bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal analyticity estimates for non-linear active–dissipative evolution equations
Authors: Papageorgiou Demetrios T , Smyrlis Yiorgos-Sokratis , Tomlin Ruben J 
Published: October 2022

Abstract
Active–dissipative evolution equations emerge in a variety of physical and technological applications including liquid film flows, flame propagation, epitaxial film growth in materials manufacturing, to mention a few. They are characterized by three main ingredients: a term producing growth (active), a term providing damping at short length scales (dissipative) and a nonlinear term that transfers energy between modes and crucially produces a nonlinear saturation. The manifestation of these three mechanisms can produce large-time spatiotemporal chaos as evidenced by the Kuramoto-Sivashinsky equation (negative diffusion, fourth-order dissipation and a Burgers nonlinearity), which is arguably the simplest partial differential equation to produce chaos. The exact form of the terms (and in particular their Fourier symbol) determines the type of attractors that the equations possess. The present study considers the spatial analyticity of solutions under the assumption that the equations possess a global attractor. In particular, we investigate the spatial analyticity of solutions of a class of one-dimensional evolutionary pseudo-differential equations with Burgers nonlinearity, which are periodic in space, thus generalizing the Kuramoto-Sivashinsky equation motivated by both applications and their fundamental mathematical properties. Analyticity is examined by utilizing a criterion involving the rate of growth of suitable norms of the $n$th spatial derivative of the solution, with respect to the spatial variable, as $n$ tends to infinity. An estimate of the rate of growth of the $n$th spatial derivative is obtained by fine-tuning the spectral method, developed elsewhere. We prove that the solutions are analytic if $\gamma $, the order of dissipation of the pseudo-differential operator, is higher than one. We also present numerical evidence suggesting that this is optimal, i.e. if $\gamma $ is not larger that one, then the solution is not in general analytic. Extensive numerical experiments are undertaken to confirm the analysis and also to compute the band of analyticity of solutions for a wide range of active–dissipative terms and large spatial periods that support chaotic solutions. These ideas can be applied to a wide class of active–dissipative–dispersive pseudo-differential equations.
Clinically undetected polyclonal heteroresistance among Pseudomonas aeruginosa isolated from cystic fibrosis respiratory specimens
Authors: Maxwell Daniel N , Kim Jiwoong , Pybus Christine A , White Leona , Medford Richard J , Filkins Laura M , Monogue Marguerite L , Rae Meredith M , Desai Dhara , Clark Andrew E , Zhan Xiaowei , Greenberg David E 
Published: October 2022

Abstract

Background
Pseudomonas aeruginosa infection is the leading cause of death among patients with cystic fibrosis (CF) and a common cause of difficult-to-treat hospital-acquired infections. P. aeruginosa uses several mechanisms to resist different antibiotic classes and an individual CF patient can harbour multiple resistance phenotypes.

Objectives
To determine the rates and distribution of polyclonal heteroresistance (PHR) in P. aeruginosa by random, prospective evaluation of respiratory cultures from CF patients at a large referral centre over a 1 year period.

Methods
We obtained 28 unique sputum samples from 19 CF patients and took multiple isolates from each, even when morphologically similar, yielding 280 unique isolates. We performed antimicrobial susceptibility testing (AST) on all isolates and calculated PHR on the basis of variability in AST in a given sample. We then performed whole-genome sequencing on 134 isolates and used a machine-learning association model to interrogate phenotypic PHR from genomic data.

Results
PHR was identified in most sampled patients (n = 15/19; 79%). Importantly, resistant phenotypes were not detected by routine AST in 26% of patients (n = 5/19). The machine-learning model, using the extended sampling, identified at least one genetic variant associated with phenotypic resistance in 94.3% of isolates (n = 1392/1476).

Conclusion
PHR is common among P. aeruginosa in the CF lung. While traditional microbiological methods often fail to detect resistant subpopulations, extended sampling of isolates and conventional AST identified PHR in most patients. A machine-learning tool successfully identified at least one resistance variant in almost all resistant isolates by leveraging this extended sampling and conventional AST.
Eosinophilic mastitis: a rare benign inflammatory condition and review of the literature
Authors: Arnott William , Leong Gregory , Davis Arie , Diab Jason , Clement Zackariah 
Published: October 2022

AbstractWe report the case of a 53-year-old nulliparous female presenting with a 9-month history of recurrent mastitis and a retro-areolar lesion. Histological assessment showed an inflammatory infiltrate predominantly composed of eosinophils without evidence of malignant changes. The patient was diagnosed with eosinophilic mastitis and commenced on a course of oral steroids with good effect. This case will outline the pathology, clinical manifestations and diagnosis of eosinophilic mastitis alongside a review of the literature.
Resection of an intrapulmonary solitary fibrous tumor in the center of the middle lobe: a case report
Authors: Hirai Yoshimitsu , Iwahashi Yoshifumi , Kogure Miwako , Shima Aya , Nakamura Rie , Hirai Issei 
Published: October 2022

Abstract
A 75-year-old female patient was referred to our hospital due to an abnormal shadow detected by chest X-ray. Computed tomography scans revealed a well-circumscribed nodule measuring 28 mm between B4 and B5 in the right middle lobe. Because the tumor was in the center of right middle lobe, a middle lobe resection was performed. The tumor was located within the lung and there were no obvious pleural surface changes. Postoperative histological findings showed 34-mm firm and round tumor, and well circumscribed without involving the visceral pleura. The pathologic examination revealed proliferating spindle-shaped cells with a random fascicular arrangement with continuity to the pulmonary interstitium. Not much cellular atypia was observed. Immunohistochemical staining indicated that the tumor was positive for STAT6, CD34. The final diagnosis was an intrapulmonary benign solitary fibrous tumor (SFT). Even benign intrapulmonary SFTs that have been completely resected may later become malignant and recur, and careful follow-up is necessary.
Translator attribution for Arabic using machine learning
Authors: Mohamed Emad , Sarwar Raheem , Mostafa Sayed 
Published: October 2022

AbstractGiven a set of target language documents and their translators, the translator attribution task aims at identifying which translator translated which documents. The attribution and the identification of the translator’s style could contribute to fields including translation studies, digital humanities, and forensic linguistics. To conduct this investigation, firstly, we develop a new corpus containing the translations of world-famous books into Arabic. We then pre-process the books in our corpus which mainly involves cleaning irrelevant material, morphological segmentation analysis of words, and devocalization. After pre-processing the books, we propose to use 100 most frequent words and/or morphologically segmented function words as writing style markers of the translators (i.e. stylometric features) to differentiate between translations of different translators. After the completion of features extraction process, we applied several supervised and unsupervised machine-learning algorithms along with our novel cluster-to-author index to perform this task. We found that the translators are not invisible, and morphological analysis may not be more useful than just using the 100 most frequent words as features. The support vector machine linear kernel algorithm reported 99% classification accuracy. Similar findings were reported by the unsupervised machine-learning methods, namely, K-mean clustering and hierarchical clustering.
Should states restrict recipient choice amongst relevant and available COVID-19 vaccines?
Authors: Cave Emma , McMahon Aisling 
Published: October 2022

Abstract
Several COVID-19 vaccinations have been authorised worldwide. Whilst some vaccines are contraindicated for certain age groups or health conditions, there are often multiple clinically suitable authorised vaccine brands available. Few states have allowed recipients to choose amongst them, though there are multiple reasons why choice would be valued. We consider the policy justifications for state controls on recipient choice amongst COVID-19 vaccine brands, focusing on European countries and drawing on the UK context as an example. We contrast justifications for not offering choice at the height of the early pandemic crisis, and as some states seek to de-escalate their response and transition towards living with COVID-19. We argue that in the latter context public expectations of choice between available vaccine brands and platforms may rise, but that several considerations may justify continued restrictions on choice. A key factor which states should continue to take into consideration is the global nature of the pandemic. Insofar as offering recipient choice at a national level might exacerbate global inequity in vaccine distribution, states retain a normative and legal justification for restricting choice amongst available and clinically suitable vaccine brands.
Intermediate redshift calibration of gamma-ray bursts and cosmic constraints in non-flat cosmology
Authors: Luongo Orlando , Muccino Marco 
Published: October 2022

ABSTRACT
We propose a new method to calibrate gamma-ray burst (GRB) correlations employing intermediate redshift data sets, instead of limiting to z ≃ 0 catalogues, and applied it to the well-consolidated Amati correlation. This model-independent calibration technique is based on the Bézier polynomial interpolation of the most updated observational Hubble data and baryonic acoustic oscillations (BAO) and alleviates de facto the well-known circularity problem affecting GRB correlations. In doing so, we also investigate the influence of the BAO scales rs, got from Planck results, and $r_{\rm s}^{\rm fid}$, got from the considered fiducial cosmology, by considering the cases $(r_{\rm s}/r_{\rm s}^{\rm fid})=1$ and $(r_{\rm s}/r_{\rm s}^{\rm fid})\ne 1$. We get constraints on the cosmic parameters, using Markov chain–Monte Carlo simulations, first fixing and then leaving free the spatial curvature density parameter Ωk. Compared to previous literature, we obtain tighter constraints on the mass density parameter Ωm. In particular, our findings turn out to be highly more compatible with those got from standard candle indicators within the Lambda cold dark matter paradigm. Finally, we critically re-examine the recent H0 tension in view of our outcomes.
Residual neural networks for the prediction of planetary collision outcomes
Authors: Winter Philip M , Burger Christoph , Lehner Sebastian , Kofler Johannes , Maindl Thomas I , Schäfer Christoph M 
Published: October 2022

ABSTRACT
Fast and accurate treatment of collisions in the context of modern N-body planet formation simulations remains a challenging task due to inherently complex collision processes. We aim to tackle this problem with machine learning (ML), in particular via residual neural networks. Our model is motivated by the underlying physical processes of the data-generating process and allows for flexible prediction of post-collision states. We demonstrate that our model outperforms commonly used collision handling methods such as perfect inelastic merging and feed-forward neural networks in both prediction accuracy and out-of-distribution generalization. Our model outperforms the current state of the art in 20/24 experiments. We provide a data set that consists of 10164 Smooth Particle Hydrodynamics (SPH) simulations of pairwise planetary collisions. The data set is specifically suited for ML research to improve computational aspects for collision treatment and for studying planetary collisions in general. We formulate the ML task as a multi-task regression problem, allowing simple, yet efficient training of ML models for collision treatment in an end-to-end manner. Our models can be easily integrated into existing N-body frameworks and can be used within our chosen parameter space of initial conditions, i.e. where similar-sized collisions during late-stage terrestrial planet formation typically occur.
USP36 promotes tumorigenesis and drug sensitivity of glioblastoma by deubiquitinating and stabilizing ALKBH5
Authors: Chang Guoqiang , Xie Gloria S , Ma Li , Li Linlin , Richard Hope T 
Published: October 2022

Abstract

Background
ALKBH5 is aberrantly activated and exerts critical roles in facilitating the development of glioblastoma. However, the underlying activation mechanism by which ALKBH5 protein is increased in glioblastoma is not completely understood. Our study aimed to elucidate the signaling pathways involved in mediating ALKBH5 protein stability.

Methods
The contribution of deubiquitinating enzymes (DUB) to the fluctuation of ALKBH5 protein expression was globally profiled with western blot analysis. Mass spectrometry and immunoprecipitation were performed to identify the USP36 and ALKBH5 interaction. The effects of USP36 on the stability of ALKBH5 were detected with in vivo and in vitro ubiquitination assays. Cell proliferation assays, neurosphere formation, limited dilution assay, and intracranial tumor growth assays were implemented to assess the collaborative capacities of USP36 and ALKBH5 in tumorigenesis.

Results
Ubiquitin-specific peptidase 36 (USP36), as a potential ALKBH5-activating DUB, played an essential role in stabilization of ALKBH5 and regulation of ALKBH5-mediated gene expression in glioblastoma. The depletion of USP36 drastically impaired cell proliferation deteriorated the self-renewal of GSCs and sensitized GSCs to temozolomide (TMZ) treatment. Furthermore, the deletion of USP36 substantially decreased the in vivo tumor growth when monitored by bioluminescence imaging. Our findings indicate that USP36 regulates the protein degradation and expression of ALKBH5, and the USP36-ALKBH5 axis orchestrates glioma tumorigenesis.

Conclusion
Our findings identify USP36 as a DUB of ALKBH5 and its role in glioblastoma progression, which may serve as a potential therapeutic target for glioblastoma treatment.
Development of a Clinical Prediction Score Including Monocyte-to-Lymphocyte Ratio to Inform Tuberculosis Treatment Among Children With HIV: A Multicountry Study
Authors: Malik Amyn A , Gandhi Neel R , Marcy Olivier , Walters Elisabetta , Tejiokem Mathurin , Chau Giang Do , Omer Saad B , Lash Timothy L , Becerra Mercedes C , Njuguna Irene N , LaCourse Sylvia M , Maleche-Obimbo Elizabeth , Wamalwa Dalton , John-Stewart Grace C , Cranmer Lisa M 
Published: October 2022

Abstract

Background
Clinical pediatric tuberculosis (TB) diagnosis may lead to overdiagnosis particularly among children with human immunodeficiency virus (CHIV). We assessed the performance of monocyte-lymphocyte ratio (MLR) as a diagnostic biomarker and constructed a clinical prediction score to improve specificity of TB diagnosis in CHIV with limited access to microbiologic testing.

Methods
We pooled data from cohorts of children aged ≤13 years from Vietnam, Cameroon, and South Africa to validate the use of MLR ≥0.378, previously found as a TB diagnostic marker among CHIV. Using multivariable logistic regression, we created an internally validated prediction score for diagnosis of TB disease in CHIV.

Results
The combined cohort had 601 children (median age, 1.9 [interquartile range, 0.9–5.3] years); 300 (50%) children were male, and 283 (47%) had HIV. Elevated MLR ≥0.378 had sensitivity of 36% (95% confidence interval [CI], 23%–51%) and specificity of 79% (95% CI, 71%–86%) among CHIV in the validation cohort. A model using MLR ≥0.28, age ≥4 years, tuberculin skin testing ≥5 mm, TB contact history, fever &gt;2 weeks, and chest radiograph suggestive of TB predicted active TB disease in CHIV with an area under the receiver operating characteristic curve of 0.85. A prediction score of ≥5 points had a sensitivity of 94% and specificity of 48% to identify confirmed TB, and a sensitivity of 82% and specificity of 48% to identify confirmed and unconfirmed TB groups combined.

Conclusions
Our score has comparable sensitivity and specificity to algorithms including microbiological testing and should enable clinicians to rapidly initiate TB treatment among CHIV when microbiological testing is unavailable.
A Fully Integrated Infectious Diseases and Antimicrobial Stewardship Telehealth Service Improves Staphylococcus aureus Bacteremia Bundle Adherence and Outcomes in 16 Small Community Hospitals
Authors: Veillette John J , May Stephanie S , Gabrellas Alithea D , Gelman Stephanie S , Albritton Jordan , Lyons Michael D , Stenehjem Edward A , Webb Brandon J , Dalto Joseph D , Throneberry S Kyle , Stanfield Valoree , Grisel Nancy A , Vento Todd J 
Published: October 2022

Abstract

Background
Infectious diseases (ID) and antimicrobial stewardship (AS) improve Staphylococcus aureus bacteremia (SAB) outcomes. However, many small community hospitals (SCHs) lack on-site access to these services, and it is not known if ID telehealth (IDt) offers the same benefit for SAB. We evaluated the impact of an integrated IDt service on SAB outcomes in 16 SCHs.

Methods
An IDt service offering IDt physician consultation plus IDt pharmacist surveillance was implemented in October 2016. Patients treated for SAB in 16 SCHs between January 2009 and August 2019 were identified for review. We compared SAB bundle adherence and outcomes between patients with and without an IDt consult (IDt group and control group, respectively).

Results
A total of 423 patients met inclusion criteria: 157 in the IDt group and 266 in the control group. Baseline characteristics were similar between groups. Among patients completing their admission at an SCH, IDt consultation increased SAB bundle adherence (79% vs 23%; odds ratio [OR], 16.9; 95% CI, 9.2–31.0). Thirty-day mortality and 90-day SAB recurrence favored the IDt group, but the differences were not statistically significant (5% vs 9%; P = .2; and 2% vs 6%; P = .09; respectively). IDt consultation significantly decreased 30-day SAB-related readmissions (9% vs 17%; P = .045) and increased length of stay (median [IQR], 5 [5–8] days vs 5 [3–7] days; P = .04). In a subgroup of SAB patients with a controllable source, IDt appeared to have a mortality benefit (2% vs 9%; OR, 0.12; 95% CI, 0.01–0.98).

Conclusions
An integrated ID/AS telehealth service improved SAB management and outcomes at 16 SCHs. These findings provide important insights for other IDt programs.
Polarly localized WPR proteins interact with PAN receptors and the actin cytoskeleton during maize stomatal development
Authors: Nan Qiong , Char Si Nian , Yang Bing , Bennett Eric J , Yang Bing , Facette Michelle R 
Published: October 2022

Abstract
Polarization of cells prior to asymmetric cell division is crucial for correct cell divisions, cell fate, and tissue patterning. In maize (Zea mays) stomatal development, the polarization of subsidiary mother cells (SMCs) prior to asymmetric division is controlled by the BRICK (BRK)–PANGLOSS (PAN)–RHO FAMILY GTPASE (ROP) pathway. Two catalytically inactive receptor-like kinases, PAN2 and PAN1, are required for correct division plane positioning. Proteins in the BRK–PAN–ROP pathway are polarized in SMCs, with the polarization of each protein dependent on the previous one. As most of the known proteins in this pathway do not physically interact, possible interactors that might participate in the pathway are yet to be described. We identified WEAK CHLOROPLAST MOVEMENT UNDER BLUE LIGHT 1 (WEB1)/PLASTID MOVEMENT IMPAIRED 2 (PMI2)-RELATED (WPR) proteins as players during SMC polarization in maize. WPRs physically interact with PAN receptors and polarly accumulate in SMCs. The polarized localization of WPR proteins depends on PAN2 but not PAN1. CRISPR–Cas9-induced mutations result in division plane defects in SMCs, and ectopic expression of WPR-RFP results in stomatal defects and alterations to the actin cytoskeleton. We show that certain WPR proteins directly interact with F-actin through their N-terminus. Our data implicate WPR proteins as potentially regulating actin filaments, providing insight into their molecular function. These results demonstrate that WPR proteins are important for cell polarization.
Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and classification in tissues
Authors: Smolen Justin A , Wooley Karen L 
Published: October 2022

Abstract
Convolutional neural networks (CNNs) and other deep-learning models have proven to be transformative tools for the automated analysis of microscopy images, particularly in the domain of cellular and tissue imaging. These computer-vision models have primarily been applied with traditional microscopy imaging modalities (e.g. brightfield and fluorescence), likely due to the availability of large datasets in these regimes. However, more advanced microscopy imaging techniques could, potentially, allow for improved model performance in various computational histopathology tasks. In this work, we demonstrate that CNNs can achieve high accuracy in cell detection and classification without large amounts of data when applied to histology images acquired by fluorescence lifetime imaging microscopy (FLIM). This accuracy is higher than what would be achieved with regular single or dual-channel fluorescence images under the same settings, particularly for CNNs pretrained on publicly available fluorescent cell or general image datasets. Additionally, generated FLIM images could be predicted from just the fluorescence image data by using a dense U-Net CNN model trained on a subset of ground-truth FLIM images. These U-Net CNN generated FLIM images demonstrated high similarity to ground truth and improved accuracy in cell detection and classification over fluorescence alone when used as input to a variety of commonly used CNNs. This improved accuracy was maintained even when the FLIM images were generated by a U-Net CNN trained on only a few example FLIM images.