bluuebunny/crossref_metadata_2025 huggingface dataset:
Photographic evidence of tagging impacts for two beluga whales from the Cumberland Sound and western Hudson Bay populations
Authors: Ryan Kasey P. , Petersen Stephen D. , Ferguson Steven H. , Breiter C-Jae C. , Watt Cortney A.
Published: March 2022

Beluga whale ( Delphinapterus leucas (Pallas, 1776)) movements, habitat use, and diving behaviour have been studied using satellite-linked transmitters for decades. The inaccessibility of Arctic and subarctic habitats makes these instruments especially valuable for beluga research. The long-term effects that tags and tag attachments have on belugas, however, are not well known because resightings occur relatively infrequently. Here, we describe two belugas photographed during photographic monitoring programs of two populations: western Hudson Bay and Cumberland Sound. The beluga photographed in western Hudson Bay had scars consistent with the tag pins migrating out, which is thought to occur when the tag is pulled posteriorly due to drag. The beluga photographed in Cumberland Sound had all three tag pins still in place 11–21 years after they were inserted. Both whales appeared to be in good body condition with no evidence of infection, and the beluga from Cumberland Sound was accompanied by a 1-year-old calf. Resightings of previously tagged whales are infrequent for the western Hudson Bay population and have never been documented in Cumberland Sound. However, through long-term photographic monitoring programs, additional sightings may provide more information regarding the method of tag loss and the long-term effects of tagging on whale health and productivity.
The problems of resolving historical specimen data, focusing on a specimen of Myotis austroriparius (Mammalia, Chiroptera, Vespertilionidae) collected by Thomas Drummond
Authors: Jenkins Paulina D. , Sealy Spencer G.
Published: March 2022

The geographical itineraries of Thomas Drummond’s two separate expeditions to Canada (1825–1827) and the United States of America (1831–1835) are used to provide historical context for the specimens collected and their localities. The coordinates for these locations are estimated and their geographical positions mapped. The difficulties of resolving various problems with historical specimens are explored and several examples are provided, including the contentious origin and identification of a southeastern myotis, Myotis austroriparius (Rhoads, 1897) (NHMUK 1837.4.8.127). Information about type specimens is discussed and the geographical position of several type localities of rodents and a mustelid in the Rocky Mountains and a lagomorph in the USA are refined.
Scaling laws of inter-event time casualties of narco-war in Mexico
Authors: Nájera-Santos A. , Sánchez-Espinosa D. B. , del Castillo-Mussot M. , Espinal-Enríquez J.
Published: March 2022

From the official report of narco-war-related casualties in Mexico from December 2006 to September 2011, we show that the inter-event time distribution (calculated for a range of minimum sizes) approximately obeys simple scaling laws similar to violent conflicts in Iraq (2003–2005), Afghanistan (2008–2010) and Northern Ireland (1969–2001). Furthermore, normalizing deaths by population municipalities (the smallest Mexican political entities) yields even better fitting results.
Protein Structure Prediction Using Quantile Dragonfly and Structural Class-Based Deep Learning
Authors: Nallasamy Varanavasi , Seshiah Malarvizhi
Published: March 2022

Predicting three-dimensional structure of a protein in the field of computational molecular biology has received greater attention. Most of the recent research works aimed at exploring search space, however with the increasing nature and size of data, protein structure identification and prediction are still in the preliminary stage. This work is aimed at exploring search space to tackle protein structure prediction with minimum execution time and maximum accuracy by means of quantile regressive dragonfly and structural class homolog-based deep learning (QRD-SCHDL). The proposed QRD-SCHDL method consists of two distinct steps. They are protein structure identification and prediction. In the first step, protein structure identification is performed by means of QRD optimization model to identify protein structure with minimum error. Here the protein structure identification is first performed as the raw database contains sequence information and does not contain structural information. An optimization model is designed to obtain the structural information from the database. However, protein structure gives much more insight than its sequence. Therefore, to perform computational prediction of protein structure from its sequence, actual protein structure prediction is made. The second step involves the actual protein structure prediction via structural class and homolog-based deep learning. For each protein structure prediction, a scoring matrix is obtained by utilizing structural class maximum correlation coefficient. Finally, the proposed method is tested on a set of different unique numbers of protein data and compared to the state-of-the-art methods. The obtained results showed the potentiality of the proposed method in terms of metrics, error rate, protein structure prediction time, protein structure prediction accuracy, precision, specificity, recall, ROC, Kappa coefficient and [Formula: see text]-measure, respectively. It also shows that the proposed QRD-SCHDL method attains comparable results and outperformed in certain cases, thereby signifying the efficiency of the proposed work.
An Efficient Design of Scalable Reversible Multiplier with Testability
Authors: Gaur Hari Mohan , Singh Ashutosh Kumar , Ghanekar Umesh
Published: March 2022

A new architecture of 4-bit reversible multiplier with scalability factor of order [Formula: see text] is presented in this paper. The design procedure is based on a unique method of gates placement, which produces parity preserving circuits. This property facilitates graceful testing and full coverage of single-bit faults at lower overhead. The circuit is designed and implemented on the top of reversible analyser for obtaining operating costs in terms of number of wires, gate cost, quantum cost, garbage output and ancilla input. Testable implementation of recently reported multiplier circuits has also been performed using the existing method of testing under the same platform. This work achieved a reduction of up to 33% in operating costs when all the parameters are combined together.
ELECTROCHEMICAL, DFT AND MULTISCALE SIMULATION STUDIES ON SELF-ASSEMBLED FILMS OF 2-AMINO-4-METHYL-PYRIDINE INHIBITOR FOR COPPER METAL CORROSION PROTECTION IN 3.5% NaCl MEDIUM
Authors: BALAJI J. , GHOREISHIAMIRI S. , RAJA P. BOTHI , OH T. H. , SETHURAMAN M. G.
Published: March 2022

Copper metal and its alloys are the most used in the industries because it holds excellent conductivity and natural corrosion protectiveness against air/moisture. However, copper undergoes degradation in presence of aggressive 3.5% NaCl. Protective coating is one of the practical methods to safeguard copper metal. In this study, 2-amino-4-methyl-pyridine (AMP) was coated as self-assembled monolayer method (SAM) with different immersion periods (12 h/24 h) and investigated the corrosion potential against 3.5% NaCl medium. Chemical composition of AMP/SAM/Cu was characterized using Fourier-transform infrared (FT-IR) spectroscopy and morphological analysis using atomic force microscopy (AFM) techniques. AMP’s corrosion inhibition potential were evaluated via electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDS) analyses. Quantum chemical calculations and molecular dynamics (MD) simulations explained the stronger adsorption of AMP over copper metal surface via self-assembly method. The corrosion analysis revealed that AMP’s SAM formation (24 h immersion) enhances the corrosion protection of Cu metal, which is mainly due to the uniform thin adhesive compact layer formation and blocks incoming corrosive chloride ions. EIS investigation evident that AMP forms an adhesive layer over the copper surface and prevents the diffusion of aggressive corrosive medium, while PP studies revealed that AMP acts via the mixed mode of corrosion inhibition mechanism. AMP’s significant inhibition efficiency 90% was confirmed via PP analysis. AFM analysis enabled the morphology image of AMP’s shielding over Cu surface and the molecular modeling investigation supported well for the Cu–N (from AMP) bond formation.
A STOCHASTIC OIL PRICE MODEL FOR OPTIMAL HEDGING AND RISK MANAGEMENT
Authors: PENNANEN TEEMU , BONATTO LUCIANE SBARAINI
Published: March 2022

In this paper, we develop a stochastic model for future monthly spot prices of the most important crude oils and refined products. The model is easy to calibrate to both historical data and views of a user even in the presence of negative prices which have been observed recently. This makes it particularly useful for risk management and design of optimal hedging strategies in incomplete market situations where perfect hedging may be impossible or prohibitively expensive to implement. We illustrate the model with optimization of hedging strategies for refinery margins in illiquid markets using a portfolio of 12 most liquid derivative contracts with 12 maturities traded on New York Mercantile Exchange (NYMEX) and Intercontinental Exchange (ICE).
A Teacher’s Perspective on the CERN Beamline for Schools Competition
Authors: Boselli Margherita , Bulson Alison , Arce-Larreta Enrique , Elorza Uriel Garcia , Felici Maria Rita , González-Trueba Laura Helena , Jacques Denis , Joos Markus , Kempa Janusz , Knapp Lise , Lima Manuela , Muscarella Maria Filomena , Riis-Johannessen Thomas , Stadermann Kirsten , Turbacci Stefania , Wuppuluri Shyam
Published: March 2022

We are a group of teachers who have independently participated in the CERN Beamline for Schools (BL4S) competition with teams of high school students from our schools between 2014 and 2021. All of our teams won the competition, and therefore our students had the opportunity to perform their proposed experiments at a particle accelerator. The experience of mentoring a team has been extremely beneficial to our professional development. In this paper, we will describe the effects that the participation in BL4S had on us, on our students and how it has impacted our approach to teaching STEM subjects.
AI-Enabled Task Offloading for Improving Quality of Computational Experience in Ultra Dense Networks
Authors: Gu Bo , Alazab Mamoun , Lin Ziqi , Zhang Xu , Huang Jun
Published: March 2022

Multi-access edge computing (MEC)
and
ultra-dense networking (UDN)
are recognized as two promising paradigms for future mobile networks that can be utilized to improve the spectrum efficiency and the
quality of computational experience (QoCE)
. In this paper, we study the task offloading problem in an MEC-enabled UDN architecture with the aim to minimize the task duration while satisfying the energy budget constraints. Due to the dynamics associated with the environment and parameter uncertainty, designing an optimal task offloading algorithm is highly challenging. Consequently, we propose an online task offloading algorithm based on a state-of-the-art
deep reinforcement learning (DRL)
technique:
asynchronous advantage actor-critic (A3C)
. It is worthy of remark that the proposed method requires neither instantaneous
channel state information (CSI)
nor prior knowledge of the computational capabilities of the base stations. Simulations show that the our method is able to learn a good offloading policy to obtain a near-optimal task allocation while meeting energy budget constraints of mobile devices in the UDN environment.
Image Semantic Analysis in Visual Media Art Using Machine Learning and Neural Machine Translation
Authors: Cao Weiran
Published: March 2022

The current archaeological work in China has the problems of high cost, large material consumption, more attention on human detection and long time-consuming. It is urgent to use modern high-precision detection technology for auxiliary work. This exploration will also use the semantic recognition system based on deep learning and neural network for the recognition of oracle bone inscriptions in archaeology. Therefore, combined with the concept of multimedia semantic recognition and analysis, a unified real-time target detection semantic analysis model named You Only Look Once (YOLOv2) is established based on the deep convolutional neural network under deep learning in the field of machine learning, to test the semantic analysis of oracle bone inscriptions. Moreover, Faster Region-Convolutional Neural Network (Faster R-CNN) and traditional YOLO models are selected to conduct the controlled experiments. A YOLOv2 recognition system is established based on Diffusion-Convolutional Neural Networks (DCNN) under deep learning. First, the concept and performance of DCNN are studied. Next, the basic information of oracle bone inscriptions is analyzed. A recognition system based on DCNN is established. On the premise that the three models can identify different directions of the same oracle bone inscriptions sample, the detection accuracy and detection loss value of YOLOv2 are better than those of the other two models, the detection accuracy is as high as 0.90, and the loss value is less than 0.10. Therefore, it is considered that this YOLOv2 semantic analysis model can be applied in oracle bone inscriptions and other archaeological work, which improves the work efficiency and simplifies the human work items for the domestic archaeological work. This semantic analysis model is of great help to the pattern recognition of cultural relics in archaeological work, and can help professionals analyze the meaning of patterns faster when there are massive similar oracle bone inscriptions.
Ion channel spectroscopy analogs for NGS
Authors: Gradov Oleg
Published: March 2022

The developing nanopore measurement techniques based on synthetic ion channels are considered by the specialists both as an innovative and classical approach, a breakthrough and a fundamental method. The effect of the sequence-dependent gating of an ion channel by DNA hairpin molecules and the dependence of the channel electrical response parameters on the molecular sequence of the nucleic acid molecule passing through a channel / pore (and, in the nearest future, also on the chemical composition of the nucleic acid, considering the developing XNA-technologies) is an excellent example of the applicability of the digital or analog methods of multiparametric pseudo-spectral processing for sequence identification from the standpoint of DSP. We have developed a set of digital signal processing methods, which includes analysis of the Prony energy spectra, several wavelet and cepstral processing procedures, analysis of the correlation dimension, phase and complex (Re | Im) spectra, which allows analyzing the fingerprints of the passing molecules. The above method is based on the mathematical and biophysical aspects of the previously developed “patch clamp spectroscopy” - a set of methods which has been first demonstrated in 2015. Preliminary testing of this method has been carried out using foreign open access data. The main goal determining the direction of our further research is to conduct similar autonomous measurements using the method proposed in the Russian Federation. Before being introduced into an autonomous experiment, this method cannot be considered NGS-validated, although it is of great interest in the aspect of signal-processing bioinformatics.
On Existence of Fixed Points for Multivalued Generalized wb‐Contractive Mappings and Applications
Authors: Latif Abdul , Al Subaie Reem Fahad , Alansari Monairah Omar
Published: March 2022

In this study, we present some new results on the existence of fixed points for multivalued generalized wb‐contractive mappings in the frame work of metric type spaces. Consequently, presented results unify and generalize several known metric fixed‐point results. In support of our main results, examples are provided to show that the results are genuine generalization of the known corresponding results of metric fixed‐point theory.
Self-Management Embedded in Daily Activities: A Photoelicitation Focus Group Study among Persons with Spinal Cord Injury and Their Primary Caregivers in Bangladesh
Authors: Begum Salma , Alve Yeasir A. , Bontje Peter
Published: March 2022

Purpose. This study explored how community-dwelling persons with spinal cord injury (SCI) and their primary caregivers execute self-management strategies in daily activities. These strategies were mapped to a preexisting self-management framework. Methods. Photoelicitation focus group discussions were conducted among 14 adults with SCI and their primary caregivers (in two groups). Moreover, a constant comparative framework was used to analyze the data. Results. This study identified nine groups of self-management strategies, some of which could not be categorized under the three main self-management components generally accepted in the literature. Accordingly, a new component is proposed based off of this analysis, entitled management of social complexities, which includes crucial strategies such as (1) relocating to another environment, (2) behaving in an assertive manner, and (3) advocating for social change. Conclusion. The results show that self-management, traditionally described as medical, emotional, and role management, should also include the management of social complexities. The identified strategies could be considered in the development of self-management enhancement programs in lower-middle-income countries.
TCM Syndrome Recognition Model of Type 2 Diabetes Mellitus in Shanghai Based on TCM Inquiry Information
Authors: Hou Chunlei , Cui Yanjie , Xu Ying , Wang Yiqin , Hao Yiming
Published: March 2022

Background. Type 2 diabetes mellitus (T2DM) has become one of the serious public health problems in China, and it affects the quality of survival of patients significantly. The long-term monitoring and early warning of T2DM and its complications should be paid attention to. Artificial diagnosis of T2DM in traditional Chinese medicine (TCM) is subjective and unrepeatable at the present stage. It is important to develop standardized collection and objective analysis methods of TCM inquiry. Therefore, we are interested in establishing syndrome recognition models. Objective. The establishment of the TCM syndrome recognition model of T2DM is helpful to the syndrome diagnosis of the disease, and the clear diagnosis of the syndrome is the prerequisite for the effective treatment of the disease by TCM. At present, there are few studies on syndrome recognition models of T2DM. Therefore, based on the inquiry information of TCM, we establish a latent structure model (LSM) of T2DM syndrome in Shanghai, hoping to provide services for the health management of TCM for diabetes in the future. Methods. A total of 587 effective samples of T2DM patients in Shanghai were collected. The gender, course of disease, and chronic complications were analyzed by one-way ANOVA and frequency analysis. Inquiry symptoms that could be included in the model were screened. In the study, 30 symptoms were used as observable variables to analyze the symptom information of TCM inquiry, and then, the TCM syndrome recognition model was established. Results. The clinical syndromes of patients with T2DM in Shanghai were mixed with deficiency and excess. The LSM divided the pattern of the disease into qi-yin deficiency pattern, yin deficiency pattern, qi deficiency pattern, and qi stagnation pattern. These patterns were mainly located in the spleen and stomach, liver, and kidney. Conclusion. The results of the syndrome classification of T2DM by LSM are basically consistent with the TCM clinical characteristics of the disease, which could reflect its main etiology and pathogenesis. The LSM of TCM inquiry syndrome diagnosis of T2DM confirmed the objectivity of TCM syndrome differentiation to some extent, and it will lay a foundation for the development of a mobile APP for TCM health management of T2DM.
The Single-Nucleotide Polymorphism of miR-27a rs895819 and the Expression of miR-27a in Helicobacter pylori-Related Diseases and the Correlation with the Traditional Chinese Medicine Syndrome
Authors: Zhang Ling , Huang Meng-Xin , Li Dan-Yan , Zhang Yun-Zhan , Lan Shao-Yang , Luo Qi , Dai Yun-Kai , Wu Yun-Bo , Ye Jin-Tong , Chen Wei-Jing , Li Ru-Liu , Hu Ling
Published: March 2022

Aims. The study aims to explore the effects of the single-nucleotide polymorphism of miR-27a and its expression in Helicobacter pylori (H. pylori)-related diseases and the relationship between gastric pathology and traditional Chinese medicine (TCM). Methods. Subjects were classified into six histopathological groups and five TCM syndrome groups. All specimens underwent H. pylori detection through rapid urease test and methylene blue staining. Histopathological characteristics were observed by hematoxylin-eosin. The expression of miR-27a and its genotype were, respectively, detected by Quantitative Real-Time PCR and direct sequencing. Results. H. pylori promoted the malignant evolution of gastric mucosa and were involved in the formation of TCM syndrome. In H. pylori-positive patients, the frequency of miR-27a CT genotype at the rs895819 locus and its expression in the gastric cancer group were higher than those in other pathological groups. TCM syndrome had a close relationship with histopathological changes, and patients with spleen-qi deficiency syndrome had a higher risk of gastric cancer than other syndromes, regardless of H. pylori infection. Conclusion. The C allele at miR-27a rs895819 locus may be an oncogene in gastric cancer. High levels of miR-27a could play an important role in gastric malignant evolution, especially cancerization. There is a certain connection between TCM syndrome and pathological changes of the gastric mucosa to some extent, where patients with SQD syndrome had a higher risk of GC.
A Graph Partition-Based Large-Scale Distribution Network Reconfiguration Method
Authors: Sha Yuanxia
Published: March 2022

This article focuses on the analysis of large-scale distribution network reconstruction fused with graph theory and graph partitioning algorithms. Graph theory and graph segmentation algorithms have been rushed by many researchers in the fields of medicine, drone, and neural network. It is a newcomer in the field of computer vision, which can not only realize the division in color but also divide it by image data. The distribution network is also indispensable for new energy, electric machines, but the traditional distribution network has many problems, such as not suitable for distributed power access and excessive network loss. To improve the performance of distribution networks and reduce network losses, this paper A multi-division model for distribution network construction and reconstruction is established, and a graph theory-based division algorithm method is proposed to effectively solve the problem of feeder-to-feeder reconstruction during large-scale distribution in distribution networks. Through its superconductivity phenomenon and the characteristics of clustering algorithm division, this paper uses formulas to show its division principle and gives examples of various distribution network reconstruction algorithms to explore which method of improvement can improve the performance of the distribution network and reduce network losses. The number of iterations is also strictly considered, and the value is taken after multiple iterations to reduce the error. Through the distribution network calculation example, the network loss reduction value is obtained, and the distribution network fault repair model is exemplified. The picture is used to briefly describe the process of distribution network reconstruction and find that the faults of the distribution network can be quickly located and isolated through the FTU, and quickly repaired. Finally, in order to reduce the network loss, reduce the load of power flow calculation, and solve the problem of local optimization, a JA-BE-JA optimization algorithm based on large-scale distribution network reconfiguration is proposed. The mixed sampling method is preferred to test the number of divisions in the four states, and the parameters are selected to test the performance of the improved annealing simulation algorithm, and the conclusion is drawn as follows: the improved graph segmentation algorithm has strong robustness, can avoid local optimization of graph data, and can reduce network loss. Compared with traditional distribution network reconstruction methods, the network loss can be reduced to 454.3 KW, which can be optimized by 10.68% compared with the initial network loss.
Systematic Evaluation Meta-Analysis of the Efficacy of Recombinant Human Endostatin Combined with Gemcitabine and Cisplatin in Non-Small-Cell Lung Cancer
Authors: Zhang Li , He Yuan , Yi ChengFeng , Huang Mei
Published: March 2022

Objective. To evaluate the efficacy of recombinant human endostatin combined with gemcitabine and cisplatin in the treatment of non-small-cell lung cancer (NSCLC). Methods. The databases of Cochrane Library, Embase, ClinicalTrials, PubMed, HowNet, Wanfang, and VIP were searched to collect randomized controlled trials (RCTs) of recombinant human endostatin combined with gemcitabine and cisplatin (experimental group) and gemcitabine combined with cisplatin (control group) for comparative study. The quality of literature was evaluated by bias risk assessment tools and related scales, and then meta-analysis was performed. Results. A total of 27 RCTs (1646 patients) were included. The results of meta-analysis showed that the effective rate (

P

 < 0.000 01) and benefit rate (

P

 < 0.000 01) of the experimental group were significantly higher than those of the control group, the incidence of leucopenia (

P

 = 0.79), thrombocytopenia (

P

 = 0.39), and gastrointestinal reaction (

P

 = 0.85) were not statistically significant. Conclusion. The combination of recombinant human endostatin, gemcitabine, and cisplatin can increase the efficacy and safety of NSCLC patients.
circRNA_101277 Influences Cisplatin Resistance of Colorectal Cancer Cells by Modulating the miR-370/IL-6 Axis
Authors: Lv Qing , Xia Qinghua , Li Anshu , Wang Zhiyong
Published: March 2022

Background. Colorectal cancer (CRC) is among the most prevalent malignancies globally. Early detection of precancerous lesions through routine colonoscopy has led to a dramatic reduction in CRC-related incidence and mortality among those between the ages of 50 and 70. However, in those where the disease progresses to an advanced stage, chemotherapy remains the primary available treatment option, and the associated 5-year survival rate remains low. The identification of genes associated with CRC chemoresistance would thus be a beneficial approach to identifying novel treatments for this deadly disease. Methods. The expression of circRNA_101277, miR-370, and IL-6 was assessed via qRT-PCR. IL-6 levels were measured with a human IL-6 ELISA kit based on the provided protocols. CRC cellular proliferation and cisplatin IC50 values were quantified via MTT assays. Luciferase assays were used to detect circRNA_101277 and miR-370 binding sites or miR-370 and IL-6 binding sites. Results. circRNA_101277 was increased in CRC tissues compared with control samples. circRNA_101277 overexpression was evident in CRC cells, and knockdown of this circRNA suppressed cellular proliferation and cisplatin resistance in these cancer cells. At a mechanistic level, circRNA_101277 was found to function by sequestering miR-370, thereby upregulating the miR-370 target gene IL-6 and promoting cisplatin resistance via this miR-370/IL-6 axis. Conclusion. In summary, our data highlight circRNA_101277 as a novel driver of CRC cell cisplatin resistance that functions by sequestering miR-370 and thereby enhancing IL-6 expression. These findings suggest that this circRNA_101277/miR-370/IL-6 axis may represent a critical axis of chemoresistance in CRC that can be targeted to diagnose and/or treat this cancer.
Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model
Authors: Liu Qing , Zhou Jing
Published: March 2022

Most of the traditional methods of English text chunk recognition are solved by setting the corresponding phrase identifier numbers and eventually transforming the chunk recognition problem into a lexical annotation problem. In language recognition, the traditional MFCC features are easily contaminated by noise and have weak noise immunity due to the insufficient amount of information on each frame of the signal. At the same time, SDC feature extraction methods commonly used today require artificial settings in parameter selection, which increases the uncertainty of recognition results. The method of identifying English text chunks by association evaluation of central word extensions identifies English text chunks from a different perspective. It has the following features: (i) each phase is considered as a cluster with the central word as the core, and the internal composition pattern of each phrase is fully considered; (ii) the results are dynamically evaluated using association and confidence. The results show that the proposed method can achieve higher recognition rate than traditional feature extraction methods. The recognition rate is faster, and the

F

-measure value of English block recognition reaches 94.05%, which is comparable to the best results so far.
Effects of hsa-mir-145-5p on the Regulation of msln Expression in Colorectal Adenocarcinoma
Authors: Li Junhua , Bulin Baila  , Xu Tian Xiang , Song Jiang , Su Rina  , Wu Ji , Wang Tegexibaiyin
Published: March 2022

Colorectal cancer (CRC) is one of the most common gastrointestinal cancers in the world, and its incidence is increasing all over the world including China. In recent years, research data show that some miRNAs are differentially expressed in cancer tissues, and their expression is closely contributed with the prognosis of CRC. Microarray technology was used, and 179 miRNAs were screened out with significantly altered expression in CRC tissues compared with adjacent tissues. The expression of mir-145-5p in tumor tissues was 3.48 times lower than that in normal tissues. Using bioinformatics technology and network resource prediction, we found that mir-145-5p had a potential target gene relationship with msln gene. Then, qRT-PCR was used to validate the expression level of mir-145-5p and msln mRNA in CRC and paracancerous tissues. The results showed that msln mRNA was higher than in normal tissues, while mir-145-5p was lower, with statistically significant difference (

P
<
0.01

,

n
=
3

). Furthermore, the expression of msln protein in CRC and normal colorectal tissues was detected by protein mass spectrometry (MRM) (

n
=
3

) and immunohistochemistry in a total case of 30 colorectal cancer tissues and normal tissues. Result showed that the positive expression of msln in CRC was higher than that in normal colorectal tissues, 1.38

e

-6 and 1.89

e

-6, respectively (

P
<
0.01

,

n
=
3

). Furthermore, in 48 h RTCA real-time monitoring experiment, mir-145-5p showed inhibitory effect on the proliferation of colo320 cells stimulated by msln. This study demonstrated that msln is a target gene of mir-145-5p in CRC. Besides, mir-145-5p negatively regulates the proliferation of CRC colo320 cells through downregulating msln gene expression in CRC colo320 cells.
Prevalence and Factors Associated with Acute Kidney Injury in Sub-Saharan African Adults: A Review of the Current Literature
Authors: Kahindo Charles Kangitsi , Mukuku Olivier , Wembonyama Stanis Okitotsho , Tsongo Zacharie Kibendelwa
Published: March 2022

Acute kidney injury (AKI) is a complex condition that can occur in both community and hospital settings and has many aetiologies. These aetiologies may be infectious, toxic, surgical, or related to the different management methods. Although it is a major public health problem worldwide, it must be emphasised that both its incidence and mortality rate appear to be very high in sub-Saharan African (SSA) countries compared to developed countries. The profile of AKI is very different from that of more developed countries. There are no reliable statistics on the incidence of AKI in SSA. Infections (malaria, HIV, diarrhoeal, and other diseases), nephrotoxins, and obstetric and surgical complications are the main aetiologies in Africa. The management of AKI is costly and associated with high rates of prolonged hospitalisation and in-hospital mortality.
Data-Driven Methodology for the Prediction of Fluid Flow in Ultrasonic Production Logging Data Processing
Authors: Song Hongwei , Li Ming , Wu Chaoquan , Wang Qingchuan , Wei Shunke , Wang Mingxing , Ma Wenhui
Published: March 2022

A new method for the determination of oil and water flow rates in vertical upward oil-water two-phase pipe flows has been proposed. This method consists of an application of machine learning techniques on the probability density function (PDF) and the power spectral density (PSD) of the power spectrum output of an ultrasonic Doppler sensor in the pipe. The power spectrum characteristic parameters of the two-phase flow are first determined by the probability density function (PDF) method. Then, the transducer signal is preprocessed by distance correlation analysis (DCA), and independent features are extracted by principal component analysis (PCA). The extracted features are used as input to a least-squares fit, which gave the oil flow rates as output. In the same way, the transducer signal is also preprocessed by partial correlation analysis (PCA), and independent features were extracted using independent component analysis (ICA). The extracted features were used as inputs to multilayer back-propagation neural networks, which water cuts as output. The present method was used to calibrate an ultrasonic Doppler sensor to estimate the flow rates of both phases in oil–water flow in a vertical pipe of diameter 159 mm. Predictions of the present method were in good agreement with direct flow rate measurements. Compared to previously used methods of feature extraction from the ultrasonic Doppler power spectrum signals, the present method provides a theoretical basis for the interpretation of ultrasonic multiphase flow logging data. Ultrasonic multiphase flow logging has potential application value in the production profile logging and interpretation evaluation of production wells with low fluid production and high water cut.
Prediction of Departure Flights’ Taxi-Out Time Based on Intelligent Algorithm Optimized BP
Authors: Xia Zheng-hong , Huang Long-yang
Published: March 2022

Taxi-out time is the main performance index to evaluate the operational efficiency of major airports. Scientifically and accurately predicting the taxi-out time of departure flights is very important to improve the operational efficiency and coordination decision-making ability of airport. Firstly, the quantifiable influence factors of taxi-out time and their correlation is analyzed, including the number of departure flights, the number of arrival flights, the number of flights pushed back in the same period, the taxi-out time by half-hour in average, the taxi distance, and the number of turns, etc. And then a prediction model of departure flights’ taxi-out time based on BP is constructed. Because the traditional BP neural network is sensitive to the initial weight and threshold and has poor accuracy and stability, the taxi-out time prediction model of BP neural network optimized by intelligent algorithm is proposed. Genetic algorithm (GA) and sparrow search algorithm (SSA) are used to obtain the optimal weight and threshold of BP neural network, which is verified by the actual operation data of a major airport in central and southern China for two weeks. The results show that ① the taxi-out time is strongly correlated with the airport surface traffic flow, moderately correlated with the average taxi-out time, and weakly correlated with the taxi distance and the number of turns. ② The predicted outcome of the 4-element combination prediction model which considers strong correlation and medium correlation factors is the best. After adding weak correlation factors, the prediction accuracy is reduced. ③ By obtaining the local optimal weight and threshold of neural network, intelligent optimization algorithm can effectively improve the accuracy of departure flights’ taxi-out time prediction results. ④ The prediction result of BP neural network optimized based on GA is 1.79% higher than that of MAPE before optimization, MAE is reduced by 7.4 s, and RMSE is reduced by 6.93 s. The prediction result of BP neural network optimized based on SSA is 3.05% higher than that of MAPE before optimization, MAE is reduced by 16.55 s, and RMSE is reduced by 14.31 s. Therefore, Sparrow search algorithm has better optimization effect on the model than genetic algorithm.
Applications and Outcomes of Internet of Things for Patients with Alzheimer’s Disease/Dementia: A Scoping Review
Authors: Sheikhtaheri Abbas , Sabermahani Farveh
Published: March 2022

Objectives. We aimed to identify and classify the Internet of Things (IoT) technologies used for Alzheimer’s disease (AD)/dementia as well as the healthcare aspects addressed by these technologies and the outcomes of the IoT interventions. Methodology. We searched PubMed/MEDLINE, IEEE Explore, Web of Science, OVID, Scopus, Embase, Cochrane, and Google Scholar. In total, 13,005 papers were reviewed, 36 of which were finally selected. All the reviews were independently carried out by two researchers. In the case of any disagreement, the problem was resolved by holding a meeting and exchanging views. Due to the diversity of the reviewed studies, narrative analysis was performed. Results. Among the technologies used for the patients including radio frequency identification (RFID), near field communication (NFC), ZigBee, Bluetooth, global positioning system (GPS), sensors, and cameras, the sensors were employed in 36 studies, most of which were switch and vital sign monitoring sensors. The most common aspects of AD/dementia care monitored using these technologies were activities of daily living (ADLs) in 27 studies, followed by sleep patterns and disease diagnosis in 19 and 14 studies, respectively. Sleeping, medication, vital signs, agitation, memory, social interaction, apathy, movement, tracking, and fall were other aspects monitored by IoT. Then, their outcomes were reported. Conclusion. Using IoT for AD/dementia provides many opportunities for considering various aspects of this disease. Moreover, the ability to use various technologies for gathering patient‐related data provides a comprehensive application for almost all aspects of the patients’ care with high accuracy.
Intelligent Drug Delivery System Based on Silk Fibroin/Wool Keratin
Authors: Yifang Shi , Huang Lingling , Wang Xiao , Li Yanru , Shen Runqing
Published: March 2022

It is critical to develop drug carriers for intelligent drug delivery systems. Normally, the drug carriers of the intelligent drug delivery system are stimulus-responsive polymers. However, as drug carriers, the biocompatibility of the materials should be the primary consideration. In this work, two smart drug systems were prepared using natural protein materials (silk and wool) as drug carriers and poly(N-isopropylacrylamide) (PNIPAM) as a functional additive material. (1) Silk fibroin (SF)/wool keratin (WK) composite films: the differences in drug release efficiency of composite films are based on the different crystallinity of composite films. Analysis of the XRD results showed that the content of the β-crystallite structure in different proportions of the composite films changed from 38.3% to 19.5%. The percentage of drugs released of the mixed films with different proportions ranged from 28% to 58% after eight hours of release, showing a gradient release. (2) SF/WK and PNIPAM composite films: the particle size of the PNIPAM microspheres changed with the temperature. The particles’ size of the prepared PNIPAM microspheres was reduced from 700 nm to 300 nm from 25°C to 35°C. After eight hours of release, the composite films with temperature-sensitive microspheres increased the percentage of drug released by up to 20% compared with the composite films without microspheres when the temperature was ranged from 25°C to 37°C. The result of drug release showed the amount of drug released could be changed with temperature stimulation, which could satisfy people’s controllable requirements for the time, location, and dose of drug release.
A Double Integral Containing the Fresnel Integral Function S(x): Derivation and Computation
Authors: Reynolds Robert , Stauffer Allan
Published: March 2022

A two‐dimensional integral containing S(x) is derived. S(x) is the Fresnel integral function, and the double integral is taken over the range 0 < x < ∞ and  0 < y < ∞. A representation in terms of the Hurwitz–Lerch zeta function is derived, from which other special function representations can be evaluated. All the results in this work are new.
Research on Dynamic Evaluation Modeling of Community e-Commerce Service Satisfaction under the Background of New Retail
Authors: Zhao Yingzi , Zhaoji Yu
Published: March 2022

In order to improve the evaluation quality of community e-commerce service satisfaction, this study designs a new dynamic evaluation modeling method of community e-commerce service satisfaction under the background of new retail. After analyzing the community e-commerce service model under the new retail background, three primary indicators including commodity, distribution, and technology indicators and 14 secondary community e-commerce service satisfaction evaluation indicators are selected to complete the dynamic evaluation of community e-commerce service satisfaction through index clustering and satisfaction information clustering. The experimental results show that the evaluation time of this method varies from 7.5 min to 18.8 min, the accuracy of satisfaction information collection varies from 87.2% to 97.4%, and the highest evaluation accuracy can reach 98.1%, which prove that this method has higher evaluation efficiency and effectiveness and has strong application advantages.
Is There Complementarity between Teaching and Research? Evidence from Pakistani Higher Education Institutions
Authors: Gebru Michael Gidey , Khan Mansoor Shaukat , Raza Saqlain
Published: March 2022

Many activities have been used to impart knowledge and foster the quality of education at higher education institutions: mainly teaching and research. Higher education institutions have typically focused on the adoption of teaching and research independently, but in many instances, both activities coexist. By taking into account the coexistence, this study empirically analyzed why teaching and research activities appear together and how joint adoption of the activities has economic impacts on the performance of the higher education institutions. To do so, this study tested the existence of complementarity between teaching and research using supermodularity through the data envelopment analysis approach. Therefore, the empirical result showed that complementarity between teaching and research confirms that the adoption of one activity strengthens the adoption decision about the other activity. This implies that the institutions that execute both activities simultaneously become more productive rather than adopting a single activity. Moreover, it is important for academic decision-makers to take decisions in order to allow universities to achieve economies of scale.
A Case of Growth Hormone Use in Dyggve–Melchior–Clausen Syndrome
Authors: Upadhyay Ravi , Ruane Claire , Umans Rachel , Pletcher Beth A. , Khokhar Aditi , Wong Kristin
Published: March 2022

Short stature has many causes including genetic disease, skeletal dysplasias, endocrinopathies, familial short stature, and nutritional deficiencies. Recombinant growth hormone (rGH) therapy may be employed to improve stature based on the underlying etiology and growth velocity. Skeletal dysplasia in Dyggve–Melchior–Clausen (DMC) syndrome tends to be progressive, typically with hip involvement, and ultimately leads to bilateral dislocation of the hip joints. Here, we present a pediatric patient with short stature treated with rGH therapy, complicated by the development of debilitating, bilateral hip pain, and found to have DMC syndrome. Our patient had limited range of motion at several joints including the hips after receiving 6 months of rGH therapy. Given the timing of the patient’s rGH therapy and the progression of her disease, it is difficult to determine if there were any benefits and instead, is concerning for worsening of her skeletal dysplasia with rGH therapy use. Consequently, patients with severe short stature should have a thorough workup for genetic causes like DMC syndrome, before initiating rGH therapy to determine any potential benefits or harms of treatment.
Characteristics and Determinants of the Presence of Respiratory Symptoms among Sewage Workers in Malaysia
Authors: Muzaini Kamarulzaman , Yasin Siti Munira , Ismail Zaliha , Ishak Ahmad Razali
Published: March 2022

Background. The purpose of this study was to determine the relationships of PM 2.5 and H2S exposure with the presence of work‐related respiratory symptoms among sewage workers. Methods. A cross‐sectional study was conducted in eleven sewage plants located in the central region of Peninsular Malaysia. One hundred ninety‐one sewage workers were assessed using the British Medical Research Council (BMRC) questionnaire. Area air sampling was performed in three different sewage plants to measure the following parameters: physical air quality and concentration of PM 2.5 and H2S. Result. Chronic cough (34.0%) was reported as the most common symptom, followed by chronic phlegm (26.2%), shortness of breath (7.9%), and chest tightness (3.7%). There were five significant determinants of the presence of respiratory symptoms among the sewage workers: shift work (AOR 23.50, 95% CI: 1.90–616.52), working at a sludge treatment facility (STF) (AOR 25.46, 95% CI: 2.06–314.29), a longer duration of working years (AOR 1,21, 95% CI: 1.01–1.44), individual cumulative exposure to PM 2.5 (AOR 9.01, 95% CI: 1.98–83.33), and individual cumulative exposure to H2S (AOR 1.04, 95% CI: 1.01–1.07). The majority of STF and non‐STF workers had higher exposure to PM 2.5 and H2S concentrations in the air than office workers. Conclusion. Sewage workers working at non‐STFs or STFs reported a significantly higher prevalence of work‐related respiratory symptoms and exposure to PM 2.5 and H2S compared with office workers. Exposure‐response relationships were also found in sewage workers’ cumulative exposure to PM 2.5 and H2S and the presence of respiratory symptoms.
The Reliability Analysis of Speaking Test in Computer-Assisted Language Learning (CALL) Environment
Authors: Khan Raja Muhammad Ishtiaq , Kumar Tribhuwan , Benyo Ahmed , Jahara Syed Farhat , Haidari Mir Mohammad Farooq
Published: March 2022

Speaking ability is regarded as among the essential aspects of language development. Oral examination appeared challenging to evaluate due to the presence of human evaluators. The speaking method depends on the test’s reliability, determined by the raters’ scores. The current study is aimed at evaluating the speaking test’s interrater reliability utilized to measure the speaking performance of Common First Year (CFY) students during remote learning. The data were obtained from 56 EFL learners using a scoring sheet and rubrics. Eight raters were responsible for rating the study. The speaking test’s reliability was estimated using quantitative data analysis. Correlation coefficients and the Bland-Altman test were employed to assess raters’ agreement. SPSS was utilized to analyze the data in this investigation. The study’s findings suggested that the speaking exam used in the CFY program during remote learning has shown some reliability on correlations and acceptable norms on the Bland-Altman test.