bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient method for twist-averaged coupled cluster calculation of gap energy: Bulk study of stannic oxide
Authors: Shaban Tameh Maliheh , Gladfelter Wayne L. , Goodpaster Jason D. 
Published: September 2024

We study the gap energy of the semiconducting oxide SnO2 through ab initio calculations including both density functional theory (DFT) and coupled cluster methods. The effectiveness of twist averaging in reducing finite-size errors is evaluated across different functionals. We report an overestimation of gap energy when applying finite-size scaling to reach the thermodynamic limit in equation-of-motion (EOM) CCSD calculations. To mitigate one-body and many-body errors, we integrate twist averaging with a post-processing correction mechanism that compares finite-size and infinite-size DFT calculations using hybrid functionals. While inspired by the Kwee, Zhang, and Krakauer approach, our method is specifically tailored to hybrid functionals for a more accurate treatment of exchange-correlation effects. Our approach ensures that the many-body interactions are accurately captured in the estimated gap for an infinite system. We introduce unique single twist angles that provide cost-effective and accurate energies compared to to full twist averaging in EOM-CCSD calculations. Applying this approach to SnO2, we calculate a fundamental gap of 3.46 eV, which closely matches the 3.59 eV gap obtained from two-photon spectroscopy experiments, demonstrating the accuracy of this method.
Semiclassical dynamics in Wigner phase space I: Adiabatic hybrid Wigner dynamics
Authors: Malpathak Shreyas , Ananth Nandini 
Published: September 2024

The Wigner phase space formulation of quantum mechanics is a complete framework for quantum dynamic calculations that elegantly highlights connections with classical dynamics. In this series of two articles, building upon previous efforts, we derive the full hierarchy of approximate semiclassical (SC) dynamic methods for adiabatic and non-adiabatic problems in Wigner phase space. In Paper I, focusing on adiabatic single surface processes, we derive the well-known double Herman–Kluk (DHK) approximation for real-time correlation functions in Wigner phase space and connect it to the linearized SC (LSC) approximation through a stationary phase approximation. We exploit this relationship to introduce a new hybrid SC method, termed Adiabatic Hybrid Wigner Dynamics (AHWD) that allows for a few important “system” degrees of freedom (dofs) to be treated at the DHK level, while treating the rest of the dofs (the “bath”) at the LSC level. AHWD is shown to accurately capture quantum interference effects in models of coupled oscillators and the decoherence of vibrational probability density of a model I2 Morse oscillator coupled to an Ohmic thermal bath. We show that AHWD significantly mitigates the sign problem and employs reduced dimensional prefactors bringing calculations of complex system–bath problems within the reach of SC methods. Paper II focuses on extending this hybrid SC dynamics to nonadiabatic processes.
Oligoyne bridges enable strong through-bond coupling and efficient triplet transfer from CdSe QD trap excitons for photon upconversion
Authors: Miyashita Tsumugi , He Sheng , Jaimes Paulina , Kaledin Alexey L. , Fumanal Maria , Lian Tianquan , Lee Tang Ming 
Published: September 2024

Polyyne bridges have attracted extensive interest as molecular wires due to their shallow distance dependence during charge transfer. Here, we investigate whether triplet energy transfer from cadmium selenide (CdSe) quantum dots (QDs) to anthracene acceptors benefits from the high conductance associated with polyyne bridges, especially from the potential cumulene character in their excited states. Introducing π-electron rich oligoyne bridges between the surface-bound anthracene-based transmitter ligands, we explore the triplet energy transfer rate between the CdSe QDs and anthracene core. Our femtosecond transient absorption results reveal that a rate constant damping coefficient of β is 0.118 ± 0.011 Å−1, attributed to a through-bond coupling mechanism facilitated by conjugation among the anthracene core, the oligoyne bridges, and the COO⊖ anchoring group. In addition, oligoyne bridges lower the T1 energy level of the anthracene-based transmitters, enabling efficient triplet energy transfer from trapped excitons in CdSe QDs. Density-functional theory calculations suggest a slight cumulene character in these oligoyne bridges during triplet energy transfer, with diminished bond length alternation. This work demonstrates the potential of oligoyne bridges in mediating long-distance energy transfer.
A high-efficiency tunable relativistic magnetron with diffraction output
Authors: Chen Kunpeng , Xu Haodong , Liu Zeyang , Fan Yuwei 
Published: September 2024

In this paper, a high-efficiency tunable relativistic magnetron with diffraction output (HTRMDO) is presented and studied numerically. To increase the tuning bandwidth and power efficiency, the outer radius of the resonant cavity and the magnetic field are simultaneously varied. The simulation results show that the HTRMDO has a tuning range between 1.280 and 2.430 GHz, corresponding to a tuning bandwidth of 62.0% with a center frequency of 1.855 GHz. In the tuning range, the maximum power efficiency is 71.9% and the minimum power efficiency is 36.0%, which complies with the 3 dB principle. Moreover, its output mode remains constant as TE31. This is a record tuning bandwidth and power efficiency for the continuously tunable relativistic magnetron.
Research on posture optimization and accuracy compensation technology in robotic side milling
Authors: Song Ci , Liu Zhibing , Wang Xibin , Qiu Tianyang , Liang Zhiqiang , Shen Wenhua , Gao Yuhang , Ma Senjie 
Published: September 2024

Abstract
Weak stiffness and poor posture accuracy are two key problems that need to be sorted out in robotic side milling. Firstly, an index for evaluating the cutting plane stiffness was proposed based on the closeness of uniformly distributed chords between the evaluated ellipse and the ideal circle. A novel comprehensive posture optimization model was constructed by considering the kinematic characteristics and milling stability. To improve the posture accuracy, a full closed-loop compensation system with laser tracker was built. Combined with the analysis of error similarity and reducer backlash, mapping posture error to joint space was regarded as the problem of selecting joint angles under certain constraints, and it was solved by introducing an improved grey wolf optimization algorithm. Experimental results show that the posture error can be less than 0.05° through two compensations. The stiffness index is effective and the machining error can be reduced by around 15% after posture optimization.
Spectral triples and Dixmier trace representations of Gibbs measures: theory and examples
Authors: Cioletti L , Hataishi L Y , Lopes A O , Stadlbauer M 
Published: September 2024

Abstract
In this paper we study spectral triples and non-commutative expectations associated to expanding and weakly expanding maps. In order to do so, we generalise the Perron–Frobenius–Ruelle theorem and obtain a polynomial decay of the operator, which allows to prove differentiability of a dynamically defined ζ-function at its critical parameter. We then generalise Sharp’s construction of spectral triples to this setting and provide criteria when the associated spectral metric is non-degenerate and when the non-commutative expectation of the spectral triple is colinear to the integration with respect to the associated equilibrium state from thermodynamic formalism. Due to our general setting, we are able to simultaneously analyse expanding maps on manifolds or connected fractals, subshifts of finite type as well as the Dyson model from statistical physics, which underlines the unifying character of noncommutative geometry. Furthermore, we derive an explicit representation of the ζ-function associated to a particular class of pathological continuous potentials, giving rise to examples where the representation as a non-commutative expectation via the associated zeta function holds, and others where it does not hold.
Thickness-dependent evolution of magnetization reversal in coupled permalloy nanocaps
Authors: Mary Anija , Thomas Senoy 
Published: September 2024

Abstract
The magnetization reversal mechanisms in permalloy (Py) nanocaps, coupled through an indirect exchange interaction, were investigated using micromagnetic simulations. This indirect exchange interaction can oscillate between ferromagnetic and antiferromagnetic types, depending on the thickness of the metallic interlayer. In ferromagnetically coupled Py nanocaps, coordinated nucleation and annihilation of magnetic vortices were observed. On the other hand, antiferromagnetically coupled caps exhibited a coherent rotation of spins, stabilizing a double onion state at remanence. The study also investigated the impact of nanocap thickness on the magnetization reversal process. As the thickness of the Py cap increased, the magnetization reversal displayed characteristics typical of a weakly coupled system. This shift in magnetization reversal behavior is attributed to the relative decrease in exchange coupling strength between the nanocaps as their thickness increases.
Occupational mechanical exposures as risk factor for shoulder osteoarthritis: a systematic review
Authors: Thorsen Ea Helene Korsgaard , Dalbøge Annett , Hovgaard Nicholas , Andersen Johan Hviid , Jahn Alexander 
Published: September 2024

Abstract

Objectives
The aim of this systematic review was to study the association between occupational mechanical exposures and shoulder osteoarthritis (OA).

Methods
A protocol was registered in PROSPERO. Four databases were systematically searched based on PECOS criteria. Outcome was defined as shoulder OA (acromioclavicular (AC) and/or glenohumeral OA) and exposure as occupational mechanical exposures (vibration, upper arm elevation, force, lifting, repetition, and combined mechanical exposures). We included epidemiological studies estimating the prevalence of shoulder OA or the association between occupational mechanical exposures and shoulder OA. Two researchers independently screened articles, performed data extraction, and assessed the risk of bias and level of evidence using GRADE.

Results
A total of 1642 articles were screened, of which 7 met the inclusion criteria. Four studies were assessed as having a high risk of bias, 1 with a moderate risk, and 2 with a low risk. Based on job titles, higher prevalence estimates (2.9% to 61.8%) were found in exposed job groups. For all occupational mechanical exposures, exposure–response relations were found in relation to AC OA. For vibration, the odds ratio (OR) ranged between 1.7 and 3.1 in the highest exposure groups, while the ORs for upper arm-elevation, force, lifting, repetition, and combined mechanical exposures ranged between 0.5 to 2.2, 1.3 to 1.8, 7.3 to 10.3, 2.4, and 2.2 to 2.9. Low or very low level of evidence was found for all exposures.

Conclusions
This systematic review found an indication of an association between occupational mechanical exposures and shoulder OA, especially AC OA. However, the level of evidence varied between low and very low. High-quality studies assessing the association and differentiating between the specific shoulder joints are highly warranted.
Molecular modification of chlorogenic acid via radiolysis with inhibitory effects on NO production
Authors: Jeong Gyeong Han , Lee Hanui , Lee Kyung-Bon , Chung Byung Yeoup , Bai Hyoung-Woo 
Published: September 2024

ABSTRACT
The molecular modification of chlorogenic acid (1) through γ-irradiation resulted in the formation of five new products: chlorogenosins A (2), B (3), C (4), D (5), and E (6) along with known compounds rosmarinosin B (7), protocatechuic acid (8), and protocatechuic aldehyde (9). The structures of the new compounds were elucidated using spectroscopic methods, including one-dimensional and two-dimensional nuclear magnetic resonance, high-resolution electrospray ionization mass spectroscopy, and circular dichroism spectroscopy. The potential anti-inflammatory activities of all the isolated compounds were determined by evaluating their inhibitory effects on the nitric oxide (NO) production in lipopolysaccharide-induced RAW 264.7 macrophages. Notably, compounds 2 and 3, which contained two hydroxymethyl functionalities instead of the trans-olefinic moiety present in the original chlorogenic acid, exhibited stronger inhibitory effects on NO production than that of the original compound. These findings suggest that the predominant chemical changes induced in chlorogenic acid by γ-irradiation may enhance its anti-inflammatory properties.
Investigation of physiological roles of UDP-glycosyltransferase UGT76F2 in auxin homeostasis through the TAA-YUCCA auxin biosynthesis pathway
Authors: Harada Mio , Kubotsu Tomoaki , Agui Takemoto , Dai Xinhua , Zhao Yunde , Kasahara Hiroyuki , Hayashi Ken-ichiro 
Published: September 2024

ABSTRACT
Cellular auxin (indole-3-acetic acid, IAA) levels are coordinately regulated by IAA biosynthesis and inactivation. IAA is synthesized through sequential reactions by two enzymes, TAA1 and YUCCA, in a linear indole-3-pyruvic acid (IPA) pathway. TAA1 converts tryptophan to IPA, and YUCCA catalyzes the oxidative decarboxylation of IPA into IAA. Arabidopsis UDP-glycosyltransferase UGT76F2 (At3g55710) was previously reported to catalyze the glycosylation of IPA and consequently modulate IAA levels. We carefully analyzed the physiological roles of UGT76F2 and its close homolog UGT76F1 (At3g55700) in IAA homeostasis. We generated two independent ugt76f1 ugt76f2 double null Arabidopsis mutants (ugt76f1f2) with a 2.7 kb deletion, along with two independent ugt76f2 single null mutants by CRISPR/Cas9 gene editing technology. Surprisingly, these null mutants exhibited indistinguishable phenotypes from the wild-type seedlings under our laboratory conditions. Our results indicate that UGT76F1 and UGT76F2 do not play important roles in regulating IAA biosynthesis via IPA glycosylation.
MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning
Authors: Huang Zhijian , Fan Ziyu , Shen Siyuan , Wu Min , Deng Lei 
Published: September 2024

Abstract

Motivation
Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts.

Results
In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to create positive samples tailored to different molecular data views. To align multi-view molecular positive samples effectively in latent space, we introduce an adaptive multi-view contrastive loss (AMCLoss). In particular, we calculate AMCLoss at various levels within the model to effectively capture the hierarchical nature of the molecular information. Eventually, we pre-train the encoders via minimizing AMCLoss to obtain the molecular representation, which can be used for various down-stream tasks. In our experiments, we evaluate the performance of our MolMVC on multiple tasks, including molecular property prediction (MPP), drug-target binding affinity (DTA) prediction and cancer drug response (CDR) prediction. The results demonstrate that the molecular representation learned by our MolMVC can enhance the predictive accuracy on these tasks and also reduce the computational costs. Furthermore, we showcase MolMVC’s efficacy in drug repositioning across a spectrum of drug-related applications.

Availability and implementation
The code and pre-trained model are publicly available at https://github.com/Hhhzj-7/MolMVC.
Multi-modal contrastive learning of subcellular organization using DICE
Authors: Nasser Rami , Schaffer Leah V , Ideker Trey , Sharan Roded 
Published: September 2024

Abstract

Summary
The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information. Here, we introduce DICE (Data Integration through Contrastive Embedding), a contrastive learning model for multi-modal data integration. We apply this model to study the subcellular organization of proteins by integrating protein–protein interaction data and protein image data measured in HEK293 cells. We demonstrate the advantage of data integration over any single modality and show that our framework outperforms previous integration approaches.

Availability
https://github.com/raminass/protein-contrastive

Contact
raminass@gmail.com
PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks
Authors: Pietz Tobias , Gupta Sukrit , Schlaffner Christoph N , Ahmed Saima , Steen Hanno , Renard Bernhard Y , Baum Katharina 
Published: September 2024

Abstract

Motivation
Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses.

Results
To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against 11 common imputation methods on 6 diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches, and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user’s needs based on the reliability of imputed values.

Availability and implementation
The code is available at https://github.com/DILiS-lab/pepermint.
HCS—hierarchical algorithm for simulation of omics datasets
Authors: Stomma Piotr , Rudnicki Witold R 
Published: September 2024

Abstract

Motivation
Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducing the dataset to include only those variables that are related to the studied phenomena. Existing simulators of the omics data were mostly developed with the goal of improving the methods for generations of high-quality data, that correspond with the highest possible fidelity to the real level of molecular markers in the biological materials. The current study aims to simulate the data on a higher level of generalization. Such datasets can then be used to perform tests of the feature selection and ML algorithms on systems that have structures mimicking those of real data, but where the ground truth may be implanted by design. They can also be used to generate contrast variables with the desired correlation structure for the feature selection.

Results
We proposed the algorithm for the reconstruction of the omic dataset that, with high fidelity, preserves the correlation structure of the original data with a reduced number of parameters. It is based on the hierarchical clustering of variables and uses principal components of the clusters. It reproduces well topological descriptors of the correlation structure. The correlation structure of the principal components of the clusters then is used to obtain datasets with correlation structures similar to the original data but not correlated with the original variables.

Availability and implementation
The code and data is available at: https://github.com/p100mma/hcrs_omics.
scNODE
: generative model for temporal single cell transcriptomic data prediction
Authors: Zhang Jiaqi , Larschan Erica , Bigness Jeremy , Singh Ritambhara 
Published: September 2024

Abstract

Summary
Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE’s predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path.

Availability and implementation
The data and code are publicly available at https://github.com/rsinghlab/scNODE.