Realistic Scenarios Research Articles

Fast and accurate distal locking of interlocked intramedullary nails using computer-vision and a 3D printed device

IntroductionDistal locking is a challenging and time-consuming step in interlocked intramedullary nailing of long bone fractures. Current methods have limitations in terms of simplicity, universality, accuracy, speed, and safety. We propose a novel device and software for distal locking using computer vision.Methods and materialsThe device consists of an universal ancillary clamp, a telescopic arm, a viewfinder clamp, and a radio-opaque cross. The software uses a camera photo from the C-arm intensifier and adjusts for geometric projection deformities. The software employs edge detection, Hough transform, perspective interpolation, and vector calculation algorithms to locate the distal hole center. The device and software were designed, manufactured, and tested using 3D CAD, FEM, DRR, and performance testing on phantom bones.ResultsThe device and software showed high accuracy and precision of 98.7% and 99.2% respectively in locating the distal hole center and calculating the correctional vector. The device and software also showed high success ratio in drilling the hole and inserting the screw. The device and software reduced the radiation exposure for the surgeon and the patient. The success ratio of the device and software was validated by the physical testing, which simulated the real clinical scenario of distal locking. The radiation exposure was as low as 5 s with a radiation dose of 0.2mSv, drastically reducing radiation exposure during distal locking.DiscussionOur device and software have several advantages over other distal locking methods, such as simplicity, universality, accuracy, speed, and safety. Our device and software also have some disadvantages, such as reliability and legislation. Our device and software can be compared with other distal locking methods based on these criteria. Our device and software have some limitations and challenges that need to be addressed in the future, such as clinical validation, and regulatory approval.ConclusionThe device showed promising results in terms of low-cost, reusability, low radiation exposure, high accuracy, fast distal locking, high stiffness, and adaptability. The device has several advantages over other distal locking techniques, such as free-hand technique, mechanical aiming devices, electromagnetic navigation systems, and computer-assisted systems. We believe that our device and software have the potential to revolutionize the distal locking technique and to improve the outcomes and quality of life of the patients with long bone fractures.

Maternal manipulation of offspring size can trigger the evolution of eusociality in promiscuous species

Eusocial organisms typically live in colonies with one reproductive queen supported by thousands of sterile workers. It is widely believed that monogamous mating is a precondition for the evolution of eusociality. Here, we present a theoretical model that simulates a realistic scenario for the evolution of eusociality. In the model, mothers can evolve control over resource allocation to offspring, affecting offspring's body size. The offspring can evolve body-size-dependent dispersal, by which they disperse to breed or stay at the nest as helpers. We demonstrate that eusociality can evolve even if mothers are not strictly monogamous, provided that they can constrain their offspring's reproduction through manipulation. We also observe the evolution of social polymorphism with small individuals that help and larger individuals that disperse to breed. Our model unifies the traditional kin selection and maternal manipulation explanations for the evolution of eusociality and demonstrates that-contrary to current consensus belief-eusociality can evolve despite highly promiscuous mating.

Evaluation of the use of box size priors for 6D plane segment tracking from point clouds with applications in cargo packing

This paper addresses the problem of 6D pose tracking of plane segments from point clouds acquired from a mobile camera. This is motivated by manual packing operations, where an opportunity exists to enhance performance, aiding operators with instructions based on augmented reality. The approach uses as input point clouds, by its advantages for extracting geometric information relevant to estimating the 6D pose of rigid objects. The proposed algorithm begins with a RANSAC fitting stage on the raw point cloud. It then implements strategies to compute the 2D size and 6D pose of plane segments from geometric analysis of the fitted point cloud. Redundant detections are combined using a new quality factor that predicts point cloud mapping density and allows the selection of the most accurate detection. The algorithm is designed for dynamic scenes, employing a novel particle concept in the point cloud space to track detections’ validity over time. A variant of the algorithm uses box size priors (available in most packing operations) to filter out irrelevant detections. The impact of this prior knowledge is evaluated through an experimental design that compares the performance of a plane segment tracking system, considering variations in the tracking algorithm and camera speed (onboard the packing operator). The tracking algorithm varies at two levels: algorithm (Awpk\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_{wpk}$$\\end{document}), which integrates prior knowledge of box sizes, and algorithm (Awoutpk\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_{woutpk}$$\\end{document}), which assumes ignorance of box properties. Camera speed is evaluated at low and high speeds. Results indicate increments in the precision and F1-score associated with using the Awpk\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_{wpk}$$\\end{document} algorithm and consistent performance across both velocities. These results confirm the enhancement of the performance of a tracking system in a real-life and complex scenario by including previous knowledge of the elements in the scene. The proposed algorithm is limited to tracking plane segments of boxes fully supported on surfaces parallel to the ground plane and not stacked. Future works are proposed to include strategies to resolve this limitation.

User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access

This paper proposes a heuristic association algorithm between access points (APs) and user equipment (UE) in user-centric cell-free massive multiple-input-multiple-output (MIMO) systems, specifically targeting scenarios where UEs share the same frequency and time resources. The proposed algorithm prevents overserving APs and ensures the connectivity of all UEs, even when the number of UEs is significantly greater than the number of APs. Additionally, we assume the use of low-resolution analog-to-digital converters (ADCs) to reduce fronthaul capacity. While realistic massive access scenarios, such as those in Internet-of-Things (IoT) environments, often involve hundreds or thousands of UEs per AP using multiple access techniques to allocate different frequency and time resources, our study focuses on scenarios where UEs within each AP cluster share the same frequency and time resources to highlight the impact of pilot contamination in dense network environments. The proposed algorithm is validated through simulations, confirming that it guarantees the connection of all UEs and prevents overserving APs. Furthermore, we analyze the required fronthaul capacity based on quantization bits and confirm that the proposed algorithm outperforms existing algorithms in terms of SE and average SE performance for UEs.

Robotic arthroplasty software training improves understanding of total knee arthroplasty alignment and balancing principles: a randomized controlled trial.

Understanding alignment and gap balancing in Total Knee Arthroplasty (TKA) can be challenging for trainee and experienced orthopedic surgeons. Traditional learning methods may not effectively translate to real-life scenarios. The advent of advanced technologies like robotic surgery and navigation systems has revolutionized intraoperative understanding of gap balancing techniques. This trial aims to investigate the effectiveness of robotic TKA planning software in educating trainees about alignment and ligament balancing. We hypothesize that a single session with the software will significantly enhance trainees' understanding of these techniques. This UK-based single-center, two-arm, group parallel randomized controlled trial was conducted during a national robotic arthroplasty symposium. It aims to evaluate the effect of robotic knee arthroplasty software training on understanding TKA alignment and gap balancing principles using Multiple Choice Questions (MCQs). The MCQ test was crafted based on established guidelines from a different institution with expert consensus in the field. Our study revealed that baseline knowledge of gap balancing and alignment principles was generally low among all participants. However, the intervention group, which received comprehensive robotic software training, demonstrated a significant improvement in their MCQ scores compared to the control group, which did not undergo the training. In conclusion, our study demonstrates that robotic arthroplasty software training significantly improves the understanding of TKA alignment and balancing principles among orthopedic trainees. Level of Evidence II.

Effects of virtual reality training on clinical skill performance in nursing students: A systematic review, meta-analysis and meta-regression.

This review examined the impact of virtual reality (VR) training on nursing students' clinical skill performance and identified essential features of VR training. VR provides immersive content, simulating real-life scenarios and preventing errors in clinical settings. This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies were obtained from Ovid-EMBASE, MEDLINE, CINAHL and PubMed. Studies related to VR training published from 1 January 2000 to 8 May 2024 were included. Comprehensive Meta-Analysis 4.0 software was used for meta-analysis and random effects meta-regression. The overall effect was assessed with Hedges' g and Z-statistics (p < 0.05). Heterogeneity was measured using I-squared (I2) statistics. Among the 31 476 studies, 11 randomized controlled trial studies were included. Meta-analysis demonstrated a significant improvement in clinical skill performance, with a medium to large effect (g = 0.61) in the VR group (Z = 3.80, p < 0.001). Subgroup analyses highlighted higher nursing skills in the VR training topic. Meta-regression revealed that the VR training topic (β = 1.23, p < 0.001) and method of VR training (β = -0.53, p = 0.05) were significant covariates influencing clinical skill performance. VR training improves nursing students' clinical skill performance, addressing shortcomings in the clinical practicum.

Technological Development of Automated Harvesting for Cultivated Button Mushroom Using Image Processing

The amount of mushrooms cultivated around the world is constantly increasing, and the most commonly consumed species in Europe is the white button mushroom (Agaricus bisporus). Mushroom producers are facing a permanent challenge to provide the labour for harvesting, with increasing wage demands. Due to high market quality requirements, early automatized technologies are currently not able to replace manual picking. Our research therefore aims at facilitating the automated picking of button mushrooms and improving the technology via image processing. We aim to develop a method that can select the right size of mushrooms from field images and produce their picking position. We used Python programming language, along with the OpenCV and NumPy libraries, to implement image processing on real scenario images. The development considered factors such as fused- or overlapping mushroom heads, emergence of mushrooms from under caps, fallen or laterally visible stumps, cover soil contamination, and white mycelia which make detection significantly more difficult. We managed a solution for handling fruiting bodies that extend beyond the edge of the image due to the small field of view. The results indicated that the quality of photographs is crucial for the program's performance, as improper lighting, the presence of shadows. The efficiency of the algorithm was significantly affected by the 82% accuracy of the OpenCV Watershed segmentation algorithm, which in some cases could not separate objects. The program processed the images at an average speed of 0.78 seconds and produced the coordinates with a 92% success rate.

Initial point positioning of weld seam and robot pose estimation based on binocular vision

Abstract In order to rapidly identify and locate the weld seam initial point in robotic automated welding, we established a binocular vision system and proposed a weld seam initial point localization algorithm named WIPL-Net. Built upon the Fully Convolutional One-Stage object detection network, WIPL-Net introduces a lightweight ResNext as its backbone network and incorporates channel attention and enhanced feature fusion mechanisms to enhance feature detection and extraction capabilities. Subsequently, WIPL-Net is utilized to obtain the weld seam’s initial point, and its three-dimensional coordinates are determined through trigonometric measurements. To further estimate the robot’s posture at the initial point, we performed sparse three-dimensional reconstruction of the local region centered on the weld seam initial point based on You Only Look At Coefficients of Tensors instance segmentation and feature point matching. Finally, we conducted comparative experiments on WIPL-Net and conducted weld seam initial point localization experiments in real welding scenarios. The results demonstrate that our proposed method achieves a positioning error of less than 1.2 mm for the weld seam’s initial point and a pose error of less than 10 degrees for the robot, meeting the requirement for real-time positioning of the weld seam’s initial point.

Reception to and Efficacy of a Serious Video Game for Correctional Intervention: Project Choices

Project Choices ( PC), a newly developed videogame for correctional intervention, consists of realistic decision-making scenarios and cognitive behavioral skills feedback. A pilot study investigated PC engagement and immersion by employing a cross-over design with a sample of 24 men on probation remanded to residential treatment. The study also examined effects of gameplay on criminogenic thinking, self-perceived criminogenic risk, and social problem-solving. As hypothesized, relative to a leisure video game ( Tetris), participants generally appeared comparably engaged by and immersed in PC. Most hypothesized effects of PC on treatment-relevant outcomes were not statistically significant; however, PC contributed to moderate to large treatment effects across most outcomes of interest. Although results are promising, PC requires further validation to determine whether it could serve as a useful adjunctive tool for practitioners hoping to further reach and engage corrections clients in criminogenic risk-reduction services. Future research directions for technology like PC are numerous and encouraged.

Risk analysis of petroleum storage tank based on uncertain data incorporated into mapped Bow-tie to Bayesian network

The petroleum industry has an important impact on the environment, which is why risk analysis for a petroleum storage tank is required in the process industry. However, conducting an exhaustive study is frequently complicated due to the incomplete data based on historical system information, feedback, or test results. The main goal of this study is to develop an innovative framework that integrates the Bow-tie method with MIMAH (Methodology for the Identification of Major Accident Hazards). This approach specifically aims to identify the most critical accident scenario and comprehensively list all realistic causes (basic events) associated with petroleum storage tanks. Acquiring historical data on basic events poses challenges due to the lack, insufficiency, and imprecision of failure data. To address this obstacle and predict the system’s evolution, a combination of established methods for handling uncertainty and multi-criteria decision-making has been proposed. This combination incorporates a modified Fuzzy Analytical Hierarchy Process with Chang’s Extent Analysis Method (FAHP-EAM), fuzzy set theory, and expert elicitation, contributing to an extensive approach for managing uncertainty and making informed decisions in the absence of comprehensive historical data. Furthermore, the Bow-tie model is converted into a dynamic Bayesian network to improve risk analysis by integrating probabilistic reasoning and capturing the dynamic interactions among multiple risk factors. This integrated approach is validated on real scenarios. The utilization of dynamic Bayesian networks further facilitates the suggestion of scenarios to assess the contribution of basic events to the critical event and evaluate the resulting environmental impacts. Finally, the prediction of threat zones for various hazardous effects, such as toxicity, thermal radiation, and overpressure linked to chemical releases following the occurrence of the critical event, is carried out. The risk analysis outcomes from this study offer valuable data for assessment, enabling managers to improve the quality of risk management and formulate effective risk mitigation strategies.

Osmotic injury and cytotoxicity for hMSCs in contact with Me2SO: The effect of cell size distribution

The paper discusses the impact of cell size on cytotoxicity and expansion lysis during the osmotic excursions resulting from the contact of hMSCs from UCB with Me2SO. It builds upon the mathematical model recently presented by the authors, which pertains to a population of cells with uniform size. The objective is to enhance the model's relevance by incorporating the more realistic scenario of cell size distribution, utilizing a Population Balance Equations approach. The study compares the capability of the multiple-sized model to the single-sized one to describe system behavior experimentally measured through cytofluorimetry and Coulter counter when, first, suspending hMSCs in hypertonic solutions of Me2SO (at varying osmolality, system temperature, and contact times), and then (at room temperature) pelleting by centrifugation before suspending the cells back to isotonic conditions. Simulations demonstrate that expansion lysis and cytotoxic effect are not affected by cell size for the specific system hMSCs/Me2SO, thus confirming what was found so far by the authors through a single-size model. On the other hand, simulations show that, when varying the adjustable parameters of the model that are expected to change from cell to cell lineages, expansion lysis is sensitive to cell size, while cytotoxicity is not, being mainly influenced by external CPA concentration and contact duration. More specifically, it is found that smaller cells suffer expansion lysis more than larger ones. The findings suggest that different cells from hMSCs may require a multiple-sized model to assess cell damage during osmotic excursions in cryopreservation.

Development of a Multi-Material 3D Printer for Functional Anatomic Models.

Anatomic models are important in medical education and pre-operative planning as they help students or doctors prepare for real scenarios in a risk-free way. Several experimental anatomic models were made with additive manufacturing techniques to improve geometric, radiological, or mechanical realism. However, reproducing the mechanical behavior of soft tissues remains a challenge. To solve this problem, multi-material structuring of soft and hard materials was proposed in this study, and a three-dimensional (3D) printer was built to make such structuring possible. The printer relies on extrusion to deposit certain thermoplastic and silicone rubber materials. Various objects were successfully printed for testing the feasibility of geometric features such as thin walls, infill structuring, overhangs, and multi-material interfaces. Finally, a small medical image-based ribcage model was printed as a proof of concept for anatomic model printing. The features enabled by this printer offer a promising outlook on mimicking the mechanical properties of various soft tissues.

Neural correlates of static and dynamic social decision-making in real-time sibling interactions.

In traditional game theory tasks, social decision-making is centered on the prediction of the intentions (i.e., mentalizing) of strangers or manipulated responses. In contrast, real-life scenarios often involve familiar individuals in dynamic environments. Further research is needed to explore neural correlates of social decision-making with changes in the available information and environmental settings. This study collected fMRI hyperscanning data (N = 100, 46 same-sex pairs were analyzed) to investigate sibling pairs engaging in an iterated Chicken Game task within a competitive context, including two decision-making phases. In the static phase, participants chose between turning (cooperate) and continuing (defect) in a fixed time window. Participants could estimate the probability of different events based on their priors (previous outcomes and representation of other's intentions) and report their decision plan. The dynamic phase mirrored real-world interactions in which information is continuously changing (replicated within a virtual environment). Individuals had to simultaneously update their beliefs, monitor the actions of the other, and adjust their decisions. Our findings revealed substantial choice consistency between the two phases and evidence for shared neural correlates in mentalizing-related brain regions, including the prefrontal cortex, temporoparietal junction (TPJ), and precuneus. Specific neural correlates were associated with each phase; increased activation of areas associated with action planning and outcome evaluation were found in the static compared with the dynamic phase. Using the opposite contrast, dynamic decision-making showed higher activation in regions related to predicting and monitoring other's actions, including the anterior cingulate cortex and insula. Cooperation (turning), compared with defection (continuing), showed increased activation in mentalizing-related regions only in the static phase, while defection, relative to cooperation, exhibited higher activation in areas associated with conflict monitoring and risk processing in the dynamic phase. Men were less cooperative and had greater TPJ activation. Sibling competitive relationship did not predict competitive behavior but showed a tendency to predict brain activity during dynamic decision-making. Only individual brain activation results are included here, and no interbrain analyses are reported. These neural correlates emphasize the significance of considering varying levels of information available and environmental settings when delving into the intricacies of mentalizing during social decision-making among familiar individuals.

FET-LM: Flow-Enhanced Variational Autoencoder for Topic-Guided Language Modeling.

Variational autoencoder (VAE) is widely used in tasks of unsupervised text generation due to its potential of deriving meaningful latent spaces, which, however, often assumes that the distribution of texts follows a common yet poor-expressed isotropic Gaussian. In real-life scenarios, sentences with different semantics may not follow simple isotropic Gaussian. Instead, they are very likely to follow a more intricate and diverse distribution due to the inconformity of different topics in texts. Considering this, we propose a flow-enhanced VAE for topic-guided language modeling (FET-LM). The proposed FET-LM models topic and sequence latent separately, and it adopts a normalized flow composed of householder transformations for sequence posterior modeling, which can better approximate complex text distributions. FET-LM further leverages a neural latent topic component by considering learned sequence knowledge, which not only eases the burden of learning topic without supervision but also guides the sequence component to coalesce topic information during training. To make the generated texts more correlative to topics, we additionally assign the topic encoder to play the role of a discriminator. Encouraging results on abundant automatic metrics and three generation tasks demonstrate that the FET-LM not only learns interpretable sequence and topic representations but also is fully capable of generating high-quality paragraphs that are semantically consistent.

Reliable biogeography requires fossils: insights from a new species-level phylogeny of extinct and living carnivores.

A central objective of historical biogeography is to understand where clades originated and how they moved across space and over time. However, given the dynamic history of ecosystem changes in response to climate change and geological events, the manifold long-distance dispersals over evolutionary timescales, and regional and global extinctions, it remains uncertain how reliable inferences based solely on extant taxa can be achieved. Using a novel species-level phylogeny of all known extant and extinct species of the mammalian order Carnivora and related extinct groups, we show that far more precise and accurate ancestral areas can be estimated by fully integrating extinct species into the analyses, rather than solely relying on extant species or identifying ancestral areas only based on the geography of the oldest fossils. Through a series of simulations, we further show that this conclusion is robust under realistic scenarios in which the unknown extinct taxa represent a biased subset of all extinct species. Our results highlight the importance of integrating fossil taxa into a phylogenetic framework to further improve our understanding of historical biogeography and reveal the dynamic dispersal and diversification history of carnivores.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors, i.e., whether testing data is sequentially streamed and whether source model is allowed to be trained with modified loss function. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domains and matches the target clusters to the source ones to improve adaptation. When source domain information is strictly absent (i.e., source-free) we further develop an efficient method to infer source domain distributions for anchored clustering. Finally, self-training (ST) has demonstrated great success in learning from unlabeled data and we empirically figure out that applying ST alone to TTT is prone to confirmation bias. Therefore, a more effective TTT approach is introduced by regularizing self-training with anchored clustering, and the improved model is referred to as TTAC++. We demonstrate that, under all TTT protocols, TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets, including corrupted target domain, selected hard samples, synthetic-to-real adaptation and adversarially attacked target domain. We hope this work will provide a fair benchmarking of TTT methods, and future research should be compared within respective protocols.

S3Net: Semi-self-supervised neural network for visibility enhancement of speckled images

The tremendous growth of imaging sensors, which visually acquire the objects of interest, has attracted substantial interest in different engineering applications, such as underwater sonar imaging, synthetic aperture radar (SAR) imaging, and medical ultrasound imaging, etc. However, due to limited imaging environments, the intelligent vision-empowered visual information often suffers from the low usability of imaging data, e.g., Gamma-distributed speckle noise of unknown level in several imaging systems. It is commonly intractable to implement blind despeckling of visual data since the speckle noise is signal-dependent and multiplicative in nature. To improve the imaging quality, we tended to propose a semi-self-supervised neural network (termed S3Net) that enables intelligent blind despeckling in different practical applications. To be specific, our S3Net is mainly composed of two sub-networks, i.e., self-supervised feature extraction network (FExNet) and supervised feature enhancement network (FEnNet). The robustness and generalization abilities of S3Net can be accordingly guaranteed in different imaging scenarios. It is thus capable of extracting meaningful features and enhancing geometrical features under different receptive fields, leading to image quality improvement. The S3Net denoiser can balance the trade-off between noise suppression and detail preservation. Numerous experiments have been implemented on synthetic and realistic speckle-degraded imaging scenarios. Results have demonstrated that S3Net remarkably outperformed other state-of-the-art methods in terms of both qualitative and quantitative evaluations, with significant boosting on metric and visual performance.

Orientation and mental representations in the decision-making process: a comparison between junior and senior professional

In the workplace, professionals have an orientation to individual or group decision-making based on ideal or real representation of the group situation. However, no previous studies investigated these dimensions in professionals of different ages. A total of 216 professionals were enrolled in this study based on their work experience and seniority. Specifically, senior professionals were from different Italian consulting companies, while juniors had not yet approached the world of work, but were completing their professional education. Participants were asked to read a realistic decision-making scenario and rate their individual or group decision-making orientation, thinking of an ideal and real group situation. A stronger group decision-making orientation was found in both groups of professionals, independent of age. However, professionals appear less group-oriented when they mentally self-represent their real compared to their ideal group. Additionally, in senior professionals, a positive correlation between individual and group orientation was found in the ideal group situation. Indeed, in junior professionals, a positive correlation between individual and group orientation was found in the real situation. Therefore, it might be that becoming older and increasing professionalism level correlate with awareness of the difference between real and ideal group representations.

The neonicotinoid acetamiprid reduces larval and adult survival in honeybees (Apis mellifera) and interacts with a fungicide mixture

Plant protection products (PPPs1), which are frequently used in agriculture, can be major stressors for honeybees. They have been found abundantly in the beehive, particularly in pollen. Few studies have analysed effects on honeybee larvae, and little is known about effects of insecticide-fungicide-mixtures, although this is a highly realistic exposure scenario. We asked whether the combination of a frequently used insecticide and fungicides would affect developing bees. Honeybee larvae (Apis mellifera carnica) were reared in vitro on larval diets containing different PPPs at two concentrations, derived from residues found in pollen. We used the neonicotinoid acetamiprid, the combined fungicides boscalid/dimoxystrobin and the mixture of all three substances. Mortality was assessed at larval, pupal, and adult stages, and the size and weight of newly emerged bees was measured.The insecticide treatment in higher concentrations significantly reduced larval and adult survival. Interestingly, survival was not affected by the high concentrated insecticide-fungicides-mixture However, negative synergistic effects on adult survival were caused by the low concentrated insecticide-fungicides-mixture, which had no effect when applied alone. The lower concentrated combined fungicides led to significantly lighter adult bees, although the survival was unaffected. Our results suggest that environmental relevant concentrations can be harmful to honeybees. To fully understand the interaction of different PPPs, more combinations and concentrations should be studied in social and solitary bees with possibly different sensitivities.

Stimulus-Response Patterns: The Key to Giving Generalizability to Text-Based Depression Detection Models.

Text content analysis for depression detection using machine learning techniques has become a prominent area of research. However, previous studies focused mainly on analyzing the textual content, neglecting the fundamental factors driving text generation. Consequently, existing models face the challenge of poor generalization to out-of-domain data as they struggle to capture the crucial features of depression. To address this, we propose a novel computational perspective of "stimulus-response patterns" that brings us closer to the essence of clinical diagnosis of depression. Adopting this computational perspective allows us to conceptually unify diverse datasets and generalize this perspective to common datasets in the field. We introduce the Stimulus-Response Patterns-aware Network (SRP-Net) as an exemplary approach within this computational perspective. To assess the performance of the SRP-Net, we constructed a multi-stimulus dataset and conducted experimental evaluations, demonstrating its exceptional cross-stimulus generalizability. Furthermore, we demonstrated the promising performance of SPR-Net in real medical scenarios and conducted an interpretability analysis of the stimulus-response patterns. Our research investigates the critical role of stimulus-response patterns in enhancing the generalizability of text-based depression detection models, which can potentially facilitate data-driven depression detection to approach the diagnostic accuracy of psychiatrists.