Browse Articles

Plant–pollinator interactions and floral and nectar traits shape the diversity of the nectar mycobiome

Abstract Beyond its essential role in plant–pollinator interactions, floral nectar serves as habitats for diverse fungal communities that can influence plant–animal mutualism. Although it has long been known that fungi can reach high densities in floral nectar, the factors structuring their diversity remain poorly understood. In this study, we evaluated how the presence of mycobiome influences nectar properties and how floral traits, nectar chemistry, and flower visitors’ presence shape nectar-inhabiting fungi diversity. We examined ten insect-pollinated plant species from nine families, encompassing a broad range of floral morphologies and flower visitor interactions. We recorded differences in sugar and amino acids presence in flowers with open and restricted visitors access. These differences are likely linked to the presence and activity of mycobiome introduced primarily through animal visits. Our results indicate that fungal diversity in nectar is associated with the presence of specific sugars, particularly fructose and glucose. In contrast, we found no significant relationships between fungal diversity and amino acid composition, floral traits, or flower visitor group. The generally weak effect of flower visitors on fungal diversity may be partially explained by a dilution effect in highly diverse floral environments, where microbial inoculation is distributed across many plant hosts.

A new mouse mutant with a discrete mutation in Pcdhgc5 reveals that the Protocadherin γC5 isoform is not essential for dendrite arborization in the cerebral cortex

There are ~ 60 clustered protocadherin (cPcdh) isoforms expressed from three gene clusters ( Pcdha , Pcdhb , Pcdhg ) arrayed in tandem across nearly 1 Mb in mammals. cPcdhs are homophilic cell adhesion molecules (CAMs) critical for a host of neural developmental functions consistent with a role in cell-cell recognition. Indeed, isoforms make recognition modules in combination to generate recognition diversity far exceeding the ~ 60 individual CAMs. However, there is also growing evidence for specialized functions for specific isoforms, particularly the C-type isoforms found at the 3’ ends of the Pcdha cluster (αC1 and αC2) and at the 3’ end of the Pcdhg cluster (γC3, γC4, and γC5). We have previously described unique roles for γC3 in dendrite arborization in the cerebral cortex and neural circuit formation in the spinal cord, as well as for γC4 in neuronal survival. Here we report a new mouse mutant specifically targeting the Pcdhgc5 exon encoding γC5. Unlike the rest of the Pcdhg cluster, expression of this isoform does not begin until postnatal stages of mouse development, increasing in the second week of life, suggesting specialized roles. We found significant expression changes in gene pathways involved in synaptic activity, learning and memory, and cognition. Despite this, we saw no major disruption in the cerebral cortex in neuronal organization, survival, dendritic arborization, or synaptic protein expression in these mutants. This new model will be an important tool for future studies delineating specific functions for γC5.

Green synthesis of silica nanoparticles using chia seeds boosts rice germination and physiological responses

Abstract This study aimed to synthesize silicon nanoparticles (SiNPs) using two different approaches: chemical (Chem-SiNPs) and biological (Bio-SiNPs) methods, utilizing chia ( Salvia hispanica L.) seed extract as a capping agent and bulk silica (SiO 2 ) as a precursor (Bulk-Si). Both routes were employed to compare the conventional chemical method with an eco-friendly, plant-based approach, enabling a comprehensive evaluation of how the fabrication technique influences the physicochemical properties and biological effectiveness of the resulting SiNPs. The synthesized SiNPs were characterized using UV-spectrophotometer, TEM, FTIR, XRD, and zeta potential analysis. Also, the impact of nano-priming with two concentrations (30 and 100 ppm) of Bulk-Si, Chem-, and Bio-SiNPs on the germination and key physiological mechanisms of Oryza sativa L. var. Sakha 108 seedlings were investigated. Characterization results showed that Bio-SiNPs surpassed Chem-SiNPs, exhibiting greater stability and a higher surface area, likely due to the available bioactive phytochemicals in the chia extract. This phytochemical capping layer enhances the stability and bioactivity of Bio-SiNPs, thereby activating key metabolic and physiological responses in rice seedlings. Bio-SiNPs at 100 ppm exhibited the highest improvement in germination rate (97.3%), germination index (65.3%), germination rate index (130.73), seedling vigour index (18.68), seedling biomass (0.033 g), and water uptake (31.53%) compared to Chem-SiNPs and Bulk-Si treatments. Likewise, Bio-SiNPs at 100 ppm concentration significantly increased total soluble sugars, α-amylase, radicle dehydrogenase activities, and Si content in rice seedlings. All SiNPs treatments induced significant changes in proline, malondialdehyde, hydrogen peroxide accumulation, glutathione level, and the antioxidant enzyme activities compared to the bulk material. This research reveals substantial variations in many critical physiological and biochemical parameters, which explain the varying responses of rice seedlings to various nano-synthesis protocols. It illustrates the efficacy of using chia seed extract as a green capping agent for nano priming as a sustainable approach to enhance germination and boost future rice productivity.

A randomized assessment of the impact of ‘Those Nerdy Girls’ newsletters on adult vaccination outcomes

Adult vaccine-preventable infectious diseases contribute substantial burden each year, and vaccine uptake remains suboptimal. As digital health communication grows, digital newsletters may represent a scalable, low-cost tool to promote vaccination. We conducted a randomized prospective study to evaluate the impact of digital newsletters on subscribers’ knowledge, attitudes, and behaviors regarding four adult vaccines: respiratory syncytial virus (RSV), influenza, shingles, and COVID-19. Between November 2023 and January 2024, half of subscribers to the Those Nerdy Girls (TNG) Substack newsletter received additional vaccine-focused newsletters, while the other half received only the standard twice-weekly newsletters. Pre- and post-intervention online surveys assessed vaccine knowledge, attitudes, and behaviors among adult subscribers who consented to participate. Across 1,327 pre-intervention and 1,208 post-intervention participants, knowledge gains were observed for RSV and shingles vaccines in the intervention group compared with controls, while knowledge of influenza and COVID-19 vaccines did not improve. Attitudes toward vaccination were generally positive at baseline and showed no significant intervention effects across vaccine types. Likelihood of vaccination increased for influenza, COVID-19, and RSV overall during the study period, but only influenza vaccination showed a significantly greater increase in the intervention group relative to controls. No significant effects were found for shingles vaccination. These results indicated that digital newsletters can improve knowledge and, in some contexts, support uptake, but knowledge gains did not consistently translate into changes in attitudes or behaviors. Findings highlighted the limitations of knowledge-deficit models for adult vaccination promotion. Digital newsletters should be considered as one component of broader public health strategies, particularly when paired with approaches that address logistical access, trust, and social norms.

A thinking innovation strategy based Northern goshawk optimizer enhanced extreme learning machine for bankruptcy prediction problems

Deep-Fed: A comprehensive solution for precise bone fracture identification in athletes

Bone fracture diagnosis is a critical aspect of sports medicine, where accurate and timely detection enables effective treatment and rapid recovery. This study proposes Deep-Fed, a federated deep learning framework for fracture diagnosis in athletes. Deep-Fed integrates convolutional neural networks with a specialized classification module, FractureNet, and trains it across distributed athletic clinics using federated averaging without exchanging raw images, thereby preserving patient privacy while leveraging diverse data sources. The framework was evaluated on three benchmark datasets—Deep-I, Deep-II, and Deep-III—representing varied imaging conditions and patient groups. Deep-Fed achieved accuracy rates of 96.23 ± 0.42%, 97.11 ± 0.35%, and 96.73 ± 0.39%, respectively, significantly outperforming Baseline 1 (87.23 ± 0.68%), Baseline 2 (90.15 ± 0.55%), and Baseline 3 (94.49 ± 0.47%). Statistical analysis using paired t-tests confirmed that Deep-Fed’s improvements were significant (p < 0.05) across all comparisons. These results demonstrate that federated learning can be effectively applied for high-accuracy fracture detection in decentralized clinical settings, enabling collaboration across institutions without compromising data privacy.

Hacking continuous-variable quantum key distribution using the photorefractive effect on proton-exchanged/annealed-proton-exchanged waveguide

Correction: Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops

Integrated morphological, molecular, and immunopathological characterization of Raillietina hymenolepidoides from Psammomys obesus reveals potent in vitro anthelmintic activity of Androctonus crassicauda venom

Study Protocol: It is time to dig deeper: A cross-country implementation mapping study of the iFightDepression® (online self-management) tool

Background The iFightDepression® (iFD) tool is an internet-based self-management program for individuals with milder forms of depression, used alongside support from trained user guides (e.g., family physicians). Despite strong evidence supporting guided internet-based cognitive behavioural therapy (iCBT), adherence to the iFD tool and its uptake across implementing countries remain variable. Methods This implementation mapping study will be conducted in Germany, Poland, and Spain. Guided by the Consolidated Framework for Implementation Research (CFIR), we aim to identify barriers, facilitators, and country-specific implementation strategies. In Phase 1, we will invite 15 trained iFD user guides to complete an online qualitative survey to explore user experiences and perceived influences on patient uptake and adherence. In Phase 2, three country-specific focus groups will be conducted with trained user guides (one in each country). Survey data will be analyzed using Interpretative Phenomenological Analysis (IPA) and focus group data will be analyzed using template analysis. Discussion Findings will be used to develop an iFD implementation manual and country-specific implementation blueprints. These outputs are intended to support consistent and sustainable adoption of this internet-based intervention across participating countries and to improve patient uptake and adherence. Study registration The study, including the analysis plan, has been preregistered via the Open Science Framework (internet archive link: https://archive.org/details/osf-registrations-wmbhy-v1 ) following ethical approval.

Targeting excessive cholesterol deposition alleviates secondary lymphoedema

Challenges and opportunities of Napier grass-derived biochar via liquefied gas for humic acid adsorption and DFT analysis

Efficient service mesh traffic management for cloud-native applications

The cloud-native architecture and microservice technologies are revolutionizing the design, development, and management of cloud applications and services by offering greater elasticity, scalability, and flexibility. However, managing service-to-service traffic and handling faults turn out to be more difficult for modern, sophisticated cloud-native applications. The research community responded to the technical challenges by exploring efficient scheduling schemes that deploy constituent services to a node. Despite those efforts, current solutions are unable to handle real-time traffic dynamics, which could lead to resource waste and unnecessary communication delays. In this work, service partitions are used to improve resource distribution and traffic control in microservice-based applications. This strategy uses graph-based techniques to effectively cluster services, optimize resource usage, and boost communication efficiency, while continually monitoring application behaviors. We found that it can reduce response times by up to 15% during times of high network latency. The performance and dependability of microservices in cloud-native environments can be significantly improved using the proposed approach.

Multi-task survival modeling of dependent failure and reimplantation events in dental implants

Building bridges to emotion: Developing a standardized film-based emotion elicitation tool for Iranian culture

Emotion elicitation through culturally relevant stimuli is crucial for psychological research that seeks to explore affective processes within specific populations. This study aimed to develop and validate a film-based emotion elicitation tool for Iranian culture. A comprehensive database of short video clips was selected to evoke distinct emotional states, including happiness, tenderness, fear, anger, sadness, and disgust, alongside neutral clips as controls. To validate the database, the emotional responses of 300 Iranian participants were assessed using key dimensions of arousal and valence, positive and negative affective states, gender differences, and mixed emotions. The results indicated that all emotional stimuli elicited significantly higher arousal levels compared to neutral clips, with fear-inducing clips generating the highest arousal levels. In terms of valence, positive emotional films, such as those inducing happiness and tenderness, were significantly associated with higher pleasantness, while anger elicited the lowest valence scores, indicating its strong negative impact. Additionally, the video clips effectively differentiated between positive and negative affective states, with clear statistical significance observed across all comparisons ( p < 0.0001 ), showing that videos designed to evoke positive emotions (e.g., happiness) and negative emotions (e.g., fear) successfully achieved these outcomes across the participant group. Gender differences were also examined, with women generally showing higher levels of emotional arousal than men, particularly in response to happiness, tenderness, sadness, and disgust, though the overall effect sizes were small. Finally, the study delved into the complexity of mixed emotions, where participants often experienced simultaneous conflicting emotions, such as happiness and sadness, challenging traditional discrete emotion frameworks. The findings affirm the cultural relevance and efficacy of the developed video clip database in eliciting a wide range of emotional responses, making it a valuable tool for future psychological studies in Iranian contexts. This study underscores the importance of culturally specific stimuli in emotion research and provides a robust resource for exploring the emotional landscape within Iranian culture.

Deep learning techniques for crop classification in complex agricultural landscapes

Genetic and pharmacologic inhibition of calcineurin reduces biofilm formation by the pathogenic fungus Trichosporon asahii in an in vivo silkworm infection model

Trichosporon asahii is a dimorphic pathogenic fungus that causes catheter-related bloodstream infection in immunocompromised patients with neutropenia. Biofilm formation by T. asahii on the surfaces of medical devices such as catheters is influenced by various host environmental factors. Calcineurin, a protein phosphatase composed of the catalytic subunit Cna1 and the regulatory subunit Cnb1, regulates multiple stress responses and virulence of T. asahii . The role of calcineurin in biofilm formation under host-derived conditions, however, remains unclear. Here, we demonstrated that calcineurin is essential for biofilm formation in vivo by T. asahii . While the cna1 gene- and the cnb1 gene-deficient mutants formed biofilms comparable to those of the parent strain in vitro , it produced significantly less biofilm than the parent strain in the in vivo silkworm infection model. Similarly, tacrolimus, a calcineurin inhibitor, did not inhibit biofilm formation by T. asahii in vitro but markedly suppressed biofilm formation in vivo . Together, these findings suggest that calcineurin plays a crucial role in biofilm formation by T. asahii under host environmental conditions.

Demonstration of a Gandolfi-type attachment for fast high-resolution synchrotron XRD of non-ideal specimens

Abstract Synchrotron powder X-ray diffraction (PXRD) offers significant advantages in the structural analysis of functional materials and enables the acquisition of high-quality data; however, accurate data collection remains challenging for samples consisting of coarse crystallites or molten samples. Specifically, obtaining reliable PXRD data from samples in their as-solidified or non-pulverized state remains challenging during melting–solidification and crystal grain growth processes, as well as for materials produced by these processes. To mitigate these limitations, a two-axis rotation Gandolfi-type attachment—comprising a 45°-tilted φ -axis and its rotational ω -axis—was developed and implemented on a high-resolution powder diffractometer at SPring-8, which is equipped with fast area detectors. This configuration improved the particle statistics by increasing the number of crystallites satisfying the Bragg condition through two-axis rotation, while also stabilizing the sample position, even for molten samples, owing to the tilted geometry. Specifically, the integration of a high-speed spinner and multiple two-dimensional photon-counting detectors allowed sub-second continuous imaging and frame-by-frame peak separation, facilitating the indexing of PXRD data for complex structures. The analytical capability was evaluated in three case studies, namely the pair distribution function analysis of molten Zn, in situ observations of LiCoO 2 electrode material synthesis in a molten flux, and high-resolution PXRD of mineral crystals within a short timeframe. The results confirmed that the Gandolfi-type attachment improved data quality and reproducibility, thereby enabling reliable measurements even for practical samples with limited availability of fine powders.

Predicting and optimizing control parameters of stir casting of Al alloy/MWCNT/RHA composite using artificial neural network and Taguchi-Grey relational analysis for multi-objective outcomes

In the present investigation, the influence of various casting parameters viz. stirrer time, stirrer speed, and processing temperature and reinforcement content on the mechanical properties of AlP0507/CNT/RHA composite is assessed. The optimum parameter combination that produces greater multi-objective performance was obtained using the GRA method. The comparison of all R 2 -score showed that the ANN model is best fitted to predict the tensile strength of HAMMC with highest R 2 - score of 99.65%. GRA established that the MWCNT content has most significant influence on the response parameters followed by stirring time, RHA content, stirring speed and processing temperature; and the best properties of stir cast HAMMCs was obtained by the combination A2B3C3D2E2. ANNOVA performed on GRA indicated that MWCNT content with contribution of 48.26% exerted maximum impact on the properties of the fabricated HAMMC, followed by stirring time with contribution 19.4%. Processing temperature contributed least with meagre contribution of 2.17%. The predicted value of GRG (0.830775) was found very close to the GRG value of the highest-ranked experiment (0.79643454) confirming the accuracy of the optimization and its validation. The improvement in GRG value by 0.09792454 shows that the optimized parameters provided the optimal results and can be recommended.

Detection of known gene fusions in cancer cell lines using whole-genome bisulfite sequencing data

Abstract Whole-genome bisulfite sequencing (WGBS) provides multiple molecular information layers including methylation, copy number variations (CNVs), single nucleotide variants (SNVs), and fragmentome patterns in cell-free DNA (cfDNA) while gene fusion detection from WGBS data has not been widely explored. We applied a custom analysis pipeline using bisulfite-aware aligners supporting split read alignment to detect known gene fusions from WGBS data and validated this approach using cancer cell lines with characterized fusion events. Using K562 cells harboring the BCR-ABL1 fusion, WGBS-derived breakpoints showed concordance with WGS-derived coordinates. Serial dilution experiments with K562 DNA in NA12878 background at average 63X coverage established a limit of detection (LoD) of 8.1% fusion-positive DNA fraction, demonstrating 100% detection rate at ≥10%. Furthermore, our method with MCF-7 cells detected 10 of 12 validated fusions with high technical reproducibility (Pearson r > 0.99), including both inter-chromosomal and intra-chromosomal events. This approach confirms that known gene fusions can be reliably detected from WGBS data, particularly well-suited for applications where methylation and fusion profiling are both required.