bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synergetic effects of combining TM single- and double-atom catalysts embedded in C2N on inducing half-metallicity: DFT study
Authors: Khan Saba , Feng Yuan-Ping , Tit Nacir 
Published: December 2022

Abstract
Designing 2D-materials that exhibit half-metallic properties is crucially important in spintronic devices that are used in low-power high-density logic circuits. The large pores in the C2N morphology can stably accommodate various configurations of transition-metal (TM) atoms that can lead to ferromagnetic (FMC) and anti-ferromagnetic coupling interactions among them, and thus paving the way for achieving half-metallic characteristics. In the present study, we use manganese ‘Mn’ as a promising catalyst and the spin-polarized density-functional theory to search for suitable configurations of metal atoms that yield half-metallicity. Test samples comprised of single-atom catalyst (SAC) and double-atom catalyst (DAC) of Mn embedded in a C2N sample of size 2 × 2 primitive cells as well as their combinations in neighboring large pores (i.e. SAC–SAC, SAC–DAC, and DAC–DAC). Tests were extended to screen many other TM catalysts and the results showed the existence of half metallicity in just five cases: (a) C2N:Mn (DAC, SAC–SAC, and SAC–DAC); (b) C2N:Fe (DAC); and (c) C2N:Ni (SAC–DAC). Our results further showed the origins of half-metallicity to be attributed to FMC interactions between the catalysts with the six mirror images, formed by the periodic-boundary conditions. The FMC interaction is found to have strength of about 20 meV and critical length scale up to about ∼21–29 Å, dependent on both the type of magnetic impurity and the synergetic effects. The potential relevance of half-metallicity to spintronic device application is discussed. Our theoretical results have been benchmarked to the available data in literature and they were found to be in good agreements.
Markov chain Monte Carlo enhanced variational quantum algorithms
Authors: Patti Taylor L , Shehab Omar , Najafi Khadijeh , Yelin Susanne F 
Published: December 2022

Abstract
Variational quantum algorithms have the potential for significant impact on high-dimensional optimization, with applications in classical combinatorics, quantum chemistry, and condensed matter. Nevertheless, the optimization landscape of these algorithms is generally nonconvex, leading the algorithms to converge to local, rather than global, minima and the production of suboptimal solutions. In this work, we introduce a variational quantum algorithm that couples classical Markov chain Monte Carlo techniques with variational quantum algorithms, allowing the former to provably converge to global minima and thus assure solution quality. Due to the generality of our approach, it is suitable for a myriad of quantum minimization problems, including optimization and quantum state preparation. Specifically, we devise a Metropolis–Hastings method that is suitable for variational quantum devices and use it, in conjunction with quantum optimization, to construct quantum ensembles that converge to Gibbs states. These performance guarantees are derived from the ergodicity of our algorithm’s state space and enable us to place analytic bounds on its time-complexity. We demonstrate both the effectiveness of our technique and the validity of our analysis through quantum circuit simulations for MaxCut instances, solving these problems deterministically and with perfect accuracy, as well as large-scale quantum Ising and transverse field spin models of up to 50 qubits. Our technique stands to broadly enrich the field of variational quantum algorithms, improving and guaranteeing the performance of these promising, yet often heuristic, methods.
Efficient Kohn–Sham density-functional theory implementation of isotropic spectroscopic observables associated with quadratic response functions
Authors: Ahmadzadeh Karan , Li Xin , Rinkevicius Zilvinas , Norman Patrick 
Published: December 2022

Abstract
For general exchange–correlation functionals with a dependence on the local spin densities and spin-density gradients, we provide computationally tractable expressions for the tensor-averaged quadratic response functions pertinent to the experimental observables in second-harmonic generation (SHG). We demonstrate how the tensor-averaged quantities can be implemented with reference to a derived minimal number of first- and second-order perturbed Fock matrices. Our consideration has the capability of treating a situation of resonance enhancement as it is based on damped response theory and allows for the evaluation of tensor-averaged resonant-convergent quadratic response functions using only ∼25% (one-photon off-resonance regions) and ∼50% (one-photon resonance regions) of the number of auxiliary Fock matrices required when explicitly calculating all the needed individual tensor components. Numerical examples of SHG intensities in the one-photon off-resonance region are provided for a sample of makaluvamine derivatives recognized for their large nonlinear optical responses as well as a benchmark set of small- and medium-sized organic molecules.
Federated data processing and learning for collaboration in the physical sciences
Authors: Huang W , Barnard A S 
Published: December 2022

Abstract
Property analysis and prediction is a challenging topic in fields such as chemistry, nanotechnology and materials science, and often suffers from lack of data. Federated learning (FL) is a machine learning (ML) framework that encourages privacy-preserving collaborations between data owners, and potentially overcomes the need to combine data that may contain proprietary information. Combining information from different data sets within the same domain can also produce ML models with more general insight and reduce the impact of the selection bias inherent in small, individual studies. In this paper we propose using horizontal FL to mitigate these data limitation issues and explore the opportunity for data-driven collaboration under these constraints. We also propose FedRed, a new dimensionality reduction method for FL, that allows faster convergence and accounts for differences between individual data sets. The FL pipeline has been tested on a collection of eight different data sets of metallic nanoparticles, and while there are expected losses compared to a combined data set that does not preserve the privacy of the collaborators, we obtained extremely good result compared to local training on individual data sets. We conclude that FL is an effective and efficient method for the physical science domain that could hugely reduce the negative effect of insufficient data.
Mildly reduced graphene oxide membranes for water purification applications
Authors: Kumar Shani , Garg Amit , Chowdhuri Arijit 
Published: December 2022

Abstract
Presently carbon allotropes namely graphene, graphene oxide (GO) and reduced graphene oxide (RGO) are being extensively utilized for water purification applications. The presence of myriad types of oxygen functional groups in the GO, however, makes this material very hydrophilic, allowing it to absorb water and to swell in moist or watery environments and to significantly damage its intended performance. In contrast, fully reduced graphene oxide membranes are not stable due to fewer oxide groups which are mainly responsible for GO flakes stacking. In the present work, the aforementioned problems are overcome by optimizing the oxygenated functional groups to develop mildly reduced graphene oxide (MRGO) membrane over PVDF (polyvinylidene fluoride) support. GO is reduced by L-Ascorbic Acid (LAA) with different amounts of wt.% and an optimized MRGO membrane is achieved at 10 wt.% of LAA, which is stable and showing comparatively lower swelling than GO membrane. All related structural and optical characterizations like XRD, SEM, EDAX, Raman, FTIR, and Contact angle have been done to evaluate the effect of mild reduction of GO. The studies are indicative of their potential application in water purification.
CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information
Authors: Peng Zhenling , Li Zixia , Meng Qiaozhen , Zhao Bi , Kurgan Lukasz 
Published: December 2022

AbstractOne of key features of intrinsically disordered regions (IDRs) is facilitation of protein–protein and protein–nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/.
Dimensionality reduction approach for many-objective epistasis analysis
Authors: Yang Cheng-Hong , Hou Ming-Feng , Chuang Li-Yeh , Yang Cheng-San , Lin Yu-Da 
Published: December 2022

Abstract
In epistasis analysis, single-nucleotide polymorphism–single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P &lt; 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.
RatesTools: a Nextflow pipeline for detecting de novo germline mutations in pedigree sequence data
Authors: Armstrong Ellie E , Campana Michael G 
Published: December 2022

Abstract

Summary
Here, we introduce RatesTools, an automated pipeline to infer de novo mutation rates from parent–offspring trio data of diploid organisms. By providing a reference genome and high-coverage, whole-genome resequencing data of a minimum of three individuals (sire, dam and offspring), RatesTools provides a list of candidate de novo mutations and calculates a putative mutation rate. RatesTools uses several quality filtering steps, such as discarding sites with low mappability and highly repetitive regions, as well as sites with low genotype and mapping qualities to find potential de novo mutations. In addition, RatesTools implements several optional filters based on post hoc assumptions of the heterozygosity and mutation rate of the organism. Filters are highly customizable to user specifications in order to maximize utility across a wide range of applications.

Availability and implementation
RatesTools is freely available at https://github.com/campanam/RatesTools under a Creative Commons Zero (CC0) license. The pipeline is implemented in Nextflow (Di Tommaso et al., 2017), Ruby (http://www.ruby-lang.org), Bash (https://www.gnu.org/software/bash/) and R (R Core Team, 2020) with reliance upon several other freely available tools. RatesTools is compatible with macOS and Linux operating systems.

Supplementary information
Supplementary data are available at Bioinformatics online.
Impact of rectal washout on recurrence and survival after anterior resection for rectal cancer
Authors: Svensson Neufert Rebecca , Jörgren Fredrik , Buchwald Pamela 
Published: December 2022

Abstract

Background
Rectal washout (RW) is routinely performed during anterior resection (AR) for rectal cancer to reduce local recurrence (LR), although is sometimes not performed during minimally invasive surgery (MIS) procedures due to technical challenges and time consumption. The aim was to investigate the impact of RW on the oncological outcome after AR for rectal cancer in a registry cohort.

Methods
Data on patients registered in the Swedish Colorectal Cancer Registry who had undergone elective radical (R0) AR for TNM stage I–III rectal cancer between 2007 and 2017 with a 3-year follow-up were analysed. Multivariable analyses were performed and the primary endpoint was LR at 3 and 5 years after AR. The occurrence of distant metastasis (DM) and overall recurrence (OAR), overall survival, and relative survival were also analysed as a secondary aim. A subgroup analysis was performed for the same outcomes in patients treated with MIS.

Results
Out of 6186 patients (1923 with TNM stage I, 1907 with TNM stage II, and 2356 with TNM stage III), RW was performed in 5706 (92.2 per cent). The median age of the cohort was 67 years. RW did not impact the 3-year risk of LR. LR within 5 years occurred in 104 of 4583 patients (2.3 per cent) in the RW group compared with 16 of 408 patients (3.9 per cent) in the no RW group (P = 0.037). In multivariable analysis of the LR risk, the HR was 0.53 (95 per cent c.i. 0.31 to 0.90), favouring RW. There were no differences in rates of DM and OAR, overall survival, and relative survival. A subgroup analysis of the 1410 patients undergoing MIS did not demonstrate any differences between the groups, given, however, the low rate of LR.

Conclusions
RW in AR for rectal cancer does not impact the 3-year oncological outcome; however, after the 5-year follow-up a reduction in LR risk was observed after RW.
Industry and academia—a perfect match
Authors: van Vuuren Hennie J J , Cooper Terrance G 
Published: December 2022

AbstractMy career developed very differently from those of most academic researchers. After school, I worked for 6 years in industries that employed yeast to manufacture ethanol and beer. At university, I was trained as a microbiologist with very little training in molecular biology. I retrained in 1987 in molecular yeast genetics and focused on genetic engineering of industrial yeasts to minimize the production of spoilage compounds in wine and ethyl carbamate, a carcinogen, in wine. The malolactic yeast ML01 and the urea-degrading yeast were the first genetically enhanced yeasts that obtained US FDA approval for commercial applications. Apart from applied research, I was fascinated by classic molecular yeast genetic studies using sophisticated techniques such as transcriptomics, proteomics, and metabolomics. Doing research at the University of British Columbia was stimulating and exciting, we established a core microarray and metabolomics facility that was used by many scientists at UBC and hospitals in Vancouver. I also established a state-of-the-art Wine Library that was used to study aging of wines produced in British Columbia. Finally, I have been fortunate to know and collaborate with leading yeast scientists who motivated me.
Comparisons between non-interferometric and interferometric passive surface wave imaging methods—towards linear receiver array
Authors: Cheng Feng , Xia Jianghai , Xu Zongbo , Ajo-Franklin Jonathan B 
Published: December 2022

SUMMARYPassive seismic methods in highly populated urban areas have gained much attention from the geophysics and civil engineering communities. Linear arrays are usually deployed for passive surface wave investigations because of their high convenience, and passive surface wave imaging methods commonly used for linear arrays can be grouped as non-interferometric methods (e.g. passive multichannel analysis of surface wave, refraction microtremor) and interferometric methods (e.g. multichannel analysis of passive surface waves and spatial autocorrelation). It is well known that the seismic interferometry method is able to retrieve Green’s function between inter-station pairs based on passive seismic data and that is how interferometric methods work. Although non-interferometric methods are also popular and effective in near-surface seismic imaging, particularly in the geotechnical industry, there is no theoretical proof to clarify the accuracy and/or the bias of these methods. In this study, we use numerical derivations and simulations to demonstrate the underlying physics for both non-interferometric and interferometric methods, under two common noise source environments including a homogeneous source distribution and a dominant in-line source distribution. We also prove the strength of interferometric methods for accurate dispersion imaging over the non-interferometric methods, and provide a way to estimate the biases in non-interferometric measurements. Finally, we present comprehensive comparisons between different passive surface wave methods with three typical field examples considering various observation systems.
Partitioning local seismogram wavefields using continuous wavelet transform methods for IRIS wavefield experiment arrays
Authors: Bolarinwa Oluwaseyi J , Langston Charles A 
Published: December 2022

SUMMARY
We applied nonlinear thresholding and scale–time gating in the continuous wavelet transform (CWT) domain to denoise, identify and characterize seismic phases contained in gradiometer and phased array waveforms of four seismic events recorded during the 2016 Incorporated Research Institutions of Seismology Wavefields Experiment in northern Oklahoma. A dense, 80-element three component phased array was subset from the linear array deployments to examine background noise, waveform coherence and seismic wave composition for local explosion and earthquake waveforms. CWT techniques were also used to significantly improve gradiometery analyses for data recorded by the geodetic array subexperiment. We observed as much as two orders of magnitude gain in the data signal-to-noise ratio. We also saw improvement in array beam quality after denoising the seismic data. Using the signal partitioning technique, we were able to extract and identify many phases based on their positions on the scale–time plane. CWT denoising and wavefield decomposition techniques also improved gradiometry analysis results from the 112-element geodetic array (also called the gradiometer) since waves could be separated before the computation of wave attributes. The operations of removing noise and gating out signal phases improved signal coherence across array records and provided clear P wave onsets on horizontal records, which can mitigate phase picking error and resulting event location uncertainty.
Population diversity analyses provide insights into key horticultural traits of Chinese native thymes
Authors: Sun Meiyu , Zhang Yanan , Bai Hongtong , Sun Guofeng , Zhang Jinzheng , Shi Lei 
Published: December 2022

Abstract
Chinese native thymes (CNTs) in the genus Thymus (family Lamiaceae) are rich in bioactive terpenes, which exert antiviral, anti-inflammatory, antioxidation, immunological, and antimicrobial effects. Plants exhibit morphological variation, including erect-type and creeping-type growth forms; however, the molecular mechanisms underlying important horticultural traits have not been determined. Here, we collected 39 CNTs providing strategic plant resources for studies of lignin, terpenoids, and glandular trichomes of thymes. Using resequencing data as well as phenotypic, metabonomic, phylogenetic, population genetic, and transcriptomic analyses, we identified and characterized key genes involved in lignin biosynthesis, terpenoid biosynthesis, and glandular trichome formation. We found many regulatory genes or transcription factors related to these three important horticultural traits, including genes encoding caffeic acid O-methyltransferase (COMT), terpene synthase (TPS), v-myb avian myeloblastosis viral oncogene homolog (MYB), and homeodomain-leucine zipper (HD-ZIP). Population diversity analyses provided insights into growth form, terpenoid, and glandular trichome evolution in CNTs. Furthermore, our results revealed that T. mongolicus accessions might be wild ancestors, and T. quinquecostatus, T. quinquecostatus var. asiaticus, and T. quinquecostatus var. przewalskii might be transitional accessions that derived from T. mongolicus accessions. Finally, T. nervulosus, T. inaequalis, T. mandschuricus, T. curtus, T. amurensis, T. proximus, T. altaicus, T. roseus, and T. marschallianus showed high divergence. We found evidence for introgression between erect-type European cultivated thymes and CNTs. These findings improve our understanding of the determinants of variation in horticultural traits and provide candidate loci for research and breeding.