Search Results (989)

Greece’s 2010–2018 adjustment programmes provide an insightful case of how timing of reforms, institutional frictions, and digital transformation jointly condition the outcomes of macroeconomic stabilization efforts. This review builds on programme evaluations, recent academic work, and empirical indicators to analyze the dynamics at the intersection of macroeconomic adjustment, institutional quality, and entrepreneurship, placing emphasis on productivity and the evolving role of digital governance. The paper argues that the asymmetric sequencing of fiscal consolidation, internal devaluation, institution-building, and digital modernization is consistent with deeper and more persistent output losses than initially anticipated, as complementary reforms in product markets and public administration were not yet in place. Recovery momentum was observed when administrative simplification, transparency reforms, and digital public services began to reduce transaction costs, uncertainty, and implementation frictions. In this perspective, digital governance—through initiatives such as Diavgeia, and interoperable registries—acted as an enabling complement to the effectiveness of structural reforms, supporting the shift towards a more innovation-oriented entrepreneurial ecosystem. While the evidence is associative rather than causally identified, the synthesis highlights mechanisms and transferable lessons for the design and sequencing of reform programmes in crisis and recovery contexts. Full article

Enzymatic browning, driven by polyphenol oxidase (PPO), remains a major postharvest challenge for potato (Solanum tuberosum L.), reducing product quality, shelf life, and consumer acceptance. To mitigate this trait in the elite tetraploid cultivar ‘Yagana-INIA’, we applied a geminivirus-derived CRISPR–Cas9 system to edit the StPPO genes most highly expressed in tubers, StPPO1 and particularly StPPO2. A paired-gRNA strategy generated a double-cut deletion in StPPO1, while StPPO2 editing required a complementary single-gRNA screening workflow. High-resolution fragment analysis and sequencing identified three StPPO2-edited lines, including one that lacked GFP, Cas9, and Rep/RepA sequences, confirming a transgene-free editing outcome. Edited tubers exhibited visibly reduced browning relative to wild type, and biochemical assays showed decreased PPO activity consistent with targeted disruption of StPPO2. Amplicon sequencing verified monoallelic editing at the gRNA2 site in the non-transgenic line. These results demonstrate the utility of a replicon-based CRISPR system for achieving targeted, transgene-free edits in tetraploid potato and identify a non-GM StPPO2-edited line with improved postharvest quality under Chile’s regulatory framework. Full article

This study presents an in silico proof of concept exploring whether deep learning models can perform conditional mitochondrial DNA (mtDNA) sequence prediction across species boundaries. A CNN–BiLSTM model was trained under a leave-one-species-out (LOSO) scheme on complete mitochondrial genomes from 21 vertebrate species, primarily archosaurs. Model behavior was evaluated through multiple complementary tests. Under context-conditioned settings, the model performed next-nucleotide prediction using overlapping 200 bp windows to assemble contiguous 2000 bp fragments for held-out species; the resulting high token-level accuracy (>99%) under teacher forcing is reported as a diagnostic of conditional modeling capacity. To assess leakage-free performance, a two-flank masked-span imputation task was conducted as the primary evaluation, requiring free-running reconstruction of 500 bp interior spans using only distal flanking context; in this setting, the model consistently outperformed nearest-neighbor and demonstrated competitive performance relative to flank-copy baselines. Additional robustness analyses examined sensitivity to window placement, genomic region (coding versus D-loop), and random initialization. Biological plausibility was further assessed by comparing predicted fragments to reconstructed ancestral sequences and against composition-matched null models, where observed identities significantly exceeded null expectations. Using the National Center for Biotechnology Information (NCBI) BLAST web interface, BLASTn species identification was performed solely as a biological plausibility check, recovering the correct species as the top hit in all cases. Although limited by dataset size and the absence of ancient DNA damage modeling, these results demonstrate the feasibility of conditional mtDNA sequence prediction as an initial step toward more advanced generative and evolutionary modeling frameworks. Full article

Adherent-invasive E. coli (AIEC) is closely related to inflammatory bowel disease (IBD). However, its pathogenic mechanism has not yet been fully elucidated. Using a BLASTP search, we discovered that the amino acid sequence of a putative protein (UFP37798.1) in the AIEC LF82 strain is highly homologous to some regulators in the SlyA family. We named it EruA. We displayed the secondary structures of EruA using bioinformatics, overexpressed the His₆-tagged EruA protein using SDS-PAGE, and dissected the genetic organization of the eruA chromosomal region using 5′RACE. We constructed an eruA deletion mutant (ΔeruA) and a complementary strain (CΔeruA) of the LF82 strain. The transcriptomes of wild-type (WT) and ΔeruA bacteria were compared using RNA sequencing and qRT-PCR, thereby identifying 32 differentially expressed genes (DEGs). Based on YASARA software and EMSA analysis, EruA directly binds to the consensus sequences (P_fimA and P_tnaB) in the promoter region of the fimA and tnaB genes from these DEGs. By using a super-resolution confocal microscope (SCM), counting CFUs of colonies on plates, indole quantification, and crystal violet staining of biofilms adhered to tubes or 96-well plates, we found that EruA activates the fimA to promote bacterial adhesion to intestinal epithelial cells and activates the tnaB to enhance bacterial indole production and biofilm formation. Moreover, EruA helps AIEC resist environmental stress and enhances bacterial survival within macrophages as well as loading in mouse tissues. Notably, EruA promotes AIEC colonization in the colons of mice and exacerbates intestinal inflammation caused by bacterial infection in mice with DSS-induced inflammatory colitis, manifested by weight loss, colon length shortening, and pathological changes in colon tissues. Therefore, EruA plays a key role in the pathogenicity of AIEC. Full article

Automated pollen identification has become an increasingly important tool for palaeoecological research; however, its application to fossil pollen assemblages remains challenging due to complex backgrounds, morphological variability, and taxonomic similarity among pollen types. In this study, we propose Pollen-YOLO, a deep learning-based object detection framework designed for automated pollen identification from microscopic images, and evaluate its performance using the TPPOL23 dataset. The model integrates a tailored backbone architecture with attention-based feature enhancement and class-specific data augmentation strategies to address the characteristics of fossil pollen images. Experimental results indicate that Pollen-YOLO achieves stable and competitive detection performance for most pollen taxa under the tested conditions, particularly for dominant taxa with distinctive morphological features. Model behavior is further examined through ablation experiments and Grad-CAM-based interpretability analysis, which provide insights into feature learning and classification mechanisms. The applicability of the framework is explored using a fossil pollen sequence from the Shaqu profile on the Tibetan Plateau. Automated results show a high level of agreement with manual identification in capturing major stratigraphic trends and vegetation succession patterns, while discrepancies persist for morphologically similar or low-abundance taxa. Overall, this study suggests that object detection-based deep learning approaches have the potential to support fossil pollen analysis and palaeoecological reconstruction. Rather than replacing expert identification, Pollen-YOLO is intended as a complementary, high-throughput tool that may assist large-scale pollen analysis under appropriate quality control when combined with expert verification. Full article

Background: Varroa destructor is the major threat to honey bee health, and selective breeding for resistance traits such as Varroa-sensitive hygiene represents a promising long-term strategy for controlling mite populations. However, breeding programs that rely on highly controlled mating schemes, including single-drone instrumental insemination, may reduce allelic diversity at the complementary sex determiner (csd) locus, potentially increasing the production of non-viable diploid males and compromising colony fitness. Methods: To evaluate whether csd diversity can be maintained under these conditions, we characterized the hypervariable region of csd in a selectively bred Apis mellifera population subjected to four years of selection. Using a validated de novo assembly pipeline, we reconstructed 43 amino-acid sequences from 33 diploid worker pupae sampled across 13 colonies. Results: Seven distinct alleles were identified, five of which were shared among multiple colonies and corresponded to variants already described in the literature, while two were private to individual colonies and novel in the literature. Colony-level frequency data revealed a moderate diversity: the most common allele was detected in nine colonies, with an allelic frequency of 31%. Moreover, the expected heterozygosity of the population was estimated at 0.79. Conclusions: Overall, these findings show that csd diversity can be partially maintained even under strong selective pressure when multiple maternal lines are retained, and they underscore the importance of incorporating genetic information into breeding decisions to support the long-term sustainability of selective breeding programs. Full article

Omics research is changing the way medicine develops new strategies for diagnosis, prevention, and treatment. With the surge of advanced machine learning models tailored for omicss analysis, recent research has shown improved results and pushed the progress towards personalized medicine. The dissection of multiple layers of genetic information has provided new insights into precision medicine, at the same time raising issues related to data abundance. Studies focusing on single-cell scale have upgraded the knowledge about gene expression, revealing the heterogeneity that governs the functioning of multicellular organisms. The amount of information gathered through such sequencing techniques often exceeds the human capacity for analysis. Understanding the underlying network of gene expression regulation requires advanced computational tools that can deal with the complex analytical data provided. The recent emergence of artificial intelligence-based frameworks, together with advances in quantum algorithms, has the potential to enhance multiomicsc analyses, increasing the efficiency and reliability of the gene expression profile prediction. The development of more accurate computational models will significantly reduce the error rates in interpreting large datasets. By making analytical workflows faster and more precise, these innovations make it easier to integrate and interrogate multi-omics data at scale. Deep learning (DL) networks perform well in terms of recognizing complex patterns and modeling non-linear relationships that enable the inference of gene expression profiles. Applications range from direct prediction of DNA sequence-informed predictive modeling to transcriptomic and epigenetic analysis. Quantum computing, particularly through quantum machine learning methods, is being explored as a complementary approach for predictive modeling, with potential applications to complex gene interactions in increasingly large and high-dimensional biological datasets. Together, these tools are reshaping the study of complex biological data, while ongoing innovation in this field is driving progress towards personalized medicine. Overall, the combination of high-resolution omics and advanced computational tools marks an important shift toward more precise and data-driven clinical decision-making. Full article

The phenomenon of transgene silencing was first observed shortly after the generation of the initial transgenic plants. The vast body of experimental data accumulated since then constitutes an invaluable resource for dissecting the mechanisms of epigenetic gene regulation. Silencing operates at either the transcriptional (TGS) or post-transcriptional (PTGS) level and is predominantly mediated by small interfering RNAs (siRNAs). Although these two epigenetic pathways involve distinct sets of proteins and enzymes, they share fundamental mechanistic features: the generation of double-stranded RNA (dsRNA), its processing into siRNAs by DICER-LIKE (DCL) enzymes, and the assembly of an Argonaute-centered effector ribonucleoprotein complex (RISC). Guided by sequence-specific siRNAs, this complex identifies complementary target sequences with high precision. A comprehensive understanding of these regulatory pathways enables the targeted induction or suppression of specific plant genes. This review traces the history of experimental findings regarding the loss of recombinant gene activity in transformants and their progeny, which collectively established the foundation for elucidating the molecular mechanisms of transgene silencing. Full article

Based on PCR with complementary primer pairs and Pfu DNA polymerase, QuickChange site-directed mutagenesis has been widely employed, but its efficiency varies from mutation to mutation. An alternative strategy relies on partially overlapping primer pairs with 3′-overhangs, and this strategy has led to the recent development of P3a and P3b site-directed mutagenesis, in which the use of SuperFi II and Q5 polymerases raises the mutagenesis efficiency to ~100%. It is unclear whether these two DNA polymerases also improve the QuickChange method. Herein, we have evaluated this possibility by engineering 46 mutations on seven expression plasmids, two of which possess extremely GC-rich sequences. As Pfu DNA polymerase is a slow enzyme, its replacement with SuperFi II and Q5 polymerases reduced PCR length. Moreover, the average efficiency for each of the seven plasmids ranged from 48% to 69%, thereby outperforming the original QuickChange method. However, this efficiency is still lower than that from the P3a and P3b methods, supporting the superiority of primer pairs with 3′-overhangs. Analysis of the incorrect plasmids from the improved QuickChange method revealed frequent insertions at primer sites. The insertions were derived from primers and varied from mutation to mutation, with certain sites much more prone to such insertions. In comparison, these insertions occurred at a much lower frequency with the P3a and P3b methods, suggesting that primer pairs with 3′-overhangs enhance mutagenesis efficiency by reducing the likelihood to introduce insertions at primer sites. Thus, this study improves the QuickChange mutagenesis method, supports the superiority of the P3a and P3b methods, and uncovers a novel molecular mechanism by which the efficiency of PCR-based mutagenesis with completely overlapping primer pairs is negatively affected. Full article

To elucidate the potential of integrated multi-omics approaches for studying systemic mechanisms of mycorrhizal fungi in mediating plant-microbe interactions, this study employed the Tuber-inoculated Corylus system as a model to demonstrate how high-throughput profiling can investigate how fungal inoculation reshapes the rhizosphere microbial community and correlates with host metabolism. A pot experiment was conducted comparing inoculated (CTG) and non-inoculated (CK) plants, followed by integrated multi-omics analysis involving high-throughput sequencing (16S/ITS), functional prediction (PICRUSt2/FUNGuild), and metabolomics (UPLC-MS/MS). The results demonstrated that inoculation significantly restructured the fungal community, establishing Tuber as a dominant symbiotic guild and effectively suppressing pathogenic fungi. Although bacterial alpha diversity remained stable, the functional profile shifted markedly toward symbiotic support, including antibiotic biosynthesis and environmental adaptation. Concurrently, root metabolic reprogramming occurred, characterized by upregulation of strigolactones and downregulation of gibberellin A5, suggesting a potential “symbiosis-priority” strategy wherein carbon allocation shifted from structural growth to energy storage, and plant defense transitioned from broad-spectrum resistance to targeted regulation. Multi-omics correlation analysis further revealed notable associations between microbial communities and root metabolites, proposing a model in which Tuber acts as a core regulator that collaborates with the host to assemble a complementary micro-ecosystem. In summary, the integrated approach successfully captured multi-level changes, suggesting that Tuber-Corylus symbiosis constitutes a fungus-driven process that transforms the rhizosphere from a competitive state into a mutualistic state, thereby illustrating the role of mycorrhizal fungi as “ecosystem engineers” and providing a methodological framework for green agriculture research. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

Ganoderma is a genus of medically significant fungi, that is used in traditional medicine and is increasingly incorporated into modern nutraceuticals and pharmaceuticals. Accurate species identification and product standardization remain major challenges due to morphological plasticity and cryptic diversity. This review articulates current advances in Ganoderma systematics, phylogenetics, and metabolomics, with an emphasis on molecular identification strategies and chemical profiling. Internal transcribed spacer (ITS) sequencing has substantially improved species delineation compared with morphology alone, but its resolving power is limited in closely related species complexes, necessitating complementary multilocus approaches. Advances in metabolomics, and LC-MS- and HPLC-based profiling of triterpenes and polysaccharides, have enhanced species discrimination, chemotaxonomic resolution, and quality control of commercial products. Integrating molecular barcoding with metabolomic fingerprints provides a more robust framework for classification, pharmacological evaluation, and standardization. This review also highlights significant geographic knowledge gaps, particularly in Africa, where molecular and metabolomic data remain scarce despite high species diversity. Full article

Staphylococcus aureus is a common opportunistic pathogen found in various environments, with the potential for rapid spread, especially in densely populated indoor settings. Integrating traditional microbiological monitoring with molecular techniques is critical for the timely detection and control of such pathogens. The aim of this study was (1) to monitor the presence and spread of S. aureus in a crowded occupational environment and (2) to optimize a PCR protocol with sequence specific primers (PCR-SSP) for precise identification and early detection of this microorganism and its antibiotic resistance genes. Sampling was conducted in two different places: a call center and a healthcare facility room. All samples were collected from indoor areas at two different time points (T0 and T1) in May 2025 (mean temperature: 22.5 °C; humidity: 59.5%). Microbiological techniques and molecular analysis using PCR-SSP were employed to confirm the presence of S. aureus and detect antibiotic resistance genes such as mecA. A total CFU (colony-forming unit) count of 587 was recorded at the dental clinic corridor, and a total CFU count of 2008 was recorded at the call center corridor. PCR-SSP successfully confirmed the identity of S. aureus with an amplicon size 267 bp and enabled the detection of antibiotic resistance markers, validating its use as a complementary method to traditional microbiological techniques. This study highlights the importance of combining environmental monitoring with molecular biology tools to enhance the early detection and accurate identification of microbial pathogens such as S. aureus and provide an insight for our future direction of producing biosensors for digital air monitoring in crowded workplaces. Full article

The Asian tiger mosquito (Aedes albopictus) is an important vector of arboviruses, including dengue, chikungunya, and Zika. Its rapid global expansion has been facilitated by climate change and human activities. Phylogenetic studies of Ae. albopictus have largely relied on mitochondrial cytochrome c oxidase subunit 1 (COX1) and NADH dehydrogenase subunit 5 (ND5) markers, while the utility of cytochrome b (CYTB) remains underexplored. We collected Ae. albopictus from 13 sites in seven provinces of South Korea and analyzed COX1, ND5, and CYTB sequences. Genetic diversity indices were calculated, and phylogenetic relationships were reconstructed using maximum-likelihood trees and haplotype networks with a dataset obtained from GenBank. COX1 revealed 46 haplotypes, including six novel variants, with the highest diversity in southern coastal regions such as Busan and Suncheon. ND5 showed limited variation, with only two haplotypes. CYTB revealed three haplotypes, including region-specific variants in Busan and Wonju, supporting its role as a complementary marker. The Busan haplotype H41 bridged domestic and international lineages, suggesting Busan as a likely entry point. This study demonstrates that integrating COX1, ND5, and CYTB improves the resolution of Ae. albopictus phylogeography in Korea and highlights the need for continued molecular surveillance to guide vector control strategies. Full article

B-cells and plasmablasts have emerged as central organizers of autoimmune pathogenesis, extending far beyond their classical role as antibody-producing cells to orchestrate immune circuits, tissue microenvironments, and therapeutic trajectories. Advances in single-cell technologies, high-dimensional cytometry, and B-cell receptor sequencing have uncovered a dynamic continuum of B-cell differentiation programs that drive clinical heterogeneity across systemic autoimmune diseases. Plasmablasts, in particular, have gained recognition as highly responsive sensors of immune activation: they expand during flares, encode interferon-driven and extrafollicular responses, and correlate with disease severity. Autoantibody profiles, long viewed as static diagnostic signatures, are now understood as durable molecular footprints of distinct B-cell pathways. In this review, we propose an endotype-based framework integrating B-cell circuits with clinical phenotypes, illustrate therapeutic decision-making through mechanistic case vignettes, and outline future strategies combining immunomonitoring, multi-omics, and precision therapeutics. We further address translational challenges and discuss complementary approaches, including T-cell modulation, FcRn inhibition, and antigen-specific tolerization. Full article