Advancing Open Science

In this study, P. persimilis was provided with T. evansi and T. urticae that had been reared on either bean or potato plants to investigate the effects of both prey and host plant species on the predator’s growth, development, and fitness. The results indicate that the reproductive potential of P. persimilis populations fed T. evansi was significantly lower than that of populations fed T. urticae from the same host plant (p < 0.01). Phytoseiulus persimilis fed T. evansi that had been reared on potatoes showed poorer performance in oviposition period, post-oviposition period, daily egg production, and total egg production compared to those fed T. evansi reared on beans (p < 0.01). The intrinsic rate of increase (r_m) of P. persimilis fed on T. evansi reared on potato was 0.08, which was 55.56% lower than that of populations fed on T. evansi reared on beans. This study sheds light on the complex interactions among host plants, pests, and their natural enemies, thereby providing a theoretical basis for developing more effective and sustainable management strategies against T. evansi that take these intricate ecological relationships into account.

Peach consumers demand good quality fruit, but premature harvests result in fruit that does not ripen properly and does not reach the required organoleptic quality, so consumers stop buying this product that does not meet their expectations. In our region, peaches are exported long distances, and it is required that when they reach the destination market their quality is adequate. Therefore, the objective of this study was to determine the storage capacity of commercial and delayed harvest in three peach cultivars. ‘Rich Lady’, ‘Summer Lady’, and ‘Merryl O’Henry’ were harvested at commercial maturity (H1) and, a few days later (H2), packed in passive modified atmosphere (PMA), and stored under refrigeration for up to 40 days to simulate marketing to distant markets. During storage and after three days of shelf-life, the physico-chemical characteristics, damage, and sensory quality of the fruit were analyzed. In general, after cold storage, peaches improve their sensory characteristics after three days at room temperature. PMA with refrigeration was suitable for exporting ‘Rich Lady’ peaches overseas for H1. The late harvest, H2, is recommended for ‘Summer Lady’, as it improves sensory quality without losing storability. ‘Summer Lady’ was the best-rated cultivar by the tasters, and ‘Merryl O’Henry’ the worst, due to its lack of ripening and high incidence of chilling injury.

Soil salinization remains a critical constraint on global land sustainability, severely limiting agricultural output and ecosystem resilience. To address this issue, a field trial was implemented to investigate the interactive benefits of vermicompost (VC) and a novel soil conditioner derived from coal gasification slag-based soil conditioner (CGSS) in mitigating saline–alkali stress. The perennial forage grass Leymus chinensis, valued for its ecological robustness and economic potential under adverse soil conditions, served as the test species. Five treatments were established: CK (unamended), T1 (CGSS alone), T2 (VC alone), T3 (CGSS:VC = 1:1), T4 (CGSS:VC = 1:2), and T5 (CGSS:VC = 2:1). Study results indicate that the combined application of CGSS and VC outperformed individual amendments, with the T4 treatment demonstrating the most effective results. Compared to CK, T4 reduced soil electrical conductivity (EC) by 12.00% and pH by 5.17% (p < 0.05), while markedly enhancing key fertility indicators—including soil organic matter and the availability of nitrogen, phosphorus, and potassium. Thus, these improvements translated into superior growth of L. chinensis, reflected in significantly greater dry biomass, expanded leaf area, and increased plant height. Additionally, the T4 treatment increased soil microbial richness (Chao1 index) by 21.5% and elevated the relative abundance of the Acidobacteria functional group by 16.9% (p < 0.05). Hence, T4 treatment (CGSS: 15,000 kg·ha⁻¹; VC: 30,000 kg·ha⁻¹) was identified as the optimal remediation strategy through a fuzzy comprehensive evaluation that integrated multiple soil and plant indicators. From an economic perspective, the T4 treatment (corresponding to a VC-CGSS application ratio of 2: 1) exhibits a lower cost compared to other similar soil conditioners and organic fertilizer combinations for saline–alkali soil remediation. This study not only offers a practical and economically viable approach for reclaiming degraded saline–alkali soils but also advances the circular utilization of coal-based solid waste. Furthermore, it deepens our understanding of how integrated soil amendments modulate the soil–microbe–plant nexus under abiotic stress.

The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated whether three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—maintain accuracy when applied to future periods with substantially different rainfall characteristics. Using the Cagayan de Oro River Basin in Northern Mindanao as a case study, models were trained on 2019–2020 data and tested on an independent 2021 period exhibiting 120% higher mean rainfall and 33% increased rainy-day frequency. During training, Random Forest and Hybrid Ensemble substantially outperformed Quantile Mapping (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested under realistic operational constraints using seasonally incomplete calibration data (January–April only), performance rankings reversed completely. Quantile Mapping maintained operational reliability (R² = 0.53, RMSE = 5.23 mm), while Random Forest and Hybrid Ensemble failed dramatically (R² dropping to 0.46 and 0.41, respectively). This demonstrates that training accuracy poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping’s percentile-based correction naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in data-limited basins with equipment failures and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability.

Precise postimplantation regulation of placental development with trophoblast invasion of uterine spiral arteries and the generation of low-resistance circulation within the utero-fetal unit are crucial for the further development of pregnancy. Cytokines, including chemokines, are crucial for ensuring placental function throughout pregnancy. The CX3CL1 chemokine (fractalkine), occurring in its membrane-bound form and as a soluble chemokine (sCX3CL1), acts on its sole receptor, namely, CX3CR1, creating a signaling axis that orchestrates the balance of cellular interactions, immune responses, and tissue remodeling needed at every stage of a healthy pregnancy. CX3CL1/CX3CR1 signaling is characterized by the activation of several downstream signaling cascades that interact with numerous pathways, coordinate with other receptors and modulate the expression of relevant genes. This review presents the current state of knowledge regarding the role of CX3CL1 and its interaction with CX3CR1 in establishing placental homeostasis during placentation, and it discusses the contribution of disturbances in this interaction to placental dysfunction. These disturbances are part of the pathomechanisms of specific pregnancy complications, including preeclampsia (PE) and diabetes. The potential to target the CX3CL1/CX3CR1 axis via therapeutic intervention at the level of the placenta in PE- and diabetes-complicated pregnancy is the subject of ongoing research.

Deep reinforcement learning (DRL) with self-play has emerged as a promising paradigm for solving combinatorial optimization (CO) problems. The recently proposed Gumbel AlphaZero Plan-to-Play (GAZ PTP) framework adopts a competitive training setup between a learning agent and an opponent to tackle classical CO tasks such as the Traveling Salesman Problem (TSP). However, in complex and multi-constrained environments like the Electric Vehicle Routing Problem (EVRP), standard self-play often suffers from opponent mismatch: when the opponent is either too weak or too strong, the resulting learning signal becomes ineffective. To address this challenge, we introduce Two-Stage Self-Play GAZ PTP (TSS GAZ PTP), a novel DRL method designed to maintain adaptive and effective learning pressure throughout the training process. In the first stage, the learning agent, guided by Gumbel Monte Carlo Tree Search (MCTS), competes against a greedy opponent that follows the best historical policy. As training progresses, the framework transitions to a second stage in which both agents employ Gumbel MCTS, thereby establishing a dynamically balanced competitive environment that encourages continuous strategy refinement. The primary objective of this work is to develop a robust self-play mechanism capable of handling the high-dimensional constraints inherent in real-world routing problems. We first validate our approach on the TSP, a benchmark used in the original GAZ PTP study, and then extend it to the multi-constrained EVRP, which incorporates practical limitations including battery capacity, time windows, vehicle load limits, and charging infrastructure availability. The experimental results show that TSS GAZ PTP consistently outperforms existing DRL methods, with particularly notable improvements on large-scale instances.

Genitourinary infections caused by Candida spp. and other yeasts have increased in incidence, and the emergence of resistant isolates to commonly prescribed antifungals is becoming more frequent. Thus, the purpose of this study was to characterize the physiological characteristics of 38 yeast isolates (Candida albicans [n = 32], Candida tropicalis [n = 3], and Nakaseomyces glabratus [n = 3]) recovered from genitourinary infections to better understand the diversity of their physiological profiles, their virulence factors, and their role in pathogenicity. In addition, an experimental study was conducted to determine the minimum inhibitory concentration (MIC) of the isolates using the M27-A3 microdilution method described by the Clinical Laboratory and Standards Institute. Clinical isolates of Candida spp. studied showed in vitro susceptibility to both fluconazole and clotrimazole, the latter having greater antifungal activity due to its lower MIC₅₀. Statistically significant differences were found between the MICs obtained for fluconazole and clotrimazole, with the latter showing the highest in vitro activity. Therefore, the clinical use of clotrimazole is recommended, as is the ongoing need for this type of analysis to monitor changes in susceptibility profiles over time.

Pedestrian crashes cause the most injuries of all types of traffic crashes. Despite their direct judicial and societal impact, the automatic classification of legal responsibility remains largely unexplored. This work addresses this gap by formulating the responsibility assessment problem as a supervised multi-class classification task and proposing a Multilayer Perceptron (MLP) based decision-support system. The objective is to establish the basis for a “robot judge” application that assists the Judicial Traffic Police (JTP), Courts, and Prosecutors in identifying cases with a clear level of responsibility in pedestrian crashes. This study draws on real-world data from reports by the Local Police of Badajoz (LPB) and Spanish Judiciary (SJ) judicial decisions. After rigorous data preprocessing, 14 meaningful binary variables were identified. The level of responsibility in a pedestrian crash depends on these 14 variables, which constitute the feature space used to model responsibility as a five-category output variable. We were able to reclassify the categories of each pedestrian crash and improve the metrics using the MLP model. More precise levels of responsibility could be determined. This would help the JPT and the Courts make more efficient and objective final decisions in similar cases. It would also enable them to focus their efforts on more complex cases requiring further investigation by human specialists. In turn, policymakers could take new measures to reduce pedestrian crashes by analyzing influential variables.