bluuebunny/crossref_metadata_2025_split huggingface dataset:
A simplified predictive model for the compression behavior of self-healing microcapsules using an empirical coefficient
Authors: Cha Jaeho , Yoon Sungho 
Published: August 2024

Abstract
This study is dedicated to predicting the compression behavior of microcapsules, a key aspect in self-healing applications. Understanding the compression behavior of microcapsules, mainly due to their liquid cores, is a complex task. Equally challenging is the evaluation of the shell properties. We aimed to streamline this prediction process by introducing the empirical coefficient C
core, which accounts for core influence. We conducted experiments on microcapsules with MUF (Melamine–Urea–Formaldehyde) shells, compressing them between two plates and recording their responses to load and displacement. The empirical coefficient, influenced by capsule size, shell properties, and core volume fraction, was then analyzed in terms of microcapsule size and Young’s modulus. The research results showed that as the diameter of microcapsule and Young’s modulus of the shell increased, the Ccore also increased. This relationship could be represented in a three-dimensional surface. These findings could significantly contribute to estimating shell properties and modeling matrices with dispersed microcapsules.
Design and manufacturing of micro/nanorobots
Authors: Liu Junmin , Zhuang Rencheng , Zhou Dekai , Chang Xiaocong , Li Longqiu 
Published: August 2024

Abstract
Micro/nanorobots (MNRs) capable of performing tasks at the micro- and nanoscale hold great promise for applications in cutting-edge fields such as biomedical engineering, environmental engineering, and microfabrication. To cope with the intricate and dynamic environments encountered in practical applications, the development of high performance MNRs is crucial. They have evolved from single-material, single-function, and simple structure to multi-material, multi-function, and complex structure. However, the design and manufacturing of high performance MNRs with complex multi-material three-dimensional structures at the micro- and nanoscale pose significant challenges that cannot be addressed by conventional serial design strategies and single-process manufacturing methods. The material-interface-structure-function/ performance coupled design methods and the additive/formative/subtractive composite manufacturing methods offer the opportunity to design and manufacture MNRs with multi-materials and complex structures under multi-factor coupling, thus paving the way for the development of high performance MNRs. In this paper, we take the three core capabilities of MNRs—mobility, controllability, and load capability—as the focal point, emphasizing the coupled design methods oriented towards their function/performance and the composite manufacturing methods for their functional structures. The limitations of current investigation are also discussed, and our envisioned future directions for design and manufacture of MNRs are shared. We hope that this review will provide a framework template for the design and manufacture of high performance MNRs, serving as a roadmap for researchers interested in this area.
Experimental investigation of hybrid reinforced A356 aluminium alloy synthesised by stir casting technique
Authors: Pawar Niteesh , Barve Shivprakash , Pesode Pralhad 
Published: August 2024

Abstract
One of the most significant alloys to be employed in the automotive, aerospace, and military industries in recent years is A356 aluminium. Because of A356’s excellent compatibility with other metals and nanoparticles, novel hybrid composites may be made using it. The characteristics of these hybrid composites are mostly the result of the additives’ interaction with the A356 alloy’s current elemental composition. Aluminium composites were synthesized through stir casting method by reinforcing 2%, and 4% SiC, 2% and 4% Al2O3 and 0.5%, 1%, 1.5%, 2% and 2.5% SiC and Al2O3 both. The homogeneous distribution of SiC and Al2O3 microparticles in reinforced composite is revealed by scanning electron microscopy (SEM). The addition of SiC and Al2O3 reinforcements greatly improved the mechanical characteristics of the synthesised composites; for example, a composite with 4% SiC reinforcement reached its maximum hardness and maximum tensile strength of 165 HV and 257 MPa respectively. Maximum elongation of 6.72% was observed for 0.5% SiC and 0.5% Al2O3 reinforced composite. Minimum wear rate is observed for 4% SiC reinforced composite material. This study aims to identify gaps in the potential variations and compatibility of various additives with one another in order to create a brand-new hybrid reinforced alloy suitable for automotive braking system applications: brake rotors made of a disc or a brake pad, depending on the properties of the hybrid reinforced alloy that was made. Hence, the current work presented focuses on the preparation of hybrid reinforcement of A356 with silicon carbide and alumina powders.
Nonlinear dynamic analysis of an inclined micro-beam under a moving mass
Authors: Zhao Liang , Wang Shun-Li 
Published: August 2024

Abstract
Nonlinear vibration of an inclined simply supported micro-beam under a moving mass is investigated for Euler–Bernoulli beam theory (EBT) and Timoshenko beam theory (TBT) respectively. Based on a modified couple stress theory (MCST) and the von-Karman geometric nonlinearity, the nonlinear coupled dynamic equations of the system are established through the Hamilton’s principle with the assumed mode method. A wide range of numerical examples are employed to study the influence of slenderness ratio, cross-section height, inclined angle, the size and velocity of the moving mass and the scale factor of the material on the solutions of nonlinear and linear, the solutions of EBT and TBT and the solutions of moving mass and moving load. By comparing the differences between the nonlinear and linear solutions under different parameters and beam theories, the importance and significance of nonlinear dynamic analysis of the inclined micro-beam are revealed.
Equivalent circuit technique for designing split ring resonator based metasurfaces
Authors: Audu Eliazar Elisha , Eteng Akaa Agbaeze , Uchendu Iyemeh , Omijeh Bourdillon O 
Published: August 2024

Abstract
Metasurfaces are two-dimensional artificially engineered structures capable of manipulating the phase, direction and orientation of electromagnetic waves by exhibiting simultaneously negative values of permittivity and permeability. These unconventional properties have been tailored and explored in many applications such as in bio-sensors, waveguides and antennas. The split ring resonators are the commonly used constituent meta-atoms of metasurfaces whose design and analysis rely on commercially available numerical electromagnetic fields (EM) solvers and experimental analysis. These numerical EM solvers are based on meshing and partitioning of graphical structures into the desire grids or patches to solve Maxwell equations in discrete form. However, graphical rendering and meshing of 3D objects requires significant space-time computational resources to analyze the structure. With the cost of licenses of EM solvers being very expensive, analytical solution were explored. The use of LC resonant frequency analytical formula provides an approximate value of resonant frequency which is less accurate and does not gives information about the current characteristics induced on the constinuent meta-atom of a metasurface. This paper presents an analytical approach to the design and analysis of a doubly split double rings (DSRR) using lumped element equivalent circuit that can be solved by mesh network analysis. The resonant frequency is extracted from the induced current characteristics which agrees with simulations and experimental results. The resonant frequency errors for a single DSRR unit cell ranged from1.05% to 7%, and for two coupled DSRR unit cells, they ranged from 1.4% to 11%.
Leveraging normalizing flows for orbital-free density functional theory
Authors: de Camargo Alexandre , Chen Ricky T Q , Vargas-Hernández Rodrigo A 
Published: August 2024

Abstract
Orbital-free density functional theory (OF-DFT) for real-space systems has historically depended on Lagrange optimization techniques, primarily due to the inability of previously proposed electron density approaches to ensure the normalization constraint. This study illustrates how leveraging contemporary generative models, notably normalizing flows (NFs), can surmount this challenge. We develop a Lagrangian-free optimization framework by employing these machine learning models for the electron density. This diverse approach also integrates cutting-edge variational inference techniques and equivariant deep learning models, offering an innovative reformulation to the OF-DFT problem. We demonstrate the versatility of our framework by simulating a one-dimensional diatomic system, LiH, and comprehensive simulations of hydrogen, lithium hydride, water, and four hydrocarbon molecules. The inherent flexibility of NFs facilitates initialization with promolecular densities, markedly enhancing the efficiency of the optimization process.
SemiH: DFT Hamiltonian neural network training with semi-supervised learning
Authors: Cho Yucheol , Choi Guenseok , Ham Gyeongdo , Shin Mincheol , Kim Daeshik 
Published: August 2024

Abstract
Over the past decades, density functional theory (DFT) calculations have been utilized in various fields such as materials science and semiconductor devices. However, due to the inherent nature of DFT calculations, which rigorously consider interactions between atoms, they require significant computational cost. To address this, extensive research has recently focused on training neural networks to replace DFT calculations. However, previous methods for training neural networks necessitated an extensive number of DFT simulations to acquire the ground truth (Hamiltonians). Conversely, when dealing with a limited amount of training data, deep learning models often display increased errors in predicting Hamiltonians and band structures for testing data. This phenomenon poses the potential risk of generating inaccurate physical interpretations, including the emergence of unphysical branches within band structures. To tackle this challenge, we propose a novel deep learning-based method for calculating DFT Hamiltonians, specifically tailored to produce accurate results with limited training data. Our framework not only employs supervised learning with the calculated Hamiltonian but also generates pseudo Hamiltonians (targets for unlabeled data) and trains the neural networks on unlabeled data. Particularly, our approach, which leverages unlabeled data, is noteworthy as it marks the first attempt in the field of neural network Hamiltonians. Our framework showcases the superior performance of our framework compared to the state-of-the-art approach across various datasets, such as MoS2, Bi2Te3, HfO2, and InGaAs. Moreover, our framework demonstrates enhanced generalization performance by effectively utilizing unlabeled data, achieving noteworthy results when evaluated on data more complex than the training set, such as configurations with more atoms and temperature ranges outside the training data.
A Systematic Review and Meta-Analysis of Synthetic Mesh Outcomes in Alloplastic Breast Reconstruction
Authors: Clark Robert Craig , Reese McKay D , Attalla Philopatir , Camacho Justin M , Hirpara Milan M , Delong Michael R , Reid Chris M 
Published: August 2024

Abstract

Background
Mesh implants are frequently employed in alloplastic breast reconstruction. Notably, no mesh to date has FDA approval for this indication. Several synthetic meshes have been introduced with heterogeneous properties and outcomes.

Objectives
This study aims to systematically review synthetic mesh use in alloplastic breast reconstruction, describe rates of short-term complications, and analyze these outcomes in reports comparing synthetic and biologic meshes. The authors hypothesized data from comparative and noncomparative studies would show no significant differences between synthetic and biological meshes.

Methods
The authors conducted a systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Thirty-one studies reporting the use of synthetic mesh and clinical outcomes were included. Eight studies directly comparing synthetic mesh and biological mesh were meta-analyzed for relative risk (RR). Nineteen noncomparative studies were analyzed for meta-rates. Outcomes, including seroma, infection, reoperation, and explant, were assessed on a per-breast basis. Resultant models were challenged for sensitivity and bias.

Results
Meta-analysis of comparative studies demonstrated no difference in the risk of infection with synthetic mesh (RR = 0.53; 95% CI [0.26-1.10]), but a reduced risk of reoperation (RR = 0.54; 95% CI [0.33-0.89]) or explant (RR = 0.43; 95% CI [0.21-0.87]). Meta-analysis of noncomparative studies demonstrated rates of seroma = 3%; 95% CI [1%-6%], infection = 4%; 95% CI [3%-6%], reoperation = 10%; 95% CI [7%-13%], and explant = 3%; 95% CI [2%-5%]).

Conclusions
Studies comparing synthetic and biologic meshes demonstrated noninferiority of synthetic in all outcomes assessed. Noncomparative studies demonstrated rates of seroma, infection, reoperation, and explant similar to literature values for biological mesh.

Level of Evidence: 2 (Risk)
CoNECo: a Corpus for Named Entity recognition and normalization of protein Complexes
Authors: Nastou Katerina , Koutrouli Mikaela , Pyysalo Sampo , Jensen Lars Juhl 
Published: August 2024

Abstract

Motivation
Despite significant progress in biomedical information extraction, there is a lack of resources for Named Entity Recognition (NER) and Named Entity Normalization (NEN) of protein-containing complexes. Current resources inadequately address the recognition of protein-containing complex names across different organisms, underscoring the crucial need for a dedicated corpus.

Results
We introduce the Complex Named Entity Corpus (CoNECo), an annotated corpus for NER and NEN of complexes. CoNECo comprises 1621 documents with 2052 entities, 1976 of which are normalized to Gene Ontology. We divided the corpus into training, development, and test sets and trained both a transformer-based and dictionary-based tagger on them. Evaluation on the test set demonstrated robust performance, with F-scores of 73.7% and 61.2%, respectively. Subsequently, we applied the best taggers for comprehensive tagging of the entire openly accessible biomedical literature.

Availability and implementation
All resources, including the annotated corpus, training data, and code, are available to the community through Zenodo https://zenodo.org/records/11263147 and GitHub https://zenodo.org/records/10693653.
Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision
Authors: Malusare Aditya , Kothandaraman Harish , Tamboli Dipesh , Lanman Nadia A , Aggarwal Vaneet 
Published: August 2024

Abstract

Summary
This article presents the Ensemble Nucleotide Byte-level Encoder-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder–decoder Transformer architecture. ENBED uses a subquadratic implementation of attention to develop an efficient model capable of sequence-to-sequence transformations, generalizing previous genomic models with encoder-only or decoder-only architectures. We use Masked Language Modeling to pretrain the foundation model using reference genome sequences and apply it in the following downstream tasks: (i) identification of enhancers, promotors, and splice sites, (ii) recognition of sequences containing base call mismatches and insertion/deletion errors, an advantage over tokenization schemes involving multiple base pairs, which lose the ability to analyze with byte-level precision, (iii) identification of biological function annotations of genomic sequences, and (iv) generating mutations of the Influenza virus using the encoder–decoder architecture and validating them against real-world observations. In each of these tasks, we demonstrate significant improvement as compared to the existing state-of-the-art results.

Availability and implementation
The source code used to develop and fine-tune the foundation model has been released on Github (https://github.itap.purdue.edu/Clan-labs/ENBED).
refseqR: an R package for common computational operations with records on RefSeq collection
Authors: Die Jose V 
Published: August 2024

Abstract

Summary
We introduce refseqR, an R package that offers a user-friendly solution, enabling common computational operations on RefSeq entries (GenBank, NCBI). The package is specifically designed to interact with records curated from the RefSeq database. Most importantly, the interoperability and integration with several Bioconductor objects allow connections to be applied to other projects.

Availability and implementation
The package refseqR is implemented in R and published under the MIT open-source license. The source code, documentation, and usage instructions are available on CRAN (https://CRAN.R-project.org/package=refseqR).
Axiomatization of interventional probability distributions
Authors: Sadeghi Kayvan , Soo Terry 
Published: August 2024

Abstract
Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions. Our axiomatizations neatly lead to a simple and clear theory of causality that has several advantages: it does not need to make use of any modelling assumptions such as those imposed by structural causal models; it relies only on interventions on single variables; it includes most cases with latent variables and causal cycles; and, more importantly, it does not assume the existence of an underlying true causal graph as we do not take it as the primitive object; moreover, a causal graph is derived as a by-product of our theory. We show that, under our axiomatizations, the intervened distributions are Markovian to the defined intervened causal graphs, and an observed joint probability distribution is Markovian to the obtained causal graph; these results are consistent with the case of structural causal models, and as a result, the existing theory of causal inference applies. We also show that a large class of natural structural causal models satisfy the theory presented here. The aim of this paper is axiomatization of interventional families, which is subtly different from causal modelling.
The incidence of lung cancer amongst primary care chest radiograph referrals—an evaluation of national and local datasets within the United Kingdom
Authors: Bhartia Bobby S K , Baldwin David , Bradley Stephen H , Callister Matthew E J , Das Indrajeet , Evison Matthew , Grundy Seamus , Kaur Jaspreet , Kennedy Martyn , O’Dowd Emma L 
Published: August 2024

Abstract

Objectives
To determine the incidence of lung cancer amongst primary care referrals for investigation with a chest radiograph (CXR).

Methods
Retrospective evaluation of datasets from the national Clinical Practice Research Datalink (CPRD) and from a single large regional centre. Data were extracted for cohorts of consecutive adults aged over 40 years for whom a CXR had been performed between 2016 and 2018. Using cancer registry data, the incidence of lung cancer within a 2 years of the CXR referral and the variations with age, gender, and smoking status were evaluated.

Results
A total of 291 294 CXR events were evaluated from the combined datasets. The incidence of lung cancer amongst primary care CXR referrals was 1.4% in CPRD with a consistent correlation with increasing age and smoking status. The incidence of lung cancer within two-years of the CXR varied between 0.03% (95%CI, 0.0-0.1) amongst never smokers aged 40-45 years to 4.8% (95%CI, 4.2-5.5) amongst current-smokers aged 70-75 years. The findings were similar for the single large centre data, although cancer incidence was higher.

Conclusions
A simple estimation and stratification of the risk of lung cancer amongst primary care referrals for investigation with a CXR is possible using age and smoking status.

Advances in knowledge
This is the first estimate of the incidence of lung cancer amongst primary care CXR referrals and a demonstration of how the demographic information contained within a request could be used to optimize investigations and interpret test results.
T2-fluid attenuated inversion recovery fat-suppressed mismatch in the identification and characterization of lesions related to radiologically isolated syndrome
Authors: Okuda Darin T , Lebrun-Frenay Christine 
Published: August 2024

Abstract
The radiologically isolated syndrome is defined by the presence of incidentally identified T2-weighted hyperintense lesions, highly suggestive of central nervous system demyelination, following an MRI study that is performed for reasons other than for the investigation of symptoms related to multiple sclerosis (MS). These individuals also have no evidence of prior neurological symptoms associated with inflammatory demyelination and no alternative explanation for the observed MRI findings. Recently, the introduction of novel imaging techniques such as the “central vein sign” has improved lesion specificity for MS. In addition, the observation of T2-fluid attenuated inversion recovery (FLAIR) mismatch characteristics associated with gliomas and in those with MS with a higher disease burden appear to provide morphological data that relate to disease severity. The value of T2-FLAIR mismatch characteristics in discrete multi-focal lesions has not yet been well defined. Here, we present the value of a fat-suppressed T2-FLAIR sequence in the identification and characterization of T2-weighted hyperintensities resulting from inflammatory demyelination.
Intranasal administration of trehalose reduces α-synuclein oligomers and accelerates α-synuclein aggregation
Authors: Tanaka Makoto T , Miki Yasuo , Mori Fumiaki , Kon Tomoya , Furukawa Tomonori , Shimoyama Shuji , Tatara Yota , Ozaki Taku , Bettencourt Conceição , Warner Thomas T , Wakabayashi Koichi 
Published: August 2024

Abstract
Abnormal α-synuclein (αSyn), including an oligomeric form of αSyn, accumulates and causes neuronal dysfunction in the brains of patients with multiple system atrophy. Neuroprotective drugs that target abnormal αSyn aggregation have not been developed for the treatment of multiple system atrophy. In addition, treating diseases at an early stage is crucial to halting the progress of neuronal damage in neurodegeneration. In this study, using early-stage multiple system atrophy mouse model and in vitro kinetic analysis, we investigated how intranasal and oral administration of trehalose can improve multiple system atrophy pathology and clinical symptoms. The multiple system atrophy model showed memory impairment at least four weeks after αSyn induction. Behavioural and physiological analyses showed that intranasal and oral administration of trehalose reversed memory impairments to near-normal levels. Notably, trehalose treatment reduced the amount of toxic αSyn and increased the aggregated form of αSyn in the multiple system atrophy model brain. In vitro kinetic analysis confirmed that trehalose accelerated the aggregate formation of αSyn. Based on our findings, we propose a novel strategy whereby accelerated αSyn aggregate formation leads to reduced exposure to toxic αSyn oligomers, particularly during the early phase of disease progression.