Complex Distribution Research Articles

Instance-level semantic segmentation of nuclei based on multimodal structure encoding

BackgroundAccurate segmentation and classification of cell nuclei are crucial for histopathological image analysis. However, existing deep neural network-based methods often struggle to capture complex morphological features and global spatial distributions of cell nuclei due to their reliance on local receptive fields.MethodsThis study proposes a graph neural structure encoding framework based on a vision-language model. The framework incorporates: (1) A multi-scale feature fusion and knowledge distillation module utilizing the Contrastive Language-Image Pre-training (CLIP) model’s image encoder; (2) A method to transform morphological features of cells into textual descriptions for semantic representation; and (3) A graph neural network approach to learn spatial relationships and contextual information between cell nuclei.ResultsExperimental results demonstrate that the proposed method significantly improves the accuracy of cell nucleus segmentation and classification compared to existing approaches. The framework effectively captures complex nuclear structures and global distribution features, leading to enhanced performance in histopathological image analysis.ConclusionsBy deeply mining the morphological features of cell nuclei and their spatial topological relationships, our graph neural structure encoding framework achieves high-precision nuclear segmentation and classification. This approach shows significant potential for enhancing histopathological image analysis, potentially leading to more accurate diagnoses and improved understanding of cellular structures in pathological tissues.

Redesign of Translocon EXP2 Nanopore for Detecting Peptide Fragments.

Nanopore sensing is a rapid, label-free technique that enables single-molecule detection and is successfully applied to nucleic acid sequencing. Extending this technology to the detection and sequencing of peptides and proteins is a key area of interest. However, the complex structures and diverse charge distributions of peptides and proteins present challenges for extensive detection using existing nanopores. In this study, the focus is on the EXP2 nanopore derived from the malaria parasite Plasmodium falciparum to address these challenges. Previously, it is characterized wild-type EXP2 (WT-EXP2) nanopores and demonstrated their ability to detect polypeptides, although intrinsic electrical noise from the pore posed difficulties for accurate detection. To overcome these limitations, several EXP2 nanopore mutants are designed, including EXP2ΔD231, EXP2NC, and EXP2NC K42D/S46F, to reduce electrical noise and improve peptide detection accuracy. The EXP2ΔD231 mutant reduced electrical noise by more than 50% compared to WT-EXP2 and improved the discrimination accuracy of oligoarginine peptides. In addition, the EXP2ΔD231 detected and discriminated eight different peptides, ranging in molecular weight from small to large, that are previously challenging to detect using a single nanopore type. These results suggest that engineered EXP2 nanopores could serve as effective tools for peptide and protein detection and sequencing, contributing to the broader application of nanopore technology in biochemical and clinical research.

Ecological overview of hard ticks (Ixodida: Ixodidae) in Nagasaki prefecture of western Japan during winter 2021–2022

In Japan, Japanese spotted fever, Lyme disease and severe fever with thrombocytopenia syndrome caused by Ixodidae species are endemic. To prevent and control the diseases, fundamental understandings in tick ecology are crucial. Hence, this study aimed to analyse tick species richness and abundance across Nagasaki prefecture including its remote islands from a wide range of environments. A total of 74 sampling points screening during winter 2021–2022, using dragging method resulted in 14,883 tick samples (279 adults, 7148 nymphs and 7456 larvae) in 11 species belonging to four genera. Haemaphysalis flava dominated adult populations, while H. formosensis was predominant among nymphs. Both species are possible vectors of Japanese spotted fever and severe fever with thrombocytopenia syndrome. The ecological analysis revealed more complex species distribution in the remote islands compering to the main island in both adults and nymphs. In addition, the ground temperature was a significant regulatory factor for both adults and nymphs. The research provides valuable insights on tick distributions, ecological groupings and environmental preferences in Nagasaki. These findings contribute to the fundamental understanding of tick ecology and could contribute to design strategies for tick population control and tick-borne disease prevention in Nagasaki or possibility to nearby areas.

Photometric Determination of Unresolved Main-sequence Binaries in the Pleiades: Binary Fraction and Mass-ratio Distribution

Abstract Accurate determination of binary fractions (f b) and mass ratio (q) distributions is crucial for understanding the dynamical evolution of open clusters. We present an improved multiband fitting technique to enhance the analysis of binary properties. This approach enables an accurate photometric determination of f b and q distribution in a cluster. The detectable mass ratio can be down to the q lim , limited by the minimum stellar mass in theoretical models. First, we derived an empirical model for magnitudes of Gaia DR3 and 2MASS bands that match the photometry of single stars in the Pleiades. We then performed a multiband fitting for each cluster member, deriving the probability density function (PDF) of its primary mass ( M 1 ) and q in the Bayesian framework. 1154 main-sequence (MS) single stars or unresolved MS+MS binaries are identified as members of the Pleiades. By stacking their PDFs, we conducted a detailed analysis of binary properties of the cluster. We found the f b of this sample is 0.34 ± 0.02. The q distribution exhibits a three-segment power-law profile: an initial increase, followed by a decrease, and then another increase. This distribution can be interpreted as a fiducial power-law profile with an exponent of −1.0 that is determined in the range of 0.3 < q < 0.8, but with a deficiency of binaries at lower q and an excess at higher q. The variations of f b and q with M 1 reveal a complex binary distribution within the Pleiades, which might be attributed to a combination of primordial binary formation mechanisms, dynamical interactions, and the observational limit of photometric binaries imposed by q lim ( M 1 ) .

Fast generation of perfect vortex and vector beams through highly scattering media via discrete convolution-based vector point-spread-function engineering

Recent advancements in optics have demonstrated that it is possible to take advantage of the feasibility of leveraging wavefront shaping to focus and image through highly scattering media (HSM). Herein, we report a method to rapidly focus perfect vortex beams (PVBs) and perfect vector vortex beams (PVVBs) through the HSM via discrete convolution-based vector point-spread-function (DC-VPSF) engineering. In our method, a vector transmission matrix (VTM)-based operator can be calculated by executing a convolution operation between the measured full VTM and the conjugate of a discrete VPSF sampled from a pre-defined spatial domain mask. The input field can be obtained by performing digital optical phase conjugation on the VTM-based operator. Then the desired output field (that is the discrete VPSF) can be generated through the HSM. Experimentally, the PVBs with different topological charges and identical optical ring sizes are generated through the HSM based on our method. Further, PVBs with controllable polarization states and a high peak-to-background ratio (PBR) are also generated after the HSM by introducing polarizers and wave plates before the recording plane. In addition, our method has advantages in fast calculating the required input field and improving the PBR of the focused light through the HSM compared with the general point-spread-function engineering. Finally, the experimentally generated PVVBs demonstrate the ability to tailor the focused light with more complex polarization distribution through the HSM. Our results may contribute to generalizing the convolution concepts for multiple degrees of freedom wavefront control and provide an effective idea for optical micromanipulation and imaging through scattering media.

Conventional and machine learning-based analysis of age, body weight and body height significance in knot position-related thyrohyoid and cervical spine fractures in suicidal hangings.

The thyrohyoid complex and cervical spine fracture distribution patterns may reflect the knot position as the force distribution by the noose to different neck regions may vary depending on it. Recently, machine learning models (MLm) were used to classify knot position through these fractures. The contribution of aging on the fracture susceptibility is better demonstrated, but data on body weight (BW) and height (BH) significance on this is more doubtful and MLm did not consider them. A retrospectively obtained autopsy data on sex, age, BW, BH and distribution of greater hyoid bone horn (GHH), superior thyroid cartilage horn (STH), and cervical spine fractures in 368 suicidal hangings were analyzed by standard statistics to determine association of the anthropometrics (age, BW, and BH) with the fracture occurrence, and by machine learning algorithms to determine if body weight and height improved MLm classification of hanging cases with typical and atypical knot positions. In the sample, unilateral GHH fracture was significantly more common in atypical hangings, while isolated STH fractures were more common in typical hangings. Age was a predictor of GHH fractures and BW of STH fractures, but BW poorly correlated with their number. BH was not a predictor of any thyrohyoid fracture. On the ROC curve analysis, the MLm that considered BW and BH did not perform statistically better than MLm that did not consider them. The study indicates that body weight and height are of no detrimental value in assessing the thyrohyoid and cervical spine fracture patterns in suicidal hangings.

A Hybrid Model Coupling Data and Hydraulic Transient Laws for Water Distribution Systems

AbstractThe physics‐informed neural network (PINN) method has been applied to solve water hammer equations in pipeline systems due to its ability of seamlessly integrate measurement data with conservation laws, offering advantages over traditional numerical method. However, existing PINN approaches require multiple neural networks to construct composite models for complex water distribution systems (WDS). This situation treats nodal information as boundary condition or labeled data during training, leading to a weaker robustness and a high demand for data. To address these issues, a hybrid water hammer model based on eXtended Physics‐Informed Neural Networks for WDS (WDS‐XPINN) is developed in this study. Unlike the standard PINN, WDS‐XPINN incorporates the nodal mechanistic model directly into the loss function, enabling to synchronously train a unified neural network jointly through sparse augmented measurement data for pipeline system. Additionally, an adaptive weights method is introduced to improve model robustness by balancing the contributions of flowrates and pressures. The proposed WDS‐XPINN is evaluated in two case studies: a series pipeline system with different operational events and noise perturbation, as well as a topological structure with looped and branched pipe. According to the simulation results and uncertainty analysis, the WDS‐XPINN model demonstrates its excellent capacity of modeling fluid transient accurately in pipeline system, even without exact operational conditions or true pipe parameters.

Spatially-aware diffusion models with cross-attention for global field reconstruction with sparse observations

Diffusion models have gained attention for their ability to represent complex distributions and incorporate uncertainty, making them ideal for robust predictions in the presence of noisy or incomplete data. In this study, we develop and enhance score-based diffusion models in field reconstruction tasks, where the goal is to estimate complete spatial fields from partial observations. We introduce a condition encoding approach to construct a tractable mapping between observed and unobserved regions using a learnable integration of sparse observations and interpolated fields as an inductive bias. With refined sensing representations and an unraveled temporal dimension, our method can handle arbitrary moving sensors and effectively reconstruct fields. Furthermore, we conduct a comprehensive benchmark of our approach against a deterministic interpolation-based method across various static and time-dependent PDEs. Our study attempts to addresses the gap in strong baselines for evaluating performance across varying sampling hyperparameters, noise levels, and conditioning methods. Our results show that diffusion models with cross-attention and the proposed conditional encoding generally outperform other methods under noisy conditions, although the deterministic method excels with noiseless data. Additionally, both the diffusion models and the deterministic method surpass the numerical approach in accuracy and computational cost for the steady problem. We also demonstrate the ability of the model to capture possible reconstructions and improve the accuracy of fused results in covariance-based correction tasks using ensemble sampling.

Effects of coding variants in the glucokinase regulatory protein gene on hepatic glucose and triglyceride metabolism suggest a gene regulatory function of glucokinase.

Regulation of glucose metabolism after a meal is the major task of hepatic glucokinase (GCK). Inhibition and nuclear retention of glucokinase during fasting is achieved by glucokinase regulatory protein (GKRP). Compounds disrupting the GCK-GKRP interaction alter glucose but not triglyceride levels, whilst GKRP coding alleles lower glucose but elevate triglycerides. The aim of this study was to identify yet unknown functions of GKRP by examining human variants both rare (p.Q234P, p.H438Y) and common (p.P446L). Fluorescently labelled human GKRP variant and GCK proteins were expressed in hepatoma cells or primary mouse hepatocytes to investigate the subcellular localization of both proteins, cellular glucose uptake, and triglyceride levels. Mutational effects on GKRP protein structure were analyzed with PyMOL. Nuclear-to-cytoplasmic distribution of the GCK-GKRP complex was modeled in MATLAB. Nuclear localization of the GKRP variants was decreased compared to wild-type. Only H438Y-GKRP still evoked WT-like GCK nuclear accumulation. Nuclear localization of Q234P-GKRP was most impaired and depended on the presence of GCK, which, supported by structural analyses, could stabilize its conformation. Nonetheless, inhibition of glucose uptake was least impaired with Q234P-GKRP. Triglyceride contents related to the glucose uptake of hepatoma cells were disproportionately high for cells expressing wild-type or H438Y-GKRP, the two variants that induced higher nuclear sequestration of GCK. Our results, supported by a modeling approach, suggest that GKRP-mediated nuclear localization of GCK has a function in liver metabolism beyond GCK inhibition and sequestration. This needs further elucidation given that GKRP disruptors have been proposed for antihyperglycemic therapy.

Residual Stress Distribution and Its Effect on Fatigue Crack Path of Laser Powder Bed Fusion Ti6Al4V Alloy

Residual stress (RS) in laser powder bed fusion (LPBF) additive manufactured structures can significantly affect mechanical performance, potentially leading to premature failure. The complex distribution of residual stresses, combined with the limitations of full-field measurement techniques, presents a substantial challenge in conducting damage tolerance analyses of aircraft structures. To address these challenges, this study developed a comprehensive simulation framework to analyze the 3D distribution of residual stresses and fatigue crack growth in LPBF parts. The 3D residual stress profiles of as-built samples in 15° and 75° build directions were computed and compared to experimental data. The fatigue crack propagation behavior of the 75° sample, considering 3D residual stress, was predicted, and the effects of residual stress redistribution under cyclic loading were discussed. It shows that the anisotropy of residual stress, influenced by the build direction, can lead to mixed-mode fracture and subsequent crack deflection. Tensile residual stress in the near-surface region and compressive stress in the inner region can cause an inverted elliptical crack front and accelerate fatigue crack growth.

ZINQ-L: a zero-inflated quantile approach for differential abundance analysis of longitudinal microbiome data

BackgroundIdentifying bacterial taxa associated with disease phenotypes or clinical treatments over time is critical for understanding the underlying biological mechanism. Association testing for microbiome data is already challenging due to its complex distribution that involves sparsity, over-dispersion, heavy tails, etc. The longitudinal nature of the data adds another layer of complexity - one needs to account for the within-subject correlations to avoid biased results. Existing longitudinal differential abundance approaches usually depend on strong parametric assumptions, such as zero-inflated normal or negative binomial. However, the complex microbiome data frequently violate these distributional assumptions, leading to inflated false discovery rates. In addition, the existing methods are mostly mean-based, unable to identify heterogeneous associations such as tail events or subgroup effects, which could be important biomedical signals.MethodsWe propose a zero-inflated quantile approach for longitudinal (ZINQ-L) microbiome differential abundance test. A mixed-effects quantile rank-score-based test was proposed for hypothesis testing, which consists of a test in mixed-effects logistic model for the presence-absence status of the investigated taxon, and a series of mixed-effects quantile rank-score tests adjusted for zero inflation given its presence. As a regression method with minimal distributional assumptions, it is robust to the complex microbiome data, controlling false discovery rate, and is flexible to adjust for important covariates. Its comprehensive examination of the abundance distribution enables the identification of heterogeneous associations, improving the testing power.ResultsExtensive simulation studies and an application to a real kidney transplant microbiome study demonstrate the improved power of ZINQ-L in detecting true signals while controlling false discovery rates.ConclusionZINQ-L is a zero-inflated quantile-based approach for detecting individual taxa associated with outcomes or exposures in longitudinal microbiome studies, providing a robust and powerful option to improve and complement the existing methods in the field.

Three-dimensional diffractive acoustic tomography

Acoustically probing biological tissues with light or sound, photoacoustic and ultrasound imaging can provide anatomical, functional, and/or molecular information at depths far beyond the optical diffusion limit. However, most photoacoustic and ultrasound imaging systems rely on linear-array transducers with elevational focusing and are limited to two-dimensional imaging with anisotropic resolutions. Here, we present three-dimensional diffractive acoustic tomography (3D-DAT), which uses an off-the-shelf linear-array transducer with single-slit acoustic diffraction. Without jeopardizing its accessibility by general users, 3D-DAT has achieved simultaneous 3D photoacoustic and ultrasound imaging with optimal imaging performance in deep tissues, providing near-isotropic resolutions, high imaging speed, and a large field-of-view, as well as enhanced quantitative accuracy and detection sensitivity. Moreover, powered by the fast focal line volumetric reconstruction, 3D-DAT has achieved 50-fold faster reconstruction times than traditional photoacoustic imaging reconstruction. Using 3D-DAT on small animal models, we mapped the distribution of the biliverdin-binding serpin complex in glassfrogs, tracked gold nanoparticle accumulation in a mouse tumor model, imaged genetically-encoded photoswitchable tumors, and investigated polyfluoroalkyl substances exposure on developing embryos. With its enhanced imaging performance and high accessibility, 3D-DAT may find broad applications in fundamental life sciences and biomedical research.

Scanning K-edge subtraction (SKES) imaging with laser-compton x-ray sources.

K-edge subtraction (KES) imaging is a dual-energy imaging technique that enhances contrast by subtracting images taken with x-rays that are above and below the K-edge energy of a specified contrast agent. The resulting reconstruction spatially identifies where the contrast agent accumulates, even when obscured by complex and heterogeneous distributions of human tissue. This method is most successful when x-ray sources are quasimonoenergetic and tunable, conditions that have traditionally only been met at synchrotrons. Laser-Compton x-ray sources (LCSs) are a compact alternative to synchrotron radiation with a quasimonoenergetic x-ray spectrum. One limitation in the clinical application of KES imaging with LCSs has been the extensive time required to tune the x-ray spectrum to two differentenergies. We introduce an imaging technique called scanning K-edge subtraction (SKES) that leverages the angle-correlated laser-Compton x-ray spectrum in the setting of mammography. The feasibility and utility of this technique will be evaluated through a series of simulation studies. The goal of SKES imaging is to enable rapid K-edge subtraction imaging using a laser-Compton x-ray source. The technique does not rely on the time-consuming process of tuning laser-Compton interactionparameters. Laser-Compton interaction physics are modeled using conditions based on an X-band linear electron accelerator architecture currently under development using a combination of 3D particle tracking software and Mathematica. The resulting angle-correlated laser-Compton x-ray beam is propagated through digitally compressed breast phantoms containing iodine contrast-enhanced inserts and then to a digital flat-panel detector using a Matlab Monte Carlo propagation software. This scanning acquisition technique is compared to the direct energy tuning method (DET), as well as to a clinically available dual-energy contrast-enhanced mammography (CEM)system. KES imaging in a scanning configuration using an LCS was able to generate a KES image of comparable quality to the direct energy tuning method. SKES was able to detect tumors with iodine contrast concentrations lower than what is clinically available today including lesions that are typically obscured by dense fibroglandular tissue. After normalizing to mean glandular dose, SKES is able to generate a KES image with equal contrast to CEM using only 3% of thedose. By leveraging the unique quasimonochromatic and angle-correlated x-ray spectrum offered by LCSs, a contrast-enhanced subtraction image can be obtained with significantly more contrast and less dose compared to conventional systems, and improve tumor detection in patients with dense breast tissue. The scanning configuration of this technique could accelerate the clinical translation of thistechnology.

Detection of weeds in vegetables using image classification neural networks and image processing

Weed management presents a major challenge to vegetable growth. Accurate identification of weeds is essential for automated weeding. However, the wide variety of weed types and their complex distribution creates difficulties in rapid and accurate weed detection. In this study, instead of directly applying deep learning to identify weeds, we first created grid cells on the input images. Image classification neural networks were utilized to identify the grid cells containing vegetables and exclude them from further analysis. Finally, image processing technology was employed to segment the non-vegetable grid images based on their color features. The background grid cells, which contained no green pixels, were identified, while the remaining cells were labeled as weed cells. EfficientNet, GoogLeNet, and ResNet models achieved overall accuracies of over 0.956 in identifying vegetables in the testing dataset, demonstrating exceptional identification performance. Among these models, the ResNet model exhibited the highest computational efficiency, with a classification time of 12.76 ms per image and a corresponding frame rate of 80.31 fps, satisfying the requirement for real-time weed detection. Effectively identifying vegetables and differentiating weeds from soil significantly reduces the complexity of weed detection and improves its accuracy.

A Review of Adaptive Control Methods for Grid-Connected PV Inverters in Complex Distribution Systems

With the growth of energy demand and the aggravation of environmental problems, solar photovoltaic (PV) power generation has become a research hotspot. As the key interface between new energy generation and power grids, a PV grid-connected inverter ensures that the power generated by new energy can be injected into the power grid in a stable and safe way, and its power grid adaptability has also received more and more close attention in the field of new energy research. This research focuses on the discussion of PV grid-connected inverters under the complex distribution network environment, introduces in detail the domestic and international standards and requirements on grid-connected inverter grid adaptability, and then analyzes in depth the impacts of the access point voltage changes, access point frequency changes, and access point harmonic changes on the inverters. In order to enhance the adaptability of grid-connected inverters under these abnormal conditions, this research systematically summarizes and concludes a series of inverter adaptive control strategies, which provide literature guidance to effectively reduce the probability of power system faults and improve the reliability of the power system. Finally, the future development direction of PV inverter technology is outlooked, pointing out that, with the increase in the proportion of PV power generation in the power system, PV inverters need to evolve gradually from adapting to the grid to supporting the grid and promote the transformation of PV power generation from the auxiliary power source to the main power source through the integration of PV and energy storage.

PyDRex: Predicting crystallographic preferred orientation in peridotites under steady-state and time-dependent strain

Abstract Crystallographic preferred orientation (CPO) of peridotite minerals is frequently invoked to explain the widespread dependence of seismic wave speed on propagation direction in Earth’s mantle — a property known as seismic anisotropy. As established by rock mechanics experiments, CPO constitutes a direct signature of past and ongoing strain regimes experienced by rocks during mantle flow. Therefore, an improved understanding of CPO generation promises to yield valuable information on the rheology and corresponding deformation mechanisms activated through mantle dynamics. Simulating CPO in geodynamical models is computationally challenging and has often been restricted to steady-state mantle flows. However, within Earth’s vigorously convecting mantle the steady-state assumption is questionable, thus motivating the need to couple CPO simulations with time-evolving mantle flow models. Here, we present a new Python implementation of the D-Rex CPO model, called PyDRex, which predicts salient features of mineral grain size and orientation evolution whilst providing a well-documented, user-friendly interface that supports flexible coupling to geodynamical modelling frameworks. PyDRex also packages numerous post-processing routines for strain analysis and visualisation of grain orientation distributions. We provide a set of benchmark simulations based on previous D-Rex implementations that validate PyDRex and demonstrate sensitivities to model parameters for both steady-state and time-dependent flows. Analysis of benchmark results highlights the role of dynamic recrystallisation in controlling competing grain growth in both the softest and hardest crystallographic orientations. When employing a commonly used value for the grain boundary mobility parameter (M* = 125), we also find that transient CPO textures are generally not well resolved if crystals are represented by fewer than 5000 ‘grains’ (weighted orientation samples) — a configuration rarely employed in most previously published studies. Furthermore, kinematic corner-flow models suggest that CPO produced at mid-ocean ridges has a non-linear dependence on depth, which implies that even ostensibly simple mantle flows can result in complex distributions of seismic anisotropy. Our analyses motivate further experimental calibration of parameters controlling dynamic recrystallisation and potential improvements to the numerical treatment of subgrain nucleation.

TRACKING MAP UNTUK MONITORING GANGGUAN JTR PADA NH FUSE GARDU DISTRIBUSI

This research develops an IoT-based fault monitoring system for Low Voltage (LV) Networks using a tracking map on NH fuses, aiming to improve response time efficiency and fault detection accuracy. The system integrates sensors with a web-based platform to monitor and visualize fault locations in real-time. Test results indicate that the system is capable of detecting faults with high accuracy, with detection errors as low as 0% to 0.32%. The response time for fault detection ranges from 0.5 to 1 second, significantly faster than conventional methods. As a result, fault recovery time can be accelerated by 30-40%, potentially enhancing the reliability of the power supply. Additionally, the use of IoT technology allows more efficient monitoring and quicker response to faults, enabling faster repair processes and minimizing downtime. Although the system demonstrates promising results, the study also identifies areas for further development, particularly in integrating it with more complex distribution management systems and enhancing data analytics. Overall, the proposed system offers an innovative solution for mitigating faults in LV networks, improving reliability, and increasing operational efficiency in power distribution systems

The actin cytoskeleton regulates danger-associated molecular pattern signaling and PEP1 RECEPTOR1 internalization.

In plants, cytoskeletal proteins assemble into dynamic polymers that play numerous roles in diverse fundamental cellular processes, including endocytosis, vesicle trafficking, and the spatial distribution of organelles and protein complexes. Plant elicitor peptides (Peps) are damage/danger-associated molecular patterns (DAMPs) that are perceived by the receptor-like kinases PEP RECEPTOR 1 (PEPR1) and PEPR2 to enhance innate immunity and inhibit root growth in Arabidopsis (Arabidopsis thaliana). To date, however, there is little evidence that the actin cytoskeleton of the host cell participates in DAMP-induced innate immunity. Here, we demonstrated that the actin cytoskeleton alters the Pep1-triggered immune response. In addition, dual-color total internal reflection fluorescence-structured illumination microscopy (TIRF-SIM) showed that PEPR1 diffusion on the plasma membrane is closely related to the actin cytoskeleton. We performed single-particle tracking to quantify individual protein particles and found that the actin cytoskeleton notably regulates PEPR1 mobility and cluster size. More importantly, we demonstrated that actin filament reconfiguration is sufficient to inhibit Pep1-induced internalization, which alters the immune response. Taken together, these findings suggest that the actin cytoskeleton functions as an integration node for Pep1 signaling and PEPR1 endocytosis.

Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas

An assessment scheme is proposed to evaluate GBM gross tumor core and T2-FLAIR hyper-intensity segmentations on preoperative multicentric MR images as a function of tumor morphology and MRI characteristics. 74 gross tumor core and T2-FLAIR hyper-intensity BraTS-Toolkit and DeepBraTumIA automatic segmentations, and 42 gross tumor core neurosurgeon manual segmentations were accordingly evaluated. Brats-Toolkit and DeepBraTumIA generally provide accurate segmentations, particularly for the most common round-shaped or well-demarked tumors, where: (1) gross tumor segmentation correctly includes necrosis and contrast enhanced tumor in 100% and 97.06% of cases (vs. 73.68% for manual segmentation) and wrongly includes healthy or non-tumor related tissues in 2.94% and 20.59% of cases (vs. 10.53% for manual segmentations); (2) T2-FLAIR hyper-intensity segmentations completely includes edema in 88.24% of cases for both software. MR image quality has little impact on the segmentation performance on these tumors. Conversely, on less common tumors with more complex tissue distribution and infiltrative behavior, manual segmentation works better than BraTS-Toolkit and DeepBraTumIA, and image quality has a larger impact on automatic segmentation performance. BraTS-Toolkit and DeepBraTumIA gross tumor segmentation properly includes necrosis and contrast enhanced areas in 50% and 37.50% of cases (vs. 66.67% for manual segmentation), all corresponding to higher image quality; T2-FLAIR hyper-intensity segmentation wrongly includes necrosis and contrast enhanced areas in 37.50% and 50% of cases.

A Radiation-Based Analytical Model for the Computation of Temperature in the Arc Column of Submerged Arc Additive Manufacturing (SAAM) Process

Abstract Submerged Arc Additive Manufacturing (SAAM) is an advanced Wire Arc-based Additive Manufacturing (WAAM) technique known for its uniform deposition structure, high deposition rate, and superior surface finish. However, it is difficult and expensive to experimentally investigate the arc column in SAAM due to its submerged nature under flux. This paper presents an effective alternative: developing a radiation-based analytical model to predict the complex temperature distribution within the SAAM arc column. It focuses on the radiation heat transfer phenomenon since the temperatures attained inside the arc column are beyond the melting temperature of the material involved, and the presence of granular flux and slag intensifies the effect of the same. The model incorporates key input process parameters like wire diameter, stick-out length, current, voltage, and travel speed alongside design parameters like bead width, penetration depth, and material reinforcement. The double ellipsoidal heat source model is the foundation for generating the temperature profile. The predicted temperature profile resembles the double ellipsoid in other arc welding-based additive manufacturing techniques. The model's predictions showed a deviation of approximately 14% from experimental results, validating its effectiveness. Extensive parametric studies were carried out using the developed model. It was observed that longer stick-out lengths dampened temperature variations, while higher currents confined the affected zone further. Increased travel speed reduces the heating and cooling rate, while voltage behaves the opposite way. This analytical approach offers a cost-effective alternative for optimizing process parameters to achieve desired dimensions and deposition quality.