Search Results (243)

The 13th International Foamy Virus (FV) Conference was held from 8 to 10 November 2023 at the BioParque/Zoological Garden in Rio de Janeiro, Brazil. This was the first conference on spumaretroviruses to be held in the Southern Hemisphere and in the unique environment of the rainforest. New developments and current perspectives in FV research were presented. Highlights of the conference included the structural biology of the envelope protein (Env) and insights into its function and evolution, epidemiologic identification of Amazonian indigenous people with a high prevalence of simian FV (SFV) infections, investigations of virus biology and genomics using synthetic FV DNAs, studies of humoral immune response, and development and applications of SFV vectors. The last day of the meeting was a special tour of the Centro de Primatologia do Rio de Janeiro, located northeast of Rio de Janeiro amidst the protected rainforest, where New World primate hosts of spumaretroviruses are rescued and studied. Our report summarizes the meeting highlights and outcomes for future discussions. Full article

Dutch elm disease is one of the most devastating plant diseases, primarily spread through bark beetles. Scolytus scolytus is a key vector of this disease. In this study, distribution data of S. scolytus were collected and filtered. Combined with environmental and climatic variables, an ensemble model was developed using the Biomod2 platform to predict its potential geographical distribution in China. The selection of climate variables was critical for accurate prediction. Eight bioclimatic factors with high importance were selected from 19 candidate variables. Among these, the three most important factors are the minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and precipitation in the driest quarter (bio17). Under current climate conditions, suitable habitats for S. scolytus are mainly located in the temperate regions between 30° and 60° N latitude. These include parts of Europe, East Asia, eastern and northwestern North America, and southern and northeastern South America. In China, the low-suitability area was estimated at 37,883.39 km², and the medium-suitability area at 251.14 km². No high-suitability regions were identified. However, low-suitability zones were widespread across multiple provinces. Under future climate scenarios, low-suitability areas are still projected across China. Medium-suitability areas are expected to increase under SSP370 and SSP585, particularly along the eastern coastal regions, peaking between 2041 and 2060. High-suitability zones may also emerge under these two scenarios, again concentrated in coastal areas. These findings provide a theoretical basis for entry quarantine measures and early warning systems aimed at controlling the spread of S. scolytus in China. Full article

Background: Pinus massoniana is a significant lipid-producing tree species in China and a susceptible host for both the pine wood nematode and its insect vector, Monochamus alternatus. The basic helix–loop–helix (bHLH) family of transcription factors play a crucial role in responding to both biotic and abiotic stresses. However, the role of bHLH in terpene-induced defense in P. massoniana remains poorly studied. Results: Transcriptome sequencing using DNA Nanoball Sequencing (DNBSEQ) and PacBio Sequel platforms was performed, revealing differences in gene expression in P. massoniana branch under the simulated feeding treatment of methyl jasmonate (MeJA) spraying. Fifteen bHLH genes were cloned and analyzed, among which eight highly upregulated PmbHLH genes showed similar temporal expression after MeJA treatment and M. alternatus adult feeding. Five highly upregulated bHLH genes with nuclear localization were highly expressed in P. massoniana after M. alternatus feeding and interacted with the promoter of the terpene synthase gene Pm TPS (−)-α-pinene, confirming their involvement in the defense response of P. massoniana against the M. alternatus adult feeding. Conclusions: Our results unveil the temporal changes and the regulation of the induced defense system in P. massoniana mediated by both MeJA signaling and M. alternatus feeding treatment. The potential application for transgenic experiments and the breeding of resistant species in the future were discussed. Full article

Cepharanthine (CEP) is a natural bisbenzylisoquinoline alkaloid known for its antibacterial, antiviral, and anti-inflammatory activities. Its antifungal effect, however, has not been well studied. In this work, we used machine learning-based virtual screening with Random Forest, Neural Network, and Support Vector Machine models to identify potential inhibitors of Fusarium solani. CEP was selected as a candidate and tested experimentally. The results showed that it inhibited the growth of Fusarium solani, Fusarium proliferatum, Fusarium oxysporum, Alternaria alternata, and Botrytis cinerea. It also reduced the sporulation and spore germination of Fusarium solani and disrupted its redox balance. Transcriptome analysis showed changes in gene expression related to basic metabolic pathways. Molecular docking suggested that CEP binds to the FsCFEM1 protein, and molecular dynamics simulations confirmed stable binding, with key roles for residues THR748 and LEU950. These results suggest that CEP is a potential bio-based antifungal agent and provide novel insights into its mechanism against Fusarium solani. Full article

As early detection of voice disorders can significantly improve patients’ situation, the automated detection using Artificial Intelligence techniques can be crucial in various applications in this scope. This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). Three publicly available voice disorder datasets have been utilized, and results have been compared based on Inverted-Generational Distance, Hypervolume, spacing, and spread. The results reveal that NSGA-II with the MLP algorithm attained the best convergence and performance. Further, the conformal prediction is leveraged to quantify uncertainty in the feature-selected models, ensuring statistically valid confidence intervals for predictions. Full article

In recent decades, ships’ ballast water and associated sediments have been recognized globally as significant vectors for the dissemination of non-indigenous species, which has attracted extensive attention due to its ecological and economic impacts. The characteristics of production of resting cysts in the dinoflagellate life cycle further increases the risk of biological invasions through ballast tank sediments. Despite extensive research which has characterized the species diversity of dinoflagellate cysts within ballast tank sediments, the possibility and importance of invasions caused by different ribosomal types of the same species have been paid little attention. In this study, two cultures of dinoflagellates were established through cyst germination from the ballast tank sediments collected from two ships (“THETIS” and “WARIYANAREE”) arriving at the Jiangyin Port (China) and identified as Wangodinium sinense Z. Luo, Zhangxi Hu, Yingzhong Tang and H.F. Gu by comprehensive phylogenetic analysis of rDNA sequences (including LSU, SSU, and ITS1-5.8S-ITS2). Despite the rDNA sequences of the isolates showing a generally high similarity to reference sequences, the LSU D1-D6 sequences contained up to 11 stable single nucleotide polymorphisms (SNPs), while SSU and ITS1-5.8S-ITS2 sequences exhibited up to five and two divergence sites, respectively. Moreover, phylogenetic analyses based on partial LSU and SSU rDNA sequences further indicated that strains germinated from ships’ ballast tank sediments formed a strongly supported sister clade to the strains previously isolated from Chinese and Korean waters, representing a novel ribo-type distinct from Chinese and Korean strains. Detailed morphological observations using light microscopy (LM) and scanning electron microscopy (SEM) did not find differences between our isolates and the holotype of the species in key diagnostic characteristics including the position and shape of the nucleus and chloroplasts, as well as the ASC structure, which suggested that no significant morphological divergence has occurred among these ribo-types. Acute toxicity exposure assays indicated that this ribo-type of W. sinense posed no lethal effect on rotifers at concentrations ≤ 10⁴ cells/mL, yet it remains necessary to maintain vigilance regarding the potential risk of algal blooms resulting from higher cell density or environmental changes in the invaded ecosystems. This study reports the first successful germination of W. sinense cysts from ballast tank sediments, indicating that its cysts may be widely transferred through ballast tank sediments, and presents a potential risk of bio-invasions of new genotypes of species to a region where other genotypes of the same species have been present as indigenous species. Full article

Increasing environmental variability and operational complexity in reservoir systems necessitate advanced optimization frameworks for flood control. This study proposes the ε-constrained Improved Zebra Optimization Algorithm (ε-IZOA), a novel metaheuristic algorithm integrating an enhanced Zebra Optimization Algorithm (ZOA) with adaptive ε-constraint handling, to address multi-reservoir flood control optimization. Three strategic modifications advance the standard ZOA: (1) Bernoulli chaotic mapping for diversified population initialization; (2) adaptive weight balancing for exploration-exploitation trade-off mitigation; and (3) golden sinusoidal vectorization for global search refinement, collectively forming the Improved ZOA (IZOA). The ε-IZOA synergizes IZOA with ε-dominance criteria to dynamically resolve constrained optimization conflicts. Applied to the Yellow River Basin’s five-reservoir cascade, ε-IZOA achieves a 52.97% peak shaving rate at Huayuankou Station, reducing the maximum discharge to 18,745.02 m³/s—a performance surpassing benchmark methods. The algorithm’s success stems from its bio-inspired hybrid architecture, which embeds swarm intelligence principles into nonlinear constraint management. This work establishes ε-IZOA as a computationally robust tool for large-scale reservoir optimization, with implications for mitigating flood risks in climate-sensitive basins. Future research should prioritize its integration with real-time hydrological forecasting systems. Full article

Vaccination has been instrumental in curbing the transmission of SARS-CoV-2 and mitigating the severity of clinical manifestations associated with COVID-19. Numerous COVID-19 vaccines have been developed to this effect, including BioNTech-Pfizer and Moderna’s mRNA vaccines, as well as adenovirus vector-based vaccines such as Oxford–AstraZeneca. However, the emergence of new variants and subvariants of SARS-CoV-2, characterized by enhanced transmissibility and immune evasion, poses significant challenges to the efficacy of current vaccination strategies. In this review, we aim to comprehensively outline the landscape of emerging SARS-CoV-2 variants of concern (VOCs) and sub-lineages that have recently surfaced in the post-pandemic years. We assess the effectiveness of existing vaccines, including their booster doses, against these emerging variants and subvariants, such as BA.2-derived sub-lineages, XBB sub-lineages, and BA.2.86 (Pirola). Furthermore, we discuss the latest advancements in vaccine technology, including multivalent and pan-coronavirus approaches, along with the development of several next-generation coronavirus vaccines, such as exosome-based, virus-like particle (VLP), mucosal, and nanomaterial-based vaccines. Finally, we highlight the key challenges and critical areas for future research to address the evolving threat of SARS-CoV-2 subvariants and to develop strategies for combating the emergence of new viral threats, thereby improving preparedness for future pandemics. Full article

Bemisia tabaci is a major agricultural pest that affects both greenhouse and field crops by feeding on plant sap, which impairs plant growth, and by secreting honeydew, promotes sooty mold growth that further reduces photosynthesis. Additionally, these insects are vectors for viruses such as the cucurbit chlorotic yellows virus (CCYV), which causes significant damage to cucurbit crops. Traditional chemical pesticide treatments have limitations, including the development of resistance, harm to non-target organisms, and environmental contamination. Traditional chemical pesticides have limitations when it comes to controlling plants infested by CCYV and whitefly. However, the underlying reasons for these limitations remain unclear, as does the impact of entomopathogenic fungi on whitefly responses. This study explores the potential of using biological control agents, specifically Beauveria bassiana and Purpureocillium lilacinum, to manage whitefly populations and control CCYV transmission. Laboratory experiments were conducted to evaluate the pathogenicity of these fungi on non/viruliferous whitefly. The results indicated that both fungi effectively reduced whitefly populations, with B. bassiana showing particularly strong adverse effects. Whiteflies infected with CCYV exhibited a higher LC₅₀ to B. bassiana and P. lilacinum. Furthermore, bio-pesticides significantly altered the bacterial microbiome dynamics of the whitefly. Interestingly, CCYV increased the susceptibility of whiteflies to entomopathogenic fungus. The findings suggest that these biocontrol agents offer a sustainable alternative to chemical pesticides. Our study unraveled a new horizon for the multiple interaction theories among bio-pesticides–insects–symbionts–viruses. Full article

Background and Clinical Significance: The occurrence of cerebral venous sinus thrombosis (CVST), both with or without thrombocytopenia, following COVID-19 vaccination, is well documented and more common in recipients of vector vaccines. Cases of CVST following immunization with the COVID-19 messenger RNA (mRNA) vaccine are rare; most of these cases occur within 28 days of the first dose of the vaccine. Case Presentation: We present the case of a 38-year-old male with a history of two episodes of deep vein thrombosis in the lower limbs, but without a specific thrombophilic condition, who developed CVST 13 days after the second dose of the Pfizer/BioNTech BNT162b2 vaccine. He suffered from diffuse tension-type headache of progressively increasing intensity, and his objective neurological findings were normal. Magnetic resonance venography showed thrombosis of the transverse and right sigmoid sinuses, and magnetic resonance imaging (MRI) of the brain revealed no cerebral infarction. Two months later, a follow-up MR venography showed partial recanalization of the affected sinuses, and a brain MRI showed no infarction. Conclusions: Given the temporal sequence and the absence of other possible causes, we speculate that the second dose of the COVID-19 BNT162b2 vaccine may have triggered the development of CVST. Full article

Clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR-Cas9), an emerging gene-editing technology, has recently gained rapidly increasing attention. However, the lack of efficient delivery vectors to deliver CRISPR-Cas9 to specific cells or tissues has hindered the translation of this biotechnology into clinical applications. Chemically synthesized nanoparticles (NPs), as attractive non-viral delivery platforms for CRISPR-Cas9, have been extensively investigated because of their unique characteristics, such as controllable size, high stability, multi-functionality, bio-responsive behavior, biocompatibility, and versatility in chemistry. In this review, the key considerations for the precise design of chemically synthesized-based nanoparticles include efficient encapsulation, cellular uptake, the targeting of specific tissues and cells, endosomal escape, and controlled release. We discuss cutting-edge strategies to integrate chemical modifications into non-viral nanoparticles that guide the CRISPR-Cas9 genome-editing machinery to specific edits. We also highlighted the rationale of intelligent nanoparticle design. In particular, we have summarized promising functional groups and molecules that can effectively optimize carrier function. In addition, this review focuses on advances in the widespread application of NPs delivery in the biomedical fields to promote the development of safe, specific, and efficient NPs for delivering CRISPR-Cas9 systems, providing references for accelerating their clinical translational applications. Full article

This paper offers an experimental approach that integrates acoustic emission (AE) monitoring with machine learning (ML) to identify damage mechanisms and predict the mechanical properties of 3D-printed biocomposites. Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers and were subjected to tensile and flexural tests. The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. The prediction models were developed using an 80/20 train–test split and further validated by 5-fold cross-validation, with performance evaluated by R-squared (R²) and Mean Squared Error (MSE) metrics. Our results demonstrate robust prediction capabilities, with the RFR model achieving the highest accuracy (R² > 0.98 and MSE as low as 0.013 for tensile stress prediction). Additionally, unsupervised clustering using K-means was applied to group AE signals into distinct clusters corresponding to different damage modes. This comprehensive methodology not only enhances our understanding of damage evolution in composite materials but also establishes a data-driven framework for non-destructive evaluation and structural health monitoring. Full article

Bio-based xylonic acid produced from inexpensive lignocellulosic biomass has enormous market potential and enhances the overall economic benefits of biorefinery processes. In this study, the introduction of genes encoding xylose dehydrogenase driven by the promoter Ppdc into Z. mobilis using a plasmid vector resulted in the accumulation of xylonic acid at a titer of 16.8 ± 1.6 g/L. To achieve stable xylonic acid production, a gene cassette for xylonic acid production was integrated into the genome at the chromosomal locus of ZMO0038 and ZMO1650 using the endogenous type I-F CRISPR-Cas system. The titer of the resulting recombinant strain XA3 reduced to 12.2 ± 0.56 g/L, which could be the copy number difference between the plasmid and chromosomal integration. Oxygen content was then identified to be the key factor for xylonic acid production. To further increase xylonic acid production capability, a recombinant strain, XA9, with five copies of a gene cassette for xylonic acid production was constructed by integrating the gene cassette into the genome at the chromosomal locus of ZMO1094, ZMO1547, and ZMO1577 on the basis of XA3. The titer of xylonic acid increased to 51.9 ± 0.1 g/L with a maximum yield of 1.10 g/g, which is close to the theoretical yield in a pure sugar medium. In addition, the recombinant strain XA9 is genetically stable and can produce 16.2 ± 0.14 g/L of xylonic acid with a yield of 1.03 ± 0.01 g/g in the lignocellulosic hydrolysate. Our study thus constructed a recombinant strain, XA9, of Z. mobilis for xylonic acid production from lignocellulosic hydrolysate, demonstrating the capability of Z. mobilis as a biorefinery chassis for economic lignocellulosic biochemical production. Full article

The main purpose of this study was to evaluate the morphological characteristics of a sample of young international elite basketball players to create new reference values, using both somatotype and Bioelectrical Impedance Vector Analysis (BIVA). Moreover, we analyze the influence of age and maturation on anthropometric characteristics. Anthropometric measures (stature, body mass, body circumferences, skinfold thicknesses, and diameters) have been collected in a sample of 153 young basketball players of different age categories. From these measures, the endo-, ecto-, and mesomorph somatotypes components were calculated. In addition, impedance measurements were performed with a hand-to-foot bioimpedance analyser, and, subsequently, BIVA was carried out and biological maturity was estimated. A principal component analysis (PCA) has been assessed to reduce somatotype dimensionality. Almost all the collected variables differed between age groups. As regards the somatotype, the majority of the athletes were mesomorph–ectomorphal and the degree of homogeneity of the sample was high; the somatotype is aligned with that of the older athletes. Strong relationships were observed between mesomorphy, age, and bio-electric phase angle. Moreover, a tolerance ellipse graph was proposed as a reference for young elite athletes in basketball, and a new PCA-based method with one component was created to synthesize somatotype contents (eigenvalue = 2.42, R² = 0.807). Reference values for basketball players are needed by researchers, coaches, and practitioners for the process of talent identification and development. There are too many biological features to easily account for during growth, and field personnel need synthetic and more reliable approaches. Full article

This study explores the application of various machine learning (ML) models for the real-time prediction of the FOS/TAC ratio in microbial electrolysis cell anaerobic digestion (MEC-AD) systems using data collected during a 160-day trial treating brewery wastewater. This study investigated models including decision trees, XGBoost, support vector regression, a variant of support vector machine (SVM), and artificial neural networks (ANNs) for their effectiveness in the soft sensing of system stability. The ANNs demonstrated superior performance, achieving an explained variance of 0.77, and were further evaluated through an out-of-fold ensemble approach to assess the selected model’s performance across the complete dataset. This work underscores the critical role of ML in enhancing the operational efficiency and stability of bio-electrochemical systems (BES), contributing significantly to cost-effective environmental management. The findings suggest that ML not only aids in maintaining the health of microbial communities, which is essential for biogas production, but also helps to reduce the risks associated with system instability. Full article