bluuebunny/crossref_metadata_2025_split huggingface dataset:
Handling dataset dependence with model ensembles for skin lesion classification from dermoscopic and clinical images
Authors: Somfai Ellák , Baffy Benjámin , Fenech Kristian , Hosszú Rita , Korózs Dorina , Pólik Marcell , Sárdy Miklós , Lőrincz András 
Published: November 2022

AbstractDataset dependence affects many real‐life applications of machine learning: the performance of a model trained on a dataset is significantly worse on samples from another dataset than on new, unseen samples from the original one. This issue is particularly acute for small and somewhat specific databases in medical applications; the automated recognition of melanoma from skin lesion images is a prime example. We document dataset dependence in dermoscopic skin lesion image classification using three publicly available medium size datasets. Standard machine learning techniques aimed at improving the predictive power of a model might enhance performance slightly, but the gain is small, the dataset dependence is not reduced, and the best combination depends on model details. We demonstrate that simple differences in image statistics account for only 5% of the dataset dependence. We suggest a solution with two essential ingredients: using an ensemble of heterogeneous models, and training on a heterogeneous dataset. Our ensemble consists of 29 convolutional networks, some of which are trained on features considered important by dermatologists; the networks' output is fused by a trained committee machine. The combined International Skin Imaging Collaboration dataset is suitable for training, as it is multi‐source, produced by a collaboration of a number of clinics over the world. Building on the strengths of the ensemble, it is applied to a related problem as well: recognizing melanoma based on clinical (non‐dermoscopic) images. This is a harder problem as both the image quality is lower than those of the dermoscopic ones and the available public datasets are smaller and scarcer. We explored various training strategies and showed that 79% balanced accuracy can be achieved for binary classification averaged over three clinical datasets.
Vegetative buffer strips show limited effectiveness for reducing antibiotic transport in surface runoff
Authors: Moody Adam H. , Lerch Robert N. , Goyne Keith W. , Anderson Stephen H. , Mendoza‐Cózatl David G. , Alvarez David A. 
Published: November 2022

AbstractVegetative buffer strips (VBS) have been demonstrated to effectively reduce loads of sediment, nutrients, and herbicides in surface runoff, but their effectiveness for reducing veterinary antibiotic (VA) loads in runoff has not been well documented. The objective of this study was to determine the effectiveness of VBS vegetation and width on surface runoff loads of the VAs sulfamethazine (SMZ) and lincomycin (LIN). Experimental design of the plots (1.5 × 25 m) was a two‐way factorial with four vegetation treatments (tall fescue [Festuca aruninacea Schreb.]; tall fescue with switchgrass [Panicum virgatum L.] hedge; warm‐season native grass mix; and continuous fallow control), and four buffer widths (0, 2, 5, and 9 m). Turkey litter spiked with SMZ and LIN was applied to the source area (upper 7 m) of each plot, and runoff was collected at each width. Runoff was generated with a rotating boom simulator. Results showed VA loads in runoff at the 0‐m sampler ranged from 3.8 to 5.9% of applied, and overall VA transport in runoff was predominately in the dissolved phase (90% for SMZ and 99% for LIN). Among vegetation treatments, only tall fescue significantly reduced loads of SMZ and LIN compared with the control, with load reductions of ∼30% for both VAs. Estimated field‐scale reductions in VA loads showed that source‐to‐buffer area ratios (SBARs) of 10:1 to 20:1 reduced VA loads by only 7 to 16%. Overall, the grass VBS tested here were less effective at reducing SMZ and LIN loads in surface runoff than has been previously demonstrated for sediment, nutrients, and herbicides.
Trends and variability of rainfall characteristics influencing annual streamflow: A case study of southeast Australia
Authors: Fu Guobin , Chiew Francis H.S. , Post David A. 
Published: November 2022

AbstractThe trend and variability in rainfall characteristics that influence annual streamflow of southeast Australia is assessed using two methods, non‐parametric Kendall test and linear slope, and two gridded daily rainfall datasets (SILO and AWAP) and 1,196 station rainfall data for 1971–2021 period. The rainfall anomaly before, during and after the Millennium Drought (1997–2009) is compared to quantify the variability. The results show: (a) a declining rainfall trend is detected as the recent dry decades were preceded by wet decades. The declining trend largely occurs from autumn through to spring with the largest trend in August to October followed by April and May; (b) the trend is more significant for number of rainfall days and for multi‐day rainfall totals (and more so for longer accumulations) than for annual rainfall; (c) the very high extreme rainfall shows a small increasing trend in summer and mixed signal in the other seasons; (d) the streamflow in the post drought period is lower than the long‐term mean, despite the mean annual rainfall being slightly higher. This can be partly explained by the rainfall‐runoff relationship of catchments not having fully recovered from the prolonged drought and partly by changes in rainfall characteristics, like the wet spell length and multi‐day rainfall, influencing annual streamflow; (e) The general conclusions for analyses with different sources of daily rainfall data are the same. However, some differences do exist, with SILO gridded rainfall and station data having larger areas with statistically significant trend than the AWAP gridded rainfall, as well as larger declining trends in the high elevation areas. The general conclusions of this study are drawn from an Australia case study but could have implications for other regions to investigate the attributions of rainfall characteristics other than annual rainfall to the non‐stationary rainfall‐streamflow relationship.
Moisture sources of the first rainy season extreme precipitation events in the hotspots of Guangdong, South China
Authors: Peng Dongdong , Lin Ailan , Liu Xiantong , Huang Xin , Xiao Hui , Li Huiqi 
Published: November 2022

AbstractThe first rainy season (April–May–June) extreme precipitation events over South China tend to frequently occur in specific hotspots, resulting in a great threat to local society and economy. Abundant moisture supply is essential to precipitation formation. In this study, we employ the FLEXPART model to investigate the geographical moisture sources for extreme precipitation events in three hotspots of Guangdong from west to east (termed as Area‐A, B, and C). During both the periods before and after the onset of the South China Sea Summer Monsoon (hereafter SCSSM), the moisture contributions of ocean regions are larger than that of land regions for all the hotspots, but the main moisture source regions are different. Before the onset of SCSSM, the main source regions are South China Sea and Southeastern Asia, which contribute 36.50 and 31.79% to Area‐A, 35.85 and 27.83% to Area‐B, while 42.27 and 23.81% to Area‐C, respectively. After the onset of SCSSM, the contribution of moisture from Indian Ocean increases a lot. Thus, the main source regions are Indian Ocean, Southeastern Asia and South China Sea, which contribute 31.52, 24.58 and 18.13% to Area‐A, 29.21, 21.82 and 14.99% to Area‐B, 26.62, 20.70%, and 28.19% to Area‐C, respectively. Among the three hotspots, the contribution of ocean regions for Area‐C is the largest. Southeastern Asia is the main land contributor before and after the onset of SCSSM for all the hotspots. This study implies the important role of both ocean and land moisture sources on extreme precipitation variations over South China.
Indian summer monsoon and winter Total Solar Irradiance signals in the tree‐ring δ18O from foothills of High Asia, northern Pakistan
Authors: Li Qiang , Liu Yu , Sinha Ashish , Kathayat Gayatri , Duan Xiangyu , Liu Ruoshi , Xu Chenxi , Song Huiming , Fang Congxi , Siddiqui Muhammad Faheem , Cheng Hai 
Published: November 2022

AbstractA tree‐ring stable oxygen isotope (δ18O) chronology was developed for the past 171 years by four Abies pindrow trees growing in southwest foothills of High Asia, northern Pakistan, which is correlated with observed and modelled δ18O of precipitation in Islamabad over the instrumental period. The averaged tree‐ring δ18O chronology is significantly negative correlated with local (r = −.651, p < .001, n = 38) and regional (r = −.664, p < .001, n = 65) summer precipitation variations. Analysis of vertically integrated water vapour flux and surface wind field suggests that the sampling site is located at the northern limit of the Indian summer monsoon (ISM). Our tree‐ring δ18O series is significantly (p < .001, n = 143) negatively correlated with observed Homogenous India rainfall, All Indian rainfall, Core‐Monsoon India rainfall and Northwest India rainfall. This is the first detected ISM signal from tree‐ring δ18O over northern Pakistan. Several ISM‐related paleoclimate proxies, including three stalagmites δ18O series (Sahiya cave, Jhumar cave and Qunf cave) and a tree‐ring δ18O series (Manali), show consistent variability and a correlation coefficient of .566 (p < .001, n = 165) with our tree‐ring δ18O, suggesting that a combination of multiple proxies may be potentially useful to understand the full perspective of ISM before instrumental era. In addition, we found that the satellite‐observed winter Total Solar Irradiance exerts influence on the tree‐ring δ18O variation (r = .643, p < .001, n = 38), which may be linked to runoff of the Indus River and ISM precipitation.
LncRNA AATBCindicates development and facilitates cell growth and metastasis of cervical cancer as a sponge ofmiR‐1245b‐5p
Authors: Liu Ying‐Qiao , Liu Chen , Bai Yang , Gao Jie 
Published: November 2022

AbstractWith the increasing incidence and mortality rate, cervical cancer has been considered one of the most frequent malignant tumors in females. Exploration of tumor progression‐related biomarkers could facilitate the identification of novel and targeted therapy strategies. To assess the significance of lncRNA AATBC (AATBC) and its potential regulatory mechanism in cervical cancer, and to identify a potential biomarker, this study enrolled 123 patients with cervical cancer. Paired tissue samples were collected. The expression levels of AATBC and miR‐1245b‐5p were analyzed by RT‐qPCR and their significance in the development and prognosis of cervical cancer was evaluated using chi‐square and Cox analyses. In vitro, the regulatory effect of AATBC on the cellular processes of cervical cancer was estimated by CCK8 and Transwell assay. The interaction between ATTBC and miR‐1245b‐5p was assessed by luciferase reporter assay. Significant upregulation of AATBC and reduced miR‐1245b‐5p level in cervical cancer were observed, which showed a negative correlation between their expression levels. Close relationships of AATBC and miR‐1245b‐5p with the FIGO stage and lymph node metastasis were revealed. AATBC showed a significant prognostic value and miR‐1245b‐5p was found to mediate the tumor inhibitory effect of AATBC knockdown, which is speculated to be the underlying molecular mechanism of AATBC in cervical cancer development. Upregulation of AATBC indicted the malignant development and adverse prognosis of cervical cancer. AATBC served as a tumor promoter of cervical cancer by modulating miR‐1245b‐5p.
Corrosion resistance improvement in 6Cr13 martensitic stainless steel via quenching‐tempering and partitioning
Authors: Song Xin , Hu Yong , Yan Zhijie , Qi Huiping 
Published: November 2022

AbstractIn this work, the effect of the temperature of quenching‐tempering and partitioning on the corrosion resistance of 6Cr13 martensitic stainless steel was investigated by immersion test and electrochemical test. The results show that tempering and partitioning can improve the stability of the retained austenite at room temperature. The volume fraction of austenite increases with the increase of the temperature of tempering and partitioning. From the results of the corrosion experiment, the corrosion resistance of 6Cr13 martensitic stainless steel first increases with the increase of heat treatment temperature. The best corrosion resistance is obtained at 400°C. Subsequently, the corrosion resistance decreased with the further increase in heat treatment temperature. The reason for the increase in corrosion resistance is that with the increase of heat treatment temperature, retained austenite's content gradually increases, enhancing the corrosion resistance. The decrease in corrosion resistance is related to the growth of retained austenite and carbide at high temperatures.
Personalized synthetic MR imaging with deep learning enhancements
Authors: Pal Subrata , Dutta Somak , Maitra Ranjan 
Published: November 2022

PurposePersonalized synthetic MRI (syn‐MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least‐squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill‐posed. This article provides a pipeline to enhance the synthesis of such images in three‐dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical.MethodsOur DL enhancements employ a Deep Image Prior (DIP) with a U‐net type denoising architecture that includes situations with minimal training data, such as personalized syn‐MRI. We provide a general workflow for syn‐MRI from three or more training images. Our workflow, called DIPsyn‐MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn‐MRI is implemented in our publicly available Python package DeepSynMRI available at:
https://github.com/StatPal/DeepSynMRI.ResultsWe demonstrate feasibility and improved performance of DIPsyn‐MRI on 3D datasets acquired using the Brainweb interface for spin‐echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn‐MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels.ConclusionThis article provides recipes and software to realistically facilitate DL‐enhanced personalized syn‐MRI.
In vivo phosphorus magnetic resonance spectroscopic imaging of the whole human liver at 7 T using a phosphorus whole‐body transmit coil and 16‐channel receive array: Repeatability and effects of principal component analysis‐based denoising
Authors: van den Wildenberg Lieke , Gursan Ayhan , Seelen Leonard W. F. , van der Velden Tijl A. , Gosselink Mark W. J. M. , Froeling Martijn , van der Kemp Wybe J. M. , Klomp Dennis W. J. , Prompers Jeanine J. 
Published: November 2022

AbstractQuantitative three‐dimensional (3D) imaging of phosphorus (31P) metabolites is potentially a promising technique with which to assess the progression of liver disease and monitor therapy response. However, 31P magnetic resonance spectroscopy has a low sensitivity and commonly used 31P surface coils do not provide full coverage of the liver. This study aimed to overcome these limitations by using a 31P whole‐body transmit coil in combination with a 16‐channel 31P receive array at 7 T. Using this setup, we determined the repeatability of whole‐liver 31P magnetic resonance spectroscopic imaging (31P MRSI) in healthy subjects and assessed the effects of principal component analysis (PCA)‐based denoising on the repeatability parameters. In addition, spatial variations of 31P metabolites within the liver were analyzed. 3D 31P MRSI data of the liver were acquired with a nominal voxel size of 20 mm isotropic in 10 healthy volunteers twice on the same day. Data were reconstructed without denoising, and with PCA‐based denoising before or after channel combination. From the test–retest data, repeatability parameters for metabolite level quantification were determined for 12 31P metabolite signals. On average, 31P MR spectra from 100 ± 25 voxels in the liver were analyzed. Only voxels with contamination from skeletal muscle or the gall bladder were excluded and no voxels were discarded based on (low) signal‐to‐noise ratio (SNR). Repeatability for most quantified 31P metabolite levels in the liver was good to excellent, with an intrasubject variability below 10%. PCA‐based denoising increased the SNR ~ 3‐fold, but did not improve the repeatability for mean liver 31P metabolite quantification with the fitting constraints used. Significant spatial heterogeneity of various 31P metabolite levels within the liver was observed, with marked differences for the phosphomonoester and phosphodiester metabolites between the left and right lobe. In conclusion, using a 31P whole‐body transmit coil in combination with a 16‐channel 31P receive array at 7 T allowed 31P MRSI acquisitions with full liver coverage and good to excellent repeatability.
Hybrid classroom approach: Virtual and live field data integration
Authors: Schlosser William E. , Aumell Aidan J. , Kilkenny Madison M. 
Published: November 2022

AbstractUniversity students stepped into synchronous remote learning as the COVID‐19 pandemic arrived in spring 2020. Temporal limitations of the live field trip‐based paradigm in teaching ecology have been complicated by remote learning. To meet the challenge, we have designed and deployed an immersive virtual ecology platform with associated field explorations and data analysis using a 360° video apparatus and software applications augmented with wildlife camera trap videos and aerial drone images at Washington State University in an undergraduate Natural Resource Ecology class. Throughout four semesters (fall 2020 to spring 2022) of synchronous online instruction, students have been using the virtual platform simulating the natural landscape at Kamiak Butte County Park. The immersive virtual ecology platform provides students with (1) access to broader learning comprehension of abiotic and biotic landscapes; (2) ability to collect and analyze tree data to describe differential woodland site characteristics; (3) visualization of native wildlife habitat uses; and (4) guidance through virtual field instruction tasks. With softening of pandemic restrictions in 2022, we designed the immersive virtual ecology platform in conjunction with in‐person learning. This proof‐of‐concept article briefly summarizes the steps followed in creating this blended delivery curriculum using a hybrid field trip experience in combining virtual data discovery with live field trips and classroom events.
A spectral collocation method for a nonlinear multidimensional Volterra integral equation
Authors: Zheng Weishan , Chen Yanping 
Published: November 2022

AbstractIn this paper, a spectral method is implemented to solve a nonlinear multidimensional Volterra integral equation. Under some condition, the spectral convergence analysis of the solution is investigated in both norm and norm. The results of a numerical example confirm the theoretical prediction.
Recasting the “middle man:” the role of charities in the age of disintermediation
Authors: Niles Meredith 
Published: November 2022

AbstractThe number of people donating to charities is declining at a time when technology is enabling a proliferation of new ways of offering support—from crowdfunding to hacking commercial platforms like AirBnB for novel purposes. There is a risk that charities could lose their traditional position as a trusted intermediary between individuals who want to help and people who need their support. This paper offers some suggestions as to why disintermediated giving may, in some situations, offer a more attractive donor experience than traditional charitable giving and suggests some possible areas for further study.
Self‐compassion and fear of cancer recurrence in Chinese breast cancer patients: The mediating role of maladaptive cognitive styles
Authors: Zhu Lei , Wei Liuyu , Xiaomin Yang , Zhao Jiang , Yu Yunlei , Sun Shasha , Wang Xiao , Yao Juntao , Xie Juan 
Published: November 2022

AbstractObjectivesPrevious studies have examined the benefits of self‐compassion for psychological symptoms in breast cancer patients; however, little is known about the role of self‐compassion for patients' fear of cancer recurrence (FCR) as well as the underlying mediating mechanisms. This study aimed to examine the effect of self‐compassion on FCR, and whether maladaptive cognitive styles mediate this relationship.MethodsThis cross‐sectional study included 304 females with breast cancer. A self‐report questionnaire was used to assess patients' self‐compassion, maladaptive cognitive styles (i.e., rumination and catastrophising), and FCR. Parallel mediation analyses were conducted to examine the research questions.ResultsApproximately half of the patients with breast cancer reported elevated levels of FCR. Self‐compassion was negatively related to FCR, and the relationship between self‐compassion and FCR was mediated by catastrophising, whereas rumination did not significantly mediate the relationship between self‐compassion and FCR.ConclusionsOur findings suggest that self‐compassion and catastrophising are closely associated with FCR in patients with breast cancer, and catastrophising is a mediator between self‐compassion and FCR. Clinicians could reduce breast cancer patients' FCR by enhancing their self‐compassion and improving their maladaptive cognitive styles.
Self‐enhanced plasma brush for therapeutic use in dermatology and its safety assessment
Authors: Zhao Yajun , Yang Jiaqi , Liu Yuzhuang , Liu Zhiyan , Zhang Li , Cui Xinglei , Jin Shanshan , Liu Feng , Fang Zhi 
Published: November 2022

AbstractIn this study, a self‐enhanced plasma brush based on a single dielectric barrier discharge was developed by optimizing the load position for treatment in dermatology. The electrical, optical, and temperature characteristics of the fabricated reactor were investigated. Results show that the length and the width of the plasma brush could reach 20 and 15 mm, respectively. The electric field and the resulting discharge between the reactor and the human impedance model were enhanced and the discharging resistance decreased when introducing a human load. The plume temperature is around 24°C and the current flowing through the human model is lower than the threshold of perception current, which is friendly for human contact. The sterilization effects are verified by killing Escherichia coli.
Improving electrochemical sensitivity of screen‐printed carbon electrodes by atmospheric pressure plasma jet treatment and electrochemical detection of dopamine
Authors: Barman Kalyani , Luhar Sunil , Rane Ramkrishna , Srivastava Divesh N. , Nema Sudhir K. , Bhattacharjee Sudeep 
Published: November 2022

AbstractScreen‐printed carbon electrode (SPCE) has various crucial applications as biosensors. The binders used in the ink formulation of the SPCEs reduce the electron transfer from an electrolyte to it. In this study, we used an argon atmospheric pressure plasma jet (APPJ), a cost‐effective device, to modify the SPCE surface. Scanning electron microscopy and X‐ray photoelectron spectroscopy results show binder removal and oxidation of the APPJ‐treated SPCE surface. Due to binder removal and exposure of graphitic surface, the hydrophilicity of the APPJ‐treated SPCE surface enhances significantly. Moreover, oxidation makes the electrode more suitable for dopamine adsorption, resulting in enhanced sensitivity. The detection limit for the APPJ‐treated electrode is 1 μm, as observed in this study. Cyclic voltammetry, linear sweep voltammetry, and electrochemical impedance spectroscopy have been performed on APPJ‐treated and untreated SPCEs.