bluuebunny/crossref_metadata_2025_split huggingface dataset:
A combined sound field reconstruction method for large cylindrical surfaces using non-conformal plane measurement
Authors: Cheng Wei , Han Shengming , Song Chao , Nie Zelin , Chen Xuefeng , Zhang Peng , Ni Jinglei 
Published: October 2021

Abstract
In practical acoustic measurement for large cylindrical surfaces, it is difficult to keep conformal and coaxial between the holographic surface and the reconstruction surface. To overcome this problem, a combined sound field reconstruction method using non-conformal plane measurement is proposed in this paper. Based on the sound pressure measured on the holographic plane, the combined method first reconstructs the sound field on the cylindrical conformal surface using statistically optimal planar near-field acoustic holography, and then reconstructs the sound field on the cylindrical reconstruction surface using statistically optimal cylindrical near-field acoustic holography. And a least square optimization method is proposed to determine the optimal position of the conformal surface. In addition, to overcome ill-posed problems, an error reduction method combining truncated singular value decomposition and Tikhonov regularization is proposed. Finally, the proposed method is applied to a test bed, and the accuracy and robustness of the sound field reconstruction for large cylindrical surfaces are obviously improved, which can provide reliable evidences for noise monitoring and control of mechanical systems.
Atomistic modeling of radiation damage in crystalline materials
Authors: Deo Chaitanya S , Chen Elton Y , Dingeville Rémi 
Published: October 2021

Abstract
This review discusses atomistic modeling techniques used to simulate radiation damage in crystalline materials. Radiation damage due to energetic particles results in the formation of defects. The subsequent evolution of these defects over multiple length and time scales requiring numerous simulations techniques to model the gamut of behaviors. This work focuses attention on current and new methodologies at the atomistic scale regarding the mechanisms of defect formation at the primary damage state.
First principles study on modulating electronic and optical properties with h-BN intercalation in AlN/MoS2 heterostructure
Authors: Yang Fei , Cao Xincheng , Han Junnan , Tao Yue , Zhuo Zhenguo , Zhang Le , Zhu Ziyue , Liu Wenjin , Dai Yuehua 
Published: October 2021

Abstract
The van der Waals (vdW) heterostructures formed by stacking layered two-dimensional materials can improve the performance of materials and provide more applications. In our paper, six configurations of AlN/MoS2 vdW heterostructures were constructed, the most stable structure was obtained by calculating the binding energy. On this basis, the effect of external vertical strain on AlN/MoS2 heterostructure was analyzed, the calculated results show that the optimal interlayer distance was 3.593 Å and the band structure was modulated. Then the h-BN intercalation was inserted into the AlN/MoS2 heterostructure, by fixing the distance between h-BN and AlN or MoS2, two kinds of models were obtained. Furthermore, the electronic properties of AlN/MoS2 heterostructure can be regulated by adding h-BN intercalation layer and adjusting its position. Finally, the optical properties show that the absorption coefficient of AlN/MoS2 heterostructure exhibits enhancement characteristic compared with that of the individual monolayers. Meantime, compared with AlN/MoS2, the AlN/h-BN/MoS2 shows a redshift effect and the light absorption peak intensity increased, which indicated that h-BN intercalation layer can be used to regulate the electronic and optical properties of AlN/MoS2 heterostructure.
Fatigue tests of superelastic NiTi wires: an analysis using factorial design in single cantilever bending
Authors: Araújo Magna Silmara de Oliveira , Nobre Dantas Grassi Estephanie , José de Araújo Carlos 
Published: October 2021

AbstractThis paper investigates the fatigue behavior of superelastic NiTi shape memory alloy thin wires with circular and rectangular cross-sections under single cantilever bending mode. A dynamic mechanical analyzer operating in single cantilever mode is employed as a testing machine. The fatigue life of the wires was analyzed at different strain amplitudes and loading frequencies. The simultaneous influence of these parameters was assessed through a factorial design with replication. Circular wires have higher fatigue life compared to rectangular wires. Higher strain amplitudes decrease the fatigue life of both circular and rectangular cross-section wires. On the other hand, the loading frequency strongly affected the rectangular cross-section wire, while having a significantly weaker effect on the circular wire. The NiTi wires showed fatigue lives between 103and 105cycles in the evaluated range of strain amplitude, which falls within the fatigue life observed for shape memory alloy (SMA) wires under similar loading conditions. Furthermore, the factorial design proved to be a valuable tool in the study of fatigue life of NiTi SMA, especially for the visualization and interpretation of the effect of the chosen variables.
Cnoidal wave in a two-level medium: an alternative approach to nonlinear susceptibilities
Authors: Leblond Hervé , Ciret Charles 
Published: October 2021

Abstract
We consider a cnoidal wave propagating in a two-level system, for which an exact analytic solution exists. Two effective components of the third-order nonlinearity

χ

(
3
)

, describing Kerr effect and third harmonics generation, can be derived from it. Some discrepancy has been found with respect to the usual formalism of nonlinear susceptibilities. Numerical computation shows that the exact cnoidal wave solution the most accurately describes the nonlinear propagation of a quasi-monochromatic wave. Especially, the third harmonic resonance of the third-order nonlinearity, predicted by the formalism of nonlinear susceptibilities, is not observed.
Characterization of fracture behavior of a nickel-based using Charpy instrumented in different conditions of heat treatment and evaluation temperatures
Authors: Taheri Morteza , Kashani-Bozorg Seyed Farshid , Teymouri Hamid Reza , Fallah-Morad Amir Hossein , Liavoli Rohallah Panahi , Davar Amirreza 
Published: October 2021

Abstract
Since gas turbine blades work in acute conditions, the study of their mechanical behavior, including impact, is one of the most important tasks recommended. The aim of this study was to investigate the impact behavior of IN738 superalloy welded by pulsed laser under different heat treatment conditions and different temperatures. The results of this study showed that the lowest fracture toughness was related to the specimens that were welded under casting conditions. This was due to the formation of liquation cracks during welding and due to the reaction between the (Ti,Ta)C carbide, γ-γ′ eutectic, and γ′ phase with the superalloy matrix. As the test temperature increased from ambient temperature to 600 °C, the impact toughness increased. This is attributed to the free presence of dislocations, which causes ductile behavior in the alloy. As the temperature increased further to 770 °C, the fracture toughness increased due to the increased strength of the superalloy yield due to locking of dislocations. At temperatures above 770 °C, the toughness of the superalloy was reduced again due to the decrease in strength due to the passage of dislocations through the obstacles.
Estimating Exposure Intensity Based on Odor
Authors: Nicas Mark , Neuhaus John 
Published: October 2021

Abstract
The odor detection threshold for a chemical typically varies widely across population members, and the values can be described by a lognormal distribution. Assuming that the parameters of a chemical’s odor detection threshold distribution can be estimated, a method is presented to retrospectively estimate exposure intensity based on detecting the chemical’s odor. The context is a single exposure period involving a single air contaminant. In brief, where k out of n identically exposed persons detect the chemical’s odor, the best estimate of the chemical’s concentration in air corresponds to the k ÷ n fractile of the odor detection threshold distribution. Where n is small and/or k ÷ n is close to zero or one, exact 100×(1−α)% confidence bounds for the fractile estimate can be computed without using the normal distribution assumption. In addition, statistical uncertainty in the parameter estimates of the chemical’s odor detection threshold distribution can be considered via a parametric bootstrap procedure, such that an overall 100×(1−α)% confidence interval on the chemical’s concentration in air is obtained. The method is illustrated for benzene. Analysis of the available benzene odor detection threshold literature provides best estimates of a population geometric mean equal to 37.8 ppm and geometric standard deviation equal to 1.92. In a hypothetical example in which two out of six workers detect the odor of benzene, the point estimate of the benzene exposure level is 28.5 ppm, and the approximate one-sided lower 95% confidence limit is 8.2 ppm. The present analysis also makes clear that odor does not provide an adequate warning of excessive levels of airborne benzene.
HyperAttentionDTI: improving drug–protein interaction prediction by sequence-based deep learning with attention mechanism
Authors: Zhao Qichang , Zhao Haochen , Zheng Kai , Wang Jianxin 
Published: October 2021

Abstract

Motivation
Identifying drug–target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. Accurately identifying DTIs in silico can significantly shorten development time and reduce costs. Recently, many sequence-based methods are proposed for DTI prediction and improve performance by introducing the attention mechanism. However, these methods only model single non-covalent inter-molecular interactions among drugs and proteins and ignore the complex interaction between atoms and amino acids.

Results
In this article, we propose an end-to-end bio-inspired model based on the convolutional neural network (CNN) and attention mechanism, named HyperAttentionDTI, for predicting DTIs. We use deep CNNs to learn the feature matrices of drugs and proteins. To model complex non-covalent inter-molecular interactions among atoms and amino acids, we utilize the attention mechanism on the feature matrices and assign an attention vector to each atom or amino acid. We evaluate HpyerAttentionDTI on three benchmark datasets and the results show that our model achieves significantly improved performance compared with the state-of-the-art baselines. Moreover, a case study on the human Gamma-aminobutyric acid receptors confirm that our model can be used as a powerful tool to predict DTIs.

Availability and implementation
The codes of our model are available at https://github.com/zhaoqichang/HpyerAttentionDTI and https://zenodo.org/record/5039589.

Supplementary information
Supplementary data are available at Bioinformatics online.
PMCE: efficient inference of expressive models of cancer evolution with high prognostic power
Authors: Angaroni Fabrizio , Chen Kevin , Damiani Chiara , Caravagna Giulio , Graudenzi Alex , Ramazzotti Daniele 
Published: October 2021

AbstractMotivationDriver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods can not infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution.ResultsWe introduce PMCE, an expressive framework that leverages mutational profiles from cross-sectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-of-the-art in terms of accuracy and robustness to noise, on simulations. The application of PMCE to 7866 samples from the TCGA database allows us to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups.Availability and implementationPMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript.Supplementary informationSupplementary data are available at Bioinformatics online.
875 Using Stress Testing to Identify Vulnerabilities in Artificial Intelligence Models for the Identification of Culprit Carotid Lesions in Cerebrovascular Events
Authors: Le E , Tarkin J , Evans N , Chowdhury M , Rudd J 
Published: October 2021

Abstract

Introduction
Carotid atherosclerosis is a major risk factor for ischaemic stroke, a leading cause of death. Carotid CT angiography (CTA) is commonly performed following a stroke or transient ischaemic attack (TIA) to help guide patient management in secondary prevention of stroke. Deep learning algorithms can help extract greater information from scans.

Method
The dataset comprised CTA scans from 40 culprit and 40 non-culprit carotid arteries of patients with recent stroke/TIA, and 40 carotid arteries of asymptomatic patients without previous stroke/TIA. A 3D convolutional neural network was trained to classify carotid artery type. Each input comprised 14 axial CTA carotid patches (centred around the carotid artery) concatenated together to form a 3D volume (capturing ∼3cm of artery). 75% of the dataset was used for training and 25% for internal validation. Following training, computer vision operations were applied to input images to assess their impact on the model’s classification decisions.

Results
The model achieved 100% accuracy on the training set and 67% on the internal validation set. However, after subjecting input images to image operations, vulnerabilities in the deep learning model were revealed, even when using input images from the training set. For example, using a Gaussian blur filter with sigma 1.0 was sufficient to change classification decisions, as was horizontally flipping the image.

Conclusions
Deep learning has exceptional capabilities for learning, however the risk with such high-capacity models is failure to learn relevant features from the data. Stress testing provides a viable method to further evaluate deep learning models before clinical deployment.
Untold diversity of floral rewards inCypella(Tigridieae: Iridaceae) and related genera: distribution and anatomy
Authors: Pastori Tamara , Kuhn Sofia Aumond , De Souza-Chies Tatiana Teixeira , Silvério Adriano , Mastroberti Alexandra Antunes , Eggers Lilian , Chauveau Olivier , Mariath Jorge Ernesto De Araujo 
Published: October 2021

AbstractFloral rewards are important elements in plant–pollinator interactions and can play an essential role in the diversification of species. The identification of these rewards has been neglected in species of Tigridieae (Iridaceae), one of the few angiosperm groups to offer lipids, considered a specialized reward. We identify and characterize the secretory structures of floral rewards in Cypella and related genera (Calydorea, Catila, Herbertia, Onira and Kelissa). Our results reveal that there are trichomatic elaiophores in the tepals of Cypella, Kelissa, Onira and Herbertia and staminal nectaries in the connectives of the anthers of Cypella and Onira. There is an unusual combination of floral rewards in Cypella and Onira, demonstrating a unique pattern in Iridaceae of the American continent.
Severe Dysbiosis and Specific Haemophilus and Neisseria Signatures as Hallmarks of the Oropharyngeal Microbiome in Critically Ill Coronavirus Disease 2019 (COVID-19) Patients
Authors: de Castilhos Juliana , Zamir Eli , Hippchen Theresa , Rohrbach Roman , Schmidt Sabine , Hengler Silvana , Schumacher Hanna , Neubauer Melanie , Kunz Sabrina , Müller-Esch Tonia , Hiergeist Andreas , Gessner André , Khalid Dina , Gaiser Rogier , Cullin Nyssa , Papagiannarou Stamatia M , Beuthien-Baumann Bettina , Krämer Alwin , Bartenschlager Ralf , Jäger Dirk , Müller Michael , Herth Felix , Duerschmied Daniel , Schneider Jochen , Schmid Roland M , Eberhardt Johann F , Khodamoradi Yascha , Vehreschild Maria J G T , Teufel Andreas , Ebert Matthias P , Hau Peter , Salzberger Bernd , Schnitzler Paul , Poeck Hendrik , Elinav Eran , Merle Uta , Stein-Thoeringer Christoph K 
Published: October 2021

Abstract

Background
At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict coronavirus disease 2019 (COVID-19) illness. However, the results are not conclusive, as critical illness can drastically alter a patient’s microbiome through multiple confounders.

Methods
To study oropharyngeal microbiome profiles in SARS-CoV-2 infection, clinical confounders, and prediction models in COVID-19, we performed a multicenter, cross-sectional clinical study analyzing oropharyngeal microbial metagenomes in healthy adults, patients with non-SARS-CoV-2 infections, or with mild, moderate, and severe COVID-19 (n = 322 participants).

Results
In contrast to mild infections, patients admitted to a hospital with moderate or severe COVID-19 showed dysbiotic microbial configurations, which were significantly pronounced in patients treated with broad-spectrum antibiotics, receiving invasive mechanical ventilation, or when sampling was performed during prolonged hospitalization. In contrast, specimens collected early after admission allowed us to segregate microbiome features predictive of hospital COVID-19 mortality utilizing machine learning models. Taxonomic signatures were found to perform better than models utilizing clinical variables with Neisseria and Haemophilus species abundances as most important features.

Conclusions
In addition to the infection per se, several factors shape the oropharyngeal microbiome of severely affected COVID-19 patients and deserve consideration in the interpretation of the role of the microbiome in severe COVID-19. Nevertheless, we were able to extract microbial features that can help to predict clinical outcomes.
FDA Public Workshop Summary—Coccidioidomycosis (Valley Fever): Considerations for Development of Antifungal Drugs
Authors: O’Shaughnessy Elizabeth , Yasinskaya Yuliya , Dixon Cheryl , Higgins Karen , Moore Jason , Reynolds Kellie , Ampel Neil M , Angulo David , Blair Janis E , Catanzaro Antonino , Galgiani John N , Garvey Edward , Johnson Royce , Larwood David J , Lewis Gareth , Purdie Rob , Rex John H , Shubitz Lisa F , Stevens David A , Page Stephen J , Shukla Sunita J , Farley John J , Nambiar Sumathi 
Published: October 2021

Abstract
Coccidioidomycosis is a fungal disease endemic to the southwestern United States, Mexico, and Central and South America. Prevalence rates are increasing steadily, and new endemic areas of Coccidioides are emerging. Standard treatment is often administered for months to decades, and intolerance to medications and treatment failures are common. No new treatments for coccidioidomycosis have been approved in the United States in nearly 40 years. On 5 August 2020, the US Food and Drug Administration convened experts in coccidioidomycosis from academia, industry, patient groups, and other government agencies to discuss the disease landscape and strategies to facilitate product development for treatment of coccidioidomycosis. This article summarizes the key topics concerning drug development for coccidioidomycosis presented by speakers and panelists during the workshop, such as unmet need, trial designs, endpoints, incentives, research and development support, and collaborations to facilitate antifungal drug development.
How microbial glycosyl hydrolase activity in the gut mucosa initiates microbial cross-feeding
Authors: Berkhout Maryse D , Plugge Caroline M , Belzer Clara 
Published: October 2021

Abstract
The intestinal epithelium is protected from direct contact with gut microbes by a mucus layer. This mucus layer consists of secreted mucin glycoproteins. The outer mucus layer in the large intestine forms a niche that attracts specific gut microbiota members of which several gut commensals can degrade mucin. Mucin glycan degradation is a complex process that requires a broad range of glycan degrading enzymes, as mucin glycans are intricate and diverse molecules. Consequently, it is hypothesized that microbial mucin breakdown requires concerted action of various enzymes in a network of multiple resident microbes in the gut mucosa. This review investigates the evolutionary relationships of microbial carbohydrate-active enzymes that are potentially involved in mucin glycan degradation and focuses on the role that microbial enzymes play in the degradation of gut mucin glycans in microbial cross-feeding and syntrophic interactions.
El espejismo de Baena: Luces y sombras de un derecho administrativo comparado latinoamericanoBaena’s mirage: Lights and shades of comparative administrative law in Latin America
Authors: García-Huidobro Luis Eugenio , Guidi Sebastián 
Published: October 2021

Abstract
In the context of a growing interest in comparative administrative law in Latin America, an emerging academic project asserts the existence of a “common administrative law” in the region, inspired by the so-called ius constitutionale commune in Latin America. In this essay, we examine the impact of the Baena decision (Inter-American Court of Human Rights, 2001) on the administrative regimes of Argentina, Chile, and Mexico so as to reveal the limitations of such a project. We speculate that the limited impact of this decision can be explained by particular characteristics germane to administrative law that tend to be overlooked from a constitutionalist optic. Finally, we draw from our case studies to make methodological recommendations to those who want to undertake comparative administrative law studies in the region.
SARS-CoV-2 Psychiatric Sequelae: A Review of Neuroendocrine Mechanisms and Therapeutic Strategies
Authors: Hornick Mary G , Olson Margaret E , Jadhav Arun L 
Published: October 2021

AbstractFrom the earliest days of the coronavirus disease 2019 (COVID-19) pandemic, there have been reports of significant neurological and psychological symptoms following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. This narrative review is designed to examine the potential psychoneuroendocrine pathogenic mechanisms by which SARS-CoV-2 elicits psychiatric sequelae as well as to posit potential pharmacologic strategies to address and reverse these pathologies. Following a brief overview of neurological and psychological sequelae from previous viral pandemics, we address mechanisms by which SARS-CoV-2 could enter or otherwise elicit changes in the CNS. We then examine the hypothesis that COVID-19–induced psychiatric disorders result from challenges to the neuroendocrine system, in particular the hypothalamic-pituitary-adrenal stress axis and monoamine synthesis, physiological mechanisms that are only further enhanced by the pandemic-induced social environment of fear, isolation, and socioeconomic pressure. Finally, we evaluate several FDA-approved therapeutics in the context of COVID-19–induced psychoneuroendocrine disorders.