Abstract: This paper presents a hardware-aware field-programmable gate array (FPGA) implementation of a layered 2-dimensional corrected normalized min-sum (2D-CNMS) decoder for quasi-cyclic low-density parity-check (QC-LDPC) codes in very small aperture terminal (VSAT) satellite communication systems. The main focus of this work is leveraging Xilinx Vitis high-level synthesis (HLS) to design and generate an LDPC decoder IP core based on the proposed algorithm, enabling rapid development and portability across FPGA platforms. Unlike conventional NMS and 2D-NMS algorithms, the proposed architecture introduces dyadic, multiplier-free normalization combined with two-level magnitude correction, achieving near-belief propagation (BP) performance with reduced complexity and latency. Implemented entirely in HLS and integrated in Vivado, the design achieves real-time operation on Zynq UltraScale+ multiprocessor system-on-chip (MPSoC) with throughput of 116-164 Mbps at 400 MHz and resource utilization of 8.7K-22.9K LUTs, 2.6K-7.5K FFs, and zero DSP blocks. Bit-error-rate (BER) results show no error floor down to 10⁻⁸ across additive white gaussian noise (AWGN) channel model. Fixed scaling factors are optimized to minimize latency and hardware overhead while preserving decoding accuracy. These results demonstrate that the proposed HLS-based 2D-CNMS IP core offers a resource-efficient, high-performance solution for multi-frequency time division multiple access (MF-TDMA) satellite links.

Abstract:

This study aimed to evaluate how dietary yeast cell wall (YCW) supplementation in the starter feed affects ruminal fermentation parameters, microbial community composition, and metabolite profiles in early-weaned Simmental calves. Twenty-four newborn Simmental heifer calves (initial body weight: 37.53 ± 2.50 kg) were randomly assigned based on birth date sequence into the experimental group and the control group (12 calves per group). Calves in the experimental group (YCW) received a daily supplement of 5 g/head/day of yeast cell wall in the starter diet, whereas those in the control group (CON) received no supplementation. The experimental period lasted for 100 days, with weaning conducted at 70 days of age. On day 70, rumen fluid samples were randomly collected from six calves per group for analysis of rumen fermentation parameters, microbial community composition, and metabolomic profiles. (1) YCW supplementation significantly increased ruminal butyrate concentration and the relative abundance of the genus Ruminococcus (p < 0.05); (2) Metabolomic analysis identified 43 differential metabolites (20 upregulated and 23 downregulated), with nucleotide metabolism–related compounds such as guanylic acid and deoxycytidine monophosphate being prominently enriched (p < 0.05); (3) Spearman correlation analysis further revealed positive associations between Ruminococcus and both butyrate levels and selected upregulated metabolites, including guanylic acid (p < 0.05). Dietary yeast cell wall supplementation enhanced ruminal fermentation in early-weaned Simmental calves by increasing butyrate concentration and altering the ruminal microbiota and metabolome. Enrichment of Ruminococcus and nucleotide-associated metabolites, with positive correlations to butyrate, indicates a coordinated shift in the microbiota–metabolite axis. These findings support YCW as an effective nutritional strategy to promote rumen development and health during the early weaning period.

Abstract: The complications that arise from pregnancy such as preterm birth (PTB), preeclampsia, and neonatal sepsis continue to pose significant threats to maternal and neonatal health worldwide. Early detection and treatment are crucial in reducing the morbidity and mortality of the complications. The human microbiome, particularly during the perinatal period, has also been seen as a critical regulator of immune and metabolic health, influencing both maternal and infant outcomes. This narrative current review responds to the application of microbiome signatures as predictors for maternal and neonatal health biomarkers. We summarize new evidence linking dysbiosis of both maternal gut and vaginal microbiomes with PTB, preeclampsia, and gestational diabetes mellitus (GDM). We further discuss how the neonatal microbiome affects immune development and what is linked to sepsis risk. The review also briefly addresses the crossroads of multi-omics data to enhance precision medicine, the shortcomings in designing adequately powered clinical trials, and the standardization and regulatory hurdles confronting the microbiome field.As much as the potential of microbiome signatures for predictive diagnostics in maternal and neonatal health is high, there are daunting challenges to overcome. They include the dynamic nature of the neonatal microbiome, the nature of multi-omics data complexity, and invoking standardized methodologies and robust clinical trials. Nevertheless, the incorporation of microbiome-based biomarkers into medicine has the potential to move towards more personalized, non-invasive, and effective management of maternal and neonatal health.

Abstract: Global deforestation is accelerating at an unprecedented scale, driven by interconnected economic, political, and environmental forces that threaten biodiversity, climate stability, and human well-being. This article synthesizes global datasets and recent evidence to assess the magnitude, spatial distribution, and structural drivers of contemporary forest loss, with particular emphasis on tropical regions. It addresses three core research questions: (i) What is the current scale and geographic concentration of global deforestation and permanent tree-cover loss? (ii) How do agricultural expansion, mining, climate-driven wildfires, and armed conflict interact to intensify forest degradation? (iii) How do global consumption patterns, financial systems, and governance failures—including the symbolic contradictions of U.N. climate summits hosted in major fossil-fuel-exporting and high-emission countries such as the United Arab Emirates, Azerbaijan, and Egypt—externalize deforestation pressures onto vulnerable regions? The analysis shows that permanent land-use change, extractive industries, and conflict-related governance breakdowns dominate forest loss dynamics, while climate change amplifies fire-driven destruction, exposing a widening credibility gap in global climate governance and the urgent need for enforceable, equity-centered forest protection strategies.

Abstract: This study presents a comparative analysis of the economics of batch and continuous lubricant supply process strategies in internal combustion engines (ICEs). A phenomenological model based on mass balance equations was developed to describe the dynamics of lubricant precursor depletion, film formation, and film removal under both supply strategies. The results demonstrate that the continuous supply system achieves a steady-state condition that ensures stable film thickness and a significant reduction in lubricant consumption compared with the batch strategy. Sensitivity analyses reveal that both the kinetic constant and the film removal rate strongly influence lubricant make-up requirements, defining a feasibility region for process operation. Under supercritical conditions, the batch strategy exhibits rapid precursor overconsumption; in contrast, the continuous strategy maintains minimal excess. The findings suggest that continuous lubrication process strategy can substantially improve economic and environmental performance in ICEs when properly designed and operated within feasible kinetic and mechanical limits.

Abstract: The human metapneumovirus (HMPV) Fusion (F) glycoprotein is a high-priority target for "fusion-locking" agents that stabilize its metastable prefusion state. While monomeric catechins like EGCG are known antivirals, the molecular basis for the superior activity of structurally complex dimeric catechins remains poorly understood. We employed an advanced biophysical workflow, integrating 100 ns all-atom Molecular Dynamics (MD), Free Energy Landscape (FEL) analysis, and MM/GBSA thermodynamic integration to decode the Structure-Dynamics Relationship (SDR) of 210 Camellia sinensis (Green tea) phytochemicals. The results reveal a "Galloylation-Driven Anchoring" mechanism: the galloyl moiety of prodelphinidin A2 3′-gallate provides critical electrostatic complementarity to the Asp325-Asp336 acidic ridge. FEL analysis quantitatively demonstrates that this anchoring traps the F protein in a deep, kinetically stabilized global minimum (ΔG = 9.357 kJ/mol), effectively raising the energy barrier for the fusogenic conformational shift. This study provides a rigorous thermodynamic proof-of-concept for the use of dimeric natural scaffolds as precision fusion-locking agents, offering a roadmap for experimental biophysical validation.

Abstract: Background: Killed Whole Cell Genome-Reduced Bacteria (KWC/GRB), a versatile vaccine platform, can produce very low cost, thermostable, easily manufactured vaccines expressing complex immunogens that include potent immunomodulators. This system supports iterative optimization through a Design–Build–Test–Learn (DBTL) workflow aimed at enhancing immunogenicity. We applied this approach to developing an HIV-1 gp41 Membrane-Proximal External Region (MPER) vaccine using the scaffolded MPER antigen, 3AGJ, a recombinant heterologous protein engineered to mimic MPER structures recognized by broadly neutralizing monoclonal antibodies (bNAbs). Methods: Five KWC/GRB vaccines expressing versions of 3AGJ were designed, including versions linked to immunomodulators and multimers of the immunogen. Display on the surface of the bacteria was evaluated by flow cytometry using the broadly neutralizing monoclonal antibody 2F5. Outbred HET3 mice were vaccinated intramuscularly, MPER-specific an-tibody responses were assessed by ELISA, and the ability of the vaccines to induce neutralizing antibodies determined. Neutralization was measured against tier 1 and tier 2 HIV-1 pseudoviruses. Results: All five vaccines were strongly expressed on the bacterial surface and induced clear MPER-specific antibody responses in every mouse. About 33% of the animals showed detectable HIV-1 neutralization. Conclusion: A KWC/GRB 3AGJ scaffold-MPER vaccine can induce HIV-1 neutralizing antibodies. While improvements in the responses would be needed for a clinically useful vaccine, the findings provide an initial validation of the concept. There are many strategies that can be used to enhance and extend immune responses induced by KWC/GRB vaccines that can be employed to yield improved anti-HIV immune responses.

Abstract: Autism spectrum disorder (ASD) is a prevalent and largely idiopathic developmental disorder with relatively widespread etiology. Currently there are no validated diagnostic or screening biomarkers for ASD, besides addressing the associated comorbidities. ASD is primarily diagnosed based on behavioral motor and cognitive characteristics. Until recently, the cerebellum had been particularly implicated in motor control, and under-researched for its potential role in the development of ASD. However, cerebellar circuitry is altered in ASD, impacting its brain interconnections, affecting brain development, and social and behavioral outcomes associated with ASD. We review the potential role of the cerebellum in ASD, how its dysfunction during development or its early postnatal injury may impact the maturation of other connected circuits, and play a role in the development of core ASD symptoms. We address cerebellar changes that may alter synaptic pruning, immune cells’ function, neurotransmitters, blood brain barrier permeability, and potential signaling pathways involved in ASD and how all these changes interplay may contribute to ASD pathophysiology. Understanding of these interactions, may provide novel therapeutic options specifically targeted to the cerebellum.

Abstract: Objectives To summarize evidence on the role of nutrition in regulating tissue crosstalk and to identify key knowledge gaps relevant to metabolic health and disease. Background Tissues continuously communicate to maintain metabolic balance. This inter-organ communication, referred to as tissue crosstalk, enables organs such as the liver, adipose tissue, skeletal muscle, gut, and immune system to coordinate responses to nutritional and environmental signals. Disruption of these pathways is increasingly recognized as a central feature of metabolic disorders, including obesity, type 2 diabetes, and non-alcoholic fatty liver disease. Nutrition plays a critical role in shaping tissue crosstalk beyond its role as a source of energy and building blocks. Dietary signals transmitted through hormones, cytokines, metabolites, and other mediators are strongly influenced by macronutrient quality and food matrix characteristics, thereby modifying metabolic and inflammatory signaling across organs. Methods This narrative review synthesizes evidence from human and experimental studies examining how nutrition regulates tissue crosstalk, with emphasis on macronutrient quality and gut microbiota–mediated mechanisms. Results The gut microbiota represents a key link between diet and systemic metabolic regulation. Dietary patterns influence microbial composition and activity, leading to the production of metabolites such as short-chain fatty acids, bile acid derivatives, and tryptophan-related2compounds. These microbial products act as signaling molecules that affect distant tissues and support coordinated metabolic responses. Evidence suggests that whole foods and food matrices may modulate these interactions more effectively than isolated nutrients or supplements. Conclusion Macronutrient quality and diet–microbiota interactions emerge as central regulators of inter- organ communication. Important gaps remain regarding the context-dependent effects of dietary protein quality and the influence of plant-based dietary patterns under conditions of positive energy balance. Addressing these gaps may help inform nutritional strategies aimed at supporting metabolic health beyond weight loss alone.

Abstract: Autonomous driving technology represents a critical component in the advancement of new energy vehicles and serves as an essential enabler for the achievement of sustainable development goals at the societal level. However, autonomous driving in nighttime scenarios suffers from unstable perception under low-light conditions and limited effectiveness, which significantly constrains the practical performance of existing perception systems. This is attributed to the fact that visual degradation and modal reliability imbalance prevalent in nighttime scenarios give rise to erratic feature fusion dynamics within 3D detection paradigms, which is the key technology in autonomous driving, consequently undermining the detection precision. In this paper, a BEV-based multi-modal 3D object detection approach is presented for nighttime autonomous driving that incorporates adaptive modeling components tailored for nighttime scenarios while preserving the original BEV representation and detection pipeline. Without modifying the core inference structure, the method improves robustness to low-light conditions and enhances the stability of cross-modal feature integration, thereby maintaining reliable perception performance under challenging illumination conditions. Extensive experiments are conducted on the nuScenes nighttime subset to evaluate the effectiveness of the proposed approach. The experimental results demonstrate that the proposed method consistently outperforms the BEVFusion baseline while introducing negligible additional model parameters and inference overhead. In particular, an overall NDS improvement of 1.13\% is achieved under nighttime conditions, validating the effectiveness and practical applicability of the proposed approach for low-light and complex autonomous driving environments.

Abstract: This paper explores the link between economic performance and multidimensional well-being in the Italian context using a combination of the ISTAT BES approach (Benessere Equo e Sostenibile) and machine learning and clustering analysis. On the basis of a dataset of 19 Italian regions and the Autonomous Provinces of Trento and Bolzano from 2012 to 2023, it will be examined how the three BES components—Benessere (B), Equità (E), and Sostenibilità (S)—are intertwined with the Gross Domestic Product of the regions. Regarding the Benessere (B) component of well-being, the Gross Domestic Product will be analyzed using a regression approach of the K-Nearest Neighbors type to reveal the complex linkages between health outcomes, education outcomes, working conditions, social participation, and economic performance. The clustering of the B indicators and the Gross Domestic Product will be done using Hierarchical Clustering analysis to identify homogeneous territories characterized by different levels of quality of life and economic prosperity. Regarding the Equità (E) component of well-being, the regression analysis will be done using the Boosting algorithm to model the linkages between the Gross Domestic Product and the indicators of income distribution, poverty, material deprivation, and inclusion in the labor market. Boosting regression analysis will be particularly useful for this purpose since it models the complex interactions and thresholds of social and economic inequalities. Hierarchical Clustering analysis will be applied to identify the territories characterized by different levels of equity and economic growth. Regarding the Sostenibilità (S) component of well-being, the Gross Domestic Product will be modeled using Boosting regression analysis to reveal the very complex linkages between the economic performance of the territories and the indicators of environmental quality, risk of climate change, innovation outcomes, and the quality of public services. For this purpose, the analysis will use the Random Forest algorithm to identify the territories characterized by different levels of sustainability and economic performance. The analysis will show that the BES approach provides a very useful framework to identify the very different levels of linkages between the economic performance of the territories and the outcomes of the BES approach. The analysis will provide evidence that the BES approach is a very useful framework for the analysis of the linkages between the economic performance of the territories and the outcomes of the BES approach.

Abstract: This study employed soybean meal as the substrate and systematically optimized its enzymatic hydrolysis process using a combination of single-factor experiments and response surface methodology. A predictive model based on the Box–Behnken design was established to improve protein hydrolysis efficiency and increase the yield of functional products. The optimal conditions identified were 1.45% enzyme addition, a reaction time of 62 h, a temperature of 36.5 °C, and a moisture content of 35%. Under these conditions, the small-peptide content increased by 16.33-fold. Structural analyses showed that enzymatic treatment markedly disrupted the compact surface of soybean meal, converting it into a loose and porous matrix. In addition, enzymolysis altered the protein secondary structure from ordered α-helices and folded conformations to more disordered and flexible forms, thereby improving the molecular-weight distribution. Composition analyses demonstrated a 114.2% increase in total free amino acids, including essential amino acids. Moreover, DPPH radical-scavenging activity increased from 18.37% to 57.99%. Overall, this study optimized the enzymatic hydrolysis conditions for soybean meal and provides valuable insights for the development of high-value protein-peptide products.

Abstract: The two most severe cosmological tensions in the Hubble constant \( H_0 \) and the matter clustering amplitude \( S_8 \) have the same relative discrepancy of 8.3%, which suggests that they may have a common origin. Modifications of gravity and exotic dark fields with numerous free parameters introduced in the Einstein field equations often struggle to simultaneously alleviate both tensions; thus, we need to look for a common cause within the standard \( \Lambda \)CDM framework. At the same time, linear perturbation analyses of matter in the expanding \( \Lambda \)CDM universe have always neglected the impact of comoving peculiar velocities \( \mathbf{v} \) (generally thought to be a second-order effect), the same velocities that in physical space cannot be fully accounted for in the observed late-time universe when the cosmic distance ladder is used to determine the local value of \( H_0 \). We have reworked the linear density perturbation equations in the conformal Newtonian gauge (sub-horizon limit) by introducing an additional drag force per unit mass \( -\Gamma(t)\mathbf{v} \) in the Euler equation with \( \Gamma \equiv \gamma(2 H) \), where \( \gamma\ll1 \) is a positive dimensionless constant and \( 2H(t) \) is the time-dependent Hubble friction. We find that a damping parameter of \( \gamma = 0.083 \) is sufficient to resolve the \( S_8 \) tension by suppressing the growth of structure at low redshifts, starting at \( z_\star\simeq 3.5-6.5 \) to achieve \( S_8\simeq 0.78-0.76 \), respectively. Furthermore, we argue that the physical source causing this additional friction (a tidal field generated by nonlinear structures in the late-time universe) is also responsible for a systematic error in the local determinations of \( H_0 \): the inability to subtract peculiar tidal velocities along the lines of sight when determining the Hubble flow via the cosmic distance ladder. Finally, the dual action of the tidal field on the expanding background—reducing both the matter and the dark-energy sources of the squared Hubble rate \( H^2 \), thereby holding back the cosmic acceleration \( \ddot a \)—is of fundamental importance in resolving cosmological tensions and can also substantially alleviate the density coincidence problem.

Abstract: Central nervous system (CNS) disorders constitute a significant global health challenge; however, the development of therapeutic agents is considerably impeded by the difficulty in delivering effective concentrations within the brain. This comprehensive review delineates the current landscape of computational modeling techniques employed to address the formidable challenges associated with CNS drug delivery, with a particular emphasis on the anatomical barriers and physiological transport mechanisms pertinent to major neurological diseases. We categorize modeling approaches ranging from the atomistic scale, including molecular dynamics simulations of drug-blood-brain barrier (BBB) interactions, to macroscopic continuum and Physiologically Based Pharmacokinetic (PBPK) models that elucidate systemic distribution and overall brain exposure. We critically assess these models concerning established delivery routes, such as intranasal and intrathecal administration, as well as emerging methods, including focused ultrasound-mediated BBB opening and targeted nanoparticle delivery. This review underscores the growing importance of integrating complex physiological phenomena, such as glymphatic flow and cerebrospinal fluid (CSF) dynamics, into predictive models. Finally, we explore the emerging opportunities involving multiscale digital twins of the CNS that integrate molecular interactions, vascular hemodynamics, CSF and perivascular flow, and parenchymal transport within patient-specific anatomical geometries. The role of machine learning and surrogate modeling in expediting the prediction of drug transport parameters and optimizing delivery strategies is also examined. By providing a structured overview of current computational tools, this review aims to guide researchers in the design of more robust computational platforms for CNS drug delivery.

Abstract: Representational alignment, defined as correspondence between distinct representations of the same underlying structure, is usually evaluated using coordinate-level similarity in high-dimensional spaces, together with correlation-based measures, subspace alignment techniques, probing performance and mutual predictability. However, these approaches do not specify a baseline for the level of agreement induced solely by dimensional compression, shared statistical structure or symmetry. We develop a methodological framework for assessing representational alignment using the Borsuk-Ulam theorem as a formal constraint. Representations are modeled as continuous maps from a state space endowed with a minimal symmetry into lower-dimensional descriptive spaces. In this setting, the Borsuk-Ulam theorem provides a lower bound on the identification of symmetry-paired states that must arise under dimensional compression. Building on this bound, we define representational alignment in terms of shared induced equivalence relations rather than coordinate-level similarity. Alignment is quantified by testing whether distinct models collapse the same symmetry-related states beyond what is guaranteed by topological necessity alone. The resulting metrics are architecture-independent, symmetry-explicit and compatible with probe-based comparisons, enabling controlled null models and scale-dependent analyses. Our framework supports testable hypotheses concerning how alignment varies with representation dimension, compression strength and symmetry structure, and applies to both synthetic and learned representations without requiring access to internal model parameters. By grounding alignment assessment in a well-defined topological constraint, this approach enables principled comparison of representations while remaining neutral with respect to the semantic or ontological interpretation of learned features.

Abstract:

Antimicrobial resistance (AMR) is recognised as a major global public health threat, with the environment increasingly acknowledged as a key reservoir and dissemination pathway for resistant bacteria and resistance genes. In this study, 148 surface water samples were collected between 2023 and 2024 from six rivers and three canals discharging wastewater into two lake waters in southern Italy to assess the occurrence and genomic features of extended-spectrum β-lactamase (ESBL)-, AmpC- and carbapenemase-producing Escherichia coli and Klebsiella pneumoniae. Relevant isolates were obtained using selective culturing, and tested for antimicrobial susceptibility by broth microdilution. Major β-lactam resistance genes were detected by Real-Time PCR. Whole-genome sequencing (WGS) was performed on presumptive carbapenemase-producing isolates. ESBL- and/or carbapenemase-producing Enterobacterales were detected in 67.6% of samples, yielding 176 non-duplicate isolates. The most prevalent gene was bla_CTX-M, detected in 79.3% of positive isolates (96/121), while carbapenemase genes were detected in 20.6% (25/121) of isolates, mainly bla_OXA-48 and bla_VIM. WGS analysis revealed occurrence of clinically relevant high-risk clones, such as K. pneumoniae ST512/ST307 carrying bla_KPC-3 and E. coli ST10 harboring bla_OXA-244. These findings demonstrate widespread contamination of surface waters with clinically relevant resistant Enterobacterales and highlight the importance of integrating environmental compartments into One Health AMR surveillance frameworks.

Abstract: In this paper a folding wing based on gear meshing deformation mechanism is developed, focusing on structural analysis and further optimization of the folding wing. Compared with existing folding wing concepts, the deformation mode of this wing is easier to manufacture and implement in engineering. A dynamic contact finite element model of gear meshing is established in ABAQUS, achieving the transmission of motion. The meshing simulation on the gear pair and dynamic strength analysis on the gear mechanism is conducted to obtain stress analysis. The results shows that the mechanism meets the strength requirements. Further dynamic numerical simulations are conducted on the outboard wing to determine the hazardous area of the load, indicating that the folding wing meets the strength requirements. At the same time, the analysis is conducted on the displacement at the tip of the outboard wing, indicating that the folding motion is relatively gentle. Finally, based on the stress analysis results, a weight reduction topology design is carried out for the spoke area of the gear and the rib structure of the folding wing using the variable density method. While ensuring strength, the optimal distribution of materials is sought by using as little material as possible, and the model is reconstructed according to the optimization results. The optimization results show that the weight reduction effect is significant.

Abstract: This paper presents the implementation of an early stage fault detection and health monitoring system for electric motors and their drive units. The study focuses on developing a cost-effective system capable of identifying abnormal behavior in both drive electronics and mechanical components before a major failure occurs. The proposed design integrates multiple sensing parameters such as vibration, acoustic signals, and electrical quantities including voltage and current. These inputs are processed using data-driven techniques to assess motor condition and identify fault patterns. A microcontroller-based platform is used for real-time monitoring and signal processing, providing early warnings through an intuitive serial interface. Experimental observations confirm that this approach can effectively detect drive faults, motor imbalance, and bearing wear at an early stage, reducing downtime and maintenance costs. This work demonstrates a practical and scalable method to enhance the reliability and operational safety of motor-driven systems, contributing to improved industrial efficiency and predictive maintenance strategies.

Abstract:

The Brown Planthopper, Nilaparvata lugens (Stål.) (Hemiptera: Delphinidae), is one of the most destructive pests of rice. Its reproductive and developmental traits are influenced by various environmental and biological factors including endosymbiotic microorganisms. Arsenophonus, a widespread endosymbiotic bacterium of insects, can affect host fitness and metabolic processes. This study investigates the role of Arsenophonus in modulating the developmental and reproductive traits of N. lugens fed on transgenic cry30Fa1 rice (KF30-14) and its parent variety Minghui 86 (MH86). Life table analysis revealed that Arsenophonus infection (Ars⁺) increased the development time and reduced the reproductive capacity of N. lugens, especially those feeding on KF30-14. The first-instar nymphs in MH86 Ars⁺ (infected) exhibited slower development compared to MH86 Ars^- (uninfected). Similarly, the third and fourth-instar nymphs in KF30-14 Ars⁺ exhibited prolonged development time compared to KF30-14 Ars^-. In addition, KF30-14 Ars⁺ females had significantly reduced reproductive capacity, smaller ovarian tubules and lower relative expression levels of reproduction-related genes including Trehalose transporter (Tret), Vitellogenin (Vg) and Cytochrome P450 hydroxylase (cyp314a1), while Juvenile hormone acid methyltransferase (JHAMT) expression was upregulated. RNA sequencing and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed significant enrichment of genes involved in lipid, amino acid, and vitamin metabolisms, with Long-chain acyl-CoA synthetase implicated as a key regulator of lipid metabolism and reproductive fitness. These results highlight the complex interactions between endosymbionts, host plants and pest biology, offering a solid foundation for sustainable approaches to control N. lugens in rice production systems.

Abstract: Image-to-PointCloud (I2P) place recognition is crucial for autonomous systems, facing challenges from modality discrepancies and environmental variations. Existing feature fusion strategies often fall short in complex real-world scenarios. We propose AttnLink, a novel framework that significantly enhances I2P place recognition through a sophisticated attention-guided cross-modal feature fusion mechanism. AttnLink integrates an Adaptive Depth Completion Network to generate dense depth maps and an Attention-Guided Cross-Modal Feature Encoder, utilizing lightweight spatial attention for local features and a context-gating mechanism for robust semantic clustering. Our core innovation is a Multi-Head Attention Fusion Network, which adaptively weights and fuses multi-modal, multi-level descriptors for a highly discriminative global feature vector. Trained end-to-end, AttnLink demonstrates superior performance on KITTI and HAOMO datasets, outperforming state-of-the-art methods in retrieval accuracy, efficiency, and robustness to varying input quality. Detailed ablation studies confirm the effectiveness of its components, supporting AttnLink's reliable deployment in real-time autonomous driving applications.