bluuebunny/crossref_metadata_2025 huggingface dataset:
Asymptotic methods applied to integrals occurring in strong-laser-field processes
Authors: Milošević D B , Jašarević A S , Habibović D , Hasović E , Čerkić A , Becker W
Published: August 2024

The quantum-mechanical transition amplitudes for atomic and molecular processes in strong laser fields are expressed in the form of multidimensional integrals of highly oscillatory functions. Such integrals are ideally suited for the evaluation by asymptotic methods for integrals. Furthermore, using these methods it is possible to identify, in the sense of Feynman’s path-integral formalism, the partial contributions of quantum orbits, which are related to particular solutions of the saddle-point equations. This affords insight into the physics of the problem, which would not have been possible by only solving these integrals numerically. We apply the saddle-point method to various quantum processes that are important in strong-field physics and attoscience. The special case of coalescing or near-coalescing saddle points requires application of the uniform approximation. We also present two modifications of the saddle-point method, for the cases where a singular point of the subintegral function exactly overlaps with a saddle point or is located in its close vicinity. Particular emphasis is on the classification of the saddle-point solutions. This problem is solved for the one-dimensional integral over the ionization time, relevant for above-threshold ionization (ATI), while for two-dimensional integrals a classification by the multi-index

(
α
,
β
,
m
)

is introduced, which is particularly useful for the medium- and high-energy spectrum of high-order harmonic generation (HHG) and backward-scattered electrons (for high-order ATI). For the low-energy structures a classification using the multi-index

(
ν
,
ρ
,
μ
)

is introduced for the forward-scattering quantum orbits. In addition to laser-induced processes such as ATI, HHG and high-order ATI, we consider laser-assisted scattering as an example of laser-assisted processes for which real solutions of the saddle-point equation exist. Particular attention is devoted to the quantum orbits that describe and visualize these processes. We also consider finite laser pulses, the semiclassical approximation, the role of the Coulomb field and the case of laser fields intense enough to lead into the relativistic regime.
Two-dimensional Janus MXTe (M = Hf, Zr; X = S, Se) piezoelectrocatalysts: a comprehensive investigation of its electronic, synthesis feasibility, electric polarization, and hydrogen evolution reaction activity
Authors: Lizzette C Lim Hermina , Tobias Ela T , Chen Guan-Yu , Lu Yen-Lin , Santos-Putungan Alexandra B , Abraham B Padama Allan , Lin Shi-Hsin , Barayang Putungan Darwin
Published: August 2024

We computationally investigated potential piezoelectrocatalysts, two-dimensional (2D) Janus MXTe (M = Hf, Zr; X = S, Se). The structural and electronic properties, synthesis feasibility, piezoelectric properties, and hydrogen evolution reaction were calculated. Our results showed that these 2D Janus MXTe are narrow-gap semiconductors, indicating great conductivity for electrocatalysis. The feasibility of synthesis was comparable to the already synthesized Janus materials. To exhibit a piezoelectrocatalytic effect, the material has to be piezoelectric and catalytically effective simultaneously. As the Janus structure breaks the centrosymmetry, the considered MXTe are intrinsically piezoelectric. We therefore calculated the dipole moments and the variation of out-of-plane polarization upon strain. The computed piezoelectric coefficient e
31 is within the same order of magnitude as that of other Janus 2D materials. Finally, although pristine 2D Janus MXTe were inert to hydrogen evolution reaction, incorporation of single-atom defects was found to boost hydrogen adsorption significantly. The catalytic efficacy can be further tuned by biaxial tensile strain, effectively controlling the Gibbs free energy of adsorption to be close to the thermoneutral value that is indicative of an excellent hydrogen evolution reaction activity, at least for ZrSTe Janus monolayer. In summary, this work proposed and comprehensively investigated a new class of possible piezoelectrocatalysts, 2D Janus materials, which is feasible to be synthesized, catalytically effective, and has great conductivity.
Critical review on the controllable growth and post-annealing on the heterojunction of the kesterite solar cells
Authors: Liu Yue , Wang Siyu , Zhang Yi
Published: August 2024

Kesterite-structured solar cells have drawn significant attention due to their low-cost and environmental friendly composition. Recently, a remarkable certified power conversion efficiency (PCE) of 14.9% has been achieved, indicating a broader prospect for kesterite solar cells. However, this PCE is still far below the theoretical efficiency and the PCE of predecessor Cu(In,Ga)Se2 solar cells, which have been commercialized successfully. The relatively low device efficiency primarily originates from the unfavorable bulk and heterojunction of kesterite solar cell. Therefore, the achievement of high PCE in kesterite solar cells heavily relies on high-quality absorber layers and appropriate heterojunction contact. In this review, we first summarize the recent studies on the controllable growth of kesterite thin film. Based on different fabrication methods, various endeavors in revealing the reaction mechanism and manipulating the growth pathway of kesterite thin films have been introduced. Subsequently, studies related to the optimization of heterojunction by post-annealing process are also summarized. This simple and convenient approach can effectively enhance the heterojunction contact and promote the carrier transportation. Finally, this article discusses the future development strategy and perspectives towards achieving enhanced PCE in kesterite thin film solar cells.
A simplified predictive model for the compression behavior of self-healing microcapsules using an empirical coefficient
Authors: Cha Jaeho , Yoon Sungho
Published: August 2024

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
GMMID: genetically modified mice information database
Authors: Xu Menglin , Fang Minghui , Chen Qiyang , Xiao Wenjun , Xu Zhixuan , Cai Bao , Zhao Zhenyang , Wang Tao , Zhu Zhu , Chen Yingshan , Zhu Yue , Dai Mingzhou , Jiang Tiancheng , Li Xinyi , Chun Siuwing , Zhou Runhua , Li Yafei , Gou Yueyue , He Jingjing , Luo Lin , You Linlin , Jiang Xuan
Published: August 2024

Genetically engineered mouse models (GEMMs) are vital for elucidating gene function and disease mechanisms. An overwhelming number of GEMM lines have been generated, but endeavors to collect and organize the information of these GEMMs are seriously lagging behind. Only a few databases are developed for the information of current GEMMs, and these databases lack biological descriptions of allele compositions, which poses a challenge for nonexperts in mouse genetics to interpret the genetic information of these mice. Moreover, these databases usually do not provide information on human diseases related to the GEMM, which hinders the dissemination of the insights the GEMM provides as a human disease model. To address these issues, we developed an algorithm to annotate all the allele compositions that have been reported with Python programming and have developed the genetically modified mice information database (GMMID; http://www.gmmid.cn), a user-friendly database that integrates information on GEMMs and related diseases from various databases, including National Center for Biotechnology Information, Mouse Genome Informatics, Online Mendelian Inheritance in Man, International Mouse Phenotyping Consortium, and Jax lab. GMMID provides comprehensive genetic information on >70 055 alleles, 65 520 allele compositions, and ∼4000 diseases, along with biologically meaningful descriptions of alleles and allele combinations. Furthermore, it provides spatiotemporal visualization of anatomical tissues mentioned in these descriptions, shown alongside the allele compositions. Compared to existing mouse databases, GMMID considers the needs of researchers across different disciplines and presents obscure genetic information in an intuitive and easy-to-understand format. It facilitates users in obtaining complete genetic information more efficiently, making it an essential resource for cross-disciplinary researchers.

Database URL: http://www.gmmid.cn
Role of 3D transoesophageal echocardiography in the study of infective endocarditis. Demonstration in a case collection
Authors: Baltodano-Arellano Roberto , Huaman-Carrasco Daniel , Cupe-Chacalcaje Kelly , Cachicatari-Beltran Angela , Benites-Yshpilco Lindsay , Urdanivia-Ruiz Dante , Rafael-Horna Eliana , Falcón-Quispe Luis , Demarini-Orellana Alessio , Velarde-Acosta Kevin , Ortiz-Leon Xochitl , Levano-Pachas Gerald
Published: August 2024

Infective endocarditis (IE) is a condition that predominantly affects native or prosthetic heart valves, which is currently on the rise due to the increase in invasive cardiology procedures, such as the utilization of cardiac implantable electronic devices and transcatheter interventions. The recommended imaging tests for diagnosis are 2D transthoracic echocardiography (2D TTE) and 2D transoesophageal echocardiography (2D TOE). However, these modalities present limitations in detecting vegetations and estimating their dimensions. These disadvantages can be overcome by 3D transoesophageal echocardiography (3D TOE), particularly with the multiplanar reconstruction tool, which allows for the visualization of infinite valve planes, thus optimizing the detection of lesions and precise measurements. Furthermore, the volume rendering provides insight into the anatomical relationships between lesions, which is particularly useful for surgical planning. In this review, we aim to discuss the role of this recent imaging modality in the diagnosis, prognosis, and therapeutic of IE. Finally, we present a collection of images that illustrate the use of 3D TOE tools.
Tissue versus mechanical mitral valve replacement in patients aged 50–70: a propensity-matched analysis
Authors: Fialka Nicholas M , Watkins Abeline R , Alam Abrar , EL-Andari Ryaan , Kang Jimmy J H , Hong Yongzhe , Bozso Sabin J , Moon Michael C , Nagendran Jeevan
Published: August 2024

OBJECTIVES

There remains debate over the optimal mitral valve replacement (MVR) option for patients aged 50–70 years. The objective of this study was to retrospectively compare the long-term outcomes of mechanical and bioprosthetic MVR in this patient population.

METHODS

Data from patients undergoing MVR between 2004 and 2018 were retrospectively reviewed. The primary outcome was all-cause mortality. Secondary outcomes included perioperative and late morbidity.

RESULTS

Two hundred and eight-six propensity-matched patients (n = 143 mechanical; n = 143 bioprosthetic) aged 50–70 years were included in the final analysis. Maximum follow-up was 15.8 years. There was no significant difference in all-cause mortality between the groups at 30 days, 1 year, 5 years, 10 years, and at the longest follow-up. Patients who underwent mechanical MVR experienced significantly lower rates of postoperative atrial fibrillation (P = 0.001). There were no significant differences in rates of sepsis, acute kidney injury, superficial and deep sternal wound infection, mediastinal bleeding, and permanent pacemaker implantation. At the longest follow-up, there were no differences in myocardial infarction, stroke, heart failure or overall rehospitalization. At the same time point, there was an increased rate of MVR in patients receiving a bioprosthetic valve (P = 0.015).

CONCLUSIONS

Survival following mechanical and bioprosthetic MVR in patients 50–70 years of age is similar to up to 15 years of follow-up. Bioprosthetic MVR is associated with an increased risk of repeat MVR. Mechanical MVR is not associated with an increased risk of stroke. Valve selection in this patient population requires diligent consideration of structural valve deterioration and subsequent reoperation risk as well as bleeding and thromboembolic risk.
The emerging H3K9me3 chromatin landscape during zebrafish embryogenesis
Authors: Duval Katherine L , Artis Ashley R , Goll Mary G
Published: August 2024

The structural organization of eukaryotic genomes is contingent upon the fractionation of DNA into transcriptionally permissive euchromatin and repressive heterochromatin. However, we have a limited understanding of how these distinct states are first established during animal embryogenesis. Histone 3 lysine 9 trimethylation (H3K9me3) is critical to heterochromatin formation, and bulk establishment of this mark is thought to help drive large-scale remodeling of an initially naive chromatin state during animal embryogenesis. However, a detailed understanding of this process is lacking. Here, we leverage CUT&RUN to define the emerging H3K9me3 landscape of the zebrafish embryo with high sensitivity and temporal resolution. Despite the prevalence of DNA transposons in the zebrafish genome, we found that LTR transposons are preferentially targeted for embryonic H3K9me3 deposition, with different families exhibiting distinct establishment timelines. High signal-to-noise ratios afforded by CUT&RUN revealed new, emerging sites of low-amplitude H3K9me3 that initiated before the major wave of zygotic genome activation (ZGA). Early sites of establishment predominated at specific subsets of transposons and were particularly enriched for transposon sequences with maternal piRNAs and pericentromeric localization. Notably, the number of H3K9me3 enriched sites increased linearly across blastula development, while quantitative comparison revealed a >10-fold genome-wide increase in H3K9me3 signal at established sites over just 30 min at the onset of major ZGA. Continued maturation of the H3K9me3 landscape was observed beyond the initial wave of bulk establishment.
Patterns of Family Conflict and Accusations of Abuse in Dementia Family Caregivers: A Latent Class Analysis
Authors: Browning Wesley R , Yildiz Mustafa , Maxwell Christopher D , Sullivan Tami P , Yefimova Maria , Hernandez Chilatra Jessica A , Pickering Carolyn E Z
Published: August 2024

Background and Objectives

Family conflict is a stressor for dementia family caregivers, yet its impact may differ based on the relationship between caregivers and their recipients. This study’s objectives were to categorize caregivers into groups based on family conflict, examine whether the relationship to the recipient influences group membership, and determine whether these groups are associated with engaging in abusive and neglectful behaviors.

Research Design and Methods

This national, cross-sectional study of 453 dementia family caregivers used latent class analysis to generate groups based on family conflict and abuse accusations. A multinomial logistic regression determined if relationship type (i.e., being a spouse, child, or grandchild to the care recipient or having a nontraditional relationship) predicted group membership. Groups were examined as predictors of abusive and neglectful behaviors using analysis of variance.

Results

A 4-class solution emerged as the best fit: 3 groups with varying probabilities of family conflict and 1 group with elevated probabilities of abuse and neglect accusations. Relationship typed predicted membership in these classes. Group membership predicted abusive and neglectful behaviors.

Discussion and Implications

Adult children were more likely to experience high amounts of family conflict, whereas nontraditional caregivers were less likely to experience abuse and neglect accusations. Membership in the accusations group was a unique risk factor for abusive and neglectful behaviors. These findings support the need for continued investigation of family conflict in dementia caregivers. They also call to examine how family relationship types, including nontraditional family structures, influence caregiving outcomes.
Performance Under Fire: Older Adult Cognitive Risks and Protections Under Heat Strain
Authors: Shin Su Hyun , Walker Susan Lee , Ji Hyunjung , Lee Hee Yun
Published: August 2024

Background and Objectives

Although extreme heat events disproportionately affect older adults and the importance of cognition is known, research examining older adult cognition under heat stress is limited. This study examines the relationship between risk/protective factors and heat strain on older adult cognition, employing a social-ecological model.

Research Design and Methods

Retrieved from the 1996–2016 waves of the Health and Retirement Study, our study used older adults aged 50 and older and their spouses residing in the United States. Individual-fixed effects models estimated changes in cognition as measured by fluid and crystallized intelligence scores in response to extreme heat days. This study further estimated interactions of extreme heat with protective/risk factors for cognition (i.e., education, physical activity, social engagement, and genetic risk for Alzheimer’s disease).

Results

Our results demonstrated that extreme heat days were associated with fluid but not crystallized intelligence scores. Educational attainment, mild physical activity, and social contacts with children moderated this relationship. Furthermore, Alzheimer’s disease polygenic scores moderated the correlation between extreme heat days and crystallized intelligence scores.

Discussion and Implications

An increasing frequency of extreme heat events and an aging population globally highlight the need for policies and interventions building resiliency in older adults. Actions promoting the protective modifiable behaviors to older adult cognition identified by our study can lead to healthier individuals and communities.
The impact of using an IoT-based automatic solar panel cleaner on building energy performance
Authors: Anber Maha , Aboshosha Bassam W
Published: August 2024

Solar energy is a great provider of renewable environmentally friendly energy. Thanks to solar technologies, it converts sunlight into electrical energy using multiple devices such as photovoltaic cells. The generated electrical energy can be used for multiple purposes such as commercial, industrial, and domestic uses. Generating electrical energy without releasing toxic gases is considered one of the biggest advantages of using solar energy. While solar panels are a reliable source of clean electrical energy, their power output can be decreased due to the lack of maintenance and especially cleaning. The efficiency of solar panels decreases by more than 40% due to the lack of cleaning. That is why routine cleaning is crucial to ensure the optimum performance of them. Providing an automatic solar panel cleaning system will ensure that routine maintenance is done and the electricity output is optimum. Integrating the use of photovoltaic cells in buildings helps improve the energy performance and energy efficiency of buildings. This research paper aims to determine the impact of integrating an internet of things (IoT)-based automatic solar panel cleaner and measure the energy efficiency of using it. The IoT-based automatic solar panel cleaner contributed to raising the solar panel power wattage by 89.33% in a hot desert climatic region.
Charging prediction for new energy electric vehicles in the context of vehicle to grid using a hybrid ROCNN-BILSTM model
Authors: Yang Ye , Wang Wen , Qin Jian , Wang Mingcai , Xia Yansong , Li Yanan , Jia Qi
Published: August 2024

Vehicle to grid refers to the interaction between electric vehicles and the power grid through charging stations. It aims to guide owners of new energy vehicles to charge in an orderly and staggered manner, and even enabling power supply back to the grid. In the context of vehicle to grid, the charging behavior of new energy vehicles becomes different from the past due to uncertainties introduced by user plug-in/plug-out actions and weather conditions, which may disrupt owners’ future scheduling plans. In this article, we propose a charging prediction study based on the Reordering Convolutional Neural Network-Bidirectional Long Short-Term Memory (ROCNN-BILSTM) hybrid model specifically designed for the vehicle to grid context. The proposed model employs wavelet threshold denoising as a data preprocessing operation to remove unnecessary noise factors that could affect predictions. Subsequently, the 2-Dimensional Convolutional Neural Network (2D-CNN) component retains temporal features while extracting spatial features. Notably, the features are rearranged, combining highly correlated ones, to facilitate the extraction of high-level, abstract spatial features by the 2D-CNN. Finally, the Bidirectional Long Short-Term Memory (BILSTM) component utilizes a bidirectional structure to capture comprehensive dynamic information and assist in achieving the final charging prediction. Our proposed ROCNN-BILSTM eliminates uncertainty in the data, allowing deep learning models to better focus on important features. Additionally, our model emphasizes high-level spatiotemporal feature extraction, which helps achieve high-performance charging prediction. In the context of vehicle to grid, a real-world dataset of new energy vehicle charging data was used for multi-step prediction, different starting point predictions, and comparison with advanced models. The experimental results show that the proposed model outperforms CNN-LSTM and 2D-CNN models by up to 50.1% and 57.1% in terms of mean absolute error (MAE), and 45.8% and 51.5% in terms of mean squared error (MSE). The results validate the strong predictive performance of the hybrid model and provide robust support for the demands of the vehicle to grid market and new energy vehicle charging prediction technology. In future work, we will place greater emphasis on designing high-performance and interpretable models to explore the fundamental reasons behind different charging trends in new energy vehicles.
Evaluation of a Benzodiazepine Immunoassay for Urine Drug Testing in Clinical Specimens
Authors: Ge Mengyuan , Alabi Adekunle , Kelner Michael J , Fitzgerald Robert L , Suhandynata Raymond T
Published: August 2024

Background

Benzodiazepines are commonly prescribed medications frequently linked to instances of abuse and overdose. Historically, FDA-cleared benzodiazepine urine immunoassays cross-react poorly with glucuronidated benzodiazepine metabolites, leading to false negatives. Clinical laboratories have addressed this deficiency by creating laboratory-developed tests (LDTs) that incorporate a beta-glucuronidase hydrolysis step to increase the clinical sensitivity of these assays.

Methods

Performance characteristics of 2 FDA-cleared benzodiazepine urine immunoassays (Benzodiazepines Plus, no glucuronidase and Benzodiazepines II, with glucuronidase; Roche Diagnostics) and a previously described benzodiazepine immunoassay LDT (with glucuronidase) were evaluated using 258 clinical urine specimens. The positive immunoassay cutoff was set at 200 ng/mL of nordiazepam and results were compared to an LC-MS/MS benzodiazepine LDT. Clinical sensitivity, specificity, precision, and immunoassay cross-reactivity were determined for all 3 immunoassays.

Results

The Benzodiazepines II and LDT immunoassays exhibited greater clinical sensitivity (100% and 95.2%) compared to the Benzodiazepines Plus assay (66.7%). Clinical specificity of 100% was observed for all 3 assays. Immunoassay response of the Benzodiazepines II assay was greater across the range of concentrations tested (100–1000 ng/mL) relative to the other immunoassays and was the most sensitive immunoassay for the detection of lorazepam glucuronide.

Conclusions

The Benzodiazepines II immunoassay demonstrated the greatest clinical and analytical sensitivity compared to the Benzodiazepines Plus and LDT immunoassays. The incorporation of beta-glucuronidase was crucial, as the Benzodiazepines II and LDT immunoassays demonstrated superior clinical sensitivity when compared to the Benzodiazepines Plus immunoassay that does not incorporate a beta-glucuronidase hydrolysis step.