bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Investigation Into the Variant‐Weight Multi‐Model Ensemble Forecasting Technique Based on the Analyses of Model Systematic Errors Calculation and Elimination
Authors: Liang Zhaoming , Wei Xiaomin , Sun Xiaogong 
Published: October 2022

AbstractEliminating the systematic errors of member models is a key step before the variant‐weight multi‐model ensemble forecasting. However, how to reasonably calculate model systematic errors is a problem worthy study. Taking surface temperature as the subject investigated, this study explores this problem through comparative analyses of a series of variant‐weight multi‐model ensemble forecasting experiments. The results showed that eliminating the systematic errors of member models dramatically improves the ensemble forecasting outcomes. The calculation of model systematic errors based on the optimal time lengths regarding the spatiotemporal distribution characteristics of model forecasting errors is a reasonable and effective approach. The surface temperature forecasts with model systematic errors calculated with the optimal time lengths and those beyond the optimal time lengths exhibit higher accuracies. Owing to the approximately uniform distribution of temperature, no significant difference happens between the accuracies of the surface temperature forecasts with model systematic errors calculated using the average and quantile methods. In addition, the updating of the models participating into the ensemble forecasting would weaken the representativeness of the optimal time lengths for model systematic errors calculation that are obtained based on historical period data, and thus, weaken the forecasting skill. The research results provide some train of thought for enhancing the variant‐weight multi‐model ensemble forecasting accuracy.
Estimation of Sea Ice Production in the North Water Polynya Based on Ice Arch Duration in Winter During 2006–2019
Authors: Ren Haiyi , Shokr Mohammed , Li Xinqing , Zhang Zhilun , Hui Fengming , Cheng Xiao 
Published: October 2022

AbstractThe North Water Polynya (NOW) is the largest recurrent Arctic coastal polynya. The formation of the NOW is critically dependent on the development of an ice arch that defines its northern boundary. In this study, high‐resolution ENVISAT Advanced Synthetic Aperture Radar data, Sentinel‐1A data, and Moderate Resolution Imaging Spectroradiometer data were employed to identify the spatio‐temporal characteristics of the ice arch during 2006–2019. Polynya pixels were identified based on the thin ice thickness (TIT), using a threshold of TIT <0.2 m, from which the polynya extent, heat flux, and ice production (IP) were estimated. The results show the different locations of the ice arch in different years, with a mean duration of 132 ± 69 days. The average annual polynya extent over the 14 years is ∼38.8 ± 8 × 103 km2, and we found that it is more closely correlated with wind speed during the winter and air temperature during early spring. The average heat flux drops from about 248 W/m2 in the winter months to about 34 W/m2 in May. The average accumulated IP varies significantly every year, with an average of 144 ± 103 km3, and peak values in March in most years. No apparent interannual trends are shown for the polynya area, heat flux, and IP during 2006–2019. The results also show that IP calculated based on the ice arch data is approximately 25% lower than that obtained by assuming a fixed time, location, and duration for the polynya.
Statistical Study of F‐Region Short Period Ionospheric Disturbances Related to Convection in the Lower Atmosphere Over Wuhan, China
Authors: Yang Zhenlin , Gu Sheng‐Yang , Qin Yusong , Teng Chen‐Ke‐Min , Huang Fuqing , Sun Wenjie , Dou Xiankang 
Published: October 2022

AbstractWe investigated the relationship between convective activity and F‐region short‐period ionospheric disturbances at middle latitudes from 2018 to 2020. The ionospheric variations were extracted from the total electron content (TEC) data obtained by the BeiDou geostationary orbit receiver in Wuhan, China, while the convective activities were represented by precipitation data from the fifth generation European Center for Medium‐Range Weather Forecasts Reanalysis (ERA5). Additionally, an ionosonde located in Wuhan, China, was used to study the variation in the sporadic E (Es)‐layer. Seasonal variations in the convective activity and TEC disturbances were studied. It was found that short‐period ionospheric disturbances occurred mainly around summer midnight and around the June solstice. In addition, the correlations between convective activity and the ionosphere have also been studied. A positive correlation between precipitation and short‐period ionospheric disturbances was found. Similarly, precipitation and also had a positive correlation. By analyzing the precipitation azimuth and upward propagation of convective gravity waves, we proposed a possible coupling mechanism between the ionosphere and the troposphere, through which convective gravity waves could indirectly affect F‐region short‐period ionospheric disturbances by modulating the Es‐layer.
72‐Hour Time Series Forecasting of Hourly Relativistic Electron Fluxes at Geostationary Orbit by Deep Learning
Authors: Son Jihyeon , Moon Yong‐Jae , Shin Seungheon 
Published: October 2022

AbstractIn this study, we forecast hourly relativistic (>2 MeV) electron fluxes at geostationary orbit for the next 72 hr using a deep learning model based on multilayer perceptron. The input data of the model are solar wind parameters (temperature, density and speed), interplanetary magnetic field (|B| and Bz), geomagnetic indices (Kp and Dst), and electron fluxes themselves. All input data are hourly averaged ones for the preceding 72 consecutive hours. We use electron flux data from Geostationary Operational Environmental Satellite (GOES)‐15 and ‐16, and perform a mapping for matching these two data. Total period of the data is from 2011 January to 2021 March (GOES‐15 data for 2011–2017 and GOES‐16 data for 2018–2021). We divide the data into training set (January–August), validation set (September), and test set (October–December) to consider the solar cycle effect. Our main results are as follows. First, our model successfully predicts hourly electron fluxes for the next 72 hr. Second, root‐mean‐square error of our model is from 0.18 (for 1 hr prediction) to 0.68 (for 72 hr prediction), and prediction efficiency is from 0.97 to 0.53, which are much better than those of the previous studies. Third, our model well predicts both diurnal variation and sudden increases of electron fluxes associated with fast solar winds and interplanetary magnetic fields. Our study implies that the deep learning model can be applied to forecasting long‐term sequential space weather events.
Approaches to long-read sequencing in a clinical setting to improve diagnostic rate
Authors: Sanford Kobayashi Erica , Batalov Serge , Wenger Aaron M. , Lambert Christine , Dhillon Harsharan , Hall Richard J. , Baybayan Primo , Ding Yan , Rego Seema , Wigby Kristen , Friedman Jennifer , Hobbs Charlotte , Bainbridge Matthew N. 
Published: October 2022

AbstractOver the past decade, advances in genetic testing, particularly the advent of next-generation sequencing, have led to a paradigm shift in the diagnosis of molecular diseases and disorders. Despite our present collective ability to interrogate more than 90% of the human genome, portions of the genome have eluded us, resulting in stagnation of diagnostic yield with existing methodologies. Here we show how application of a new technology, long-read sequencing, has the potential to improve molecular diagnostic rates. Whole genome sequencing by long reads was able to cover 98% of next-generation sequencing dead zones, which are areas of the genome that are not interpretable by conventional industry-standard short-read sequencing. Through the ability of long-read sequencing to unambiguously call variants in these regions, we discovered an immunodeficiency due to a variant in IKBKG in a subject who had previously received a negative genome sequencing result. Additionally, we demonstrate the ability of long-read sequencing to detect small variants on par with short-read sequencing, its superior performance in identifying structural variants, and thirdly, its capacity to determine genomic methylation defects in native DNA. Though the latter technical abilities have been demonstrated, we demonstrate the clinical application of this technology to successfully identify multiple types of variants using a single test.
Review of surface defect detection of steel products based on machine vision
Authors: Tang Bo , Chen Li , Sun Wei , Lin Zhong‐kang 
Published: October 2022

AbstractSteel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision‐based surface defect inspection for steel products including hardware system, automated vision‐based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre‐processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.
Simulation of Reconstructive Microsurgery in Soft Embalmed Cadavers: A Teaching Module for Plastic Surgery Residents
Authors: Manikumari Baswa , Jaggavarapu Sasidhar Reddy , Subha Kakumanu , Kalpana Tumma , Muni Roopesh Kumar Nambi , Kishve Prajakta , Bheemesh P. 
Published: October 2022

Abstract
Background Cadaveric dissection courses—comprising flap harvesting techniques, vessel dissections, flap transfers to the defect, and microvascular anastomosis—would help residents gain confidence and master these difficult major reconstructive microsurgery procedures. Formalin embalmed bodies lack natural softness and many other features of a live body. Many soft embalming techniques have evolved to mimic live tissue and Theil technique is the most popular one among them. We explored alternate soft embalming options and started using Genelyn.
Materials and Methods Over a span of 2 years (2019–2021), we have conducted three flap dissection workshops using soft-embalmed cadavers. Six soft-embalmed and two formalin-embalmed cadavers were used. Total number of participants was 80.
Results Feedback of experience from the third course participants in the form of grades (1–5) for different criteria was obtained and evaluated. Confidence in the dissection of the various flaps and microsurgery is noticeable in all the participants.
Conclusion Based on our experience, we propose that flap dissection and microsurgery training on soft-embalmed cadavers be included as a teaching module in the plastic surgery postgraduate curriculum.
Nutritional stress and population density influence risk/reward decisions by elk
Authors: Found Rob 
Published: October 2022

Context Optimal foraging strategies can be influenced at the group level by population density, which can increase competition, hunger, and activity levels. Optimal foraging can also be influenced at the individual level by personality, which in part governs how individuals assess risk versus reward. Aims The purpose of this study was to quantify the influence of population density on risk-taking. I focused on captive elk that had previously been studied in tandem with wild elk in order to quantify personality trait characteristics. I hypothesised that risk-taking would be shown to be a density-dependent affect, in addition to varying individually. Methods Automated cameras recorded visitation to four rotating treatments sites in each of high and low density pens, and which had either: forage, a novel object, forage + novel object, or a control site. Novel objects were regularly changed to elicit a spectrum of responses ranging from neofilia to neophobia. Selection or avoidance of sites was determined by comparing elk visitation to visits to control sites. The experiment was done in summer when natural forage was most abundance, and in again in autumn when natural forage was limited. Key results High density elk showed year-round selection for all treatment sites. In summer low density elk showed weak selection for forage and forage + novel and no selection for novel objects, and no selection for any site in autumn. These patterns showed that elk in the high density pen, which were in poorer condition, had to increase their levels of both activity and risk taking. Neither elk pen exhibited any group-wide avoidance of novel objects, indicating a group-wide gradient of neophobia to neofilia, known personality traits in elk. Conclusions These results show that risk-taking behaviour can be a density dependent effect. This increase in neofilia may be driven by hunger stress, but also through increases in competition that may favour elk of certain personalities. Implications Increasing density may increase the prevalence of both neofilia and other personality traits correlated with neofilia, which include aggression, and so may contribute to increasing human–wildlife conflicts in areas where human disturbance has resulted in ungulate hyperabundance.
Binding energies of proton-rich nuclei determined from their mirror pairs*
Authors: Liu 刘 Shuai 帅 , Jia 贾 Hao-Kang 昊康 , Wang 王 Teng-Fei 腾飞 , Qian 钱 Yi-Bin 以斌 
Published: October 2022

Abstract
In addition to the Coulomb displacement energy, the residual differences between the binding energies of mirror nuclei (a pair of nuclei with the same mass number plus interchanged proton and neutron numbers) contribute to the shell effect via the valence scheme in this study. To this end, one linear combining type of valence nucleon number, namely,

, is chosen to tackle this shell correction, in which

and

are the valence proton and neutron numbers with respect to the nearest shell closure, respectively. The mass differences of mirror nuclei, as the sum of the empirical Coulomb displacement energy and shell effect correction, are then used to obtain the binding energies of proton-rich nuclei through the available data of their mirror partners to explore the proton dripline of the nuclear chart.
Anti-Stokes excitation of optically active point defects in semiconductor materials
Authors: Lin Wu-Xi , Wang Jun-Feng , Li Qiang , Zhou Ji-Yang , Xu Jin-Shi , Li Chuan-Feng , Guo Guang-Can 
Published: October 2022

Abstract
Optically addressable point defects in semiconductor materials have been identified as promising single-photon sources and spin qubits in quantum information technologies. The traditional method of exploring the optical and spin properties of these defects is using a laser with a wavelength shorter than the point defects’ zero-phonon-line (ZPL) to Stokes exciting and detecting the Stokes photoluminescence (PL). On the other hand, anti-Stokes excitation with the pumping laser’s wavelength longer than the defects’ ZPL can also be used to investigate their optical and spin properties. The anti-Stokes excitation has shown many advantages and attracted great interest. Here, we provide a brief review of the anti-Stokes excitation of optically active point defects in semiconductor materials. The Stokes and anti-Stokes PL spectra of different point defect systems in semiconductor materials are compared. We then discuss the main mechanisms of the anti-Stokes excitation of different physical systems and conclude that the anti-Stokes excitation of the point defect system in the semiconductor is a single-photon absorption phonon-assisted process. Finally, we summarize some practical applications of anti-Stokes excitation, including laser cooling of semiconductor materials, high-sensitivity quantum thermometry, and enhancement of the readout signal contrast of the point defect spin states. The anti-Stokes excitation of point defects in semiconductors extends the boundary of quantum technologies.
‘It’s just incredible the difference it has made’: family carers’ experiences of a specialist Lewy body dementia Admiral Nurse service
Authors: Brown Laura J E , Aldridge Zena , Pepper Amy , Leroi Iracema , Dening Karen Harrison 
Published: October 2022

Abstract

Background
Lewy body dementia (LBD) is characterised by a complex array of symptoms. Being a family carer of someone with LBD can be challenging, and is associated with high levels of stress, depression and anxiety. Admiral Nursing services support family carers of people with dementia in the UK by providing tailored information, advice and therapeutic support. Recently, the Admiral Nurse model has been adapted to the needs of more specific populations, including the introduction of a new, specialist LBD Admiral Nurse.

Objective
to explore family carers’ experiences of the LBD Admiral Nurse service.

Methods
fourteen family carers of people with LBD were interviewed about their experiences of the LBD Admiral Nurse service. Interview transcripts were analysed using thematic analysis.

Results
four themes were identified: theme 1 described how participants valued the LBD Admiral Nurse’s specialist expertise, which contrasted with the lack of knowledge they encountered in other services. In theme 2, participants described how the practical, tailored support provided by the LBD Admiral Nurse enabled them to better manage a variety of caring-related challenges. Theme 3 captured the emotional benefits that participants gained from being able to talk to someone who understood their situation. In theme 4, participants explained how the reliability of the LBD Admiral Nurse’s support helped them feel supported in their role.

Conclusions
the specialist knowledge and expertise of the LBD Admiral Nurse enabled family carers to better support the people they cared for, and to relieve some of the emotional stress associated with caring.
Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity
Authors: Prasad Bodhayan , Bjourson Anthony J , Shukla Priyank 
Published: October 2022

Abstract
Multimorbidity generally refers to concurrent occurrence of multiple chronic conditions. These patients are inherently at high risk and often lead a poor quality of life due to delayed treatments. With the emergence of personalized medicine and stratified healthcare, there is a need to stratify patients right at the primary care setting. Here we developed multimorbidity analysis pipeline (MulMorPip), which can stratify patients into multimorbid subgroups or endotypes based on their lifetime disease diagnosis and characterize them based on demographic features and underlying disease–disease interaction networks. By implementing MulMorPip on UK Biobank cohort, we report five distinct molecular subclasses or endotypes of multimorbidity. For each patient, we calculated the existence of broad disease classes defined by Charlson's comorbidity classification using the International Classification of Diseases-10 encoding. We then applied multiple correspondence analysis in 77 524 patients from UK Biobank, who had multimorbidity of more than one disease, which resulted in five multimorbid clusters. We further validated these clusters using machine learning and were able to classify 20% model-blind test set patients with an accuracy of 97% and an average Jaccard similarity of 84%. This was followed by demographic characterization and development of interlinking disease network for each cluster to understand disease–disease interactions. Our identified five endotypes of multimorbidity draw attention to dementia, stroke and paralysis as important drivers of multimorbidity stratification. Inclusion of such patient stratification at the primary care setting can help general practitioners to better observe patients’ multiple chronic conditions, their risk stratification and personalization of treatment strategies.
NetTDP: permutation-based true discovery proportions for differential co-expression network analysis
Authors: Cai Menglan , Vesely Anna , Chen Xu , Li Limin , Goeman Jelle J 
Published: October 2022

Abstract
Existing methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network differences. In this work, a novel method, permutation-based Network True Discovery Proportions (NetTDP), is proposed to quantify the number of edges (correlations) or nodes (genes) for which the co-expression networks are different. In the NetTDP method, we propose an edge-level statistic and a node-level statistic, and detect true discoveries of edges and nodes in the sense of differential co-expression network, respectively, by the permutation-based sumSome method. Furthermore, the NetTDP method could further localize the differences by inferring the TDPs for edge or gene subsets of interest, which can be selected post hoc. Our NetTDP method allows inference on data-driven modules or biology-driven gene sets, and remains valid even when these sub-networks are optimized using the same data. Experimental results on both simulation data sets and five real data sets show the effectiveness of the proposed method in inferring the quantification and localization of differential co-expression networks. The R code is available at https://github.com/LiminLi-xjtu/NetTDP.
PPMS: A framework to Profile Primary MicroRNAs from Single-cell RNA-sequencing datasets
Authors: Ji Jiahui , Anwar Maryam , Petretto Enrico , Emanueli Costanza , Srivastava Prashant Kumar 
Published: October 2022

Abstract

Motivation
Single-cell/nuclei RNA-sequencing (scRNA-seq) technologies can simultaneously quantify gene expression in thousands of cells across the genome. However, the majority of the noncoding RNAs, such as microRNAs (miRNAs), cannot currently be profiled at the same scale. MiRNAs are a class of small noncoding RNAs and play an important role in gene regulation. MiRNAs originate from the processing of primary transcripts, known as primary-microRNAs (pri-miRNAs). The pri-miRNA transcripts, independent of their cognate miRNAs, can also function as long noncoding RNAs, code for micropeptides or even interact with DNA, acting like enhancers. Therefore, it is apparent that the significance of scRNA-seq pri-miRNA profiling expands beyond using pri-miRNA as proxies of mature miRNAs. However, there are no computational methods that allow profiling and quantification of pri-miRNAs at the single-cell-type resolution.

Results
We have developed a simple yet effective computational framework to profile pri-MiRNAs from single-cell RNA-sequencing datasets (PPMS). Based on user input, PPMS can profile pri-miRNAs at cell-type resolution. PPMS can be applied to both newly produced and publicly available datasets obtained via single cell or single-nuclei RNA-seq. It allows users to (i) investigate the distribution of pri-miRNAs across cell types and cell states and (ii) establish a relationship between the number of cells/reads sequenced and the detection of pri-miRNAs. Here, to demonstrate its efficacy, we have applied PPMS to publicly available scRNA-seq data generated from (i) individual chambers (ventricles and atria) of the human heart, (ii) human pluripotent stem cells during their differentiation into cardiomyocytes (the heart beating cells) and (iii) hiPSCs-derived cardiomyocytes infected with severe acute respiratory syndrome coronavirus 2.