Search Results (363)

Objectives: To evaluate the presence and dominance of Lactobacillus in the endometrial microbiome and their age-related associations in a large group of Bulgarian patients with recurrent implantation failure (RIF) and recurrent pregnancy loss (RPL) who attend our clinic. Methods: This retrospective study included 199 patients (mean age: 35.69 ± 5.16) with RIF (n = 103) and RPL (n = 96) who visited our fertility clinic between October 2019 and November 2022. Endometrial samples were analyzed using real-time PCR for target DNA sequences. Results: Overall, 62.8% (n = 125) exhibited an absence of Lactobacilli in their endometrial samples, with 63.1% (n = 65) of the RIF group and 62.5% (n = 60) of the RPL group showing a lack of Lactobacilli, with no statistically significant difference between the groups (p = 0.926). A Lactobacillus-dominant microbiome was found in 23.6% of the entire cohort (n = 47), 25.2% of the RIF group (n = 26) and 21.9% of the RPL group (n = 21). A reduced abundance of Lactobacilli was identified in 13.5% of the cohort (n = 27), though to differing degrees. There was no significant relationship between the abundance of Lactobacilli and belonging to the RIF or RPL group. A statistically significant difference was found in the mean age of two groups in cases with a Lactobacillus-dominant microbiome (mean age of 36.4 ± 4.8 years in the RIF group and 32.5 ± 3.5 years in the RPL group) (p = 0.004). Conclusions: Our findings demonstrate a high prevalence of non-Lactobacillus-dominant microbiomes in a large group of Bulgarian patients with RIF and RPL and significant age-related Lactobacillus changes in the microbiome of patients with RPL. These results point to the potential role of the uterine microbiome and support the need for further prospective studies, especially in cases of advanced maternal age. Full article

Long-term fertilization regulates soil microbial communities and is essential for black soil health and sustainable productivity, yet its key drivers remain unclear. Using a 39-year field experiment, we evaluated the effects of four fertilization regimes: no fertilizer (CK), chemical fertilizer (NPK), organic fertilizer (M), and combined organic-inorganic fertilizer (MNPK). Soil properties and bacterial communities were analyzed using Illumina MiSeq sequencing, quantitative real-time PCR (qRT-PCR), and multivariate analyses. Proteobacteria, Actinobacteriota, Acidobacteriota, Chloroflexi, and Gemmatimonadota dominated (>80% of the community), and all treatments significantly altered their relative abundances. Compared with CK, NPK reduced soil pH by 8.3% and bacterial abundance by 29.7%, increased soil organic matter (SOM) by 22.9%, and decreased community evenness. MNPK reduced pH by only 2.0%, increased SOM by 53.8% and bacterial abundance by 38.9%, and improved community evenness, mitigating acidification while maintaining high diversity. M increased pH by 2.3%, SOM by 73.3%, and bacterial abundance by 71.8%. Soil pH, available phosphorus, and SOM were the main drivers of community structure. Overall, MNPK showed the strongest synergistic effects on soil fertility and microbial stability, making it an optimal strategy for sustainable black soil management. Full article

Preclinical multimodal imaging is widely applied in small animal models for longitudinal studies of human diseases. Beyond murine systems, cost-effective and ethically sustainable models such as the chicken embryo and its chorioallantoic membrane are gaining increasing interest in accordance with the 3Rs principles. This study evaluated the feasibility of using both high-frequency ultrasound and magnetic resonance imaging for the non-invasive longitudinal monitoring of chicken embryo development in ovo. Fifty fertilized eggs were incubated under controlled conditions and examined up to embryonic day 14. High-frequency ultrasound (15–71 MHz) enabled real-time imaging and quantitative assessment of superficial structures, including cranial biometry and limb growth, while magnetic resonance imaging (7T) provided high-resolution three-dimensional visualization of internal organs and extraembryonic compartments. Together, these modalities allowed the progressive identification of key anatomical structures from ED5 onward, with HFUS enabling earlier linear measurements and MRI facilitating detailed anatomical and volumetric evaluation. The integration of these techniques allowed the generation of a developmental imaging timeline and quantitative reference dataset of normal embryogenesis. This multimodal approach represents a promising strategy for in vivo developmental studies, offering a robust baseline to characterize structural alterations induced by experimental conditions. Moreover, the use of the chicken embryo model provides significant ethical and economic advantages, supporting its application in preclinical research and imaging-based studies. Full article

The increasing frequency of extreme climate events challenges farmland nutrient management, yet single-year fertilization assessments fail to capture system adaptability. This study quantifies interannual changes in partial factor productivity (PFP) of rice, wheat, and rapeseed under contrasting climate years (typhoon–high temperature in 2024 vs. drought in 2025) using fixed-point monitoring data from 160 farming entities in the middle and lower Yangtze River, China. Fertilization rates, yields, and PFP were analyzed with paired t-tests and Kruskal–Wallis tests. Rice PFP increased significantly from 21.48 to 23.54 kg kg⁻¹ (p < 0.001) as yields rebounded under normal climate, while wheat PFP dropped sharply from 16.50 to 12.89 kg kg⁻¹ (p < 0.001) under drought, with farmers reducing fertilizer by only 1.1% despite a 22.7% yield loss. Rapeseed PFP remained persistently low (<7 kg kg⁻¹) with no significant changes. Family farms and cooperatives achieved higher PFP than ordinary farmers (p < 0.05). These findings demonstrate that fertilizer use efficiency is highly climate-sensitive and that single-year assessments are misleading. We recommend a dynamic, climate-smart fertilization framework integrating disaster type, crop species, and site-specific thresholds (e.g., real-time weather monitoring to adjust topdressing timing). Full article

Precision monitoring of crops using drone or unmanned aerial vehicle (UAV) technology is rapidly growing as a climate-smart agriculture practice in rice farming systems in Sri Lanka and globally. In rice fields, the Leaf Color Chart (LCC) is traditionally used for manual comparison of a leaf to the standard LCC categories in the field to determine the fertilizer condition of the plant. However, this lacks autonomous monitoring, rapid monitoring of larger fields, scalability, and the digital transformation of the scores with sprayer drones for targeted fertilizer application. Drones with multispectral cameras could pose a greater rapid and digitalized solution for delineation of leaf color instead of LCC, in the field. Thus, this paper presents a novel attempt of digitization of conventional LCC levels 3 and 4, rice plant leaf greenness levels in the field, with classification and production of a spatial map using drone multispectral images and machine learning algorithms. The experimental setup consisted of ground sampling of LCC levels 3 and 4 from farmer fields and acquisition of drone imagery data above the field with a DJI Phantom 4 Multispectral UAV, from which fifteen vegetation indices related to crop spectra were extracted. The vegetation indices were then employed for training (70%) and testing (30%) with machine learning algorithms: Random Forest (RF), as well as SVM-linear and SVM-RBF, focusing on LCC 3–4 class classification. The results showed good classification performance, with the RF algorithm reporting a test accuracy of 98.2%, outperforming SVM-linear (82.5%) and SVM-RBF (87.5%). The RF model outputs SR, EVI, MSR, NDVI, and TCARI as feature importance indices for the classification of LCC levels 3 and 4 in the rice field. The findings of this proposed method greatly encourage the adaptation of drone technology for real-time monitoring of rice leaf fertilizer levels linked to LCC levels three and four, and spatial identification of the zones across the field. This imposes greater advancement towards climate-smart rice cultivation, targeted fertilizer application and rice field landscape pattern change analysis, underpinning the importance of field digitization. Full article

Nitrate (NO₃⁻) serves as a pivotal molecule with dual functions in nutrient supply and signaling during plant growth and development. Precise monitoring of its spatiotemporal dynamics in planta is therefore essential for dissecting the regulatory mechanisms underlying plant nitrogen metabolism. However, conventional nitrate detection methods suffer from inherent limitations, including destructive sampling, insufficient spatiotemporal resolution, and an inability to achieve real-time whole-plant monitoring. Here, we report a genetically encoded nitrate biosensor, designated NitNRCL1, constructed using a split firefly luciferase complementation system. Functional validation in both prokaryotic and eukaryotic systems demonstrates that NitNRCL1 responds to changes in nitrate availability and generates stable chemiluminescent signals in bacteria and diverse plant species. Importantly, NitNRCL1 enables non-invasive, real-time, and whole-plant monitoring of nitrate levels in living plants. Using NitNRCL1, we successfully imaged the spatiotemporal dynamics of nitrate signaling in Arabidopsis thaliana. Collectively, our findings establish NitNRCL1 as a robust and novel tool for investigating nitrate transport, signaling, and metabolic pathways in plants. This biosensor advances our mechanistic understanding of plant nitrate biology and provides a technical foundation for breeding nitrogen-use-efficient crops and developing precision fertilization strategies. Full article

Contemporary problems ranging from allergies, myopia, and obesity to chronic anxiety, loneliness, and ultralow fertility can be understood as consequences of evolutionary mismatch intensified by the polycrisis, in which accelerating technological and socioeconomic changes push human adaptations beyond what they evolved to handle. We sought to provide a conceptual review that maps these problems to adaptive needs that are disrupted in highly modernized environments. We then introduce the social evolutionary mismatch and competition hypothesis, which proposes that social aspects of evolutionary mismatch—e.g., increasing population sizes, fragmented communities, rising socioeconomic inequality, constant exposure to inflated social status cues—have a distinct effect of heightening both real and perceived competition. In turn, this perspective can help us make sense of predictable variation in psychosocial outcomes, including obsessive status pursuit, hostility, and social withdrawal. Finally, we outline strategies to lessen the impact of these dynamics by reducing sources of evolutionary mismatch. In sum, we contribute (1) an exposition of how the polycrisis exacerbates evolutionary mismatch and the adaptive needs that are impacted, (2) a theoretical advance identifying mismatch-driven competition as a predictor of multiple problematic outcomes, and (3) a translational framework showing how evolutionary insights can inform interventions to promote well-being in a time of profound societal strain. Full article

Novel brominated flame retardants (NBFRs), including 1,2-bis(2,4,6-tribromophenoxy)ethane (BTBPE), are emerging endocrine-disrupting chemicals, though their direct effects on female gamete maturation remain insufficiently characterized. In this study, we used a refined zebrafish oocyte in vitro maturation (IVM) model integrating germinal vesicle breakdown (GVBD) assessment with real-time, image-based oocyte diameter quantification. The workflow incorporated donor-condition optimization and diameter-based quality control during sorting. Oocytes from donors 4 to 5 months post-fertilization (mpf) showed more consistent diameter dynamics at the dish level than those from donors 3 to 4 mpf. Mixed-sex co-housing was associated with higher GVBD and larger Δdiameter than separated housing, although this comparison should be considered preliminary. Under DHP induction, BTBPE (1–1000 nM) consistently suppressed GVBD and attenuated maturation-associated diameter increases, with a non-monotonic-like response pattern. These findings indicate that BTBPE impairs oocyte maturation competence in vitro and supports real-time morphometric tracking as a practical QC component for zebrafish IVM workflows. Full article

Background/Objectives: Adenomyosis is a common disorder of the uterus in those of reproductive age. Robotic-assisted surgery has been adopted to address the technical challenges of adenomyomectomy. This systematic review evaluated the current evidence regarding the feasibility, safety, and clinical outcomes of robotic-assisted conservative surgery for uterine adenomyosis. Methods: A systematic review of literature was performed on five databases, from the beginning to 21 December 2025, to identify studies reporting robotic-assisted uterus-sparing surgical approaches to adenomyosis. Data were collected on patient characteristics, surgical techniques used, pre- and post-operative pain, fertility outcomes, and complications. Risk of bias was evaluated using the ROBINS-I framework. Results: A total of 514 articles were found; six studies met the inclusion criteria. Most included studies were small and retrospective. The operative time ranged from 279 to 147 min. Mean blood loss ranged between 25 and 296 mL with a low rate of conversion and perioperative complications. Dysmenorrhea improved after surgery as reflected by the post operative visual analog scale pain score and serum CA-125 level. Few reproductive data were collected about successive spontaneous pregnancies. Risk of bias was serious or moderate in all studies included. Conclusions: Robotic-assisted conservative surgery for adenomyosis may represent a feasible and safe option for women with symptomatic adenomyosis who wish preserve the uterus, with a positive impact on patients’ symptoms. Large prospective, multicenter studies with standardized protocols and long-term follow-up are needed to clarify the real impact of robotic surgery in adenomyosis management. Full article

Accurate crop yield prediction is fundamental to sustainable agricultural management, enabling optimized resource allocation and informed decision-making. However, a critical gap exists in current prediction models: existing approaches overlook the temporal alignment between meteorological conditions and production management phases—defined as the intervals between consecutive agronomic operations (e.g., sowing, fertilization, thinning). This oversight results in suboptimal predictive performance, as conventional whole-season weather aggregation fails to capture phase-sensitive crop–weather interactions. While machine learning (e.g., XGBoost) and deep learning approaches (e.g., CNN, LSTM) have been applied to yield prediction, these models typically treat weather variables as temporally homogeneous inputs, inadequately modeling the correlation between historical yields and phase-specific meteorological patterns. To address this gap, this study proposes CNN-LSTM-AM, an innovative hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM), and attention mechanisms (AMs), utilizing weather data explicitly aligned with production management phases as input. The CNN component extracts cross-phase weather patterns, the LSTM captures sequential dependencies across growth stages, and the attention mechanism dynamically weights phase importance based on meteorological conditions. The proposed model is validated using a real-world case study of Bok choy production from an agricultural cooperative in Yunlin County, Taiwan, encompassing 1714 production cycles over eight years (2011–2019). Experimental results demonstrate that CNN-LSTM-AM achieves an RMSE of 1448.24 kg/ha, MAPE of 3.60%, and R² of 0.98, outperforming five baseline models—CNN (RMSE = 2919.18), LSTM (RMSE = 2529.74), CNN-LSTM (RMSE = 1516.44), LSTM-AM (RMSE = 2284.64), and XGBoost (RMSE = 3452.47)—representing a notable reduction in prediction error (58% lower RMSE) compared to XGBoost. Furthermore, prediction accuracy improves progressively as harvest time approaches, and phase-specific weather encoding enhances accuracy by 16.5% compared to whole-season averaging. These findings underscore the critical importance of integrating agronomic domain knowledge into data-driven prediction frameworks. Full article

This study addresses the inefficiencies, subjectivity, and poor adaptability to lighting variations inherent in traditional candling methods used in large-scale egg incubation. We developed a high-throughput transmissive imaging system capable of capturing 30 eggs simultaneously. Based on this system, we propose PDA-YOLO, an enhanced YOLOv8-based object detection model featuring a position-decoupled attention strategy. Specifically, a lightweight C2f-SE module is integrated into the backbone to amplify subtle feature responses in low-contrast regions, while a CBAM is deployed prior to the detection head to mitigate background clutter through precise spatial attention. Experimental results on a self-constructed Hailan White egg dataset show that at the critical 60 h incubation stage, PDA-YOLO achieves a Recall of 91.5% and an mAP@0.5 of 97.4%, outperforming the YOLOv8 baseline while maintaining a real-time inference speed of 62.1 FPS. Grad-CAM visualizations confirm the model’s ability to focus on vascular textures and suppress noise. Furthermore, the model demonstrates robust performance under varying illumination (180–540 lumens), effectively mitigating missed detections in low light and recognition degradation from overexposure. This work provides a scalable, real-time solution for non-destructive, early-stage detection of poultry health and fertilization status in commercial hatcheries. Full article

With the advancement of agricultural modernization, issues related to resource conservation, intensive utilization, and green, low-carbon development have become increasingly prominent. To enhance water and fertilizer use efficiency in Henan Province and promote green, low-carbon, and sustainable agricultural development, field experiments were conducted during 2023–2024. The experiment employed a randomized complete block design with three replications. Each plot measured 30 m² (5 m × 6 m), totaling 36 plots. An IoT-based real-time coordinated water-fertilizer regulation technology, driven by continuous WSH-TDR310S sensor monitoring of soil moisture and nitrogen status with automated threshold-based control logic, was implemented. By transforming the traditional static scheduling approach into a dynamic feedback mechanism driven by real-time sensor data, the synchronization between resource supply and crop demand was achieved. This study aimed to elucidate the response characteristics of summer maize growth dynamics and farmland N₂O emissions under the proposed regulation strategy. The experiment included three levels of water deficit (mild, moderate, and severe) and three fertilization levels (low, medium, and high), resulting in a total of nine real-time water–fertilizer coordinated regulation treatments, along with three local border irrigation control treatments. The results showed that under real-time water–fertilizer regulation, plant height, stem diameter, and leaf area index of summer maize exhibited unimodal variation patterns, with the medium irrigation–medium fertilization (B2) treatment performing optimally. Compared with the border-irrigation medium-fertilization control (D2), plant height and stem diameter under the B2 treatment increased significantly. Cumulative farmland N₂O emissions increased with higher irrigation and fertilization levels, with the border-irrigation high-fertilization treatment producing the highest emissions. Yield formation was mainly governed by structural growth traits, with plant height showing the strongest predictive ability, followed by stem diameter, whereas leaf area index showed weaker explanatory power. Summer maize yield exhibited a unimodal response to both irrigation and nitrogen input levels. Compared with the D2 treatment, the B2 treatment increased grain yield by 41.33%, while achieving water-saving and fertilizer-saving rates of 38.10% and 35.75%, respectively, thereby achieving an optimal balance between high yield and efficient water–fertilizer utilization. These findings provide theoretical support for summer maize production in the North China Plain and contribute to the promotion of green and sustainable agricultural development. Full article

The application of organic residues in agriculture helps to replenish soil organic carbon (OC), improve soil fertility and biodiversity, reinforce aggregate stability, and favour water infiltration. Moreover, its application as a soil amendment alters the fate of herbicides applied to the soil. The objective here was (i) to evaluate soil quality by determining the physicochemical and biological parameters of an agricultural soil (Soil) amended with green compost (Soil + GC) over an arable pea–wheat crop rotation in a short-term experiment; and (ii) to study the dissipation and persistence of iodosulfuron-methyl-sodium applied in field plots sown with winter wheat under real field conditions. The experimental field design consisted of 24 plots (10 m²) involving 12 with control and 12 with GC-amended soils. The plots were sown with pea after GC application (~11 t ha⁻¹) in February 2023, and with winter wheat in October 2023. Iodosulfuron-methyl-sodium (Hussar^® Plus, Bayer CropScience S.L., Barcelona, Spain) was applied in post-emergence at the agronomic dose (D1 = 176 mL ha⁻¹) and double dose (D2 = 352 mL ha⁻¹). Soil samples were taken from the plots to assess the soil physicochemical and biological parameters at six sampling times after GC application, with extraction and determination of residual herbicide and metabolite (metsulfuron-methyl) concentrations. In addition, the yield and characteristics of the pea and wheat grain crops were determined. The application of GC to the soil significantly increased pH (0.5 units by July 2024) and electrical conductivity (up to 5.2 times) compared to control soil, which remained constant throughout the experiment. The OC in Soil + GC increased by 40% in July 2024 compared to control soil. Total nitrogen content increased up to 2.0 and 1.3 times during the pea–wheat growing seasons in Soil + GC compared to unamended soil. Soil dehydrogenase activity, respiration, and biomass increased by up to 1.4, 2.2 and 1.4 times, respectively, in Soil + GC compared to unamended soil over the growing seasons. The soil microbial structure, determined by phospholipid fatty acid (PLFA) analysis, recorded no significant differences between the microbial groups in both soil treatments. A non-significant increase in pea and wheat yield was observed in Soil + GC compared to unamended soil. The results revealed an increase in the residual amounts of herbicide and metabolite, being slightly more persistent, with DT₅₀ and DT₉₀ values up to 1.6 times higher, in the Soil + GC plots over time. Much higher amounts of metabolite (DT₅₀ = 24.8–29.7 days) than iodosulfuron-methyl (DT₅₀ = 5.2–8.8 days) were found in all the treatments. This may be due to wheat plants intercepting the herbicide initially at the time of application in post-emergence, the rapid dissipation of the herbicide reaching the soil, and/or the higher persistence of the metabolite compared to that of the herbicide. Overall, the soil’s physicochemical and biological properties were improved in GC-amended soil, and organic amendment increased slightly the persistence of iodosulfuron-methyl-sodium and its metabolite in the soil. Full article

Rice (Oryza sativa L.) grain quality is a critical determinant of market value, consumer acceptance, and nutritional security. This multifaceted trait is governed by the dynamic interaction of genotype (G), environment (E), and management practices (M). In this review, we synthesize recent advances in understanding these multifaceted determinants. We first delineate the genetic architecture, emphasizing key genes and quantitative trait loci (QTLs) such as Wx, ALK, Chalk5, and the GS3/GW families, which control starch composition, gelatinization temperature, chalkiness, and grain dimensions, forming the foundational blueprint for quality potential. We examine how this genetic potential is influenced by environmental factors, focusing on the detrimental impacts of abiotic stresses, particularly high temperatures during grain filling and drought, which impair milling yield, increase chalkiness, and modify starch and protein profiles. Furthermore, we discuss how optimized agronomic strategies—including precision water management (e.g., alternate wetting and drying), balanced nitrogen fertilization, and targeted micronutrient (e.g., silicon) application—can mitigate these adverse effects and potentially improve specific quality parameters. Post-harvest handling is identified as the final determinant of product quality. We conclude that achieving high and stable rice quality under climate variability requires an integrated G × E × M approach. Prospects include next-generation breeding for climate-resilient quality, precision agronomy guided by real-time sensing, synergistic soil health management, and the integration of systems biology with digital agriculture to design sustainable, high-quality rice production systems. Full article

Branching processes are stochastic models describing the evolution of populations in which individuals reproduce and die independently over time. In the classical setting, an individual’s reproductive capacity is fixed throughout its lifetime. However, in real-world situations, fertility typically rises during a juvenile phase, peaks at maturity, and subsequently declines. In order to capture this feature, we introduce a branching random walk with ageing, as an extension of the classical branching random walk, by assigning each individual an age-dependent reproductive rate. Our model differs from classical age-dependent processes such as the Bellman–Harris model, where the remaining lifespan depends on age, while the rate of reproduction is fixed within that lifetime. As in the classical case, branching random walks with ageing are parametrised by $\lambda >0$, which tunes the reproductive speed and may be seen as a characteristic of the population. The thresholds of $\lambda$ separating extinction and survival are the global and local critical parameters. We characterise the value of the local critical parameter and provide a lower bound for the global critical parameter. We identify a class of ageing branching random walks for which this lower bound coincides with the global critical parameter. We study how local modifications to the reproduction and ageing rates may change the critical parameters. This is of practical interest: in species preservation, one may want to lower the critical parameters, so that $\lambda$ exceeds them, and there is a positive probability of survival. On the other hand, in epidemic control, the goal is to increase the critical parameters, since if $\lambda$ is below them, then the epidemic is eventually going to disappear. We compute the expected number of individuals alive in a branching process with ageing and show that, contrary to the behaviour of classical branching processes, it may exhibit an initial growth even when the population is ultimately destined for extinction. Full article