bluuebunny/crossref_metadata_2025_split huggingface dataset:
Learning stable seismic wave equations for porous media from real data
Authors: Xiong Fansheng , Yong Wen-An 
Published: February 2022

SUMMARYThis work presents a machine-learning-based framework to determine unknown coefficients in seismic wave equations for porous media saturated with fluids by using real data as labels, which are velocities of P and S waves. The coefficients are functions of basic rock physics parameters. By using this framework, the trained neural networks incorporate certain mathematical and physical constraints on the coefficients. Working on a single-fluid model, we train the networks with synthetic as well as real data sets. The prediction results show that the learned model is inherently stable, has good physical properties and can accurately predict synthetic data as well as real logging data of shale reservoirs with relative mean square errors less than 5 per cent. They also demonstrate that the wave propagation phenomenon corresponding to the logging data can be well described with the single-fluid model.
Trans-dimensional gravity and magnetic joint inversion for 3-D earth models
Authors: Ghalenoei Emad , Dettmer Jan , Ali Mohammed Y , Kim Jeong Woo 
Published: February 2022

SUMMARY
Studying 3-D subsurface structure based on spatial data is an important application for geophysical inversions. However, major limitations exist for conventional regularized inversion when applied to potential-field data. For example, global regularization parameters can mask model features that may be important for interpretation. In addition, 3-D inversions are typically based on data acquired in 2-D at the Earth’s surface. Such data may contain significant spatial error correlations in 2-D due to the choice of spatial sampling, acquisition geometry, ambient noise and model assumptions. These correlations can cause trade-offs with spatial resolution and should be accounted for. However, correlations are often ignored, particularly 2-D correlations in spatial data, such as potential field data recorded on the Earth’s surface. Non-linear Bayesian methods can address these shortcomings and we present a new hierarchical model for 2-D correlated errors. Nonetheless, limitations also exist. For example, non-linear Bayesian estimation requires numerical integration with a considerable computational burden to collect a posterior ensemble of models. For 3-D applications, this cost can be prohibitive. This paper presents a non-linear Bayesian inversion with trans-dimensional (trans-D) partitioning of space by a hierarchy of Voronoi nodes and planes (VP), and trans-D estimation of the data noise covariance matrix. The addition of planes permits the introduction of prior information which reduces non-uniqueness. The covariance matrix estimation uses a trans-D autoregressive (AR) noise model to quantify correlated noise on 2-D potential-field data. We address computational cost by wavelet compression in the forward problem and by basing susceptibility on an empirical relationship with density contrast. The method is applied to simulated data and field data from off-shore Abu Dhabi. With simulated data, we demonstrate that subsurface structures are well-resolved with the trans-D model that applies hierarchical VP partitioning. In addition, the model locally adapts based on data information without requiring regularization. The method is also successful in reducing 2-D error correlation via trans-D AR models in 2-D. From field data, the inversion efficiently resolves basement topography and two distinct salt diapirs with a parsimonious and data-driven parametrization. Results show a considerable reduction in 2-D spatial correlations of field data using the proposed trans-D AR model.
Language, historical culture and the gentry of later Stuart Cornwall and south-west Wales
Authors: Harris James 
Published: February 2022

Abstract
This article considers how gentry antiquarian communities in later Stuart Cornwall and south-west Wales constructed distinctive local identities. It focuses on four case studies: William Scawen, the West Penwith coterie, Edward Lhuyd and the Teifi Valley group. These antiquaries conceived of the Cornish and the Welsh as ‘ancient Britons’ and established them as historically and culturally distinct from the English, usually through reference to their indigenous languages. However, the reception of their work among wider landed society was shaped by the vitality of each respective language (with still-ubiquitous Welsh contrasting with near-extinct Cornish). By exploring the relationship between intellectual culture and identity formation, the article contributes to a broader understanding of the various and overlapping identities that permeated the British archipelago.
The technical landscape for patient-centered CDS: progress, gaps, and challenges
Authors: Dullabh Prashila , Heaney-Huls Krysta , Lobach David F , Hovey Lauren S , Sandberg Shana F , Desai Priyanka J , Lomotan Edwin , Swiger James , Harrison Michael I , Dymek Chris , Sittig Dean F , Boxwala Aziz 
Published: February 2022

AbstractSupporting healthcare decision-making that is patient-centered and evidence-based requires investments in the development of tools and techniques for dissemination of patient-centered outcomes research findings via methods such as clinical decision support (CDS). This article explores the technical landscape for patient-centered CDS (PC CDS) and the gaps in making PC CDS more shareable, standards-based, and publicly available, with the goal of improving patient care and clinical outcomes. This landscape assessment used: (1) a technical expert panel; (2) a literature review; and (3) interviews with 18 CDS stakeholders. We identified 7 salient technical considerations that span 5 phases of PC CDS development. While progress has been made in the technical landscape, the field must advance standards for translating clinical guidelines into PC CDS, the standardization of CDS insertion points into the clinical workflow, and processes to capture, standardize, and integrate patient-generated health data.
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure
Authors: Jabbour Sarah , Fouhey David , Kazerooni Ella , Wiens Jenna , Sjoding Michael W 
Published: February 2022

Abstract

Objective
When patients develop acute respiratory failure (ARF), accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients.

Materials and Methods
Machine learning models were trained to predict the common causes of ARF (pneumonia, heart failure, and/or chronic obstructive pulmonary disease [COPD]). Models were trained using chest radiographs and clinical data from the electronic health record (EHR) and applied to an internal and external cohort.

Results
The internal cohort of 1618 patients included 508 (31%) with pneumonia, 363 (22%) with heart failure, and 137 (8%) with COPD based on physician chart review. A model combining chest radiographs and EHR data outperformed models based on each modality alone. Models had similar or better performance compared to a randomly selected physician reviewer. For pneumonia, the combined model area under the receiver operating characteristic curve (AUROC) was 0.79 (0.77–0.79), image model AUROC was 0.74 (0.72–0.75), and EHR model AUROC was 0.74 (0.70–0.76). For heart failure, combined: 0.83 (0.77–0.84), image: 0.80 (0.71–0.81), and EHR: 0.79 (0.75–0.82). For COPD, combined: AUROC = 0.88 (0.83–0.91), image: 0.83 (0.77–0.89), and EHR: 0.80 (0.76–0.84). In the external cohort, performance was consistent for heart failure and increased for COPD, but declined slightly for pneumonia.

Conclusions
Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of ARF. Further work is needed to determine how these models could act as a diagnostic aid to clinicians in clinical settings.
Statistical tolerance–cost–service life optimization of blade bearing of controllable pitch propeller considering the marine environment conditions through meta-heuristic algorithm
Authors: Zeng Wenhui , Yi Jin , Lin Rongfu , Lu Wenlong 
Published: February 2022

Abstract
Blade bearing is the key mechanism of the controllable pitch propellers (CPPs) to adjust the pitch to change the thrust. In the marine environment, wear and deformation of the contact surface of the blade bearing are quite large, which may result in leakage to reduce the service life of CPP. However, most studies on tolerance optimization of blade bearing disregard deformations and wear, and it could result in unreasonable tolerances and impair the CPP’s service life. In this paper, to establish the statistical tolerance–cost–service life optimization model of the blade bearing of the CPP considering the marine environment effects in propellers, a prediction model about the relationship between the thrust and rotation speed of the CPP considering environment conditions of wind, wave, and ocean currents is constructed. Based on this, the wear and deformation are simulated according to the predicted rotation speed in the marine environment. Considering the deformation and wear in the marine environment, a tolerance analysis model of the assembly deviations of the blade bearing is introduced, and the assembly characteristics and functional requirements are established and converted into probability constraints to establish the service-life model based on statistical tolerance analysis and Monte Carlo simulation methods. Afterward, a statistical tolerance–cost–service life optimization model is established and transformed into a single-objective optimization problem through the normalization method and the weighted sum method, and solved by a meta-heuristic algorithm named cuckoo search. Finally, the application of the proposed model is demonstrated through a case involving the blade bearing of a CPP, and the optimized results show that the manufacturing cost of the blade bearing is reduced and its service life is extended.
Psychometric Properties of a Measure Assessing Anxiogenic Parenting Practices in Food Allergy
Authors: Wilton Emily P , Luke Anna K , Gladstone Theresa R , Lahoud Ashley A , Biscarri Clark Sydney D , Flessner Christopher A 
Published: February 2022

Abstract

Aim
Pediatric food allergy represents a significant public health burden. In order to avoid allergen consumption, adequate management requires daily vigilance and involvement from parents, frequently leading to increased parental anxiety. While specific anxiogenic parenting practices (i.e., parenting behaviors which may aid in the development and/or exacerbation of childhood anxiety) have been documented within this population, to this point, these behaviors have not been systematically measured.

Objectives
The current study aimed to develop and examine a parent-report scale designed to measure anxiogenic parenting behaviors related to food allergy.

Methods
Participants included 177 parents of children with food allergy recruited online using Amazon Mechanical Turk (mTurk). An exploratory factor analysis was conducted to determine the factor structure of the newly developed scale. Subsequently, psychometric properties (e.g., construct validity) were examined via correlational analyses.

Results
Results indicated a 24-item, 3 factor (Factor 1: Involvement in Food Allergy; Factor 2: Worry about Food Allergy; Factor 3: Autonomy Promotion) scale, which accounted for 53.11% of the total variance. The Kaiser–Meyer–Olkin measure was acceptable, KMO = 0.872 and Bartlett’s test of sphericity indicated sufficient correlations between items (χ2(378)=2568.95). All subscales demonstrated strong internal consistency (Involvement in Food Allergy: α = .880; Worry about Food Allergy: α = .892; Autonomy Promotion α = .796) as well as convergent and discriminant validity.

Conclusions
Results support the overall psychometric properties of the scale. Interpretations, limitations, and future directions are discussed.
Autologous tissue reconstruction sternal cleft: a rare congenital malformation
Authors: Mongkornwong Atthawit , Kongpanichakul Laliphat , Tawaranurak Niti , Chansanti Orawan , Chitithavorn Voravit , Chuangsuwanich Apirag 
Published: February 2022

Abstract
Sternal clefts are rare congenital chest-wall deformities, which can be complete or incomplete; therefore, reconstruction with autologous tissue is essential to protect the heart and prevent respiratory infection. In this report, we present the case of a 16-month-old baby girl from a preterm, twin pregnancy with a partial superior sternal cleft. A moist dressing for promoting wound healing was used until cutaneous layer complete epithelialization at which time we performed reconstruction with autologous tissue. After surgery the patient recovered with close-to-natural chest wall contour and adequate heart and lung function.
Estimating Population Size from a Privatized Network Sample
Authors: Fellows Ian E 
Published: February 2022

Abstract
Link-tracing designs, such as respondent-driven sampling (RDS), are extensively used to perform inference on marginalized and hard-to-reach populations. Privatized network sampling (PNS), where the identities of each subject’s connections are collected in a manner that preserves their privacy, is an extension of RDS that admits new inferential procedures. We derive two new population size estimators for PNS studies. We explore their performance in both simulated and empirically collected network datasets and find them to have reduced bias along with considerably lower variance than previously developed estimators.
Strong Lyman-α emission in an overdense region at z = 6.8: a very large (R ∼ 3 physical Mpc) ionized bubble in COSMOS?
Authors: Endsley Ryan , Stark Daniel P 
Published: February 2022

ABSTRACT
Our understanding of reionization has advanced considerably over the past decade, with several results now demonstrating that the intergalactic medium transitioned from substantially neutral at z = 7 to largely reionized at z = 6. However, little remains known about the sizes of ionized bubbles at z ≳ 7 as well as the galaxy overdensities which drive their growth. Fortunately, rest-ultraviolet (UV) spectroscopic observations offer a pathway towards characterizing these ionized bubbles thanks to the resonant nature of Lyman-alpha photons. In a previous work, we presented Ly α detections from three closely separated Lyman-break galaxies at z ≃ 6.8, suggesting the presence of a large (R &gt; 1 physical Mpc) ionized bubble in the 1.5 deg2 COSMOS field. Here, we present new deep Ly α spectra of 10 UV-bright ($\mathrm{\mathit{ M}}_{\mathrm{UV}}^{} \le -20.4$) z ≃ 6.6–6.9 galaxies in the surrounding area, enabling us to better characterize this potential ionized bubble. We confidently detect (S/N &gt; 7) Ly α emission at z = 6.701–6.882 in nine of ten observed galaxies, revealing that the large-scale volume spanned by these sources (characteristic radius R = 3.2 physical Mpc) traces a strong galaxy overdensity (N/〈N〉 ≳ 3). Our data additionally confirm that the Ly α emission of UV-bright galaxies in this volume is significantly enhanced, with 40 per cent (4/10) showing strong Ly α emission (equivalent width &gt;25 Å) compared to the 8–9 per cent found on average at z ∼ 7. The median Ly α equivalent width of our observed galaxies is also ≈2 times that typical at z ∼ 7, consistent with expectations if a very large (R ∼ 3 physical Mpc) ionized bubble is allowing the Ly α photons to cosmologically redshift far into the damping wing before encountering H i.
Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning
Authors: Akiyama Manato , Sakakibara Yasubumi 
Published: February 2022

Abstract
Effective embedding is actively conducted by applying deep learning to biomolecular information. Obtaining better embeddings enhances the quality of downstream analyses, such as DNA sequence motif detection and protein function prediction. In this study, we adopt a pre-training algorithm for the effective embedding of RNA bases to acquire semantically rich representations and apply this algorithm to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-training algorithm to embed the four bases of RNA in a position-dependent manner using a large number of RNA sequences from various RNA families, a context-sensitive embedding representation is obtained. As a result, not only base information but also secondary structure and context information of RNA sequences are embedded for each base. We call this ‘informative base embedding’ and use it to achieve accuracies superior to those of existing state-of-the-art methods on RNA structural alignment and RNA family clustering tasks. Furthermore, upon performing RNA sequence alignment by combining this informative base embedding with a simple Needleman–Wunsch alignment algorithm, we succeed in calculating structural alignments with a time complexity of O(n2) instead of the O(n6) time complexity of the naive implementation of Sankoff-style algorithm for input RNA sequence of length n.
Effect of lanthanum carbonate on serum calciprotein particles in patients with stage 3–4 CKD—results from a placebo-controlled randomized trial
Authors: Tiong Mark K , Smith Edward R , Pascoe Elaine M , Elder Grahame J , Lioufas Nicole M , Pedagogos Eugenia , Hawley Carmel M , Valks Andrea , Holt Stephen G , Hewitson Tim D , Toussaint Nigel D 
Published: February 2022

ABSTRACT

Background
Calciprotein particles (CPP) are colloidal aggregates of calcium phosphate and the mineral-binding protein fetuin-A, and are potential mediators of cardiovascular disease in chronic kidney disease (CKD). Emerging evidence suggests non-calcium-containing phosphate binders may reduce serum CPP in patients with kidney failure who require dialysis; however, it is unclear whether similar interventions are effective in patients with earlier stages of CKD.

Methods
The IMpact of Phosphate Reduction On Vascular End-points in CKD (IMPROVE-CKD) was a multi-centre, placebo-controlled, randomized trial of lanthanum carbonate on cardiovascular markers in 278 participants with stage 3b/4 CKD. In this pre-specified exploratory analysis, primary (CPP-I) and secondary CPP (CPP-II) were measured in a sub-cohort of participants over 96 weeks. Treatment groups were compared using linear mixed-effects models and the relationship between serum CPP and pulse wave velocity (PWV) and abdominal aortic calcification (AAC) was examined.

Results
A total of 253 participants had CPP data for baseline and at least one follow-up timepoint and were included in this analysis. The mean age was 62.4 ± 12.6 years, 32.0% were female and the mean estimated glomerular filtration rate (eGFR) was 26.6 ± 8.3 mL/min/1.73 m2. Baseline median serum CPP-I was 14.9 × 104 particles/mL [interquartile range (IQR) 4.6–49.3] and median CPP-II was 3.3 × 103 particles/mL (IQR 1.4–5.4). There was no significant difference between treatment groups at 96 weeks in CPP-I [22.8% (95% confidence interval −39.2, 36.4), P = 0.65] or CPP-II [−18.3% (95% confidence interval −40.0, 11.2), P = 0.20] compared with a placebo. Serum CPP were not correlated with baseline PWV or AAC, or with the progression of either marker.

Conclusions
Lanthanum carbonate was not associated with a reduction of CPP at 96 weeks when compared with a placebo in a CKD cohort.
On-demand synthesis of high-quality, blue-light-active ZnSe colloidal quantum wires
Authors: Li Yi , Zhang Chong , Tian Jie , Wu Liang , Liu Guo-Qiang , Li Hui-Hui , Zhang Yu-Zhuo , Shao Zhen-Chao , He Zhen , Yu Shu-Hong 
Published: February 2022

Abstract
Beyond the state-of-the-art Cd-containing quantum wires (QWs), heavy-metal-free semiconductor QWs, such as ZnSe, are of great interest for next-generation environmental-benign applications. Unfortunately, simultaneous, on-demand manipulation of their radial and axial sizes—that allows strong quantum confinement in the blue-light region—has so far been challenging. Here we present a two-step catalyzed growth strategy that enables independent, high-precision and wide-range controls over the diameter and length of ZnSe QWs. We find that a new epitaxial orientation between the cubic-phase Ag2Se solid catalyst and wurtzite ZnSe QWs kinetically favors the formation of defect-free ultrathin QWs. Thanks to their high uniformity, the resulting blue-light-active, phase-pure ZnSe QWs exhibit well-defined excitonic absorption with the 1Se–1Sh transition linewidth as narrow as sub-13 nm. Combining the transient absorption spectroscopy, we further show that surface electron traps in these ZnSe QWs can be eliminated by thiol passivation, which results in long-lived charge carriers and high-efficiency solar-to-hydrogen conversion.
Fabrication and biological evaluation of 3D-printed calcium phosphate ceramic scaffolds with distinct macroporous geometries through digital light processing technology
Authors: Wang Jing , Tang Yitao , Cao Quanle , Wu Yonghao , Wang Yitian , Yuan Bo , Li Xiangfeng , Zhou Yong , Chen Xuening , Zhu Xiangdong , Tu Chongqi , Zhang Xingdong 
Published: February 2022

Abstract
Digital light processing (DLP)-based 3D printing technique holds promise in fabricating scaffolds with high precision. Here raw calcium phosphate (CaP) powders were modified by 5.5% monoalcohol ethoxylate phosphate (MAEP) to ensure high solid loading and low viscosity. The rheological tests found that photocurable slurries composed of 50 wt% modified CaP powders and 2 wt% toners were suitable for DLP printing. Based on geometric models designed by computer-aided design (CAD) system, three printed CaP ceramics with distinct macroporous structures were prepared, including simple cube, octet-truss and inverse face-centered cube (fcc), which presented the similar phase composition and microstructure, but the different macropore geometries. Inverse fcc group showed the highest porosity and compressive strength. The in vitro and in vivo biological evaluations were performed to compare the bioactivity of three printed CaP ceramics, and the traditional foamed ceramic was used as control. It suggested that all CaP ceramics exhibited good biocompatibility, as evidence by an even bone-like apatite layer formation on the surface, and the good cell proliferation and spreading. A mouse intramuscular implantation model found that all of CaP ceramics could induce ectopic bone formation, and foam group had the strongest osteoinduction, followed by inverse fcc, while cube and octet-truss had the weakest one. It indicated that macropore geometry was of great importance to affect the osteoinductivity of scaffolds, and spherical, concave macropores facilitated osteogenesis. These findings provide a strategy to design and fabricate high-performance orthopedic grafts with proper pore geometry and desired biological performance via DLP-based 3D printing technique.