bluuebunny/crossref_metadata_2025_split huggingface dataset:
Explaining the Zonal Asymmetry in the Air‐Sea Net Heat Flux Climatology Over the Antarctic Circumpolar Current
Authors: Song Xiangzhou 
Published: April 2020

AbstractSurface heating occurs over the streamline of the Antarctic Circumpolar Current (ACC) and balances the heat transport associated with the global meridional overturning circulation. With a combination of ocean assimilation model output, objectively analyzed products, and atmospheric reanalysis, this paper investigates the mechanism of climatological surface cooling in the Pacific sector with zonal asymmetry. The poleward shift of the ACC path in the Pacific with barotropic potential vorticity conservation was found to account for this cooling. The southward transport of warm/wet surface waters encounters relatively cold/dry air at high latitudes, causing strong turbulent heat fluxes. Sensible heat flux related to the sea‐air heat convection plays an equivalent role alongside the sea surface evaporative latent heat flux, both of which dominate the surface cooling in the Pacific sector. Solar radiation decreases with a poleward meandering of the ACC, which aggravates the cooling process. A significant seasonal cycle of the ensemble mean net surface heat flux (Qnet) is demonstrated, that is, approximately half‐year heating (cooling) during austral warm (cool) seasons. The estimated magnitude of Qnet over the ACC is approximately 20 W m−2 by averaging 10 heat flux climatologies and 4 W m−2 in an ocean state estimate model. However, efforts are still needed to reduce the excess heat flux over the sea surface in the Southern Ocean for the heat flux products.
Analysis of E-mail Account Probing Attack Based on Graph Mining
Authors: Wen Yi , Chen Xingshu , Zeng Xuemei , Wang Wei 
Published: April 2020

AbstractE-mail has become the main carrier of spreading malicious software and been widely used for phishing, even high-level persistent threats. The e-mail accounts with high social reputation are primary targets to be attacked and utilized by attackers, suffering a lot of probing attacks for a long time. In this paper, in order to understand the probing pattern of the e-mail account attacks, we analyse the log of email account probing captured in the campus network based on graph mining. By analysing characteristics of the dataset in different dimensions, we find a kind of e-mail account probing attack and give it a new definition. Based on the analysis results, its probing pattern is figured out. From the point of probing groups and individuals, we find definitely opposite characteristics of the attack. Owing to the probing pattern and its characteristics, attacks can escape from the detection of security devices, which has a harmful effect on e-mail users and administrators. The analysis results of this paper provide support for the detection and defence of such distributed attacks.
Tracing the retina to analyze the integrity and phagocytic capacity of the retinal pigment epithelium
Authors: Valiente-Soriano Francisco J. , Salinas-Navarro Manuel , Di Pierdomenico Johnny , García-Ayuso Diego , Lucas-Ruiz Fernando , Pinilla Isabel , Cuenca Nicolás , Vidal-Sanz Manuel , Villegas-Pérez María Paz , Agudo-Barriuso Marta 
Published: April 2020

AbstractWe have developed a new technique to study the integrity, morphology and functionality of the retinal neurons and the retinal pigment epithelium (RPE). Young and old control albino (Sprague-Dawley) and pigmented (Piebald Virol Glaxo) rats, and dystrophic albino (P23H-1) and pigmented (Royal College of Surgeons) rats received a single intravitreal injection of 3% Fluorogold (FG) and their retinas were analyzed from 5 minutes to 30 days later. Retinas were imaged in vivo with SD-OCT and ex vivo in flat-mounts and in cross-sections. Fifteen minutes and 24 hours after intravitreal administration of FG retinal neurons and the RPE, but no glial cells, were labeled with FG-filled vesicles. The tracer reached the RPE 15 minutes after FG administration, and this labeling remained up to 30 days. Tracing for 15 minutes or 24 hours did not cause oxidative stress. Intraretinal tracing delineated the pathological retinal remodelling occurring in the dystrophic strains. The RPE of the P23H-1 strain was highly altered in aged animals, while the RPE of the RCS strain, which is unable to phagocytose, did not accumulate the tracer even at young ages when the retinal neural circuit is still preserved. In both dystrophic strains, the RPE cells were pleomorphic and polymegathic.
Forewarned is forearmed: Queensland fruit flies detect olfactory cues from predators and respond with predator-specific behaviour
Authors: Kempraj Vivek , Park Soo Jean , Taylor Phillip W. 
Published: April 2020

AbstractAnimals can gain significant advantages from abilities to detect cues from predators, assess risks, and respond adaptively to reduce the likelihood of injurious interactions. In contrast, predator cue-induced changes in behaviour may interfere with fitness-associated activities such as exploration, foraging and reproduction. Despite the ecological importance of predator-prey interactions in insects, remarkably little is known about the abilities of insects to detect and respond to olfactory cues from predators, or the potential costs of such responses. We here demonstrate that a tephritid fruit fly, the Queensland fruit fly Bactrocera tryoni, is able to detect and respond differentially to volatile olfactory cues from four potential predators (three spiders and an ant) that vary in prevalence and diurnal activity. Male and female flies increased or decreased motility (velocity, active time, distance moved), or exhibited no change in motility, depending on which predator volatiles they encountered. Further, flies significantly reduced foraging, oviposition and mating propensity in the presence of volatiles from any of the predators. This study is the first report of predator-specific responses to olfactory cues in a tephritid fruit fly, and highlights that such anti-predator responses can impose costs on general activity and reproductive behaviour.
Inappropriate Peak Inspiratory Flow Rate with Dry Powder Inhaler in Chronic Obstructive Pulmonary Disease
Authors: Chen Shih-Yu , Huang Chun-Kai , Peng Hui-Chuan , Yu Chong-Jen , Chien Jung-Yien 
Published: April 2020

AbstractOptimal peak inspiratory flow rate (PIFR) is crucial for optimizing dry powder inhaler (DPI) effectiveness for chronic obstructive pulmonary disease (COPD). This study provide an insight that there was a substantial proportion of improper PIFRs (not only insufficient but also excessive) among COPD patients using DPIs. We enrolled 138 COPD patients from a medical center in Taiwan and measured PIFRs against different internal resistances of DPIs. Proportion of excessive, optimal, suboptimal, and insufficient PIFRs were 2%, 54%, 41%, 3%, respectively, against medium-high resistance; 2%, 77%, 20%, 1%, respectively, against medium resistance; 27%, 63%, 9%, 1%, respectively, against medium-low resistance; and 42%, 57%, 1%, 0%, respectively, against low resistance (p < 0.01). Although most PIFRs against medium-high (54%), medium (77%), medium-low (63%) and low (57%) resistance were optimal, a substantial proportion of PIFRs against low resistance were excessive (42%, p < 0.01), irrespective of age, body-mass index, dyspnea severity score, and COPD severity. Insufficient PIFRs were infrequent, but suboptimal/insufficient PIFRs were most prevalent in patients older than 75 years than in younger patients (36% vs. 56%, p = 0.036) against medium-high resistance. Regularly monitoring PIFRs against the specific resistance of the DPIs and instructing patients to employ a proper inspiration effort may help to optimize the effects of DPIs.
Compared to conventional, ecological intensive management promotes beneficial proteolytic soil microbial communities for agro-ecosystem functioning under climate change-induced rain regimes
Authors: Lori Martina , Piton Gabin , Symanczik Sarah , Legay Nicolas , Brussaard Lijbert , Jaenicke Sebastian , Nascimento Eduardo , Reis Filipa , Sousa José Paulo , Mäder Paul , Gattinger Andreas , Clément Jean-Christophe , Foulquier Arnaud 
Published: April 2020

AbstractProjected climate change and rainfall variability will affect soil microbial communities, biogeochemical cycling and agriculture. Nitrogen (N) is the most limiting nutrient in agroecosystems and its cycling and availability is highly dependent on microbial driven processes. In agroecosystems, hydrolysis of organic nitrogen (N) is an important step in controlling soil N availability. We analyzed the effect of management (ecological intensivevs. conventional intensive) on N-cycling processes and involved microbial communities under climate change-induced rain regimes. Terrestrial model ecosystems originating from agroecosystems across Europe were subjected to four different rain regimes for 263 days. Using structural equation modelling we identified direct impacts of rain regimes on N-cycling processes, whereas N-related microbial communities were more resistant. In addition to rain regimes, management indirectly affected N-cycling processes via modifications of N-related microbial community composition. Ecological intensive management promoted a beneficial N-related microbial community composition involved in N-cycling processes under climate change-induced rain regimes. Exploratory analyses identified phosphorus-associated litter properties as possible drivers for the observed management effects on N-related microbial community composition. This work provides novel insights into mechanisms controlling agro-ecosystem functioning under climate change.
An artificial chromosome ylAC enables efficient assembly of multiple genes in Yarrowia lipolytica for biomanufacturing
Authors: Guo Zhong-peng , Borsenberger Vinciane , Croux Christian , Duquesne Sophie , Truan Gilles , Marty Alain , Bordes Florence 
Published: April 2020

AbstractThe efficient use of the yeast Yarrowia lipolytica as a cell factory is hampered by the lack of powerful genetic engineering tools dedicated for the assembly of large DNA fragments and the robust expression of multiple genes. Here we describe the design and construction of artificial chromosomes (ylAC) that allow easy and efficient assembly of genes and chromosomal elements. We show that metabolic pathways can be rapidly constructed by various assembly of multiple genes in vivo into a complete, independent and linear supplementary chromosome with a yield over 90%. Additionally, our results reveal that ylAC can be genetically maintained over multiple generations either under selective conditions or, without selective pressure, using an essential gene as the selection marker. Overall, the ylACs reported herein are game-changing technology for Y. lipolytica, opening myriad possibilities, including enzyme screening, genome studies and the use of this yeast as a previous unutilized bio-manufacturing platform.
Pathologisch-genetisch orientierte Diabetes-Reklassifizierung
Authors: Wagner Robert , Fritsche Andreas 
Published: April 2020

AbstractDiabetes mellitus has been defined by hyperglycemia, but in addition to hyperglycemia, there are several other factors determining the clinical course and complications. We review the current classification of diabetes and recent attempts to identify new subphenotypes. Notably, there are anthropometry-pathophysiology based and genome-based subphenotyping approaches. They aim to improve the prediction of disease course and complications and could pave the way for precision medicine in the therapy of diabetes.
Analysis of Feature Extraction Methods for Prediction of 30-Day Hospital Readmissions
Authors: Sumner Joel , Alaeddini Adel 
Published: April 2020

Abstract
Objectives This article aims to determine possible improvements made by feature extraction methods to the machine learning prediction methods for predicting 30-day hospital readmissions.
Methods The study evaluates five feature extraction methods including principal component analysis (PCA), kernel principal component analysis (KPCA), isomap, Laplacian eigenmaps, and locality preserving projections (LPPs) for improving the accuracy of nine machine learning prediction methods in predicting 30-day hospital readmissions. The specific prediction methods considered include logistic regression, Cox regression, linear discriminant analysis, k-nearest neighbor (KNN), support vector machines (SVMs), bagged trees, boosted trees, random forest, and artificial neural networks. All models are developed in MATLAB and validated using area under the curve based on two population-based data sets from partner hospitals.
Results Laplacian eigenmaps and isomap feature extraction provide the most improvement to the readmission predictive accuracy of KNN, SVM, bagged trees, boosted trees, and linear discriminant analysis methods. The results for artificial neural networks, random forest, Cox regression, and logistic regression show improvement for only one of the data sets. Also, PCA and LPP provided the best computation efficiency followed by KPCA, Laplacian eigenmaps, and isomap.
Conclusion Feature extraction methods can improve the predictive performance of machine learning methods for predicting readmissions. However, the improvement depended on the specific choice of the prediction method, feature extraction method, and the complexity of the data set features.
MANDIBULAR THIRD MOLAR FUSED WITH A SUPERNUMERARY TOOTH: REPORT OF A RARE CASE.
Authors: Shetty Urvashi Ashwin , Hegde Padmaraj , Shetty Pushparaja , M. Vidya , V. Sreelatha S. , D'Cruz Audrey Madonna , Shenoy Mangesh 
Published: April 2020

AbstractFusion is a developmental anomaly defined as the union of two normally separated tooth buds. Depending on the stage of development, fusion may be either complete or incomplete. The significance of this particular case was that this fusion occurred in a posterior permanent mandibular tooth with a supernumerary tooth which was impacted, while such a manifestation is more reported in maxillary anterior teeth; either in the primary (0.5%) or permanent (0.1%) dentition. The genetic basis for this anomaly is probably autosomal dominant with reduced penetrance. The clinical features, radiographic findings and the various etio-pathogenic possibilities of this unique tooth anomaly are discussed. In addition, the essential findings for differential diagnosis include number of teeth, radiography and clinical features are discussed.
The Role of Duplex Ultrasound in Microsurgical Reconstruction: Review and Technical Considerations
Authors: Cho Min-Jeong , Kwon Jin Geun , Pak Changsik John , Suh Hyunsuk Peter , Hong Joon Pio 
Published: April 2020

Abstract
Background With the technological advances, microsurgery has evolved to the era of supermicrosurgery since its inception. With the popularization of these flaps, proficiency in using tools such as color Doppler ultrasound (CDU) has become crucial. Despite the increased interest, studies regarding the role of ultrasound in microsurgical reconstruction are scarce. Therefore, in this study, we discussed currently available imaging modalities for reconstructive surgeons, types of ultrasound, and the role of ultrasound in microsurgical reconstruction, and made recommendations for ultrasound use in reconstructive surgery.
Methods We performed a computerized search of ultrasound in reconstructive surgery using the MEDLINE database. Data regarding ultrasound indications, usage, and outcomes were collected.
Results Of the 115 articles identified, the majority was written on nonreconstructive ultrasound uses (51.4%). For the reconstructive uses, mapping of perforators was the most common usage (39.1%), followed by flap monitoring and volume measurement. In addition, we found that there are a limited number of literature on the role of CDU in reconstructive surgery, especially on its intraoperative and postoperative use.
Conclusion CDU is a valuable and powerful tool for any reconstructive surgeons who are interested in performing microsurgery or supermicrosurgery. As the demand of customized flaps using superthin flaps, perforator-to-perforator anastomosis, and supermicrosurgery increases, understanding and becoming versatile in CDU will be critical. We hope our experience with using ultrasound and refinements that we made are helpful for those who would like to include it as a part of their armamentarium.
The Malt stone as evidence for a morphological archaism
Authors: Heltoft Lars 
Published: April 2020

Abstract
The form fauþr ‘father’ on the Malt stone is normally understood as a carving error for
faþur, but could very well be read at face value as a one-syllable form fǫðr, an archaic
accusative singular. In a wider Proto-Germanic context, I propose that this form is part of an early levelling process of the
kinship terms to one-syllable stem forms, an alternative paradigm co-existing with the classical hysterodynamic paradigm
documented in the Gothic singular. This levelling takes place not only in the plural, but also in the oblique cases of the
singular. In a Scandinavian context, this reading sheds light on a handful of seemingly aberrant forms.
Die Runeninschrift auf dem Rinderknochen von Břeclav, Flur Lány (Südmähren, Tschechische Republik)
Authors: Macháček Jiří , Nedoma Robert 
Published: April 2020

Abstract
During recent excavations at Břeclav-Lány (southern Moravia, Czech Republic), archaeologists have found a
fragmented bovine rib with runes. The rib was unearthed in an early Slavic pit-house and is radiocarbon- dated to ca. 600. The
inscription begins at the break line and reads xbemdo (probably tbemdo), representing six of the last
eight runes of the older fuþark – it seems that the lost piece of the rib exhibited the preceding part of the rune row. There is
reason to believe that the carver was a Langobard who did not join the migration into northern Italy in 568 (or, alternatively, a
Slav who learned and used the Germanic script?).
Framed motives of algebraic varieties (after V. Voevodsky)
Authors: Garkusha Grigory , Panin Ivan 
Published: April 2020

A new approach to stable motivic homotopy theory is given. It is based on Voevodsky’s theory of framed correspondences. Using the theory, framed motives of algebraic varieties are introduced and studied in the paper. They are the major computational tool for constructing an explicit quasi-fibrant motivic replacement of the suspensionP1\mathbb P^1-spectrum of any smooth schemeX∈Sm/kX\in Sm/k. Moreover, it is shown that the bispectrum(Mfr(X),Mfr(X)(1),Mfr(X)(2),…),\begin{equation*} (M_{fr}(X),M_{fr}(X)(1),M_{fr}(X)(2),\ldots ), \end{equation*}each term of which is a twisted framed motive ofXX, has the motivic homotopy type of the suspension bispectrum ofXX. Furthermore, an explicit computation of infiniteP1\mathbb P^1-loop motivic spaces is given in terms of spaces with framed correspondences. We also introduce big framed motives of bispectra and show that they convert the classical Morel–Voevodsky motivic stable homotopy theory into an equivalent local theory of framed bispectra. As a topological application, it is proved that the framed motiveMfr(pt)(pt)M_{fr}(pt)(pt)of the pointpt=Spec⁡kpt=\operatorname {Spec} kevaluated atptptis a quasi-fibrant model of the classical sphere spectrum whenever the base fieldkkis algebraically closed of characteristic zero.
Network-principled deep generative models for designing drug combinations as graph sets
Authors: Karimi Mostafa , Hasanzadeh Arman , Shen Yang 
Published: April 2020

Abstract

Motivation
Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery.

Results
We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene–gene, gene–disease and disease–disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space.

Availability and implementation
https://github.com/Shen-Lab/Drug-Combo-Generator.

Supplementary information
Supplementary data are available at Bioinformatics online.
From the butterfly’s point of view: learned colour association determines differential pollination of two co-occurring mock verbains byAgraulis vanillae(Nymphalidae)
Authors: Drewniak M Eugenia , Briscoe Adriana D , Cocucci Andrea A , Beccacece Hernán M , Zapata Adriana I , Moré Marcela 
Published: April 2020

AbstractLearning plays an important role in the location and utilization of nectar sources for pollinators. In this work we focus on the plant-pollinator interaction between the butterfly Agraulis vanillae (Nymphalidae) and two Glandularia plant species (Verbenaceae) that grow in sympatry. Bioassays using arrays of artificial flowers (red vs. lilac-purple) showed that naïve A. vanillae butterflies do not have innate colour preferences for any of the tested colours. Trained butterflies were able to learn to associate both floral colours with the presence of nectar rewards. Wild A. vanillae butterflies visited the red flowers of Glandularia peruviana much more frequently than the lilac-purple flowers of Glandularia venturii. Standing nectar crop measurements showed that G. peruviana flowers offered three times more sucrose than the flowers of G. venturii. Analyses confirmed that corolla colour of G. peruviana (red flowers) and G. venturii (lilac-purple flowers) were discriminable in the butterfly’s colour space. These findings may indicate flexibility in A. vanillae preferences due to a learned association between red coloration and higher nectar rewards.