bluuebunny/crossref_metadata_2025_split huggingface dataset:
Noninvasive Intracranial Pressure Waveforms for Estimation of Intracranial Hypertension and Outcome Prediction in Acute Brain-Injured Patients
Authors: Brasil Sérgio , Frigieri Gustavo , Taccone Fabio Silvio , Robba Chiara , Solla Davi Jorge Fontoura , Nogueira Ricardo Carvalho , Yoshikawa Marcia Harumy , Teixeira Manoel Jacobsen , Malbouisson Luiz Marcelo Sá , Paiva Wellingson Silva 
Published: August 2022

Abstract
Background: Analysis of intracranial pressure waveforms (ICPW) provides information on intracranial compliance. We aimed to assess the correlation between noninvasive ICPW (NICPW) and invasively measured intracranial pressure (ICP) and to assess the NICPW prognostic value in this population. Methods: In this cohort, acute brain-injured patients were included within 5 days from admission in six Intensive Care Units. ICP values and the P2/P1 ratio derived from NICPW were analyzed and correlated with outcome, which was defined as: a) early death (ED); survivors on spontaneous breathing (SB) or survivors on mechanical ventilation (MV) at 7 days from inclusion. The target sample size was calculated for the primary objective of evaluating the NICPW accuracy to discriminate outcomes. Results: A total of 72 patients were included (mean age 39, 68% TBI). Mean ICP and P2/P1 values were significantly correlated (r=0.49, p<0.001). P2/P1 was significantly higher in patients with intracranial hypertension and had an area under the receiving operator curve (AUROC) to predict intracranial hypertension of 0.88 (95% CI 0.78-0.98). ICP and P2/P1 ratio was also significantly higher in non-survivors (n=10) than survivors. The AUROC of P2/P1 to predict early mortality was 0.71 [95% CI 0.53-0.87], and the threshold P2/P1 > 1.2 showed a sensitivity of 60% [95% CI 31-83%] and a specificity of 69% [95% CI 57-79%]. Similar results were observed when decompressive craniectomy patients were excluded. Conclusions: In this study, P2/P1 derived from noninvasive ICPW assessment, was well correlated with ICP and was higher in non-survivors compared to survivors. Trial registration: NCT03144219, Registered 01 May 2017 Retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT03144219
Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion
Authors: Lu Zhihao , Zhang Zhongfu , Zhang Xiaojun , Yang Wanneng , Zhu Longfu , Huang Chenglong 
Published: August 2022

Abstract
Background
Verticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labour-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the full spectrum and feature band spectrum were used as comparison for SVM and BPNN classification respectively.
Result
The results showed that the average accuracy of EfficientNet network for cotton verticillium wilt identification was 93.00%. By full spectrum, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.57%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.80%.
Conclusions
The study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.
ARRB1 reverses 5-FU-induced chemotherapeutic intestinal mucosal injury by protecting intestinal barrier and stem cell function
Authors: zhang quan , Zhao Qinying , Ye Yingquan , Liu Li , Zhang Mei 
Published: August 2022

Abstract
Chemotherapy-induced intestinal mucosal injury(CIMI)is a common side effect of chemotherapy, and its mechanism is complex. Previous studies have demonstrated that Armillariella tabescens polysaccharides (ATPS) exert intestinal mucosal protective effects by upregulating tight junction protein expression and reducing apoptosis of intestinal epithelial cells, and the expression of β-arrestin 1 (ARRB 1) is altered during this process. The aim of this study was to investigate the role of ARRB 1 in ATPS protection against chemotherapeutic intestinal mucosal injury induced by 5-fluorouracil (5-FU). A CIMI model was established by intraperitoneal injection of 5-FU (50 mg/kg), and ATPS (200 mg/kg) was administered by gavage once a day for treatment. Body weight changes of mice were monitored. The gross appearance of intestinal tissues was observed, and hematoxylin and eosin staining was performed for histological analysis and histological activity score. Immunohistochemistry was performed to detect the expression of tight junction ( TJ ) protein markers. Organoid cultures were performed to detect the status of intestinal stem cells. The results showed that ATPS ameliorated 5-FU-induced intestinal inflammation in WT mice by increasing body weight, reducing histopathological damage, and increasing intestinal tight junction protein expression, and organoid formation rate. However, the alleviating effect of ATPS on CIMI was not significant in ARRB1-KO mice. Therefore, we suggest that ARRB1 is involved in the repair of CIMI damage by ATPS, and the mechanism may be related to the regulation of small intestinal tissue TJ protein production by ARRB1 to protect intestinal stem cells from chemotherapeutic damage.
Impact on the soil microbiota of marine sediment content as an agricultural substrate: metagenomic 16S rRNA analyzes
Authors: Núñez-Gómez Dámaris , Melgarejo Pablo , Martínez-Nicolás Juan José , Hernández Francisca , Martínez-Font Rafael , Lidón Vicente , Legua Pilar 
Published: August 2022

Abstract
Background: The soil microorganisms play a fundamental role in maintaining the key functions of these ecosystems, with emphasis on agricultural soils, since they present a direct impact on the plant development and other metabolic systems as the degradation of organic matter, availability of microelements and/or metabolites, among others. In this context, the present study aimed to identify, characterise and quantify the microbial communities of four types of soils with different proportions of port sediment used to cultivate lemons.
Results: The composition and structure of the sampled microbial communities were assessed through the amplification and sequencing of the V3-V4 variable regions of the 16S rRNA gene. The results were processed with microbiome bioinformatics platform QIIME2 and an open-source software package, Dada2. A total of 41 phylum, 113 classes, 266 orders, 405 families, 715 genera and 1513 species were identified in all the soil samples, where Proteobacteria, Bacteriodota, Planctomycetota, Patescibacteria, Chloroflexi, Actinobacteriota, Acidobacteriota, Verrucomicrobiota and Gemmatimonadota, represented more than 90% of the identified bacterial reads.
Conclusions: The impact of the substrate origin on the diversity, qualitatively and quantitatively, of the soil microbiota was confirmed. The higher content of beneficial bacterial communities for plant development identified in peat could explain why is considered an ideal agricultural substrate. Development of "beneficial for plants" bacterial communities in alternative agricultural substrates, regardless of the edaphic characteristics, opens the possibility of studying the forced and specific inoculation of these culture media aiming to be agriculturally ideals.
Bayesian Variable Selection for Mixture Process VariableDesign Experiment
Authors: Aljeddani Sadiah 
Published: August 2022

Abstract
This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian variable selection is easy to implement due to the improvement in computing via MCMC sampling. We described the Bayesian methodology by introducing the Bayesian framework, and explaining Markov Chain Monte Carlo (MCMC) sampling. The Metropolis-Hastings within Gibbs sampling was used to draw dependent samples from the full conditional distributions which were explained. In mixture experiments with process variables, the response depends not only on the proportions of the mixture components but also on the effects of the process variables. In many such mixture-process variable experiments, constraints such as time or cost prohibit the selection of treatments completely at random. In these situations, restrictions on the randomisation force the level combinations of one group of factors to be fixed and the combinations of the other group of factors are run. Then a new level of the first-factor group is set and combinations of the other factors are run. We discussed the computational algorithm for the Stochastic Search Variable Selection (SSVS) in linear mixed models. We extended the computational algorithm of SSVS to fit models from split-plot mixture design by introducing the algorithm of the Stochastic Search Variable Selection for Split-plot Design (SSVS-SPD). The motivation of this extension is that we have two different levels of the experimental units one for the whole plots and the other for subplots in the split-plot mixture design.
The effects of adipose-derived stem cell transplantation on tendon-bone healing in a model of chronic rotator cuff tear with suprascapular nerve injury
Authors: Eshima Kenichiro , Ohzono Hiroki , Gotoh Masafumi , Nakamura Hidehiro , Shimokobe Hisao , Tanaka Suguru , Kanazawa Tomonosin , Okawa Takahiro , Shiba Naoto 
Published: August 2022

Abstract
Background
Recurrence of massive rotator cuff tears (RCTs) are relatively common due to resultant muscle atrophy and fatty infiltration, possibly caused by scapular nerve (SN) injury. Growing evidence suggests that adipose-derived stem cells (ADSCs) are useful tools for regeneration in chronic RCT models. However, data on the effects of ADSCs on in chronic RCT models with SNI are limited. This study aimed to investigate the effect of ADSCs on tendon-bone healing in a chronic rotator cuff tear (RCT) model with scapular nerve (SN) injury.
Methods
Overall, 36 adult rats (weight = 344 ± 33 g) were equally divided into the ADSC (+) and ADSC (-) group. Both groups underwent right shoulder surgery in which the supraspinatus was detached, and SN injury was induced; he untreated left shoulder in the ADSC (-) group was used as a control. In the ADSC (+) group, abdominal fat was collected to culture ADSCs. At 6 weeks postoperatively, the ADSC (+) group underwent surgical tendon repair with ADSC injection, whereas the ADSC (-) group underwent tendon repair with saline injection. Shoulders were harvested at 10, 14, and 18 weeks and underwent histological, fluorescent, and biomechanical analyses.
Results
In bone-tendon junction, a firm enthesis, including dense mature fibrocartilage and well-aligned cells, was observed in the ADSC (+) group. Conversely, decreased cell density and immature fibrocartilage were observed in the ADSC (-) group. Fatty infiltration was more uncommon in the ADSC (+) group compared to that in the ADSC (-) group. The mean maximum stress and linear stiffness was higher in the ADSC (+) group than in the ADSC (-) group at 18 weeks. Neither the mean Young’s modulus nor the cross-sectional area were significantly different throughout the study.
Conclusion
ADSC injection produced better histological and biomechanical outcomes. Thus, ADSC supplementation showed a positive effect on tendon-bone healing in a chronic RCT model with SN injury; therefore, ADSC injection possibly accelerates recovery in large RCT repair.
Research on the improvement method of social recommendation model based on efficient non-sampling
Authors: Li Yuqiang , Zhou Zhiwei , Liu Chun , Zhan Zhilong 
Published: August 2022

Abstract
With the rapid development of social networks, social recommendation systems based on graph neural networks have received widespread attention. Most of them usually adopt the strategy of random negative sampling to train the models. Although this training method has low time complexity, the recommendation performance of the models obtained after training is usually less stable. To this end, we propose an efficient non-sampling strategy to replace the random negative sampling strategy in order to speed up the training of the model and improve the recommendation accuracy of the model. First, we take DiffNet as an example, change its random negative sampling strategy to an efficient non-sampling strategy, and call the improved model NS-DiffNet. Meanwhile, it is verified through multiple sets of experiments that the recommendation accuracy of NS-DiffNet improves nearly 17% compared to DiffNet on the Yelp dataset and nearly 21% on the Flickr dataset, while the training speed of NS-DiffNet improves nearly 23% compared to DiffNet on the yelp dataset. In addition, NS-DiffNet shows significant improvement in recommendation accuracy and training speed compared to other social recommendation models. Finally, in order to verify the generalizability of the improved method proposed in this paper, we change the training method of multiple recommendation models from the original random negative sampling strategy to an efficient non-sampling strategy. The comparison experimental results show that the improved model has improved in both recommendation accuracy and training speed compared with the original model, which also indicates that the improved idea proposed in this paper has good generalizability.
Low and Mid-rise hybrid CLT Infilled Steel Moment Resisting Frames Displacement Based Design and application advantages
Authors: mirzazadah ainullah , Sabbagh-Yazdi Saeed-Reza 
Published: August 2022

Abstract
Recently, a novel hybrid structure has been developed as an alternative lateral-load resisting system at The University of British Columbia. The hybrid structure includes Cross Laminated Timber (CLT) shear panels as an infill in steel moment-resisting frames (SMRFs). To increase the suitability of the suggested system, first in this paper types of hybridization for timber-steel structures are discussed. In the second step, an iterative design method is expanded and formulated for a hybrid timber-steel structure. The iterative design procedure comprises the following primary modeling variables: the gap between CLT panel and steel frame, bracket spacing, panel thickness and strength, and post-yield stiffness ratio. Later, the design displacement profile is developed by allocating primary relative strength between the CLT wall and frame component. This profile is then applied to acquire the distinctive of an equivalent single degree of freedom (SDOF) system. A mathematical term for the system ductility is formulated based on the proportions of the overturning moment resistance of the CLT wall and SMRF. A determined equivalent viscous damping-ductility relationship is applied to get the energy dissipation of the equivalent SDOF system. In the third study, A new reiterative direct displacement-based design method SMRFs with CLT-infill walls has been expanded and analytically confirmed by designing 3-, 6-, and 9-story hybrid buildings primary shear proportions between the wall and frame are allocated at the start of the design process. The system ductility and equivalent viscous damping are clearly accounted.
Leveraging state-of-the-art engines for large-scale data analysis in High Energy Physics
Authors: Padulano Vincenzo Eduardo , Kabadzhov Ivan Donchev , Saavedra Enric Tejedor , Alonso-Jordá Pedro 
Published: August 2022

Abstract
The Large Hadron Collider (LHC) at CERN has generated a vast amount of information from physics events, reaching peaks of TB of data per day which are then sent to large storage facilities. Traditionally, data processing workflows in the High Energy Physics (HEP) field have leveraged grid computing resources. In this context, users have been responsible for manually parallelising the analysis, sending tasks to computing nodes and aggregating the partial results. Analysis environments in this field have had a common building block in the ROOT software framework. This is the de facto standard tool for storing, processing and visualising HEP data. ROOT offers a modern analysis tool called RDataFrame, which can parallelise computations from a single machine to a distributed cluster while hiding most of the scheduling and result aggregation complexity from users. This is currently done by leveraging Apache Spark as the distributed execution engine, but other alternatives are being explored by HEP research groups. Notably, Dask has rapidly gained popularity thanks to its ability to interface with batch queuing systems, widespread in HEP grid computing facilities. Furthermore, future upgrades of the LHC are expected to bring a dramatic increase in data volumes. This paper presents a novel implementation of the Dask backend for the distributed RDataFrame tool in order to address the aforementioned future trends. The scalability of the tool with both the new backend and the already available Spark backend is demonstrated for the first time on more than two thousand cores, testing a real HEP analysis.
Fabrication of DTC-g-PE-DA@GO@PU chelator for extraction of heavy metals from e-waste
Authors: Jalili Mahrokh , Mokhtari Mehdi , Teimouri Fahimeh , Aali Rahim , Abedi Mohammad , Salmani Mohammad Hosein 
Published: August 2022

Abstract
In this study, mill polyurethane was functionalized for use in the sequential recovery of precious and toxic metals from electronic waste as a solution containing a mixture of metals and elements. For this purpose, graphene oxide (GO), polyethylene imine-dopamine, and dithiocarbamate were used to create functional groups on the outer surface of the Millipolyurethane. Scanning Electron Microscope (SEM) was used to evaluate the morphology and activation of Millipolyurethane sites and to examine the characteristics of the cavities after exposure to acidic solutions during functionalization steps. Raw and functional Millipolyurethane functional groups were evaluated using FOURIER TRANSFORM INFRARED SPECTROMETER (FTIR). The results showed that the cavities in functionalized Millipolyurethane were 100 to 200 μm in size and the changes in the surface to volume ratio were appropriate. Also, the N-H bond was related to the first type of amine in 3297.26 cm-1, and double bond C = N in 1640.5 cm-1 was specified. Due to double bond existence and its activation, this factor can bond with other elements, including metals. Sulfonic bonds (S = O) at 1337 cm-1 indicate the presence of sulfur bases combined with alkyl and aryl, which can bond with OH, which eventually converts to sulfonic acid. Under these conditions, the exchange of metal ions by the sulfonic base is also expected. Given the presence of such bands in the FTIR spectrum obtained from raw PU, it can be concluded that PU functionalization has been performed properly. Metals can bind to existing functional groups that form the solid-metal phase complex (MS), which is very effective in removing it from the aqueous solution, and the process of metal recovery and complex formation with them is maintained to the desired level. Open cavities and high porosity, especially in functionalized PU samples, are well illustrated by electron imagery, which provides a high level of metal recovery.
Comprehensive analysis to long non-coding RNA-mediated high expression of GNG5 correlates with better prognosis and tumor immune infiltration of colon carcinoma
Authors: Zhao Bo , Wang Han , Tang Weizhong 
Published: August 2022

Abstract
Background
Colorectal cancer is the third most common cancer and the fourth leading cause of cancer deaths, and prognosis is poor and the majority of patients are diagnosed with locally advanced or metastatic disease.Increasing evidence suggests GNG5 play key roles in several types of human cancer, The key gating mechanisms in colorectal cancer remails unkown.
Methods
In this study, Pan-cancer analyses have been performed for GNG5’s expressionon and prognosis using The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) data and found that GNG5 might be activated oncogenes in CRC.Noncoding RNAs (ncRNAs) play increasingly appreciated gene-regulatory roles and long noncoding RNAs (lncRNAs) contributing to GNG5 overexpression were identified by a combination in silico computational analyses, we determine candidate regulators controlling colon carcinoma survival analysis and correlation analysis.
Results
The SNHG4/DRAIC-let-7c-5p axis was identifiedas the most potential upstream lncRNA-related pathway of GNG5 in CRC.GNG5 level was significantly positively correlated with tumor immune cell infiltration, immune cell biomarkers and immune checkpoint expression.
Conclusions
Our findings elucidated that lncRNAs-mediated downregulation of GNG5 correlated with better prognosis and tumor immune infiltration in colorectal cancer.