bluuebunny/crossref_metadata_2025 huggingface dataset:
Smoking associates with increased BAFF and decreased interferon-γ levels in patients with systemic lupus erythematosus
Authors: Raymond Warren David , Hamdorf Matthew , Furfaro Michael , Eilertsen Gro Ostli , Nossent Johannes Cornelis
Published: November 2021

Objective

In SLE, smoking increases the burden of cutaneous disease and organ damage, and leads to premature mortality. However, the effect of smoking on disease manifestations and cytokine levels of patients with SLE is unclear. This study compared characteristics of patients with SLE across smoking status, and determined the association of smoking with serum cytokine levels.

Method

A cross-sectional study of patients with SLE (n=99) during a research visit in which smoking status was ascertained. Smoking status was compared across classification criteria (American College of Rheumatology Classification Criteria for SLE (ACR97)), disease activity (SLE Disease Activity Index), autoantibody levels, accrued damage (Systemic Lupus International Collaborating Clinics/ACR Damage Index), and circulating concentrations of serum interferon-gamma (IFN-γ), interleukin (IL)-1β, IL-4, IL-6, IL-10, IL-12, IL-17, B cell-activating factor (BAFF), tumour necrosis factor-alpha, transforming growth factor beta 1 (TGF-β1), macrophage inflammatory protein 1 alpha (MIP-1α), MIP-1β and monocyte chemoattractant protein 1. Linear regression models determined the association between smoking and cytokine levels, adjusting for age and sex, clinical characteristics (model 1), and anti-inflammatory (IL-4, IL-10 and TGF- β1) and regulatory (IL-1β) cytokines (model 2).

Results

Among patients with SLE (97.9% ANA+; mean 48.48 years old; 86.9% female; mean 10 years of disease duration), 35.4% (n=35 of 99) were smoking (an average of 7 cigarettes/day for 24 years). Smokers had increased odds of prevalent ACR97 malar rash (OR 3.40, 95% CI 1.23 to 9.34) and mucosal ulcers (OR 3.31, 95% CI 1.36 to 8.05). Smokers had more arthritis (OR 3.19, 95% CI 1.19 to 8.60), migraine (OR 2.82, 95% CI 1.07 to 7.44), Raynaud’s phenomenon (OR 5.15, 95% CI 1.95 to 13.56) and increased non-steroidal anti-inflammatory drug use (OR 6.88, 95% CI 1.99 to 23.72). Smoking associated with 27% increased BAFF levels (95% CI 6% to 48%) and 42% decreased IFN-γ levels (95% CI −79% to −5%) in model 2.

Conclusion

In patients with SLE, smoking independently associated with increased BAFF and decreased IFN-γ levels, and an increased frequency of arthritis, migraine and Raynaud’s phenomenon. Smoking cessation is advisable to reduce systemic inflammation, reduce disease activity and improve host defence.
Elk (Cervus elaphus) grazing reduces volunteer soybean density
Authors: Barteaux Emily N. , Lamb Eric G.
Published: November 2021

Management of glyphosate resistant volunteer soybean can be challenging and costly in other glyphosate resistant crops. This study examined the effect of elk (Cervus elaphus) grazing on volunteer soybean (Glycine max) management. Volunteer soybean density was assessed in four fields grazed by elk and three without elk in western Manitoba, Canada. This study determined that elk grazing of soybean stubble significantly reduced soybean volunteer density in the following crop. These results demonstrate that winter elk grazing can be an effective management technique for volunteer soybeans.
Virial series for a system of classical particles interacting through a pair potential with negative minimum
Authors: Procacci Aldo
Published: November 2021

In this paper, we revisit the recent and less recent developments concerning rigorous results on the Mayer series and the virial series of a continuous system of classical particles interacting via a stable and tempered pair potential and we provide new lower bounds for the convergence radius of the virial series when the potential has a strictly positive stability constant. As an application we obtain a new estimate for the convergence radius of the virial series of the Lennard-Jones gas which improves sensibly previous estimates available in the literature.
A virtual finite element method for two-dimensional Maxwell interface problems with a background unfitted mesh
Authors: Cao Shuhao , Chen Long , Guo Ruchi
Published: November 2021

A virtual element method (VEM) with the first-order optimal convergence order is developed for solving two-dimensional Maxwell interface problems on a special class of polygonal meshes that are cut by the interface from a background unfitted mesh. A novel virtual space is introduced on a virtual triangulation of the polygonal mesh satisfying a maximum angle condition, which shares exactly the same degrees of freedom as the usual [Formula: see text]-conforming virtual space. This new virtual space serves as the key to prove that the optimal error bounds of the VEM are independent of high aspect ratio of the possible anisotropic polygonal mesh near the interface.
Error estimates of local energy regularization for the logarithmic Schrödinger equation
Authors: Bao Weizhu , Carles Rémi , Su Chunmei , Tang Qinglin
Published: November 2021

The logarithmic nonlinearity has been used in many partial differential equations (PDEs) for modeling problems in various applications. Due to the singularity of the logarithmic function, it introduces tremendous difficulties in establishing mathematical theories, as well as in designing and analyzing numerical methods for PDEs with such nonlinearity. Here, we take the logarithmic Schrödinger equation (LogSE) as a prototype model. Instead of regularizing [Formula: see text] in the LogSE directly and globally as being done in the literature, we propose a local energy regularization (LER) for the LogSE by first regularizing [Formula: see text] locally near [Formula: see text] with a polynomial approximation in the energy functional of the LogSE and then obtaining an energy regularized logarithmic Schrödinger equation (ERLogSE) via energy variation. Linear convergence is established between the solutions of ERLogSE and LogSE in terms of a small regularization parameter [Formula: see text]. Moreover, the conserved energy of the ERLogSE converges to that of LogSE quadratically, which significantly improves the linear convergence rate of the regularization method in the literature. Error estimates are also presented for solving the ERLogSE by using Lie–Trotter splitting integrators. Numerical results are reported to confirm our error estimates of the LER and of the time-splitting integrators for the ERLogSE. Finally, our results suggest that the LER performs better than regularizing the logarithmic nonlinearity in the LogSE directly.
MODELING AND ANALYSIS OF MACHINING PARAMETERS OF ECAP PROCESSED COMMERCIAL PURE ALUMINUM USING RSM AND ANN
Authors: SURENDARNATH S. , RAMACHANDRAN T. , RAVISANKAR B.
Published: November 2021

Equal channel angular pressing (ECAP) processed materials have higher grain refinement and strength, and they exhibit more surface roughness when they are machined. This enhancement in the properties highly influences the surface roughness and material removal rate of the materials. The commercial pure aluminum has a wide variety of applications when it is enhanced with high strength properties. In this paper, the machinability of commercially pure aluminum processed through ECAP is investigated in turning operations. Different ECAP processes are carried out to study the microstructural characterization and mechanical properties of the material. The material removal rate and surface roughness are tested by performing the turning operation in the CNC lathe with chemical vapor deposited carbide tool such that the feed rate, spindle speed and depth of cut are considered as the machining variables. To create a hypothesis for the experimentation, the empirical models are developed for the objective functions using the statistical technique response surface methodology (RSM) such that the response models are the objective functions and the model variables are the machining parameters. The response models are verified for the adequacy through ANOVA and [Formula: see text]-test, and also verified for the closeness with the experimental results. Artificial neural network (ANN)-based empirical equations are also developed for the objective functions using the RSM design matrix and the results of both the RSM and ANN are compared for the suitability.
Joint Design of Control Chart, Production Cycle Length, and Maintenance Schedule for Imperfect Manufacturing Systems with Deteriorating Products under Stochastic Shift Size
Authors: Salmasnia Ali , Hajihosseini Zahra , Maleki Mohammad Reza
Published: November 2021

This paper presents a hybrid model based on the integration of three interrelated topics including statistical process monitoring (SPM), production cycle length, and maintenance scheduling for imperfect production systems. The proposed model takes both process deterioration and product deterioration into account i.e., the process can go out of control and the products can deteriorate over time. In this regard, a nonuniform sampling strategy is presented for calculating the sampling intervals to avoid exceeding the hazard rate from a threshold value. Moreover, in order to make the model more adapted to real industrial systems, the shift magnitude in the mean parameter of quality characteristic is considered as a stochastic variable. To elaborate the model effectiveness, three comparative studies and a sensitivity analysis are presented. The first comparison shows that by using the variable sampling interval scheme instead of the fixed one, the ratio of in-control time interval to the whole production cycle increases dramatically. Moreover, the use of variable sampling interval scheme leads to a significant cost savings and decreases the out-of-control time interval. The second comparative study indicates that although using statistical considerations in establishment of the control chart imposes a higher cost on the manufacturer, both values of the in-control average run length ([Formula: see text]) and out-of-control average run length ([Formula: see text]) improve significantly. In other words, the use of economic-statistical model reduces the amount of non-conforming products as well as the risk of issuing a false alarm by the control chart. The third comparative study indicates that, in terms of total inventory holding cost, the proposed model performs better than the model without considering deterioration possibility due to the significant reduction in the deterioration cost.
Reducing catastrophic forgetting problem in streaming data by Hybrid Shark Smell with Jaya Optimization-based Deep Neural Networks
Authors: Singh Maisnam Niranjan , Khaiyum Samitha
Published: November 2021

Catastrophic forgetting is a well-known characteristic of diverse parameterized supervised learning models. Artificial Neural Networks (ANN) face severe catastrophic forgetting or catastrophic interference in the sequential learning of information that is intolerable to both engineering applications and the human memory model. This problem can be solved through the machine learning community. The main intent of this paper is to implement a novel continuous learning model that could overcome the problem of catastrophic forgetting. The stream data from diverse benchmark sources are used for experimenting. The proposed model adopts the Deep Neural Network (DNN) with testing weight updates. When new data are given, the hybrid meta-heuristic algorithm helps to update the weight that manages to prevent the learning from catastrophic forgetting. The weight update strategy for solving the catastrophic forgetting is done by the hybridization of Shark Smell Optimization (SSO) and Jaya Algorithm (JA) that is named as Hybrid Shark Smell with Jaya Optimization (HSS-JO). A multi-objective function concerning the parameters associated with catastrophic forgetting like accuracy and remembering is used for framing the proposed continuous learning. The proof of the proposed model over conventional models on three publicly available datasets is given for final validation. From the analysis, in Table 3, the accuracy of the proposed HSS-JO-DNN is 38%, 0.5%, 0.55%, 0.14%, 0.98%, and 0.5% superior to DT, NB, KNN, SVM, NN, and RNN, respectively. For dataset 2, the FNR of the proposed HSS-JO-DNN is 90% superior to DT, 89% improved than NB, KNN, and NN, 94% improved than SVM, and 24% improved than RNN. Similarly, the proposed HSS-JO performance is higher in all the results. Thus, the proposed HSS-JO performs better than all the other traditional methods.
Groundwater Markets in Water Abundant Conditions: Factors Affecting Farmers’ Decision to Sell Water in Assam in North East India
Authors: Tamuli Jitu , Dutta Mrinal Kanti , Kashyap Payel Priya
Published: November 2021

Development of groundwater-based irrigation technology resulted in the emergence of groundwater markets in many parts of India. Due to the localized nature of the markets, it deems important to capture influence of spatial variation in the functioning of the market. This study is carried out to unearth the factors influencing water selling decisions of farmers in water abundant regions of India taking the case of Assam in Eastern India. Using field-level data from two districts of the state and with the help of logit regression analysis, the study examines the determinants of water selling decision of farmers. The results have shown that excess capacity of the pump sets, availability of diesel operated pump sets and conveyance facilities, depth of the boring, caste of the farmer, etc. are some of the significant factors influencing the probability of water selling decision of a farmer. The results, on the whole, indicate that water selling decision of a farmer in water abundant regions is the result of a combined effect of a number of socio-economic, farm specific and tube well specific factors.
Blockchain-based Framework for Reducing Fake or Vicious News Spread on Social Media/Messaging Platforms
Authors: Dhall Sakshi , Dwivedi Ashutosh Dhar , Pal Saibal K. , Srivastava Gautam
Published: November 2021

With social media becoming the most frequently used mode of modern-day communications, the propagation of fake or vicious news through such modes of communication has emerged as a serious problem. The scope of the problem of fake or vicious news may range from rumour-mongering, with intent to defame someone, to manufacturing false opinions/trends impacting elections and stock exchanges to much more alarming and mala fide repercussions of inciting violence by bad actors, especially in sensitive law-and-order situations. Therefore, curbing fake or vicious news and identifying the source of such news to ensure strict accountability is the need of the hour. Researchers have been working in the area of using text analysis, labelling, artificial intelligence, and machine learning techniques for detecting fake news, but identifying the source or originator of such news for accountability is still a big challenge for which no concrete approach exists as of today. Also, there is another common problematic trend on social media whereby targeted vicious content goes viral to mobilize or instigate people with malicious intent to destabilize normalcy in society. In the proposed solution, we treat both problems of fake news and vicious news together. We propose a blockchain and keyed watermarking-based framework for social media/messaging platforms that will allow the integrity of the posted content as well as ensure accountability on the owner/user of the post. Intrinsic properties of blockchain-like transparency and immutability are advantageous for curbing fake or vicious news. After identification of fake or vicious news, its spread will be immediately curbed through backtracking as well as forward tracking. Also, observing transactions on the blockchain, the density and rate of forwarding of a particular original message going beyond a threshold can easily be checked, which could be identified as a possible malicious attempt to spread objectionable content. If the content is deemed dangerous or inappropriate, its spread will be curbed immediately. The use of the Raft consensus algorithm and bloXroute servers is proposed to enhance throughput and network scalability, respectively. Thus, the framework offers a proactive as well as reactive, practically feasible, and effective solution for curtailment of fake or vicious news on social media/messaging platforms. The proposed work is a framework for solving fake or vicious news spread problems on social media; the complete design specifications are beyond scope of the current work and will be addressed in the future.
Cross-lingual Text Reuse Detection Using Translation Plus Monolingual Analysis for English-Urdu Language Pair
Authors: Muneer Iqra , Nawab Rao Muhammad Adeel
Published: November 2021

Cross-Lingual Text Reuse Detection (CLTRD) has recently attracted the attention of the research community due to a large amount of digital text readily available for reuse in multiple languages through online digital repositories. In addition, efficient machine translation systems are freely and readily available to translate text from one language into another, which makes it quite easy to reuse text across languages, and consequently difficult to detect it. In the literature, the most prominent and widely used approach for CLTRD is Translation plus Monolingual Analysis (T+MA). To detect CLTR for English-Urdu language pair, T+MA has been used with lexical approaches, namely, N-gram Overlap, Longest Common Subsequence, and Greedy String Tiling. This clearly shows that T+MA has not been thoroughly explored for the English-Urdu language pair. To fulfill this gap, this study presents an in-depth and detailed comparison of 26 approaches that are based on T+MA. These approaches include semantic similarity approaches (semantic tagger based approaches, WordNet-based approaches), probabilistic approach (Kullback-Leibler distance approach), monolingual word embedding-based approaches siamese recurrent architecture, and monolingual sentence transformer-based approaches for English-Urdu language pair. The evaluation was carried out using the CLEU benchmark corpus, both for the binary and the ternary classification tasks. Our extensive experimentation shows that our proposed approach that is a combination of 26 approaches obtained an
F
1
score of 0.77 and 0.61 for the binary and ternary classification tasks, respectively, and outperformed the previously reported approaches [
41
] (
F
1
= 0.73) for the binary and (
F
1
= 0.55) for the ternary classification tasks) on the CLEU corpus.
A Comprehensive Guideline for Bengali Sentiment Annotation
Authors: Mukta Md. Saddam Hossain , Islam Md. Adnanul , Khan Faisal Ahamed , Hossain Afjal , Razik Shuvanon , Hossain Shazzad , Mahmud Jalal
Published: November 2021

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as
Positive, Negative,
or
Neutral
. Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.
Facile Material Design Concept for Co-Free Lithium Excess Nickel-Manganese Oxide as High-Capacity Positive Electrode Material
Authors: Tabuchi Mitsuharu , Kitta Mitsunori , Yazawa Koji , Kubota Kei
Published: November 2021

High-capacity Li1+x(Ni0.3Mn0.7)1-xO2, (0 < x < 1/3) samples were synthesized by the coprecipitation–calcination method. Both electrochemical cycle and high-rate performances were drastically improved by selecting an N2 atmosphere as final calcination. Scanning transmission electron microscopy—energy dispersive X-ray spectroscopy analysis showed that the sample calcined in an N2 atmosphere had a more homogeneous transition metal distribution into primary particles than that calcined in air. The solid-state 7Li nuclear magnetic resonance data showed that electrochemically inactive domains were only diminished for the sample calcined in an N2 atmosphere after electrochemical activation. X-ray Rietveld analysis revealed that the suitable transition metal distribution and content of the samples were different from those of typical layered rock-salt materials. Only that calcined in an N2 atmosphere had no spinel formation during charging and no oxide ion insertion reaction during discharging. No positive Co substitution effect was observed under the optimized preparation conditions. At the 100th cycle, the discharge capacity was 216 mAh g−1, which corresponds to 87% of the initial capacity (251 mAh g−1) at optimizing synthetic condition.
Comparison of Rhenium and Iodine as Contrast Agents in X-Ray Imaging
Authors: De La Vega José Carlos , Esquinas Pedro Luis , Gill Jovan Kaur , Jessa Selin , Gill Bradford , Thakur Yogesh , Saatchi Katayoun , Häfeli Urs O.
Published: November 2021

Purpose. The majority of X-ray contrast agents (XCA) are made with iodine, but iodine-based XCA (I-XCA) exhibit low contrast in high kVp X-rays due to iodine’s low atomic number (Z = 53) and K-edge (33.1 keV). While rhenium is a transition metal with a high atomic number (Z = 75) and K-edge (71.7 keV), the utilization of rhenium-based XCA (Re-XCA) in X-ray imaging techniques has not been studied in depth. Our study had two objectives: (1) to compare both the image quality and the absorbed dose of I- and Re-XCA and (2) to prepare and image a rhenium-doped scaffold. Procedures. I- and Re-XCA were prepared and imaged from 50 to 120 kVp by Micro-computed tomography (µCT) and digital radiography and from 120 to 220 kVp by planar X-ray imaging. The scans were repeated using 0.1 to 1.6 mm thick copper filters to harden the X-ray beam. A rhenium-doped scaffold was prepared via electrospinning, used to coat catheters, and imaged at 90 kVp by µCT. Results. I-XCA have a greater contrast-to-noise ratio (CNR) at 50 and 80 kVp, but Re-XCA have a greater CNR at >120 kVp. The difference in CNR is increased as the thickness of the copper filters is increased. For instance, the percent CNR improvement of rhenium over iodine is 14.2% with a 0.6 mm thick copper filter, but it is 59.1% with a 1.6 mm thick copper filter, as shown at 120 kVp by µCT. Upon coating them with a rhenium-doped scaffold, the catheters became radiopaque. Conclusions. Using Monte Carlo simulations, we showed that it is possible to reduce the absorbed dose of high kVp X-rays while allowing the acquisition of high-quality images. Furthermore, radiopaque catheters have the potential of enhancing the contrast during catheterizations and helping physicians to place catheters inside patients more rapidly and precisely.
Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis
Authors: Li Yiran , Zhang Hongyan , Shao Jinyan , Chen Jindong , Zhang Tiancheng , Meng Xiaoyan , Zong Ruiqing , Jin Guangzhi , Wu Feixiang
Published: November 2021

Purpose. Sepsis becomes the main death reason in hospitals with rising incidence, causing a growing economic and medical burden. However, the genes related to the pathogenesis and prognosis of sepsis are still unclear, which is a problem that needs to be solved urgently. Materials and Methods. Gene expression profiles of GSE69528 were obtained from the National Center for Biotechnology Information. Limma software package got employed to search for differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for enrichment analysis. Protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes (STRING) database. Results. We screened 101 DEGs, containing 81 upregulated DEGs and 20 downregulated DEGs. GO analysis demonstrated that the upregulated DEGs were chiefly concentrated in negative regulation of response to interferon-gamma and regulation of granulocyte differentiation. KEGG analysis revealed that the pathways of upregulated DEGs were concentrated in prion diseases, complement and coagulation cascades, and Staphylococcus aureus infection. The PPI network constructed by upregulated DEGs contained 67 nodes (proteins) and 110 edges (interactions). Analysis of bioinformatics results showed that CEACAM8, MPO, and RETN were hub genes of sepsis. Conclusion. Our analysis reveals a series of signal pathways and key genes related to the mechanism of sepsis, which are promising biotargets and biomarkers of sepsis.
A Lightweight Hierarchical Model with Frame-Level Joints Adaptive Graph Convolution for Skeleton-Based Action Recognition
Authors: Jiang Yujian , Yang Xue , Liu Jingyu , Zhang Junming
Published: November 2021

In skeleton-based human action recognition methods, human behaviours can be analysed through temporal and spatial changes in the human skeleton. Skeletons are not limited by clothing changes, lighting conditions, or complex backgrounds. This recognition method is robust and has aroused great interest; however, many existing studies used deep-layer networks with large numbers of required parameters to improve the model performance and thus lost the advantage of less computation of skeleton data. It is difficult to deploy previously established models to real-life applications based on low-cost embedded devices. To obtain a model with fewer parameters and a higher accuracy, this study designed a lightweight frame-level joints adaptive graph convolutional network (FLAGCN) model to solve skeleton-based action recognition tasks. Compared with the classical 2s-AGCN model, the new model obtained a higher precision with 1/8 of the parameters and 1/9 of the floating-point operations (FLOPs). Our proposed network characterises three main improvements. First, a previous feature-fusion method replaces the multistream network and reduces the number of required parameters. Second, at the spatial level, two kinds of graph convolution methods capture different aspects of human action information. A frame-level graph convolution constructs a human topological structure for each data frame, whereas an adjacency graph convolution captures the characteristics of the adjacent joints. Third, the model proposed in this study hierarchically extracts different levels of action sequence features, making the model clear and easy to understand; further, it reduces the depth of the model and the number of parameters. A large number of experiments on the NTU RGB + D 60 and 120 data sets show that this method has the advantages of few required parameters, low computational costs, and fast speeds. It also has a simple structure and training process that make it easy to deploy in real-time recognition systems based on low-cost embedded devices.
Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm
Authors: Liu Dianting , Huang Kangzheng , Zhang Chenguang , Wu Danling , Wu Shan
Published: November 2021

According to the needs of scientific research project research and development, the research of cooperative team excavation methods was carried out. Aiming at the current difficulties in accurately and reliably defining and identifying cooperative research teams from co-author network, an improved Louvain algorithm that integrates core node recognition was proposed: Louvain-LSCR algorithm. Based on the analysis of the Louvain algorithm, considering the local topology of the node in the network and the communication range of the node, a new algorithm LSCR for core node identification was constructed. The LSCR algorithm and Louvain algorithm were merged to obtain a new and improved algorithm, Louvain-LSCR. In this algorithm, the leaf nodes in phase 1 of Louvain algorithm were first pruned to reduce calculations; then, seed nodes were selected according to the LSCR algorithm in phase 2. The experimental results on related datasets show that LSCR algorithm has certain advantages in identifying core nodes. The modularity of Louvian-LSCR algorithm is better than other algorithms, and the community structure is more reasonable. It was verified that the algorithm can mine potential cooperative research teams in co-author network.
A Nomogram for Predicting the Residual Back Pain after Percutaneous Vertebroplasty for Osteoporotic Vertebral Compression Fractures
Authors: Li Qiujiang , Shi Lin , Wang Yinbin , Guan Tao , Jiang Xiaocheng , Guo Donggeng , Lv Jinhan , Cai Lijun
Published: November 2021

Objective. Current findings suggest that percutaneous vertebroplasty (PVP) is a suitable therapeutic approach for osteoporotic vertebral compression fractures (OVCFs). However, a significant minority of patients still experience residual back pain after PVP. The present retrospective study was designed to determine the risk factors for residual back pain after PVP and provides a nomogram for predicting the residual back pain after PVP. Methods. We retrospectively reviewed the medical records of patients with single-segment OVCFs who underwent bilateral percutaneous vertebroplasty. Patients were divided into group N and group R according to the postoperative VAS score. Group R is described as the VAS score of residual back pain ≥ 4. Pre- and postoperative factors that may affect back pain relief were evaluated between two groups. Univariate and multivariate logistic regression analysis were performed to identify risk factors affecting residual back pain after PVP. We provided a nomogram for predicting the residual back pain and used the receiver operating characteristic curve (ROC), concordance index (C-index), calibration curve, and decision curve analyses (DCA) to evaluate the prognostic performance. Results. Among 268 patients treated with PVP, 37 (13.81%) patients were classified postoperative residual back pain. The results of the multivariate logistical regression analysis showed that the presence of an intravertebral vacuum cleft (IVC) (OR 3.790,

P
=
0.026

), posterior fascia oedema (OR 3.965,

P
=
0.022

), severe paraspinal muscle degeneration (OR 5.804,

P
=
0.01

; OR 13.767,

P
<
0.001

), and blocky cement distribution (OR 2.225,

P
=
0.041

) were independent risk factors for residual back pain after PVP. The AUC value was 0.780, suggesting that the predictive ability was excellent. The prediction nomogram presented good discrimination, with a C-index of 0.774 (0.696∼0.852) and was validated to be 0.752 through bootstrapping validation. The calibration curve of the nomogram demonstrated a good consistency between the probabilities predicted by the nomogram and the actual probabilities. The nomogram showed net benefits in the range from 0.06 to 0.66 in DCA. Conclusions. The presence of IVC, posterior fascia oedema, blocky cement distribution, and severe paraspinal muscle degeneration were significant risk factors for residual back pain after PVP for OVCFs. Patients with OVCFs after PVP who have these risk factors should be carefully monitored for the possible development of residual back pain. We provide a nomogram for predicting the residual back pain after PVP.
On Computation Degree-Based Topological Descriptors for Planar Octahedron Networks
Authors: Zhen Wang , Ali Parvez , Ali Haidar , Dustigeer Ghulam , Liu Jia-Bao
Published: November 2021

A molecular graph is used to represent a chemical molecule in chemical graph theory, which is a branch of graph theory. A graph is considered to be linked if there is at least one link between its vertices. A topological index is a number that describes a graph’s topology. Cheminformatics is a relatively young discipline that brings together the field of sciences. Cheminformatics helps in establishing QSAR and QSPR models to find the characteristics of the chemical compound. We compute the first and second modified K-Banhatti indices, harmonic K-Banhatti index, symmetric division index, augmented Zagreb index, and inverse sum index and also provide the numerical results.
Detection of MSH2 Gene Methylation in Extramammary Paget’s Disease by Methylation-Sensitive High-Resolution Melting Analysis
Authors: Dong Liu , Zhu Yingfeng , Wu Liting , Zhang Qiaoan , Xu Feng , Zhang Xinju , Xu Xiao , Tang Yiting , Ren Guoqiang , Kang Zhihua , Guan Ming
Published: November 2021

Background. Extramammary Paget’s disease (EMPD) is a rare skin tumor. Hypermethylation in the MSH2 promoter resulting in the downregulation of its protein expression shows a high detection rate in EMPD tumor tissue, which indicates that the methylation of MSH2 may play an important role in the pathogenesis of EMPD. Objective. This study aims to establish a rapid analysis strategy based on the methylation-sensitive high-resolution melting curve (MS-HRM) to detect the methylation level of the MSH2 promoter. Methods. With the use of universal methylated human DNA products, we established the MS-HRM standard curve to quantitatively detect the methylation level of the MSH2 promoter. Then, all 57 EMPD tumor DNA samples were analyzed. Pyrosequencing assay was also carried out to test the accuracy and efficacy of MS-HRM. Besides, a total of 54 human normal and other cancerous tissues were included in this study to test the reliability and versatility of the MS-HRM standard curve. Results. In this study, by using the established MS-HRM, we found that 96.5% (55/57) EMPD tumor samples had varying methylation levels in the MSH2 promoter ranging from 0% to 30%. Then, the methylation data were compared to the results obtained from pyrosequencing, which showed a high correlation between these two techniques by Pearson’s correlation (r = 0.9425) and Bland–Altman plots (mean difference = −0.1069) indicating that the methylation levels analyzed by MS-HRM were consistent with DNA pyrosequencing. Furthermore, in 23 normal and 31 other cancerous tissue samples, there were two colorectal cancer (CRC) tissues that tested MSH2 methylation positive (1% and 5%) which confirmed that our established MS-HRM can be widely applied to various types of samples. Conclusion. MS-HRM standard curve can be used for the detection of the methylation level of MSH2 in EMPD tumor samples and other cancerous tissues potentially, which presents a promising candidate as a quantitative assay to analyze MSH2 promoter methylation in routine pathological procedure.
Seismic Fragility Assessment of RC Plan‐Asymmetric Wall‐Frame Structures Based on the Enhanced Damage Model
Authors: Wang Ning , Huang Xiaoning , Zhang Dan
Published: November 2021

The seismic response of reinforced concrete (RC) plan‐asymmetric structures is significantly influenced by the input angle of seismic ground motions. Hence, it is challenging to assess the performance of plan‐asymmetric structures. In this study, the classic probabilistic seismic fragility assessment method is used to assess RC plan‐asymmetric wall‐frame structures based on the enhanced damage model. First, the worst‐case input angle of seismic ground motions for plan‐asymmetric structures is identified using the wavelet transforms coefficient method, considering the coupling of bidirectional seismic ground motions. Accordingly, the maximum deformation and hysteretic energy dissipation can be determined. Then, an enhanced damage model, which is based on the combination of deformation and hysteretic energy dissipation, is used to identify floor damage factor. Note that the importance coefficients of structural components are considered in the identification. Meanwhile, the incremental dynamic analysis (IDA) is conducted to create the fragility curves by assuming floor damage factor as seismic performance index. In particular, the randomness of the threshold for floor damage factor is considered during the assessment. Afterwards, numerical simulations are employed to verify the fragility assessment method. Results show that the wavelet transforms coefficient method can evaluate the worst‐case input angles with low time‐consuming and high efficiency. Meanwhile, the story damage factors confirmed that the proposed damage model could accurately assess the structure during the failure process. Moreover, the typical failure modes of the RC wall‐frame structure, which significantly depend on the story damage distribution, can be defined using the enhanced damage model. Note that the randomness of the threshold could significantly affect the probability of exceedance, which is important for fragility analysis.
Allelopathic Potential of Green Manure Cover Crops on Germination and Early Seedling Development of Goose Grass [Eleusine indica (L.) Gaertn] and Blackjack (Bidens pilosa L.)
Authors: Rugare Joyful Tatenda , Pieterse Petrus Jacobus , Mabasa Stanford
Published: November 2021

Green manure cover crops (GMCCs), which are recommended for improving soil fertility, also have the potential of reducing weed populations in cropping systems through allelopathy. The objective of this study was to evaluate the effect of eight GMCCs on the germination and seedling development of two weeds of divergent morphology, namely, goose grass [Eleusine indica (L.) Gaertn] and blackjack (Bidens pilosa L.). Aqueous leaf, stem, and root extracts of hyacinth bean (Lablab purpureus L), red sunnhemp [Crotalaria ochroleuca (G.) Don], showy rattlebox (Crotalaria grahamiana Wight & Arn.), common bean (Phaseolus vulgaris L.), common rattlepod (Crotalaria spectabilis Roth.), radish (Raphanus sativus L.), tephrosia (Tephrosia vogelii L.), and black sunnhemp (Crotalaria juncea L.) at 0, 1.25, 2.5, 3.75, and 5% wv−1 were applied to weed seeds in Petri dishes to determine their effect on germination, radicle and plumule growth, and germination vigor index. The experimental design was 3(tissue types) ∗ 5(concentrations) treatment combinations replicated four times in a completely randomized design. In the pot study, 25 seeds of either goose grass or blackjack were planted separately in approximately 400 g of soil mixed with cover crop tissue powder at 1% concentration per pot. The experimental design was cover crop residues + control replicated four times in randomized complete blocks. There was a significant (p<0.05) extract ∗ concentration interaction on all germination parameters across all GMCCs. The different cover crop aqueous extracts differentially reduced all germination parameters of both weeds in the order leaf > stem > root extract except for radish root extracts being most inhibitory to all germination parameters of goose grass. The leaf, stem, and root soil-incorporated residues of GMCCs significantly (p<0.05) affected seedling emergence, dry weight, and vigor indices of both weeds. Based on the results of this study, it was concluded that the different GMCC tissues contain allelochemicals that inhibit the emergence of both monocotyledonous and dicotyledonous weeds.
Undernutrition and Associated Factors among Under-Five Orphan Children in Addis Ababa, Ethiopia, 2020: A Cross-Sectional Study
Authors: Sewnet Selam Shegaw , Derseh Hunegnaw Almaw , Desyibelew Hanna Demelash , Fentahun Netsanet
Published: November 2021

Background. Undernutrition contributes to the death of around 3 million children and threatens the futures of hundreds of millions, undermining healthy development and the strength of their societies by preventing children from achieving their full potential. Orphans are at greater risk of undernutrition because they are more likely to be extremely poor and receive less medical and social care. However, there is little information about the prevalence of undernutrition and associated factors among under-five orphan children. Objective. This study aimed to assess undernutrition and associated factors among under-five orphan children in orphanages in Addis Ababa, Ethiopia. Methods. An institution-based cross-sectional study was conducted in Addis Ababa from February 28 to March 28, 2020. A simple random sampling technique was employed to recruit a total of 275 orphan children. An interviewer-administered questionnaire and anthropometric measurements were used to collect data. Data were entered using EpiData version 3.1 and analysis was done by WHO Anthro version 3.2.2 and SPSS version 23. Multivariable logistic regression analysis was performed to identify determinants of undernutrition at a p value of less than 0.05 with an adjusted odds ratio of 95% confidence interval. Results. The prevalence of wasting, stunting, and underweight were 11.1%, 45.8%, and 25.5%, respectively. Presence of illness (AOR = 2.23; 95% CI: 1.41, 12.73), children who received less than three meals per day (AOR = 2.11; 95% CI: 1.58, 7.71), and children who were not vaccinated (AOR = 2.86; 95% CI: 2.07, 11.61) were significantly associated with stunting. Children who were not vaccinated (AOR = 2.04; 95% CI: 1.29, 9.71) and who had inadequate dietary diversity scores (AOR = 1.32, 95% CI: 1.16, 12.65) were significantly associated with wasting and underweight, respectively. Conclusion. The prevalence of undernutrition was very high compared to national data. Health status, meal frequency, and vaccination status were associated factors of stunting. Vaccination status and dietary diversity score were associated factors with wasting and underweight, respectively. Therefore, improving meal frequency, dietary diversity, and early treatment during childhood illness are important to reduce orphan undernutrition.
Penetration of Multimedia Technology in Piano Teaching and Performance Based on Complex Network
Authors: Niu Yitian
Published: November 2021

Multimedia network teaching, as a modern teaching mode, provides an opportunity for the establishment of new educational methods and creates conditions for the all-round expansion of educational functions. This article mainly studies the penetration analysis of complex network and multimedia technology to piano teaching and performance. This article introduces complex network and multimedia technology into piano teaching cases to classify, analyze, research and evaluate, remove the rough and save the essence, and screen out some typical teaching cases that meet modern learning theories, teaching requirements, and the characteristics of piano discipline. On this basis, carry forward the advantages, correct the disadvantages, and develop and design some network teaching cases for piano teachers to apply and research. Adopt a new teaching model to improve the original single-class teaching model that focuses on teacher lectures. The new teaching mode is supported by multimedia and network technology, especially multimedia technology, which makes piano teaching move towards personalized learning, and use the method of questionnaire survey to conduct questionnaire surveys on students and teachers to investigate the impact of multimedia piano teaching. Experimental data show that, in terms of multimedia attitudes, 29.50% of teachers believe that multimedia technology is very important in piano teaching and performance, 66.10% of teachers believe that multimedia technology is important in piano teaching and performance, and 4.40% of teachers believe that multimedia technology is in piano teaching and more important in performance. The experimental results show that multimedia and network technology have a good auxiliary effect on piano teaching and performance.
Application of Apriori Improvement Algorithm in Asthma Case Data Mining
Authors: Zheng Yi , Chen Peipei , Chen Biyu , Wei Dengjun , Wang Meifang
Published: November 2021

In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (doctors in this case) and (2) symptoms and medications for a particular disease in the hospitals. In this paper, initially, a thorough analysis of expected performance and shortcomings of the Apriori algorithm in mining of medical case data is presented. Secondly, we propose an extended version of the traditional Apriori algorithm which is primarily based on the fast response of computer to bit-string logic operation. A comparative evaluation of the proposed and existing Apriori algorithms is presented particularly in terms of running time, mining of frequent items set and strong association rules. Both experimental and simulation results have proved that the proposed extended Apriori algorithm has outperformed existing algorithms when it is applied to asthma medication and combined symptom-medication data for the association analysis. Furthermore, the association relationship between mind asthma case data and medication is effective in the analysis of asthma case data with significant application value which is verified by the experimental data and observations.
Mechanisms of Resistance to CDK4/6 Blockade in Advanced Hormone Receptor–positive, HER2-negative Breast Cancer and Emerging Therapeutic Opportunities
Authors: Lloyd Maxwell R. , Spring Laura M. , Bardia Aditya , Wander Seth A.
Published: November 2021

The cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) have become the standard of care, in combination with antiestrogen therapy, for patients with hormone receptor–positive (HR+)/HER2− advanced breast cancer. Various preclinical and translational research efforts have begun to shed light on the genomic and molecular landscape of resistance to these agents. Drivers of resistance to CDK4/6i therapy can be broadly subdivided into alterations impacting cell-cycle mediators and activation of oncogenic signal transduction pathways. The resistance drivers with the best translational evidence supporting their putative role have been identified via next-generation sequencing of resistant tumor biopsies in the clinic and validated in laboratory models of HR+ breast cancer. Despite the diverse landscape of resistance, several common, therapeutically actionable resistance nodes have been identified, including the mitotic spindle regulator Aurora Kinase A, as well as the AKT and MAPK signaling pathways. Based upon these insights, precision-guided therapeutic strategies are under active clinical development. This review will highlight the emerging evidence, in the clinic and in the laboratory, implicating this diverse spectrum of molecular resistance drivers.