bluuebunny/crossref_metadata_2025 huggingface dataset:
3D printed skin dressings manufactured with spongin-like collagen from marine sponges: physicochemical properties and in vitro biological analysis
Authors: de Souza Amanda , Amaral Gustavo Oliva , do Espirito Santo Giovanna , dos Santos Jorge Sousa Karolyne , Martignago Cintia Cristina Santi , Souza e Silva Lais Caroline , de Lima Lindiane Eloisa , Vitor de Souza Daniel , Cruz Matheus Almeida , Ribeiro Daniel Araki , Granito Renata Neves , Renno Ana Claudia Muniz
Published: January 2025

The search for innovative materials for manufacturing skin dressings is constant and high demand. In this context, the present study investigated the effects of a 3D printed skin dressing made of spongin-like collagen (SC) extract from marine sponge (Chondrilla caribensis), used in 3 concentrations of SC and alginate (C1, C2, C3). For this proposal, the physicochemical, morphological and in vitro biological results were investigated. The results demonstrated that, after immersion, C2 presented a higher mass loss and C3 present a higher pH in experimental periods. Also, a higher porosity was observed for C1 and C2 skin dressings, with a higher swelling ratio for C2. For Fourier transform infrared, peaks of Amide A, –CH2, –COOH and C–O–C were seen. Moreover, the macroscopic image demonstrated a skin dressing with rough surface and grayish color that is naturally observed in Chondrilla caribensis. For scanning electron microscopy analysis the presence of pores could be observed for all skin dressings, with fibers disposed in layers. The in vitro analyses demonstrated the viability of HFF-1 and L929 cell lines 70% of the values found for cell proliferation compared to Control Group. Furthermore, the cell adhesion analysis demonstrated that both cell lines adhered to the 3 different skin dressings and non-cytotoxicity was observed. Taking together, all the results suggest that the skin dressings are biocompatible and present non-cytotoxicity in the in vitro studies, being considered a suitable material for tissue engineering proposals.
Unraveling the response of the apparent temperature sensitivity of ecosystem respiration to rising temperature
Authors: Liu Zhentao , Liu Junguo , Chen Deliang
Published: January 2025

Global warming is expected to intensify carbon loss, as ecosystem respiration (RECO) rates increase exponentially with rising temperatures. However, a comprehensive analysis of the response of the apparent temperature sensitivity of RECO (

Q

10

) to rising temperature is lacking. This study leverages observational data from 254 sites from the FLUXNET2015 and AmeriFlux datasets to address this knowledge gap. We found a strong influence of non-temperature factors on the seasonality of RECO. The similar seasonality of this effect and temperature can lead to underestimating or overestimating

Q

10

. In this study,

Q

10

was quantified using a temporal moving window and a linear-mixed effect model to account for the effects of non-temperature factors on RECO. Our results show that

Q

10

decreases from 1.55 ± 0.24 (mean ± one standard error) at 5 °C to 1.35 ± 0.18 at 25 °C over all sites. The mean slope of

Q

10

to temperature across all sites is about −0.02 °C−1. In this study, we found lower values of Q
10 and a lower decreasing rate of Q
10 with rising temperature compared to previous studies. Our study suggests that

Q

10

might be systematically overestimated due to the confounding effect of non-temperature factors, potentially leading to overestimated simulation of RECO rate. Our study also emphasizes the necessity of developing a process-based model, rather than simply incorporating the influences of non-temperature factors into

Q

10

.
Elucidating diverse population exposure to compound drought and heatwave events from two meteorological drought indices (SPI and SPEI)
Authors: Wang Tianyue , She Dunxian , Bao Zhengfeng , Zhang Qin , Wang Lvlv , Wei Yiheng , Niu Qing
Published: January 2025

Anthropogenic climate change has significantly exacerbated the frequency and severity of Compound Drought and Heatwave (CDHW) events, increasing risks such as water shortages, wildfires, and heat-related mortality. Previous studies often use a single drought index, such as the Standardized Precipitation Index (SPI) or the Standardized Precipitation Evapotranspiration Index (SPEI), while our study uses both SPI and SPEI to elucidate the effect of different drought indices on the quantification of population exposure to CDHW events. Six General Circulation Models under four future Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) are considered. Exposure is defined as the product of CDHW Magnitude (°C) and the population in each region (million people), providing a quantitative measure of how CDHW events affect populations. The role of potential evapotranspiration (PET) in CDHW population exposure projections is examined by comparing SPI, which considers only precipitation, with SPEI, which accounts for both precipitation and PET in drought measurements. Results show that after 2050, CDHW Magnitude population exposure diverges significantly across scenarios, with SSP3-7.0 exhibiting the largest increase, reaching 0.72 (SPI) and 1.78 (SPEI) million person-°C by the end of the century. Regions such as Western Africa (WAF), Southeastern Africa, and South Asia (SAS) experience the largest increases in population exposure under SSP3-7.0 with SPEI, reaching 6.93, 6.77, and 5.56 million person-°C, respectively. Additionally, regions like Western & Central Europe, the Mediterranean, WAF, Western Central Africa, Eastern Asia, and SAS display heightened sensitivity to PET, with discrepancies between SPEI and SPI projections exceeding 1 million person-°C. Attribution analysis reveals that climate change, particularly when drought is calculated using PET by SPEI, is the primary factor, followed by interaction change and population change. These findings emphasize the critical role of PET in CDHW projections and the need for region-specific adaptation strategies to manage escalating risks in highly vulnerable areas.
Magnetic polydimethylsiloxane microspheres as hydrophobic collectors for oil removal
Authors: Cabal-Velarde J G , Lobo Guerrero A , Araujo E , Romero-Tela E , Sánchez-Llamazares J L , Encinas A
Published: January 2025

Oil spills represent a significant environmental challenge, requiring materials that are efficient, cost-effective, and reusable for oil–water separation. This study presents a scalable method for fabricating magnetic polydimethylsiloxane (PDMS) microspheres using commercially available oil-based ferrofluid. The choice of ferrofluid simplifies synthesis by providing pre-dispersed superparamagnetic nanoparticles, ensuring compatibility with PDMS. The ferrofluid-PDMS mixture was emulsified in warm water containing a surfactant, stabilizing the microparticles during polymerization. The resulting microspheres were characterized by optical microscopy, and magnetic hysteresis measurements confirm that the microspheres are superparamagnetic. X-ray diffraction confirmed the ferrofluid had magnetite nanoparticles. Oil absorption tests revealed that the microspheres achieved an absorption capacity of up to 580% of their weight. Moreover, the microspheres retained nearly 90% of their original absorption capacity after 10 reuse cycles, demonstrating excellent durability and reusability. This approach combines simplicity, scalability, and cost-efficiency while achieving high performance in oil absorption and magnetic separability. By exploiting the compatibility of ferrofluid and PDMS, the study provides a practical and effective solution for environmental remediation. These microspheres offer an attractive alternative to traditional methods, addressing the need for advanced materials that combine high efficiency with low operational costs and reusability.
Predictions of gyrokinetic turbulent transport in proton-boron plasmas on EHL-2 spherical torus
Authors: TAN 谭 Muzhi 沐芝 , XU 许 Jianqiang 健强 , DU 杜 Huarong 华荣 , DONG 董 Jiaqi 家齐 , XIE 谢 Huasheng 华生 , WANG 王 Xueyun 雪韵 , HUANG 黄 Xianli 贤礼 , WANG 王 Yumin 嵎民 , GU 顾 Xiang 翔 , LIU 刘 Bing 兵 , SHI 石 Yuejiang 跃江 , LIANG 梁 Yunfeng 云峰 , Team the EHL-2
Published: January 2025

The EHL-2 spherical torus at ENN is the next-generation experimental platform under conceptual design, aiming at realizing proton-boron (p-11B) thermonuclear fusion, which is an attractive pathway towards neutron-free fusion. To achieve high-performance steady-state plasma, it is extremely necessary to study the turbulence transport characteristics with high boron content in the plasma core. This study investigates the transport properties in the core internal transport barrier (ITB) region of p-11B plasma utilizing the gyrokinetic code GENE in view of the high ion temperature scenario of EHL-2, specifically focusing on the impact of boron fractions and plasma β on the microinstabilities and corresponding transport features. Numerical findings indicate that the inclusion of boron species effectively suppresses the trapped electron modes (TEMs) as well as promoting a transition from electromagnetic to electrostatic turbulence with increased boron fraction, which is a result of the suppression of microinstabilities by effective charge and mass. Moreover, it has been identified that the external
E
×
B
rotational shear has a notable inhibitory influence on transport, which can reduce the transport level by two to three orders of magnitude, especially at medium boron content. The suppressive effect of
E
×
B
on turbulence is weakened once the kinetic ballooning mode (KBM) is excited and the transport shows a rapid increase with β together with a reduction in zonal flow amplitude, which is consistent with previous findings. Therefore, it is strongly suggested that exploring advanced strategies for mitigating turbulent transport at high β regimes is necessary for the active control of plasma behavior regarding p-11B plasma-based fusion devices such as EHL-2.
Monte Carlo simulation of interferometric measurement and wavefront shaping under influence of shot noise and camera noise
Authors: Lee Chunghyeong , Oh Jaeyeon , Ko Hakseok , Jang Mooseok
Published: January 2025

Interferometry often serves as an essential building block of wavefront shaping systems to obtain optimal wavefront solutions. In this tutorial, we provide a Monte Carlo simulation tool to calculate the accuracy of interferometric measurements and its impact on wavefront shaping in the context of focusing through disordered media. In particular, we have focused on evaluating wavefront shaping fidelity under the influence of shot noise with practical considerations on the operation of digital image sensors, including readout noise, dark current noise, and digitization with finite bit-depth. Based on some exemplary simulation results, we provide practical guidance for setting up an interferometric measurement system for wavefront shaping applications.
Accelerated dynamic light sheet microscopy: unifying time-varying patterned illumination and low-rank and sparsity constrained reconstruction
Authors: Tobia Vitali Marco , Candeo Alessia , Farina Andrea , Pozzi Paolo , Brix Alessia , Bassi Andrea , Correia Teresa M
Published: January 2025

Light Sheet Fluorescence Microscopy (LSFM) enables rapid and gentle 3D fluorescence imaging of dynamic processes over extended periods in translucent samples at the mesoscopic scale. However, its temporal resolution is constrained by the sequential acquisition of individual two-dimensional planes at varying depths, making it challenging to capture rapid dynamics such as the beating of a zebrafish heart. To address this limitation, we recently developed spatially modulated Selective Volume Illumination Microscopy, which utilizes a compressed sensing approach to reconstruct the entire imaging volume from measurements where multiple planes are illuminated simultaneously using spatially modulated light. Building on this advancement, we now introduce a novel spatio-temporal patterned illumination strategy and volume reconstruction method that incorporates low-rank and sparsity constraints, effectively leveraging the temporal and spatial redundancy present in sequential volumetric acquisitions. This method was applied to the volumetric imaging of embryonic zebrafish hearts, achieving an improvement in imaging speed of 4-fold compared to standard LSFM and a 2-fold improvement compared to traditional compressed sensing approaches, while preserving reconstruction accuracy and enabling the visualization of fast dynamic events with a resolution of a few tens of milliseconds. Our approach represents a step forward in enhancing the temporal resolution of LSFM for studying fast biological dynamics.
Voltage profile enhancement of an AVR system using ant lion optimization algorithm
Authors: Kar Manoj Kumar , Sabat Jogeswara , Kanungo Sanjeet
Published: January 2025

This paper addresses voltage regulation, which is a critical issue in power systems. The automatic voltage regulator (AVR) plays a crucial role in maintaining voltage regulation within permissible limits The AVR system’s dynamic behaviour is improved by applying the Proportional (KP) Integral (KI) Derivative (KD) (PID) controller. The primary challenges of using a PID controller in AVR include difficulties in tuning parameters to handle nonlinearities, load disturbances, and system dynamics, leading to potential instability, overshoot, or slow response. The controller parameters are tuned with Ant Lion Optimisation (ALO) algorithm and Integral Time Absolute Error (ITAE) as an objective function. Also, the proposed system has the ability to improve the transient response of AVR by decreasing the maximum overshoot, settling time, rise time, and peak time magnitudes of the generator terminal voltage with eliminating the steady state error. Once the suggested method had produced the best values for the three gains i.e., KP, KI, and KD of the PID controller, a transient response analysis is examined and compared with some of the existing techniques such as Bat Algorithm (BA), Grey Wolf Optimisation (GWO) and Biogeography Based Optimisation (BBO) algorithm to highlight the novelty of the proposed ALO optimized PID controller. Transient response analysis, root locus analysis, and bode analysis are used to assess the AVR system’s stability. Comparisons to three well-known optimization techniques corroborate the findings. The results show that this new suggested method performs exceptionally well even when there are significant variations in power system parameters and load uncertainties.
Design and analysis of optical devices with ultra-high quality factor based on a plasmonic photonic hybrid structure
Authors: Gholinezhad Shafagh Sara , Kaatuzian Hassan , Danaie Mohammad
Published: January 2025

In the present paper, the design of a tunable, high transmitting, and optical ultra-narrow band-pass filter using a plasmonic-photonic hybrid structure comprised of a multilayer stack of dielectrics and a thin sheet of silver is proposed. This current design can create more energy coupling, thus having a higher transmission peak in comparison to prior studies. To obtain a filtering operation, two different topologies are designed to achieve better performance specifications. The materials used in the structure include silicon, silicon-dioxide, and silver. The Drude model is employed for the silver. It has been shown that the geometrical parameters are sensitive to choose such that transmission properties and resonance wavelengths are arbitrarily tunable. The structure’s design enables a single-mode as well as a multi-mode spectrum for each topology. We have achieved a maximum quality factor of 432.87 with an ultra-small full-width-at-half-maximum bandwidth of 1.43 nm, while the maximum transmission values are greater than 75%. Most of the various advantages include adjustability, high detection resolution, and integration at the nanoscale for optical applications owing to the basic merits of the hybrid structures of plasmonic and photonic crystals.
Thin profile UWB antenna with improved bandwidth for wearable applications
Authors: Sharma Mamta Devi , Yadav Ajay , Singhal Sarthak , Sharma Ritu
Published: January 2025

This work investigates a compact tapered Coplanar Waveguide (CPW) fed wearable antenna. The wide band antenna incorporates a modified patch inspired by a flower shape and a semi-elliptical ground plane to realize a wide bandwidth. The flower-shaped geometry is implemented to increase the electrical perimeter of the radiating element to improve impedance bandwidth. The antenna design is also elaborated by mathematical and analytical modeling. The antenna prototype is printed on a thin FR-4 (0.0026

λ

thick) substrate with an overall footprint of 0.21

λ

× 0.28

λ

(where

λ

is the lower operating wavelength of the proposed structure, i.e., f = 3.95 GHz). The measured results, like reflection coefficient, far-field plots, and gain, are compared with the simulated results. The measured bandwidth is 19.92 GHz with a gain of 3.9 dBi. An investigation is carried out underwater to analyze the performance of the antenna in wet conditions. Specific Absorption Rate (SAR) is calculated to ensure the safety of human tissue with antenna proximity. The maximum SAR observed is 0.844 W kg−1, within the acceptable limit. Thus, the reported results validate the application of the proposed antenna for wearable devices and sensors.
Mathematical modeling for detecting variable gear tooth cracks in a wind turbine gearbox operating under a ramp-up wind speed
Authors: Kumar Rishi , Roy Sankar Kumar
Published: January 2025

Wind is natural and possesses renewable energy. This energy is an opportunity when fossil fuel is shrinking very fast and creating heavy ecological imbalance. Wind energy (WE) is an input to wind turbine (WT) system that produces electricity. Therefore, knowledge of wind is a prerequisite for the dynamic modelling of wind turbine drivetrain system (WTDS) in case of defective gear drives. Wind speed is not certain and its ramp-up component can be very detrimental in poor occasions. Input torque to drivetrain system is modelled based on ramp-up wind speed for the diagnosis under healthy and defective WTDS. Lumped parameter model simplifies the mathematical model of gearbox and the Lagrange’s formulation gives insights to derive the equation of motion of the system. Time varying mesh stiffness (TVMS) computed at mean speed is interpolated in MATLAB to get the same at ramp-up condition. The interpolated stiffness is incorporated in the mathematical formulation for both healthy and defective system. Numerical discretization of differential equation is done in MATLAB using Houbolt method to produce the dynamic responses of WTDS with gear defects under ramp-up speed. Here, time frequency (TF) domain analysis is explored to perform dynamic analysis. Hence, short time Fourier transforms (STFT) is applied to low pass filtered signal; wavelet packet transform is helpful in filtering process. The signal processing technique is proposed to investigate the defective WTDS under variable wind speed. This can be helpful in future investigations in WT under more real situations.
Multi-strategy hybrid dung beetle optimization algorithm for parameter identification in photovoltaic systems
Authors: Yu Zhentao , Cheng Jiatang , Zheng Xinpeng
Published: January 2025

The dung beetle optimization algorithm (DBO) is a novel meta-heuristic algorithm inspired by the behaviors of dung beetle populations, including rolling, dancing, foraging, breeding and stealing. As so far, the DBO algorithm has demonstrated success in addressing a wide range of complex engineering optimization problems. However, like many other meta-heuristic algorithms, it is also prone to certain limitations, such as slow convergence rates and the tendency to become trapped in local optima during the later stages of optimization. To overcome these limitations, this paper proposes a multi-strategy hybrid dung beetle optimization algorithm (MSDBO), which introduces the tangent flight strategy, golden sine search strategy, adaptive t-distribution sparrow perturbation strategy, and vertical crossover mutation strategy. To comprehensively evaluate the performance of MSDBO, simulations are conducted on 59 benchmark functions from CEC2014 and CEC2017. Experimental results demonstrate that MSDBO outperforms DBO, four advanced DBO variants, and several other popular algorithms in overall performance. Furthermore, MSDBO is employed for parameter identification in photovoltaic system models, further showcasing its effectiveness and reliability in real-world engineering applications.
The seepage flow state characteristics of SMA-13 asphalt mixture under triaxial fatigue load
Authors: Li Yan , Zhou Zhigang , Li Shuaishuai , Guo Anshun , Guo Shihui
Published: January 2025

The seepage flow state of water in asphalt pavement may not accordant with Darcy law due to the high hydrodynamic pressure generated under high-speed traffic load, In order to study the seepage flow state of water in asphalt pavement under high water pressure conditions and analyze the critical conditions for non Darcy flow to occur, the SMA-13 asphalt mixture with different compaction methods and different percent air voids were prepared, and triaxial fatigue tests and high water pressure seepage tests were conducted on the specimens, hydraulic gradient and critical Reynolds number were used to distinguish Darcy and non Darcy phenomena in the seepage process. The results indicate that the linear flow occurs at low flow velocity in the SMA-13 asphalt mixture with different compaction methods and different percent air voids, it shows obvious nonlinear flow at high flow velocity. The values of linear component coefficient a and nonlinear component coefficient b obtained by nonlinear fitting by using Forchheimer’s law decrease with the increase of percent air voids, and the values of the coefficients of specimens formed by rotary compaction molding are larger than those formed by Marshall compaction molding. The critical flow velocity and critical Reynolds number increase with the increase of percent air voids before and after triaxial fatigue loading with different molding methods.
Multilayer magnetic skyrmion devices for spiking neural networks
Authors: Lone Aijaz H , Rahimi Daniel N , Fariborzi Hossein , Setti Gianluca
Published: January 2025

Spintronic devices -based on magnetic solitons, such as the domain wall motion and the skyrmions, have shown a significant potential for applications in energy-efficient data storage and beyond CMOS computing architectures. Based on the magnetic multilayer hetero-structures, we propose a magnetic skyrmion-magnetic tunnel junction device structure, mimicking leaky integrate and fire LIF neuron characteristics. The device is controlled by spin-orbit torque-SOT driven skyrmion motion in the ferromagnetic thin film. The modified leaky integrate and fire LIF neuron-like characteristics are shown using the combination of SOT and the skyrmion position dependence of the demagnetization energy. The device characteristics are modeled as the modified LIF neuron. The LIF neuron is one of the fundamental spiking neuron models; we integrate the model in the three-layer spiking neural network (SNN) and convolutional CSNN framework to test these spiking neuron models to classify the MNIST and FMNIST datasets. In both architectures, the network achieves classification accuracy above 97.10%. Additionally, the LIF neuron latency is in ns; thus, when integrated with the CMOS, the proposed device structures and associated systems exhibit an excellent future for energy-efficient neuromorphic computing.
Delineating urban flooding when incorporating community stormwater knowledge
Authors: Scolio Madeline , Kremer Peleg , Smith Virginia , Amur Achira , Wadzuk Bridget , Homet Kate , Devlin Eric , Al Mehedi Md Abdullah , Moore Laura
Published: January 2025

Accurately delineating both pluvial and fluvial flood risk is critical to protecting vulnerable populations in urban environments. Although there are currently models and frameworks to estimate stormwater runoff and predict urban flooding, there are often minimal observations to validate results due to the quick retreat of floodwaters from affected areas. In this research, we compare and contrast different methodologies for capturing flood extent in order to highlight the challenges inherent in current methods for urban flooding delineation. This research focuses on two Philadelphia neighborhoods, Manayunk and Eastwick, that face frequent flooding. Overall, Philadelphia, PA is a city with a large proportion of vulnerable populations and is plagued by flooding, with expectations that flood risk will increase as climate change progresses. An array of data, including remotely sensed satellite imagery after major flooding events, Federal Emergency Management Agency’s Special Flood Hazard Areas, First Street Foundation’s Flood Factor, road closures, National Flood Insurance Program claims, and community surveys, were compared for the study areas. Here we show how stakeholder surveys can illuminate the weight of firsthand and communal knowledge on local understandings of stormwater and flood risk. These surveys highlighted different impacts of flooding, depending on the most persistent flood type, pluvial or fluvial, in each area, not present in large datasets. Given the complexity of flooding, there is no single method to fully encompass the impacts on both human well-being and the environment. Through the co-creation of flood risk knowledge, community members are empowered and play a critical role in fostering resilience in their neighborhoods. Community stormwater knowledge is a powerful tool that can be used as a complement to hydrologic flood delineation techniques to overcome common limitations in urban landscapes.
From just transitions to just transformations; observations from Aotearoa New Zealand in addressing the human wellbeing impacts of climate change
Authors: Lenihan-Ikin Isabella , Ariana Proochista , Atuire Caesar
Published: January 2025

This paper interrogates the utility of a just transition framework for climate change and human wellbeing in the context of Aotearoa New Zealand. Drawing on historical conceptualisations and applications of just transitions, their limitations and potential, we argue that an expansive and anti-reductionist conceptualisation of the just transition is best suited to address complex and inter-sectoral issues across time and space. Expansive just transitions are characterised by four features; relationality, systems-thinking, place-based and inter-generational approaches, where emancipation is held as the overarching objective. While we argue in favour of an expansive and anti-reductionist just transition framework being employed in the face of complex issues, such as the human wellbeing impacts of climate change, we introduce the concept of a just transformation: achieving true equity and justice in the face of climate change requires a transformative approach, situated outside of the confines of the hegemonic economic system and linear transitions. Transformations and not transitions are required for the health and wellbeing outcomes desired amidst threats of climate change.
Deep learning–based image classification reveals heterogeneous execution of cell death fates during viral infection
Authors: Centofanti Edoardo , Oyler-Yaniv Alon , Oyler-Yaniv Jennifer
Published: January 2025

Cell fate decisions, such as proliferation, differentiation, and death, are driven by complex molecular interactions and signaling cascades. While significant progress has been made in understanding the molecular determinants of these processes, historically, cell fate transitions were identified through light microscopy that focused on changes in cell morphology and function. Modern techniques have shifted toward probing molecular effectors to quantify these transitions, offering more precise quantification and mechanistic understanding. However, challenges remain in cases where the molecular signals are ambiguous, complicating the assignment of cell fate. During viral infection, programmed cell death (PCD) pathways, including apoptosis, necroptosis, and pyroptosis, exhibit complex signaling and molecular cross-talk. This can lead to simultaneous activation of multiple PCD pathways, which confounds assignment of cell fate based on molecular information alone. To address this challenge, we employed deep learning–based image classification of dying cells to analyze PCD in single herpes simplex virus-1 (HSV-1)-infected cells. Our approach reveals that despite heterogeneous activation of signaling, individual cells adopt predominantly prototypical death morphologies. Nevertheless, PCD is executed heterogeneously within a uniform population of virus-infected cells and varies over time. These findings demonstrate that image-based phenotyping can provide valuable insights into cell fate decisions, complementing molecular assays.
Insights into the role of phosphorylation on microtubule cross-linking by PRC1
Authors: Tai Ellinor , Henglein Austin , Alfieri Angus , Saxena Gauri , Forth Scott
Published: January 2025

The mitotic spindle is composed of distinct networks of microtubules, including interpolar bundles that can bridge sister kinetochore fibers and bundles that organize the spindle midzone in anaphase. The cross-linking protein PRC1 can mediate such bundling interactions between antiparallel microtubules. PRC1 is a substrate of mitotic kinases including CDK/cyclin-B, suggesting that it can be phosphorylated in metaphase and dephosphorylated in anaphase. How these biochemical changes to specific residues regulate its function and ability to organize bundles has been unclear. Here, we perform biophysical analyses on microtubule networks cross-linked by two PRC1 constructs, one a wild-type reflecting a dephosphorylated state, and one phosphomimetic construct with two threonine to glutamic acid substitutions near PRC1’s microtubule binding domain. We find that the wild-type construct builds longer and larger bundles that form more rapidly and are much more resistant to mechanical disruption than the phosphomimetic PRC1. Interestingly, microtubule pairs organized by both constructs behave similarly within the same assays. Our results suggest that phosphorylation of PRC1 in metaphase could tune the protein to stabilize smaller and more flexible bundles, while removal of these post-translational modifications in anaphase would promote the assembly of larger, more mechanically robust bundles to resist chromosome and pole separation forces at the spindle midzone.
Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation
Authors: Xu Yongjie , Zang Zelin , Hu Bozhen , Yuan Yue , Tan Cheng , Xia Jun , Li Stan Z
Published: January 2025

Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches. To address these issues, we propose the Poincaré deep manifold transformation (PoincaréDMT) method, which maps high-dimensional scRNA-seq data to a hyperbolic Poincaré disk. This approach preserves global structure from a graph Laplacian matrix while achieving local structure correction through a structure module combined with data augmentation. Additionally, PoincaréDMT alleviates batch effects by integrating a batch graph that accounts for batch labels into the low-dimensional embeddings during network training. Furthermore, PoincaréDMT introduces the Shapley additive explanations method based on trained model to identify the important marker genes in specific clusters and cell differentiation process. Therefore, PoincaréDMT provides a unified framework for multiple key tasks essential for scRNA-seq analysis, including trajectory inference, pseudotime inference, batch correction, and marker gene selection. We validate PoincaréDMT through extensive evaluations on both simulated and real scRNA-seq datasets, demonstrating its superior performance in preserving global and local data structures compared to existing methods.
Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity
Authors: Huang Wendong , Hu Yaofeng , Wang Lequn , Wu Guangsheng , Zhang Chuanchao , Shi Qianqian
Published: January 2025

Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or states within tissue microenvironments. Despite these advances, the field has not adequately addressed the regulatory heterogeneity influenced by microenvironmental factors. Here, we propose a novel Spatially Aligned Graph Transfer Learning (SpaGTL), pretrained on a large-scale multi-modal SRT data of about 100 million cells/spots to enable inference of context-specific spatial gene regulatory networks across multiple scales in data-limited settings. As a novel cross-dimensional transfer learning architecture, SpaGTL aligns spatial graph representations across gene-level graph transformers and cell/spot-level manifold-dominated variational autoencoder. This alignment facilitates the exploration of microenvironmental variations in cell types and functional domains from a molecular regulatory perspective, all within a self-supervised framework. We verified SpaGTL’s precision, robustness, and speed over existing state-of-the-art algorithms and show SpaGTL’s potential that facilitates the discovery of novel regulatory programs that exhibit strong associations with tissue functional regions and cell types. Importantly, SpaGTL could be extended to process multi-slice SRT data and map molecular regulatory landscape associated with three-dimensional spatial-temporal changes during development.
Imp

utation for

Li

pidomics and

Met

abolomics (ImpLiMet): a web-based application for optimization and method selection for missing data imputation
Authors: Ou Huiting , Surendra Anuradha , McDowell Graeme S V , Hashimoto-Roth Emily , Xia Jianguo , Bennett Steffany A L , Čuperlović-Culf Miroslava
Published: January 2025

Motivation

Missing values are prevalent in high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. The three missingness patterns, including missing completely at random, missing at random, and missing not at random, describe unique dependencies between the missing and observed data. The optimal imputation method for each dataset depends on the type of data, the cause of the missingness, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset.

Results

ImpLiMet: is a user-friendly web-platform that enables users to impute missing data using eight different methods. For a given dataset, ImpLiMet suggests the optimal imputation solution through a grid search-based investigation of the error rate for imputation across three missingness data simulations. The effect of imputation can be visually assessed by histogram, kurtosis, and skewness, as well as principal component analysis comparing the impact of the chosen imputation method on the distribution and overall behavior of the data.

Availability and implementation

ImpLiMet is freely available at https://complimet.ca/shiny/implimet/ and https://github.com/complimet/ImpLiMet.
Ileal pouch-anal anastomosis for ulcerative colitis: 30-year analysis on surgical evolution and patient outcome
Authors: Bislenghi Gabriele , Luberto Antonio , De Coster Wout , van Langenhoven Leen , Wolthuis Albert , Ferrante Marc , Vermeire Severine , D’Hoore André
Published: January 2025

Background

Proctocolectomy with ileal pouch-anal anastomosis is the treatment of choice for patients with ulcerative colitis with medical refractory disease or dysplasia. The aim of this research was to describe the evolution of ileal pouch-anal anastomosis surgery and surgical outcomes over a three-decade interval in a high-volume referral centre.

Methods

All consecutive patients undergoing ileal pouch-anal anastomosis for ulcerative colitis between 1990 and 2022 at the University Hospitals of Leuven were retrospectively included. Patients were divided into three interval arms (interval A 1990–2000, interval B 2001–2010 and interval C 2011–2022). The primary outcomes of interest were anastomotic leakage at 30 days and pouch failure.

Results

Overall, 492 patients were included. The use of preoperative advanced therapies increased over time (P < 0.001). An increase in laparoscopic procedures (23.2% in interval A, 66.4% in interval B, 86.0% in interval C; P < 0.001) and a shift towards delayed ileal pouch-anal anastomosis (colectomy-first approach with delayed ileal pouch-anal anastomosis construction: 23.0% in interval A, 40.9% in interval B, 85.8% in interval C; P < 0.001) were observed. Anastomotic leakage rate decreased from 16.7% (interval A) to 8.4% (interval C) (P = 0.04). Delayed ileal pouch-anal anastomosis was the most relevant factor in limiting leakage (OR 0.49 (95% c.i. 0.27 to 0.87); P = 0.016). Median follow-up was 7.5 years (interquartile range 2.5–16). Cumulative pouch failure incidence was 8.2%, not significantly different between the three intervals (P = 0.580). Anastomotic leakage was the only significant risk factor for pouch failure (HR 2.82 (95% c.i. 1.29 to 6.20); P = 0.010).

Conclusion

Significant changes in the management of ulcerative colitis patients occurred. Despite the widespread use of advanced therapies and the expanded surgical indications, anastomotic leakage rate decreased over time. In the context of a delayed ileal pouch-anal anastomosis, diverting ileostomy could be avoided in selected cases. Anastomotic leakage remains the most relevant risk factor for pouch failure. Pouch failure incidence remained stable over the years.
Down-regulation of histone deacetylase 2 attenuates ventricular arrhythmias in a mouse model of cardiac hypertrophy through up-regulation of Kv channel-interacting protein 2 expression
Authors: Liu Wenjuan , Liu Jianping , Wang Gang , Cheng Wanwen , Gong Haochen , Song Yujuan , Song Ming , Zhuge Yixin , Li Ying , Liu Jie
Published: January 2025

Aims

Decrease in repolarizing K+ currents, particularly the fast component of transient outward K+ current (Ito,f), prolongs action potential duration (APD) and predisposes the heart to ventricular arrhythmia during cardiac hypertrophy. Histone deacetylases (HDACs) have been suggested to participate in the development of cardiac hypertrophy, and Class I HDAC inhibition has been found to attenuate pathological remodelling. This study investigated the potential therapeutic effects of HDAC2 on ventricular arrhythmia in pressure overload–induced cardiac hypertrophy.

Methods and results

An in vivo cardiac hypertrophic model was produced by performing transverse aortic constriction (TAC) surgery and an in vitro cardiomyocyte hypertrophy model by stimulating neonatal rat ventricular myocytes (NRVMs) with phenylephrine (PE). HDAC2 expression was up-regulated in TAC mouse hearts and in PE-stimulated cardiomyocytes. Susceptibility to ventricular arrhythmia was increased in TAC mice, while Ito,f was decreased and APD was prolonged in TAC cardiomyocytes. Heart-specific knockdown (HKD) of HDAC2 by RNA interference increased Ito,f, shortened APD, and decreased susceptibility to ventricular arrhythmia. Concomitantly, HKD increased the expression of the obligatory β sub-unit of Ito,f, Kv channel-interacting protein 2 (KChIP2), which is down-regulated in hypertrophic hearts. The effects of HKD on KChIP2 expression, Ito,f and APD were also observed in PE-stimulated cardiomyocytes. Mechanistically, HKD increased H3K4me3 abundance and H3K4me3 enrichment at the Kcnip2 promoter in cardiomyocytes. HKD also decreased the expression of KDM5, the H3K4me3 demethylase, which resulted in H3K4me3 up-regulation. While investigating the regulatory mechanisms underlying the effect of HDAC2 on KDM5 stability, we identified CNOT4 as the active KDM5 ubiquitinase in cardiomyocytes. HKD increased CNOT4 expression and CNOT4–KDM5 interactions and thus enhanced the polyubiquitinated degradation of KDM5.

Conclusion

HDAC2 inhibition serves as a novel therapeutic strategy for preventing cardiac hypertrophy-associated electrophysiological remodelling. Furthermore, we identified a novel signalling pathway of CNOT4-mediated KDM5 degradation contributing to the up-regulation of H3K4me3-mediated KChIP2 expression in response to HDAC2 inhibition.
Reconciling blockchain technology and data protection laws: regulatory challenges, technical solutions, and practical pathways
Authors: Zafar Ammar
Published: January 2025

This paper thoroughly explores the complex interplay between blockchain technology and the General Data Protection Regulation (GDPR) of the European Union, alongside the substantial challenges and potential opportunities stemming from their interaction. While the challenges of decentralization and immutability in blockchain are well-documented, this paper advances the discussion by incorporating legal developments, such as evolving interpretations of joint controllership and new advisory opinions. It also evaluates emerging use cases, including blockchain integration in digital currencies like Worldcoin, highlighting contemporary compliance challenges and innovative solutions. By proposing actionable frameworks that leverage technological advancements like chameleon hashes and zero-knowledge proofs, this paper provides a forward-looking analysis of how blockchain systems can align with GDPR principles, offering theoretical insights and practical pathways for compliance. The conclusion underscores the urgent need for clear regulatory frameworks. These frameworks are crucial to enable a balanced approach that fosters innovation while ensuring robust data protection compliance, and their absence could hinder the potential impact of the research.
Standardized pipelines support and facilitate integration of diverse datasets at the Rat Genome Database
Authors: Smith Jennifer R , Tutaj Marek A , Thota Jyothi , Lamers Logan , Gibson Adam C , Kundurthi Akhilanand , Gollapally Varun Reddy , Brodie Kent C , Zacher Stacy , Laulederkind Stanley J F , Hayman G Thomas , Wang Shur-Jen , Tutaj Monika , Kaldunski Mary L , Vedi Mahima , Demos Wendy M , De Pons Jeffrey L , Dwinell Melinda R , Kwitek Anne E
Published: January 2025

The Rat Genome Database (RGD) is a multispecies knowledgebase which integrates genetic, multiomic, phenotypic, and disease data across 10 mammalian species. To support cross-species, multiomics studies and to enhance and expand on data manually extracted from the biomedical literature by the RGD team of expert curators, RGD imports and integrates data from multiple sources. These include major databases and a substantial number of domain-specific resources, as well as direct submissions by individual researchers. The incorporation of these diverse datatypes is handled by a growing list of automated import, export, data processing, and quality control pipelines. This article outlines the development over time of a standardized infrastructure for automated RGD pipelines with a summary of key design decisions and a focus on lessons learned.
A change language for ontologies and knowledge graphs
Authors: Hegde Harshad , Vendetti Jennifer , Goutte-Gattat Damien , Caufield J Harry , Graybeal John B , Harris Nomi L , Karam Naouel , Kindermann Christian , Matentzoglu Nicolas , Overton James A , Musen Mark A , Mungall Christopher J
Published: January 2025

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of “apply patch” and “diff” commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders—e.g. ontology edits can be specified by commands like “add synonym ‘arm’ to ‘forelimb’” or “move ‘Parkinson disease’ under ‘neurodegenerative disease’.” We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs.

Database URL: https://github.com/INCATools/kgcl