bluuebunny/crossref_metadata_2025_split huggingface dataset:
Recurrence Pattern and Mapping of Lymph Node Metastases from Rectal Neuroendocrine Tumors
Authors: Takaoka Ayumi , Tsukamoto Shunsuke , Takamizawa Yasuyuki , Moritani Konosuke , Imaizumi Jun , Kinugasa Yusuke , Kanemitsu Yukihide 
Published: October 2024

Abstract

Purpose
There are as yet no data on the long-term clinical outcomes or the frequency of lymph node metastasis after surgical resection in patients with rectal neuroendocrine tumors (NETs). Our study investigated the long-term clinical outcomes and the frequency of lymph node metastasis in patients with rectal NETs treated by surgical resection.
Methods
Fifty-one patients with rectal NETs who underwent rectal resection with total mesorectal excision between January 2000 and July 2020 at our hospital were enrolled. Twenty-eight patients had undergone preoperative endoscopic mucosal resection or endoscopic submucosal dissection before surgical resection. The indication for surgery was determined based on the Japan Neuroendocrine Tumor Society guidelines. Patients diagnosed with neuroendocrine carcinoma were excluded. Main Outcome Measures is frequency of pathological diagnosis of lymph node metastasis, site of lymph node metastasis, and the long-term prognosis after surgical resection.
Results
Lymph node metastasis was observed in 20 patients (39.2%) with lateral lymph node metastasis occurring in 4 patients (7.8%). There was no significant association between lymph node metastasis and 5-year relapse-free survival (90% for metastasis-negative patients vs 58.1% for metastasis-positive patients, p = 0.094).
Conclusions
The rate of lymph node metastasis in patients with rectal NETs treated by surgical resection with total mesorectal excision was found to be high. However, a good prognosis was achieved by surgical resection even in patients with lymph node metastasis.
Growing functional artificial cytoskeletons in the viscoelastic confinement of DNA synthetic cells
Authors: Walther Andreas , Chen Weixiang , Song Siyu , Samanta Avik , Sethi Soumya , Drees Christoph , Kappl Michael , Butt Hans-Jürgen 
Published: October 2024

Abstract

Intracellular structures, like the cytoskeletons, form within a crowded cytoplasm with defined viscoelastic properties. While crowding effects on self-assembly are well studied, the role of viscoelasticity in regulating structure formation remains elusive. Here, we engineer crowded all-DNA synthetic cells (SCs) with tunable viscoelastic interiors to investigate this phenomenon. We introduce a facile approach to integrate multiple DNA barcodes with adjustable concentrations into SCs, enabling selective enrichment of DNA tiles to form artificial cytoskeletons coupled to the viscoelastic SC interior. Distinct mechanistic differences in assembly occur compared to solution or simple crowding. Additionally, we develop light, molecular, and metabolic controls to direct structure formation and create self-sorted SC populations with distinct artificial cytoskeletons. These cytoskeletons enhance the mechanical stability of SCs and support stable contacts with mammalian cells. Our SC system serves as a versatile molecular engineering platform to study self-assembly under viscoelastic constraints and leverage sub-SC structures for challenging applications.
The footprint of endolithic algae in shaping the skeletal structure of massive coral skeletons: insights into micro and macro-porosity
Authors: Uribe Edwin S. , Murgueitio Amalia , Gómez Carlos E. , Acosta Alberto , Sánchez Juan A. 
Published: October 2024

Abstract

Coral skeletons provide habitat for a euendolithic community, forming a green band within the skeleton, where Ostreobium spp. is the dominant group. Euendoliths, actively penetrate live coral skeletons, but how they use and modify skeletal structure is not properly understood. This study explores the microstructural characteristics of skeletal microenvironments through a micro-CT technique that analyzes the "footprint" of the euendolithic community on the porosity of coral skeleton. We compared three Porites species based on the percentage of the relative volume of microporosity, macroporosity, total porosity, and solid volume fraction of CaCO3 among three distinct zones within the coral colony: coral tissue, the green band (characterized by eundolithic community) and the bare skeletal region. We found a significant increase in microporosity within the green band, while the opposite occurs for macroporosity that decreased within this zone, for all analyzed species. We describe a model to explain the porosity gradient along the vertical axis for Porites coral colonies, and suggests that within the “green band” microenvironment, the metabolic activity of the community is the responsible for this pattern. Our findings provide insights on the ecological relationship with the coral holobiont: macroerosion mitigation and microporosity filling.
Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks
Authors: Lin Emily , Zhong Yang , Chen Gang , Deng Sili 
Published: October 2024

Abstract

The continual growth of anthropogenic CO2 has motivated the introduction of carbon capture techniques, such as metal-organic framework (MOF)-based physisorption. The large design space of adsorbents resulting from the inherently high tunability of MOFs has prompted the utilization of deep learning to drive material design via property prediction. Nonetheless, the inferencing of certain critical adsorption properties, such as heat of adsorption (ΔHads), is an unexplored area of study. To address this knowledge gap, a predictive learning model employing crystal graph convolution neural network (CGCNN) and graph attention (GAT) was developed for high-throughput uptake and (ΔHads) estimation at pressures ranging from 0 to 50 bars. We further bridged these predicted adsorption properties to uptake behaviors (i.e., isotherm shapes/types) and structural information by performing a detailed ablation study to investigate the relative importance of local (atomic) and global (textural) features in relation to predictive performance. This comparative analysis facilitated the discovery of data-informed universal descriptors capable of illustrating the interdependencies between easily-computed, accessible textural information and extrinsic adsorption properties (uptake and (ΔHads). We demonstrate the generalizability of these descriptor functions via robust validation using unseen datasets.
Bio-primed machine learning to enhance discovery of relevant biomarkers
Authors: Henke David , Renwick Alexander , Zoeller Joseph , Meena Jitendra , Neill Nicholas , Bowling Elizabeth , Karlin Kristen , Westbrook Thomas , Simon Lukas 
Published: October 2024

Abstract

Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. Traditional statistical methods often fall short in this context, necessitating novel computational approaches that harness the full potential of big data in bioinformatics. Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. This bio-primed approach prioritizes variables that are both statistically significant and biologically relevant. Applying our method to multiple dependency datasets, we identified biomarkers which traditional methods overlooked. Our biologically informed LASSO method effectively identifies relevant biomarkers from high-dimensional collinear data, bridging the gap between statistical rigor and biological insight. This method holds promise for advancing personalized medicine by uncovering novel therapeutic targets and understanding the complex interplay of genetic and molecular factors in disease.
A metal-wall compatible high-confinement tokamak plasma regime for fusion energy
Authors: Xu Guosheng , Ding Genfan , Jian Xiang , Wang Yifeng , Guo Houyang , Zhang Tao , Wu Kai , Li Kedong , Yang Qingquan , Chen Ran , Yu Lin , Meng Lingyi , Wang Liang , Wang Huiqian , Ding Siye , Li Nami , Xu Xueqiao , Yan Ning , Xu Liqing , lin lin , Zhang Bin , Qian Jinqing , Zhou Tianfu , Li Pan , Zhou Chu , Zang Qing , Liu Haiqing , Ding Fang , Zhang Ling , Jin Yifei , Duan Yanmin , Yu Yaowei , Ding Rui , Li Guoqiang , Gong Xianzu , Lu Kun , Hu Jiansheng , Song Yuntao , Wan Baonian 
Published: October 2024

Abstract

The international thermonuclear experimental reactor (ITER) is now considering to use tungsten as the material for plasma-facing components (PFCs) [1] and operate in the so-called high-confinement mode (H-mode) [2]. Heat pulse eruptions caused by the plasma instability named the edge localized mode (ELM) pose a critical threat to the PFCs, which can lead to severe material damage and generate metallic impurities that contaminate the core plasma, rendering stable H-mode operation difficult [3]. Therefore, ITER and future fusion reactors are desired to operate in a plasma regime without ELM but with good energy confinement, simultaneously coupled with the plasma detachment to relieve heat load on the material surface [4]. However, such an operational regime has not yet been demonstrated for long pulse without expense of energy confinement, and the mechanism for maintaining ELM suppression in a detached H-mode plasma remains unclear. Here, we report the first demonstration of the achievement of a long-pulse detached H-mode plasma regime without ELM but with energy confinement even better than the standard H-mode in a metal-wall environment, and elucidate, for the first time, the underlying physics. We find that a high-frequency turbulence is excited at the plasma edge that provides a radial transport channel, thus preventing ELM generation. Simulations suggest that in ITER the plasma edge will be more prone to turbulences, as turbulence suppression by the ExB flow shear is expected to be significantly weaker than that in present tokamaks [5]. These findings may lead to a promising operational scenario for future tokamak fusion reactors compatible with metal-wall conditions, which is essential for harvesting fusion energy.
Influence of Cell Size and Porosity on Permeability in Bone Scaffold TPMS Structures
Authors: Shen Hangming , Song Chaojie , Yang Lihong , Gan Yi 
Published: October 2024

Abstract

In bone tissue engineering, bone scaffold structures based on triply periodic minimal surfaces (TPMS) are of significant interest, with permeability being a key indicator of scaffold performance. This paper constructs three types of bone scaffold TPMS structures—Diamond (D), Gyroid (G), and IWP—in both sheet (S) and network (N) forms, and it specifically investigates the impact of two structural parameters—cell size and porosity—on the permeability of these porous structures through numerical simulation. The results demonstrate that permeability increases with both cell size and porosity. Among the structures, the IWP type exhibits significantly higher permeability compared to the other two types. Under identical conditions, N-form structures show superior permeability over S-form structures within the same type. Additionally, empirical formulae for calculating the permeability of the three N-form structures, based on the two parameters of cell size and porosity, are derived. These formulae have been experimentally validated, demonstrating that the errors within a reasonable and acceptable range. These findings provide theoretical guidance for the optimized design and practical application of bone scaffold TPMS structures.
Highly Magneto-electric-mechanical Coupling Effect in Self-biased Magnetoelectric Composite Induced by Laser Thermal Annealing
Authors: Liu Ming , XIAN DAN , Zhao Yanan , Du Yongjun , Xu Yiwei , Qiao Jiacheng , Wu Jingen , Lin Qijing , Jiang Zhuangde 
Published: October 2024

Abstract

The development of advanced magnetoelectric (ME) composites necessitates high-performance materials is capable of achieving high levels of ME coupling, minimal magnetic loss, absence or limited reliance on external excitation sources. In this paper, a (2-2) connectivity magnetoelectric (ME) laminate integrates multiple layers of FeSiB alloy (Metglas) and Pb (Mg, Nb) O3-PbTiO3 (PMN-PT) single crystal, achieving a remarkable ME coupling coefficient of 2033.4 V/Oe·cm (sevenfold rise) by laser thermal annealing treatment. Here, the laser-induced nanostructures on Metglas, with oxidized insulation layer and soft and hard magnetic dipole layer improve the Magneto-electric-mechanical coupling with a mechanical quality factor exceeding 350. More importantly, the interaction between amorphous and nanocrystalline dipoles triggers an EB effect, leading to self-biasing performance of 67.45 V/Oe·cm. Furthermore, the composite exhibits excellent passive DC magnetic detection limit of 22 nT, and an improved weak AC magnetic detection limit down to 383 fT. These explorations offer the potential to enhance passive current measurement, underwater communication, extend weak magnetic positioning, and extend brain magnetic detection.
Economics and health status in China: A repeated cross-sectional study of China Family Panel Studies (CFPS) 2016 and 2020
Authors: Liu Jiayin , Yu Peizhi , Ye Xiaoyu , Yang Yirui , Feng Zhixin 
Published: October 2024

Abstract

This study examines the association between economic status and health outcomes across China, utilizing data from the China Family Panel Studies (CFPS) for 2016 and 2020. The "Healthy China 2030" initiative underscores the importance of addressing key determinants of health, prompting a closer examination of the interplay between economic factors and health indicators. This study employs multivariate multilevel logistic regression models to explore how individual, household, and provincial economic variables influence Body Mass Index (BMI) and self-rated health (SRH) status. Results reveal nuanced associations between economic status and health outcomes. At the province level, increased mean income negatively correlates with optimal BMI levels, while higher provincial expenditure is positively associated with optimal BMI. At the individual or household level, higher expenditure on tourism associates with better self-rated health, suggesting a positive link between leisure spending and perceived health. Per capita living space and the total value of durable consumer goods also associate with one’s BMI and SRH, reflecting the broader socio-economic context of individuals' health. Geographical disparities in health outcomes persist, with southern provinces generally exhibiting healthier BMI profiles compared to their northern counterparts. However, Shanxi (a province in the North) stands out with the highest odds of good SRH, indicating regional variations in subjective health assessments. Spatial analysis reveals a negative correlation trend between BMI and SRH across provinces, highlighting the complex interplay between objective health measurements and subjective health perceptions. This study emphasizes the importance of targeted interventions to address health disparities and promote healthy lifestyles across different regions in China. Enhancing healthcare accessibility in economically disadvantaged areas, investing in tourism and recreational infrastructure, and tailoring public health initiatives to local socio-economic contexts are essential steps towards fostering a more equitable and healthy society.
Pre-adolescent Brain Asymmetry: Developmental Trajectory, Cognitive and Psychiatric Effects, Neurobiological and Environmental Influences in ABCD Study
Authors: Zhang Jie , Wu Xinran , Zhang Kai , Kuang Nanyu , Kong Xiang-Zhen , Cao Miao , Zhang Yufeng , Lian Zhengxu , Liu Yu , Fan Huaxin , Yu Gechang , Liu Zhaowen , Cheng Wei , Jia Tianye , Sahakian Barbara , Robbins Trevor , Feng Jianfeng , Schumann Gunter , Palaniyappan Lena 
Published: October 2024

Abstract

Cerebral asymmetry is fundamental to various cognitive functions but is often disrupted in neuropsychiatric disorders. While adolescent brain growth has been extensively studied, the maturation of brain asymmetry in children and its influencing factors remain poorly understood. Using longitudinal data from 11,000 children aged 10–14 in the ABCD Study, we mapped the developmental trajectory of structural brain asymmetry and revealed significant age-related, modality-specific development patterns, particularly linked to crystallized intelligence and general psychiatric risks. Genetically, structural asymmetry were related to synaptic processes and neurogenesis, likely through asymmetric synaptic pruning. At the macrostructural level, corpus callosum integrity emerged as a key factor in modulating longitudinal asymmetry. Environmentally, favorable perinatal conditions were associated with prolonged corpus callosum development, affecting future asymmetry patterns and cognitive outcomes. These findings underscore the dynamic yet predictable interactions between brain structural asymmetry, its determinants, and cognitive and psychiatric outcomes during this pivotal developmental stage. Our results provide empirical support for the adaptive plasticity theory in cerebral asymmetry and offer new insights into both cognitive maturation and potential risk for early-onset psychiatric disorder risks.
TFDN: A Reliable Hybrid Time and Frequency Domain-based Model for Photovoltaic Power Generation Time Series Forecasting
Authors: Hua Qiang , Chu HaoRan , Zhang Feng , Zhang Yong , Dong ChunRu 
Published: October 2024

Abstract

The rapid expansion of photovoltaic (PV) power generation has highlighted the critical need for accurate PV power forecasting. This paper proposes a novel deep learning-based model for PV power forecasting, which effectively integrates information from both the time and frequency domains to achieve more accurate predictions. Within the time-domain module, the input data undergoes an initial decomposition into cycle segments using Fast Fourier Transform (FFT). These segments are then processed through a mask-based multilayer perceptron and a parameter-efficient inception block, designed to more easily capture both global and local dependencies within the data. In parallel, a distinct module leverages multiscale convolutional kernels of varying sizes to model cross-dimensional dependencies among different variables in the PV data. In the frequency-domain module, a low-pass filter removes high-frequency noise from the frequency components extracted by the FFT. The filtered components are then fed into a complex linear layer to perform linear interpolation, generating the forecasted frequency components. The inverse FFT subsequently converts the frequency components back into the time domain. This process leverages frequency-domain processing to mitigate information loss in the time domain, while also enabling the integration of both time-domain and frequency-domain information for a more comprehensive analysis of the data. Experimental results demonstrate that the proposed model not only delivers accurate time series forecasts for PV power generation across three different sites but also surpasses state-of-the-art models in the field of PV power forecasting.
STFFormer-GCN: Spatial–Temporal Fusion Transformer Based Graph Convolutional Network for Traffic Flow Prediction
Authors: Teng Guoqing , Wu Han , Wang Yixing , He Ao , Long Yangsheng , Zhao Meng 
Published: October 2024

Abstract

Traffic flow prediction plays a crucial role in improving traffic management efficiency and optimizing traffic resource allocation. However, existing traffic flow prediction models are often difficult to accurately capture complex spatial–temporal dependencies. To this end, this paper proposes a spatial–temporal fusion Transformer based graph convolutional network (STFFormer-GCN) for traffic flow prediction, aiming to address the issues of long- and short-term dynamic spatial–temporal relationships, traffic propagation delays, and spatial–temporal feature fusion. First, we propose a multi-scale time convolution module employing recursion and sliding windows to model long- and short-term temporal dependencies. Second, a new dynamic relational convolution matrix is proposed for learning dynamic spatial dependencies that change over time and road structure. Then, we propose a time-delay cycle module that combines the current traffic flow and spatially propagated time delay information to model the propagation process of traffic flow in space. Finally, we employ Transformer's encoder to fuse dynamic spatial–temporal features with time delayed spatial–temporal features. We conducted extensive experiments on four real datasets and the results show that STFFormer-GCN achieves state-of-the-art performance,especially the MAPE value of the PeMS08 dataset experiment is improved by 4.05%. In addition, we conducted an ablation study and a single-step prediction performance study to evaluate the contribution of each component to the prediction accuracy of the model.
Camouflaged Object Detection Based on Deformable Convolution and Edge Guidance
Authors: Wang Xu , Chen Chong , Ruan Kang 
Published: October 2024

Abstract

Camouflaged Object Detection (COD), which aims to accurately segment objects that perfectly blend into the surrounding environment, is a complex task. Although many deep learning-based COD methods have emerged, they are still unable to effectively segment camouflaged objects from the background in a complete and fine-grained manner. Based on this, we will propose a novel network DCEGNet (Deformable Convolutional and Edge-Guided Networks) for camouflaged object detection using deformable convolution as well as edge-attached semantic features. Specifically, in order to address the situation that complex objects are difficult to be segmented effectively, we design a deformable convolution based on a self-attention mechanism to change the shape of the convolution kernel, on top of which we also propose an Edge Detection Module (EDM) to explore the additional edge semantic information to guide the feature learning of the COD, which enables the model to utilize the boundary information to accurately locate and segment the object. Extensive experiments on three COD benchmark datasets show that our DCEGNet significantly outperforms 20 existing methods on four widely used evaluation metrics.
Energy Recuperation of Driven Colloids in Non-Equilibrium Baths
Authors: Ginot Felix , Bechinger Clemens 
Published: October 2024

Abstract

When objects move within a classical fluid, this leads to viscous friction, which is irreversibly converted into heat. In particular, at microscopic length scales, such energy loss is detrimental to applications making use of externally and self-propelled colloidal particles such as microscopic heat engines and microrobots. Here, using a colloidal particle in a viscoelastic fluid, we experimentally demonstrate energy recuperation (ER), where up to 30\% of the energy coupled to the surrounding can be recovered in useful work. This effect is due to the time-delayed structural response of viscoelastic fluids to external forces, which prevents an immediate relaxation back to equilibrium. As a consequence this allows the temporary storage and bidirectional energy exchange between the such a non-equilibrium bath and the particle. Our results are in excellent agreement with a micro-mechanical generic model which only makes use of the time-delayed response of the surrounding to a driven particle. Therefore, we expect our results to equally apply for critical fluids or active baths.