bluuebunny/crossref_metadata_2025_split huggingface dataset:
The teaching of the cello in Spain: An analysis of the planning frameworks used for teaching in conservatories
Authors: Hernández-Dionis Paula , Pérez-Jorge David , Gisbert-Caudeli Vicenta , de la Rosa Olga María Alegre 
Published: April 2022

AbstractOne might think that the elementary teachings of an instrument should follow uniform planning patterns. The present study shows discrepancies between the models developed by different Spanish conservatories, mainly attributable to the guidelines established by each autonomous community concerning the order of the curriculum markedly influenced by each community’s circumstances. This article analyzes and compares first-year didactic programs in the elementary music teachings of 40 Spanish conservatories within the cello specialty. This work shows significant heterogeneity in planning, marked mainly by the variety of content in planning. The lack of unified criteria and standards for teaching planning is evident in teachers’ freedom to establish the contents and evaluation criteria within the framework of regulations in each autonomous community, whose generality prevents the establishment of standard criteria. It is urgent and necessary to design training plans and standard content that consider improved learning practices and strategies to offer quality training based on common standards.
The magnetic structure of DyFeO3 revisited: Fe spin reorientation and Dy incommensurate magnetic order
Authors: Ritter Clemens , Vilarinho Rui , Moreira Joaquim Agostinho , Mihalik Matus , Mihalik Marian , Savvin Stanislav 
Published: April 2022

Abstract
High resolution and high intensity neutron powder diffraction is used to study the ground state magnetic order and the spin reorientation transition in the orthoferrite DyFeO3. The transition from the high temperature k = 0 Γ4 (G

x

A

y

F

z
) to the low temperature Γ1 (A

x

G

y

C

z
) type order of the Fe-sublattice is found at T
SR = 73 K and does not show any thermal hysteresis. Below T
N2 = 4 K the Dy-sublattice orders in an incommensurate magnetic structure with k = [0, 0, 0.028] while the Fe-sublattice keeps its commensurate Γ1 type order. DyFeO3 is the first orthoferrite RFeO3 to possess an incommensurate magnetic order of the rare earth sublattice under zero field conditions; an important piece of information neglected in the recent discussion of its multiferroic properties.
Quantum statistics of phonon radiations in optomechanical systems
Authors: Chen Menghan , Chang Yue , Shi Tao 
Published: April 2022

Abstract
We study the correlation statistics of phonon radiations in a weakly driven optomechanical system. Three dominated scattering processes are identified by the scattering theory analytically and the master equation numerically, whose interplay determines the phonon statistical properties. Our results show that for the large detuning, the driving field off-resonant with the system induces a small emission rate of two anti-bunched phonons. For the resonant driving field, there is a relatively large emission rate of two bunched phonons.
Generating images of hydrated pollen grains using deep learning
Authors: Grant-Jacob James A , Praeger Matthew , Eason Robert W , Mills Ben 
Published: April 2022

Abstract
Pollen grains dehydrate during their development and following their departure from the host stigma. Since the size and shape of a pollen grain can be dependent on environmental conditions, being able to predict both of these factors for hydrated pollen grains from their dehydrated state could be beneficial in the fields of climate science, agriculture, and palynology. Here, we use deep learning to transform images of dehydrated Ranunculus pollen grains into images of hydrated Ranunculus pollen grains. We also then use a deep learning neural network that was trained on experimental images of different genera of pollen grains to identify the hydrated pollen grains from the generated transformed images, to test the accuracy of the image generation neural network. This pilot work demonstrates the first steps needed towards creating a general deep learning-based rehydration model that could be useful in understanding and predicting pollen morphology.
The carbon footprint of cold chain food flows in the United States
Authors: Wang Junren , Karakoc Deniz Berfin , Konar Megan 
Published: April 2022

Abstract
The food system is an important contributor to carbon dioxide (CO2) emissions. The refrigerated food supply chain is an energy-intensive, nutritious and high-value part of the food system, making it particularly important to consider. In this study, we develop a novel model of cold chain food flows between counties in the United States. Specifically, we estimate truck transport via roadways of meat and prepared foodstuffs for the year 2017. We use the roadway travel distance in our model framework rather than the haversine distance between two locations to improve the estimate for long-haul freight with a temperature-controlled system. This enables us to more accurately calculate the truck fuel consumption and CO2 emissions related to cold chain food transport. We find that the cold chain transport of meat emitted 8.4 × 106 t CO2 yr−1 and that of prepared foodstuffs emitted 14.5 × 106 t CO2 yr−1, which is in line with other studies. Meat has a longer average refrigerated transport distance, resulting in higher transport CO2 emissions per kg than processed foodstuffs. We also find that CO2 emissions from cold chain food transport are not projected to significantly increase under the temperatures projected to occur with climate change in 2045. These county-level cold chain food flows could be used to inform infrastructure investment, supply chain decision-making and environmental footprint studies.
Less Incorrect Ways of Doing Jurisprudence
Authors: Fasel Raffael N 
Published: April 2022

Abstract:
Theorists interested in the question of how to do jurisprudence often have the aspiration of developing a method that is the correct one. The article challenges this aspiration. Focusing on Julie Dickson’s claim that Indirectly Evaluative Legal Theory is the correct method, I show that any method claiming to be the correct one runs into the problem that law is not the kind of thing that a legal theorist could capture independently of her underlying conception of law, and without potentially influencing what law is through her own theory. However, this does not spell the end of jurisprudential methodology. The article proposes that methodologists should shift their focus away from the pursuit of single correct methods and toward studying and weeding out incorrect methods. I outline some of the principles that theorists can use for this purpose and point to promising avenues of research that this shift opens up.
Effects of honeybee (Apis cerana) visiting behaviour on toxic plant (Tripterygium hypoglaucum) reproduction
Authors: Chen Shunan , Wang Yunfei , Li Yi , Zhang Xuewen , Wu Jie 
Published: April 2022

Abstract
Honeybees play a significant role in the plant–pollinator interactions of many flowering plants. The ecological and evolutionary consequences of plant–pollinator interactions vary by geographic region, and the effects of honeybees on the reproduction of toxic plants have not been well studied. We measured the florescence of toxic plants, the flower-visiting behaviour of honeybees and the effects of pollination on the fertility, weight and moisture content of seeds. The effects of climatic factors on the number of flowers, and the spatial and temporal variation in pollinator visits were evaluated, and the effects of pollinator visits on seed quality were evaluated. Flower visitors were diverse, climatic factors had a great impact on spatio-temporal flowering variation and the number of bee visits was strongly correlated with the spatio-temporal variation in the number of flowers. Honeybees strongly increase the fullness and weight of seeds. Our study demonstrated a good ecological fit between the spatio-temporal variation in the flowering of toxic plants and the general validity of honeybee pollination syndrome in the south of Hengduan Mountains in East Asia. A linear relationship between honeybee visitation and plant reproduction can benefit the stabilization of plant reproduction.
Assessing Preseason Baseline Cognitive Functioning in High School Student Athletes: A “Hybrid” Approach
Authors: Ransom Danielle M , Ahumada Luis , Amankwah Ernest K , Katzenstein Jennifer M , Goldenberg Neil A , Mularoni P Patrick 
Published: April 2022

AbstractObjectiveInternational consensus statements highlight the value of neuropsychological testing for sport-related concussion. Computerized measures are the most frequently administered assessments of pre-injury baseline and post-injury cognitive functioning, despite known measurement limitations. To our knowledge, no studies have explored the convergent validity of computerized Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) and traditional, well-validated paper and pencil (P&P) neuropsychological tests in high school student athletes. This study aimed to assess a “hybrid” adolescent test battery composed of ImPACT and P&P measures to determine the extent of shared variance among ImPACT and P&P tests to inform comprehensive yet streamlined assessment.MethodParticipants included male and female high school student athletes in the Southeastern United States participating in American football, hockey, and soccer who completed a battery of ImPACT and P&P tests (N = 69).ResultsWe performed principal component analysis with ProMax rotation to determine components of the hybrid battery that maximally accounted for observed variance of the data (Kaiser–Meyer–Olkin factor adequacy = 0.71). Our analysis revealed four independent factors (Verbal Learning and Memory, ImPACT Memory and Speed, Verbal Processing Speed/Executive Functions, and Nonverbal Processing Speed/Executive Functions) explaining 75% of the variance.ConclusionsFindings of this study in adolescent student athletes support those from the adult literature demonstrating the independence of ImPACT and P&P tests. Providers should be aware of limitations in using standalone ImPACT or P&P measures to evaluate cognitive functioning after concussion. If confirmed in a larger, clinical sample, our findings suggest that a hybrid battery of computerized and P&P measures provides a broad scope of adolescent cognitive functioning to better inform recovery decisions, including return to play after concussion.
plotsr: visualizing structural similarities and rearrangements between multiple genomes
Authors: Goel Manish , Schneeberger Korbinian 
Published: April 2022

AbstractSummaryThird-generation genome sequencing technologies have led to a sharp increase in the number of high-quality genome assemblies. This allows the comparison of multiple assembled genomes of individual species and demands new tools for visualizing their structural properties. Here, we present plotsr, an efficient tool to visualize structural similarities and rearrangements between genomes. It can be used to compare genomes on chromosome level or to zoom in on any selected region. In addition, plotsr can augment the visualization with regional identifiers (e.g. genes or genomic markers) or histogram tracks for continuous features (e.g. GC content or polymorphism density).Availability and implementationplotsr is implemented as a python package and uses the standard matplotlib library for plotting. It is freely available under the MIT license at GitHub (https://github.com/schneebergerlab/plotsr) and bioconda (https://anaconda.org/bioconda/plotsr).Supplementary informationSupplementary data are available at Bioinformatics online.
Prediction of recovery from multiple organ dysfunction syndrome in pediatric sepsis patients
Authors: Fan Bowen , Klatt Juliane , Moor Michael M , Daniels Latasha A ,   Swiss Pediatric Sepsis Study, Agyeman Philipp K A , Berger Christoph , Giannoni Eric , Stocker Martin , Posfay-Barbe Klara M , Heininger Ulrich , Bernhard-Stirnemann Sara , Niederer-Loher Anita , Kahlert Christian R , Natalucci Giancarlo , Relly Christa , Riedel Thomas , Aebi Christoph , Schlapbach Luregn J , Sanchez-Pinto Lazaro N , Agyeman Philipp K A , Schlapbach Luregn J , Borgwardt Karsten M 
Published: April 2022

AbstractMotivationSepsis is a leading cause of death and disability in children globally, accounting for ∼3 million childhood deaths per year. In pediatric sepsis patients, the multiple organ dysfunction syndrome (MODS) is considered a significant risk factor for adverse clinical outcomes characterized by high mortality and morbidity in the pediatric intensive care unit. The recent rapidly growing availability of electronic health records (EHRs) has allowed researchers to vastly develop data-driven approaches like machine learning in healthcare and achieved great successes. However, effective machine learning models which could make the accurate early prediction of the recovery in pediatric sepsis patients from MODS to a mild state and thus assist the clinicians in the decision-making process is still lacking.ResultsThis study develops a machine learning-based approach to predict the recovery from MODS to zero or single organ dysfunction by 1 week in advance in the Swiss Pediatric Sepsis Study cohort of children with blood-culture confirmed bacteremia. Our model achieves internal validation performance on the SPSS cohort with an area under the receiver operating characteristic (AUROC) of 79.1% and area under the precision-recall curve (AUPRC) of 73.6%, and it was also externally validated on another pediatric sepsis patients cohort collected in the USA, yielding an AUROC of 76.4% and AUPRC of 72.4%. These results indicate that our model has the potential to be included into the EHRs system and contribute to patient assessment and triage in pediatric sepsis patient care.Availability and implementationCode available at https://github.com/BorgwardtLab/MODS-recovery. The data underlying this article is not publicly available for the privacy of individuals that participated in the study.Supplementary informationSupplementary data are available at Bioinformatics online.
Simulating domain architecture evolution
Authors: Cui Xiaoyue , Xue Yifan , McCormack Collin , Garces Alejandro , Rachman Thomas W , Yi Yang , Stolzer Maureen , Durand Dannie 
Published: April 2022

AbstractMotivationSimulation is an essential technique for generating biomolecular data with a ‘known’ history for use in validating phylogenetic inference and other evolutionary methods. On longer time scales, simulation supports investigations of equilibrium behavior and provides a formal framework for testing competing evolutionary hypotheses. Twenty years of molecular evolution research have produced a rich repertoire of simulation methods. However, current models do not capture the stringent constraints acting on the domain insertions, duplications, and deletions by which multidomain architectures evolve. Although these processes have the potential to generate any combination of domains, only a tiny fraction of possible domain combinations are observed in nature. Modeling these stringent constraints on domain order and co-occurrence is a fundamental challenge in domain architecture simulation that does not arise with sequence and gene family simulation.ResultsHere, we introduce a stochastic model of domain architecture evolution to simulate evolutionary trajectories that reflect the constraints on domain order and co-occurrence observed in nature. This framework is implemented in a novel domain architecture simulator, DomArchov, using the Metropolis–Hastings algorithm with data-driven transition probabilities. The use of a data-driven event module enables quick and easy redeployment of the simulator for use in different taxonomic and protein function contexts. Using empirical evaluation with metazoan datasets, we demonstrate that domain architectures simulated by DomArchov recapitulate properties of genuine domain architectures that reflect the constraints on domain order and adjacency seen in nature. This work expands the realm of evolutionary processes that are amenable to simulation.Availability and implementationDomArchov is written in Python 3 and is available at http://www.cs.cmu.edu/~durand/DomArchov. The data underlying this article are available via the same link.Supplementary informationSupplementary data are available at Bioinformatics online.
On the reliability and the limits of inference of amino acid sequence alignments
Authors: Rajapaksa Sandun , Sumanaweera Dinithi , Lesk Arthur M , Allison Lloyd , Stuckey Peter J , Garcia de la Banda Maria , Abramson David , Konagurthu Arun S 
Published: April 2022

AbstractMotivationAlignments are correspondences between sequences. How reliable are alignments of amino acid sequences of proteins, and what inferences about protein relationships can be drawn? Using techniques not previously applied to these questions, by weighting every possible sequence alignment by its posterior probability we derive a formal mathematical expectation, and develop an efficient algorithm for computation of the distance between alternative alignments allowing quantitative comparisons of sequence-based alignments with corresponding reference structure alignments.ResultsBy analyzing the sequences and structures of 1 million protein domain pairs, we report the variation of the expected distance between sequence-based and structure-based alignments, as a function of (Markov time of) sequence divergence. Our results clearly demarcate the ‘daylight’, ‘twilight’ and ‘midnight’ zones for interpreting residue–residue correspondences from sequence information alone.Supplementary informationSupplementary data are available at Bioinformatics online.
Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data
Authors: Edrisi Mohammadamin , Valecha Monica V , Chowdary Sunkara B V , Robledo Sergio , Ogilvie Huw A , Posada David , Zafar Hamim , Nakhleh Luay 
Published: April 2022

AbstractMotivationSingle-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data.ResultsHere, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases.Availability and implementationPhylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar.
DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms
Authors: Kulmanov Maxat , Hoehndorf Robert 
Published: April 2022

AbstractMotivationProtein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50 000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require a significant amount of training data and cannot make predictions for GO classes that have only few or no experimental annotations.ResultsWe developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted.Availability and implementationhttp://github.com/bio-ontology-research-group/deepgozero.Supplementary informationSupplementary data are available at Bioinformatics online.
PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information
Authors: Ahsan Faizy , Yan Zichao , Precup Doina , Blanchette Mathieu 
Published: April 2022

AbstractMotivationThe computational prediction of regulatory function associated with a genomic sequence is of utter importance in -omics study, which facilitates our understanding of the underlying mechanisms underpinning the vast gene regulatory network. Prominent examples in this area include the binding prediction of transcription factors in DNA regulatory regions, and predicting RNA–protein interaction in the context of post-transcriptional gene expression. However, existing computational methods have suffered from high false-positive rates and have seldom used any evolutionary information, despite the vast amount of available orthologous data across multitudes of extant and ancestral genomes, which readily present an opportunity to improve the accuracy of existing computational methods.ResultsIn this study, we present a novel probabilistic approach called PhyloPGM that leverages previously trained TFBS or RNA–RBP binding predictors by aggregating their predictions from various orthologous regions, in order to boost the overall prediction accuracy on human sequences. Throughout our experiments, PhyloPGM has shown significant improvement over baselines such as the sequence-based RNA–RBP binding predictor RNATracker and the sequence-based TFBS predictor that is known as FactorNet. PhyloPGM is simple in principle, easy to implement and yet, yields impressive results.Availability and implementationThe PhyloPGM package is available at https://github.com/BlanchetteLab/PhyloPGMSupplementary informationSupplementary data are available at Bioinformatics online.