bluuebunny/crossref_metadata_2025_split huggingface dataset:
What is the actual relationship between neutrophil extracellular traps and COVID-19 severity? A longitudinal study
Authors: de Diego Cristina , Lasierra Ana Belen , Lopez-Vergara Lucia , Torralba Laura , de Gopegui Pablo Ruiz , Lahoz Raquel , Abadía Claudia , Godino Javier , Cebollada Alberto , Jimeno Beatriz , Bello Carlota , Tejada Antonio , Bello Salvador 
Published: August 2023

Abstract
Background Neutrophil extracellular traps (NETs), have repeatedly been related to COVID-19 severity and mortality. However, there is no consensus on their quantification, and there are scarce data on their evolution during the disease. We studied circulating NET markers in patients with COVID-19 throughout their hospitalization.Methods We prospectively included 93 patients (201 blood samples), evaluating the disease severity in 3 evolutionary phases (viral, early, and late inflammation). Of these, 72 had 180 samples in various phases. We also evaluated 55 controls with similar age, sex and comorbidities. We measured 4 NET markers: cfDNA, CitH3, and MPO-DNA and NE-DNA complexes; as well as neutrophil-related cytokines IL-8 and G-CSF.Results The COVID-19 group had higher CitH3 (p = 0.022), and cfDNA, MPO-DNA, and NE-DNA (p < 0.001) than the controls throughout hospitalisation. cfDNA was the only NET marker clearly related to severity, and it remained higher in non-survivors during the 3 phases. Only cfDNA was an independent risk factor for mortality and need for intensive care. Neutrophil count, IL-8, and G-CSF were significantly related to severity. MPO-DNA and NE-DNA showed significant correlations in all 3 phases and across all severity grades, and they only remained significantly higher on days 10–16 of evolution in those who died. Correlations among the other NET markers were lower than expected.Conclusions Although NETs were present in patients with COVID-19 throughout hospitalization, their markers, except cfDNA, showed little or no association with severity and mortality. Neutrophil activity and neutrophil count were also associated with severity. MPO-DNA and NE-DNA better reflected NET formation. cfDNA appeared to be more associated with overall tissue damage; previous widespread use of this marker could have overestimated the relationship between NETs and severity. Currently, there are limitations to accurate NET markers measurement that make it difficult to assess its true role in COVID-19 pathogenesis.
First Report of Ageratum yellow vein virus infecting Papaya in Lampung, Indonesia
Authors: Helina Selvi , Akin Hasriadi Mat , Pramono Sudi , Lestari Puji , Nurdin Muhammad , Afandi Auliana , Dewi Lionita 
Published: August 2023

Abstract
Begomovirus attacks on papaya plants have been identified in several regions in Indonesia. Detection based on viral nucleic acid in each category of papaya plants in Tanggamus, Pringsewu, West Lampung, Pesawaran, Central Lampung, and Bandar Lampung using universal primers SPG1/SPG2 showed positive results for Begomovirus infection with visible DNA bands measuring ± 900 bp. Direct nucleotide sequencing using SPG1/SPG2 primers for the AC2 dan AC1 genes of the Begomovirus and confirmed by the BLAST program showed that papaya samples were infected with Ageratum yellow vein virus (AYVV). The phylogenetic results show that AYVV from papaya samples has a close relationship with the AYVV group from several other countries with 98% homology. Overall, our study further indicated Ageratum acts as an alternative host and reservoir for Begomovirus.
Role of imidacloprid and chlorfenapyr nanocapsule in control of hunting billbug, Sphenophorus venatus and white grub, Phyllophaga crinita as principal pests of golf courses
Authors: Hussein Mona Ahmed , Sabry Al-Kazafy Hassan 
Published: August 2023

Abstract
Both hunting billbug, Sphenophorus venatus and white grub, Phyllophaga crinita are considered the most destructive pest for golf courses not only in Egypt but worldwide. This work is considered the first record of P. crinita in Egypt. So, many efforts were carried out to control these pests. In this work nanopesticides formulations were used against these pests; imidacloprid nanocapsule and chlorfenapyr nanocapsule under laboratory conditions. Three concentrations were used for each nanocapsule. Two stages for each pest were treated; the third and sixth instar larvae of S. venatus and the first and third instar larvae of P. crinita. The obtained results showed that the nanocapsule formulations of both imidacloprid and chlorfenapyr were very effective against S. venatus and P. crinita larvae. The LC50 of imidacloprid and chlorfenapyr nanocapsule against the sixth instar larvae of S. venatus were 10.3 and 11.8 ppm, respectively, and 8.8 and 9.1 ppm, respectively, against the third instar larvae. The corresponding results with the third and first instar larvae of P. crinita were 11.2 and 17.2 ppm, and 8.7 and 11.6 ppm, respectively. The results showed also imidacloprid nanocapsule was more effective than chlorfenapyr nanocapsule especially, with the first concentration; the percentages of mortality ranged between 91.7 to 96.7% with imidacloprid nanocapsule compared with 75 to 88.3% with chlorfenapyr nanocapsule. The obtained results found that the nanocapsule formulations were very promising in the control of S. venatus and P. crinita larvae. These formulations can be reduced soil contamination compared with the traditional formulations.
Landscape connectivity for predicting the spread of ASF in the European wild boar population
Authors: Goicolea Teresa , Cisneros-Araújo Pablo , Vega Cecilia Aguilar , Sánchez-Vizcaíno Jose Manuel , Bosch Jaime , Mateo-Sánchez MCruz 
Published: August 2023

Abstract
African swine fever (ASF) is an infectious and highly fatal disease affecting wild and domestic swine, which is unstoppably spreading worldwide. In Europe, wild boars are the main driver of local spread, transmission, and maintenance of the disease in endemic areas, and introduction into ASF-free countries. Landscape connectivity studies are the main discipline to analyze wild-species dispersal networks, and it can be an essential tool to predict dispersal-wild boars’ movement routes and probabilities and therefore the associated potential ASF-spread. We aimed to integrate wild boar connectivity predictions with their occurrence, population abundance, and ASF notifications to calculate the impact (capacity of a landscape feature to favor ASF-spread) and the risk (likelihood of a habitat patch becoming infected) of wild boars’ infection across Europe. Furthermore, we tested the accuracy of the risk of infection by comparing the results with the temporal distribution of ASF cases. Our findings highlighted that the impact and risk factors were generally higher in Europe's central and Eastern regions in the current ASF-situation. Additionally, the impact factor was 31 times higher on habitat patches that were infected the next year, proving the utility of the proposed approach and the key role of wild boars’ movements in ASF-spread.
A Statistical Model for Identifying Collusive Bidding Based on Historical Quotations
Authors: Li Jieni , Guo Hongchao 
Published: August 2023

Abstract
Open bidding is widely used in Sinopec's material procurement due to its advantages of openness, transparency, and cost savings. However, there have been many complaints about collusive bidding in recent years. Collusive bidding will pose significant challenges to fair competition and increase project costs for material procurement. Therefore, there is an urgent need for an analysis model to identify and prevent the collusive bidding. In this paper, we propose a statistical model for identifying collusive bidding using the coefficient of variation cv, the ratio of high to low average quotation rP and the deviation rate of quotation δ as indicators, based on the statistics of the historical quotations for material A. Through a case study of the framework agreement bidding for material B, we identified the collusion behavior of bidders effectively, thereby verifying the applicability of this statistical model.
Association of Time in Range with Cognitive Impairment in Type 2 Diabetic Patients
Authors: Liu Yanting , Liu Yanlan , Qiu Huina , Haghbin Nahal , Li Jingbo , Li Yaoshuang , Jiang Weiran , Xia Longfei , Wu Fan , Lin Chenying , Lin Jingna , Li Chunjun 
Published: August 2023

Abstract
OBJECTIVE
This study investigated the association of Time In Range (TIR) obtained from Blood Glucose Monitoring (BGM) with Cognitive Impairment (CI) inpatients with Type 2 Diabetes Mellitus (T2DM) and further explored whether a TIR goal for T2DM in adults with > 70% possess a protective effect on cognitive function.
RESEARCH DESIGN AND METHODS
A total of 274 inpatients with T2DM aged 40–64 years, who underwent seven-point BGM (120 mins pre and post meals and at bedtime) were recruited in this cross-sectional study. TIR was defined as the percentage of blood glucose within the target range of 3.9-10.0mmol/L. Subjects were divided into Normal Cognitive Function (NCF) (n = 160) and CI (n = 114) groups according to the results of the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). The association of TIR and other glycemic metrics, calculated from seven-point BGM data, with cognitive dysfunction was analyzed.
RESULTS
The prevalence of CI was 41.6% in patients with T2DM (median age 58 years). TIR was lower in CI group than in NCF group (28.6% vs. 42.9%, P = 0.004). The prevalence of CI decreased with ascending tertiles of TIR (p for trend < 0.05). Binary logistic regression analysis showed a significant association between TIR and CI (odds ratio [OR] = 0.84, p < 0.001) after adjusting for confounders (age, education, marital status, age at Diabetes Mellitus (DM) onset, cerebrovascular disease). Further adjustment of Standard Deviation (SD)(OR = 0.84, p = 0.001) or Coefficient of Variation (CV)(OR = 0.83, p < 0.001), TIR was still associated with CI. While a TIR goal of > 70% probably possessed independent protective effect on cognitive function (OR = 0.25, p = 0.001) after controlling for confounders above.
CONCLUSIONS
TIR obtained from BGM was related to CI in T2DM patients and a TIR goal of > 70% probably possessed a protective effect on cognitive function for T2DM adults.
Spray dried VCrO/SiO2 micro-spheroidal catalyst for the ammoxidation of p-chlorotoluene
Authors: Du Chuanqing , Huang Yeyin , Tang Wanjun , Sun Lang , You Qingliang , Li Tingcheng , Xie Guangyong 
Published: August 2023

Abstract
Formulation and shaping of heterogeneous catalysts are vital in the successful industrial application. Here micro-sized vanadium chromium composite oxides catalysts with the spherical shape were prepared via spray drying with colloidal silica as a binder material. The structural properties of catalysts with different Cr/V molar ratios were characterized by XRD, XPS, FT-IR, TPR, and particle size distribution analysis. It was revealed that the addition of Cr inhibited the formation of the crystalline phase V2O5 and decreased the reduction temperature of pentavalent vanadium species, and also resulted in the formation of monoclinic CrVO4 and a highly dispersed state of vanadia species. VCrO/SiO2 particles with various Cr/V atomic ratio were studied as catalysts for p-chlorotoluene ammoxidation to p-chlorobenzonitrile, in which the catalyst with Cr/V ratio of 1 exhibited the best catalytic performance. When the Cr/V ratio was less than 1, mixed phases of orthorhombic CrVO4 and monoclinic Cr2V4O13 were formed and resulted in a low catalytic activity. With the increase of Cr/V ratio, the ratio of V4+/V5+ in the catalysts increased, thus the activation energy required for V reduction decreased, resulting in the catalytic activity of the catalysts improved. However, too large an amount of Cr led to the formation of highly oxidizing hexagonal-Cr2O3 phase, which reduced the selectivity of the catalytic reaction.
Stochastic Variational Inference for GARCH Models
Authors: Xuan Hanwen , Maestrini Luca , Chen Feng , Grazian Clara 
Published: August 2023

Abstract
Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skew-t response GARCH models and fit these using Gaussian variational approximating densities. We implement efficient stochastic gradient ascent procedures based on the use of control variates or the reparameterization trick and demonstrate that the proposed implementations provide a fast and accurate alternative to Markov chain Monte Carlo sampling. Additionally, we present sequential updating versions of our variational algorithms, which are suitable for efficient portfolio construction and dynamic asset allocation.
3D Quantum-Corrected Monte Carlo Device Simulator of n-FinFETs
Authors: Soares Caroline dos Santos , Furtado Gabriela , Rossetto Alan Carlos Junior , Wirth Gilson Inácio , Vasileska Dragica 
Published: August 2023

Abstract
The Effective Potential approach was successfully incorporated as a quantum correction to a Monte Carlo simulator of n-FinFETs to take into account the electron confinement in nanoscale n-FinFETs. The electron line density calculated by the Effective Potential approach agrees very well with the one calculated by a Schrödinger-Poisson solver. We compared the results obtained by the semiclassical and quantum-corrected Monte Carlo simulator. The quantum-corrected Monte Carlo device simulator can predict volume inversion, which reduces the impact of surface roughness scattering, improving the electron velocity. Additionally, the quantum corrections allow the modelling of the reduction of electron density in the n-FinFETs channel. This is a result of the reduced density of states in two-dimensional confined transistors and degrades the on-current in comparison with the one predicted by a semiclassical Monte Carlo simulator.
Direct in Situ Determination and Modulation of Asymmetric Surface Reconstruction on Rutile TiO2
Authors: Wang Yong , Yuan Wentao , Chen Bingwei , Han Zhongkang , You Ruiyang , Jiang Ying , Qi Rui , Li Guanxing , Wu Hanglong , Ganduglia-Pirovano Maria Veronica 
Published: August 2023

Abstract
Rutile TiO2 (110)-(1×2) reconstructions have triggered long time debate due to the conflicts among different experimental observations. Here, by combining state-of-the-art in situ spherical aberration corrected scanning transmission electron microscopy and density functional theory calculations, we unravel the asymmetric surface reconstruction of rutile TiO2 (110)-(1×2). We conclude that the repulsive electrostatic interaction among the small polarons, formed by the excess electrons after removal of neutral oxygen, and the consequent small polaron induced surface relaxations play critical roles in the asymmetric surface reconstruction. Importantly, the emergence and dissolution of these asymmetric structures can be modulated by tuning the oxygen partial pressure. This research offers an innovative approach to better understand, predict, and control surface reconstructions, holding significant implications for various applications and technological advancements.
DPHFM: A Deep Parallel Hybrid Fusion Model for Disaster Tweet Classification on Twitter Data
Authors: DASARI SIVA KRISHNA , Gorla Srinivas , D Prasad Reddy P V G 
Published: August 2023

Abstract
In recent years, disaster tweet classification has garnered significant attention in natural language processing (NLP) due to its potential to aid disaster response and emergency management. The goal of disaster tweet classification is to automate the identification of informative tweets containing information related to various types of disasters, such as floods, earthquakes, wildfires, and more. This classification task plays a crucial role in real-time monitoring, situational awareness, and timely response coordination during emergency situations. In this context, we propose a deep parallel hybrid fusion model (DPHFM) that combines features extracted from Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) as base learners. The extracted features from the base learners are combined using a fusion mechanism, and the resulting features are then reconstructed and supplied to a meta-learner as input for making predictions. The DPHFM is trained on disaster datasets, such as crisisMMD, which consists of seven natural disaster events. The model was thoroughly evaluated using various metrics, demonstrating an average performance improvement of 90–96%. Furthermore, the proposed model's performance surpassed that of other state-of-the-art models, showcasing its potential for disaster tweet classification using deep learning techniques.
In silico secretome prediction and expression analysis of potential effector candidates of fall armyworm (Spodoptera frugiperda)
Authors: Shilpi Sundaram , Choudhary Chhavi , Meghwanshi Keshav Kumar , Verma Vivek , Shukla Jayendra Nath 
Published: August 2023

Abstract
Effector proteins, one of the major insect salivary gland components, alter host defence mechanism(s) and facilitate pests for successful infestation of host plant. Fall armyworm, Spodoptera frugiperdais a polyphagous lepidopteran insect infesting a wide range of agricultural crops. Despite being one of the world’s deadliest pests, no information about the effector proteins of S. frugiperda is available, till date. Therefore, in this study, we have performed an in silico analysis of interproscan-annotated protein sequences of S. frugiperda (derived from its transcriptome) using established secretome prediction pipelines. Out of 21,779protein sequences of S. frugiperda, 821 proteins were predicted to be secretory in nature, leading to the generation of an in silico secretome database of S. frugiperda. The proteins of S. frugiperda secretome were categorised into different functional groups as per their annotated functions. The expression of 40 selected candidates was analysed in different tissues (head, gut, salivary gland and fat body) of S. frugiperda, which revealed 14 candidates to be exclusive to a single tissue. In addition, expression of 13 candidates were found to be exclusive to gut or salivary glands or to both the tissues indicating that they may be secreted out from the insect's body and serve as potential effector proteins. The study described here is the first report of S. frugiperda's secretome prediction which will help in the identification of candidates which are secretory in nature, ultimately opening the door to the identification of possible effector proteins of S. frugiperda.
Topic extraction and categorization using a glove based technique for representing topics
Authors: Scholar Farha Naznin Research , Hazarika Irani , Mahanta Anjana K. 
Published: August 2023

Abstract
Topic extraction and categorization is an important task because by doing that it is easy to find out which are the topics most discussed by the users in their tweets or opinions and need to be analyzed. In this work, topics are extracted from positive and negative opinions and then categorized into different groups. For performing this, first a collection of opinions are divided into two sets- positive opinions and negative opinions by using a sentiment analyzer. Then a method is proposed to find out the most discussed topics in the set of positive opinions and negative opinions. For extracting the topics from a set of opinions the noun words are extracted from the set of the opinions. After extracting the topics, the similar topics have been combined by using synonymy relation. Then the frequent topic words are represented with the help of GloVe embedding technique. Finally, the topics are categorized by using a clustering algorithm by applying it on the frequent topic words. For the evaluation of the proposed method, tweets from a Twitter User dataset are used. The results obtained from the experiments by applying the proposed method on the dataset give promising result and provide interesting and meaningful clusters of topics. Moreover an analysis of the result obtained for both positive and negative opinions is also presented.
Revealing Delay Propagation Patterns and Interactions in the Chinese Air Traffic System Using Bayesian Network Analysis
Authors: Li Qiang , Guan Xinjia , Liu Jinpeng 
Published: August 2023

Abstract

Flight delays in the air traffic system exhibit strong correlations among different airports, making it crucial to comprehend the dynamics of delay propagation for optimal structural design and prevention of cascading delays. In this study, we adopt a Bayesian network (BN) approach, assuming that flight delays in individual airports are influenced by various potential airports. Through the application of complex network analysis, we enhance our understanding of delay propagation characteristics, while employing a BN-based analytical framework to uncover spatial and temporal patterns of delay propagation. Using Chinese air traffic data from 2015 to 2017 as a case study, our findings reveal that each airport's flight delays have an impact on approximately eight external airports, indicating a significant degree of interconnectedness within the air traffic system. Larger airports tend to be affected by delays originating from upstream airports, whereas smaller airports typically influence downstream airports. Additionally, economically developed regions experience broader delay propagation, impacting a larger number of airports compared to less developed regions. These insights provide valuable guidance for the development of delay propagation damping strategies and the implementation of efficient delay management techniques. By understanding the patterns of delay propagation and their spatial-temporal dynamics, stakeholders can take proactive measures to mitigate the impact of delays and optimize overall system performance.
Experimental Analysis of Multiwalled CNT-incorporated Self-compacting Mortar (SCM)
Authors: Raza Ali , Ndiaye Mamadou , Myler Prof. Peter 
Published: August 2023

Abstract

The placement of reinforcement and mortar in narrow sections of a framework is a significant challenge in the construction industry. Self-compacting mortar (SCM), despite its ability to flow under its own weight, exhibits reduced durability, flexure, and compressive strength due to its porous and brittle nature. As the demands for high-rise structures, coastal tunnels, pass bridges, and sustainability increase, the development of high-performance SCM is crucial. Carbon nanotubes (CNTs) are indeed the strongest known materials to date and could be utilized to enhance the functionality and strength of traditional SCM. Therefore, it is imperative to investigate the effect of multiwalled carbon nanotubes (MWCNTs) on the fundamental properties of SCM. In this study, the fresh and mechanical properties of conventional SCM were evaluated with the addition of MWCNTs. Specifically, 0.05%, 0.1%, 0.15%, and 0.5% MWCNTs by mass of cement were incorporated into SCM. The results indicate that the incorporation of MWCNTs has a retarding effect on the workability of fresh SCMs, yet the flow spread values still comply with the EFNARC standard for SCMs without segregation or bleeding. Additionally, the optimal content of MWCNTs was 0.1%, which led to a significant enhancement in flexural and compressive strength of SCMs at 28 days, 54.6% and 61.2%, respectively, compared to the control specimen of SCMs, a breakthrough that could have important implications in the construction industry.