bluuebunny/crossref_metadata_2025_split huggingface dataset:
Iterative Guided Machine Learning-Assisted Systematic Literature Reviews: A Diabetes Case Study
Authors: Zimmerman John , Soler Robin , James Lavender , Sarah Murphy , Charisma Atkins , LaShonda Hulbert , Richard Lusk , Peng Ng Boon 
Published: July 2020

Abstract
Background: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and Machine Learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs.Methods: In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to articles that meet inclusion criteria. Our approach differs from previous approaches to using ML as a SR tool in that it incorporates ML configurations guided by previously conducted SRs, and human confirmation on ML predictions of relevant articles during multiple iterative reviews on smaller tranches of citations. We applied the tailored method to a new SR review effort to validate performance.Results: The case study test of the approach proved a sensitivity (recall) in finding relevant articles during down selection that may rival many traditional processes.Conclusions: We believe this iterative method can help overcome bias in initial ML model training by having humans reinforce ML models with new and relevant information, and is an applied step towards transfer learning for ML in SR.
Exploration of prognostic factors for critical COVID-19 patients：a nomogram analysis
Authors: Wang Lili , Liu Chun , Wang Zhengquan , Lin Yi , Fan Rui 
Published: July 2020

Abstract
To discuss influencing factors on critical COVID-19 patient’s prognosis, construct a basic model and predict their mortality risks. Retrospectively analyzed the general condition and respective laboratory biomarkers of critical patients with duration≥24 h from Feb. 10th, 2020 to Mar. 30th, 2020 to separate them into a survival group and death group based on their clinical features. Multiple logistic regression analysis was performed to assess risk factors for critical COVID-19 patient’s and a nomogram was constructed based on screened risk factors. A receiver operating curve (ROC) was created to evaluate the accuracy of the nomogram. Multi-factor Logistic recovery analysis results show: Age, Peripheral blood leucocyte count,Lymphocyte percentage, Thrombocyte count and Hyper C-reactive protein are single danger factors of critical COVID-19 patient’s mortality risk (p＜0.05). ROC curve indicates Nomogram predictive model AUC is 0.958 (95%CI: 0.923-0.993), which has high predictive value. Findings from this study suggest advanced age, high peripheral blood leucocyte count, high hyper C-reactive protein, low lymphocyte percentage and low thrombocyte count are risk factors of critical COVID-19 patient’s death.The Nomogram model is helpful for timely intervention to reduce the incidence of critical COVID-19 patients.
Molecular Docking Studies of SARS-Cov-2 Mainprotease Potential Inhibitors
Authors: Utami Wiji , Fitriani Ika Nur , Aziz H A , Tanti Tanti , Santoso Pugoh 
Published: July 2020

Abstract
BACKGROUND : SARS-Cov-2 causes an coronavirus disease 2019 (COVID-19), and the vaccines or drugs of this disease have not been found to inhibit this replication of the virus. Researchers are engaged in all fields of study to discover new potential inhibitors. This study aimed to compute the binding energy (BE) and interactions between the new potential inhibitors and the SARS-Cov-2 Mainprotease (Mpro).METHODS: In this study, we docked between twenty-seven patented drugs and the Mpro receptor (PDB ID: 6W63). The molecular docking calculation was performed using AutoDock Tools 1.5.6. software. Moreover, the information about the biological activity of ligands was calculated using the PASS online server. The result of the calculation was then analyzed and visualized using Biovia Discovery Studio Visualizer. Further calculation, such as the ligand-protein interaction using STITCH database and Lipinski’s rule five were employed in this research.RESULTS: The molecular docking calculation results showed that nelfinavir was strongly bound to Mpro with BE of -9,51 kcal/mol, followed by lopinavir, vitamin D, ritonavir, and dexamethasone. From these ligands, we considered dexamethasone because this ligand works as an anti-inflammatory agent. CONCLUSIONS: Following the calculation, nelfinavir, lopinavir, and dexamethasone are proposed as a potential inhibitor of the Mpro receptor, but there is a need for further investigation.
Knowledge, Attitudes, and Practices of Medical Interns Toward COVID-19 in Saudi Arabia: A Cross-Sectional Survey, April-May 2020
Authors: ALNohair Sultan Fahad , Mahmud Ilias , ALShehri Fahad , ALShuqayran Rakan , ALBatanouni Manal 
Published: July 2020

Abstract
Background: COVID-19 is a highly infectious, rapidly spreading disease without any proven vaccine or treatment. Poor knowledge, attitude and practices (KAP) toward COIVD-19 prevention measures among healthcare workers may lead to further transmission of the disease. Lack of attention towards non-frontline healthcare workers may put them at higher risk of infection from undetected cases. Here, we evaluate medical interns’ KAP toward COVID-19 prevention measures in the Kingdom of Saudi Arabia (KSA). Methods: We conducted a cross-sectional online survey. We distributed a structured questionnaire to the medical interns through different social media. Data was collected between April and May 2020. We estimated the prevalence of good KAP toward COVID-19 prevention measures. We did T-test or ANOVA to investigate the mean differences in KAP between socio-demographic groups. In addition, we did multivariable logistic regression analysis to investigate the socio-demographic determinants of good KAP.Results: Of the total 250 participants, 60% were males, 64% were aged between 20 and 25 years, and 90% was Saudi. 24% interns are relying on social media, newspaper, television or friends as primary source of COVID-19 information, while others are relying on formal sources like official websites of national and international bodies. Overall, the prevalence of good knowledge, attitude and practices are 38% (95% CI: 32.2-44.2), 55.2% (48.9-61.3) and 24% (19.1-29.7), respectively. Graduating from government universities are associated with higher odds of good knowledge (aOR: 3.87; 95% CI: 1.05-14.22) and positive attitude (aOR: 4.84; 95% CI: 1.28-18.23) than private or foreign universities. While, practicing in the west region-Mecca and Medina (aOR: 2.35; 95% CI: 1.05-5.23); and the North region-Hail, Jouf, Tabuk & Northern borders (aOR: 3.2; 95% CI: 1.32-7.75) are associated to higher odds of moderate/good practice compared to practicing in the central region- Riyadh and Qassim. Conclusions: Our findings reveal gaps in KAP among medical interns. Medical interns in Saudi Arabia are not deployed as front-line health worker to combat COVID-19. However, community transmission of COVID-19 make it critical to improve KAP of medical interns toward COVID-19 prevention measures. Repeated training to improve KAP and competency of the medical interns in this regard are warranted.
Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks
Authors: Qur'ania Arie , Harsani Prihastuti , Triastinurmiatiningsih Triastinurmiatiningsih , Wulandhari Lili Ayu , Gunawan Alexander Agung Santoso 
Published: July 2020

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and Pand P and K. The r esearchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.
The ice cover shapes the spatial and temporal characteristics of water quality in Hulun Lake during winter
Authors: Ao Wen , Dou Hua-shan , Yu Cen-cen , Wang Wen-lin , Wang Zeng-long , Wang Qi , Lu Lu , Zhou Xing-jun , Han Rui-ming , Zou Chang-xin 
Published: July 2020

Abstract
Being the largest boreal steppe lake in Northeast China, Hulun Lake has been characterized by eutrophication induced by abnormally high organic matter. This work investigated therefore the physicochemical and water quality parameters of Hulun Lake, and that of the inflowing Crulen River and Orshen River through winter, to reveal the spatial and temporal characteristics of water quality without impact of algal bloom, atmospheric deposition, wind or allochthonous nutrient input. Results showed that the prevention of wind-induced sedimentary resuspension accompanied with the minimized volume of tributary inflows is supposed to predominate the eutrophication alleviation. The formation of ice cover had a slight concentrative effect on water ion content. However, ice cover resulted in an increasingly homogenous distribution of phosphorus and oxygen-depleting organic matter over the entire lake. The two headwater streams demonstrated limited impact on water quality in estuaries in winter without showing evident coordination in upstream water nutrient level. It is suggested that the prevention of wind disturbance by ice cover and the subsequently modified hydrodynamic and water ecological processes are the determinant factors on water quality in Hulun Lake during winter.
The Interaction of the Microtubule Targeting Anticancer Drug Colchicine with Human Glutathione Transferases
Authors: Premetis Georgios , Marugas Panagiotis , Fanos Georgios , Vlachakis Dimitrios , Chronopoulou Evangelia G. , Perperopoulou Fereniki , Dubey Kashyap Kumar , Shukla Pratyoosh , Foudah Ahmed Ibrahim , Muharram Magdy Mohamed , Aldawsari Mohammed F. , Papageorgiou Anastassios C. , Labrou Nikolaos E. 
Published: July 2020

Background:
Glutathione transferases (GSTs) are a family of Phase II detoxification enzymes that
have been shown to be involved in the development of multi-drug resistance (MDR) mechanism toward chemotherapeutic
agents. GST inhibitors have, therefore, emerged as promising chemosensitizers to manage and
reverse MDR. Colchicine (COL) is a classical antimitotic, tubulin-binding agent (TBA) which is being explored
as anticancer drug.

Methods:
In the present work, the interaction of COL and its derivative 2,3-didemethylcolchicine (2,3-DDCOL)
with human glutathione transferases (hGSTA1-1, hGSTP1-1, hGSTM1-1) was investigated by inhibition analysis,
molecular modelling and molecular dynamics simulations.

Results:
The results showed that both compounds bind reversibly to human GSTs and behave as potent inhibitors.
hGSTA1-1 was the most sensitive enzyme to inhibition by COL with IC50 22 μΜ. Molecular modelling predicted
that COL overlaps with both the hydrophobic (H-site) and glutathione binding site (G-site) and polar interactions
appear to be the driving force for its positioning and recognition at the binding site. The interaction of COL with
other members of GST family (hGSTA2-2, hGSTM3-3, hGSTM3-2) was also investigated with similar results.

Conclusion:
The results of the present study might be useful in future drug design and development efforts towards
human GSTs.
The Quantitative Structure-Activity Relationships between GABAA Receptor
and Ligands based on Binding Interface Characteristic
Authors: Cheng Shu , Ding Yanrui 
Published: July 2020

Background:
Quantitative Structure Activity Relationship (QSAR) methods based on machine learning play a
vital role in predicting biological effect.

Objective:
Considering the characteristics of the binding interface between ligands and the inhibitory neurotransmitter
Gamma Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of ligands that bind to the human GABAA
receptor.

Method:
After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked ligand molecular
descriptors. Three QSAR models are built using gradient boosting regression tree algorithm based on the different
combinations of docked ligand molecular descriptors and ligand-receptor interaction characteristics.

Results:
The features of the optimal QSAR model contain both the docked ligand molecular descriptors and ligand-receptor
interaction characteristics. The Leave-One-Out-Cross-Validation (Q2 LOO) of the optimal QSAR model is 0.8974, the
Coefficient of Determination (R2) for the testing set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this
model to predict the pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02 and
0.03 respectively.

Conclusion :
We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features, which can help to
build the QSAR model more accurately.
16x16-Bit Binary Multiplier Using High-Speed Modified Compressor
Authors: Maitra Subhashis 
Published: July 2020

Background:
For higher-order multiplications, a huge number of adders or compressors are
used to perform the addition of the partial products.

Objective:
Hence, the area and the propagation delay will increase. Researchers are trying to reduce
the number of additions of partial products.

Method:
In this paper, different modified compressors have been proposed and based on these compressors,
16x16-bit binary multiplier has been discussed.

Results:
The proposed design provides better area, power consumption, critical path delay and less
number of transistor counts when compared to other designs using the conventional compressors.
Here, the proposed method has been used in the Wallace tree multiplier or Dadda tree multiplier. The
compressor used here has been implemented using Microwind DSCH 3.8 lite.

Conclusion:
The modified compressor makes the multiplier faster and reduces the number of addition
of partial products.
Research and Design of Pipe Cleaning Device with Self-Rotation
Authors: Chen Zhong , Qiu Xiaoyang , Yang Hu , Yang Lingling 
Published: July 2020

Background:
With more and more blockages in the drainage pipe, recent patents on the design
of the pipe cleaning device are also being addressed increasingly. But the current pipe cleaning
device has only a single dredging function, and due to the inefficiency of the nozzle head, it cannot be
used for cleaning of the seriously blocked pipelines.

Objective:
In order to solve these problems, a novel pipe cleaning device with self-rotation is proposed
and the fluid simulation analysis is adopted for the low-efficiency problem of the nozzle head in this
paper.

Methods:
Firstly, the overall structure of the drainage pipeline cleaning device was designed. Secondly,
the size of the nozzle and the nozzle head was determined. Thirdly, the fluid simulation analysis of the
nozzle head was carried out to realize the optimal design of the nozzle head. Finally, according to the
above design, a prototype was manufactured.

Results:
This paper presents a novel pipe cleaning device with self-rotation, which is different from
current patents. It is divided into the nozzle head system, walking system, cleaning system, threedimensional
modeling and assembly of the above three mechanical systems. The simulation results
demonstrated that if a deflector is not fixed inside the nozzle head, the pressure of the nozzle head is
about 8542751.89Pa, and the flow rate at the outlet of the nozzle head is 354.897m/s. If a deflector is
fixed, the corresponding data is 1.32e + 008Pa and 446.336m/s. The result shows that the proposed new
nozzle head optimization is effective.

Conclusion:
The new design solves the problems of the current patents on pipe cleaning device, and
solves the key technical problems of inconvenient cleaning of pipe blockage and low efficiency nozzle
head under complex working conditions. As a whole, this paper provides new ideas and new methods
for the efficient work of pipe cleaning device and the removal of hard dirt on the inner wall of the pipeline.
A Secure Framework to Preserve Privacy of Biometric Templates on Cloud Using Deep Learning
Authors: Arora Shefali , Bhatia M.P.S. 
Published: July 2020

Introduction:
Cloud computing involves the use of maximum remote services through a network using minimum
resources via internet. There are various issues associated with cloud computing, such as privacy, security and reliability.
Due to rapidly increasing information on the cloud, it is important to ensure security of user information. Biometric template
security over cloud is one such concern. Leakage of unprotected biometric data can serve as a major risk for the privacy of
individuals and security of real-world applications.

Method:
In this paper, we improvise a secure framework named DeepCrypt, that can be applied to protect biometric
templates during authentication of biometric templates. We use deep Convolutional Neural Networks to extract features
from these modalities. The resulting features are hashed using a secure combination of Blowcrypt (Bcrypt) and SHA-256
algorithm, which salts the templates by default before storing on the server.

Results:
Experiments conducted on the CASIA-Iris-M1-S1, CMU-PIE and FVC-2006 datasets achieve around 99% Genuine
accept rates, proving that this technique helps to achieve better performance along with high template security.

Discussion:
The proposed method is robust and provides cancellable biometric templates, high security
and better matching performance as compared to traditional techniques used to protect the biometric
template.
Quantitative Methods for Analyzing Intimate Partner Violence in Microblogs: Observational Study
Authors: Homan Christopher Michael , Schrading J Nicolas , Ptucha Raymond W , Cerulli Catherine , Ovesdotter Alm Cecilia 
Published: July 2020

Background
Social media is a rich, virtually untapped source of data on the dynamics of intimate partner violence, one that is both global in scale and intimate in detail.

Objective
The aim of this study is to use machine learning and other computational methods to analyze social media data for the reasons victims give for staying in or leaving abusive relationships.

Methods
Human annotation, part-of-speech tagging, and machine learning predictive models, including support vector machines, were used on a Twitter data set of 8767 #WhyIStayed and #WhyILeft tweets each.

Results
Our methods explored whether we can analyze micronarratives that include details about victims, abusers, and other stakeholders, the actions that constitute abuse, and how the stakeholders respond.

Conclusions
Our findings are consistent across various machine learning methods, which correspond to observations in the clinical literature, and affirm the relevance of natural language processing and machine learning for exploring issues of societal importance in social media.
Barriers and Enablers in Implementing Electronic Consultations in Primary Care: Scoping Review
Authors: Baines Rebecca , Tredinnick-Rowe John , Jones Ray , Chatterjee Arunangsu 
Published: July 2020

Background
Often promoted as a way to address increasing demands, improve patient accessibility, and improve overall efficiency, electronic consultations are becoming increasingly common in primary care, particularly in light of the current COVID-19 pandemic. However, despite their increasing use, a theoretically informed understanding of the factors that support and inhibit their effective implementation is severely limited.

Objective
With this scoping review, we sought to identify the factors that support and inhibit the implementation of electronic consultations in primary care.

Methods
In total, 5 electronic databases (PubMed, Medline, Embase, CINAHL, and PsycINFO) were systematically searched for studies published in 2009-2019 that explored the impact and/or implementation of electronic consultations in primary care. Database searches were supplemented by reference list and grey literature searches. Data were analyzed using inductive thematic analysis and synthesized using Normalization Process Theory (NPT).

Results
In total, 227 articles were initially identified and 13 were included in this review. The main factors found to hinder implementation included awareness and expectations; low levels of engagement; perceived suitability for all patient groups, conditions, and demographics; cost; and other contextual factors. Reports of information technology reliability and clinical workload duplication (as opposed to reduction) also appeared detrimental. Conversely, the development of protocols and guidance; patient and staff education; strategic marketing; and patient and public involvement were all identified as beneficial in facilitating electronic consultation implementation.

Conclusions
This review highlights the need for proactive engagement with patients and staff to facilitate understanding and awareness, process optimization, and delivery of coherent training and education that maximizes impact and success. Although the necessity to use online methods during the COVID-19 pandemic may have accelerated awareness, concerns over workload duplication and inequality of access may remain. Future research should explore health inequalities in electronic consultations and their economic impacts from multiple perspectives (eg, patient, professional, and commissioner) to determine their potential value. Further work to identify the role of meaningful patient involvement in digital innovation, implementation, and evaluation is also required following the rapid digitization of health and social care.