Multiple environmental gradients shape the functional structure of macrobenthic communities across the Pacific Arctic shelf.

The Pacific Arctic shelf is undergoing significant environmental changes that are expected to impact the functioning of Arctic benthic ecosystem. By utilizing trait-based methods, we can better understand the effects of environmental changes on the functional structure of macrobenthic communities, offering a more detailed interpretation that complements traditional biodiversity assessments based on community structure. Using Biological Trait Analysis (BTA), we investigated shifts in the functional composition of macrobenthic communities across the subarctic to Arctic regions of the Pacific Arctic shelf, examining how these communities are responding to various environmental gradients. The study analyzed data from 14 environmental variables and 355 taxa, using 13 functional traits coded with 51 modalities collected from 78 boxcore stations. Multivariate statistics, including fuzzy correspondence analysis (FCA) and RLQ/fourth-corner combined analysis, were utilized. We find that the northern Bering Sea (NB) and southeastern Chukchi Sea (SEC) shelves exhibit shared functional similarities (e.g., small, chitinous skeletons, gregarious behavior, and low body flexibility) and significant regional differences from other subregions. The analysis revealed that sediment characteristics and sea ice cover influenced macrobenthic trait composition. The ongoing retreat of sea ice is expected to lead to rapid functional shifts in the Pacific Arctic shelves, potentially causing the migration of smaller, deposit-feeding, shorter-lived taxa to the Arctic seas. This could result in structural transformation in Arctic communities characterized by greater longevity, suspension-feeding, and larger size. These findings can inform future polar environmental management and help develop adaptive management strategies.

Emerging sensing, imaging, and computational technologies to scale nano-to macroscale rhizosphere dynamics - Review and research perspectives.

The soil region influenced by plant roots, i.e., the rhizosphere, is one of the most complex biological habitats on Earth and significantly impacts global carbon flow and transformation. Understanding the structure and function of the rhizosphere is critically important for maintaining sustainable plant ecosystem services, designing engineered ecosystems for long-term soil carbon storage, and mitigating the effects of climate change. However, studying the biological and ecological processes and interactions in the rhizosphere requires advanced integrated technologies capable of decoding such a complex system at different scales. Here, we review how emerging approaches in sensing, imaging, and computational modeling can advance our understanding of the complex rhizosphere system. Particularly, we provide our perspectives and discuss future directions in developing in situ rhizosphere sensing technologies that could potentially correlate local-scale interactions to ecosystem scale impacts. We first review integrated multimodal imaging techniques for tracking inorganic elements and organic carbon flow at nano- to microscale in the rhizosphere, followed by a discussion on the use of synthetic soil and plant habitats that bridge laboratory-to-field studies on the rhizosphere processes. We then describe applications of genetically encoded biosensors in monitoring nutrient and chemical exchanges in the rhizosphere, and the novel nanotechnology-mediated delivery approaches for introducing biosensors into the root tissues. Next, we review the recent progress and express our vision on field-deployable sensing technologies such as planar optodes for quantifying the distribution of chemical and analyte gradients in the rhizosphere under field conditions. Moreover, we provide perspectives on the challenges of linking complex rhizosphere interactions to ecosystem sensing for detecting biological traits across scales, which arguably requires using the best-available model predictions including the model-experiment and image-based modeling approaches. Experimental platforms relevant to field conditions like SMART (Sensors at Mesoscales with Advanced Remote Telemetry) soils testbed, coupled with ecosystem sensing and predictive models, can be effective tools to explore coupled ecosystem behavior and responses to environmental perturbations. Finally, we envision that with the advent of novel high-resolution imaging capabilities at nano- to macroscale, and remote biosensing technologies, combined with advanced computational models, future studies will lead to detection and upscaling of rhizosphere processes toward ecosystem and global predictions.

Allopolyploidy enhances survival advantages for urban environments in the native plant genus Commelina.

BACKGROUND AND AIMS: Urbanization-induced environmental changes affect the geographical distribution of natural plant species. This study focused on how polyploidization, a dynamic genome change, influences the survival and distribution of Commelina communis L. (Cc) and its subspecies, C. communis f. ciliata (Masam.) Murata (Ccfc) which have different chromosome numbers (e.g. Cc: 2n = 88, Ccfc: 2n = 46). The aim is to investigate polyploidization effects on natural plant distribution in urban environments. METHODS: The geographical distribution across urban-rural gradients was investigated at a total of 218 sites in Japan. Stomata size and density were measured and compared between Cc and Ccfc. Flow cytometry determined genome size and polyploidy. Chromosome karyotyping was performed using genomic in situ hybridization (GISH) method. KEY RESULTS: Urban areas were exclusively dominated by Cc, while Cc and Ccfc coexisted in rural areas. Cc had larger and fewer stomata and more than twice the genome size than Ccfc. GISH results indicated that Cc possesses Ccfc and another unknown genome, suggesting allopolyploidy. CONCLUSIONS: Our results show that the ploidy difference affects the geographical distribution, the stomata traits, and genome size between two distinct taxa in the genus Commelina, C. communis as a neo-tetraploid and C. communis f. ciliata, the diploid. Cc is an allopolyploid, therefore, not only polyploidy but also an additional genome with new sets of genes and alleles contributes to Cc having enhance survival potentials in urban environments compared to Ccfc. This is the first investigation to clarify the distribution difference related to urban environments, the difference in stomata traits and genome size, and to conduct chromosome composition in Commelina species.

A study of stress, composition and grain interaction gradients in energy-dispersive X-ray stress analysis on materials with cubic symmetry.

The influence of various combinations of residual stress, composition and grain interaction gradients in polycrystalline materials with cubic symmetry on energy-dispersive X-ray stress analysis is theoretically investigated. For the evaluation of the simulated sin2ψ distributions, two different strategies are compared with regard to their suitability for separating the individual gradients. It is shown that the separation of depth gradients of the strain-free lattice parameter a 0(z) from residual stress gradients σ(z) is only possible if the data analysis is carried out in section planes parallel to the surface. The impact of a surface layer z* that is characterized by a direction-dependent grain interaction model in contrast to the volume of the material is quantified by comparing a ferritic and an austenitic steel, which feature different elastic anisotropy. It is shown to be of minor influence on the resulting residual stress depth profiles if the data evaluation is restricted to reflections hkl with orientation factors Γ hkl close to the model-independent orientation Γ*. Finally, a method is proposed that allows the thickness of the anisotropic surface layer z* to be estimated on the basis of an optimization procedure.

TIANA: transcription factors cooperativity inference analysis with neural attention.

BACKGROUND: Growing evidence suggests that distal regulatory elements are essential for cellular function and states. The sequences within these distal elements, especially motifs for transcription factor binding, provide critical information about the underlying regulatory programs. However, cooperativities between transcription factors that recognize these motifs are nonlinear and multiplexed, rendering traditional modeling methods insufficient to capture the underlying mechanisms. Recent development of attention mechanism, which exhibit superior performance in capturing dependencies across input sequences, makes them well-suited to uncover and decipher intricate dependencies between regulatory elements. RESULT: We present Transcription factors cooperativity Inference Analysis with Neural Attention (TIANA), a deep learning framework that focuses on interpretability. In this study, we demonstrated that TIANA could discover biologically relevant insights into co-occurring pairs of transcription factor motifs. Compared with existing tools, TIANA showed superior interpretability and robust performance in identifying putative transcription factor cooperativities from co-occurring motifs. CONCLUSION: Our results suggest that TIANA can be an effective tool to decipher transcription factor cooperativities from distal sequence data. TIANA can be accessed through: https://github.com/rzzli/TIANA .

Strength of Sexual Selection and Sex Roles Vary between Social Groups in a Coral Reef Cardinalfish.

AbstractThe strength and direction of sexual selection can vary among populations. However, spatial variability is rarely explored at the level of the social group. Here we investigate sexual selection and sex roles in the paternally mouthbrooding, socially monogamous, and site-attached pajama cardinalfish, Sphaeramia nematoptera. Females were larger and more aggressive and had a longer dorsal fin filament, indicating reversed sex roles. At the scale of social groups, we show that the Bateman gradient and reproductive variance depend on the sex ratio and size of groups. In small and medium-sized groups with balanced or male-biased sex ratios, Bateman gradients were steeper for females, whereas gradients were equally steep for both sexes in large groups or when the sex ratio was female biased. For both sexes, reproductive variance increased with group size and with a higher male-to-female sex ratio. In S. nematoptera, mating opportunities outside the socially monogamous pair appear to impact sexual selection. We conclude that strength and direction of sexual selection can be masked by social dynamics in group-living species when considering only population and large-scale demographic processes.

The Art of Speaking Up: Supporting a Culture of Safety in the OR.

Considering the high-risk, stressful, and fast-paced nature of the perioperative environment and vulnerability of surgical patients, the quest for maintaining a safety culture in the OR is ongoing. Speaking up-an interaction between perioperative team members to address a concern-requires team member empowerment to advocate for patient safety when needed. Hierarchical gradients, lack of psychological safety, incivility, and a nonsupportive organizational culture can impede speaking-up behaviors. Strategies to improve speaking up include using multimethod education initiatives, enhancing psychological safety, and managing conflict. Perioperative nurses can experience barriers to speaking up, such as lack of team familiarity, normalization of deviance, and differing perceptions among team members. The logistics of whole-team training initiatives can be challenging; however, such initiatives can help participants improve their understanding of the perspectives and communication goals of all involved personnel. Perioperative nurses and leaders should collaborate to promote speaking up for safety when warranted.

Orbits, Spirals, and Trapped States: Dynamics of a Phoretic Janus Particle in a Radial Concentration Gradient.

A long-standing goal in colloidal active matter is to understand how gradients in fuel concentration influence the motion of phoretic Janus particles. Here, we present a theoretical description of the motion of a spherical phoretic Janus particle in the presence of a radial gradient of the chemical solute driving self-propulsion. Radial gradients are a geometry relevant to many scenarios in active matter systems and naturally arise due to the presence of a point source or sink of fuel. We derive an analytical solution for the Janus particle's velocity and quantify the influence of the radial concentration gradient on the particle's trajectory. Compared to a phoretic Janus particle in a linear gradient in fuel concentration, we uncover a much richer set of dynamic behaviors including circular orbits and trapped stationary states. We identify the ratio of the phoretic mobilities between the two domains of the Janus particle as a central quantity in tuning their dynamics. Our results provide a path for developing optimum protocols for tuning the dynamics of phoretic Janus particles and mixing fluid at the microscale. In addition, this work suggests a method for quantifying the surface properties of phoretic Janus particles, which have proven to be challenging to probe experimentally.

Exploiting the pH-Cross Sensitivity of Ion-Selective Optodes to Broaden Their Response Range.

While the pH cross-sensitivity of chromoionophore-based ion-selective optodes (ISOs) has often been regarded as a significant limitation, this paper demonstrates how this apparent drawback can be transformed into a beneficial feature. The response range of chromoionophore-based ISOs shifts proportionally with changes in the sample pH. Thus, integrating them with a stable pH gradient across the optode surface, such as those provided by immobilized pH gradient (IPG) gels, allows for significant enhancement of the effective measuring range of chromoionophore-based ISOs while preserving their maximum sensitivity. We show that the measuring range of sodium-selective chromoionophore-based optodes can be increased up to 2.5 log units when used with commercially available IPG gels. This improvement in measuring range is directly correlated with the pH difference in the pH gradient across the optode, suggesting that even greater enhancements are possible with more substantial pH gradients. Furthermore, this approach is not confined to sodium-selective optodes but can be readily adapted to other ion-selective chromoionophore-based optodes, broadening their potential applications and impact in the field of chemical sensing. This work paves the way for the development of more versatile and highly sensitive optodes across a broad range of analytes, leveraging the pH cross-sensitivity as a tool for enhanced performance.

Fast and silent MRI using nonlinear gradient fields at the ultrasonic gradient switching frequency of 20 kHz with a Point Spread Function framework reconstruction.

PURPOSE: To demonstrate the feasibility of using a nonlinear gradient field for spatial encoding at the ultrasonic switching frequency of 20 kHz and present a framework to reconstruct data acquired in this way. METHODS: Nonlinear encoding at 20 kHz was realized by using a single-axis silent gradient insert for imaging in the periphery, that, is the nonlinear region, of the gradient field. The gradient insert induces a rapidly oscillating gradient field in the phase-encode direction, which enables nonlinear encoding when combined with a Cartesian readout from the linear whole-body gradients. Data from a 2D gradient echo sequence were reconstructed using a point spread function (PSF) framework. Accelerated scans were also simulated via retrospective undersampling (R = 1 to R = 8) to determine the effectiveness of the PSF-framework for accelerated imaging. RESULTS: Using a nonlinear gradient field switched at 20 kHz and the PSF-framework resulted in images of comparable quality to images from conventional Cartesian linear encoding. At increased acceleration factors (R ≤ 8), the PSF-framework outperformed linear SENSE reconstructions by improved controlling of aliasing artifacts. CONCLUSION: Using the PSF-framework, images of comparable quality to conventional SENSE reconstructions are possible via combining traditional linear and ultrasonic oscillating nonlinear encoding fields. Using nonlinear gradient fields relaxes the demand for strictly linear gradient fields, enabling much higher slew rates with a reduced risk of peripheral nerve stimulation or cardiac stimulation, which could aid in extension to ultrasonic whole-body MRI. The lack of aliasing artifacts also highlights the potential of accelerated imaging using the PSF-framework.

Assessment of carbon flux gradients and dominant processes in a subtropical highly urbanized coastal ecosystem.

Highly urbanized coastal ecosystems are vital in the global carbon budget. However, there are limited researches on carbon flux gradients in these nearshore areas, considering both natural and anthropogenic influences. Through on-site measurements and field samplings during wet-to-dry season in 2023, this study investigated spatial variations and factors affecting carbon fluxes, focusing on the impacts of salinity and eutrophic status in five geographically connected coastal waters of the Guangdong-Hong Kong-Macau Greater Bay Area (GBA). By estimating carbon exchange at land-sea-air interface, dominant processes in carbon dynamics were identified as well. Results showed that partial pressure of CO2 (pCO2) varied from 391 to 2290 µatm, and sea-air CO2 exchange fluxes (FCO2) ranged from -3.07 to 70.07 mmol m-2 d-1, indicating significant geographical distinctions among five coastal waters of the GBA. The total carbon transport from rivers to these nearshore waters was approximated at 6.44 Tg C yr-1, with the Pearl River (PR) contributing 99.7%, primarily in dissolved forms. Atmospheric CO2 release was calculated at 0.29 Tg C yr-1 for studied five coastal waters, primarily as carbon sources, except for Dapeng Bay (DPB) as a sink. CO2 emissions inversely correlated with salinity, yet positively with eutrophication status, particularly in river-dominated estuaries. Moreover, CO2 flux decreased 23 times as eco-status shift from eutrophic to non-eutrophic. River plumes, terrestrial pollutant inputs, and economic structure were underlying drivers, influencing carbon species concentrations and fluxes. Elevated CO2 concentrations in eutrophic coastal waters were mainly attributed to terrestrial carbon and nutrients inputs, supporting active biological respiration and microbial decomposition. Conversely, carbon dynamics potentially depend on the balance of respiration and photosynthesis in non-eutrophic coastal waters. This study offers high geographic precision and specificity of carbon species, and provides land-sea integration insight to understand carbon dynamic mechanisms, promoting advancements in water quality management and climate mitigation.

MULTIMODAL NEURAL CORRELATES OF CHILDHOOD PSYCHOPATHOLOGY.

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

Diffusion MRI in prostate cancer with ultra-strong whole-body gradients.

Diffusion-weighted MRI (dMRI) is universally recommended for the detection and classification of prostate cancer (PCa), with PI-RADS recommendations to acquire b-values of ≥1.4 ms/µm2. However, clinical dMRI suffers from a low signal-to-noise ratio (SNR) as the consequence of prolonged echo times (TEs) attributable to the limited gradient power in the range of 40-80 mT/m. To overcome this, MRI systems with strong gradients have been designed but so far have mainly been applied in the brain. The aim of this work was to assess the feasibility, data quality, SNR and contrast-to-noise ratio (CNR) of measurements in PCa with a 300 mT/m whole-body system. A cohort of men without and with diagnosed PCa were imaged on a research-only 3T Connectom Siemens MRI system equipped with a gradient amplitude of 300 mT/m. dMRI at high b-values were acquired using high gradient amplitudes and compared with gradient capabilities mimicking clinical systems. Data artefacts typically amplified with stronger gradients were assessed and their correction evaluated. The SNR gains and lesion-to-healthy tissue CNR were statistically tested investigating the effect of protocol and b-value. The diagnostic quality of the images for different dMRI protocols was assessed by an experienced radiologist using a 5-point Likert scale and an adapted PI-QUAL scoring system. The strong gradients for prostate dMRI allowed a significant gain in SNR per unit time compared with clinical gradients. Furthermore, a 1.6-2.1-fold increase in CNR was observed. Despite the more pronounced artefacts typically associated with strong gradients, a satisfactory correction could be achieved. Smoother and less biased parameter maps were obtained with protocols at shorter TEs. The results of this study show that dMRI in PCa with a whole-body 300-mT/m scanner is feasible without a report of physiological effects, SNR and CNR can be improved compared with lower gradient strengths, and artefacts do not negate the benefits of strong gradients and can be ameliorated. This assessment provides the first essential step towards unveiling the full potential of cutting-edge scanners, now increasingly becoming available, to advance early detection and diagnostic precision.

Positional Information-Based Organization of Surfactant Droplet Swarms Emerging from Competition Between Local and Global Marangoni Effects.

Positional information is key for particles to adapt their behavior based on their position in external concentration gradients, and thereby self-organize into complex patterns. Here, position-dependent behavior of floating surfactant droplets that self-organize in a pH gradient is demonstrated, using the Marangoni effect to translate gradients of surface-active molecules into motion. First, fields of surfactant microliter-droplets are generated, in which droplets floating on water drive local, outbound Marangoni flows upon dissolution of surfactant and concomitantly grow myelin filaments. Next, a competing surfactant based on a hydrolysable amide is introduced, which is more surface active than the myelin surfactant and thereby inhibits the local Marangoni flows and myelin growth from the droplets. Upon introducing a pH gradient, the amide surfactant hydrolyses in the acidic region, so that the local Marangoni flows and myelin growth are reestablished. The resulting combination of local and global surface tension gradients produces a region of myelin-growing droplets and a region where myelin growth is suppressed, separated by a wave front of closely packed droplets, of which the position can be controlled by the pH gradient. Thereby, it is shown how "French flag"-patterns, in synthetic settings typically emerging from reaction-diffusion systems, can also be established via surfactant droplet systems.

An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements.

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose a Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE), to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress-detection methodologies in terms of precision, recall, and interpretability. The results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on.

Dynamic range and optimization strategies for radiochromic film calibration using gradient radiation fields.

This investigation aimed to optimize gradient positioning for radiochromic film calibration to facilitate a uniform distribution of calibration points. The study investigated the influence of various parameters on gradient dose profiles generated by a physical wedge, assessing their impact on the field's dose dynamic range, a scalar quantity representing the span of absorbed doses. Numerical parameterization of the physical wedge profile was used to visualize and quantify the impact of field size, depth, and energy on the dynamic range of dose gradients. This concept enabled the optimization of the gradient positioning and estimation of the necessary number of exposures for the desired calibration dose range. An optimization algorithm based on histogram bin height minimization was developed and presented. The maximum dynamic range was achieved with a 20 × $\times$ 20 cm 2 $\textrm {cm}^{2}$ field size at 5 cm depth. Optimization of wedge gradient positioning yielded the most uniform dose distribution with 7 exposures for the [1,10] Gy range and 8 exposures for the [1,20] Gy range. Film calibration using gradients centered at 1.6, 3, 3.5, and 7 Gy central axis (CAX), obtained through optimized gradient positioning, was showcased. The presented work demonstrates the potential for an improved film calibration process, with efficient material utilization and enhanced dosimetric accuracy for clinical applications. While the method was described for the use of a physical wedge, the methodology can be easily extended to the use of a more convenient dynamic wedge.

ESR Essentials: basic physics of MR safety-practice recommendations by the European Society for Magnetic Resonance in Medicine and Biology.

OBJECTIVES: The use of magnetic resonance imaging (MRI) is safe from a long-term perspective since there are no known cumulative risks for patients or personnel. However, the technique comes with several acute risks associated with the powerful electromagnetic fields that are necessary to produce medical images. These risks include, among other things, a projectile hazard, loud noise, and the risk of heating. Safe use of MRI requires knowledge about the different hazards related to MRI and organizational structured work including the implementation of routines describing a safe workflow from the referral of a patient to the signed report. In this article, the risks associated with MRI are described along with suggestions for how each risk can be minimized or eliminated. CONCLUSION: The aim of this article is to provide support for the development of, and compliance with, MRI safety routines, and to work with the technique in a safe way. The scope of this treatise does not cover specific details of implant safety, however, the physical principles described can be applied to the risk assessment of implants. KEY POINTS: Establish whether any MR contraindications apply to the patient. Evaluate means to deal with identified risks for both patients and personnel. It is imperative to always perform and document a risk-benefit assessment.

Differences in cortical microstructure according to body mass index in neurologically healthy populations using structural magnetic resonance imaging.

Associations between brain structure and body mass index (BMI) are increasingly gaining attention. Although BMI-related regional alterations in brain morphology have been previously reported, the effect of BMI on the microstructural profiles, which provide information on the proxy of neuronal density within the cortex, is unexplored. In this study, we investigated the links between cortical layer-specific microstructural profiles and BMI in 302 neurologically healthy young adults. Using the microstructure-sensitive proxy based on the T1-and T2-weighted ratio, we estimated microstructural profile covariance (MPC) by calculating linear correlations of cortical depth-wise intensity profiles between different brain regions. Then, low-dimensional gradients of the MPC matrix were estimated using dimensionality reduction techniques, and the gradients were associated with BMI. Significant effects in the heteromodal association areas were observed. The BMI-gradient association map was related to the geodesic distance along the cortical surface, curvature, and sulcal depth, suggesting that the microstructural alterations occurred along the cortical topology. The BMI-gradient association map was further linked to cognitive states related to negative emotions. Our findings may provide insights into understanding the atypical cortical microstructure associated with BMI.

Social selection analysis reveals limited effect of neighbors' traits in Tree swallows.

Social interactions are ubiquitous in nature and can shape fitness of individuals through social selection. This type of selection arises when phenotypes of neighbors influence the fitness of a focal individual. Quantifying social selection is crucial to better characterise the overall selective landscape. For example, if intraspecific competition is strong, traits that are beneficial for an individual could be detrimental for competitors. In this study, we quantified social selection acting on three key ecological traits (body mass, wing length and laying date) in wild Tree swallow (Tachycineta bicolor) females. We used reproductive success measured at three stages throughout the breeding season as fitness proxies to assess selection acting at those decisive moments. We also quantified the effects of environment on selection using measures of conspecifics' density, type of agricultural landscape and presence of interspecific competitors. Overall, we found no strong evidence of social selection on these traits in our study system, although there were marginally non-significant selection gradients suggesting positive effect of larger neighbors. Environmental variables affected reproductive success but did not strongly affect social selection gradients. Our study calls for more social selection estimates to be reported across environments to better understand its importance in wild populations.

Effectiveness of intra-arterial manometry in guiding femoral popliteal stent implantation: a prospective randomized controlled trial.

BACKGROUND: Femoropopliteal artery occlusion is a prevalent peripheral arterial disease, and endovascular therapy has become the preferred treatment. Accurate assessment of balloon dilation efficacy is crucial for determining the necessity for subsequent stent implantation. This study aims to investigate the use of interlesion arterial pressure gradients as a novel approach to assess balloon dilation efficacy and guide stent implantation decisions. METHODS: A prospective, randomized, controlled trial was conducted on 100 patients with femoropopliteal artery occlusion. Patients were randomized into a control group (n=50) and an experimental group (n=50). Stent implantation was performed in the control group according to standard indications, while the experimental group underwent stent implantation only if the mean arterial pressure gradient exceeded 10 mmHg or fractional flow reserve (FFR) fell below 0.85. Post-intervention, pressure measurements and angiography were used to evaluate residual stenosis, dissection, and pressure gradients. RESULTS: Lesions were categorized into stent-indicated and non-indicated groups. In the non-stent-indicated lesions, the experimental group demonstrated significantly higher patency rates for lesions with pFFR < 0.85 or ΔP > 10 mmHg compared to the control group (92.9% vs. 50.0%, P=0.039). There was no significant difference in patency rates between the experimental and control groups for stent-indicated lesions. CONCLUSION: Combining pressure measurement with angiography provides a more precise evaluation of balloon dilation efficacy and stent implantation indicators in femoropopliteal artery occlusive disease. Further research is needed to establish optimal pressure threshold values and refine treatment guidelines.