bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhanced bearing capacity prediction using hybrid tree-based ensemble learning with advanced meta-heuristic optimization
Authors: Ibrahim Awaisu Shafiu , Idris Ahmad , Umar Ibrahim Haruna , Lin Hang , Shehu Hamza Tijjani 
Published: January 2025

Abstract
The accurate prediction of soil bearing capacity remains a critical challenge in geotechnical engineering, particularly given the complex non-linear relationships between soil properties and foundation performance. Traditional analytical methods often struggle to capture these complexities, leading to potential overestimation or underestimation of bearing capacity across different footing types. This study investigates the application of machine learning techniques for predicting soil bearing capacity across different footing types. The research utilized 200 datasets, comprising 175 institutional sources and 25 laboratory direct shear test experiments, with an 80–20 split ratio for model development and validation. A hybrid tree-based ensemble learning (HTBEL) methodology was developed and compared against conventional models (M5P, CatBoost, AdaBoost, SVR, and Decision Tree) and Terzaghi analytical equation. The HTBEL model demonstrated superior predictive accuracy with R2 values exceeding 0.96 across all footing types, maintaining errors below 5% throughout the sample range. Square footings showed the highest bearing capacity (median ∼3,400 kN m−2) due to favorable area-to-depth ratio, followed by circular footings (∼3,200 kN m−2) benefiting from symmetrical stress transmission, while strip footings (∼2,000 kN m−2) showed lower performance due to concentrated stress distribution along their length. Clustering analysis identified optimal configurations at 3 clusters (Silhouette Score: 0.5236) and 10 clusters (0.5315). This research establishes HTBEL as a robust methodology for bearing capacity prediction in geotechnical engineering applications.
Development of a biaxial fibre Bragg grating (FBG) extensometer with axis misalignment error compensation and cloud integration for advanced slope monitoring
Authors: Ramalingam Nimalrajh , Tan Daryl , Lim Kok-Sing , Mohd Sa'ad Muhammad Syamil , Ahmad H 
Published: January 2025

Abstract
In the paper, an FBG-based biaxial extensometer is presented to detect ground movement, which is a crucial element in slope monitoring systems. Multiple FBGs were used as displacement sensors to establish sensing nodes at various locations along an HDPE pipe. Each sensing node comprises two FBGs for biaxial displacement measurement. A numerical model based on characterization matrix equation is formulated to estimate the lateral displacement vectors using the displacement data obtained from all FBG sensors. The inclusion of torsion effects in the characteristic matrix equation addresses the misalignment error in the principal axis at each sensing node, thereby enhancing estimation accuracy. Through characterization tests, there is a notable enhancement in the accuracy of the displacement vector estimation. Finally, the proposed extensometer is being examined to determine if it can be integrated with the cloud network for remote monitoring. Various random displacement shapes and curvature angles have been tested. The results demonstrate a strong similarity between the shapes reconstructed by the model and the actual ones.&#xD;
An integrated toolbox for creating neuromorphic edge applications
Authors: Niedermeier Lars , Dutt Nikil , Krichmar Jeffrey L 
Published: January 2025

Abstract
spiking neural networks (SNNs) and neuromorphic models are believed to be more efficient in general and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small data sets, and can adapt through neuromodulation. Although research has shown their advantages, there are still few compelling practical applications, especially at the edge where sensors and actuators need to be processed in a timely fashion. One reason for this might be that SNNs are much more challenging to understand, build, and operate due to their intrinsic properties. For instance, the mathematical foundation involves differential equations rather than basic activation functions. To address these challenges, we have developed CARLsim++, an integrated toolbox that facilitates the creation of neuromorphic applications. It extends the highly efficient CARLsim open-source SNN simulator. CARLsim++ encapsulates the mathematical intrinsics and low-level C++ programming by providing a graphical user interface for users to easily create their SNNs and a means to configure sensors and actuators for robotics and other edge devices. These can be accurately simulated before deploying on physical devices. CARLsim++ can lead to rapid development of neuromorphic applications for simulation or edge processing. We introduce CARLsim++ with a closed loop robotic demonstration using neuromorphic computing.
Introduction: Listening Intersectionally to the Chocolate City
Authors: Martin Allie 
Published: January 2025

Abstract
The introduction explores the theoretical and historical framing for the rest of the book. In this chapter the author outlines the history of gentrification and Black life in DC, as well as introducing the theoretical framework that undergirds the book, “intersectional listening.” Intersectional listening is a theoretical approach that considers sound to be a speculative method, considering a multiple of axes of identity and being. This kind of listening practice is intended to support the complexity of stories about gentrification and sound in Washington, DC. In doing so, the chapter also considers the emancipatory potential of listening to gentrification in DC.
Toward a comprehensive understanding of the phenological responses of non-native plants to climate warming: a review
Authors: Dawson-Glass Emma , Schiafo Rory , Miller Chelsea N , Kuebbing Sara E , Stuble Katharine L 
Published: January 2025

Abstract

Background
Plants often shift their phenology in response to climate warming, with potentially important ecological consequences. Relative differences in the abilities of native and non-native plants to track warming temperatures by adjusting their phenologies could have cascading consequences for ecosystems. Our general understanding of non-native species leads us to believe these species may be more phenologically sensitive than native species, but evidence for this has been mixed, likely due, in part, to the myriad of diverse ecological contexts in which non-natives have been studied.

Scope
Here, we review the current state of knowledge on non-native plant phenological responses to climate warming. From observational and experimental studies, we synthesize: (1) the ways in which non-native plant phenology shifts with increased temperature, (2) the relative differences between natives and non-natives in phenological timing and sensitivity to warming, (3) the contingencies driving variable non-native phenological responses to warming, and (4) the ecological consequences of warming-induced phenological shifts in non-natives.

Conclusions
Early-season phenophases tend to advance with warming, sometimes (but not always) more so in non-native species relative to native species. Late-season phenophases, on the other hand, tend to be more variable: advancing, delaying, or remaining unchanged. Similarly, relative differences in phenological sensitivity between native and non-native plants were less consistent for late-season phenophases. However, our ability for inference is limited by the scope of studies done to date, which best represent temperate ecosystems in the Northern Hemisphere. We found phenological shifts in non-native species to be driven by various factors, including their evolutionary histories and the environmental context of the invaded system. Shifts in non-native phenologies result in varied ecological consequences, from shifting demographics of the non-native species themselves to changes in ecosystem level processes such as carbon cycling. Additional study addressing key gaps is vital to improving understanding of non-native phenological responses to warming.
Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences
Authors: Yang Tingpeng , He Yonghong , Wang Yu 
Published: January 2025

Abstract
The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in life processes, highlighting the necessity to enhance the performance of state-of-the-art models. Here, we introduced TEC-LncMir, a novel approach for predicting lncRNA-miRNA interaction using Transformer Encoder and convolutional neural networks (CNNs). TEC-LncMir treats lncRNA and miRNA sequences as natural languages, encodes them using the Transformer Encoder, and combines representations of a pair of microRNA and lncRNA into a contact tensor (a three-dimensional array). Afterward, TEC-LncMir treats the contact tensor as a multi-channel image, utilizes a four-layer CNN to extract the contact tensor’s features, and then uses these features to predict the interaction between the pair of lncRNA and miRNA. We applied a series of comparative experiments to demonstrate that TEC-LncMir significantly improves lncRNA-miRNA interaction prediction, compared with existing state-of-the-art models. We also trained TEC-LncMir utilizing a large training dataset, and as expected, TEC-LncMir achieves unprecedented performance. Moreover, we integrated miRanda into TEC-LncMir to show the secondary structures of high-confidence interactions. Finally, we utilized TEC-LncMir to identify microRNAs interacting with lncRNA NEAT1, where NEAT1 performs as a competitive endogenous RNA of the microRNAs’ targets (mRNAs) in brain cells. We also demonstrated the regulatory mechanism of NEAT1 in Alzheimer’s disease via transcriptome analysis and sequence alignment analysis. Overall, our results demonstrate the effectivity of TEC-LncMir, suggest a potential regulation of miRNAs by NEAT1 in Alzheimer’s disease, and take a significant step forward in lncRNA-miRNA interaction prediction.
TransBic: bucket trend-preserving biclustering for finding local and interpretable expression patterns
Authors: Li Jing , Mei Qinglin , Yang Chaoxia , Zhu Naibo , Li Guojun 
Published: January 2025

Abstract
Biclustering has emerged as a promising approach for analyzing high-dimensional expression data, offering unique advantages in uncovering localized co-expression patterns that traditional clustering methods often miss and thus facilitating advancements in complex disease research and other biomedical applications. However, state-of-the-art methods identify distinct patterns at the expense of losing information about specific patterns, some of which have been used to define cancer subtypes or reflect the progression of a disease or cellular processes. Additionally, these methods exhibit poor effectiveness in noisy environments. To address these limitations, we propose the bucket trend-preserving (BTP) pattern, a novel generalization of existing patterns. And we have developed an algorithm, TransBic, to extract significant biclusters of BTP-patterns. Specifically, TransBic transforms the problem into identifying common multipartite acyclic tournament subdigraphs shared by distinct subsets of acyclic tournament digraphs derived from a given expression matrix. Compared with prominent tools, TransBic demonstrates superior performance in identifying biclusters of all non-row-constant patterns, especially under noise and data fluctuations. Furthermore, TransBic successfully identifies the most disease-related pathways for type 2 diabetes (T2D), colorectal cancer, hepatocellular carcinoma, and breast cancer, outperforming other tools in this regard. Different from previous generalizations, BTP-patterns capture specific up-regulation and down-regulation dynamics. Through targeted analysis of BTP-patterns in T2D expression data, TransBic uncovers biological processes affected by disease risk factors, extending the application of trend-preserving biclustering in expression data analysis.
Vascraft: new decellularized and re-endothelialized human vascular graft produced by tissue engineering for use in arterial bypass
Authors: Iraola Picornell G , Gálvez-Montón C , Onetti Y , Martorell J , López-Puerto L , Savio A , Castells-Sala C , Muñoz Guijosa C 
Published: January 2025

Abstract

Introduction
Small-diameter vascular grafts are crucial for patients undergoing coronary artery bypass surgery. Inherent comorbidities and the absence of optimal autologous vessels have prompted the development of vascular grafts through tissue engineering. The objective of the study is to develop a human biological substitute, VasCraft, for use as a coronary bypass graft.

Methods
VasCraft is an inter-institutional project carried out in four phases (F1–F4) for the design, development, and production of the bioimplant.F1: Decellularization of human cadaveric saphenous veins. The optimal decellularization protocol was identified by evaluating extracellular matrix (ECM) preservation, residual DNA content, morphohistological structure, and cytotoxicity.F2: Reendothelialization using endothelial cells derived from Wharton’s jelly mesenchymal stem cells (EC-MSC, WJ) in 3D bioreactors.F3: Generation of an Advanced Therapy Medicinal Product (ATMP) under Good Manufacturing Practice (GMP) conditions.F4: Pre-clinical validation of the VasCraft ATMP in the femoro-femoral position in a porcine model. Graft implantation was monitored fluoroscopically, and structural and inflammatory assessments were performed at 30 days post-implantation.

Results
Eleven decellularization protocols were tested. All protocols achieved a DNA concentration of less than 50 ng/mg of dry tissue. Protocol 1 was selected due to its high preservation of the morphology and ECM of the decellularized vein and the low cytotoxicity of the decellularization reagents. Additionally, a 3D bioreactor was developed to facilitate the recellularization of grafts with EC-MSC, WJ under continuous flow conditions.

Conclusion
The VasCraft bioimplant aims to solve an unmet need in the development of artificial vascular grafts. Phases F1 and F2 are well underway, with plans to move into Phases F3 and F4 in the coming months.
Temporal study of myocardial infarction to optimize the application of cardiosphere-derived cells’ secretome as a therapeutic strategy
Authors: Pulido Fresneda M , de Pedro M Á , Álvarez V , Marchena A M , Sánchez-Margallo F M , López E , Crisóstomo Ayala V , Báez Díaz C 
Published: January 2025

Abstract

Introduction
Myocardial infarction (MI) initiates an acute inflammatory response that is crucial for heart repair. However, an exaggerated inflammatory response can lead to adverse left ventricular remodeling and heart failure. The secretome of cardiosphere-derived cells (CDCs) offers promising immunomodulatory properties. This study seeks to determine the optimal timing for CDC secretome application by evaluating CD163 expression in monocytes and plasma levels post-MI to maximize its immunomodulatory properties.

Methods
CD163 expression in monocytes was evaluated under basal conditions and after MI at 24, 48, 72 hours, and one week post-MI. Plasma protein levels were also assessed at the same time points.

Results
The results indicate a decrease in CD163 expression in monocytes immediately following MI, with a progressive increase observed over the subsequent 72 hours. Plasma protein analysis revealed elevated levels of TNF-α and IL-6 within the first 24 hours post-MI, which then gradually declined. These findings suggest that the inflammatory response is most acute within the first 24 hours post-MI, characterized by high pro-inflammatory cytokine levels and reduced CD163 expression.

Conclusion
The application of CDC-derived secretome treatment could be more beneficial during the first 24 hours post-MI. This study highlights the importance of timing in the application of CDC-based therapies to optimize their immunomodulatory effects and improve outcomes in MI patients.
Artificial intelligence applications for learning in robotic surgery
Authors: Caballero Jorna D , Sánchez Margallo J A , Sánchez Margallo F M 
Published: January 2025

Abstract

Introduction
Robot-assisted surgery (RAS) has become the standard surgical technique in several specialties due to its many advantages. Furthermore, artificial intelligence (AI) has seen an exponential increase in its applications over recent years, leading to the development of numerous algorithms designed to assist in decision support. The primary objective of this study is to evaluate the current applications of AI in the learning process of RAS.

Methods
An exhaustive search was conducted in various scientific databases, including PubMed, Scopus, and WoS. A set of studies that met the search criteria was selected, and from these, the key current applications of AI in learning in RAS were extracted.

Results
The key current applications of AI in learning in RAS include:Providing feedback to surgeons through algorithms that assist in improving the surgical procedure, offering virtual assistants with useful recommendations, and highlighting common errors.Enhancing the realism of simulation textures, thereby improving the quality of visual environments.Planning optimal trajectories for learning in laparoscopic activities, automating trajectory calculation from preoperative imaging studies.Adapting and optimizing the learning process by self-adjusting based on the surgeon’s progress, common errors, strengths, and weaknesses.

Conclusion
AI applications in learning for RAS remain an evolving research field, with significant potential for future advancements. The synergy between technical and clinical aspects offers exciting opportunities for enhancing surgical training.
Cell-type-specific networks during hippocampal seizures at the micro- and macroscale
Authors: Wang Jiaoyang , Yan Jiaqing , Li Donghong , He Shipei , Li Xiaonan , Xing Yue , Lai Huanling , Gui Yue , Zhang Nannan , Huang Wenyao , Yang Xiaofeng 
Published: January 2025

Abstract
Epilepsy is a network disorder, involving neural circuits at both the micro- and macroscale. While local excitatory-inhibitory imbalances are recognized as a hallmark at the microscale, the dynamic role of distinct neuron types during seizures remain poorly understood. At the macroscale, interactions between key nodes within the epileptic network, such as the central median thalamic nucleus (CMT), are critical to the, hippocampal epileptic process. However, precise mechanisms underlying these interactions remain unclear. In this study, we investigated the microcircuit dynamics within the seizure onset zone and secondary spreading regions, as well as the network connectivity between the hippocampus and the CMT, using a 4-aminopyridine (4-AP) induced hippocampal seizure model.
Rats were allocated into three experimental groups. The first group used a 3D tetrode array to monitor hippocampal seizure activity and microcircuit dynamics, including seizure propagation across the macroscale network. In the second group, a chemical lesion was induced in the CMT to assess its impact on hippocampal seizures. In the third group, chemogenetic techniques were used to selectively suppress pyramidal neurons in the CMT and observe changes in neural network connectivity between the CMT and hippocampus during seizures. Offline single-unit sorting was performed using KlustaKwik and further analysis was conducted with CellExplorer.
At seizure onset, the narrow interneurons exhibited increased firing rates, initiating recruitment of other neurons, followed by increased activity in pyramidal neuron. Wide interneurons also showed heightened activity subsequent to pyramidal neurons. Interneurons played a more prominent role in the microcircuit during seizures compared to baseline. The CMT exhibited characteristic seizure activity and a decrease in narrow interneuron activity, whereas the cortex did not display seizure activity during hippocampal seizures. Lesioning the CMT resulted in the loss of the tonic component of hippocampal seizures and reduced overall neuronal activity in the hippocampal. Selective suppression of CMT pyramidal neurons resulted in shortened hippocampal seizures while preserving the tonic component. Narrow interneuron activity remained unchanged, while pyramidal neuron and wide interneuron activity significantly decreased.
Our findings underscore the critical role of interneurons in the micronetwork of the seizure onset zone and secondary spreading region. Narrow interneurons were particularly vital in seizure initiation, whereas wide interneurons may contribute to seizure termination within the onset zone but not in the secondary spreading region. Pyramidal neurons in the CMT influence hippocampal seizures by modulating of both hippocampal pyramidal neurons and wide interneurons.
Receptor activity-modifying protein 1 regulates the differentiation of mouse skin fibroblasts by downregulating α-SMA expression via suppression of high mobility group AT-hook 1 to promote skin wound repair
Authors: Song Ru , Ma Jiaxu , Yin Siyuan , Wu Zhenjie , Liu Chunyan , Sun Rui , Cao Guoqi , Lu Yongpan , Liu Jian , Su Linqi , Wang Yibing 
Published: January 2025

Abstract

Background
Skin innervation is very important for normal wound healing, and receptor activity-modifying protein 1 (RAMP1) has been reported to modulate calcitonin gene-related peptide (CGRP) receptor function and thus be a potential treatment target. This study aimed to elucidate the intricate regulatory effect of RAMP1 on skin fibroblast function, thereby addressing the existing knowledge gap in this area.

Methods
Immunohistochemical staining and immunofluorescence (IF) staining were used to measure the dynamic changes in the expression of RAMP1 and α-smooth muscle actin (α-SMA) in skin wound tissue in mice. Mouse skin fibroblasts (MSFs) stably transfected with Tet-on-Flag-RAMP1 overexpression (OE) and Tet-on-Flag control (Ctrl) lentiviruses were constructed for in vitro experiments. High mobility group AT-hook 1 (HMGA1) plasmids and α-SMA plasmids were used to overexpress HMGA1 and α-SMA, respectively. An α-SMA siRNA was used to silence α-SMA. Quantitative real-time polymerase chain reaction (qPCR), western blot and IF staining analyses were used to determine the mRNA and protein levels in the cells in different groups. A scratch wound healing assay was used to evaluate the cell migration ability of different groups. Cleavage under targets and release using nuclease (CUT & RUN) assays and dual-luciferase reporter assays were used to predict and verify the interaction between HMGA1 and the α-SMA promoter.

Results
RAMP1 and α-SMA protein expression levels in the dermis changed dynamically and were negatively correlated during dorsal skin wound healing in mice. RAMP1 OE in vitro inhibited the differentiation and promoted the migration of MSFs by decreasing α-SMA expression via the suppression of HMGA1, which was shown for the first time to bind to the α-SMA promoter and increase α-SMA transcription. RAMP1 OE also modulated extracellular matrix (ECM) synthesis and remodeling by promoting collagen III and MMP9 expression and decreasing collagen I, MMP2, and tissue inhibitor of metalloproteinases 1 expression.

Conclusions
Our findings suggest that RAMP1 OE decreases differentiation and promotes migration in MSFs by downregulating α-SMA expression via the suppression of HMGA1 and modulates ECM synthesis and remodeling, revealing a novel mechanism regulating α-SMA transcription, providing new insights into the RAMP1-mediated regulation of fibroblast function, and identifying effective nerve-related targets for skin wound repair.