bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association Between Heart Rate Variability and Parkinson’s Disease: A Meta-analysis
Authors: Li Yuan , Wang Junjie , Li Xinyi , Jing Wei , Omorodion Itohan , Liu Lei 
Published: September 2020

Aim:
A systematic review which aims to assess the evidence regarding the function of the autonomic
heart rate regulation system among Parkinson’s disease (PD) patients. The main objective of the study is to compare
heart rate variability (HRV) between those with and without PD from published studies. The subgroup analyses
aimed to investigate the impact of treatment and disease duration on heart rate variability (HRV), assessed
by measuring sympathetic and parasympathetic activity via low-frequency (LF) and high-frequency (HF) power
spectrum scores, in patients with Parkinson’s disease (PD).

Methods:
PubMed, Cochrane Library, Embase and Web of Science were searched using the keywords “Parkinson’s
disease” and “heart rate variability”. Studies that reported at least one HRV variable were included. The
quality of the included studies was evaluated, and the relevant information was extracted. A meta-analysis was
carried out with Stata software. We followed the PRISMA guidelines for all stages of the meta-analysis. PROSPERO
registration number: CRD42021242766.

Results:
Thirteen references (16 studies) were included in our analysis. The LF values (g -0.27; 95% confidence
interval (CI) -0.53 to -0.01) of the patients with PD were lower than the controls. No significant differences in HF
values (g -0.11; 95% CI -0.28 to 0.06) were observed between groups. Subgroup analyses of HRV outcomes in
patients stratified by treatment status and disease duration were performed. For LF, patients with a disease duration
of less than 5 years presented lower HF (g -0.25; 95% CI -0.44 to -0.06) values than controls. Regarding HF,
patients receiving treatment presented lower HF (g -0.22; 95% CI -0.40 to 0.05) values than controls, and patients
with a disease duration greater than 5 years also presented lower HF (g -0.29; 95% CI -0.56 to -0.03) values than
controls.

Discussion:
We have confirmed and elaborated on the hypothesis of sympathovagal imbalance in PD.
Knowledge of the effect of sympathovagal balance on HRV may inform the design of therapeutic regimens for
PD. However, between-study heterogeneity and methodological issues limit the generalizability of the evidence;
thus, future studies employing strict methodologies are warranted.

Conclusion:
Our meta-analysis found that PD is associated with reduced HRV values, which indicates that both
sympathetic and vagal activities are decreased. Patients in the early stage of PD have sympathetic autonomic
nerve dysfunction, with only minor damage to sympathetic activity.
Benfotiamine Reduces Dendritic Cell Inflammatory Potency
Authors: Djedovic Neda , Božić Iva , Miljković Đorđe , Lavrnja Irena 
Published: September 2020

Background:
Benfotiamine is a synthetic liposoluble derivative of vitamin B1 that has
been shown to have anti-inflammatory properties.

Objective:
To study the effects of benfotiamine on dendritic cells.

Methods:
Dendritic cells were obtained from murine bone marrow precursor cells in the presence
of GM-CSF. Benfotiamine was applied to the cell culture during the process of bone marrow cell
differentiation into dendritic cells. Dendritic cells were stimulated with lipopolysaccharide (LPS)
and expression of MHC class II molecules and CD86 was determined by flow cytometry, while levels
of tumor necrosis factor (TNF) and interleukin (IL)-1β in cell culture supernatants were measured
by ELISA. F-Actin, NF-κB and Nrf2 were visualized by immunofluorescent staining and microscopy.

Results:
Benfotiamine potently reduced LPS-induced expression of MHC class II molecules and
CD86, in addition to suppressing the release of pro-inflammatory cytokines TNF and IL-1β. It also
prevented LPS-imposed morphological changes of dendritic cells, i.e. enlargement and intensified
protrusions. The effects were paralleled with the reduction of NF-κB translocation to the nucleus,
but not of Nrf2 activation inhibition.

Conclusion:
Having in mind the importance of dendritic cells for the configuration of the immune
response, our results imply that benfotiamine has the ability to regulate the immune response
through inhibition of inflammatory properties of dendritic cells.
A Nanoparticles based Microbiological Study on the Effect of Rosemary and Ginger Essential Oils against Klebsiella pneumoniae.
Authors: Abozahra Rania , Abdelhamid Sarah M. , Wen Ming Ming , Abdelwahab Ibrahim , Baraka Kholoud 
Published: September 2020

Background:

Klebsiella pneumoniae is a nosocomial pathogen in outbreaks of hospital infections. It is one of the major factors for morbidity and mortality in hospitalized patients especially those infected with colistin-resistant pathogens. Many plant essential oils have antimicrobial activities and have been investigated as natural sources to combat multiple antibiotic resistances. Moreover, recent advances in phytonanotechnology have created exciting opportunities for the management of many infections.

Objective:
This study aims at investigating the antimicrobial and antibiofilm effect of rosemary and ginger essential oil-based nano-sized formulations on colistin resistant K. pneumonia clinical isolates.

Methods:
Isolation and identification of 30 K. pneumonia isolates from different human samples were done followed by antibiotic susceptibility testing and detection of biofilm gene (mrkD). Examination of the activity of the tested essential oils and their chitosan nanoparticle formulations against the selected isolates was made by determination of their MICs using broth microdilution method followed by biofilm inhibition test and quantitative real-time PCR for the expression of mrkD gene in the presence of the oils and nanoparticles formulations compared to untreated bacterial isolates.

Results:
Our results showed that the minimum inhibitory concentration of rosemary and ginger oils was 1250 μg/ml, that of nanostructured lipid carrier-rosemary oil and nanostructured lipid carrier-ginger oil was 625 μg/ml and rosemary oil loaded chitosan nanoparticles and ginger oil loaded chitosan nanoparticles possessed minimum inhibitory concentration of 156 μg/ml. Results also revealed complete (100%) inhibition for mrkD gene expression when compared to untreated K. pneumonia.

Conclusion:
Oil loaded chitosan nanoparticles showed the highest antimicrobial and antibiofilm activity.
Eight Weeks of Interval Training Led to no Improvement in Cardiovascular Variables in the Elderly
Authors: Sant’Ana Leandro de Oliveira , Vianna Jeferson Macedo , dos Reis Natália Rodrigues , de Souza Ribeiro Aline Aparecida , de Oliveira Soares Bruno , da Silva Novaes Jefferson , Scartoni Fabiana Rodrigues , Machado Sérgio 
Published: September 2020

Background:
Interval training is a method with high acceptance in prescription to increase health and can be an essential intervener in improving cardiovascular function.

Objective:
This study aimed to verify the effects of eight weeks of interval training with different intensities on hemodynamic and autonomic function, which were assessed through resting heart rate, blood pressure, dual product, and heart rate variability.

Methods:
The sample consisted of 24 older men (age: 68.8 ± 6.8 years, body mass: 74.4 ± 18.1 kg, height: 1.70 ± 0.8 m; BMI: 25.1 ± 2, 2) who were physically active. Participants were randomized into 3 experimental groups: training group A (TGA, n = 8), training group B (TGB, n = 8) and control group (CG, n = 8). For trained groups, interventions were developed twice a week for eight weeks, with an interval of 48 hours between the sessions. The evaluations were carried out at the pre (baseline) and after the eighth week of intervention. The control group did not perform any intervention. The variables were analyzed for 10 minutes with subjects at rest in the sitting position before and after the intervention. Statistics with a significance level of p <0.05 were applied.

Results:
After the intervention, no statistically significant results were found in the variables assessed (p> 0.05).

Conclusion:
The intervention was not sufficient to promote statistical differences in hemodynamic and autonomic variables.
The Effect of Counseling on Stress in Infertile Women Admitted to an Infertility Center in Hamadan City, Iran
Authors: Khodakarami Batul , Masoumi Seyedeh Zahra , Shayan Arezoo , Ahmadnia Hasan , Dastgerdian Fatemeh , Rabiei Sogra 
Published: September 2020

Background:Infertility can result in many problems for couples. In most cases, the stress and anxiety of treating infertility can reduce fertility and affect marital disputes. The provision of counseling and informing can reduce stress and improve couples’ relationships and positively influence infertility treatment in many cases. The present study, therefore, was conducted to evaluate the effect of counseling on the stress of infertile women admitted to an infertility center in Hamadan City, Iran.Methods:The present study was performed as a two-group randomized clinical trial with two pretest and posttest stages on 104 eligible women admitted to Fatemieh Hospital in Hamadan during 2017-18. Members of the intervention group received group counseling with a cognitive-behavioral approach to stress reduction in four 1-h sessions during the 4 weeks. Data were obtained using demographic characteristics and Harry’s Stress Assessment questionnaires. Data were analyzed using SPSS 21 software, paired and independent t-test, and analysis of covariance at a significance level of < 0.05.Results:Mean stress scores were 181.82 ± 15.9 and 189.11 ± 2.1 in the pre-intervention stage, and 174.6 ± 29.6 and in the test group 78.10 ± 1.3 in the post-intervention phase in the test and control groups, respectively. High-stress levels were observed in both groups before the intervention. Independent t-test revealed that mean stress scores were significantly different in the two groups at both pre- and post-intervention stages (P < 0.05). The paired t-test indicated a significantly reduced stress score in the intervention group (P < 0.05).Conclusion:Stress management counseling in infertile women can play a positive and effective role in stress mitigation and promotion of their mental health. Therefore, the effect of this counseling can be more reliably emphasized on improving the effectiveness of infertility treatment.
IoT Enabled Crop Prediction and Irrigation Automation System Using
Machine Learning
Authors: Kumar Raj , Singhal Vivek 
Published: September 2020

Aim:
India, an agriculture country with more than 50% population depends on agriculture as the main source of
income. With increasing population and shrinking agricultural land and fragmentation may create severe challenges to
food safety in India. Therefore, there is an intense need for the application of technological innovations in the agriculture
sector to improve its growth.

Objectives:
The objective of the study is to design and develop an automated crop yield maximization system with an
irrigation automation process using the Internet of Things (IoT) and machine learning. The proposed study focuses on the
maximization of crop yield and profits of the farmer using crop prediction and automation of the irrigation process leading
to water conservation.

Methods:
The proposed system firstly rolls around making the irrigation system automated to ease the burden of getting
water for the plants when they need it. In automatic irrigation system, data sensed by temperature sensor and soil sensor in
real-time are sent to microcontroller board and based on the values received, it will instruct the relay to turn on/off the
water pump and thus automate the irrigation process. Secondly, it involves to predicting suitable crop types using soil
parameters and weather conditions that may be implanted by the farmers, using machine learning techniques, displayed on
an android based application.

Results:
The system proposed in this work recommends suitable crop as per the current weather and soil conditions in a
particular geographical area by applying XGBoost classifier with the automation of the irrigation process. The study also
does a comparative performance analysis of XGBoost classifier based on F1 Score and Accuracy.

Conclusion:
The study proposed a micro-controller-based model for crop maximization and irrigation automation. The
system mainly fulfills two major objectives of agriculture-maximization of crop yield and farmer's profit using crop
prediction and automation of irrigation process leads to water conservation.
Compatibility Study of Installation of an Operating System with Boot Modes and Partitioning Styles Configuration
Authors: Tuli Sandeep , Jain Nitin 
Published: September 2020

Introduction:
This manuscript is explicating the mutual compatibility of boot modes in a
computer system and partitioning styles of Hard Disk Drive. Most of us are familiar with these terms
and know a little about these.

Methods:
This manuscript contains ample information about the boot modes of a computer system
and partitioning styles of Hard Disk Drive (HDD) and their related configuration through which we
get to know about their configuration and endurability. It also contains some practically verified case
studies of the problems which occur due to the wrong configuration of boot modes and partitioning
styles, though there are a lot more, the most common ones are discussed along with their solutions.

Result:
In order to achieve the compatibility, it might require to convert the primary HDD into either GPT or MBR
partitioning schemes. It should be marked that this interconversion wipes data on HDD, so cautiously the data on the hard
drive must be backed-up before any conversion process.

Discussion:
This is helpful when the system is equipped with the latest configuration, i.e., UEFI,
and if there is a need to install an older operating system that does not support UEFI boot mode (for
example, Windows XP), then CSM can help. In addition, some graphics cards (for example, GTX
680) do not support UEFI and hence, require CSM to boot. This means that the CSM can help as a
runner for all that hardware configuration (in an updated system with new configuration), which can
run only through legacy BIOS configuration.

Conclusion:
The information contained had been practically verified and is helpful in coping with the newer technology
trends which contains more features along with backward compatibility.
A Study of Cognitive Radio Sensing Techniques for Optimum Spectrum
Utilization
Authors: A. Darwin Nesakumar , T.M. Inbamalar 
Published: September 2020

Background:
The spectrum scarcity plays a vital role in wireless communications. We are in the situation to
use it through an efficient methodology.

Objective:
To identify the holes in the spectrum through an efficient spectrum sensing and to allocate the bands to the
unlicensed users (Secondary Users- SU).

Methods:
It has been proposed to make a comparative study among the existing spectrum sensing methods based on the
following parameters such as Probability detection (Pd) measurement, Algorithm, Decision fusion method, Network
model.

Results:
Comparative study has been made to find the pros and cons of the existing techniques with their limitations and
the field of application. CSS (Cooperative Spectrum Sensing) technique significantly consumes less energy and reporting
time to FC (Fusion Centre) since it utilizes LLR (Log Likelihood Ratio) Method to find the Probability of detection using
Chair-varshney rule in local sensing with parallel report approach in the cluster based network.

Conclusion:
Through the study and the comparison of parameters in literature, it is found that CSS provide better
detection. Therefore, this technique can be considered as an efficient technique to find the holes and to share the
frequency with SU’s.
An Enhanced Approach for Number Plate Detection and Recognition
Authors: Ansari Mohd. A. , Kurchaniya Diksha , Patel Durga 
Published: September 2020

Introduction:
The number of vehicles is increasing day by day in our life. The vehicle may violate traffic rules
and cause accidents. The automatic number plate detection system (ANPR) plays a significant role to identify these
vehicles. Number plate detection is very difficult sometimes because each country has its own format for representing the
number plate and font types and sizes may also vary for different vehicles. The number of ANPR systems is available
nowadays but still, it is a big problem to detect the number plate correctly in various scenarios like high-speed vehicle,
number plate language, etc.

Methods:
In the development of this method, we mainly used wiener filter for noise removal, morphological operations
for number plate localization, connected component algorithm for character segmentation, and template based matching
for character recognition.

Results:
Our proposed methodology is providing promising results in terms of detection accuracy.

Discussion:
The automatic number plate detection system (ANPR) has wide range of applications because the license
number is the crucial, commonly putative and essential identifier of motor vehicles. These applications include ticketless
parking fee management, parking access automation, car theft prevention, security guide assistance, Motorway Road
Tolling, Border Control, Journey Time Measurement, Law Enforcement and many more.

Conclusion:
In this paper, an enhanced approach of automatic number plate detection system is proposed using some
different techniques which not only detect the number plate of the vehicle but also recognize each character present in the
detected number plate image.
Plant Leaf Classification using Convolutional Neural Network
Authors: Yadav Jay K.P.S. , Nidhi  
Published: September 2020

Introduction:
Convolutional Neural Network (CNNet) has proven the indispensable system in order to perform
the recognition and classification tasks in different computer vision applications. The purpose of this study is to exploit
the marvelous learning ability of CNNet in the image classification field.

Method:
In order to circumvent the overfitting issues and to enhance the generalization potential of the proposed
FLCNNet, augmentation has been performed on the Flavia dataset that impose translation and rotation techniques to
perform the augmentation with the transformed leaves having the same labels as the original ones. Both the classification
models executed using; one without augmentation and one with the augmentation data are compared to check the
effectiveness of the augmentation hence the aim of the proposed work. Moreover, Edge detection technique has been
applied to extract the shape of the leaf images, in order to classify them accordingly. Thereafter, the FLCNNet is trained
and tested for the dataset, with and without augmentation.

Results:
The results are gathered in terms of accuracy and training time for both datasets. The Augmented dataset (dataset
2) has been found effective and more feasible for classification without misguiding the network to learn (avoid
overfitting) as compared to the dataset without augmentation (dataset 1).

Conclusion:
This paper proposed the Five Layer Convolution Neural Network (FLCNNet) method to classify plant leaves
based on their shape. This approach can classify 8 types of leaves using automatic feature extraction, by utilizing their
shape characteristics. To avoid the overfitting condition and make the performance better. We aimed to perform the
classification of the augmented leaf dataset.

Discussion:
We proposed a five Layer CNNet (FLCNNet) to classify the leaf image data into different classes or labels
based on the shape characteristics of the leaves.
Performance Analysis of Hybrid Protocol Under Distributed Denial of
Service Attack in Remote Healthcare Systems
Authors: Gautam Ashu , Mahajan Rashima , Zafar Sherin 
Published: September 2020

Introduction:
The future of healthcare systems relies on faster communication between sensors and smart
devices, which can assist in taking decisions for betterment of patients. The responsiveness of wireless health care system
in case of medical urgencies is crucial factor for survival of patient’s life. The routing protocol of infrastructure less based
are playing key role in sending the information in such medical related emergencies. Therefore, it is important to study and
identify the best routing protocol in wireless healthcare based system using Mobile Ad-hoc networks (MANETS).

Methods:
MANET routing protocols such as Ad-hoc On Demand Vector (AODV), Secure AODV (SAODV) and Hybrid
Wireless Mesh Protocol (HWMP) through their routing environment are simulated through this research study. It is essential
to highlight the effect of various attacks affecting the routing methodology of these protocols. Since Distributed Denial of
Service (DDoS) attack is popularly talked about and is most vulnerable attack present in MANET environment, so this
research study analyses AODV, SAODV and HWMP under DDoS attacks through various simulation parameters.

Results:
In this research study, most suitable routing protocols to handle DDoS attacks are simulated and estimated in terms
of delay and packet delivery ratio in the scenario of changing nodes. This aids in providing implications to enhance existing
protocols and alleviate the consequences of DDoS investigation by such attacks.

Discussion:
For ensuring the optimized routing, privacy and security of patient’s data during transmission in healthcare
sector, MANET could be used as one of the important technology combined with IoT. For dealing with end to end data
transmission of patient's sensitive data, MANET protocol plays a vital role in sending the information securely.

Conclusion:
The performance of AODV, SAODV and HWMP (Hybrid Wireless Mesh Protocol) are compared and
tabularized which are the most popularly utilized protocols in healthcare environment. The simulation results shows that
the HWMP out performed than AODV and SAODV routing protocol, in terms of evaluation metrics namely end-to-end
delay and Packet Delivery Ratio (PDR) and could be considered as much less vulnerable against DDoS attacks prevailing
in wireless healthcare sector using MANET.
Prediction of Cardiovascular Risk Using Extreme Learning Machine-Tree
Classifier on Apache Spark Cluster
Authors: S. Venkatramaphanikumar , A Jaya Lakshmi , Kolli Venkata K. K. 
Published: September 2020

Introduction:
In the recent scenario, machine learning is considered a prevailing area in the diverse fields of science and
technology include image processing, automobiles, banking, finance, etc. The availability of data and adverse
improvements over machine learning techniques have become more feasible to understand and to work on various
channels of real-time health analytics.

Method:
In this paper, a health status prediction system is proposed to detect cardiovascular diseases through patients’
tweets. Further, analytics will be carried on a distributed Apache Spark framework to reduce the time taken for both
training and testing when compared with regular standalone machines.

Results:
Performance of the proposed framework with Extreme Learning Machine (ELM) - Tree classifier is evaluated on
two different corpora, and which outperforms other classifiers such as Decision Trees, Naïve Bayes, and Linear SVC,
DNN, etc. in both accuracy and time.

Discussion:
Social media streaming data is considered as one of the major sources for data in the proposed system. Based
on the model, the attributes of the incoming user tweets are analyzed, and accordingly cardiovascular risk is predicted.
Further, current health status is tweeted back as a reply to the respective user along with a copy to the family and
caretakers.

Conclusion:
This proposed work provides the development of the alert-based system for heart status prediction by adding
some additional features impacting the accuracy besides reducing the response time by using Big data Apache Spark
Distributed Framework.