optimising deep learning models for gesture-based recognition. By integrating image classification and skeleton recognition, we can better capture both global and detailed motion patterns, which are crucial for improving early ASD diagnosis and intervention strategies. Full article

Tuberculosis (TB) remains one of the leading causes of mortality worldwide, exacerbated by the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis strains. In the pursuit of novel therapeutic strategies, thiazolidin-4-one derivatives have gained significant attention due to their structural diversity and broad-spectrum biological activities. This review provides a comprehensive summary of recent advances (2021–present) in the synthesis, structure–activity relationship (SAR), and mechanisms of action of thiazolidin-4-one derivatives as promising antitubercular agents. A detailed discussion of synthetic pathways is presented, including classical and multi-component reactions leading to various subclasses such as thiazolidine-2,4-diones, rhodanines, and pseudothiohydantoins. The SAR analysis highlights key functional groups that enhance antimycobacterial activity, such as halogen substitutions and heterocyclic linkers, while molecular docking and in vitro studies elucidate interactions with key Mtb targets including InhA, MmpL3, and DNA gyrase. Several compounds demonstrate potent inhibitory effects with MIC values lower than or comparable to first-line TB drugs, alongside favorable cytotoxicity profiles. These findings underscore the potential of thiazolidin-4-one scaffolds as a valuable platform for the development of next-generation antitubercular therapeutics. Full article