bluuebunny/crossref_metadata_2025_split huggingface dataset:
Selenium nanoparticles are effective in penetrating pine and causing high oxidative damage to Bursaphelenchus xylophilus in pine wilt disease control
Authors: Shang Hongyi , Zhang Hongyan , Zhao Rui , Yu Meng , Ma Yingjian , Sun Zhe , Wu Xuemin , Xu Yong 
Published: May 2022

AbstractBACKGROUNDResearch on selenium nanoparticles (SeNPs) in chemical defense and chemotherapy of plants has developed rapidly owing to their high microbial toxicity, environmental safety, and degradability. Pine wilt disease (PWD) threatens pine forests worldwide; however, it is difficult to kill the nematodes (Bursaphelenchus xylophilus) inside the tree that cause PWD using traditional pesticide formulations. SeNPs could be the key to controlling PWD.RESULTSIn this study, approximately 50 nm SeNPs were prepared using a simple and green method, and chitosan was used to increase their biocompatibility and stability. The preparation and characterization results showed that the prepared SeNPs coated with chitosan (SeNPs@CS) were spherical and evenly dispersed. The bioassay results showed that SeNPs@CS had an LC50 of 15.627 mg L−1 against B. xylophilus. In addition, the killing mechanism of SeNPs@CS against B. xylophilus was studied. Confocal microscopy and transmission electron microscopy demonstrated that B. xylophilus were killed by reactive oxygen species, and the penetration of nano‐form materials to B. xylophilus was higher than that of non‐nano‐form materials. To verify the effective penetration of SeNPs in pine tissues, Cy5‐labeled SeNPs@CS was observed inside pine needles and branches using frozen sections and confocal microscopy. In addition, the cytotoxicity of SeO2 and SeNPs@CS was tested, and the results showed that the cytotoxicity of SeNPs@CS to MC3T3‐E1 cells was reduced.CONCLUSIONThese results show that SeNPs are expected to be used as a new strategy for the control of PWD with oxidative damage and high penetration to B. xylophilus and effective target penetration and biosafety. © 2022 Society of Chemical Industry.
Natural product‐based nanomedicine applied to fungal infection treatment: A review of the last 4 years
Authors: Marena Gabriel Davi , Ramos Matheus Aparecido dos Santos , Carvalho Gabriela Corrêa , Junior José Alberto Paris , Resende Flávia Aparecida , Corrêa Ione , Ono Gabriela Yuki Bressanim , Sousa Araujo Victor Hugo , de Camargo Bruna Almeida Furquim , Bauab Tais Maria , Chorilli Marlus 
Published: May 2022

AbstractFungal infections are one of the main public health problems, especially in immunocompromised patients, nosocomial environments, patients with chronic diseases, and transplant recipients. These diseases are increasingly frequent and lethal because the microorganism has a high capacity to acquire resistance to available therapy. The main resistance factors are the emergence of new strains and the uncontrolled use of antifungals. It is, therefore, important to develop new methods that contribute to combating fungal diseases in the clinical area. Natural products have considerable potential for the development of new drugs with antifungal activity, mainly due to their biocompatibility and low toxic effect. This promising antimicrobial activity of natural products is mainly due to the presence of flavonoids, terpenes, and quinones, which explains their antifungal potential. Pharmaceutical nanotechnology has been explored to enhance the delivery, selectivity, and clinical efficacy of these products. Nanotechnological systems provide a safe and selective environment for various substances, such as natural products, improving antifungal activity. However, further safety experiments (in vivo or clinical trials) need to be carried out to prove the therapeutic action of natural products, since they may have undesirable, toxic, and mutagenic effects. Therefore, this review article addresses the main nanotechnological methods using natural products for effective future treatment against the main fungal diseases.
Illustration of an object‐based approach to identify structural differences in tropical cyclone wind fields
Authors: Zick Stephanie , Matyas Corene , Lackmann Gary , Tang Jingyin , Bennett Brian 
Published: May 2022

AbstractThe meteorology community primarily assesses tropical cyclone (TC) forecast skill using track and intensity errors. These metrics are frequently uncorrelated and can offer conflicting information about model performance. Continued improvements in intensity forecasting require improved representation of physical processes over multiple scales, and model verification of TC spatial structure can contribute to these improvements. To date, there are limited studies into forecast model representation of wind fields. More work is needed to better understand model deficiencies in skillfully predicting TC size metrics. In this study, we demonstrate an object‐based approach that can reveal structural differences in TC wind fields. Object‐based methods have been underutilized, and these methods, along with spatial metrics, can serve as additional verification methods for assessing storm structure in both observations and model simulations. Specifically, we illustrate this approach by examining a major difference between the Tiedtke and Kain–Fritsch cumulus parametrization schemes: The Tiedtke scheme includes convective momentum transport while the Kain–Fritsch scheme does not. We create three experiments of Hurricane Isabel (2003) using the Weather Research and Forecasting model using the Kain–Fritsch and Tiedtke cumulus parametrization schemes and an altered Tiedtke scheme with convective momentum transport turned off. Within the three experiments, we generate a small ensemble of four simulations to avoid drawing erroneous conclusions due to growth of numerical noise. Then, we use object‐based methods to measure and compare spatial attributes of the low‐level wind fields to confirm the dominant influence of momentum transport in influencing the TC spatial structure. Our spatial metric approach offers an objective suite of structural attributes that could be useful in diverse applications. A future goal is to use spatial metrics in systematic verification studies of TCs in operational model forecasts and climate model simulations, which may offer great benefit to operational forecasters and numerical model developers.
Defining relative yield for soil test correlation and calibration trials in the Fertilizer Recommendation Support Tool
Authors: Pearce Austin W. , Slaton Nathan A. , Lyons Sarah E. , Bolster Carl H. , Bruulsema Tom W. , Grove John H. , Jones John D. , McGrath Josh M. , Miguez Fernando E. , Nelson Nathan O. , Osmond Deanna L. , Parvej Md. Rasel , Pena‐Yewtukhiw Eugenia M. , Spargo John T. 
Published: May 2022

AbstractThe Fertilizer Recommendation Support Tool (FRST) will perform correlations between soil nutrient concentrations and crop response to fertilization from user‐selected datasets in the FRST national database. Yield response for the nutrient of interest in a particular site‐year is presented as relative yield (RY), a ratio of unfertilized yield to the maximum attainable yield (A). Several methods exist in the literature for estimating A and calculating RY but the effect of method choice on soil test correlation outcomes is undocumented. We used six published methods to calculate RY from site‐year yield data for five published correlation datasets, and fit a generalized linear plateau (LP) model to each. The critical soil test value (at the LP join point) and RY intercept coefficients were not significantly affected by RY method for any of the datasets, and RY plateau was significantly affected by method for only one dataset. The top options after robust group discussions were the so‐called MAX and FITMAX methods. We selected the MAX method, which defines A as the numerically highest treatment yield mean, as the most appropriate method for FRST because MAX represents maximal yield in responsive sites, is inclusive of trial data having a range of treatment numbers, limits RY to 100% (which allows options for transforming data), and is simpler to implement than FITMAX, which requires a decision tree to calculate RY for diverse trials.
Towards a win‐win situation for innovation and sustainable development: The role of environmental regulation
Authors: Lyu Yanwei , Zhang Jinning , Wang Lingli , Yang Fei , Hao Yu 
Published: May 2022

AbstractInnovation and sustainable development are important concerns for countries around the world, and strengthening environmental regulation (ER) may have a critical implication for the promotion of green technology innovation. On the basis of elaborating the mechanism of ER on technological innovation, the Super‐SBM‐Windows‐DEA model is employed to measure the green innovation efficiency (GIE) using provincial‐level panel data from 2004–2017 in China. The system generalised method of moments is used to estimate the direct effects of ER on GIE, an innovative mediating effect model that based on structural equation model is utilised to explore their indirect effects. Further, a spatial econometric model is developed to examine the possible spatial effects between ER and GIE. The ‘U’ shaped relationship between ER and GIE is verified in the direct effect test. The results of the indirect effects indicate that ER is not conducive to GIE by discouraging the inflows and outflows of FDI, while it contributes to GIE by promoting the structural upgrading of industry. The spatial effect results show that the impact of ER on GIE in neighbouring areas has an obvious spatial spillover characteristic, which is manifested as an inverted ‘U’‐shaped relationship of promotion followed by inhibition. The paper further proposes policy implications including strengthening cross‐regional joint prevention and control, integrating ER strategies, and rational application of ER tools, so as to promote a win‐win development of innovation and sustainability, and the positive effects of ER are better developed.
Deprivation and fire injury
Authors: Taylor Mark , Francis Hulya , Oakford Gary , Appleton Debbie , Fielding John 
Published: May 2022

AbstractThe association between deprivation and accidental dwelling fire injuries was examined in Merseyside which has a high proportion of deprived areas. For the years 2006 to 2016, over half of the accidental dwelling fire injuries in Merseyside occurred in deprived areas, and almost three quarters of alcohol and drug related accidental dwelling fire injuries occurred in such areas. In particular, we examine the relationship between the established accidental dwelling fire injury risk factors of old age, disability and alcohol and drug consumption and the level of deprivation. There were strong correlations between deprivation and the overall number of fire injuries, and between deprivation and fire injuries relating to alcohol/drug consumption, disability/mobility related fire injuries, and fire injuries sustained by those aged 65+.
Wind of change brought by COVID‐19: Exploring the response and the new normal from the perspectives of Indian flexpatriates
Authors: Sengupta Santoshi , Patel Parth , Mohyuddin Syed , Prikshat Verma , Rajesh J. Irudhaya , Rana Vishal 
Published: May 2022

AbstractDrawing from the positive organizational change theory, this paper aims to explore how Indian flexpatriates responded to the change brought by the pandemic of COVID‐19 and what is the new normal according to them. Thematic analysis of 19 in‐depth interviews with flexpatriates from the IT industry revealed four explicit phases of change process – reflection, communication, collaboration, and transformation. Further, the analysis brought out four tenets of the new normal. First, it is time to blend physical and virtual work; second, the “personal” touch of Indians in international assignments is irreplaceable; third, working from home amidst the entire household being housebound is the new normal; and last, international travel will resume soon with some changed protocols. This is the first qualitative study combining the issue of global talent management with Indian flexpatriates vis‐à‐vis the impact of COVID‐19, the findings of which expand the positive organizational change theory and have important implications.
Herausforderung: Revision eines fest eingewachsenen kurvierten Kurzschaftes in der Hüftendoprothetik
Authors: Kutzner Karl Philipp , Stoffel Karl , Hochreiter Josef 
Published: May 2022

Zusammenfassung
Operationsziel
Sichere und knochensparende Extraktion eines fest eingewachsenen kurvierten Kurzschaftes ohne die Notwendigkeit eines transfemoralen Zuganges.

Indikationen
Revision eines fest eingewachsenen Kurzschaftes beispielsweise bei periprothetischer Infektion oder Malposition.

Kontraindikationen
Korrekte Platzierung der Konusschablone nicht möglich.

Operationstechnik
Wahl eines Standardzugangs zum Hüftgelenk. Luxation des Gelenkes. Abschlagen des einliegenden Kopfimplantats. Sorgfältiges Freipräparieren des Konus und der lateralen Schulter. Aufbringen und Befestigen der Konusschablone. Beginn des vorsichtigen Einschlagens der Eröffnungsmeißel, zunächst lateral, anschließend ventral und dorsal über die Führungen der Schablone. Es ist darauf zu achten, dass stets der Schliff vom Implantat weg zeigt. Anschließend Wiederholung des Vorganges mit den Startermeißeln in gleicher Reihenfolge. Entfernung der Schablone. Vorsichtiges Einschlagen der Finalen Meißel in gleicher Reihenfolge. Versuch einer Schaftextraktion mittels Ausschlaginstrumentarium. Fakultativ Wiederholung des Gesamtvorganges. Zur Vermeidung von Frakturen, erst nach Mobilisation des Interfaces lateral, ventral und dorsal, Freilegung des Interfaces auch medial mittels medialer Meißel in der entsprechenden Reihenfolge. Abschließend Schaftextraktion.

Weiterbehandlung
Mobilisierung und Belastungsaufbau gemäß der gewählten Revisionsimplantate und Verankerungstechnik.

Ergebnisse
Das beschriebene Vorgehen hat sich in der klinischen Praxis in den 3 Autorenkliniken in insgesamt 14 Fällen bewährt. In 3 (21,4 %) Fällen musste trotz Nutzung des Extraktions-Meißel-Systems zusätzlich ein transfemoraler Zugang bzw. eine Fensterung erfolgen, um das Schaftimplantat zu entfernen. Als Revisionsimplantate wurden in über der Hälfte der Fälle (57,8 %) primäre Geradschäfte verwendet, in 4 Fällen (36,4 %) konnte erneut ein zementfreier Kurzschaft verwendet werden.
Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above
Authors: Oosterhoff Jacobien Hillina Froukje , Savelberg Angelique Berit Marte Corlijn , Karhade Aditya Vishwas , Gravesteijn Benjamin Yaël , Doornberg Job Nicolaas , Schwab Joseph Hasbrouck , Heng Marilyn 
Published: May 2022

Abstract
Purpose
Preoperative prediction of mortality in femoral neck fracture patients aged 65 years or above may be valuable in the treatment decision-making. A preoperative clinical prediction model can aid surgeons and patients in the shared decision-making process, and optimize care for elderly femoral neck fracture patients. This study aimed to develop and internally validate a clinical prediction model using machine learning (ML) algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.

Methods
A retrospective cohort study at two trauma level I centers and three (non-level I) community hospitals was conducted to identify patients undergoing surgical fixation for a femoral neck fracture. Five different ML algorithms were developed and internally validated and assessed by discrimination, calibration, Brier score and decision curve analysis.

Results
In total, 2478 patients were included with 90 day and 2 year mortality rates of 9.1% (n = 225) and 23.5% (n = 582) respectively. The models included patient characteristics, comorbidities and laboratory values. The stochastic gradient boosting algorithm had the best performance for 90 day mortality prediction, with good discrimination (c-statistic = 0.74), calibration (intercept = − 0.05, slope = 1.11) and Brier score (0.078). The elastic-net penalized logistic regression algorithm had the best performance for 2 year mortality prediction, with good discrimination (c-statistic = 0.70), calibration (intercept = − 0.03, slope = 0.89) and Brier score (0.16). The models were incorporated into a freely available web-based application, including individual patient explanations for interpretation of the model to understand the reasoning how the model made a certain prediction: https://sorg-apps.shinyapps.io/hipfracturemortality/

Conclusions
The clinical prediction models show promise in estimating mortality prediction in elderly femoral neck fracture patients. External and prospective validation of the models may improve surgeon ability when faced with the treatment decision-making.

Level of evidence
Prognostic Level II.
Radiation synthesis and anticancer drug delivery of poly(acrylic acid/acrylamide) magnetite hydrogel
Authors: Hosny Nasser Mohammed , Abbass Mohammed , Ismail Faten , El‐Din Horia M. Nizam 
Published: May 2022

AbstractHydrogels had gained considerable importance in drug delivery systems. Polyacrylic acid/acrylamide (AAc/AAm) and polyacrylic acid/acrylamide doped with magnetite (AAc/AAm)Fe3O4 hydrogels have been prepared by ɣ-radiations and characterized by Fourier transform infrared spectra (IR), X-ray diffractions (XRD), scanning electron microscope (SEM) and transmittance electron microscope (TEM). The morphology and the particle size were determined from TEM images. The average particle size of Fe3O4 was found to be 12 nm. The degree of cross-linking of Poly (AAc/AAm) hydrogel was determined from the gel fraction at different radiation doses and compositions. Swelling of the hydrogel was studied at different conditions. The magnetism of (AAc/AAm) Fe3O4 was studied by vibrating sample magnetometer and the results indicated super-paramagnetic character of (AAc/AAm)Fe3O4. The band gap (Eg) was determined and the values suggested semi-conductivity of the hydrogels. Doxorubicin loading and release by (AAc/AAm)Fe3O4 hydrogel was studied and indicated that the hydrogel can load 78% of doxorubicin.
Sparse reconstruction for integral Fourier holography using dictionary learning method
Authors: Kuruguntla Lakshmi , Dodda Vineela Chandra , Wan Min , Elumalai Karthikeyan , Chinnadurai Sunil , Muniraj Inbarasan , Sheridan John T. 
Published: May 2022

AbstractA simplified (i.e., single shot) method is demonstrated to generate a Fourier hologram from multiple two-dimensional (2D) perspective images (PIs) under low light level imaging conditions. It was shown that the orthographic projection images (OPIs) can be synthesized using PIs and then, following incorporation of corresponding phase values, a digital hologram can be generated. In this work, a fast dictionary learning (DL) technique, known as Sequential Generalised K-means (SGK) algorithm, is used to perform Integral Fourier hologram reconstruction from fewer samples. The SGK method transforms the generated Fourier hologram into its sparse form, which represented it with a linear combination of some basis functions, also known as atoms. These atoms are arranged in the form of a matrix called a dictionary. In this work, the dictionary is updated using an arithmetic average method while the Orthogonal Matching Pursuit algorithm is opted to update the sparse coefficients. It is shown that the proposed DL method provides good hologram quality, (in terms of peak signal-to-noise ratio) even for cases of ~ 90% sparsity.
Multiparametric MRI identifies subtle adaptations for demarcation of disease transition in murine aortic valve stenosis
Authors: Quast Christine , Kober Frank , Becker Katrin , Zweck Elric , Hoffe Jasmina , Jacoby Christoph , Flocke Vera , Gyamfi-Poku Isabella , Keyser Fabian , Piayda Kerstin , Erkens Ralf , Niepmann Sven , Adam Matti , Baldus Stephan , Zimmer Sebastian , Nickenig Georg , Grandoch Maria , Bönner Florian , Kelm Malte , Flögel Ulrich 
Published: May 2022

AbstractAortic valve stenosis (AS) is the most frequent valve disease with relevant prognostic impact. Experimental model systems for AS are scarce and comprehensive imaging techniques to simultaneously quantify function and morphology in disease progression are lacking. Therefore, we refined an acute murine AS model to closely mimic human disease characteristics and developed a high-resolution magnetic resonance imaging (MRI) approach for simultaneous in-depth analysis of valvular, myocardial as well as aortic morphology/pathophysiology to identify early changes in tissue texture and critical transition points in the adaptive process to AS. AS was induced by wire injury of the aortic valve. Four weeks after surgery, cine loops, velocity, and relaxometry maps were acquired at 9.4 T to monitor structural/functional alterations in valve, aorta, and left ventricle (LV). In vivo MRI data were subsequently validated by histology and compared to echocardiography. AS mice exhibited impaired valve opening accompanied by significant valve thickening due to fibrotic remodelling. While control mice showed bell-shaped flow profiles, AS resulted not only in higher peak flow velocities, but also in fragmented turbulent flow patterns associated with enhanced circumferential strain and an increase in wall thickness of the aortic root. AS mice presented with a mild hypertrophy but unaffected global LV function. Cardiac MR relaxometry revealed reduced values for both T1 and T2 in AS reflecting subtle myocardial tissue remodelling with early alterations in mitochondrial function in response to the enhanced afterload. Concomitantly, incipient impairments of coronary flow reserve and myocardial tissue integrity get apparent accompanied by early troponin release. With this, we identified a premature transition point with still compensated cardiac function but beginning textural changes. This will allow interventional studies to explore early disease pathophysiology and novel therapeutic targets.
Generative Adversarial Network to evaluate quantity of information in financial markets
Authors: Santoro Domenico , Grilli Luca 
Published: May 2022

AbstractNowadays, the information obtainable from the markets are potentially limitless. Economic theory has always supported the possible advantage obtainable from having more information than competitors, however quantifying the advantage that these can give has always been a problem. In particular, in this paper we study the amount of information obtainable from the markets taking into account only the time series of the prices, through the use of a specific Generative Adversarial Network. We consider two types of financial instruments traded on the market, stocks and cryptocurrencies: the first are traded in a market subject to opening and closing hours, whereas cryptocurrencies are traded in a 24/7 market. Our goal is to use this GAN to be able to “convert” the amount of information that the different instruments can have in discriminative and predictive power, useful to improve forecast. Finally, we demonstrate that by using the initial dataset with the 5 most important feature useds by traders, the prices of cryptocurrencies present higher discriminatory and predictive power than stocks, while by adding a feature the situation can be completely reversed.