bluuebunny/crossref_metadata_2025_split huggingface dataset:
The whole is greater than its parts: ensembling improves protein contact prediction
Authors: Billings Wendy M. , Morris Connor J. , Della Corte Dennis 
Published: April 2021

AbstractThe prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, can increase the accuracy of protein contact predictions by combining the outputs of different neural network models. We show that ensembling the predictions made by different groups at the recent Critical Assessment of Protein Structure Prediction (CASP13) outperforms all individual groups. Further, we show that contacts derived from the distance predictions of three additional deep neural networks—AlphaFold, trRosetta, and ProSPr—can be substantially improved by ensembling all three networks. We also show that ensembling these recent deep neural networks with the best CASP13 group creates a superior contact prediction tool. Finally, we demonstrate that two ensembled networks can successfully differentiate between the folds of two highly homologous sequences. In order to build further on these findings, we propose the creation of a better protein contact benchmark set and additional open-source contact prediction methods.
Intratympanic steroid therapy for Bell’s palsy with poor prognostic results
Authors: Inagaki Akira , Katsumi Sachiyo , Sekiya Shinji , Murakami Shingo 
Published: April 2021

AbstractIn Bell’s palsy, electrodiagnosis by electroneurography (ENoG) is widely used to predict a patient’s prognosis. The therapeutic options for patients with poor prognostic results remain controversial. Here, we investigated whether early intervention with intratympanic steroid therapy (ITST) is an effective treatment for Bell’s palsy patients with poor electrodiagnostic test results (≤ 10% electroneurography value). Patients in the concurrent ITST group (n = 8) received the standard systemic dose of prednisolone (410 mg total) and intratympanic dexamethasone (16.5 mg total) and those in the control group (n = 21) received systemic prednisolone at the standard dose or higher (average dose, 605 ± 27 mg). A year after onset, the recovery rate was higher in the ITST group than in the control group (88% vs 43%, P = 0.044). The average House-Brackmann grade was better in the concurrent ITST group (1.13 ± 0.13 vs 1.71 ± 0.16, P = 0.035). Concurrent ITST improves the facial nerve outcome in patients with poor electroneurography test results, regardless of whether equivalent or lower glucocorticoid doses were administered. This may be ascribed to a neuroprotective effect of ITST due to a higher dose of steroid reaching the lesion due to dexamethasone transfer in the facial nerve.
Association of common TMPRSS6 and TF gene variants with hepcidin and iron status in healthy rural Gambians
Authors: Jallow Momodou W. , Campino Susana , Prentice Andrew M. , Cerami Carla 
Published: April 2021

AbstractGenome-wide association studies in Europeans and Asians have identified numerous variants in the transmembrane protease serine 6 (TMPRSS6) and transferrin (TF) genes that are associated with changes in iron status. We sought to investigate the effects of common TMPRSS6 and TF gene SNPs on iron status indicators in a cohort of healthy Africans from rural Gambia. We measured iron biomarkers and haematology traits on individuals participating in the Keneba Biobank with genotype data on TMPRSS6 (rs2235321, rs855791, rs4820268, rs2235324, rs2413450 and rs5756506) and TF (rs3811647 and rs1799852), n = 1316. After controlling for inflammation, age and sex, we analysed the effects of carrying either single or multiple iron-lowering alleles on iron status. TMPRSS6 rs2235321 significantly affected plasma hepcidin concentrations (AA genotypes having lower hepcidin levels; F ratio 3.7, P = 0.014) with greater impact in individuals with low haemoglobin or ferritin. No other TMPRSS6 variant affected hepcidin. None of the TMPRSS6 variants nor a TMPRSS6 allele risk score affected other iron biomarkers or haematological traits. TF rs3811647 AA carriers had 21% higher transferrin (F ratio 16.0, P < 0.0001), 24% higher unsaturated iron-binding capacity (F ratio 12.8, P < 0.0001) and 25% lower transferrin saturation (F ratio 4.3, P < 0.0001) compared to GG carriers. TF rs3811647 was strongly associated with transferrin, unsaturated iron-binding capacity (UIBC) and transferrin saturation (TSAT) with a single allele effect of 8–12%. There was no association between either TF SNP and any haematological traits or iron biomarkers. We identified meaningful associations between TMPRSS6 rs2235321 and hepcidin and replicated the previous findings on the effects of TF rs3811647 on transferrin and iron binding capacity. However, the effects are subtle and contribute little to population variance. Further genetic and functional studies, including polymorphisms frequent in Africa populations, are needed to identify markers for genetically stratified approaches to prevention or treatment of iron deficiency anaemia.
Effect of slow-release fertilizer on soil fertility and growth and quality of wintering Chinese chives (Allium tuberm Rottler ex Spreng.) in greenhouses
Authors: Wang Cheng , Lv Jian , Xie Jianming , Yu Jihua , Li Jing , Zhang Jing , Tang Chaonan , Niu Tianhang , Patience Bakpa Emily 
Published: April 2021

AbstractTo avoid the negative impact of excessive fertilization on vegetable production, a decreased fertilization experiment was conducted in a multi-layer covered plastic greenhouse in 2017 to 2018. Treatments included no fertilizer (CK), traditional fertilization (TF), slow-release fertilizers (SRF), and decreased fertilization with slow-release fertilizers (DSRF). Results showed that the SRF and DSRF increased leaf length (13% and 8.3%) and chlorophyll content (7.1% and 8.2%) of Chinese chives compared to TF. Similarly, DSRF was found to increase the accumulation of dry matter accumulation of roots (22%) and the dry matter accumulation of shoots (36%) of Chinese chives. Flavonoid, soluble sugar, and soluble protein content were enhanced by 18%, 8.5%, and 4.6%, respectively, in DSRF compared to TF. Nitrate content of the SRF and SRFR decreased significantly by 26% and 35%, respectively. In addition, there was a significant increase in soil nutrient and enzyme activity in the middle and late harvest of Chinese chives under DSRF compared to TF, and there was a high correlation between soil nutrients and the quality of Chinese chives. The available P and total P content significantly differed among the different greenhouse soil samples, and this significantly affected the quality of Chinese chives. The content of available P and total P in greenhouse soil was 125.07 g kg−1 and 1.26 mg kg−1, respectively. Optimal quality was obtained. Hence, the application of DSRF promoted the growth of Chinese chives and improved soil fertility, thereby enhancing the productivity and quality of Chinese chives.
An association between fibroblast growth factor 21 and cognitive impairment in iron-overload thalassemia
Authors: Theerajangkhaphichai Wasan , Sripetchwandee Jirapas , Sriwichaiin Sirawit , Svasti Saovaros , Chattipakorn Nipon , Tantiworawit Adisak , Chattipakorn Siriporn C. 
Published: April 2021

AbstractAlthough an increased fibroblast growth factor 21 (FGF21) level was related to mild cognitive impairment (MCI) in metabolic syndrome patients, any association regarding FGF21 and MCI in thalassemia patients as well as mechanistic insight are questionable. Therefore, the objectives of this study were: (1) to investigate the prevalence and associative risk factors of MCI in thalassemia patients, (2) to evaluate the association between levels of FGF21 and MCI in thalassemia patients, and (3) to investigate brain FGF21 signaling in iron-overload thalassemia. Thalassemia patients were enrolled onto the study (n = 131). Montreal cognitive assessment (MoCA) was used to determine cognitive performance. Plasma FGF21 level was determined in all patients. Iron-overload β-thalassemic (HT) mice were used to investigate brain FGF21 level and signaling, the expression of synaptic proteins, and Alzheimer’s like pathology. We found that 70% of thalassemia patients developed MCI. FGF21 level was positively correlated with the MCI. Interestingly, brain FGF21 resistance, as indicated by increased brain FGF21 levels with impaired FGF21 signaling, was found in iron-overload HT mice. The reduced synaptic protein expression and increased Alzheimer’s like pathology were also observed. These suggest that FGF21 may play a role in MCI in thalassemia patients.
Central human B cell tolerance manifests with a distinctive cell phenotype and is enforced via CXCR4 signaling in hu-mice
Authors: Alves da Costa Thiago , Peterson Jacob N. , Lang Julie , Shulman Jeremy , Liang Xiayuan , Freed Brian M. , Boackle Susan A. , Lauzurica Pilar , Torres Raul M. , Pelanda Roberta 
Published: April 2021

Significance
A better understanding of how central B cell tolerance operates in human B cells can lead to developing approaches that reduce bone marrow egress of autoreactive B cell clones and their burden in circulation. Using a human immune system mouse model, this study characterizes the phenotype of human autoreactive immature B cells undergoing central tolerance and shows that cells with a similar but more subtle phenotype are present within fresh human bone marrow tissue. Treatment of mice with a pharmacologic antagonist of CXCR4, one of the markers of central B cell tolerance, demonstrates CXCR4 is necessary to prevent autoreactive human and mouse B cell clones from egressing the bone marrow, thus contributing to the establishment of tolerance.
Lepidium latifolium (perennial pepperweed).
Authors: Stutz Sonja , Hinz Hariet , Parker Chris 
Published: April 2021

Abstract

L. latifolium is an erect, branching perennial native to southern Europe and western Asia. It was accidentally introduced into countries outside of its native range as a contaminant of seeds such as Beta vulgaris. L. latifolium exhibits a wide ecological adaptation to different environmental factors, tolerating a range of soil moisture and salinity conditions, which has allowed it to spread explosively in recent years in wetlands and riparian areas especially in the western USA. L. latifolium thrives in many lowland ecosystems and is extremely competitive, forming monospecific stands that can crowd out desirable native species and a number of threatened and endangered species. L. latifolium alters the ecosystem in which it grows, acting as a 'salt pump' which takes salt ions from deep in the soil profile and deposits them near the surface, thereby shifting plant composition and altering diversity.
Cucurbit yellow stunting disorder virus.
Authors: Janssen Dirk 
Published: April 2021

Abstract

Cucurbit yellow stunting disorder virus (CYSDV) is a crinivirus that is non-circulative, semi-persistently transmitted by the whitefly Bemisia tabaci. It multiplies in cucurbitaceous plant species but not inside its insect vector. The main pathways of CYSDV introduction and long-distance spread are through infected plants for planting. Short-distance spread is predominantly through viruliferous (infected) adults of B. tabaci. CYSDV is considered an 'emerging virus' and is increasingly recorded from a number of European, American and Asiatic countries. Infected cucurbit crops display yellowing symptoms on the leaves and this damage leads to an approximate yield reduction of 30-50% in Spain and Lebanon (Célix et al., 1996; Hourani and Abou-Jawdah, 2003). In Arizona, the outbreak of CYSDV in 2006 caused an estimated 60% reduction in marketable melon yield and a subsequent US$ 18 million loss (McGinley, 2008; James, 2011). CYSDV was added to the EPPO A2 List in 2004.
Cymbopogon citratus (lemongrass).
Authors: Rojas-Sandoval Julissa 
Published: April 2021

Abstract

Genetics: The chromosome number reported for C. citratus is 2n = 40 (Flora of China, 2016). Reproductive biologyIn most places Cymbopogon citratus flowers very rarely or not at all. It mostly reproduces vegetatively (Oyen, 1999). Physiology and phenology: In China, C. citratus has been recorded flowering and fruiting during summer (Flora of China, 2016). Longevity: Cymbopogon citratus has a life-span of 4-6 years (Oyen, 1999). Environmental requirements: Cymbopogon citratus grows best under sunny, warm and humid conditions at elevations below 750 m, with mean annual temperatures ranging from 23°C to 30°C and mean annual rainfall between 2500-3000 mm. It is adapted to grow on a wide variety of soils types, but prefers well-drained soils with pH ranging from 5.5 to 7.5. However, this species has been recorded growing in Australia on clay soils with pH=9.6. It does not tolerate saline soils or frosty conditions (Oyen, 1999).
A deep learning-based approach for direct PET attenuation correction using Wasserstein generative adversarial network
Authors: Li Yongchang , Wu Wei 
Published: April 2021

Abstract
Positron emission tomography (PET) in some clinical assistant diagnose demands attenuation correction (AC) and scatter correction (SC) to obtain high-quality imaging, leading to gaining more precise metabolic information in tissue or organs of patient. However, there still are some inevitable issues, such as imperceptible mismatching precision between PET and CT imaging, or plenty of ionizing radiation dose exposure in many after-treatment inspections. To cope with the abovementioned issues, we introduced a deep learning-based technique to achieve a direct attenuation correction for PET imaging in this article. Moreover, wasserstein generative adversarial networks and hybrid loss, including adversarial loss, L2 loss and gradient difference loss, were utilized to enforce the deep network model to synthesize PET images with much richer detail information. A comprehensive research was designed and carried out on a total of forty-five sets of PET images of lymphoma patients for the model training stage and test stage. Final performances analysis was totally based on our experimental outcomes, which demonstrated that the proposed algorithm has definitely improved the quality of PET imaging according to qualitative and quantitative study.
Bone metastasis segmentation based on Improved U-NET algorithm
Authors: Zhang Jingyi , Huang Mengge , Deng Tao , Cao Yongchun , Lin Qiang 
Published: April 2021

Abstract
Whole body bone scan image analysis is widely used in nuclear medicine to assist nuclear medicine physicians in the detection of bone metastases. At present, the analysis of whole-body bone scan images mainly relies on the manual reading of nuclear medicine doctors. The doctors, based on personal knowledge and experience, look for abnormal lesion locations and diagnose them by examining the whole-body bone scan images. However, this method is prone to misdiagnosis and missed diagnosis. To solve the above problems, this study proposes an image segmentation method based on deep learning, which can automatically identify the location of bone metastases, so that doctors can make more accurate diagnosis. The Methods Attention mechanism was added to the jump connection of the original U-NET network to enhance the image feature selection. Experiments show that the algorithm in this study teaches traditional U-Net to show better results on the three indicators of MIoU Dice and MAP.
Credit evaluation with a data mining approach based on gradient boosting decision tree
Authors: Liu Zhenlong , Zhang Yuheng 
Published: April 2021

Abstract
In recent years, credit evaluation has become an issue of increasing concern for financial institutions. However, since most research focuses on the risk classification process, the problem of data imbalance is ignored. In real data sets, there are often far more users with good credit than users with bad credit, and the imbalance of data often easily leads to a decline in the classification performance of the model. Therefore, previous research is very limited in practical application scenarios. In this paper, we establish a new integration method for credit evaluation, which is classified into three steps: First, data preprocessing. Before inputting samples into the model, we take a series of preprocessing steps, such as missing data processing, data dimensionality reduction. Secondly, in view of the imbalance problem, the data is divided into multiple clusters using an unsupervised clustering algorithm, and the SMOTE method is used to generate minority samples in the clusters whose ratio exceeds the threshold. Finally, the GBDT2NN and Factorization Machine methods are integrated to classify the samples. In order to verify the effectiveness of this method, we use the Kaggle competition data set for verification. The results show that this method is better than other algorithms in the field of credit evaluation in terms of recall rate and AUC value.
Research on Ethnic Original Ecological Music Clustering of MFCC Based on HHT
Authors: Fu Qinjian , Lv Danju , Zhang Yan , Xu Haifeng , Yao Wang , Zi Jiali , Liu Jiang 
Published: April 2021

Abstract
In view of the characteristics of ethnic original ecological music and the limitation of human ear’s understanding of hearing. This paper presents a MFCC feature extraction method based on HHT transformation. In this method, the ethnic original ecological music signals were decomposed into several inherent mode functions (IMFs) by EEMD and Hilbert transform, in which the Hilbert marginal spectrum of each IMF was used to complete the feature extraction of HHTMFCC through the MEL scale filter. The experiment collected 13 kinds of original ecological music of ethnic minorities in Yunnan. Based on the feature extraction results of HHTMFCC, Kmeans clustering analysis method was adopted to compare and analyze the clustering effect of HHTMFCC. The experimental results showed that the HHTMFCC feature was 0.49 higher than that of MFCC in the Purity index, 1.2 lower than that of Entropy index, and 3% higher than that of F index. The experimental results show that the HHTMFCC features extracted from ethnic original music are better than the traditional MFCC features.
Research on Weighted Cluster Analysis Method of Panel Data
Authors: Ao Xiqin , Zhang Yiwen , Zhou Yinyin , Xu Deyu 
Published: April 2021

Abstract
Panel data is composed of three dimensions: object, index and time, it has no mature clustering algorithm because of its complexity and dynamic data structure. Two clustering methods for multi index panel data are proposed in this paper. These two methods are respectively weighted from the index perspective and time perspective, and on this basis, the two-step clustering method is used for clustering. Then these methods are applied to cluster analysis of regional economy. The empirical results show that these two methods can effectively complete the clustering of the research objects, and have their own emphasis on the feature extraction angle of the panel data.
Time Series Data Classification on Grassmann Manifold
Authors: Al-Samhi Waheeb , Al-Soswa Mohammed , Al-Dhabi Yunes 
Published: April 2021

Abstract
Time Series Classification has become one of the most challenging problems in many signal processing and machine learning applications, e.g., audio/video signal processing and EEG signal processing. We propose a novel method of extracting features from data for classification and representing data on a Grassmann manifold by parameterizing it using the autoregressive moving model (ARMA). Then we perform classification on these extracted features by training support vector machines (SVM), with appropriate kernels on the Grassmann manifold. We performed tests on several publicly available datasets. We found that an SVM with a proper kernel on the Grassmann manifold consistently performs better than an SVM using a typical Gaussian kernel that acts on the data in Euclidean space. Furthermore, we found that the Grassmann SVM technique overperforms the literature in some high-dimensional datasets, without the need for any other preprocessing techniques. This work demonstrates the power of data-based manifold techniques in improving the performance of existing algorithms.
Research on optimization of deep learning algorithm based on convolutional neural network
Authors: Yiyue Luo , Yu Fan , Xianjun Chen 
Published: April 2021

Abstract
Convolutional neural network is a representative one of deep learning algorithms. It plays an important role in improving the learning level of artificial intelligence. Therefore, this article discusses the optimization of convolutional neural networks and deep learning algorithms. The optimization of deep learning algorithms and its application in actual image recognition are discussed for reference.
Development Route Analysis of Intangible Cultural Heritage Industry of China Based on Data Mining
Authors: Chen Zhengyu , Huang Jiajing , Dai Hao , Liu Jian 
Published: April 2021

Abstract
Industrialization is an important way to protect and inherit intangible cultural heritage, which can enhance the vitality of intangible cultural heritage. The development of intangible cultural heritage industrialization can not only meet the needs of intangible cultural heritage protection and inheritance, but also meet the diverse cultural needs of the people. This paper uses DEA model to analyse the development status of intangible cultural heritage industry of thirty-one provinces and cities of China, discusses the main problems of intangible cultural heritage industry, and gives three suggestions of promoting brand building to enhance market positioning; accelerating creative research and development to promote the value-added of intangible cultural heritage products; innovating business model to promote industrial transformation and upgrading to provide some references for relevant researchers.
Crowdsensing big data: sensing, data selection, and understanding
Authors: Zhai Shuying , Li Ru , Yang Yuange 
Published: April 2021

Abstract
Mobile Crowdsensing (MCS) has become an emerging paradigm for large-scale sensing. It empowers ordinary citizens to contribute data sensed or generated from their mobile devices (e.g., smartphones, wearable devices), aggregates and fuses the data in the cloud for crowd intelligence extraction and human-centric service delivery. The data contributed by the crowd in MCS systems presents the features such as multi-modal, rich-content, spatio-temporal, and human-centric. The key challenges and techniques about crowdsensing big data were discussed. The recent progress of our group in this promising research area was described.